AS1.2

This talk focusses on progress in ensemble forecasting methodology (Part I) and ensemble verification methodology (Part II).

Operational ECMWF ensemble forecasts are global predictions from days to months ahead. At all forecast ranges, model uncertainties are represented stochastically with the Stochastically Perturbed Parametrization Tendency scheme (SPPT). Recently, considerable progress has been made in developing the Stochastically Perturbed Parametrization scheme (SPP). The SPP scheme offers improved physical consistency by naturally preserving the local conservation properties for energy and moisture of the unperturbed version of the corresponding parametrization. In contrast, the SPPT scheme lacks such local conservation properties, mainly because the scheme does not perturb fluxes at the surface and at the top of the atmosphere consistently with the tendency perturbations in the column.

NWP research and development relies on scoring rules to judge whether or not a change to the forecast systems results in better ensemble forecasts. A new tool will be presented that can improve the understanding of score differences between sets of forecasts for a widely used proper score, the Continuous Ranked Probability Score (CRPS). An analytical expression has been derived for the CRPS when a homogeneous Gaussian (hoG) forecast-observation distribution is considered. This leads to an approximation of the CRPS when actual verification data are considered, which deviate from a homogeneous Gaussian distribution. The hoG approximation of the CRPS permits a useful decomposition of score differences. The methodology will be illustrated with verification data for medium-range weather forecasts.

How to cite: Leutbecher, M., Ben Bouallegue, Z., Haiden, T., Lang, S., and Lock, S.-J.: Progress in ensemble forecasting and verification methodologies at ECMWF, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-108, https://doi.org/10.5194/egusphere-egu21-108, 2020.

The GLM instruments aboard the GOES-16 and 17 satellites provides nearly uniform spatiotemporal coverage of total lightning over the Americas and adjacent vast oceanic regions of the western hemisphere. This work summarizes recent efforts from our group at CIMMS/NSSL geared towards the evaluation of the potential added value of assimilating GLM-observed total lightning data on short-term, convection-allowing scale (dx = 2-3 km) forecasts for higher impact weather events. Results using data assimilation (DA) approaches ranging from single deterministic three-dimensional variational (3DVAR) methods applied in real time to experimental ensemble-based VAR hybrid methods (3DEnVAR) will be highlighted. 
The lightning data assimilation (DA) scheme in these frameworks follow the same core philosophy wherein background water vapor mass mixing ratio is adjusted (increased) locally at or around observed lightning locations, either throughout the entire atmospheric column or within a fixed, confined layer above the lifted condensation level. Toward a more systematic assimilation of real GLM data, emphasis will be directed toward: (i) sensitivity tests with deterministic 3DVAR experiments aimed at evaluating the impact of the horizontal decorrelation length scale, DA cycling frequency as well the length of the accumulation window for the lightning data, (ii) aggregate statistics from real time CONUS-scale experiments over the Spring 2020 and (iii) preliminary results employing ensemble of 3DEnVARs with hybrid (static + flow dependent) background error covariances. 
Aggregate statistical results from all deterministic 3DVAR exercises in (i) and (ii) revealed that the assimilation of either radar (radial wind and reflectivity factor) or total lightning (GLM) resulted in overall notably more skillful, shorter term (0-3 h) forecast of composite reflectivity fields, accumulated rainfall, as well as individual storm tracks – with optimal skill obtained when both radar and lightning data were assimilated. In (iii) forecast impacts related to the following will be summarized: (1) the respective weights assigned to the flow-dependent component and static components of the background error covariances, (2) the inclusion of three time-level sampling for each member during each cycle and (3) the usage of Gaussian noise coupled with a fixed 3 to 12 h spin-up period prior to the beginning of the cycled 3DVAR.

How to cite: Fierro, A., Hu, J., Wang, Y., Gao, J., and Mansell, E.: Evaluation of the impact of assimilating spaceborne (GLM) total lightning data and radar data on short-term forecasts of convective events in the 3DVAR framework, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-133, https://doi.org/10.5194/egusphere-egu21-133, 2020.

As the computational capacity has been largely improved in the last decades, the grid configuration of numerical weather prediction models has stepped into microscale resolutions. Even if mesoscale models are not originally designed to reproduce fine scale phenomena, a large effort is being made by the research community to improve and adapt these systems. However, reasonable doubts exist regarding the ability of the models to forecast this type of events, due to the unfit parametrizations and the appearance of instabilities and lack of sensitivity in the variables. Here, the Weather Research and Forecasting (WRF) model effective resolution is evaluated for several situations and grid resolutions. This is achieved by assessing the curve of dissipation for the wind kinetic energy. Results show that the simulated energy spectrum responds to different synoptic conditions. Nevertheless, when the model is forced into microscale grid resolutions the dissipation curves present an unrealistic atmospheric energy. This may be a partial explanation to the aforementioned issues and imposes a large uncertainty to forecasting at these resolutions.

How to cite: Bolgiani, P., Díaz-Fernández, J., Quitián-Hernández, L., Sastre, M., Santos-Muñoz, D., Farrán, J. I., González-Alemán, J. J., Valero, F., and Martín, M. L.: On the effective resolution of WRF simulations at microscale grid resolution., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-229, https://doi.org/10.5194/egusphere-egu21-229, 2020.

This study investigates sensitivity of  cloud and precipitation parameterized microphysics  to stochastic representation of parameter uncertainty as formulated by the stochastically perturbed parameterization (SPP) scheme.  SPP is applied to multiple microphysical parameters within a lagrangian column model, used in several prior published studies to characterize  parameter uncertainty by means of multivariate nonlinear inversions using remote sensing observations. The 1D column microphysics model is forced with prescribed time-varying profiles of temperature, humidity and vertical velocity.  This modeling framework allows for investigation of the effect of changes in model physics parameters on the model output in isolation from any feedback to the cloud-scale dynamics.

The test case selected in this study of an idealized representation of mid-latitude squall-line convection is the same as in the prior studies. This enabled using the estimates of multi-parameter distributions from the inversions in the prior studies as the basis for setting the second-moment statistics in the SPP scheme implementation. Additionally impacts of the non-stochastic and stochastic multi-parameter representation of parameterization uncertainty on the microphysics model solution could be directly compared.

The sensitivity experiments with the SPP scheme involve ensemble simulations where each member is evolved with a different stochastic sequence of parameter perturbations, as is done in the standard practice with this scheme.  The experiments explore impacts of using different decorrelation times and different estimates of second moment statistics for the parameter perturbations.  These include uncorrelated perturbations between the parameters for several values of variance for each parameter and correlated perturbations based on multi-parameter empirical statistical distributions from the prior studies.  The selection of physical parameters for the perturbations is based on the significance of their impacts derived from the prior studies . 

The results are evaluated in terms of changes to the ensemble mean and variance of time evolving profiles of hydrometeor mass quantities, the microphysics processes within the model as well as in terms of the simulated column integral microphysics-sensitive satellite-based  observables. The latter include PR (Precipitation Rate) , LWP (Liquid Water Path), IWP (Ice water path), TOA-LW and TOA-SW (-Long and -Short Wave, respectively).  In each experiment six parameters were perturbed.

The analyses performed so far indicate a high sensitivity of the microphysics model to the SPP scheme. The ensemble simulations with the standard uncorrelated parameter perturbations exhibit a significant bias relative to the control simulation which uses the unperturbed parameters.  For the selected test case the skewness toward small parameter values in the SPP sampling based on the underlying log-normal distributions leads to less precipitating ice and more precipitating liquid and accumulated precipitation. The response is due to nonlinear relationships between the parameters and modeled microphysics output. The changes in microphysics output result in large mean changes in PR, LWP, IWP, TOA- LW and SW, suggesting a potential for using these and other microphysics sensitive satellite observations to evaluate and if needed correct properties of the underlying sampling distribution in the stochastic scheme.  Further analyses will be presented at the conference.

How to cite: Vukicevic, T., Stankovic, A., and Posselt, D.: Sensitivity of modeled microphysics to stochastically perturbed parameters, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2587, https://doi.org/10.5194/egusphere-egu21-2587, 2021.

Previous work by this team has demonstrated that assimilation of IR radiances in partially cloudy regions is beneficial to numerical weather predictions (NWPs), improving the representation of tropical cyclones (TCs) in global analyses and forecasts. The specific technique used by this team is based on the “cloud-clearing CC” methodology. Cloud-cleared hyperspectral IR radiances (CCRs), if thinned more aggressively than clear-sky radiances, have shown a strong impact on the analyzed representation and structure of TCs. However, the use of CCRs in an operational context is limited by 1) latency; and 2) external dependencies present in the original cloud-clearing algorithm. In this study, the Atmospheric InfraRed Sounder (AIRS) CC algorithm was (a) ported to NASA high end computing resources (HEC), (b) deprived of external dependencies, and (c) parallelized improving the processing by a factor of 70. The revised AIRS CC algorithm is now customizable, allowing user’s choice of channel selection, user’s model's fields as first guess, and could perform in real time. This study examines the benefits achieved when assimilating CCRs using the NASA’s Goddard Earth Observing System (GEOS) hybrid 4DEnVar system. The focus is on the 2017 Atlantic hurricane season with three infamous hurricanes (Harvey, Irma, and Maria) investigated in depth.  The impact of assimilating customized CCRs on the analyzed representation of tropical cyclone horizontal and vertical structure and on forecast skill is discussed.

How to cite: Boukachaba, N., Reale, O., L. McGrath-Spangler, E., Ganeshan, M., McCarty, W., and Gelaro, R.: Progress toward Cloud-Cleared Infrared radiance assimilation in a global modeling framework: Application to the 2017 Atlantic Tropical Cyclone Season., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2870, https://doi.org/10.5194/egusphere-egu21-2870, 2021.

Shallow cumulus clouds play an important role in the weather in the Atlantic Tropical Convergence Zone.  Their interaction with the atmospheric environment and oceanic mixing processes has a significant impact on the convective organization and tropical dynamics.  It is still a scientific challenge for numerical weather prediction models to accurately simulate them due to deficiencies in the model’s representation of physical processes. 

In this study, we investigate how the physics parameterization schemes in NOAA’s most recent operational global forecast system (GFSv16) perform in the simulation of shallow cumulus clouds in the western Atlantic in terms of their interaction with the large-scale atmospheric dynamics.  Previous studies have indicated that the impact of physics parameterization schemes on model’s tendencies during the first few hours can provide critical information on their suitability for short- and medium-range forecasts.  Therefore, we first evaluate the GFSv16 forecasts against the observations obtained from the European field campaign called the ATOMIC/EUREC4A that occurred between 12 January and 23 February 2020.  We then diagnose the sensitivity of the GFSv16 physics tendencies to changes to the physics parameterization schemes over the first 6 hours of the forecast, which is the timescale before dynamical feedback becomes significant. Using the information from the observational evaluation and physics tendency diagnosis, we further explore possible improvement in the physical process representation that can positively affect the physics tendencies and lead to overall forecast improvement beyond 6 hours.

How to cite: Bao, J.-W., Michelson, S., and Grell, E.: Evaluation of the physics suite in NOAA’s GFSv16 using field-campaign observations and diagnosis of physics tendencies, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3387, https://doi.org/10.5194/egusphere-egu21-3387, 2021.

This study evaluates the short-to-medium range precipitation forecasts from Global Forecast System for 14 major transboundary river basins in Africa against GPM IMERG “Early”, IMERG “Final”, and CHIRPSv2 products. Daily precipitation forecasts with lead times of 1-day, 5-day, 10-day, and 15-day and accumulated precipitation forecasts with periods of 1-day, 5-day, 10-day, and 15-day are investigated. The 14 selected basins are (1) Senegal; (2) Volta; (3) Niger; (4) Chad; (5) Nile; (6) Awash; (7) Congo; (8) Omo Gibe; (9) Tana; (10) Pangani; (11) Zambezi; (12) Okavango; (13) Limpopo and (14) Orange. For each basin, several sub-basins are defined by the major dams in the basin. Our preliminary results in the Nile river basin show that in terms of temporal variability, there was a good agreement between the forecasted and observed accumulated precipitation on a 15-day basis. When compared to IMERG “Final”, the correlation coefficients of accumulated GFS forecasts scored as high as 0.75. Thus, GFS products provide relatively reliable accumulated precipitation forecasts. However, the precipitation forecasts were mostly biased: they tend to overpredict rainfall for the eastern part of the Nile river, underestimate rainfall for the northern part of the Nile river and produce almost unbiased estimates for the southern part of the river. Additionally, GFS forecasts have a general tendency to underpredict the area of precipitation across the Nile basin. Although the performance of GFS varies at different locations, the GFS precipitation forecasts can be a good reference to dam operators in Africa. 

How to cite: Yue, H. and Gebremichael, M.: Evaluation of Precipitation Forecast from Global Forecast System Over Transboundary Rivers in Africa, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3652, https://doi.org/10.5194/egusphere-egu21-3652, 2021.

The Japan Meteorological Agency (JMA) is conducting the third Japanese global atmospheric reanalysis named Japanese Reanalysis for Three Quarters of a Century (JRA-3Q) using the JMA operational data assimilation system that has been upgraded and improved since the Japanese 55-year Reanalysis (JRA-55) was conducted. Main points of improvement in the specifications of the data assimilation system are as follows (specifications of the JRA-55 data assimilation system are shown in parentheses for comparison): Vertical levels are increased up to 100 (60) layers; The top level of the system is 0.01 (0.1) hPa; The inner model resolution for 4D-Var is also increased up to TL319 (T106); Various parameterization schemes have been improved and several new schemes have been implemented. In addition, we use observations newly rescued and digitized by the ERA-CLIM and other projects as well as newly reprocessed and improved satellite observations. As for GNSS radio occultation, bending angle is assimilated up to 60 km (refractivity up to 30 km).

The early results show that both overestimation of precipitation in the tropics and dry bias in the middle troposphere are diminished compared with those in JRA-55, and the representation of diabatic heating rate is also improved. In addition, biases of surface heat fluxes and radiation fluxes at the top of the atmosphere are also reduced.

How to cite: Harada, Y., Kobayashi, S., Kosaka, Y., Chiba, J., and Tokuhiro, T.: Early results of the evaluation of the JRA-3Q reanalysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3762, https://doi.org/10.5194/egusphere-egu21-3762, 2021.

Improving the accuracy of numerical weather predictions still poses a challenging task. The lack of sufficiently detailed spatio-temporal real-time in-situ measurements constitutes a crucial gap concerning the adequate representation of atmospheric moisture fields, such as water vapor, which are critical for improving weather predictions accuracy. Information on total vertically integrated water vapor (IWV), extracted from global positioning systems (GPS) tropospheric path delays, can enhance various atmospheric models at global, regional, and local scales. Currently, numerous existing atmospheric numerical models predict IWV. Nevertheless, they do not provide accurate estimations compared with in-situ measurements such as radiosondes. In this work, we demonstrate a novel approach for assimilating 2D IWV regional maps estimations, extracted from GPS tropospheric path delays combined with METEOSAT satellite imagery data, to enhance Weather Research and Forecast (WRF) model predictions accuracy above the Eastern Mediterranean area. Unlike previous studies, which assimilated IWV point measurements, here, we assimilate quasi-continuous 2D GPS IWV maps, augmented by METEOSAT-11 data, over Israel and its surroundings. Using the suggested approach, our results show a decrease of more than 30% in the root mean square error (RMSE) of WRF forecasts after assimilation relative to the standalone WRF when verified against in-situ radiosonde measurements near the Mediterranean coast. Furthermore, substantial improvements along the Jordan Rift Valley and Dead Sea Valley areas are achieved when compared to 2D IWV regional maps. Improvements in these areas suggest the importance of the assimilated high resolution IWV maps, in particular when assimilation and initialization times coincide with the Mediterranean Sea Breeze propagation from the coastline to highland stations.

How to cite: Reuveni, Y., Leontiev, A., and Rostkier-Edelstein, D.: Enhancing WRF Model Forecasts by Assimilating High-Resolution GPS-Derived Water-Vapor Maps combined with METEOSAT-11 Data , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4503, https://doi.org/10.5194/egusphere-egu21-4503, 2021.

The use of numerical modeling for atmospheric research is complicated by the problem of verification by a limited set of measurement data. Comparison with radar measurements is widely used for assessing the quality of the simulation. The probabilistic nature of the development of convective phenomena determines the complexity of the verification process: the reproduction of the pattern of the convective event is prior to the quantitative agreement of the values at a particular point at a particular moment.

We propose a method for verifying the simulation results based on comparing areas with the same reflectivity. The method is applied for verification of WRF-modeling of convective events in the Aragats highland massif in Armenia. It is shown that numerical simulation demonstrates approximately the same form of distribution of areas of equal reflectivity as for radar-measured reflectivity. In this case, the model tends to overestimate on average reflectivity, while enabling us to obtain the qualitatively correct description of the convective phenomenon.

The proposed technique can be used to verify the simulation results using data on reflectivity obtained by a satellite or a meteoradar. The technique allows one to avoid subjectivity in the interpretation of simulation results and estimate the quality of reproducing the “general pattern” of the convective event.

How to cite: Svechnikova, E., Ilin, N., and Mareev, E.: Verification of modeling of convective events based on radar reflectivity, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5224, https://doi.org/10.5194/egusphere-egu21-5224, 2021.

 It is an essential problem for forecasting Mesoscale Convection Systems to understand the mechanism of interaction between atmospheric flow and vortices with the development of cumulonimbus clouds using a numerical weather model. In this research, potential temperature gradient based vorticity which is the expression of baroclinic is obtained to analyze the energy structure of the vorticity field in developing cumulonimbus. First, applying the variational method enables us to obtain a diagnostic equation in which the equation of motion, conservation law of mass, and entropy are considered as constraints. Second, Fourier analysis was performed on the vorticity field in the cross-section of the convective core in the isolated cumulonimbus simulation. The temporal change of the spectrum of the vorticity field indicates that the rotational intensity of potential temperature gradient based vorticity increases at the same time as the degree of baroclinicity increases. It was also found that the same tendency can be seen in the analysis of the vorticity field of developing clouds using the environment of the heavy rainfall event in the Kuma River basin that occurred on July 4, 2020. We are planning to analyze the vorticity field in the cluster of cumulonimbus clouds and consider the difference in the energy structure of the vorticity field due to the difference in model resolution. Third, we conducted the data assimilation experiment assuming the use of vertical vorticity estimated by doppler radar observation. As a result, the change in the potential temperature and vertical wind through the error covariance matrix generates coherent convection in the computations.

How to cite: Ono, A., Yamaguchi, K., and Nakakita, E.: Energy Structure Analysis of Vorticity Driven by Thermal Gradient in Developing Cumulonimbus Clouds and Application to Data Assimilation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5570, https://doi.org/10.5194/egusphere-egu21-5570, 2021.

This study evaluates the latest Japanese Reanalysis for Three Quarters of a Century (JRA-3Q) conducted by the Japan Meteorological Agency (JMA), focusing on a semi-period of pre-satellite era (1960s and 1970s). The reanalysis is the third Japanese global atmospheric reanalysis covering the period from late 1940s onward, which is produced with the JMA's operational system as of December 2018. The atmospheric model has a TL479 horizontal resolution and 100 vertical layers up to 0.01 hPa, and the core component of the JRA-3Q data assimilation system is the 6-hourly 4D-Var of the atmospheric state with a T319-resolution inner model. Because there are only few global-covered observational datasets during the pre-satellite era, evaluation of the JRA-3Q is mainly to conduct an intercomparison of other reanalysis datasets such as representation Japanese 55-year Reanalysis (JRA-55), a JRA-55's subset of atmospheric reanalysis assimilating conventional observations only (JRA-55C), and version 3 of the Twentieth Century Reanalysis (20CRv3), and also an intercomparison of JRA-3Q between the pre-satellite and satellite eras. Emphasis of this evaluation during the non-satellite era is placed on the representation of tropical circulation, the consistency in time of the reanalysed fields, detection of tropical cyclones, and the quality of the stratospheric water vapor and ozone. For example, the surface circulation over the tropical Africa is improved by means of reducing spurious anticyclonic circulation anomalies that were found in JRA-55. Although the atmospheric model can produce self-generated quasi-biennial oscillation (QBO) by introducing non-orographic gravity wave drag, the evaluation reveals that JRA-3Q has a shorter period of around one year in the middle stratosphere and diminished QBO amplitude in the lower stratosphere, indicating that representation of the QBO in JRA-3Q is not as good as that in JRA-55.

How to cite: Naoe, H., Kobayashi, S., Kosaka, Y., Chiba, J., Tokuhiro, T., and Harada, Y.: Evaluation of a new Japanese reanalysis (JRA-3Q) in a pre-satellite era, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6881, https://doi.org/10.5194/egusphere-egu21-6881, 2021.

The ensemble data assimilation system is beneficial to represent the initial uncertainties and flow-dependent background error covariance (BEC). In particular, the inevitable model uncertainties can be expressed by ensemble spread, that is the standard deviation of ensemble BEC. However, the ensemble spread generally suffers from under-estimated problems. To alleviate this problem, recent studies employed stochastic perturbation schemes to increases the ensemble spreads by adding the random forcing in the model tendencies (i.e., physical or dynamical tendencies) or parameterization schemes (i.e., PBL, convective scheme, etc.). In this study, we focus on the near-surface uncertainties which are affected by the interactions between the land and atmosphere process. The land surface model (LSM) provides various fluxes as the lower boundary condition to the atmosphere, influencing the accuracy of hourly-to-seasonal scale weather forecasting, but the surface uncertainties were not much addressed yet. In this study, we developed the stochastically perturbed parameterization (SPP) scheme for the Noah LSM. The Weather Research and Forecasting (WRF) ensemble system is used for regional weather forecasting over East Asia, especially over the Korean Peninsula. As a testbed experiment with the newly-developed Noah LSM-SPP system, we first perturbed the soil temperature — a crucial variable for the near-surface forecasts by affecting sensible heat fluxes, land surface skin temperature and surface air temperature, and hence lower-tropospheric temperature. Here, the random forcing used in perturbation is made by the tuning parameters for amplitude, length scale, and time scales: they are commonly determined empirically by trial and error. In order to find optimal tuning parameter values, we applied a global optimization algorithm — the micro-genetic algorithm (micro-GA) — to achieve the smallest root-mean-squared errors. Our results indicate that optimization of the random forcing parameters contributes to an increase in the ensemble spread and a decrease in the ensemble mean errors in the near-surface and lower-troposphere uncertainties. Further experiments will be conducted by including soil moisture in the testbed.

How to cite: Lim, S., Cassardo, C., and Park, S. K.: Development of stochastically perturbed parameterization scheme for the Noah Land Surface Model with the optimized random forcing parameters using the micro-genetic algorithm, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6396, https://doi.org/10.5194/egusphere-egu21-6396, 2021.

The Japan’s Big Data Assimilation (BDA) project started in October 2013 and ended its 5.5-year period in March 2019. Here, we developed a novel numerical weather prediction (NWP) system at 100-m resolution updated every 30 seconds for precise prediction of individual convective clouds. This system was designed to fully take advantage of the phased array weather radar (PAWR) which observes reflectivity and Doppler velocity at 30-second frequency for 100 elevation angles at 100-m range resolution. By the end of the 5.5-year project period, we achieved less than 30-second computational time using the Japan’s flagship K computer, whose 10-petaflops performance was ranked #1 in the TOP500 list in 2011, for past cases with all input data such as boundary conditions and observation data being ready to use. The direct follow-on project started in April 2019 under the Japan Science and Technology Agency (JST) AIP (Advanced Intelligence Project) Acceleration Research. We continued the development to achieve real-time operations of this novel 30-second-update NWP system for demonstration at the time of the Tokyo 2020 Olympic and Paralympic games. The games were postponed, but the project achieved real-time demonstration of the 30-second-update NWP system at 500-m resolution using a powerful supercomputer called Oakforest-PACS operated jointly by the Tsukuba University and the University of Tokyo. The additional developments include parameter tuning for more accurate prediction and complete workflow to prepare all input data in real time, i.e., fast data transfer from the novel dual-polarization PAWR called MP-PAWR in Saitama University, and real-time nested-domain forecasts at 18-km, 6-km, and 1.5-km to provide lateral boundary conditions for the innermost 500-m-mesh domain. A real-time test was performed during July 31 and August 7, 2020 and resulted in the actual lead time of more than 27 minutes for 30-minute prediction with very few exceptions of extended delay. Past case experiments showed that this system could capture rapid intensification and decays of convective rains that occurred in the order of less than 10 minutes, while the JMA nowcasting did not predict the rapid changes by its design. This presentation will summarize the real-time demonstration during August 25 and September 7 when Tokyo 2020 Paralympic games were supposed to take place.

How to cite: Miyoshi, T., Honda, T., Amemiya, A., Otsuka, S., Maejima, Y., Taylor, J., Tomita, H., Nishizawa, S., Sueki, K., Yamaura, T., Ishikawa, Y., Satoh, S., Ushio, T., Koike, K., Hoshi, E., and Nakajima, K.: Big Data Assimilation: Real-time Demonstration Experiment of 30-second-update Forecasting in Tokyo in August 2020, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6890, https://doi.org/10.5194/egusphere-egu21-6890, 2021.

One important aspect of successfully implementing an ensemble Kalman filter (EnKF) in a high dimensional geophysical application is covariance localization. But for satellite radiances whose vertical locations are not well defined, covariance localization is not straightforward. The global group filter (GGF) is an adaptive localization algorithm, which can provide adaptively estimated localization parameters including the localization width and vertical location of observations for each channel and every satellite platform of radiance data, and for different regions and times. This adaptive method is based on sample correlations between ensemble priors of observations and state variables, aiming to minimize sampling errors of estimated sample correlations. The adaptively estimated localization parameters are examined here for typhoon Yutu (2018), using the regional model WRF and a cycling EnKF system. The benefits of differentiating the localization parameters for TC and non-TC regions and varying the localization parameters with time are investigated. Results from the 6-h priors verified relative to the conventional and radiance observations show that the adaptively estimated localization parameters generally produce smaller errors than the default Gaspari and Cohn (GC) localization. The adaptively estimated localization parameters better capture the onset of RI and yield improved intensity and structure forecasts for typhoon Yutu (2018) compared to the default GC localization. The time-varying localization parameters have slightly advantages over the time-constant localization parameters. Further improvements are achieved by differentiating the localization parameters for TC and non-TC regions.

How to cite: Wang, C., Lei, L., Tan, Z.-M., and Chu, K.: Adaptive Localization for Tropical Cyclones With Satellite Radiances in an Ensemble Kalman Filter, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8774, https://doi.org/10.5194/egusphere-egu21-8774, 2021.

ESIAS is an atmospheric modeling system including the ensemble version of the Weather Forecasting and Research Model (WRF V3.7.1) and the ensemble version of the EURopean Air pollution Dispersion-Inverse Model (EURAD-IM), the latter uses the output of the WRF model to calculate, amongst others, the transportation of aerosols. To capture extreme weather events causing the uncertainty in the solar radiation and wind speed for the renewable energy industry, we employ ESIAS by using stochastic schemes, such as Stochastically Perturbed Parameterization Tendency (SPPT) and Stochastic Kinetic Energy Backscatter (SKEBS) schemes, to generate the random fields for ensembles of up to 4096 members.

     Our first goal is to produce 48 hourly weather predictions for the European domain with a 20 KM horizontal resolution to capture extreme weather events affecting wind, solar radiation, and cloud cover forecasts. We use the ensemble capability of ESIAS to optimize the physics configuration of WRF to have a more precise weather prediction. A total of 672 ensemble members are generated to study the effect of different microphysical schemes, cumulus schemes, and planetary boundary layer parameterization schemes. We examine our simulation outputs with 288 simulation hours in 2015 using model input from the Global Ensemble Forecast System (GEFS). Our results are validated by the cloud cover data from EUMETSAT CMSAF. Besides the precision of weather forecasting, we also determine the greatest spread by generating total 768 ensemble members: 16 stochastic members for each different configurations of physical parameterizations (48 combinations). The optimization of WRF will help for improving the air quality prediction by EURAD-IM, which will be demonstrated on a test case basis.

     Our results show that for the performed analysis the Community Atmosphere Model (CAM) 5.1, WRF Single-Moment 6-class scheme (WSM6), and the Goddard microphysics outstand the other 11 microphysics parameterizations, where the highest daily average matching rate is 64.2%. The Mellor–Yamada Nakanishi Niino (MYNN) 2 and MYNN3 schemes give better results compared to the other 8 planetary boundary layer schemes, and Grell 3D (Grell-3) works generally well with the above mentioned physical schemes. Overall, the combination of Goddard and MYNN3 produces the greatest spread comparing to the lowest spread (Morrison 2-moment & GFS) by 40%.

How to cite: Lu, Y.-S., Franke, P., and Jerger, D.: Optimizing cloud cover prediction by the Ensemble for Stochastic Integration of Atmospheric Simulations (ESIAS), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9299, https://doi.org/10.5194/egusphere-egu21-9299, 2021.

Ensembles of numerical weather prediction models are currently used to represent the forecast uncertainty of forecast variables. However due to the computationally expensive nature of these ensembles, these uncertainties are only known with a large sampling error, and often the underlying distributions are assumed to be gaussian for Data Assimilation purposes. Furthermore, it is unclear how many members are required in an ensemble to obtain a designated level of sampling error. This work endeavours to understand how this error decreases as ensembles become larger, and how the forecast uncertainty evolves over a 24 hour free forecast period, before answering the pressing question of: how many ensembles are required in an NWP ensemble in order to sufficiently resolve the uncertainty? To do this, a simple 1D modified shallow water model which replicates the main features of convection is employed in the form of a massive ensemble with over 100,000 members. The shape of the distributions from this ensemble, which develop significant non-gaussianity, resembles those of the operational NWP ensembles of SCALE-RM and ICON, indicating that this model is sufficiently realistic in representing the forecast uncertainty. The simple model will be used to determine the rate of convergence of different forecast variables as ensemble size increases, and to evaluate the errors resulting from using the small ensemble sizes that are typical in operational NWP.

How to cite: Tempest, K. and Craig, G.: What ensemble size is required for accurate forecasts? Idealised model experiments with very large ensembles, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4068, https://doi.org/10.5194/egusphere-egu21-4068, 2021.

The Met Office hourly 4D-Var was introduced operationally to its convective-scale limited area model (UKV) in summer 2017, improving forecast skill for nowcasting and short-range purposes. However, in recent tests a downscaler run from a global analysis tends to be better than hourly 4D-Var, especially for some variables (e.g. screen temperature). This is probably due to a poor representation of large-scale dynamics in the LAM DA system, which is now integrated on an extended domain, whilst the global model has improved to a 10km resolution and with better DA (hybrid 4D-Var). Therefore, the MO recognises the necessity of coupling large scale dynamics with convective systems using the better estimation of these motions from the global model.
We opted for a solution similar to spectral nudging, which uses large scale increments derived from a model with a better representation of these scales. At the same time, the short scales from UKV are maintained. We call this method ‘Background Increments’ (BGInc), as it updates the UKV background fields using a spectrally filtered increment derived from a different (global) model. This update is calculated just prior to computing the analysis increments from the hourly DA cycle. We investigated different set-ups for the implementation, changing the cut-off wavelength, the vertical weights, the frequency of updates of BGInc and other set-up features.
This novel system is now in a testing phase for operational purposes. From preliminary results, the forecast is improved for about the first 12 hours for different variables. We also notice a reduction in the gravity wave activity generated when new lateral boundary conditions are introduced to the LAM from the latest global forecast. This research shows the benefits of a better representation of large-scale motions for LAM forecasts.
In the short term, future development involves the computation of new static covariances using a better representation of the large-scale error. In the longer term, this technique could be useful in a hybrid 4D-Var scheme while enabling the use of large-scale ensemble perturbations in the analysis without causing large adjustments at the lateral boundaries.

How to cite: Milan, M., Clayton, A., Lorenc, A., Dow, G., Tubbs, R., and Macpherson, B.:  Spectral nudging in an hourly 4DVar framework: Status and Plans, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6618, https://doi.org/10.5194/egusphere-egu21-6618, 2021.

The western Mediterranean region is frequently disrupted by heavy precipitation and flash flood episodes. Designing convection-permitting ensembles capable of accurately forecasting socially relevant aspects of these natural hazards such as timing, location, and intensity at basin scales of the order of a few hundred of squared kilometers is an extremely challenging effort. The usual forecast underdispersion prevailing at these scales motivates the research of sampling methodologies which are able to provide an adequate representation of the uncertainties in the initial atmospheric state and its time-integration by means of numerical models. This work investigates the skill of multiple techniques to sample model uncertainty in the context of heavy precipitation in the Mediterranean. The performance of multiple stochastic schemes is analyzed for a singular event occurred on 12 and 13 September 2019 in València, Murcia, and Almería (eastern Spain). This remarkable and enlightening episode caused seven casualties, the flooding of hundreds of homes and economic exceeding 425 million EUR.

Stochastic methods are compared to the popular multiphysics strategy in terms of both diversity and skill. The considered techniques include stochastic parameterization perturbation tendencies of state variables and perturbations to specific and influential parameters within the microphysics scheme (cloud condensation nuclei, fall speed factors, saturation percentage for cloud formation). The introduction of stochastic perturbations to the microphysics parameters results in an increased ensemble spread throughout the entire simulation. A conclusion of special relevance for the western Mediterranean, where local topography and deep moist convection play an essential role, is that stochastic methods significantly outperform the multiphysics-based ensemble, indicating a clear potential of stochastic parameterizations for the short-range forecast of high-impact events in the region.

How to cite: Hermoso, A., Homar, V., and Plant, R.: Improving heavy precipitation forecasting over the western Mediterranean: Benefits of stochastic techniques for model error sampling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6225, https://doi.org/10.5194/egusphere-egu21-6225, 2021.

In the context of Numerical Weather Prediction (NWP), continuous improvement from one version to another is made possible by the improvement of individual parts of the models. Thus the evaluation of those parts is crucial. Within a time step, we see the sequence of the resolved dynamic part and physical parametrizations. Similarly, within a data assimilation cycle, we see the sequence of forecast and analysis. These cyclical behaviours are responsible for a high coupling between the different parts of a NWP system. This means that, when evaluating an individual physical parametrization, a forecast only approach is not enough and simulations of the whole system with data assimilation over a long period are required.

In this work, we focus on the evaluation of the physical parametrization of deep convection in the French model ARPEGE. We evaluate the direct impact of this parametrization in a forecast only study as well as the indirect impact with a 4D-Var and the study of the analysis. We have replaced the previous parametrization by the one used in the Integrated Forecast System (IFS) developed at the ECMWF. We seize the opportunity of using an other model parametrization to rearrange physical tendencies in the same way as in the IFS. This diagnostic is new for the ARPEGE environment and it leads to an intecomparison between the two model physics. To evaluate the coupling, we use several ARPEGE 4D-Var to compare the change in analysis with an estimate of the analysis error. Those studies show a significant impact of the new scheme both in the tendencies and in the analysis.

How to cite: Hubans, A., Berre, L., Bouteloup, Y., Loo, C., and Marquet, P.: Impacts of a change in deep convection scheme on the ARPEGE data assimilation system, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7184, https://doi.org/10.5194/egusphere-egu21-7184, 2021.

Forecasting high impact weather events is a major challenge for numerical weather prediction. Initial condition uncertainty plays a major role but so potentially do uncertainties arising from the representation of physical processes, e.g. cloud microphysics. In this project, we investigate the impact of these uncertainties for the forecast of cloud properties, precipitation and hail of a selected severe convective storm over South-Eastern Germany.
To investigate the joint impact of initial condition and parametric uncertainty a large ensemble including perturbed initial conditions and systematic variations in several cloud microphysical parameters is conducted with the ICON model (at 1 km grid-spacing). The comparison of the baseline, unperturbed simulation to satellite, radiosonde, and radar data shows that the model reproduces the key features of the storm and its evolution. In particular also substantial hail precipitation at the surface is predicted. Here, we will present first results including the simulation set-up, the evaluation of the baseline simulation, and the variability of hail forecasts from the ensemble simulation.
In a later stage of the project we aim to assess the relative contribution of the introduced model variations to changes in the microphysical evolution of the storm and to the fore- cast uncertainty in larger-scale meteorological conditions.

How to cite: Kuntze, P., Miltenberger, A., Hoose, C., and Kunz, M.: Role of initial condition and parametric uncertainty in a severe hailstorm forecast, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7406, https://doi.org/10.5194/egusphere-egu21-7406, 2021.

Dynamic sea ice and ocean have long been recognised as an important components in the Earth System Models used to generate climate change projections and more recently seasonal forecasts. However, the benefit of forecasts on the timescales of days to weeks has received less attention. Until recently it was assumed that sea-ice-ocean fields change so slowly that it is acceptable to keep them fixed in short and medium-range forecasts. However, at the ice edge the presence of sea ice dramatically influences surface fluxes, particularly when the overlying atmosphere is much colder than the open ocean so errors in the position of the sea ice, caused by simply persisting this field, have the potential to degrade atmospheric skill. To address this and similar issues, the European Centre for Medium-range Weather Forecasts (ECMWF) recently took the pioneering step of coupling a dynamic–thermodynamic sea ice-ocean model to the Integrated Forecast System, developing the first coupled medium-range forecasting system. This was a major step towards making ECMWF’s forecasts seamless across all timescales.

In this study we assess the benefits of including coupled sea-ice ocean processes in the medium-range by comparing set of ten-day forecasts with and without dynamic ice-ocean coupling, focussing on forecast performance at the edge of the sea ice and in the surrounding region. We demonstrate that dynamic coupling improves forecasts of the sea ice edge at all leadtimes. Further, the skill gained is larger during periods when the ice edge is advancing or retreating rapidly. We will also explore whether dynamic coupling has an impact on forecast skill in atmospheric parameters downstream of the ice edge.  

How to cite: Day, J., Keeley, S., Mogensen, K., Tietsche, S., and Magnusson, L.: Benefits of ice-ocean coupling for medium-range forecasts in polar and sub-polar regions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8054, https://doi.org/10.5194/egusphere-egu21-8054, 2021.

Since their first operational application in the 1950s, atmospheric numerical models have become essential tools in weather and climate prediction. As such, they are a constant subject to changes, thanks to advances in computer systems, numerical methods, and the ever increasing knowledge about the atmosphere of Earth. Many of the changes in today's models relate to seemingly unsuspicious modifications, associated with minor code rearrangements, changes in hardware infrastructure, or software upgrades. Such changes are meant to preserve the model formulation, yet the verification of such changes is challenged by the chaotic nature of our atmosphere - any small change, even rounding errors, can have a big impact on individual simulations. Overall this represents a serious challenge to a consistent model development and maintenance framework.

Here we propose a new methodology for quantifying and verifying the impacts of minor atmospheric model changes, or its underlying hardware/software system, by using ensemble simulations in combination with a statistical hypothesis test. The methodology can assess effects of model changes on almost any output variable over time, and can also be used with different hypothesis tests.

We present first applications of the methodology with the regional weather and climate model COSMO. The changes considered include a major system upgrade of the supercomputer used, the change from double to single precision floating-point representation, changes in the update frequency of the lateral boundary conditions, and tiny changes to selected model parameters. While providing very robust results, the methodology also shows a large sensitivity to more significant model changes, making it a good candidate for an automated tool to guarantee model consistency in the development cycle.

How to cite: Zeman, C. and Schär, C.: A new ensemble-based statistical methodology to verify changes in weather and climate models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4255, https://doi.org/10.5194/egusphere-egu21-4255, 2021.

Most of the precipitation formation in extratropical cyclones occurs in the warm sector along an elongated air stream ahead of the cold front - the so-called warm conveyor belt (WCB). The WCB ascends slantwise from the planetary boundary layer into the upper troposphere, where its outflow interacts with the upper-level jet and modifies the Rossby wave structure. The ascent of WCBs is strongly driven by cloud-condensational processes, which are parametrized in numerical weather prediction models, and is therefore associated with forecast uncertainty. In the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS), model uncertainty related to parametrizations is represented by the so-called stochastically perturbed parametrization tendencies (SPPT)-scheme, which introduces multiplicative noise to the physics tendencies.

In this study, we investigate the systematic effect of the SPPT-scheme on rapidly ascending air streams in the extratropics (i.e. WCBs) and on tropical convection by conducting sensitivity experiments with the ECMWF EPS based on the Integrated Forecasting System (IFS) model. The comparison of an experiment with an operational setup (initial condition and model physics perturbations) to one where model physics perturbations are switched off demonstrates that the SPPT-scheme systematically influences the activity of WCBs and tropical convection.

Globally, rapidly ascending air streams, which are detected by applying trajectory analysis in each ensemble member, are enhanced by about 37% when SPPT is activated. Also the dynamical and physical characteristics of the trajectories are systematically modified: the latent heat release and the ascent speed are increased, while the outflow latitude is decreased. This systematic modulation is stronger in the tropics and weaker in the extratropics. A detailed investigation of vertical velocities indicates that SPPT increases the frequency of relatively strong upward motion related to WCBs and tropical convection, while slower upward motion is suppressed compared to the unperturbed experiment. Despite the symmetric, zero-mean nature of the perturbations, the response of rapidly ascending air streams to the SPPT-scheme is systematically unidirectional, pointing towards non-linearities in the underlying processes.

This study shows that process-oriented diagnostics of weather systems help to advance the understanding of upscale impacts of the ensemble configuration on the representation of the large-scale circulation in numerical models.

How to cite: Pickl, M., Grams, C. M., Lang, S. T. K., and Leutbecher, M.: The effect of stochastically perturbed parametrization tendencies on rapidly ascending air streams, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9397, https://doi.org/10.5194/egusphere-egu21-9397, 2021.

For any ensemble-based data assimilation system sampling errors are introduced as a consequence of limited ensemble size, generating spurious backgound error covariances and leading to erroneous adjustments to the analysis. As a way to reduce the impact of these sampling errors, as well as improve rank deficiency, covariance localization is applied, which artifically reduces the weighting of error covariances beyond a defined physical distance between the background and observations deemed to be false.

In this study we perform sensitivity tests to find the appropriate horizontal localization scale for the SCALE-LETKF, a numerical weather prediction model that combines the SCALE regional model with the local ensemble transform Kalman filter. The system has been in development since 2013 to provide very high resolution modelling of convective weather systems and is unique in its ability to perform near real-time NWP operation at 500-m resolution refreshed every 30 seconds with observations from Phased Array Weather Radar (PAWR).  Here, we perform sensitivity tests at 500-m resolution with 30-second update cycling of PAWR data for several testcases of heavy convective rainfall over Tokyo metropolitan area from August/September 2019. Test scores showed horizontal localization scale of 2-km generally provided optimal forecast skill for lead times up to 30 minutes, although there were variations on this dependent upon lead time and case study. We show that by reducing localization scale, systematic errors leading to over-intensification of convective activity in forecasts were reduced, resulting in improved consistency with observations. This was a conseqence of generating more convectively stable, less dynamically active environment with smaller localization scale.

How to cite: Taylor, J., Honda, T., Amemiya, A., and Miyoshi, T.: Optimizing the localization scale for a convective-scale ensemble radar data assimilation , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9533, https://doi.org/10.5194/egusphere-egu21-9533, 2021.

The growing share of renewable energy in power generation increases the impact of the weather on the stability of the power grid.
Especially prior to severe weather events, not only high-quality weather forecasts but also information about forecast uncertainties is needed by the transmission system operators (TSOs) to prepare stability provisions. 
To this end, in the research project gridcast the German Meteorological Service (DWD) aims at an improved representation of the inherent model error in its recently introduced convection-permitting ensemble prediction system ICON-D2-EPS.

We describe the model error using the following stochastic ansatz: The tendency equations for a set of relevant variables for power prediction like temperature, and zonal and meridional winds are extended by an additive tendency error approximated by the solution of a partial stochastic differential equation (SDE). This SDE consists – similar to an Ornstein-Uhlenbeck equation – of a damping term and a random field. However, the SDE is augmented with an additional diffusion term that ensures spatial correlations.
Each of the three terms has a strength parameter that is assumed to be a function of (possibly different) flow-dependent predictor variables. Hence the relative importance of the three terms varies in space and time according to the respective weather conditions.
The functional form of the parameters can be approximated from past estimates of the model error based on ICON-D2 ensemble forecasts.

We present theoretical properties of the SDE and motivate its choice as representation of the model error. Furthermore, we investigate a method to determine the parameters of the SDE and apply this method to the operational ICON-D2-EPS at DWD for the model error of relevant forecast variables.
First numerical results along the development of the scheme are presented.

How to cite: Sprengel, M. and Gebhardt, C.: Characterization of the model error in ICON-D2-EPS using a flow-dependent partial SDE, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9772, https://doi.org/10.5194/egusphere-egu21-9772, 2021.

Stochastic parameterisations are an important way to represent uncertainty in the deterministic forecasting models underlying ensemble prediction systems. In many of the currently used stochastic parameterisation approaches, random generators produce correlation patterns that induce spatially and temporally coherent perturbations to the parameterisation parameters or tendencies. The patterns that are currently used in the Harmonie ensemble prediction system are therefore unrelated to the atmospheric flow or weather situation. Here we investigate the potential of replacing such random patterns by accumulated tendency fields from parameterized physical processes in the model. The rationale hereby is that by perturbing the parameterisations with a field that reflects where parameterisations are most active, rather than a random pattern, the model obtains a more targeted increase in the degrees-of-freedom to represent forecasting uncertainty.

As an initial test case, we consider a large cold-air outbreak during 23-25 Dec 2015 that affected large parts of Scandinavia. During that time period, strong heat fluxes persisted near the ice edge, while widespread shallow convection dominated in the center of the model domain. For diagnosing the perturbation fields, we utilise an implementation of individual tendency diagnostics implemented in AROME-Arctic within the ALERTNESS project. Total physical tendencies for the horizontal wind components, for air temperature and humidity are accumulated with a time filtering throughout the 66 h forecast period.

The accumulated tendencies from all parameterisations for the different variables show overlapping and differing centers of activity. Wind parameterisations are active near the ice edge, and with smaller scale variability over land areas, in particular at lower model levels. Temperature tendency patterns show activity that is more confined to the ice edge, and a narrow coastal stripe along Northern Scandinavia. These first results show that the approach provides spatially coherent patterns of parameterisation activity, which are meaningfully related to the dominating weather situation. Based on sensitivity tests of cloud parameterisation parameters in a single-column version, we outline the next steps in the path towards diagnostic perturbation patterns for stochastically perturbed perturbations in the Harmonie EPS system.

How to cite: Sodemann, H., Kähnert, M., Valkonen, T. M., Ekrem, P., and Frogner, I.-L.: Potential of accumulated AROME-Arctic parameterisation tendency for stochastic parameterisation perturbation patterns, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9878, https://doi.org/10.5194/egusphere-egu21-9878, 2021.

Spain, having a complex topography, has many climate and weather particularities, acting in many aspects like a mini continent. This is shown in many aspects, such as supercells, which count for more than 1000 in the last 10 years. This indicates that severe weather happens yearly, and supercell thunderstorms are one of the biggest threats, producing damage to population and economical assets, which makes reliable supercell forecast for risk management and mitigation a priority.

This research evaluates supercell forecasts from the Weather Research and Forecasting (WRF-ARW) model over Spain. This first iteration analyzes 2018 supercells, trying to predict this events using three nested domains (15-3-1 km), feeded with GFS operational datasets. The configuration chosen for the model has been used in the past for a master's thesis, with great results, and thus this work aims to evalute the operational usage of this configuration for prediction with 12-36 hours of anticipation. Results so far show that around 80% of supercells could be perfectly forecasted, and another 15% could have medium forecasting skill. This results show that risk alarms could have been issued if this forecasts had being operative at the moment.

How to cite: Granda-Maestre, R., Calvo-Sancho, C., and Martín, Y.: Supercell predictability on Iberian Peninsula using WRF-ARW model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10376, https://doi.org/10.5194/egusphere-egu21-10376, 2021.

Floods and puddles are incidents that occur every year in Mexico City. The surface runoff that occurs in areas of hills and mountains, such as torrential rains where precipitation is greater than the drainage capacity, are the main factors that give rise to floods in the city. The measures that have been implemented to control floods have focused more on reactive planning instead of implementing prevention measures; so the city is completely dependent on its drainage system to mitigate flooding. For these reasons, the forecast has become essential to respond to the demand for better risk management due to the exposure of infrastructure and people to flood events; and coupled with the uncertainty of future events in Mexico City.

Rainfall is the main source of uncertainty in flood prediction; That is why, in recent years, the Numerical Climate Prediction Models (NWP) have focused on the generation of Ensemble Prediction Systems (EPS); which constitute a feasible method to predict the probability distribution function of atmospheric evolution.

The objective of this work is to evaluate the Operational Ensemble Prediction System issued by the European Centre for Medium-Range Weather Forecasts (ECMWF) to open the doors to the development of a Flood Forecasting System in Mexico City. The EPS was evaluated against observed rainfall for two study zones: Mexico Valley Basin and Mexico City, where for the latter, the forecasts were compared against information of real time observed rainfall. To carry out an objective analysis of the quality of the forecast, metrics were applied for the scalar attributes: precision, reliability, resolution, discrimination and performance. The probabilities given by the ensembles were estimated using a predictive model.

The results show the EPS do represent the probability distribution of the observed events. The first 36 hours of forecasting are the most reliable, after which uncertainty increases. Finally, the predictive model shows good performance in estimating probabilities according to the area under the receiver operating characteristic curve.

How to cite: López López, M. R. and Pedrozo Acuña, A.: Evaluation of the ECMWF Operational Forecasting System for Probabilistic Flood Prediction in Mexico City, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13602, https://doi.org/10.5194/egusphere-egu21-13602, 2021.

Heavy snowfall associated with the passage of a cold front was observed over the Australian Snowy Mountains (ASM) from 05 to 07 Aug, 2018, producing more than 60 mm of snow at some mountain gauges. The snowfall was mainly observed after the passage of the cold front (in postfrontal period) when north-westerly and westerly cross-barrier winds were observed in the lower and mid troposphere. According to the observations of Cabramurra parsivel located at windward slopes of northern part of the ASM snow intensities exceeded 20 mm h^-1 during short time episodes. Furthermore, Himawari-8 observations show convective clouds over the ASM with isolated cold cloud top temperatures varying from -45 to -40 ^oC. The Weather Research and Forecasting (WRF) model version 4.2 was used to further investigate this event. The WRF model was run at 1 km spatial resolution using Thompson, Morrison, NSSL and WDM7 microphysical schemes. Overall, Thompson scheme (our CONTROL run) successfully simulated the precipitation and cloud pattern over the ASM, but showing underestimation of upwind and near top precipitation amount. Morrison and NSSL schemes produce more snow over highly elevated parts of the ASM leading to overestimation of observed snow at top and leeward gauges. The WDM7 simulates unrealistically high amount of precipitation over entire ASM due to strong glaciation processes produced by this scheme. The evaluation of simulated water vapor and cloud water paths against radiometer observations at Cabramurra location show that all sensitivity runs consistently underestimate water vapor path (WVP) despite strong relationship in the simulated and observed WVP time-variations throughout the event. The underestimation of supercooled liquid water (SLW) path is strongest in the WDM7 scheme, while the overestimation of SLW content is greatest in the Thompson scheme. 

How to cite: Gevorgyan, A., Ackermann, L., Huang, Y., Siems, S., and Manton, M.: Simulation of postfrontal heavy snowfall over the Australian Snowy Mountains , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13936, https://doi.org/10.5194/egusphere-egu21-13936, 2021.

The new Copernicus European Regional Re-Analysis (CERRA) is a 5.5km reanalysis, starting in 1984 and ending “near-real-time”, 2021. The reanalysis was delivered using the ALADIN model under the HARMONIE scripting garb. The upper-air is analysed using a 3DVAR technique cycled 3-hourly, while the surface analysis is achieved through a conventional OI technique (MESCAN). Analyses produced by CERRA at 5.5km are assisted through an accompanying 10-member Ensemble Data Assimilation (EDA) system with 11km horizontal resolution cycled 6-hourly. The EDA system is used mainly for serially updated background error covariance estimation (B-matrix) used in the deterministic upper-air 3DVAR minimisation to produce the upper-air analysis.

The B-matrix comprises 2 principal EDA-derived components. The first component is estimated from same-resolution (5.5km) forecast differences, run in the winter and the summer periods, to represent seasonal climatology. This component also varies in time, such that a linearly appropriated proportion of summer or winter differences is taken, based on the current time of year of the reanalysis. The second component comes from the lower-resolution (11km) set of forecast differences, which represents ‘errors of the day’. This second component is a 2.5 day moving average ingested into a new B-matrix every 2 days. The B-matrix is thus comprised of 80% forecast differences coming from the first component and 20% coming from the second component. 

We show results from our study on the primacy of varying the weighting on the 2 components of forecast differences mentioned above, and how it has the potential, given a suitable observation network, to provide better B-matrix statistics.

How to cite: El-Said, A., Brousseau, P., and Ridal, M.: Investigating the primacy of B-matrix EDA flow dependence within the Copernicus Regional Re-Analysis (CERRA), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15543, https://doi.org/10.5194/egusphere-egu21-15543, 2021.

The German Weather Service (DWD) introduces the regional NWP model ICON-LAM (ICON Limited Area Mode) in 2021 to replace the COSMO model. For the ICON-LAM data assimilation, a novel EnVAR (Ensemble VARiational data assimilation) setup is currently evaluated in comparison to the operational deterministic run of KENDA-LETKF (Local Ensemble Transform Kalman Filter). This requires special care as the observation handling differs for the global assimilation (via EnVAR) and the regional assimilation (KENDA). Furthermore, the variational quality control for the regional EnVAR may require a setup differing from the global setup. We will give an introduction to the observation processing in DWD's data assimilation framework (DACE).

For future development, we give an outlook on how a regional EnVAR can be used for a regional deterministic analysis by using a global ICON ensemble in combination with a regional deterministic ICON-LAM run. This is potentially of interest for DWD's partners with smaller computational capacities, because a regional EnVAR analysis is computationally less expensive than running a full KENDA ensemble assimilation cycle.

How to cite: Burba, M., Ulbrich, S., Hollborn, S., Potthast, R., and Knippertz, P.: EnVAR for ICON-LAM: observations and quality control, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15603, https://doi.org/10.5194/egusphere-egu21-15603, 2021.

Extreme Rainfall Events (EREs) in India has increased many folds in recent decades. These severe weather events are generally destructive in nature causing flash floods, catastrophic loss of life and property over densely populated urban cities. Various cities in Karnataka, a southern state in India, witnessed many EREs recently. Appropriate advanced warning systems to predict these events are crucial for preparedness of mitigation strategy to reduce human casualty and socio economic loss. Mesoscale models are essential tools for developing an integrated platform for disaster warning and management. From a stakeholder/user pint of view, primary requirement to tackle ERE related damages is accurate prediction of the observed rainfall location, coverage and intensity in advance. Weather prediction models have inherent limitations imposed primarily by approximations in the model and inadequacies in data. Hence, it is important to evaluate the skill of these models for many cases under different synoptic conditions to quantify model skill before using them for operational applications. The objective of the study is to evaluate performance of the Weather Research and Forecasting (WRF) model for several ERE cases in Karnataka at different model initial conditions. The EREs were identified from the distribution of rainfall events over different regions in Karnataka and those events comes under 1% probability were considered. We examined 38 ERE’s distributed over Karnataka for the period June to November for the years 2015-2019. WRF model is configured with 3 nested domains with outer, inner and innermost domains having resolution of 12 km, 9 km and 3 km respectively. Two sets of simulations are conducted in this study, i) staring at 12 hours prior to the ERE day (i.e. -1200 UTC) & ii) starting at 0000 UTC of the ERE day. Performance of the WRF model forecast is validated against 15 minutes rainfall observations from ~6000 rain gauge stations over Karnataka. During initial hours forecasts initiated at 1200 UTC has distinct advantage in terms of accuracy compared to those initiated at 0000 UTC for most of the cases. In general, model underpredict EREs and underprediction is relatively low for forecasts initiated at 12 00 UTC.

How to cite: Bankar, A. and Vasudevan, R.:  Evaluation of high resolution WRF forecasts for Extreme Rainfall Events over Karnataka against high density in-situ observations , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15786, https://doi.org/10.5194/egusphere-egu21-15786, 2021.