The next and final update to the deterministic Rapid Refresh, version 5 (RAPv5), and High-Resolution Rapid Refresh, version 4 (HRRRv4), is currently scheduled for an operational implementation at NOAA/NCEP in mid-2020. Numerous physics, dynamics and data assimilation changes are being included as part of this upgrade.  This presentation will discuss the scope of this implementation including  an emphasis on the development of an hourly-cycled regional 36-member storm-scale ensemble analysis system with demonstrated improvements to deterministic forecasts at the convective scale.  The design of this HRRR data assimilation system (HRRRDAS) will be motivated through choices to include multiple-scales of perturbations from both a global and this regional convection-allowing ensemble, scale-dependent use of different observation types in an Ensemble Kalman Filter and use of inflation to maintain ensemble spread.  While not included in the HRRRv4 operational implementation, an experimental storm-scale ensemble forecast system leveraging a subset of the 36 members will also be described.  Design of the HRRR ensemble (HRRRE) forecasts will be discussed including use of stochastic parameter perturbations across the single model physics suite along with comparisons to other storm-scale multi-model/physics ensemble designs highlighting both the benefits of this single-model/physics ensemble approach along with challenges in maintaining appropriate spread at longer forecast lengths.  This final configuration of the RAP/HRRR model systems will serve as an operational baseline for the transition to a regional FV3-based convection allowing application in a Unified Forecast System (UFS), known as the Rapid Refresh Forecast System (RRFS), and this transition will be described.

How to cite: Alexander, C.: Storm-Scale Ensemble Data Assimilation and Forecast Development for the High-Resolution Rapid Refresh (HRRR) and Future Applications in the Unified Forecast System, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22670, 2020.

Model error is one of major contributors to forecast uncertainty. In addition, statistical representations of possible model errors substantially affect the data assimilation results. We investigate variety of methods of taking into account model error in ensemble based convective scale data assimilation. This is done using the operational convection-permitting COSMO model and data assimilation system KENDA of German weather service, for a two-week convective period in May 2016 over Germany. Conventional and radar reflectivity observations are assimilated hourly by the LETKF. For example, to take into account the model error due to unresolved scales and processes, we use the additive noise with samples coming from the difference between high-resolution model run and low-resolution experiment. We compare this technique for assimilation of radar reflectivity data to other methods such as RTPS, warm bubble initialization, stochastic boundary layer perturbation and estimation of parameters. To further improve on additive noise technique, which consists of perturbing each ensemble member with a sample from a given distribution, we propose a more flexible approach in which the model error samples are treated as additional synthetic ensemble members that are used in the update step of data assimilation but are not forecasted. In this way, the rank of the model error covariance matrix can be chosen independently of the ensemble. This altered additive noise method is analyzed as well.

How to cite: Janjic, T., Zeng, Y., de Lozar, A., Ruckstuhl, Y., Blahak, U., and Seifert, A.: Representation of model error in convective scale data assimilation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11232, https://doi.org/10.5194/egusphere-egu2020-11232, 2020.

In the operational NWP, the assimilation of ozone causes large wind and temperature analysis increments in the stratosphere to accommodate for the differences between background and observations. In such cases, unless the ozone feedback on dynamics is switched off, the strong-constraint 4D-Var internal dynamics without comprehensive bias correction makes spurious flow adjustments, especially in the regions with larger gradients in the tracer background field, and when there are insufficient constraints such as large background flow uncertainties and a lack of observations of dynamic variables. The wind-tracer feedback is also turned off for the aerosols and the trace gases. Thus, their useful information on the wind advection is not accounted for anywhere in the domain at any time instance. In this way, the tracer analysis quality is also deteriorated. Somewhat smarter, selective use of tracer information would be beneficial to alleviate unphysical analysis increments in certain regions and at the same time to retain the benefits of wind extraction in other areas.

Thus, we formulate the method for flow-dependent 4D-Var wind extraction, which switches the wind-tracer feedback on or off in the tracers’ tangent-linear model and wind adjoint model. The objective criterion for the selection is deduced from the ensemble of simulations and is based on the ratio of the tracer physical forcings’ uncertainty and the mean tracer advection rate. The numerical tests with an intermediate-complexity incremental 4D-Var system MADDAM show promising results for both wind and tracer analyses. We also demonstrate that the aerosols have theoretically an even larger potential as the carriers of the advection information than humidity due to larger relative spatial gradients, which are crucial for successful wind extraction. The flow-dependent wind extraction method is compared with the weak-constraint 4D-Var, where the tracer model error obtained from the ensemble implicitly controls the amount of wind-tracer coupling.

How to cite: Zaplotnik, Ž. and Žagar, N.: Inferring atmospheric dynamics from tracer observations in 4D-Var: flow-dependent aspects, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12103, https://doi.org/10.5194/egusphere-egu2020-12103, 2020.

This work is devoted to the urgent task of assessing regional flows of greenhouse gases from the Earth's surface according to satellite observations. The article presents the practical and theoretical results of the first year of study in the PhD program, later they will be included in the final dissertation. Flows will be estimated based on the observational data assimilation system for a three-dimensional model of diffusive transport of gas components in the atmosphere (MOZART-4). Model for Ozone and Associated Chemical Indicators, Version 4 (MOZART-4) is an autonomous global model for the transport of chemicals in the atmosphere.

The development of a modern system for the assimilation of real satellite data for assessing greenhouse gas sources is currently a very important theoretical and practical area in science. The ensemble approach is relevant and has great potential for using both stochastic and variational methods. In the process of implementation, this is an order of magnitude simpler, since there are no cumbersome matrix calculations using the model.

To solve the problem of estimating methane flows, the parameter estimation problem was solved: an algorithm for data assimilation was developed; the Kalman filter with the transformation of the local ensemble was used as the basis for it. Using an example of a model problem, an algorithm for estimating the concentration of a passive impurity and a parameter is developed. The case was also considered when only one parameter can be estimated in the assimilation system. In this case it is considered that at the forecasting stage the parameter does not change, and the calculations in accordance with the transport model are included in the operator H, for example, as in Feng (2009,2017). H is the observation operator; transfers predicted values to observation points (and observed variables). For example, for satellite methane data, H includes:

1. a) interpolation to the observation point;
2. b) vertical averaging (using the middle core);
3. c) if the observation data is obtained from a large time interval, then the operator H also includes a forecast for the model in time.

Numerical experiments were carried out with model and real data. Using numerical experiments with the model, it was shown that a large problem (global) can be solved sequentially by subregion, independently in each subregion, which allowed the use of MPI and OpenMP.

How to cite: Platonova, M.: Data assimilation system for estimating methane flows using satellite data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-661, https://doi.org/10.5194/egusphere-egu2020-661, 2020.

The FORMOSAT-7/COSMIC-2 (simplified as FS-7/C-2 in the following descriptions) is the constellation of satellites for meteorology, ionosphere, climatology, and space weather research. FS-7/C-2 was a joint Taiwan-U.S. satellite mission that makes use of the radio occultation (RO) measurement technique. FORMOSAT-7 is the successor of FORMOSAT-3 which was launched in 2006. the FORMOSAT-3 RO data has been shown to be extremely valuable for numerical weather prediction, such as improving the prediction of tropical cyclogenesis and reducing the typhoon track error. The follow-on FS-7/C-2 mission was launched on 25 June 2019, and is currently going through preliminary testing and evaluation. After it is fully deployed, FS-7/C-2 is expected to provide 6,000 GNSS (Global Navigation Satellite System) RO profiles per day between 40S and 40N.  

In this study, we conduct a preliminary evaluation of FS-7/C-2 GNSS RO data on heavy precipitation events associated with typhoon and southwesterly monsoon flows based on the operational NWP system of the Central Weather Bureau (CWB) in Taiwan. The FS-7/C-2 GNSS RO data are assimilated using a dual-resolution hybrid 3DEnVare system with a 15-3 km nested-grid configuration. In the 15km resolution domain, flow-dependent background error covariances (BECs) derived from the perturbation of ensemble adjustment Kalman filter (EAKF), will be used to conduct hybrid 3DEnVar analysis. In the 3 km resolution domain, the 15 km resolution flow-dependent BECs will be inserted to the 3 km grid using a Dual-Resolution (DR) technique, and then combined with 3 km resolution static BECs, to perform the high-resolution 3DEnVar analysis. The performance of the CWB operational NWP system on quantitative precipitation forecast of significant precipitation events with and without the assimilation of FS-7/C-2 GNSS RO data will be evaluated.

How to cite: Hong, J.-S., Chen, W.-J., Chen, Y.-J., Jiang, S.-Y., and Fong, C.-T.: Impact of FORMOSAT-7/COSMIC-2 RO on High-Resolution Hybrid 3DEnVar System at Central Weather Bureau of Taiwan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4161, https://doi.org/10.5194/egusphere-egu2020-4161, 2020.

NCEP has implemented the first version of the Finite Volume Cubed Sphere (FV3) dynamic core based Global Forecast System (GFS v15) into operations in June 2019, replacing the spectral model-based GFS. This is the first instantiation of NOAA's Unified Forecast System (UFS), which is being built as a comprehensive coupled Earth system model using modern tools and software infrastructure (e.g., NEMS, NUOPC, and ESMF) to support research and operations. Advancements in model physics and data assimilation are in development using CCPP and JEDI frameworks. Testing and evaluation of UFS are facilitated through the development of unified workflow and METplus capabilities. All these initiatives involve significant engagement with the research community, with emphasis on more efficient and streamlined transition of research advances to operations (R2O).

The next major operational upgrade towards UFS is for the Global Ensemble Forecast System (GEFSv12) planned for implementation later this year.  Compared to the currently operational GEFS, the next GEFS version 12 incorporates the following advances: the same FV3 based global model and UFS infrastructure as in GFS,  higher resolution (~25km), increased membership (31), GFSv15 physics, advanced stochastic physics perturbations (SKEB and SPPT), and 2-tiered SST approach using SST anomalies from CFSv2 as input.  For the first time, GEFSv12 will provide ensemble based operational weather predictions for sub-seasonal scales with daily 00z forecasts going out to 35 days. GEFSv12 also comes with 20-year reanalysis, 30-year reforecasts and 3-year retrospective forecasts to support stakeholder needs for calibration and validation.  In addition, GEFSv12 absorbs the global wave ensembles and aerosol capabilities (control member only) through one-way coupling, taking major steps towards building a unified system and simplifying NCEP’s production suite.

This presentation describes the design and development of GEFSv12 and discusses results from the evaluation of the retrospective and reforecast experiments.  Significant improvements were noted in both deterministic and probabilistic forecast metrics for several variables including 500 hPa geopotential height anomaly correction, 850 hPa temperature and winds, near surface variables, precipitation, tropical cyclone tracks and intensity, and modes of variability including MJO and NAO.  Substantial improvements were also noted in the performance of wave ensemble and aerosol predictions.

This presentation also describes NOAA’s efforts towards accelerating further development of fully coupled UFS consisting of six component models of the Earth system: the FV3 dynamical core for the atmosphere, MOM6 for the ocean, Noah MP for the land surface, GOCART for aerosols, CICE5/CICE6 for sea ice and WW3 for ocean surface waves.  Combined with data assimilation advances, an ambitious goal of unifying both the high-resolution deterministic (GFSv17) and probabilistic (GEFSv13) predictions for global medium range and sub-seasonal time scales is planned to significantly advance the global prediction capabilities at NCEP.

How to cite: Stajner, I., Tallapragada, V., Zhu, Y., Alves, H., McQueen, J., Hamill, T., Whitaker, J., Grell, G., and Levit, J.: NOAA’s Unified Forecast System for Sub-Seasonal Predictions: Development and operational implementation plans of Global Ensemble Forecast System v12 (GEFSv12) at NCEP, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6212, https://doi.org/10.5194/egusphere-egu2020-6212, 2020.

We will present some recent improvements to the GF parameterization. These include two features that were added to the Grell-Freitas (GF) Cumulus Parameterization to improve the representation of the particle size distribution and to allow parameterized deep convection to propagate. These also include the treatment of tracer transport, wet scavenging, and aqueous phase chemistry, and improvements on interactions with aerosols. A more complete implementation for transport and treatment of atmospheric composition variables was necessary to complement recent new developments at NOAA/ESRL coupling chemical modules within the NWP model.

Estimates of cloud water and ice crystal number concentrations are added to GF base on the water-friendly aerosol content, temperature, and the cloud water and ice crystal mixing ratios. This modification is designed to diminish the artificial modification of the particle size distribution that occurs when the single moment cumulus schemes are used with the double-moment microphysics schemes. Simulations demonstrate that the addition of GF ice number concentrations substantially increases ice content aloft in the tropics, which shifts the outgoing longwave radiation distribution towards colder brightness temperatures.

The key modification used to enable the propagation of parameterized deep convective is the addition of an advected scalar that represents the cloud base mass flux associated with GF downdrafts. Our implementation of this advected scalar allows the impact of downdrafts from previous time steps to foster propagation. Evaluation and tuning of the new downdraft mass advection term is ongoing.

How to cite: Grell, G., Barnes, H., Freitas, S., and Li, H.: Ongoing Development and Applications of the Grell-Freitas Cumulus Parameterization , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20855, https://doi.org/10.5194/egusphere-egu2020-20855, 2020.

Global numerical weather prediction models (NWP) such as the European Centre for Medium-Range Weather Forecasts (ECMWF) and Global Forecast System (GFS) generate atmospheric data for the entire world. However, these models provide the data at large spatiotemporal resolutions because of computational limitations. Weather Research and Forecasting (WRF) Model is one of the models, which is capable of dynamically downscaling the NWP models’ output. In this study, all combinations of 4 microphysics and 3 cumulus parametrization schemes, 2 planetary boundary layers (PBL), 2 initial and lateral boundary conditions and 2 horizontal grid spacing (i.e., an ensemble consisting of 96 different scenarios) are simulated to measure the sensitivity of WRF-derived precipitation against different model configurations. The sensitivity analyses are performed for 4 separate events. These events are selected among the extreme precipitation events in the Mediterranean (MED) and eastern Black Sea (EBLS) regions. For each region, a summer and an autumn event are chosen. Here, the fundamental aim is to determine the spatiotemporal differences in WRF input parameters that yield better outcomes. A total of 72 hours simulations are started 24 hours before the event day to avoid spin-up time error. The model is adjusted to produce hourly precipitation outputs. The relative performance of scenarios is measured using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method considering 5 categorical validation indices and 4 pairwise statistics calculated between the model estimations and the ground-based precipitation observations. According to the TOPSIS results, microphysics scheme, initial and lateral boundary condition, and horizontal grid spacing are substantially influential on WRF precipitation estimates, while cumulus parameterization has a comparatively low effect. The choice of PBL scheme is essential for the summer events, but the results of the autumn events are independent of PBL selection. WRF products are better for the events of the EBLS basin when ERA5 is used as the initial and lateral boundary condition. On the contrary, GFS is superior in the MED region. In terms of spatial resolution, 9 km horizontal grid spacing is commonly preferable for all the events rather than 3 km. Besides, the model underestimates the area-averaged precipitation amounts except for the MED-autumn incident. Still, the model is successful at catching the peak hours of all events. Moreover, the precipitation detection ability of WRF is better for the autumn months. The probability of detection index is higher than 0.5 at 35% of MED stations and 68% of EBLS stations for the autumn events. The local and convective summer events are investigated considering the event centers. Albeit relatively low relationships are defined for the MED-summer event, a statistically significant correlation is obtained between the central station of the EBLS-summer event and the closest grid for the predictions of 52 scenarios (i.e., 54% of the ensemble).

How to cite: Duzenli, E., Pilatin, H., Yucel, I., Kilicarslan, B. M., and Yilmaz, M. T.: Evaluation of the performance of WRF model in extreme precipitation estimation concerning the changing model configuration and the spatial and temporal variations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1026, https://doi.org/10.5194/egusphere-egu2020-1026, 2020.

Subgrid-scale cloud representation and the closely related surface-energy balance continue to be a central challenge from subseasonal-to-seasonal models down to storm-scale models applied for forecast duration of only a few hours. Previously, NOAA/ESRL confirmed this issue from 3-km model (HRRR using WRF-ARW) for short-range forecasting including sub-grid-scale cloud representation up to a 25-km subseasonal model (FV3-GFS) testing a common suite of scale-aware physical parameterizations.  

In a major physics suite component -- modified representation of subgrid cloud water resulted in much improved agreement with radiation measurements as shown with 2018-2020 testing of the 3km HRRR model. Latest results will be shown using SURFRAD radiation and METAR ceiling observations, indicating much improved bias in downward solar radiation and in cloud location (via mean absolute error metric), as well as with 2m temperature and precipitation.

In addition, new evaluations with the same convection-allowing suite (“mesoscale” suite) of physical parameterizations revised further for subseasonal 30-day tests over summer and winter periods with the 25km NOAA FV3-GFS model. These results are compared with CERES-estimated cloud and downward solar radiation fields. The radiation results from this very preliminary subseasonal test with the ESRL-HRRR physics suite will be compared with previous subseasonal tests using the GFS physics suite and at different horizontal resolution.  This global application now confirms much better downward solar-radiation results over oceans for both January and June from a Nov-2019 version over a 2018 of the “mesoscale” suite.

Background: NOAA Earth System Research Laboratory, together with NCAR, has developed this parameterization suite (turbulent mixing, deep/shallow convection, 9-layer land/snow/vegetation/lake model) to improve PBL biases (temperature and moisture) including better representation of clouds and precipitation. This parameterization suite development has been accompanied by an effort for improved data assimilation of clouds, near-surface observations and radar for the atmosphere-land system.

Subgrid-scale cloud representation continues to be a central challenge from subseasonal-to-seasonal models down to storm-scale models applied for forecast duration of only a few hours.   Previously, NOAA/ESRL confirmed this issue from 3-km model (HRRR) for short-range forecasting including sub-grid-scale cloud representation up to a 60-km subseasonal model testing a common suite of scale-aware physical parameterizations.   Some progress has been made in 2018-2019 to substantially reduce cloud deficiency and excessive downward solar radiation at least over land areas.

Recent development and refinements to this common suite of physical parameterizations for scale-aware deep/shallow convection and boundary-layer mixing over this wide range of time and spatial scales will be reported in this presentation showing some progress. Evaluation of components of this suite is being evaluated for cloud/radiation (using SURFRAD, CERES, METAR ceiling) and near-surface (METAR, mesonet, aircraft, rawinsonde).

NOAA Earth System Research Laboratory, together with NCAR, has developed this parameterization suite (turbulent mixing, deep/shallow convection, 9-layer land/snow/vegetation model) to improve PBL biases (temperature and moisture) including better representation of clouds and precipitation. This parameterization suite development has been accompanied by an effort for improved data assimilation of clouds, near-surface observations and radar for the atmosphere-land system.  

The MYNN boundary-layer EDMF scheme (Olson, et al 2019), RUC land-surface model (Smirnova et al. 2016 MWR), Grell-Freitas scheme (2014, Atmos. Chem. Phys.), and aerosol-aware cloud microphysics (Thompson and Eidhammer 2015) have been applied and tested extensively for the NOAA hourly updated 3-km High-Resolution Rapid Refresh (HRRR) and 13-km Rapid Refresh model/assimilation systems over the United States and North America.   This mesoscale but also scale-aware suite is being tested,

How to cite: Benjamin, S., Joseph Olson, J., Sun, S., Georg Grell, G., and Curtis Alexander, C.: Common evaluation/evolution of cloud-radiation processes from 25km S2S to 3km NWP, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22677, https://doi.org/10.5194/egusphere-egu2020-22677, 2020.

With the successful upgrade of its deterministic model GFS (v15) on June 12, 2019, NCEP has scheduled the implementation of its next Global Ensemble Forecast System (GEFS v12) in the summer of 2020. These two model upgrades on deterministic and ensemble forecast systems are substantially different from previous upgrades. A new dynamical core (FV3) is adopted for the first time in the NCEP operational models, replacing the previous spectral dynamical core. The previous 3-category Zhao-Carr microphysics scheme is also being replaced by a more advanced 6-category GFDL microphysics scheme. From an ensemble model perspective, the previous GEFS has already demonstrated great success in past decades for weather and week-2 prediction by providing reliable probabilistic forecasts. Recently, there has been a large demand for subseasonal prediction, and GEFS v12 forecasts will be extended to 35 days to cover this time range. To better represent large uncertainties associated with this time scale, SPPT (stochastic physics perturbed tendency) and SKEB (stochastic kinetic energy backscatter) stochastic schemes are taking the place of the original STTP (stochastic total tendency perturbation), and a prescribed SST generated from combination of NSST and bias corrected CFS forecasts is also applied to simulate the sub-seasonal variation of SST forcing.    

As a major system upgrade,  a 2.5-year retrospective run of GEFS v12 is carried out to evaluate the model performance. A 30-year reforecast will be provided to stakeholders and the public to calibrate the forecast. The improvement of predictability and prediction skill will be studied through various measurements across tropical to extratropical areas in terms of deterministic (ensemble mean) and probabilistic (ensemble distribution) forecasts. The characteristics of model systematic error will be identified from comparing the major changes of the two state-of-art ensemble systems. As GEFS serves the most crucial model guidance for 5-7 day hurricane forecasts in support of the NHC (National Hurricane Center) and other customers, model capability in predicting tropical cyclone track and intensity is also examined from the retrospective runs. The results show there are significant improvements for tropical cyclone track forecast in North Atlantic and the western North Pacific, in particular, the intensity forecast is improved remarkably in all the basins.

How to cite: Fu, B., Zhu, Y., Zhou, X., and Hou, D.: Evaluation of NCEP next Global Ensemble Forecast System (GEFS v12), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6379, https://doi.org/10.5194/egusphere-egu2020-6379, 2020.

In recent years a strong relationship has been found between Atmospheric Rivers (ARs) and extreme precipitation and floods across western Europe, with some regions having 8 of their top 10 annual maxima precipitation events related to ARs. In the case of the Iberian Peninsula, the association between ARs and extreme precipitation days has also been well documented, particularly for western Iberia river basins.

Since ARs are often associated with high impact weather, it is important to study their medium-range predictability. Here we perform such an assessment using the ECMWF ensemble forecasts up to 15 days, for events that made landfall in western Iberian Peninsula during the winters spanning between 2012/2013 and 2015/16. IVT and precipitation from the 51 ensemble members of the ECMWF Integrated Forecasting System (IFS) ensemble (ENS) were processed over a domain including western Europe and contiguous North Atlantic Ocean.

Metrics concerning the ARs location, intensity and orientation were computed, in order to compare the predictive skill (for different prediction lead times) of IVT and precipitation analyses in the IFS. We considered several regional boxes over Western Iberia, where the presence of ARs is detected in analysis/forecasts, enabling the construction of contingency tables and probabilistic evaluation for further objective verification of forecast accuracy. Our results indicate that the ENS forecasts have skill to detect upcoming ARs events, which can be particularly useful to improve the prediction of associated hydrometeorological extremes. We also characterized how the ENS dispersion and confidence curves change with increasing forecast lead times for each sub-domain. We employed the standard ROC analysis to evaluate the probabilistic component of these predictions showing that for short lead times precipitation forecasts are more accurate than IVT forecasts, while for longer lead times this result is reversed (~10 days). Furthermore, we show that this reversal occurs at shorter lead times in areas where the ARs contribution is more relevant for winter precipitation totals (e.g. northwestern Iberia).

Acknowledgements

The work done was supported by the project Landslide Early Warning soft technology prototype to improve community resilience and adaptation to environmental change (BeSafeSlide) funded by Fundação para a Ciência e a Tecnologia, Portugal (FCT, PTDC/GES-AMB/30052/2017). A.M.R. was also supported by the Scientific Employment Stimulus 2017 from FCT (CEECIND/00027/2017).

How to cite: Ramos, A. M., Sousa, P. M., Dutra, E., and Trigo, R. M.: Predictive skill of atmospheric rivers in the Iberian Peninsula , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7354, https://doi.org/10.5194/egusphere-egu2020-7354, 2020.

Current regional forecasting systems particularly aim at the forecast of convective events and related hazards. Most weather centers apply high-resolution ensemble forecasts that resolve convection explicitly but can only afford a limited ensemble size of less than 100 members. Given that the degrees of freedom of atmospheric models are several magnitudes higher implies sampling errors. Sampling errors and fast error growth on convective scales in turn lead to a low predictability. Consequently, improving initial conditions and subsequent forecasts requires a better understanding of error correlations in both space and time.
For this purpose, we conducted the first convective-scale 1000-member ensemble simulation over central Europe. Several 1000-member ensemble forecasts are investigated during a high impact weather period in summer 2016 using ensemble sensitivity analysis. Spatial and spatiotemporal correlations are used to quantify sampling errors on convective scales. Correlations of the 1000-member ensemble forecast serve as truth to assess the performance of different localization approaches. Those approaches include a standard distance-based localization technique and a statistical sampling error correction method as proposed by Anderson (2012). Our study highlights advantages and disadvantages of existing methods and emphasises the need of different localization approaches for different scales and variables. Several results are published in Necker et al (2020a) and (2020b).

How to cite: Necker, T., Weissmann, M., Ruckstuhl, Y., Ruiz, J., Miyoshi, T., and Anderson, J.: Sampling errors on convective scales: What can we learn from a 1000-member ensemble?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-273, https://doi.org/10.5194/egusphere-egu2020-273, 2020.

The Marine Meteorology Division of the U.S. Naval Research Laboratory (NRL) has developed and transitioned a 3DVAR reflectivity data assimilation (DA) system into operations at Fleet Numerical Meteorology and Oceanography Center (FNMOC), located in Monterey, California.  The system assimilates hourly, volumetric, radar reflectivity data into the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS^®)¹ high-resolution NWP model within the ship-following COAMPS – On demand System (COAMPS-OS^®)¹.  Both Next-Generation Radar (NEXRAD) land-based radar data and U.S. Navy shipboard SPS-48/Hazardous Weather Detection and Display Capability (HWDDC) radar data are assimilated depending on their data coverage provided to the COAMPS^® nested grids. The SPS-48/HWDDC units are installed on eighteen U.S. Navy aircraft carriers and amphibious assault ships, and when underway on a mission, the available units automatically transmit compressed, radar data files to FNMOC near the top of the hour.  Through previously reported NRL and FNMOC demonstrations, and more recent operationally testing at FNMOC, the COAMPS-OS^® radar DA system’s nowcasting products have demonstrated their ability to provide improved predictions of precipitation events out to at least 6 hour forecasts compared to 3DVAR conventional DA into COAMPS^® alone.  Shipboard SPS-48/HWDDC radar data and their assimilation into COAMPS-OS^® at FNMOC provide critical environmental awareness in the data sparse oceanic regions of the world that the Navy warfighter encounters.

The SPS-48 radar is a S-band, phased-array, azimuthally scanning, air-search radar that scans electronically in elevation and completes a volume scan in four seconds. The HWDDC combines the volume scans into motion-compensated, one-minute composites with limited clutter filtering applied. The SPS-48 beams are combined to yield full PPI scans at 22 different elevation angles ranging from 0.1° to 24°. The azimuthal resolution of the data is 1° and the range resolution is 1 km. The maximum range for reflectivity (radial velocity) data is 250 (81) km.  The Doppler data are only produced for the lowest three elevation scans whereas reflectivity data are produced for all elevation scans; all these data are archived in Universal Format and compressed before dissemination to FNMOC.   Owing to the limited HWDDC Doppler data both in range and elevation, and the single-polarization of the SPS-48 radar waveform, reflectivity data quality control is particularly challenging.  New algorithms have been developed to handle sea clutter and constant power function artifacts, such as bullseyes and sun strobes.  There are two algorithms for sea clutter; the first one deals with anomolus propagation sea clutter caused by sea-water evaporation into the atmospheric surface layer, and the second one deals with the more widespread and distant sea clutter due to surface-based and elevated electromagnetic ducts resulting from trapped moist air under temperature inversions often encountered off the coasts of California and the Arabian Gulf region.  An overview of the ship-following COAMPS-OS^® radar data quality control and assimilation system will be presented along with examples of quality controlled SPS-48/HWDDC radar data and the impact on COAMPS^® forecast skill scores.

¹ COAMPS and COAMPS-OS are registered trademarks of the U.S. Naval Research Laboratory

How to cite: Harasti, P.: Quality Control of Sea Clutter and Constant Power Function Artifacts for Operational U.S. Navy Shipboard Radar Data Assimilation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11989, https://doi.org/10.5194/egusphere-egu2020-11989, 2020.

To be well prepared for rapidly-developing meteorological hazards in advance, quick and qualified information on real-time and very-short range (within 6 hours) forecasts is required. KLAPS (Korea Local Analysis and Prediction System) was developed for the operational very-short range forecasts in KMA (Korea Meteorological Administration), based on the LAPS (Local Analysis and Prediction System) from NOAA and WRF from NCAR in 2009. Recently, KLAPS is updated to use new observation datasets and physics schemes from KIM (Korea Integrated Model) to improve its very-short range precipitation forecast skills. New observation data sources (geostationary satellite, RADAR, ground-based GNSS(Global Navigation Satellite System), ceilometer, local radiosonde, etc.) are ingested into KLAPS in real-time to resolve rapidly developing mesoscale systems. Physics schemes (WDM7, KSAS(Kiaps SAS), RRTMG, Shing-Hong PBL, etc.) based on KIM physics package are implemented in KLAPS to support the high-resolution physics. The new KLAPS is now operated in 10-minute interval, so that it could provide 10-minute interval precipitation forecasts to the public(www.weather.go.kr) every 10 minutes. The advantages of 10-minute interval analysis and forecast system will be presented.

How to cite: Kim, M., Lee, K., and Lee, Y. H.: Development of 10-minute interval analysis and prediction system in KMA, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6451, https://doi.org/10.5194/egusphere-egu2020-6451, 2020.

Taiwan, a subtropical island with steep mountains, is influenced by diverse weather systems, including typhoons, monsoons, frontal, and convective systems. Of these, the prediction of deep, moist convection here is particularly challenging due to complex topography and apparent landsea contrast. This study explored the benefits of assimilating surface observations on prediction of afternoon thunderstorms using a 2-km resolution WRF and WRFDA model system with rapid update cycles. Consecutive afternoon thunderstorm events during 30 June to 08 July 2017 are selected. Five experiments, consisting of 240 continuous cycles are designed to evaluate the data assimilation strategy and observation impact. Statistical results show that assimilating surface observations systematically improves the accuracy of wind and temperature prediction near the surface. Also, assimilating surface observations alone in one-hour intervals improves model quantitative precipitation forecast (QPF) skill, extending the forecast lead time in the morning. Furthermore, radar data assimilation can benefit by the additional assimilation of surface observations, particularly for improving the model QPF skill for large rainfall thresholds. An afternoon thunderstorm event that occurred on 06 July 2017 is further examined. By assimilating surface and radar observations, the model is able to capture the timing and location of the convection. Consequently, the accuracy of the predicted cold pool and outflow boundary is improved, when compared to the surface observations.

How to cite: Chen, I.-H., Hong, J.-S., Tsai, Y.-T., and Fong, C.-T.: Improving Operational Numerical Prediction of Afternoon Thunderstorms over Taiwan through Surface Data Assimilation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7244, https://doi.org/10.5194/egusphere-egu2020-7244, 2020.

Optimizing the performance of numerical weather prediction models is a very complicated process due to the numerous parameterization choices provided to the user. In addition, improving the predictability of one model’s variable (e.g., temperature) does not necessarily imply the improvement of another (e.g., precipitation). In this work the Technique of Preference by Similarity to the Ideal Solution (TOPSIS) is suggested as a method to optimize the performance of a numerical weather prediction model. TOPSIS provides the ability of using multiple statistical measures as ranking criteria for multiple forecasting variables. The Weather Research and Forecasting model (WRF) is used here for application of TOPSIS in order to optimize the model’s performance by the combined assessment of temperature and precipitation over Europe. Six ensembles optimize model’s physics performance (i.e., microphysics, planetary boundary layer, cumulus scheme, Long–and Short– wave and Land Surface schemes). The best performing option for each ensemble is selected by using multiple statistical criteria as input for the TOPSIS method, based on the integration of entropy weights. The method adopted here illustrates the importance of an integrated evaluation of weather prediction models’ performance and suggests a pathway for its improvement.

Acknowledgments LIFE CLIMATREE project “A novel approach for accounting & monitoring carbon sequestration of tree crops and their potential as carbon sink areas” (LIFE14 CCM/GR/000635).

How to cite: Sotiropoulou, R.-E., Stergiou, I., and Tagaris, E.: A Methodology for Optimizing Numerical Weather Prediction Models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4149, https://doi.org/10.5194/egusphere-egu2020-4149, 2020.

Low-level wind shear could occur not only in rainy weather conditions but also in non-rainy weather conditions, which is dangerous to aircraft safety for its rapid changes in wind direction or velocity. Recently, dry wind shear occurred in non-rainy condition has drawn more and more attention. Rain-detecting Doppler radar has no capabilities in detecting dry wind shear occurred in non-rainy condition, while Doppler Lidar observations with higher spatial and temporal resolution provide valuable information for dry wind shear. For this, considering dry wind shear cases reported by pilots at Lanzhou Zhongchuan International Airport as study object, lidar observations (radial velocities) were assimilated along with surface data to improve the prediction skill of dry wind shear events.

All experiments were conducted with Weather Research and Forecasting (WRF) model and its three-dimensional variational (3D-VAR) system. Three-nested domains were employed with 1-km horizontal resolution in the innermost domain. The model was derived by the NCEP FNL data. Lidar data was processed and only assimilated in the innermost domain. Experimental results show that the low-level wind shear can not be found in the experimental results without lidar data assimilation, while lidar data assimilation experiment successfully represented wind shear small-scale characteristics and simulated radial wind pattern was close to lidar observation. In addition, assimilation cycles with short time intervals effectively improved simulation accuracy of wind shear events.

How to cite: Li, L. and Shao, A.: Impact of Lidar Data Assimilation on Analysis and Prediction of Low-level Wind Shears at Lanzhou Zhongchuan International Airport, China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5030, https://doi.org/10.5194/egusphere-egu2020-5030, 2020.

Lacking of high-resolution observations over oceans is one of the major problems for the numerical simulation of the tropical cyclones (TC), especially for the tropical cyclone inner-core structure’s simulation. Satellite observations plays an important role in improving the forecast skills of numerical weather prediction (NWP) systems. Many studies have suggested that the assimilation of satellite radiance data can substantially improve the numerical weather forecast skills for global model. However, the performance of satellite radiance data assimilation in limited-area modeling systems is still controversial.

This study attempts to investigate the impact of assimilation of the Advanced Technology Microwave Sounder (ATMS) satellite radiances data and its role to improve the model initial condition and forecast of typhoon LEKIMA(2019) using a regional mesoscale model. In this study, detailed analysis of the data impact will be presented, also the results from different data assimilation methods and different data usage schemes will be discussed.

How to cite: Zhang, L. and Chen, B.: Improving numerical simulation of typhoon LEKIMA(2019) through assimilating ATMS radiance data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6256, https://doi.org/10.5194/egusphere-egu2020-6256, 2020.

With violent wind and severe rainfall, the tropical cyclone is one of the most disastrous weather systems over ocean and the coastal area. To provide accurate tropical cyclone (TC) track and intensity forecasts is one of the most important tasks of the national weather service of countries affected. Taiwan is one of the areas frequently influenced by tropical cyclones. Improving the tropical cyclone forecast is the highest priority task of Taiwan’s Central Weather Bureau (CWB).

Recent improvement of the TC forecast is due to the improvement of the numerical weather prediction. A version of the Advanced Research Weather Research and Forecasting Model (WRF), named TWRF (Typhoon WRF), was developed and implemented in CWB for operational TC forecasting from 2011. During the years, partial update cycling, cyclone bogus scheme, relocation scheme, 3DVAR with outer loop, analysis blending scheme, new trigger Kain–Fritsch cumulus scheme, and so on have been studied and applied in TWRF (Hsiao et al. 2010, 2012, 2015) to improve the model. We also improved the model by changing the TWRF configuration from a triple nested to a double nested grid and increasing the model resolution from 45/15/5 km 45-levels to 15/3 km 52-levels from 2016. Results showed increasing the model resolution improving the track, intensity and rainfall forecast. However, The averaged 24/48/72 hours TC track forecast errors of TWRF are 91/147/223, 84/133/197, 74/127/215, 64/122/202, 70/120/194 and 70/122/180 km in year 2014, 2015, 2016, 2017, 2018 and 2019 respectively.

In this study, WRF Four-dimensional data assimilation (FDDA) is adopted to assimilate the temperature, pressure, water vapor content which processed from the FORMOSAT-7 constellation, high-temporal frequency atmospheric motion vector (AMV) retrieved from Himawari-8 satellite images and radar data to generate a model balanced TC structure and thermodynamic state at the initial time. The specific goal is to improve the track, structure and intensity prediction of TCs and their associated rainfall distribution in Taiwan. The detail will be presented in the conference.

Keywords: tropical cyclone, Himawari-8 AMV, Four-dimensional data assimilation, FORMOSAT-7, radar data.

Corresponding author address:

Der-Song Chen,  song@cwb.gov.tw

Central Weather Bureau, 64 Gongyuan Rd., Taipei, Taiwan, R.O.C., 10048.

How to cite: Chen, D.-S., Hsiao, L.-F., Xie, J.-H., Hong, J.-S., Fong, C.-T., and Yeh, T.-C.: Improve Tropical Cyclone Prediction of TWRF with the Application of Advanced Observation Data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4327, https://doi.org/10.5194/egusphere-egu2020-4327, 2020.

The system of multigrid NLS-4DVar data assimilation for Numerical Weather Prediction (SNAP) is established, building upon the multigrid NLS-4DVar assimilation scheme, the operational Gridpoint Statistical Interpolation (GSI)-based data-processing and observation operator and widely used numerical forecast model WRF (easily replaced by others global/regional model). The multigrid assimilation framework can adequately correct errors from large to small scales to achieve higher assimilation accuracy. Meanwhile, the multigrid strategy can accelerate iteration solution improving the computational efficiency. NLS-4DVar, as an advanced 4DEnVar method, employs the Gauss-Newton iterative method to handle the nonlinear of the 4DVar cost function and provides the flow-dependent background error covariance, which both contribute to the assimilation accuracy. The efficient local correlation matrix decomposition approach and its application in the fast localization scheme of NLS-4DVar and obviating the need of the tangent linear and adjoint model further improve the computational efficiency. The numerical forecast model of SNAP is any optional global/regional model, which makes the application of SNAP very flexible. The analysis variables of SNAP are rather the model state variables than the control variables adopted in the usual 4DVar system. The data-processing and observation operator modules are used from the National Centers for Environmental Prediction (NCEP) operational GSI analysis system, prominent in the various observation operators and the ability to assimilate multi-source observations. Currently, we have achieved the assimilation of conventional observations and we will continue to improve the assimilation of radar and satellite observations in the future. The performance of SNAP was investigated assimilating conventional observations used for the generation of the operational global atmospheric reanalysis product (CRA-40) by the National Meteorological Information Center of China Meteorological Administration. Cyclic assimilation experiments with two windows, which is 6-h for each window, are designed. The results of numerical experiments show that SNAP can absorb observations, improve initial field, and then improve precipitation forecast.

How to cite: Zhang, H. and Tian, X.: System of Multigrid NLS-4DVar Data Assimilation for Numerical Weather Prediction (SNAP)：System Formulation and Preliminary Evaluation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3812, https://doi.org/10.5194/egusphere-egu2020-3812, 2020.

The global numerical weather prediction (NWP) system based on the FV3GFS model jointly developed by U.S. National Centers for Environmental Prediction (NCEP) and Geophysical Fluid Dynamics Laboratory (GFDL) has been implemented in the Taiwan’s Central Weather Bureau (CWB) forecast system for the next generation global NWP operations. Currently, NCEP FV3GFS model provides land use dataset retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations. The MODIS 20-category data is composed of roughly 12 km resolution data elements. However, the modified of the 20-class MODIS land cover dataset with a resolution of 500 m which defined by the International Geosphere-Biosphere Program (IGBP) is provided by WRF model. A significant difference between these two datasets is MODIS data from NCEP FV3GFS as being extremely urbanized in western Taiwan. In a case of weaker synoptic-scale forcing, the modified MODIS land cover dataset from WRF model result in a larger improvement in 2-m temperature and 2-m mixing ratio when compare to the surface observations over Taiwan. The reason results from the overestimation of urban area in NCEP FV3GFS model, which contains previous and low-resolution MODIS dataset. Moreover, there is a bias reduction in 10-m wind speed as well as thermal effects. The detailed results will be presented in the conference.

How to cite: Hsiao, L.-F. and Wang, F.-J.: Impact of MODIS land cover data on surface predictions over Taiwan in the FV3GFS model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6752, https://doi.org/10.5194/egusphere-egu2020-6752, 2020.

In this talk we use the data from an operational ensemble prediction system to investigate recent developments in practical predictability across scales. Furthermore, we separate the estimated forecast error data into components representing the two dominant regimes in the atmosphere, the Rossby and inertia-gravity regimes. The latter is used to discuss aspects of tropical predictability.

We define the practical predictability limit of a meteorological field (e.g., meridional wind at 500 hPa) or of a variability mode (e.g., the equatorial Kelvin wave) by the forecast time at which the root mean square (rms) forecast error normalized by its saturation value reaches a prescribed threshold value (e.g., 60%).

The investigative technique fits a parametric function to the curve that describes the growth of the rms error of the forecasts with forecast time for a sample of forecasts. The parametric model describes the functional dependence of the magnitude of the forecast error on the magnitude of the initial error. Thus, it can be used for the estimation of the forecast error reduction that can be achieved by reducing the magnitude of the analysis error by a presumed percentage. Likewise, it can be used for the quantitative attribution of the forecast improvements between the years to analysis or model improvements.

The calculations are carried out for the different spatial scales and the two regimes separately.

How to cite: Žagar, N. and Szunyogh, I.: Recent and potential future evolution of practical predictability across scales , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12466, https://doi.org/10.5194/egusphere-egu2020-12466, 2020.

The accuracy of a large set of high-resolution wind speed forecasts with different lead times is assessed for different parts of orographic flows, including upstream blockings, gap winds, corner winds, wakes and downslope winds.  The by far largest errors are in areas of downslope windstorms, but there are also considerable errors in the other parts of orographic disturbances to the flow and they are greater than in non-orographic flows in the same region.  The errors are discussed in view of the different dynamics and kinematics of the flows.  They are partly related to intermittency of i.e. gravity waves as well as strong spatial gradients in the wind field.

How to cite: Ólafsson, H.: Uncertainties in forecasting orographic flows, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11997, https://doi.org/10.5194/egusphere-egu2020-11997, 2020.

The Normalized Difference Vegetation Index (NDVI) has been retrieved and analyzed for Iceland.  There are only limited trends in the total integrated NDVI in the period 2001 - 2018.  However, there is a positive trend in recent decades in the occurrence of signal corresponding to woodland and forests.   Locally, there may however be great changes; some small deserts have turned green and systematic planting of trees in certain regions is well detectable. The impact, and the driver of these changes are discussed in the context of climate and the implication for thermally driven weather systems and local weather forecasting is explained. 

How to cite: Rousta, I. and Ólafsson, H.: Changes in land surface and weather forecasting, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22110, https://doi.org/10.5194/egusphere-egu2020-22110, 2020.

How to cite: Ollinaho, P.: Ensemble prediction with OpenIFS, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18197, https://doi.org/10.5194/egusphere-egu2020-18197, 2020.

Icing occurs when an unheated solid structure is exposed to liquid cloud droplets at temperatures below the freezing point. Supercooled liquid water (SLW) in the atmosphere can persist in a physically metastable state until coming into contact with a solid object “In-cloud icing” occurs when super cooled liquid droplets (SLD) like clouds collide with a structure or object and freezes.

Atmospheric icing prediction has gain attention in the last years. Despite the progress made in meteorology, both weather forecasting modelling and atmospheric observations through advanced experimental technologies, there are still limitations in the accurate forecast and detection of icing conditions. The GFA‐ULE group has carried out some NWPs. In a previous work, we investigated the capability of the Weather Research and Forecasting model to detect regions containing supercooled cloud drops, proposing a multiphysics ensemble approach. Four microphysics and two planetary boundary layer schemes were used. Morrison and Goddard parameterizations with the YSU scheme, yielded superior results in evaluating the presence of liquid water content.

Concerning the remote detection of icing conditions, some European research centres (i.e. DLR, CIRA, ONERA, INCAS) as well as University of Leon (GFA-ULE) already have nowcasting or forecasting activities for detection of clouds and icing conditions. In this work a multichannel, microwave radiometer (MMWR) was used to detect the appearance of SLW. Consequently, we present both comparison between indirect detection of SLW and the output obtained by WRF with the two combination of parametrizations selected.

In our work we have taken into account:

The results show a good concordance between the number of events found by the MMWR and the result of the two numerical modeling performed. Therefore, everything seems to indicate that indirect detection by MMWR can be an accurate technology to detect the appearance of SLW and that the models can be qualitatively validated.

Acknowledgments: Data support came from the Atmospheric Physics Group, IMA, University of León, Spain, and the National Institute of  Aerospace Technology (INTA). This research was carried out in the framework of the SAFEFLIGHT project, financed by MINECO (CGL2016‐78702) and LE240P18 project (Junta de Castilla y León). We also thank R. Weigand for computer support.

How to cite: Sanchez, J. L., Melcon, P., Merida, G., Merino, A., Garcia-Ortega, E., Marcos, J. L., Lopez, L., Sanchez-Muñoz, L., Valero, F., Fernandez, J., Bolgiani, P., Martin, M. L., Fernandez-Gonzalez, S., and Navarro, A.: Evaluating the ability of a microwave radiometer and wrf to detect and simulate in-cloud icing conditions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14316, https://doi.org/10.5194/egusphere-egu2020-14316, 2020.

In this study, Korea Rapid Developing Thunderstorms (K-RDT) product from geostationary meteorological satellite which represents developing stage of convective cells is nudged to the Simplified Arakawa Schubert (SAS) deep convection scheme using a simple nudging technique in order to improve prediction skill of a heavy rainfall caused by mesoscale convective system over South Korea in the short-term forecast. Impact of the K-RDT information is investigated on the Global/Regional Integrated Model system (GRIMs) regional model program (RMP) system. For the selected heavy rainfall cases, the control run without nudging and two nudging experiments with different nudging period are performed. Although the simulated precipitations in the nudging experiments tend to depend on the distribution of convective cells detected in the K-RDT algorithm, the nudging experiment shows improved precipitation forecast than the control experiment. Particularly, the experiment with nudging for longer time produces better prediction skill. The results present that the small-scale convective cells from the K-RDT which are detected with a 1-km resolution have clear impacts to large-scale atmospheric fields. Therefore, it is suggested that utilizing small-scale information of convective system in the numerical weather prediction can have critical impact to improve forecast skill when the model system, which cannot properly represent sub-grid scale convections.

How to cite: Yeo, N., Chang, E.-C., and Min, K.-H.: Impact of the Korea Rapid Developing Thunderstorms (K-RDT) product nudging to the convective parameterization over the Korean Peninsula, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20986, https://doi.org/10.5194/egusphere-egu2020-20986, 2020.

The Weather Research and Forecasting (WRF) model dynamically downscales NCEP FNL Operational Global Analysis data in order to assess the grid size resolution effect on the simulated variables. Simulations where conducted over Europe for the year 2015 using 36km, 12km and 4km grid size resolutions. The sensitivity analysis assesses the grid size resolution effect on the simulated mean, maximum and minimum daily temperatures as well as precipitation. The simulated data are evaluated using reanalysis dataset. The statistical variables used are the bias, mean absolute error, root mean square error and the index of agreement for each grid cell. Results show that model performance for mean and maximum temperature, is better when increasing the spatial resolution from 36Km to 12Km but no significant change is found when the spatial resolution is further increased to 4Km, in general. In addition, model performance for minimum temperatures and precipitation does not change significantly when moving to higher spatial resolution grids (i.e., 12Km and 4Km) compared to the 36Km domain,

Acknowledgments LIFE CLIMATREE project “A novel approach for accounting & monitoring carbon sequestration of tree crops and their potential as carbon sink areas” (LIFE14 CCM/GR/000635).

How to cite: Tagaris, E., Stergiou, I., and Sotiropoulou, R.-E.: WRF forecast sensitivity to spatial resolution, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4151, https://doi.org/10.5194/egusphere-egu2020-4151, 2020.

A new scale-adaptive three-dimensional (3D) turbulent kinetic energy (TKE) subgrid mixing scheme is developed using the Advanced Research version of the Weather Research and Forecasting Model (WRF-ARW) to address the gray-zone problem in the parameterization of subgrid turbulent mixing. This scheme is based on the full 3D TKE prognostic equation and combines the horizontal and vertical subgrid turbulent mixing into a single energetically consistent framework.

A series of real tropical cyclone(TC) simulations with varying horizontal resolutions from 9km to 1km are carried out to compare the performance of the 3D mixing scheme and the conventional 1D planetary boundary layer (PBL) schemes to the observations, including conventional ones such as radiosonde and surface synoptic observations, as well as intensive ones obtained during the landfall of TC, such as mobile boundary layer wind profiler and Dual-pol Doppler Radar. This study aims to determine if the new scheme performs appropriate on TC simulation, and to evaluate the sensitivity of TC simulation to boundary layer schemes.

How to cite: liu, M. and Zhang, X.: Application of a new scale-aware three-dimensional subgrid mixing parameterization on the simulations of tropical cyclone, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6581, https://doi.org/10.5194/egusphere-egu2020-6581, 2020.

  A crucial step of the application of WRF in regional climate research is selection of the proper combinations of physical parameterizations. In this study, we performed experiments in WRF to assess the predict skill of various parametrization schemes sets in simulating precipitation, temperature over the Haihe river basin. The experiments driven by ERA-INTERIM reanalysis data are performed for a period of summer (1 June to 31 August, 2016) in this domain with 13 km grid spacing. Fifty-eight members of physics combinations thoroughly covering five types of physics options are assessed against the available observational data by utilizing the multivariable integrated evaluation (MVIE) method. It is deduced that the best performing setup consists of CAM5.1 microphysics, MRF PBL, BMJ Cumulus, CAM Longwave/Shortwave radiation, and Noah Land Surface schemes. To identify the robustness of the optimal scheme set, the vector field evaluation (VFE) diagram for displaying all simulations reveals that the optimal one is distinguished from others by higher vector field similarity coefficient(Rν), smaller root mean square vector deviation(RMSVD). The model deviations spatially for the precipitation show a promising tendency that a strong overestimation about 5 mm/day for the default configuration evolves small biases of the optimal setup with a range between -1 and 1 mm/day, and the surface temperature forecasts have improved to some extent although not significant as that of precipitation. The temporally analysis of the spatial average of all simulations exhibits that for temperature the optimal setup is more approaching to the observational data, but for precipitation no remarkable difference between all simulation and the observations. Further analysis of the sensitivities of model output to different types of physics option suggests that, microphysics, PBL, and Cumulus schemes have more significant impact on the model performances measured by a multivariable integrated evaluation index (MIEI) than radiation scheme and Land Surface schemes.

How to cite: Dai, D.: Evaluation of the WRF Physics Ensemble using Multivariable Integrated Evaluation Approach over Haihe river basin in north China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8832, https://doi.org/10.5194/egusphere-egu2020-8832, 2020.

The airborne research center called CIAR is placed in the airfield of Rozas (Lugo, Spain). It is a center for experimentation and development of new Unmanned Aerial Vehicles. Since you need to have a good planning of the flights of the prototypes, it is necessary to have a good prediction of the wind at different levels of height.

To obtain a reliable database for wind at different vertical levels, three types of instruments have been used: anemometers installed at 10 meters high to determine surface wind, a sodar for levels below 150 meters and a wind radar for those between 200 and 3000 m high above the CIAR level.

Concerning the mesoscale modelling: we have used the WRF with 48 sigma levels and horizontal resolution of up to 3 x 3 km. Therefore, we have applied multiphysics ensemble techniques. Five combinations of microphysics schemes (AEROSOL THOMPSON, MORRISON 2 MOMENTS, THOMPSON, GODDARD and WRF 2 MOMENTS), three of PBL (MYNN3, YSU and MYJ), and two of Surface (NOAA and RUC) have been selected.

Once the wind data databases were obtained, by means of the different instrumentation indicated above, it has been compared with each of the 20 WRF scenarios. To visualize the results, Taylor diagrams have been used for the different heights.

In summary, some conclusions have been found:

1. It’s necessary distinguish between low levels and those of slightly higher heights. On the surface, the scenarios with the PBL parameterizations called YSU and MYNN3 show better results.
2. It seems that the microphysics schemes settings have a less importance in wind forecast, which is consistent with the physical interpretation.
3. Above 200 meter, the 20 scenarios behave more satisfactorily with excellent correlation coefficients and low standard deviations

Acknowledgment

Data support came from the Atmospheric Physics Group, IMA, University of León, Spain, and the National Institute of Aerospace Technology (INTA). This research was carried out in the framework of the SAFEFLIGHT project, financed by MINECO (CGL2016‐78702) and LE240P18 project (Junta de Castilla y León). We also thank R. Weigand for computer support to the research group.

How to cite: Merino, A., Mérida, G., Melcón, P., López, L., Marcos, J. L., Romo, C. V., Seoane, N., Navarro, A., García-Ortega, E., and Sánchez Gómez, J. L.: Sensitivity analysis to physical parameterizations schemes applied for wind forecasting, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10586, https://doi.org/10.5194/egusphere-egu2020-10586, 2020.

IITM provides seasonal monsoon rainfall forecast using modified CGCM CFSv2. The present operational CFSv2 initilized with the INCOIS-GODAS ocean analysis based on MOM4p0d and 3DVar assimilation schemes. Recently new Ocean analysis GODAS-Mom4p1 using Moduler Ocean Model (MOM) upgraded physical model MOM4p1 is generated. This analysis has shown improvement in terms of subsurface temperature, salinity , current as well as sea surface temperature (SST), sea surface salinity (SSS) and surface currents over the Indian Ocean domain with respect to present operational INCOIS-GODAS analysis (Rahaman et al. 2017;Rahman et al. 2019). This newly generated ocean analysis is used to initialize NCEP Climate Forecast System (CFSv2) for the retrospective run from 2011 to 2018. The simulated coupled run has shown improvement in both oceanic as well atmospheric parameters. The more realistic nature of coupled simulations across the atmosphere and ocean may be promising to get better forecast skill.

How to cite: Pokhrel, S., Rahaman, H., Chaudhari, H., Saha, S. K., and Hazra, A.: Impact of improved Ocean initial condition in Climate Forecast System (CFSv2) Hindcast run, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19593, https://doi.org/10.5194/egusphere-egu2020-19593, 2020.

It is well known that horizontal resolution has a great deal of impact on tropical cyclone simulations using numerical weather prediction models.  It is relatively less discussed in the literature how vertical resolution affects the solution convergence of tropical cyclone simulations.  In this study, the resolved kinetic energy spectrum, the Richardson number probability density function and resolved flow features are used as metrics to examine the behavior of solution convergence in tropical cyclone simulations using the Weather and Forecast Model (WRF).  It is found that for convective-scale simulations of a real tropical cyclone case with 3-km horizontal resolution, the model solution does not converge until a vertically stretched vertical resolution approaches 200 layers or more.  The results from this study confirm the results from a few previous studies that the subgrid turbulent mixing, particularly, the vertical mixing, plays a significant role in the behavior of model solution convergence with respect to vertical resolution.  They also provide a basis for the vertical grid configuration selection for the operational tropical cyclone model of Shanghai Meteorological Service.

How to cite: Huang, W., Liu, M., Zhang, X., and Bao, J.: The impact of vertical resolution on tropical cyclone simulation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6352, https://doi.org/10.5194/egusphere-egu2020-6352, 2020.

Numerical weather predictions (NWP) models are increasingly run using kilometer-scale horizontal grid spacing at which convection is partially resolved and the use of a subgrid convection parameterization scheme is still required. Traditionally, subgrid deep convection has been represented by mass flux-based convection parameterizations based on the ensemble-mean closure concept. Recently, a great effort has been made to develop scale-aware subgrid convection schemes that can be used in kilometer-scale NWP models. However, direct evaluation of these schemes is rarely done using coarse-grained large-eddy simulation (LES).

In this study, an idealized LES of deep moist convection is performed to assess the performance of three widely-used scale-aware subgrid convection schemes in the Weather Research and Forecast (WRF) model that is run at 3-km horizontal resolution. It is found that the simulations using the three schemes not only differ from each other but also do not converge to the coarse-grained LES, indicating that further investigation is required as to what “scale-awareness” means in theory and practice.

How to cite: Zhang, X., Bao, J.-W., and Chen, B.: Evaluation of scale-aware convection schemes at the kilometer-scale resolution, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5337, https://doi.org/10.5194/egusphere-egu2020-5337, 2020.

A new parameterization of the accretion of cloud water by snow for use in bulk microphysics schemes is derived by analytically solving the stochastic collection equation (SCE), where the theoretical collision efficiency for individual snowflake–cloud droplet pairs is applied. The snowflake shape is assumed to be nonspherical with the mass- and area-size relations suggested by an observational study. The performance of the new parameterization is compared to two parameterizations based on the continuous collection equation, one with the spherical shape assumption for snowflakes (SPH-CON), and the other with the nonspherical shape assumption employed in the new parameterization (NSP-CON). In box model simulations, only the new parameterization reproduces a relatively slow decrease in the cloud droplet number concentration which is predicted by the direct SCE solver. This results from considering the preferential collection of cloud droplets depending on their sizes in the new parameterization based on the SCE. In idealized squall-line simulations using a cloud-resolving model, the new parameterization predicts heavier precipitation in the convective core region compared to SPH-CON, and a broader area of the trailing stratiform rain compared to NSP-CON due to the horizontal advection of greater amount of snow in the upper layer. In the real-case simulations of a line-shaped mesoscale convective system that passed over the central Korean Peninsula, the new parameterization predicts higher frequencies of light precipitation rates and lower frequencies of heavy precipitation rates. The relatively large amount of upper-level snow in the new parameterization contributes to a broadening of the area with significant snow water path.

How to cite: Jin, H.-G. and Baik, J.-J.: A new parameterization of the accretion of cloud water by snow and its evaluation through simulations of mesoscale convective systems, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1994, https://doi.org/10.5194/egusphere-egu2020-1994, 2020.

Considering aerosol effects via microphysics parameterization is an imperative work in high-resolution numerical weather prediction. This paper uses two bulk microphysics parameterizations, Aerosol-Aware Thompson and CLR schemes, with the Weather and Research Forecast model to study the impacts of aerosols and microphysics scheme on an idealized supercell storm. Our results show that the implementation of aerosols can successfully modify the cloud droplet size and influence the subsequent warm-rain, mixed-phase, and accumulated precipitation. It implies that aerosols can make numerous differences to cloud microphysics properties and processes but the uncertainty in the magnitude of aerosol effects is huge because the two schemes are different from each other since the warm-rain process including CCN activation and rainwater formation. On the other hand, it is also found that the two schemes make tremendous differences in the rainfall pattern and storm dynamics due to the presence of graupel below the freezing level. The Thompson scheme has hail-like graupel which can fall below the freezing level to chill the air temperature effectively, intensify the downdraft, and enhance the uplifting on the front of cold pools. The mean graupel size represented by the two schemes plays a much more important role than the fall-speed formula for the dynamical feedbacks. Our results suggest that particle size is the core of a myriad of microphysics processes and highly associated with key cloud and dynamical signatures.

How to cite: Wu, W., Huang, W., and Chen, B.: A comparison study of aerosol impacts on idealized supercell between two bulk microphysics parameterizations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6235, https://doi.org/10.5194/egusphere-egu2020-6235, 2020.

The next generation of weather and climate models will have an unprecedented level of resolution and model complexity, while also increasing the requirements for calculation and memory speed. Reducing the accuracy of certain variables and using mixed precision methods in atmospheric models can greatly improve Computing and memory speed. However, in order to ensure the accuracy of the results, most models have over-designed numerical accuracy, which results in that occupied resources have being much larger than the required resources. Previous studies have shown that the necessary precision for an accurate weather model has clear scale dependence, with large spatial scales requiring higher precision than small scales. Even at large scales the necessary precision is far below that of double precision. However, it is difficult to find a guided method to assign different precisions to different variables, so that it can save unnecessary waste. This paper will take CESM1.2.1 as a research object to conduct a large number of tests to reduce accuracy, and propose a new discrimination method similar to the CFL criterion. This method can realize the correlation verification of a single variable, thereby determining which variables can use a lower level of precision without degrading the accuracy of the results.

How to cite: Lai, J.: A Guiding Principles for Choosing Numerical Precision in Atmospheric Model based on CESM, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13445, https://doi.org/10.5194/egusphere-egu2020-13445, 2020.

In order to provide multiple choices of dynamic cores for the next generation global numerical prediction system at Chinese Meteorological Administration (CMA), a Z-grid based dynamic core is under development. Among other important features of a Z-grid scheme, better dispersion relation, natural geostrophic adjustment and conservation attract numerical modeler’s interests. In this presentation, we will share the progress of such a development at CMA along with other dynamic cores, improving its accuracy on a sphere, efficient solvers and software design and implementation. We also developed some standard unit test cases for software reliability, which are also available and convenient for other dynamic cores. Some numerical experiment results will be presented as well.

How to cite: Xie, Y.: A Z-grid Based Dynamic Core for Global Numerical Prediction Model of Chinese Meteorological Agency, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21150, https://doi.org/10.5194/egusphere-egu2020-21150, 2020.

We examine the weakening of the Atlantic Meridional Overturning Circulation (AMOC) in response to increasing CO₂ at different horizontal resolutions in HadGEM3-GC3.1 and in a small ensemble of models with differing resolutions. There is a strong influence of the ocean mean state on the AMOC weakening: models with a more saline western subpolar gyre have a greater formation of deep water there. This makes the AMOC more susceptible to weakening from an increase in CO₂ since weakening ocean heat transports weaken the contrast between ocean and atmospheric temperatures and hence weaken the buoyancy loss. In models with a greater proportion of deep water formation further north (in the Greenland-Iceland-Norwegian basin), deep-water formation can be maintained by shifting further north to where there is a greater ocean-atmosphere temperature contrast.

We show that ocean horizontal resolution can have an impact on the mean state, and hence AMOC weakening. In the models examined, those with higher resolutions tend to have a more westerly path of the North Atlantic Current and hence greater impact of the warm, saline subtropical Atlantic waters on the western subpolar gyre. This results in greater dense water formation in the western subpolar gyre. Although there is some improvement of the higher resolution models over the lower resolution models in terms of the mean state, both still have biases and it is not clear which biases are the most important for influencing the AMOC strength and response to increasing CO₂.

How to cite: Hewitt, H., Jackson, L., Roberts, M., Iovino, D., Koenigk, T., Meccia, V., Roberts, C., Ruprich-Robert, Y., and Wood, R.: Does ocean resolution affect the rate of AMOC weakening?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11447, https://doi.org/10.5194/egusphere-egu2020-11447, 2020.

Previous work showed that simulations of extratropical cyclones and their intensity are significantly impacted by model resolution. This might be explained by the impact of resolution on cloud diabatic processes occurring within the warm conveyor belt (WCB), as these are linked to the strength of cyclones. To investigate this link, we move gradually from very coarse (80km) to very fine resolution (2.5km) simulations and study if there is a systematic impact of resolution on the simulated WCB processes. For this purpose, we analyse ICON simulations in a regional North Atlatnic setup for a specific case from the NAWDEX campaign -  cyclone Vladiana - that occurred on 23^rd September, 2016. Furthermore, we compare simulations with 1- and 2- moment cloud microphysics and with explicit and parametrized convection. From these simulations WCB trajectories are calculated over 48 hours by means of the Lagranto tool and 1-hourly model output to sample cloud diabatic processes within the WCB. We find a systematic increase in the number of WCB trajectories with finer resolution, which also ascent higher. Moreover, the fine-resolution simulations show a new class of anticyclonic trajectories that is absent in the low-resolution simulations. This effect becomes more pronounced when convection is represented explicitly, but is not strongly affected by the treatment of cloud microphysics. We diagnose the impact of increasing resolution on WCB in terms of changes in processes like updraft velocity, diabatic heating and modification of potential vorticity by total diabatic heating and individual diabatic processes.

How to cite: Choudhary, A. and Voigt, A.: Sensitivity of simulated warm conveyor belt associated with an extratropical cyclone to model resolution and treatment of cloud processes - a NAWDEX case study, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21165, https://doi.org/10.5194/egusphere-egu2020-21165, 2020.

With climate change and the conjoint challenges of food availability, clean water and geo-energy resources, our society is facing major challenges in the near future. These challenges are hard to address, because projections of Earth system change involve uncertainties that require quantification. Therefore, the Earth system science community tries to develop tools that provide decision-makers with the information required to effectively manage these issues.

The Advanced Earth System Modelling Capacity project (ESM) aims to enable such tools, investigating problems by looking at interactions between different Earth system components and improve their representation in numerical models. The project was funded by the German Helmholtz Association in April 2017 and involves eight research centers across Germany. The ultimate goal of the project is to represent the Earth system and how it changes with a world-leading modelling infrastructure that will support the process of developing solutions for the grand challenges we are facing.

The five different work packages of the project are working on topics such as enhancing the representation of Earth system model compartments, develop a flexible framework for coupling of Earth system model components, advance the Earth system data assimilation capacity, diagnose Earth system models, further develop cutting-edge frontier simulations, cross-scale modelling, and contribute to the shaping of a national strategy for Earth system modelling. The project also engages in training activities to educate and transfer knowledge to the next generation of scientists.

Since its initiation the project contributed with important results to several key model systems and platforms. In this presentation, we will highlight some current results and discuss advances in our Earth system modelling community and the way forward.

How to cite: Cristini, L. and the ESM Project Consortium: Advanced Earth System Modelling Capacity (ESM): Solving Grand Challenges by improving the representation of the components of the Earth system and their coupling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8322, https://doi.org/10.5194/egusphere-egu2020-8322, 2020.

A well-known drawback of Eulerian models is excessive numerical diffusion of the transported species. This applies to chemical species but also to water vapor. We present a new way of dealing with this problem in the vertical direction by using the Després and Lagoutière (1999) scheme – hereinafter DL99, a simple 1^st-order advection scheme with antidiffusive properties. These authors have only studied this scheme in the context of 1d transport. Here we test the applicability of this scheme in atmospheric modelling by applying it to tracer transport in the vertical direction in idealized 2d (zonal-vertical) atmospheric circulations and quantify the gain compared to classical advection schemes.

In this idealized framework, we have tested the efficiency of DL99 in two cases representative of important situations for atmospheric transport :
- Formation of a thin plume from an initially thick column of tracer (e.g. volcanic plume, biomass burning plume etc.) under the effect of zonal wind shear in presence of large-scale variations in vertical wind
- Long-range advection of a thin polluted layer under the action of zonal wind in presence of large scale oscillations in the vertical wind.

In these idealized case studies, we show that using DL99 in the vertical direction yields dramatically improved performance compared to any other scheme we have tested, including the 3^rd-Order Piecewise Parabolic Method (PPM). As an example, in a simulation with a vertical resolution of 500m and a zonal resolution of 25 km initialized by a 1000m-thick, zonally uniform layer of tracer, after being transported horizontally over 2000km over 48 hours by a uniform zonal wind ~11.5ms^-1 together with an oscillating vertical wind of +-0.05ms^-1, maximal tracer concentration at the end of the simulation with DL99 is 94% of its initial value instead of 51% with PPM, l² relative error is 10% instead of 61%, and 92% of the tracer mass is still confined in the correct 1000m-thick envelope instead of 50%.

This and other numerical experiments shows that, by design, DL99 reduces numerical diffusion, but it also proves it to be able to preserve the areas of uniform tracer concentration even if these areas cover only a very small number of cells in the vertical direction. We argue that this unique set of properties, along with the simplicity of its formulation and its minimal computational cost make the DL99 an extremely attractive candidate for a robust and non-diffusive representation of vertical advection in Eulerian meteorological and chemistry-transport models. This scheme has been implemented in the state-of-the-art CHIMERE chemistry-transport model (Mailler et al., 2017), and we have shown that it brings a clear improvement in the representation of the structure of a volcanic plume from the Etna volcano (Lachâtre et al., 2020, Atmos. Chem. Phys.)

Main references:

Després, B., and F. Lagoutière, 1999, Un schéma non linéaire anti-dissipatif pour l'équation d'advection linéaire, Comptes Rendus de l’Académie des Sciences

How to cite: Mailler, S., Lachatre, M., and Menut, L.: Using an antidiffusive transport scheme for vertical transport: a promising path towards solving the long-standing problem of excessive vertical diffusion in Eulerian models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11706, https://doi.org/10.5194/egusphere-egu2020-11706, 2020.

High-resolution numerical weather and climate models have a great advantage in both prediction and simulation for their ability to resolve small-scale systems, but suffer from expensive computational cost. The aim of this study is to explore a cost-effective variable-resolution modeling approach within a newly developed global nonhydrostatic dynamical core based on an unstructured mesh. We provide a size-controllable formulation of hierarchical refinement mode by an adapted density function for more realistic high-resolution simulations. The dynamical core is tested regarding both dry and moist atmosphere to evaluate variable-resolution simulations against quasi-uniform ones. In baroclinic wave tests, the variable-resolution model, which owns much less grid points, captures a comparable fine-scale fluid structure with the high-resolution quasi-uniform one in the refinement region. In the coarse region, the result of the variable-resolution simulation matches that of the low-resolution quasi-uniform one, which contributes to smaller global errors of the variable-resolution simulation. A series of sensitivity tests regarding parameters of the hierarchical refinement mode validate the high stability of the variable-resolution model to preserve the intensity and vertical structure of tropical cyclones moving through the transition zone. The variable-resolution modeling lays a strong foundation for potential improvement of regional high-resolution simulations.

How to cite: Zhou, Y., Zhang, Y., Liu, Z., Li, J., and Yu, R.: Exploring a Variable-Resolution Modeling Approach within a Global Nonhydrostatic Dynamical Core, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1328, https://doi.org/10.5194/egusphere-egu2020-1328, 2020.

Reanalysis is a key contribution to the Copernicus Climate Change Service (C3S) that is implemented at the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission. The most recent ECMWF reanalysis, ERA5, provides hourly estimates of the global atmosphere, land surface and ocean waves at a horizontal resolution of 31km. Daily updates are provided with a latency of 5 days, while an extension back to 1950 is to be made available in the 2nd quarter of 2020.
ERA5 uses a 2016 version of the ECMWF numerical weather prediction model and data assimilation system (Integrated Forecasting System Cy41r2) to assimilate both in situ and satellite observations (95 billion for the period 1979 - 2019), many of which stem from reprocessed data records. The assimilation method includes a variational method for estimating observation biases that respects the heterogeneity within the observing system. Information on random uncertainties in the state estimates is provided by a 10-member ensemble of data assimilations at half the horizontal resolution (63km).
This presentation provides a concise overview of the ERA5 data assimilation system. A basic evaluation of characteristics and performance is presented, which includes an inter-comparison with other reanalysis products, such as its predecessor ERA-Interim and several major reanalyses produced elsewhere. Attention is given to the importance of the specification of the background error covariance matrix that determines the weight given to the model's first guess in the assimilation. In addition, a special focus will be on the back extension from 1950 to 1978, where the absence of satellite data prior to the 1970s puts a more demanding constraint on the data assimilation system.

How to cite: Hersbach, H., Bell, B., Berrisford, P., Dahlgren, P., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Radu, R., Schepers, D., Simmons, A., and Soci, C.: The ERA5 Global Reanalysis: achieving a detailed record of the climate and weather for the past 70 years., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10375, https://doi.org/10.5194/egusphere-egu2020-10375, 2020.

The National Center for Atmospheric Research (NCAR) has recently released version 2.1 of the Community Earth System Model (CESM 2.1). A twenty-year, 80-member ensemble atmospheric reanalysis with 1-degree resolution in the CAM6 atmospheric model is being produced using NCAR’s Data Assimilation Research Testbed (DART) to support a variety of climate research goals. A standard configuration of CAM and the CLM5 land surface model will be coupled to a prescribed ocean and sea ice. Eventually, the reanalyisis will generate a final product that extends from 1999 to the present. Observations being assimilated include in situ observations used in the operational NCEP CFSR reanalysis along with GPS occultation observations and remote sensing temperature retrievals. The primary goal is to provide an ensemble of atmospheric forcing that can be used to generate additional ensemble reanalyses for other components of CESM including CLM, the POP and MOM6 ocean models, and the CICE sea ice model. Highlights of results from the first 10-years of the reanalysis will be presented. Results will include evaluation of short-term forecasts in observation space for root mean square error, ensemble spread, and ensemble consistency. In addition, key aspects of the atmospheric forcing files for other components of the climate system will be discussed. 

How to cite: Raeder, K., Anderson, J., Hoar, T., Collins, N., and El Gharamti, M.: Results from An Ensemble Reanalysis with the Community Earth System Model 2.1 , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3131, https://doi.org/10.5194/egusphere-egu2020-3131, 2020.

Under the Quantitative Observing System Assessment Program, the National Oceanic and Atmospheric Administration's (NOAA's) Atlantic Oceanographic and Meteorological Laboratory (AOML) is preparing to utilize the 9-km-resolution European Centre for Medium-Range Weather Forecasts (ECWMF) Cubic Octahedral grid global Nature Run (ECO1280) for observation simulation and conducting Observing System Simulation Experiments (OSSEs).   As part of the OSSE calibration, and before experiments can be run, it needs to be shown that the forecast model used in the OSSEs does not do a better job in predicting the Nature Run meteorology than it does in predicting the real world. Otherwise, the conclusions from the OSSEs in such a configuration may misstate the potential impact of a given instrument. In this presentation, the predictability of the new global OSSE system being developed at NOAA will be discussed. The NOAA/National Centers for Environmental Prediction (NCEP) Finite-Volume Cubed-Sphere Global Forecast System (FV3GFS) is used to test predictability over the first two months of ECO1280 (October-November 2015), comparing forecasts using simulated observations with added errors to real-world observations.  Only conventional observations will be utilized in both cases.  

How to cite: Casey, S., Cucurull, L., and Vidal, A.: Predictability of ECMWF Cubic Octahedral Nature Run (ECO1280) using Finite-Volume Cubed-Sphere Global Forecast System (FV3GFS) Compared to Real-World Predictability, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2012, https://doi.org/10.5194/egusphere-egu2020-2012, 2020.

Utilizing a multitude of in situ and remote sensing instruments, a comprehensive dataset was collected during the transatlantic field campaign NAWDEX in autumn 2016. Cycled data denial experiments with the global model of the ECMWF showed that additionally collected dropsonde and radiosonde observations contributed to a reduction in the short-range forecast error, with the most prominent error reductions being linked to Tropical Storm Karl, cyclones Matthew and Nicole and their subsequent interaction with the midlatitude waveguide. While the short-range forecast quality was improved, Schäfler et al. (2019, in review) demonstrated that ECMWF IFS analyses exhibit deficiencies in capturing observed wind speeds at and above the dynamical tropopause during NAWDEX. Therefore, data assimilation output from the ECMWF IFS is used to evaluate the observational influence on the tropopause. Statistics of data assimilation diagnostics such as the analysis increment and first guess departure will be assessed in observation space in a tropopause relative framework to quantify the impact of assimilated radiosonde observations on tropopause location and sharpness.

How to cite: Schindler, M., Weissmann, M., Schäfler, A., and Radnoti, G.: The impact of NAWDEX dropsonde and extra radiosonde observations on forecast quality and tropopause structure, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17945, https://doi.org/10.5194/egusphere-egu2020-17945, 2020.

How to cite: Nuterman, R., Häfner, D., Jochum, M., and Vinter, B.: Next-generation geophysical modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20536, https://doi.org/10.5194/egusphere-egu2020-20536, 2020.

How to cite: Jüling, A., von der Heydt, A., and Dijkstra, H.: Effects of mesoscale ocean flows on multidecadal climate variability, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20598, https://doi.org/10.5194/egusphere-egu2020-20598, 2020.

The purpose of this study is to examine the impact of climate change on the changes on summer surface temperatures between present (2000-2010) and future (2050-2060) over the Arabian Peninsula and Kuwait. In this study, the influence of climate change in the Arabian Peninsula and especially in Kuwait was investigated by high resolution (36, 12, and 4 km grid spacing) dynamic downscaling from the Community Climate System Model CCSM4 using the WRF Weather Research and Forecasting model. The downscaling results were first validated by comparing National Centers for Environmental Prediction NCEP model outputs with the observational data. The global climate change dynamic downscaling model was run using WRF regional climate model simulations (2000-2010) and future projections (2050-2060). The influence of climate change in the Arabian Peninsula can be projected from the differences between the two period’s model simulations. The regional model simulations of the average maximum surface temperature in summertime predicted an increase from 1◦C to 3 ◦C over the summertime in Kuwait by midcentury.

How to cite: Alsarraf, H.: Projected climate change over Kuwait simulated using a WRF high resolution regional climate model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1867, https://doi.org/10.5194/egusphere-egu2020-1867, 2020.

    The EARS (East Asia Reanalysis System) is a project of China Meteorological Administration for developing a high resolution regional atmospheric reanalysis dataset in the East Asia with higher quality for meso-scale weather system simulation and regional climate analysis. The reanalysis system was established with a domain covers the East Asia with horizontal resolution of 12km and some sub-domains with horizontal resolution of 3km. In EARS, besides the Global Communication System (GTS) shared observations, dense local observations such as the ground-based automatic observations, and weather radar data are used. New data assimilation operator for radar data was developed, and data assimilation methods for high-density surface observation were tested. Based on the observation dataset and reanalysis system, 10 years primary test reanalysis dataset was established. Verifications of the dataset indicate a better quality in comparison with global reanalysis data.

How to cite: Liang, X., Yin, J., Xie, Y., and Li, F.: East Asia Reanalysis System of CMA, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1914, https://doi.org/10.5194/egusphere-egu2020-1914, 2020.

The aim of this presentation is to compare the occurrence of discontinuities in the ozone concentration data from the MERRA-2, ERA-5 and JRA-55 reanalyse, with the help of the Pettitt homogeneity test. We distinguish between the significant and insignificant discontinuities, according to the relation between the dispersion and the average ozone values before and after the discontinuity.    This occurrence is important for trend analyses, because the presence of discontinuities influences the values of trends and their significance. Discontinuities arise from the changing in the assimilation procedure, introducing new observation to the reanalyse, and changing of data quality. We search for their spatial, temporal and geographical occurrence. There are differences among these reanalyses. In the case of the MERRA-2 data, the transition from SBUV to EOS Aura data in 2004 has great impact on discontinuity behaviour. The frequent occurrence of discontinuities is seen in the uppermost model layers. The uppermost MERRA-2 layer is 0.1 hPa, while for ERA-5 this layer is 1 hPa. So there are differences in the vertical distribution of discontinuities among the reanalyses. The ozone data with the strong occurrence of the significant discontinuities is not suitable for trend analyses.   

How to cite: Krizan, P.: Occurrence of discontinuities in the ozone concentration data from three reanalyses, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1942, https://doi.org/10.5194/egusphere-egu2020-1942, 2020.

A new 10-type urban Local Climate Zone (LCZ) classification with 100-m resolution was developed, following the guidelines of the World Urban Database and Access Portal Tools (WUDAPT) over the Greater Bay Area (GBA). This LCZ dataset was incorporated into the Building Environment Parameterization (BEP)-Building Energy Model (BEM) multi-layer urban canopy scheme used by the Weather Research and Forecasting (WRF) model, with key parameters (such as fraction of impervious surface, building height/width, road width, air conditioning usage) determined from local building morphology and energy consumption patterns. The impacts of using such detailed 10-type LCZ, as compared to using remapped 3-type LCZ and using default WRF 1-type urban land cover were assessed, based on parallel integrations of the WRF system at 1-km resolution for a historical hot-and-polluted event over the GBA. It was found that the model surface temperature, air temperature, humidity and wind speed in the 10-type LCZ run were in closer agreement with in-situ observations, demonstrating the value of detailed urban LCZ data in improving the model performance. Smaller diurnal temperature range and higher nighttime temperature were found in the 10-type LCZ run compared to the 3-type LCZ and 1-type runs. Increased building height in the 10-type LCZ setting also reduces positive bias of wind speed in the lower planetary boundary layer at urban locations. The cold and dry biases over the non-urban areas in the 10-type LCZ run could be further reduced through considering updated land cover, soil type, soil hydraulic/thermal parameters, soil moisture/temperature. Owing to the improvement in capturing the urban meteorology, incorporating more detailed LCZ classification might also improve air-quality simulations. These findings should be relevant to the development of comprehensive, high-resolution earth system models, which are an indispensable tool for mitigation of and adaption to regional environmental and climate changes.

How to cite: Chen, J., Tam, C.-Y., Yim, S. H. L., Cai, M., Wang, R., Li, X., Ren, C., Zhang, T., and Gao, P.: High-resolution Simulations of a Hot-and-polluted Event with Detailed Local Climate Zone Information over the Greater Bay Area in South China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3225, https://doi.org/10.5194/egusphere-egu2020-3225, 2020.

Numerical simulation of nonlinear gravity internal waves with non-hydrostatic ocean models, especially these which using the terrain-following sigma-coordinate, is challenging. The expensive computation cost, which is caused by the dynamic pressure Poisson solver in cases using fine grid resolution in both directions (horizontal and vertical), is the main reason. A non-hydrostatic ocean model named NH-GOMO is constructed based on a partially implicit finite difference scheme for the dynamic pressure and adopts an idea of “decimation and interpolation”. A significant optimization for the pressure Poisson solver, which brings no obvious accuracy loss, is obtained with these technologies. The automatic parallel operator library named OpenArray is used as the bottom layer of this model and make it easy to transport between different computing platforms. Accuracy and efficiency have been validated by several ideal test cases.

How to cite: Tang, Q., Huang, X., and Huang, X.: NH-GOMO, an efficient and easily portable non-hydrostatic ocean model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4579, https://doi.org/10.5194/egusphere-egu2020-4579, 2020.

How to cite: Zeng, Y., Janjic, T., de Lozar, A., Blahak, U., and Seifert, A.: An idealized Testbed for Radar Data Assimilation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5403, https://doi.org/10.5194/egusphere-egu2020-5403, 2020.

Previous studies showed that high resolution GCMs overestimate land precipitation when compared against gridded observations or reanalysis (Demory et al. 2014, Vannière et al. 2019). In particular, grid point models (eg. HadGEM3) show a significant increase of precipitation on regions dominated by complex orography, where the scarcity of gauge stations increase the uncertainty of gridded observations. The goal of this work is to assess the effect of such differences in precipitation on river discharge, considering it as an integrator of the water balance at catchment scale. A set of JULES and CLM simulations have been conducted turning rivers on with Total Runoff Integrating Pathways (TRIP) and the River Transport Model (RTM) respectively. The simulations form three ensembles for each land surface model (LSM) which main difference is given by the forcing dataset. The forcings are WFDEI (reanalysis), LR (~1° resolution in meteorological data from GCMs) and HR (~0.25° resolution in meteorological data from GCMs). These ensembles are evaluated in a set of 280 catchments distributed around the world.

In terms of correlation between simulated and observed river discharge observations, the results show that LSMs forced by reanalysis have higher performance than LSMs forced by GCMs as expected. In terms of biases, the river discharge is underestimated in eight out of eleven major basins when LSMs are forced by reanalysis. On those basins, the extra precipitation estimated by GCMs help to simulate an amount of river discharge closer to observations (Eg. Yenisey and Lena). Moreover, 37 small basins with a strong component of orographic precipitation over the Andes, the Rocky Mountains, the Alps and in the Maritime Continent were evaluated. In most cases HR offers notably better results than LR and WFDEI, suggesting that high resolution models produce orographic precipitation in the correct place and time.

In future works offline TRIP simulations will be carried out directly forced by runoff and subsurface runoff from GCMs. It will allow to discard errors in evapotranspiration produced by JULES or CLM when they are used to simulate river discharge. This work is part of the European Process-based climate sIMulation: AdVances in high resolution modelling and European climate Risk Assessment (PRIMAVERA) Project. PRIMAVERA is a collaboration between 19 funded by the European Union’s Horizon 2020 Research & Innovation Programme.

Demory, M. E., Vidale, P. L., Roberts, M. J., Berrisford, P., Strachan, J., Schiemann, R., & Mizielinski, M. S. (2014). The role of horizontal resolution in simulating drivers of the global hydrological cycle. CLIM DYNAM, 42(7-8), 2201-2225.

Vannière, B., Demory, M. E., Vidale, P. L., Schiemann, R., Roberts, M. J., Roberts, C. D., ... & Senan, R. (2018). Multi-model evaluation of the sensitivity of the global energy budget and hydrological cycle to resolution. CLIM DYNAM, 1-30.

How to cite: Müller, O., Vidale, P. L., McGuire, P., Vannière, B., Schiemann, R., and Peano, D.: Do high resolution GCMs overestimate precipitation over land?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8733, https://doi.org/10.5194/egusphere-egu2020-8733, 2020.

Neglecting subgrid-scale (SGS) variability can lead to substantial bias in calculations of microphysical process rates. The solution to the SGS variability bias problem lies in representing the variability using two-dimensional joint probability distribution functions (JPDFs) for the pairs of different microphysical variables. The JPDFs have also been shown to vary in the vertical: as a result their implementation in mesoscale models presents a challenging task.

We developed a more efficient formulation of cloud inhomogeneity by using a concept of “generic” JPDF. Using Large Eddy Simulation (LES) studies of shallow cumulus and cumulus congestus clouds we showed that JPDFs calculated based on datasets representing “typical” cloud types (“generic” JPDFs) provide good approximation of microphysical process rates. The generic JPDF, therefore, represent the cloud type in general, i.e. they do not depend on changing ambient conditions. The advantage of generic JPDFs is that they can be a-priory integrated and yield a one-dimensional variability factor (V-factor) specific for each cloud type. A quite accurate approximation of V-factors by an analytical function in the form of a 3^rd order polynomial was obtained and can be easily implemented in mesoscale models.

How big is the effect of cloud inhomogeneity on precipitation? To answer this question we evaluated the effect of accounting for cloud inhomogeneity on precipitation in sensitivity simulations. In the shallow Cu case over the 24 hr simulation the surface precipitation increased by about 40% when inhomogeneity was accounted. In the congestus Cu case the increase in precipitation was even more significant: by more than 75% over only 8 hours since rain first appeared at the surface. The sensitivity experiments also revealed that most of the increase resulted from the augmented autoconversion process. The effect of modified by the V-factor accretion rates was much less significant, primarily, because of the nearly linear dependence of accretion on its parameters.  This shows importance of the most accurate formulation of the autoconversion process.

How to cite: Kogan, Y.: An efficient method to account for microphysical inhomogeneity in mesoscale models by using one-dimensional variability factor., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5951, https://doi.org/10.5194/egusphere-egu2020-5951, 2020.

A reliable simulation of the spatiotemporal characteristics of the meteorological field is of great significance for hydrological impact studies. To approach this target, a number of weather generators (WGs) have been developed over the past few decades. However, a detailed literature review shows that currently developed WGs are subject to one or several aspects of the following limitations: (1) low spatial and temporal resolutions to describe the real spatiotemporal dynamics of meteorological processes; 2) incapability to simulate a spatially coherent, temporally consistent, and physically meaningful meteorological field; and 3) inability to extend into the future in a climate change context. To tackle these problems, this study proposes some potential solutions: (1) using the multi-site multivariate WGs (MMWGs) to simulate the spatial, temporal, and inter-variable dependencies in the meteorological field; (2) coupling the MMWGs with the resampling-based algorithms to generate high-resolution spatiotemporal meteorological data; and (3) perturbing the parameters of the distribution and dependency models based on the future climate projection. A case study is carried out and shows that the proposed solutions are effective in addressing the aforementioned challenges. These findings could assist in developing high-resolution MMWGs for weather simulation and impact assessment.

How to cite: Li, X.: Challenges in constructing a high-resolution, multi-site, and multivariate weather generator and potential solutions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12075, https://doi.org/10.5194/egusphere-egu2020-12075, 2020.

The present study aims to design an optimal CO₂ monitoring network over India to better constrain the Indian terrestrial surface fluxes using Lagrangian Particle Dispersion Model FLEXPART and Bayesian inversion methods. Prior and posterior cost functions are calculated using potential emission sensitivity from FLEXPART, prior flux uncertainties from CASA-GFED biosphere fluxes and CDIAC fossil fuel fluxes, and assumed uniform observational uncertainty of 2 ppm. A total of 73 regular grid cells are identified over the Indian land mass in 2°x2° latitude by longitude resolution assuming each cell can hold a potential site. Further, using incremental optimization methodology, the effectiveness of CO₂ observations from these locations to reduce the Indian terrestrial flux uncertainty is quantified. The study is carried out in three parts. Firstly, we evaluated the existing stations over India in terms of reduction in uncertainty brought out by them in the surface flux estimation over the Indian landmass. This provides a unique opportunity for the representative stations to restart the observational programs based on their role in the flux estimation. In second part, we devised a methodology to design an extended network by adding a few more potential stations to the existing stations. Thirdly, we identified a completely new set of optimal stations for measuring atmospheric CO₂ over India, which do not have any liabilities of pre-existing stations. The study depicts that the existing stations could bring down the uncertainty in the range of 18% to 36%. Among the existing stations, Kharagpur, Sagar, Shadnagar, Kodaikanal and Pondicherry are the best stations, which are indeed adding value to the CO₂ flux inversions by reducing the uncertainty in the range of 4% to 13%. Addition of five new stations to the base network formed an extended network, which could reduce the uncertainty by an additional 15% for all the seasons reaching up to 45%. The new stations are mainly located over the east and north-east India with few exceptions during post-monsoon where stations are identified over the west and south India as well. The study identified 12 stations for each season and formed a ‘new network’ that could achieve the equivalent uncertainty reduction as compared with the 14 stations in the ‘extended network’. From this, an ‘optimal network’ and the best network consisting of 17 stations were identified that could best represent flux scenario and transport over India in all the four seasons. In northeast India, flux uncertainty is quite large, also the prevailing westerly wind in most parts of the year contributes to the surface CO₂ signature of India to that location, demanding requirement of CO₂ observations throughout the year. The study highlights a major zone of CO₂ ‘observational void’ that exists in potential locations near east and northeast parts of India. Immediate requirement of CO₂ monitoring initiative in these areas is highly recommended.

How to cite: Krishnankutty, N., Sivaraman, S., Valsala, V., Tiwari, Y., and Ramachandran, R.: Ground-based CO2 monitoring network design over India to constrain the regional terrestrial fluxes , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7881, https://doi.org/10.5194/egusphere-egu2020-7881, 2020.

The distribution of dissolved oxygen in the tropical Pacific acts as a major control on marine ecosystems habitats and the foraging range of tuna fiheries in this region. A basic understanding of processes driving the mean structure and variability of the oxygen minimum zones (OMZs) in this region, however, remains challenged by sparse observations and coarse model resolution. In this study, we examine the influence of mesoscale processes on equatorial Pacific oxygen distribution and variability, with a particular focus on tropical instability vortices (TIVs). We employ an eddy-resolving configuration of the Community Earth System Model (CESM) and Lagrangian analysis to evaluate the impacts and governing mechanisms by which TIVs influence oxygen distribution and budgets in this region. The westward seasonal propagation of TIVs from summer through winter is found to drive a deepening of the oxygen minima along the equatorial Pacific band (10^oN-10^oS), and thus a seasonal expansion of the equatorial oxygenated tongue separating the north and south tropical Pacific OMZs. Strong hemispheric asymmetry is evident in TIV impacts on oxygen due to relatively weaker TIV activity and less pronounced oxygen gradients south of the equator. Mechanisms governing TIV oxygenation of the upper equatorial Pacific include a complex interplay of physical and biogeochemical processes. Isopycnal displacements act in concert with vortex trapping and lateral stirring to mix oxygenated waters from the upper layers into the equatorial boundaries of the north and south tropical Pacific OMZs. TIV-induced advection and upwelling, on the other hand, intensifies nutrient supply and productivity, organic carbon export, and oxygen respiration demand at depth, thus acting (though only slightly) to counteract the physical effects. The influence of these processes varies with TIV phase, from vortex generation in the eastern Pacific through vortex dissipation in the west. TIVs are found to have a profound influence on upper equatorial Pacifc oxygen distribution and budget, with major implications for understanding the coupling between oxygen and ocean circulation, predicting marine ecosystem dynamics, and designing observation networks in this region.

How to cite: Eddebbar, Y.: Mesoscale Modulation of Dissolved Oxygen in the Tropical Pacific, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11976, https://doi.org/10.5194/egusphere-egu2020-11976, 2020.

In this study, we developed a new urban parameterization method of wind speeds. The parameterization method uses building morphology parameters (the volumetric fraction, the plane area fraction, and the average height of buildings) for three different areas. For this, we investigated the relationships between the wind speed change rates by buildings and the urban parameters in three target areas. Each target area includes an automated weather station (AWS) at its center. We conducted the multiple regression analysis to make look-up tables for the relationships between the wind speed change rates and the urban morphology parameters for 32 inflow directions in the target areas. For validation, we simulated the wind speeds at the AWSs using a CFD model coupled to the local data assimilation and prediction system (LDAPS), one of the operational numerical prediction systems of the Korean Meteorological Administration. The results showed that the estimated wind speeds at the AWSs in the three target areas were very similar to those simulated by the LDAPS-CFD coupled model as well as those observed at the AWSs.

How to cite: Wang, J.-W., Kim, J.-J., and Yang, H.-J.: Estimation of Wind Speeds in Urban Areas Using Look-up Tables for Wind-speed Change Rate Simulated by a CFD Model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12129, https://doi.org/10.5194/egusphere-egu2020-12129, 2020.

A regional coupled prediction system for the Asia-Pacific area (AP-RCP) has been established. The AP-RCP system consists of WRF-ROMS (Weather Research and Forecast and Regional Ocean Model System) coupled models combined with local observing information through dynamically downscaling coupled data assimilation. The system generates 18-day atmospheric and oceanic environment forecasts on a daily quasi-operational schedule at Qingdao Pilot National Laboratory for Marine Science and Technology (QNLM). The AP-RCP system mainly includes 2 different coupled model resolutions: 27km WRF coupled with 9km ROMS, and 9km WRF coupled with 3km ROMS. This study evaluates the impact of enhancing coupled model resolution on the extended-range forecasts, focusing on forecasts of typhoon onset, and improved precipitation and typhoon intensity forecasts. Results show that enhancing coupled model resolution is a necessary step to realize the extended-range predictability of the atmosphere and ocean environmental conditions that include a plenty of local details. The next challenges include improving the planetary boundary physics and the representation of air-sea and air-land interactions when the model can resolve the kilometer or sub-kilometer processes.

How to cite: Li, M. and Zhang, S.: Introduction to A Regional Coupled Forecasting System, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12626, https://doi.org/10.5194/egusphere-egu2020-12626, 2020.

The col field (a region between two lows and two highs in the isobaric surface) is a common pattern leading to the generation of mesoscale vortex and heavy rainfall in China. The mesoscale vortex usually forms near the col point and the dilatation axis of the col field in the low-level troposphere.

The Mesoscale model WRF was used to numerically simulate a rainfall process in col field. A temperature perturbation column (TPC) was introduced into the low-level col field near the col point, and the effects of TPC on mesoscale vortex and rainfall was analyzed.

It was shown that in the region of strong wind background, the TPC moves downstream and has little effect on the environment, while near the col point, the wind speed and the vertical wind shear are small, the TPC can stay in the col field for a long time, which can have a greater impact on the environment. The strong TPC near the col point can trigger the vortex. As the temperature of the air column increases, the pressure drops, leading to the low-level convergence and the upper-level divergence, and the low-level cyclonic vorticity form under the effect of ageostrophic winds, which is favor of the formation of mesoscale vortex in the weak wind field. The formation of vortex promotes the intensification of precipitation. The release of the latent heat of the condensation induced by the TPC makes a positive feedback for the mesoscale vortex. The southwestly low-level jet enhances through the thermodynamic action, resulting in convergence of the leeward low-level jet and increase of precipitation, and divergence of the upwind low-level jet and decrease of precipitation, respectively. The col field is a favorable circumstance for the formation of mesoscale vortex.

Acknowledgements. This research was supported by the National Natural Science Foundation of China (Grant Nos. 41975128 and 41275099).

How to cite: Jiang, Y., Chen, C., He, H., Liu, Y., Huang, H., Wang, X., and Wang, H.: Numerical Simulation of the Effects of Warm Temperature Perturbation on Mesoscale Vortex and Rainfall in Col Field, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13552, https://doi.org/10.5194/egusphere-egu2020-13552, 2020.

The assimilation of observations in local area models (LAMs) assures that the states of meteorological variables are as close to reality as possible. Water vapor is an important constituent in terms of cloud and precipitation formation. Its highly variable nature in space and time is often insufficiently represented in models.

The aim of our work is to improve the simulation of water vapour in the Weather Research and Forecasting model WRF by assimilation of different observations. At the current stage, temperature, relative humidity, and surface pressure derived from climate stations are applied as well as zenith total delay (ZTD) data from global navigation satellite system (GNSS) stations. We try to identify the best setup of assimilation parameters which all of them directly or indirectly influence water vapour simulations. We will show case studies of high-resolution WRF simulations (2.1 km) between 2016 and 2018 for different seasons in southwest Germany. The impact of assimilation (3D-VAR) of different variables, combinations of variables, background error option as well as the temporal resolution of assimilation is evaluated. We look at column values and also at profiles derived from radiosondes. Our results show a positive impact when assimilating measured data, but deteriorations are also possible. A distinct influence of assimilation is only apparent for a few time steps. If the temporal resolution of the assimilated variables is too coarse and there is no assimilation close to these time steps, the positive effect vanishes.

How to cite: Wagner, A., Fersch, B., Yuan, P., and Kunstmann, H.: The sensitivity of data assimilation on water vapour fields on convection-permitting WRF simulations over the GNSS Upper Rhine Graben Network (GURN), Germany, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18092, https://doi.org/10.5194/egusphere-egu2020-18092, 2020.

Global simulations with 1.45 km grid-spacing are presented that were performed with the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). Simulations are uncoupled (without ocean, sea-ice or wave model), using 62 or 137 vertical levels and the full complexity of weather forecast simulations including recent date initial conditions, real-world topography, and state-of-the-art physical parametrizations and diabatic forcing including shallow convection, turbulent diffusion, radiation and five categories for the water substance (vapour, liquid, ice, rain, snow). Simulations are evaluated with regard to computational efficiency and model fidelity. Scaling results are presented that were performed on the fastest supercomputer in Europe - Piz Daint (Top 500, Nov 2018). Important choices for the model configuration at this unprecedented resolution for the IFS are discussed such as the use of hydrostatic and non-hydrostatic equations or the time resolution of physical phenomena which is defined by the length of the time step. 

Our simulations indicate that the IFS model — based on spectral transforms with a semi-implicit, semi-Lagrangian time-stepping scheme in contrast to more local discretization techniques — can provide a meaningful baseline reference for O(1) km global simulations.

How to cite: Düben, P., Wedi, N., Saarinen, S., and Zeman, C.: Global simulations of the atmosphere at 1.45 km grid-spacing with the Integrated Forecasting System, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19344, https://doi.org/10.5194/egusphere-egu2020-19344, 2020.

Large-scale river routing schemes are essential to close the hydrological cycle in fully coupled Earth System Models (ESMs). The availability of a realistic water flow is a powerful instrument to evaluate modeled land surface, a crucial component of the global climate whose properties are often simplified by heavy parameterization, due to lack of process knowledge and validation data. We built up a new concept of river routing model, named HYDROS (HYdro-Dynamic ROuting Scheme), that replaces the present scheme embedded in the CMCC-CM2 global coupled model. The new scheme aims at overcoming one of the current major limitations, that is the use of time-independent flow velocities parameterized as a function of topography. Through the imposition of hydraulic equations, HYDROS defines a time-varying flow velocity associated with the amount of lateral runoff generated by the ESM's land component and the flow through the river system. Compared to the scheme currently in place, HYDROS show improvements in the simulation of mean annual discharge phase, especially for the Arctic rivers and the Amazon. In the Mississippi case, an extreme flood episode is better caught by the new representation, indicating that the improved flow velocity better catches the discharge peaks after extreme rainfalls. The new routing model is not able to improve the volumes of simulated river discharge, whose magnitude depends on the ability of the ESM land surface scheme to generate correct surface and sub-surface runoff. Once implemented in coupled mode, HYDROS will guarantee a plausible amount and timing of freshwater discharge into the global ocean, unveiling possible unresolved feedback mechanisms occurring in proximity of river mouths.

How to cite: Materia, S., Peano, D., Benassi, M., Lovato, T., Gualdi, S., Cherchi, A., Alessandri, A., and Navarra, A.: An enhanced river routing scheme for the closure of global water budget, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19869, https://doi.org/10.5194/egusphere-egu2020-19869, 2020.

By using a multi-source data set consisting of high resolution satellite, radar, lightning, and model data this study presents the analysis of characteristics of deep convective systems over Germany and first results of a new model to predict the remaining lifetime of existing thunderstorms. Contrary to previous studies, the analysis was performed for the full mixture of observed convective systems regardless of their organization type, since our focus is an operational forecasting environment where no simple method is available to differentiate organization types. Basis for the study are all deep convective cell detections in satellite data (using Cb-TRAM, Thunderstorm Tracking and Monitoring) in a five month period (June 2016, May, June, and July 2017, and June 2018). The lifetimes of all cells are normalized, averaged and separated into life cycle phases to investigate the behavior of the parameters from the different data sources during the detected lifetime. Furthermore, the thunderstorm cells are sorted by their lifetime to determine differences between the characteristics of long- and short-lived convective systems. Parameters with predictive skill are then combined with fuzzy logic to determine the actual stage of a thunderstorm, and to nowcast its remaining lifetime. It will be shown that the new lifetime prediction model contributes to an improvement of the thunderstorm nowcasting.

How to cite: Zöbisch, I., Forster, C., Zinner, T., and Wapler, K.: Analysis and nowcasting of deep convective systems over Germany in multi-source data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3521, https://doi.org/10.5194/egusphere-egu2020-3521, 2020.

Based on the characteristics of sudden and local, short life history, serious disasters and so on, the severe convection weather system is difficult to be captured by the conventional meteorological observation network, and is still challenging for catastrophic weather forecasting. In order to improve the service ability in strong weather monitoring and prediction, the following researches have been carried out recently: (1) The new mesocyclone and tornado vortex feature recognition algorithms are developed and proved to be successfully in identifying tornado vortex characteristics in more than a dozen tornado cases. Extracted from Doppler radar volume scan data, a number of parameters (exceed thirty) have been used in the research on the automatic recognition and warning technology of classified severe convective weather (downburst, tornado, hail and short-time strong precipitation). Based on large sample data and results of a variety of analysis methods, a thunderstorm winds Bayes discriminant model has also been established. The testing results show that its Heidke skill score is 0.836, along with the accuracy rate and hit rate are greater than 95%, and the empty rate is below 5%. (2) Rapid update cycle forecast system can effectively improve the quality of model initial values that is very suitable for short time forecast application. For the sake of improving severe thunderstorm prediction, a novel pseudo-observation and assimilation approach involving water vapor mass mixing ratio is proposed to better initialize numerical weather prediction (NWP) at convection-resolving scales. In addition, a new set of simplified and parameterized dual-polarization radar simulators for horizontal reflectivity (Z_H), differential reflectivity (Z_DR), specific difference phase (K_DP), and correlation coefficient (ρ_HV) have been co-developed, and some preliminary data assimilation experiments have shown that the assimilation of dual polarization variables including differential reflectivity and specific difference phase in addition to radar radial velocity and horizontal reflectivity can help improve the accuracy of initial conditions for model hydrometer variables and ensuing model forecasts. (3) Although not yet mature enough for meteorological application, blending technology which is expected to overcome the deficiency of the quantitative precipitation (QPF) by a mesoscale NWP model for the short term at convective scales and the rapidly descending skill of rainfall forecast based on radar extrapolation method beyond the first few hours is under development and debugging, and also has potential in enhancing the ability of rainfall forecast within the nowcasting period. (4) The above methods and systems were applied and provided technical support for meteorological services during the 7^th Wuhan World Military Games in 2019, and a good service effect had been achieved.

How to cite: Cui, C., Xiao, Y., Lai, A., and Du, M.: Advances in the Study of Severe Convection Weather Nowcasting in Central China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1656, https://doi.org/10.5194/egusphere-egu2020-1656, 2020.

The Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) international field campaign occurred June 1, 2018, to April 30, 2019. This campaign was comprised of more than 150 scientists from 10 organizations. Data was collected to investigate different phases of the life cycle of thunderstorms that occur in Argentina to better understand the physical mechanisms that cause the initiation and growth of organized convective systems in some of the most intense storms on the planet. The main focus of the project was to develop new conceptual models for forecasting extreme weather events that will hopefully lead to reductions in future loss of life and property.

This presentation shows the performance of a recently developed model for estimating ice mass aloft, a key component in the atmospheric electrification process, and a method for nowcasting lightning activity using C-band weather radar and Global Lightning Dataset (GLD360) data from RELAMPAGO. This nowcasting method uses a grid-based approach to make specific forecasts of lightning in space and time. The method estimates ice mass aloft in the region where electrification occurs using a numerical optimization approach to essentially reframe a simplified bulk microphysical model into a completely data-driven model. Previous results using WSR-88D S-band radar data in the United States showed that using this model significantly improved nowcasts of first-flash lightning occurrence versus the traditional weather radar-based ice mass estimator as well as using lightning flash-rate density directly.

How to cite: Ruzanski, E., Chandrasekar, V., and Arias, I.: Nowcasting lightning during RELAMPAGO, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13272, https://doi.org/10.5194/egusphere-egu2020-13272, 2020.

This study uses C-band polarimetric radar (C-POL) measurements to classify the hydrometeors and retrieve rain drop size distributions (DSDs) during the IMFRE (Investigative Monsoon Frontal Rainfall Experiment) field campaign in central China.  Three types of precipitation in a Meiyu frontal heavy rainfall case are classified to further investigate the microphysical characteristics and processes based on C-POL observations and classified hydrometeors. When raindrops fall from the freezing level, collision–coalescence plays an equally important role as break-up and/or evaporation in stratiform regions, but is the dominant process for convective-related precipitation and is an attribute of intensive precipitation. There are more supercooled liquid water droplets above the freezing level in convective cores due to strong updrafts, which can bring more cloud droplets into the upper levels to help the formation of graupel and hail. To the best of our knowledge, this work is the first time that C-POL-classified hydrometeors and rain-parameter retrievals have been validated against in-situ aircraft and surface disdrometer measurements over the middle reaches of the Yangtze River Valley in central China, which will pave the way for future studies related to Meiyu frontal rainfall systems.

How to cite: Wang, B., Dong, X., Fu, Z., and Zhou, L.: Investigation of Hydrometeors Using a C-band Poloarimetric Radar and In-situ Measurements during IMFRE in Central China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1657, https://doi.org/10.5194/egusphere-egu2020-1657, 2020.

The majority of heavy rainfall and flooding events in the central China during the Meiyu season are caused by the multi-scale monsoon frontal systems. However, there are limited studies of the vertical distributions of monsoon frontal rainfall. This work for the first time analyzed the vertical structures of the different stages of Meiyu precipitation systems over the Yangtze‐Huai River Valley in central China using measurements and retrievals from the Global Precipitation Measurement Mission Dual‐Frequency Precipitation Radar (GPM‐DPR) and Feng Yun satellites. GPM‐DPR retrieved near‐surface rain and drop size distributions were first validated against the surface disdrometer measurements and showed good agreement. Then we analyzed three cases from the Integrative Monsoon Frontal Rainfall Experiment to demonstrate the different characteristics of convective precipitation (CP) and stratiform precipitation (SP) in the developing, mature, and dissipating stages of the Meiyu precipitation systems, respectively. For statistical analysis, all Meiyu cases during the period 2016–2018 detected by GPM‐DPR were collected and classified into different types and stages. In the stratiform regions of Meiyu precipitation systems, coalescence slightly overwhelms break‐up and/or evaporation processes, but it was dominant in the convective regions when raindrops fall. There were large numbers of large ice particles during the developing stage due to strong updrafts and abundant moisture, whereas there were both large ice and liquid particles in the mature stage. The vertical structures of the SP examined in this study were similar to those over the ocean regions due to high relative humidity but different to the mountainous west regions of the USA. The findings of the stage‐dependent SP vertical structures provide better understanding of the evolution of monsoon frontal precipitation, as well as the associated microphysical properties, and provide insights to improve microphysical parameterization in future models.

How to cite: Sun, Y., Dong, X., Cui, W., Zhou, Z., Fu, Z., Zhou, L., Deng, Y., and Cui, C.: Vertical Structures of Typical Meiyu Precipitation Events Retrieved from GPM‐DPR, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2512, https://doi.org/10.5194/egusphere-egu2020-2512, 2020.

Numerical weather prediction models with high resolution (of order kms or less) can deliver very accurate low-level winds. The problem is that one cannot afford to run simulations at very high resolution over global or other large domains for long periods because the computational power needed is prohibitive.

Instead, we propose using neural networks to downscale low-resolution wind-field simulations (input) to high-resolution fields (targets) to try to match a high-resolution simulation. Based on short-range forecasts of wind fields (at the 100m level) from the ECMWF ERA5 reanalysis, at 31km resolution, and the HRES (deterministic) model version, at 9km resolution, we explore two complementary approaches, in an initial “proof-of-concept” study.

In a first step, we evaluate the ability of U-Net-type convolutional neural networks to learn a one-to-one mapping of low-resolution input data to high-resolution simulation results. By creating a compressed feature-space representation of the data, networks of this kind manage to encode important flow characteristics of the input fields and assimilate information from additional data sources. Next to wind vector fields, we use topographical information to inform the network, at low and high resolution, and include additional parameters that strongly influence wind-field prediction in simulations, such as vertical stability (via the simple, compact metric of boundary layer height) and the land-sea mask. We thus infer weather-situation and location-dependent wind structures that could not be retrieved otherwise.

In some situations, however, it will be inappropriate to deliver only a single estimate for the high-resolution wind field. Especially in regions where topographic complexity fosters the emergence of complex wind patterns, a variety of different high-resolution estimates may be equally compatible with the low-resolution input, and with physical reasoning. In a second step, we therefore extend the learning task from optimizing deterministic one-to-one mappings to modelling the distribution of physically reasonable high-resolution wind-vector fields, conditioned on the given low-resolution input. Using the framework of conditional variational autoencoders, we realize a generative model, based on convolutional neural networks, which is able to learn the conditional distributions from data. Sampling multiple estimates of the high-resolution wind vector fields from the model enables us to explore multimodalities in the data and to infer uncertainties in the predictand.

In a future customer-oriented extension of this proof-of-concept work, we would envisage using a target resolution higher than 9km - say in the 1-4km range - to deliver much better representivity for users. Ensembles of low resolution input data could also be used, to deliver as output an “ensemble of ensembles”, to condense into a meaningful probabilistic format for users. The many exciting applications of this work (e.g. for wind power management) will be highlighted.

How to cite: Kern, M., Höhlein, K., Hewson, T., and Westermann, R.: Towards Operational Downscaling of Low Resolution Wind Fields using Neural Networks, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5447, https://doi.org/10.5194/egusphere-egu2020-5447, 2020.

    Weather forecasts play an important role in the Olympic game,especially the mountain snow projects, which will help to find a "window period" for the game. The winter Olympics track is located on very complex terrain, and a detailed weather forecast is needed. A Post-processing method based on machine learning is used for the future-10-days weather prediction with 1-km spatial resolution and 1-hour temporal resolution, which can greatly improve accuracy and refinement of numerical weather prediction(NWP). The ECWMF/RMAPS model data and the automatic weather station data(AWS) from 2015-2018 are prepared for the training data and test data, included 48 features and 4 labels (the observed 2m temperature, relative humidity , 10m wind speed and wind direction ). The model data are grid point, while the AWS data are station point. We take the nearest 9 model point to predict the station point, instead of making an interpolation between the grid point and station point. Then the feature number will be 48*9 in dataset. The interpolation error from grid point to station is eliminated,and the spatial distribution is considered to some extent. Machine leaning method we used are SVM, Random Forest, Gradient Boosting Decision Tree(GBDT) and XGBoost. We find that XGBoost method performs best, slightly better than GBDT and Random Forest. It is noted that we did some feature engineering work before training, and we found that it’s not that the more features, the better the model, while 10 features are enough. Also there is an interesting thing that the features that closely related the labels values becomes less important as the forecast time increases,such as the model outputed 2m temperature, 10m wind speed and wind direction. While some features that forecasters don’t pay attention to become more important in the 6-10 days prediction, such as latent heat flux, snow depth and so on. So it’s necessary to train the model based on dynamic weight parameters for different forecast time. Through the post-processing based on the machine learning method, the forecast accuracy has been greatly improved compared with EC model. The averaged forecast accuracy of 0-10 days for 2m relative humidity, 10m wind speed and direction has been increased by almost 15%, and the temperature accuracy has been increased by 20%~40% ( 40% for 0-3 days, and the accuracy decreased with the forecast time ). 

How to cite: Yanyan, K., Haochen, L., Jiangjiang, X., and Yingxin, Z.: Post-processing for NWP Outputs Based on Machine Learning for 2022 Winter Olympics Games over Complex Terrain, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10463, https://doi.org/10.5194/egusphere-egu2020-10463, 2020.

Visualization of numerical weather prediction data and atmospheric observations has always been an important and ubiquitous tool in weather forecasting. Visualization research has made much progress in recent years, in particular with respect to techniques for ensemble data, interactivity, 3D depiction, and feature-detection. Transfer of new techniques into weather forecasting, however, is slow.

In this contribution, we will discuss the potential of recent developments in 3D and ensemble visualization research for weather forecasting. We will introduce our work on 3D feature-detection methods for jet-stream and front features, which facilitate analysis of the evolution of jet-stream core lines and frontal surfaces in an (ensemble) forecast. The techniques have been integrated into the 3D visual ensemble analysis framework Met.3D (https://met3d.wavestoweather.de), in which they can be combined with traditional 2D depictions as well as further 3D visual elements and be displayed in an interactive 3D context. We will present and discuss 3D ensemble forecast products created with Met.3D based on forecast data from ECMWF and DWD, and demonstrate their use in the exploration of example cases including an extratropical transition over the North Atlantic and a European winter storm.

In addition, we will introduce new semi-operational 3D forecast products based on our techniques that we provide experimentally on the web, in order to gather user feedback and to initiate discussion about potential benefit of such products for operations.

How to cite: Rautenhaus, M., Hewson, T., Modali, K. R., Beckert, A., and Kern, M.: Interactive 3D visual analysis in weather forecasting, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15011, https://doi.org/10.5194/egusphere-egu2020-15011, 2020.

Lightning strikes from convective storms are a serious safety concern for public and businesses alike. Accurate assessment of local lightning risk is therefore crucial for various industries. However, it is usually not possible to obtain lightning climatologies with reasonable spatial detail, due to the scarcity of well distributed, long term observations. At this respect, meteorological models serve as a useful tool for creating lightning risk maps, provided that their output can be verified with available observations. In this study, a high resolution (3 km) lightning risk map has been constructed for Turkey, using output from Weather Research and Forecasting Model (WRF). The model was forced by the ECMWF’s ERA-5 reanalysis data, and run for the period of January 2014 – December 2018 (5 years). Simulations were conducted on high-performance computers offered by Amazon Web Services. Lightning flash rates were estimated from WRF output using the parameterization scheme proposed by McCaul et al. (2009). Model-derived lightning rates have been calibrated and validated by observed lightning data for the determined region. The spatial pattern and average rate of lightning flashes over the validation region have been found to agree reasonably well with available observations. The high resolution lightning risk map produced in this study is the first one for Turkey that is based on numerical modeling, and it will serve as an objective guidance for location-based lightning risk assessment in the country.

How to cite: Yılmaz, M. Y., Göktürk, O. M., and Fidan, G.: A High-Resolution, Model-Based Lightning Risk Map for Turkey, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5031, https://doi.org/10.5194/egusphere-egu2020-5031, 2020.

As the grid resolution continues to increase, it is essential to utilize more data to describe the occurrence and evolution of mesoscale system more precisely. Based on Local Analysis and Prediction System (LAPS), NCEP reanalysis data (FNL) is used as the background field. By improving and developing LAPS, a high spatial-temporal resolution mesoscale data is generated and a sample database is established by fusing a variety of observation data.

Moreover, the synoptic and physical variables that may affect the short- duration heavy precipitation are fully considered when establishing the diagnosis-statistical forecast model. After the calculation, three discriminant formulas of strong and weak precipitation, heavy precipitation classification I and II are obtained.

Furthermore, the grouping accuracy of the discriminant formula and T_s score were calculated, and after the independent sample test and typical case test, it can be concluded that these discriminant formulas can be used to distinguish strong and weak precipitation and heavy precipitation classification.

How to cite: Ke, D., Wang, X., and Cui, C.: Characteristics and classifications of short-duration heavy precipitation in mesoscale system evolution, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7810, https://doi.org/10.5194/egusphere-egu2020-7810, 2020.

1.Introduction

A key issue in developing the ensemble prediction technique is the recognition of uncertain factors in numerical forecasting and how to use appropriate perturbation techniques to reflect these uncertain processes and improve ensemble prediction levels.Plenty of corresponding perturbation techniques have been developed. Such as Initial uncertainty and model uncertainty,In addition to the influence of IC and model uncertainty ,precipitation is closely related to terrain.The influence of terrain on the heavy rain includes the following three aspects:(1) The terrain has significant effect on the climatic distribution of precipitation.(2)The windward slope and leeward slope and other dynamic effects generated by terrain impact the triggering and intensity of precipitation.(3)The thermal effect is triggered by the heating of land surface of terrain at different height and latent heat release when airflow rises ,and this thermal action makes mountain precipitation closely related to terrain distribution .What are the terrain uncertainties in the model?(1)Different vertical coordinate systems lead to significant differences in terrain treatment(2)The conversion from real terrain to model terrain is closely associated with the resolution of the model and different terrain interpolation schemes, and it affects the simulation results of precipitation .(3)Measuring error of real terrain, etc.In this report, A terrain perturbation scheme (ter) has been firstly incorporated into an ensemble prediction system (EPS) and preliminarily tested in the simulation of the extremely heavy rain event occurred on 21 July, 2012 in Beijing, along with other three perturbation schemes.

2.Case,data and schemes

(1)Case: Based on the extremely heavy rain case in Beijing on July 21,2012, maximum precipitation center more than 400mm.(2)Data: GEPS of NCEP were used as initial background fields and lateral boundary condition , surface and upper-level observation of GTS,Rain gauge etc.(3)Model: WRFv4.3, 9km horizontal resolution ,511*511 grid point, 51 vertical layers,KF Eta,WSM6,etc(4)Experiments schemes: Four different perturbation schemes were used in the experiments and six members in each experiment. Sch_1(IC) considered the IC uncertainty ,the parameterization schemes were same but IC/LBC came from different GEPS members. Sch_2(phy) considered the Phy uncertainty ,the IC/LBC were same but PHY schemes were comprised of different parameterization schemes. Sch_3-4(ter and icter) considered the terrain uncertainty ,the second aspect of terrain uncertainty was considered in this study. Two different model terrain smoothing schemes and 3 terrain interpolation schemes were used to reflect the forecast error caused by terrain height. Icter is the mixed scheme of ter and ic.

3.Preliminary test and results

(1)Precipitation is closely related to terrain, terrain uncertainties have significant effect on the intensity and falling area of precipitation.(2) Only a simple terrain perturbation can produce a significant forecast spread , and its ensemble mean forecast is also improved compared with control forecast. for this case, it has a slightly positive contribution to the spread and probability forecast of precipitation on the basis of not impacting the quality of ensemble mean forecast.(3) In this case, the magnitude of spread generated by the terrain perturbation scheme is significantly smaller than that generated by the initial perturbation and physics process perturbation schemes.

How to cite: jun, L., jun, D., yu, L., and jianyu, X.: Preliminary study on terrain uncertainty and its perturbing scheme, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1856, https://doi.org/10.5194/egusphere-egu2020-1856, 2020.

The objective extrapolation forecast is the main method for 0-1 hour convective storm nowcasting. Radar echo extrapolation was performed by using the 6 minute interval radar mosaics obtained from the radar images of 8 multi-radars in Beijing-Tianjin-Hebei region. A comparative study of two extrapolated forecasts of eighteen typical convective precipitation events occurred in Beijing-Tianjin-Hebei region from 2016 to 2018 was conducted. Compared with the tracking radar echoes by correlation method, the variational echo tracking method utilizes variational technique to compute the motion vector fields, and uses two strict constraints to get a better motion vector field. The results indicated that the variational echo tracking method performed better in prediction of the radar echo pattern, echo location, and echo intensity at 30- and 60-min forecast lead times: 1) A comparative study of the two extrapolated forecasts of four precipitation events in Beijing-Tianjin-Hebei region was conducted. The result indicated that the radar echo location, the echo pattern and echo intensity produced by the variational echo tracking method were closer to the real observation within one hour. 2) Quantitative evaluation for the two extrapolated forecasts of the eighteen typical convective precipitation events was conducted. Compared with the tracking radar echoes by correlation method, the probability of detection and the critical success index of the 30- or 60-min extrapolated forecast produced by the variational echo tracking method were higher, meanwhile the false alarm rate was lower when the radar echo threshold was 35dBz and 45dBz. Also, a quantitative evaluation classified by the weather type indicated that the variational echo tracking method performed better than the tracking radar echoes by correlation method in most weather types.

How to cite: Wu, J., Chen, M., Qin, R., Gao, F., and Song, L.: The variational echo tracking method and its application in convective storm nowcasting, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1293, https://doi.org/10.5194/egusphere-egu2020-1293, 2020.

The Ili Valley is an area with frequent heavy rain in Xinjiang. In this paper, a heavy rainstorm process in this area on June 26, 2015 is taken as an example. The observational data and WRF high-resolution numerical simulation results are used to analyze the synoptic background and the process of the precipitation. The results show that: (1) The Central Asian low vortex and the upper-level jet provides a favorable circulation background for this heavy rain. Northerly winds and westerly winds forms a low-level convergence line in Ili Valley. (2) In addition to the convergence of low-level airflow, the uplifting effect of the terrain on the westerly winds also intensifies the low-level ascending motion. At the same time, the uplifting effect of the terrain on the northerly winds causes the middle-level ascending motion. After the low-level ascending motion is connected with that of the middle level, precipitation begins to occur. The convection further develops, superimposed with the upwards phase of upper-level wave, and the precipitation increases strongly. (3) Through spectral analysis methods, the characteristics of the upper-level wave are obtained, and the wave is an inertial gravity wave. It is further obtained from the mesoscale three-dimensional Eliassen-Palm (EP) flux that during the period of heavy precipitation, the energy of the upper-level inertial gravity wave is transported down to the low level of the precipitation area. (4) Convective instability plays an important role in the enhancement of the precipitation in the Ili Valley. The analysis of potential divergence further indicates that the convective instability in the precipitation area is mainly caused by the vertical shear part of potential divergence, while the divergence part of the potential divergence can strengthen the convective instability in the leeward slope of the terrain. It indicates that the dynamic and thermodynamic factors are coupled with each other, which affects the precipitation location, intensity and evolution.

How to cite: Huang, X. and Zhou, Y.: Numerical Simulation and Mechanism Analysis of an Extreme Precipitation in Ili Valley, Xinjiang, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1717, https://doi.org/10.5194/egusphere-egu2020-1717, 2020.

Central Asian Vortices (CAVs) are deep cyclonic systems that occur in the Central Asian and are identified at the 500 hPa level. CAVs are significantly associated with many convective events in the Xinjiang province. In order to strengthen the understanding of the mesoscale systems development mechanisms in torrential rain under the influence of CAVs, we analyzes the rainstorm process occurred in the Aksu region that is near the west of Tianshan Mountains, during June 17 to 18, 2013 basing on a variety of data. The results show that the precipitation process occurs under the background of the circulation of the two ridges in a trough over the middle and high latitudes, and the CAV provides favorable large-scale dynamic and water vapor conditions for this rainstorm. The convergence line is the important mesoscale system, which is formed by the superposition of the CAV circulation and the flow stream around the special topography of the west Tianshan Mountains. Due to the difference of thermal properties between the mountain and desert, the slope wind drives convergence line to move and the strong convection developed along the convergence line triggers strong precipitation in the Aksu region. The WRF is able to well simulate not only the location and intensity of the heavy rain but also the evolution of wind field. Preliminary analysis combined with observations and simulation data shows that under the blockage of west Tianshan Mountains, the south wind accumulates and convergences near the valley. As a result, a local convergence line is formed. Meanwhile, with the adjustment of the large-scale circulation situation, especially after the CAV moves to the vicinity of the Aksu area, one part of the westward flow that comes from the south of the vortex turns into northwest wind after crossing the west Tianshan Mountains, and the other part turns into the northeast wind after passing through the Yili Valley, these two flow aggravate the northerly airflow and enhance the intensity of convergence, thereby promote the formation of mesoscale convergence lines and strengthen it. The eastward airflow-induced water vapor accumulates in front of the southern foot of the Tianshan Mountains, and strengthens as the convergence line moves towards southeast with the enhancement of the valley wind at night. Accompanied with the convergence uplift, the accumulation of water vapor at the foot of the mountain promotes the release of unstable energy and brings heavy precipitation to the Aksu region.

How to cite: Guo, N. and Zhou, Y.: Analysis of a severe precipitation process in Aksu Area under the background of the Central Asian Vortex, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1718, https://doi.org/10.5194/egusphere-egu2020-1718, 2020.

During the Meiyu period, floods are prone to occur in the middle and lower reaches of the Yangtze River due to the highly concentrated and heavy rainfall, which caused huge life and economic losses. Based on numerical simulation by assimilating Doppler radar, radiosonde, and surface meteorological observations, the evolution mechanism for the initiation, development and decaying of a Meiyu frontal rainstorm that occurred from 4th to 5th July 2014 is analyzed in this study. Results show that the numerical experiment can well reproduce the temporal variability of heavy precipitation and successfully simulate accumulative precipitation and its evolution over the key rainstorm area. The simulated “rainbelt training” is consistent with observed “echo training” on both spatial structure and temporal evolution. The convective cells in the mesoscale convective belt propagated from southwest to northeast across the key rainstorm area, leading to large accumulative precipitation and rainstorm in this area. There existed convective instability in lower levels above the key rainstorm area, while strong ascending motion developed during period of heavy rainfall. Combined with abundant water vapor supply, the above condition was favorable for the formation and development of heavy rainfall. The Low level jet (LLJ) provided sufficient energy for the rainstorm system, and the low-level convergence intensified, which was an important reason for the maintenance of precipitation system and its eventual intensification to rainstorm. At its mature stage, the rainstorm system demonstrated vertically tilted structure with strong ascending motion in the key rainstorm area, which was favorable for the occurrence of heavy rainfall. In the decaying stage, unstable energy decreased, and the rainstorm no longer had sufficient energy to sustain. The rapid weakening of LLJ resulted in smaller energy supply to the convective system, and the stratification tended to be stable in the middle and lower levels. The ascending motion weakened correspondingly, which made it hard for the convective system to maintain.

How to cite: Li, H., Hu, Y., and Zhou, Z.: Analysis of the Evolution of a Meiyu Frontal Rainstorm Based on Doppler Radar Data Assimilation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1975, https://doi.org/10.5194/egusphere-egu2020-1975, 2020.

Convection initiation (CI)  occurred near the oasis region surrounded by gobi desert in the Southwestern Xinjiang, Northwest China is investigated using a real-data, high-resolution Weather Research and Forecasting (WRF) simulation. Observations revealed that many CIs occurred successively near oasis region, some of which developed significantly in both size and intensity and eventually become a strong mesoscale convective system (MCS). The WRF simulation captured the general features of the CIs and MCS. Lagrangian vertical momentum budgets were conducted along the backward trajectories of air parcels within three convective cells. The total vertical acceleration was decomposed into dynamic and buoyant components. The results showed that the buoyant acceleration played a decisive role for about half of the air parcels during the CI, which was contributed by the dry air buoyancy. However, the dynamic acceleration mainly contributed during the CI for about one fourth of the air parcels. The dynamic acceleration can be further decomposed into five terms based on anelastic approximation. The positive dynamic acceleration was mainly caused by the vertical twisting term associated with the mid-level vertical shear, while the extension term contributed negatively to the dynamic acceleration. The other two terms related to horizontal curvature and height variation of density were negligibly small.

How to cite: Ming, J. and Abulikemu, A.: A numerical study of convection initiation in Southwestern Xinjiang, Northwest China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2285, https://doi.org/10.5194/egusphere-egu2020-2285, 2020.

Using hourly air pollutants concentration from six environmental monitor stations, meteorological data and wind profile radar data in winter during 2013-2015, the influences of shallow foehn on diffusion conditions and air pollution concentration over Urumqi were analyzed. The results showed the occurrence frequency of shallow foehn was 57.3% in Urumqi in winter. The flow depth, base height and top height of shallow foehn were about 1500 m, 600 m and 2100 m, respectively. The maximum mixing layer depth, the inversion depth, the temperature difference between the top and bottom of inversion layer on foehn days were 200 m lower, 344m thicker and 4.4℃ higher than the corresponding values on non-foehn days, respectively. However, the differences of wind speed and inversion intensity between on foehn days and on non-foehn days were slight. Also, the frequency of each pollution level on foehn days was higher than on non-foehn days with extra frequency of 18% from level Ⅲ to level Ⅵ. Moreover, there was foehn existence on days with air pollution level Ⅵ. Except for O₃, the other five air pollutant concentrations at each environmental station on foen days were all higher than on non-foehn days but with similar diurnal variation. The spatial distributions of six air pollutants on foehn days and non-foehn days were almost same. Overall, the air quality at south urban area was relative excellent than other areas.

How to cite: keming, Z.: The Influence of Shallow Foehn on Atmospheric Diffusion Conditions and Air Quality over Urumqi in Winter, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2579, https://doi.org/10.5194/egusphere-egu2020-2579, 2020.

It is well known that climate changes sometimes may cause natural disasters，especially the disastrous weather days，as downpour，flood，landslide and mudslide，and their derivatives disasters not only have relationship with precipitation，but also，closely with rainfall intensity. In the practice of Xinjiang disaster prevention，it’s urgent to know the temporal and spatial distributions of precipitation intensity and the maximum of precipitation intensity in different recurrence periods. In this paper，based on the observed hourly precipitation data over 16 national-standard stations during May to September from 1991 to 2013 in Xinjiang，some large-scale，multisites and long-paying observed hourly precipitation data have been used firstly together with the methods of probability distributions，statistical tests，variant difference analysis and extreme value analysis，the temporal and spatial distributions and the diurnal variation of hourly rain in summer in Xinjiang have been analyzed. The results show that the hourly rain presents high frequency in northwest and low frequency in southeast of Xinjiang. The high value center of the frequency with hourly rainfall intensity over 0.1 mm·h-1 or 4 mm·h-1 both in Western Tianshan Mountains. The frequency of heavy rainfall is increasing in places such as Ruoqiang where rains less. The high frequent periods of heavy rainfall，with hourly rainfall intensity over 4 mm·h-1，are often occurred in the afternoon，and the first and second half of the night in Northern Xinjiang，while it occurs at night in Southern Xinjiang. The hourly rain frequencies share obviously different diurnal variation in all regions of Xinjiang，where the hourly rainfall is not well-distributed. The distribution characteristic of daily rain in Northern Tacheng and Altay Prefecture is bimodal and in the rest regions of Northern Xinjiang is unimodal. Nevertheless，in Southern Xinjiang，most are in bimodal distribution. The total frequency of hourly rainfall intensity larger than 0.1 mm·h-1 or 4.0 mm·h-1 in Northern and Southern Xinjiang both appears to be an evident increase trend，and it would increase more significance in Southern Xinjiang in the 2010s. The high value region of hourly rainfall intensity occurring once in 50 or 100 years，respectively 45 mm·h-1 and 50 mm·h-1，both is in the western Aksu.

How to cite: chunyan, C.: Temporal and spatial distributions of hourly rain intensity under the warm background in Xinjiang, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2592, https://doi.org/10.5194/egusphere-egu2020-2592, 2020.

Severe downslope wind triggered by the interaction between the gap jet and the asymmetrical topography in the Tianshan canyon which caused severe disasters of trains rolloverin the Turpan Depression in Xinjiang on February 28, 2007. To understand the mechanism of downslopewindstorm between the interaction of large-scale circulation background，mesoscale system and complex topography in this extreme windstorm.We use a WRF model to simulated it.Base on the mesoscale diagnostic analysis to simulative results，We propose a mechanism for the windstorm : Under the pressure gradient between north-south sides of the Tianshan Mountain, the air parcel climbs windward slope and flowsinto the Tianshan Gorge and then forms gap jet due to effect of narrow，the jet generated gravity waves forced by the asymmetric terrain of the Tianshan Canyon, and produces a lee waves in the leeward, which transmits the energy of the gap jet to the ground，andSevere downslope windstorm formed finally. In this process, the turbulence formed by the wave breaking and the critical layer absorb the upper layer energy downward, which strengthens the energy of the gap jet,and the atmospheric stability stratification exacerbates the sinking movement, which sinks energy to the ground.

How to cite: hao, T.: Mesoscale Analysis of Severe Downslope Windstorm Caused by Gap Jet in Tianshan Canyon, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2794, https://doi.org/10.5194/egusphere-egu2020-2794, 2020.

In order to solve the problem of excessive energy dissipation near the sub-grid scale in numerical weather model, the Stochastic Kinetic Energy Backscatter (SKEB) method is introduced into the GRAPES-REPS regional ensemble prediction system, and the first-order autoregressive stochastic process is used in the horizontal direction. Calculate the random pattern obtained by spherical harmonic expansion in the direction, calculate the local dynamic energy dissipation rate caused by the numerical diffusion scheme, construct the random flow function forcing, and convert it into horizontal wind speed disturbance, compensate the dissipated kinetic energy, and carry out A 10-day ensemble prediction test and a randomized time and space scale sensitivity test in September and October 2018 (choose 1st, 7th, 13th, 19th, and 25th), and evaluate the test results. The main conclusions of the research work are as follows: By comparing the ensemble prediction results of the test using the SKEB method and the test without the SKEB method, the use of the SKEB scheme increases the large aerodynamic energy of the GRAPES regional model in the small and medium-scale region, and improves the GRAPES model to the actual atmosphere to some extent. The simulation ability of kinetic energy spectrum; the introduction of SKEB scheme in regional ensemble prediction can significantly improve the dispersion of U and V in horizontal wind field of regional model, and the problem of insufficient dispersion of large-scale dynamic energy dissipation rate in Qinghai-Tibet Plateau region is improved. The SKEB program has improved the forecasting skills to a certain extent, such as reducing the CRPS scores of the horizontal wind fields U and V, reducing the outliers scores of the horizontal wind field, temperature, and 10 m wind speed; the introduction of the SKEB method can improve the light rain. The precipitation probability prediction skill score, but the improvement of the score did not pass the significance test, so it is considered that the SKEB method is difficult to effectively improve the probability prediction technique of precipitation.

Sensitivity tests based on the SKEB method for five time scales of random pattern (1h, 3h, 6h, 9h and 12h of the time series τ) show that the ensemble prediction is sensitive to the five time scales of the stochastic model of the SKEB method. And the 12h experiment show the best performance than the others.

How to cite: Li, H. and Chen, J.: Sensitivity analysis of SKEB method in Regional ensemble forecast system GRAPES-REPS, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3444, https://doi.org/10.5194/egusphere-egu2020-3444, 2020.

        This study demonstrates how model bias can adversely affect the quality assessment of an ensemble prediction system (EPS) by verification metrics. A regional EPS [Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS)] was verified over a period of one month over China. Three variables (500-hPa and 2-m temperatures, and 250-hPa wind) are selected to represent "strong" and "weak" bias situations. Ensemble spread and probabilistic forecasts are compared before and after a bias correction. The results show that the conclusions drawn from ensemble verification about the EPS are dramatically different with or without model bias. This is true for both ensemble spread and probabilistic forecasts. The GRAPES-REPS is severely underdispersive before the bias correction but becomes calibrated afterward, although the improvement in the spread' spatial structure is much less; the spread-skill relation is also improved. The probabilities become much sharper and almost perfectly reliable after the bias is removed. Therefore, it is necessary to remove forecast biases before an EPS can be accurately evaluated since an EPS deals only with random error but not systematic error. Only when an EPS has no or little forecast bias, can ensemble verification metrics reliably reveal the true quality of an EPS without removing forecast bias first. An implication is that EPS developers should not be expected to introduce methods to dramatically increase ensemble spread (either by perturbation method or statistical calibration) to achieve reliability. Instead, the preferred solution is to reduce model bias through prediction system developments and to focus on the quality of spread (not the quantity of spread). Forecast products should also be produced from the debiased but not the raw ensemble.

How to cite: Wang, J., Chen, J., and Du, J.: Sensitivity of ensemble forecast verification to model bias, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4374, https://doi.org/10.5194/egusphere-egu2020-4374, 2020.

Abstract

The aim of this study is to assess the skills of a stochastic weather generator (SWG) to forecast precipitation in Europe. The SWG is based on the random sampling of circulation analogues, which is a simple form of machine learning simulation. The SWG was developed and tested by Yiou and Déandréis (2019) to forecast daily average temperature and the NAO index. Ensemble forecasts with lead times from 5 to 80 days were evaluated with CRPSS scores against climatology and persistence forecasts. Reasonable scores were obtained up to 20 days.  In this study, we adapt the parameters of the analogue SWG to optimize the simulation of European precipitations. We then analyze the performance of this SWG for lead times of 2 to 20 days, with the forecast skill scores used by Yiou and Déandréis (2019). To achieve this objective, the SWG will use ECA&D precipitation data (Haylock. 2002), and the analogues of circulation will be computed from sea-level pressure (SLP) or geopotential heights (Z500) from the NCEP reanalysis. This provides 100-member ensemble forecasts on a daily time increment. We will evaluate the seasonal dependence of the forecast skills of precipitation and the conditional dependence to weather regimes. Comparisons with “real” medium range forecasts from the ECMWF will be performed.

References

Yiou, P., and Céline D.. Stochastic ensemble climate forecast with an analogue model. Geoscientific Model Development 12, 2 (2019): 723‑34.

Haylock, M. R. et al.. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. J. Geophys. Res. - Atmospheres 113, D20 (2008): doi:10.1029/2008JD010201.

Acknowledge

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813844.

How to cite: Krouma, M., Yiou, P., Déandréis, C., and Thao, S.: Ensemble weather forecast of precipitation with a stochastic weather generator based on analogues circulation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4705, https://doi.org/10.5194/egusphere-egu2020-4705, 2020.

Cloud analysis module (GCAS) in GRAPES model can combine radar reflectivity data, satellite data and surface observations to provide there-dimensional cloud information. To show application effect of GCAS on 3-km resolution forecasts in arid and semi-arid areas of Northwest China, three sets of forecast experiments were conducted with GRAPES_Meso model, which includes control experiment (Con_exp), gcas experiment (Gcas_exp) and hot-start experiment (Hot_exp). The impact of cloud analysis on the prediction effect was investigated using 13 heavy rainfall cases and one month continuous experiments.

These experimental results show the use of cloud analysis can significantly improve forecasting skills of precipitation. Compared with hourly precipitation observations, Gcas_exp performed better than Con_exp and Hot_exp, which gets a higher threat scores of precipitation both for 13 cases and for one-month continuous experiments. Hot_exp presented an positive effect only in the first few hours. Oftentimes, Hot_exp got a worse forecast than Con_exp after the first several hours. In addition, gcas_exp has a positive effect on the prediction of 2m temperature, 10m wind and other variables, but forecasted composite reflectivity was stronger than its observations. Hot_exp can reduce this strength bias to some extent.

How to cite: Ren, X., Shao, A., Liu, W., and Chen, X.: Performance Evaluation of Cloud Analysis in GRAPES 3km Model over Northwest China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5217, https://doi.org/10.5194/egusphere-egu2020-5217, 2020.

 Based on conventional meteorological observation data and particulate matter monitoring data, combined with traceability of pollution sources, the main causes of sand-dust heavy pollution and the characteristics of dust transmission in eastern Qinghai in May 2019 were analyzed by using the principles of meteorology and trajectory analysis.The results show as following：The heavy dusty weather is mainly affected by the development of the enhanced low-slot eastward of Lake Baikal. The low-slots carry strong cold air moving eastward ,leading to heavy pollution in the eastern part of Qinghai. During the dusty weather, the cold air from the Hexi Corridor was poured into the eastern part of Qinghai from the valley . The dust from Gansu Province entered and then transported from east to west to easdtern Qinghai,causing pollution. The presence of the inversion layer stabilizes the atmospheric stratification in the eastern boundary layer of Qinghai, which is not conducive to the outward spread of pollutants caused by surface turbulence activities. The long-term maintenance of dust that cannot be diffused in time is the main cause of heavy pollution. In the early stage of sand and dust weather, the humidity conditions in the eastern part of Qinghai gradually deteriorated. Before the sand-dust occurred, the sensible heat on the ground increased significantly. The water vapor in the atmosphere weakened, the air was dry, and the water vapor condition, which is an important condition for the formation of sand and dust,was poor. The dust storm transmission route affecting the eastern part of Qinghai is transmitted from southeast to northwest. The mixed layer height and static weather index of the EC numerical forecasts have a good predictive indication during the process. The results of the trajectory analysis also indicate that the dusty weather in the eastern part of Qinghai (Xining, Haidong, etc.) was caused by backward irrigation of sandfrom the Hexi Corridor, and affected Haidong and Xining areas under the influence of the terrain.

How to cite: guo, X. and ma, Y.: Analysis of a typical heavy dust pollution weather in Semi-arid region- A Case Study in Eastern Qinghai , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5220, https://doi.org/10.5194/egusphere-egu2020-5220, 2020.