The impact of assimilating radar reflectivity and radial velocity data with an intermittent, cycled three-dimensional variational assimilation (3DVAR) system is explored using an idealized thunderstorm case and a real data case on 8 May 2003. A new forward operator for radar reflectivity is developed that uses a background temperature field provided by a numerical weather prediction model for automatic hydrometeor classification. Three types of experiments are performed on both the idealized and real data cases. The first experiment uses radial velocity data only, the second experiment uses both radial velocity and reflectivity data without hydrometeor classification, and the final experiment uses both radial velocity and reflectivity data with hydrometeor classification. All experiments advance the analysis state to the next observation time using a numerical model prediction, which is then used as the background for the next analysis. Results from both the idealized and real data cases show that, assimilating only radial velocity data, the model can reconstruct the supercell thunderstorm after several cycles, but the development of precipitation is delayed because of the well-known spinup problem. The spinup problem is reduced dramatically when assimilating reflectivity without hydrometeor classification. The analyses are further improved using the new reflectivity formulation with hydrometeor classification. This study represents a successful first effort in variational convective-scale data assimilation to partition hydrometeors using a background temperature field from a numerical weather prediction model.

A real-time, weather-adaptive three-dimensional variational data assimilation (3DVAR) system has been adapted for the NOAA Warn-on-Forecast (WoF) project to incorporate all available radar observations within a moveable analysis domain. The key features of the system include 1) incorporating radar observations from multiple Weather Surveillance Radars-1988 Doppler (WSR-88Ds) with NCEP forecast products as a background state, 2) the ability to automatically detect and analyze severe local hazardous weather events at 1-km horizontal resolution every 5 min in real time based on the current weather situation, and 3) the identification of strong circulation patterns embedded in thunderstorms. Although still in the early development stage, the system performed very well within the NOAA's Hazardous Weather Testbed (HWT) Experimental Warning Program during preliminary testing in spring 2010 when many severe weather events were successfully detected and analyzed. This study represents a first step in the assessment of this type of 3DVAR analysis for use in severe weather warnings. The eventual goal of this real-time 3DVAR system is to help meteorologists better track severe weather events and eventually provide better warning information to the public, ultimately saving lives and reducing property damage.

A hybrid 3DVAR-EnKF data assimilation algorithm is developed based on 3DVAR and ensemble Kalman filter (EnKF) programs within the Advanced Regional Prediction System (ARPS). The hybrid algorithm uses the extended alpha control variable approach to combine the static and ensemble-derived flow-dependent forecast error covariances. The hybrid variational analysis is performed using an equal weighting of static and flow-dependent error covariance as derived from ensemble forecasts. The method is first applied to the assimilation of simulated radar data for a supercell storm. Results obtained using 3DVAR (with static covariance entirely), hybrid 3DVAR-EnKF, and the EnKF are compared. When data from a single radar are used, the EnKF method provides the best results for the model dynamic variables, while the hybrid method provides the best results for hydrometeor related variables in term of rms errors. Although storm structures can be established reasonably well using 3DVAR, the rms errors are generally worse than seen from the other two methods. With two radars, the results from 3DVAR are closer to those from EnKF. Our tests indicate that the hybrid scheme can reduce the storm spin-up time because it fits the observations, especially the reflectivity observations, better than the EnKF and the 3DVAR at the beginning of the assimilation cycles.

To improve severe thunderstorm prediction, a novel pseudo-observation and assimilation approach involving water vapor mass mixing ratio is proposed to better initialize NWP forecasts at convection-resolving scales. The first step of the algorithm identifies areas of deep moist convection by utilizing the vertically integrated liquid water (VIL) derived from three-dimensional radar reflectivity fields. Once VIL is obtained, pseudo–water vapor observations are derived based on reflectivity thresholds within columns characterized by deep moist convection. Areas of spurious convection also are identified by the algorithm to help reduce their detrimental impact on the forecast. The third step is to assimilate the derived pseudo–water vapor observations into a convection-resolving-scale NWP model along with radar radial velocity and reflectivity fields in a 3DVAR framework during 4-h data assimilation cycles. Finally, 3-h forecasts are launched every hour during that period. The performance of this method is examined for two selected high-impact severe thunderstorm events: namely, the 24 May 2011 Oklahoma and 16 May 2017 Texas and Oklahoma tornado outbreaks. Relative to a control simulation that only assimilated radar data, the analyses and forecasts of these supercells (reflectivity patterns, tracks, and updraft helicity tracks) are qualitatively and quantitatively improved in both cases when the water vapor information is added into the analysis.

In this study, the ensemble of three-dimensional variational data assimilation (En3DVar) method for convective-scale weather is adopted and evaluated using an idealized supercell storm simulated by the Weather Research and Forecasting (WRF) Model. Synthetic radar radial velocity, reflectivity, satellite-derived cloud water path (CWP), and total precipitable water (TPW) data are produced from the simulated supercell storm and then these data are assimilated into another WRF Model run that starts with no convection. Two types of experiments are performed. The first assimilates radar and satellite CWP data using a perfect storm environment. The second assimilates additional TPW data using a storm environment with dry bias. The first set of experiments indicates that incorporating CWP and radar data into the assimilation leads to a much faster initiation of supercell storms than found using radar data alone. Assimilating CWP data primarily improves the analyses of nonprecipitating hydrometeor variables. The results from the second set of experiments demonstrate the critical importance of the storm environment. When using the biased storm environment, assimilation of CWP and radar data enhances the analyses, but the forecast skill rapidly decreases over the subsequent 1-h forecast. Further experiments show that assimilating the TPW data has a large impact on storm environment that is essential to the accuracy of the storm forecasts. In general, the combination of radar data and satellite data within the En3DVar results in better analyses and forecasts than when only radar data are used, especially for an imperfect storm environment.

Assimilating radar reflectivity into NWP models is one of the keys to improve the accuracy of convective‐scale numerical weather prediction (NWP). There are generally two major branches of research in radar reflectivity assimilation: directly assimilating radar reflectivity, and indirectly assimilating reflectivity, i.e. assimilating hydrometeors retrieved from radar reflectivity. In this study, the indirect assimilation method based on background‐dependent hydrometeor retrievals is compared with the direct assimilation method using frequent data assimilation cycles for five real data cases. The retrieved hydrometeors in the indirect assimilation method are first verified against the hydrometeor types obtained from a polarimetric hydrometeor classification method. It is illustrated that the background‐dependent hydrometeor retrieval method can obtain reasonable model‐equivalent hydrometeors from radar reflectivity. The analysis increments for hydrometeors with both radar data assimilation methods show similar patterns but their magnitudes are different. Both quantitative and qualitative evaluations of forecasted composite reflectivities and accumulated precipitation indicate that the indirect assimilation method predicts the location and intensity of the simulated convection more accurately than the direct method. Furthermore, the indirect assimilation method is more efficient, which is valuable in real‐time applications by helping deliver forecasts quickly and thus helping forecasters make more timely warning decisions.