Polarimetric radar data (PRD) have potential to be used in numerical weather prediction (NWP) models to improve convective-scale weather forecasts. However, thus far only a few studies have been undertaken in this research direction. To assimilate PRD in NWP models, a forward operator, also called a PRD simulator, is needed to establish the relation between model physics parameters and polarimetric radar variables. Such a forward operator needs to be accurate enough to make quantitative comparisons between radar observations and model output feasible, and to be computationally efficient so that these observations can be easily incorporated into a data assimilation (DA) scheme. To address this concern, a set of parameterized PRD simulators for the horizontal reflectivity, differential reflectivity, specific differential phase, and cross-correlation coefficient were developed. In this study, we have tested the performance of these new operators in a variational DA system. Firstly, the tangent linear and adjoint (TL/AD) models for these PRD simulators have been developed and checked for the validity. Then, both the forward operator and its adjoint model have been built into the three-dimensional variational (3DVAR) system. Finally, some preliminary DA experiments have been performed with an idealized supercell storm. It is found that the assimilation of PRD, including differential reflectivity and specific differential phase, in addition to radar radial velocity and horizontal reflectivity, can enhance the accuracy of both initial conditions for model hydrometer state variables and ensuing model forecasts. The usefulness of the cross-correlation coefficient is very limited in terms of improving convective-scale data analysis and NWP.

A dual-resolution, hybrid, three-dimensional ensemble-variational (3DEnVAR) data assimilation method combining static and ensemble background error covariances is used to assimilate radar data, and pseudo-water vapor observations to improve short-term severe weather forecasts with the Weather Research and Forecast (WRF) model. The higher-resolution deterministic forecast and the lower-resolution ensemble members have 3 and 9 km horizontal resolution, respectively. The water vapor pseudo-observations are derived from the combined use of total lightning data and cloud top height from the Fengyun-4A(FY-4A) geostationary satellite. First, a set of single-analysis experiments are conducted to provide a preliminary performance evaluation of the effectiveness of the hybrid method for assimilating multisource observations; second, a set of cycling analysis experiments are used to evaluate the forecast performance in convective-scale high-frequency analysis; finally, different hybrid coefficients are tested in both the single and cycling experiments. The single-analysis results show that the combined assimilation of radar data and water vapor pseudo-observations derived from the lightning data is able to generate reasonable vertical velocity, water vapor and hydrometeor adjustments, which help to trigger convection earlier in the forecast/analysis and reduce the spin-up time. The dual-resolution hybrid 3DEnVAR method is able to adjust the wind fields and hydrometeor variables with the assimilation of lightning data, which helps maintain the triggered convection longer and partially suppress spurious cells in the forecast compared with the three-dimensional variational (3DVAR) method. A cycling analysis that introduced a large number of observations with more frequent small adjustments is able to better resolve the observed convective events than a single-analysis approach. Different hybrid coefficients can affect the forecast results, either in the single deterministic or cycling analysis experiments. Overall, we found that a static coefficient of 0.4 and an ensemble coefficient of 0.6 yields the best forecast skill for this event.

This study assesses the impact of assimilating pseudo‐observations for water vapor mass derived from the Geostationary Operational Environmental Satellites GOES‐16 lightning data on short‐term quantitative precipitation forecasts (QPFs) over the contiguous United States (CONUS), with an emphasis given to regions characterized by an overall poor radar coverage. The GOES‐16/17 provide nearly uniform and high temporal resolution total lightning observations over most of the Americas. To leverage this information for convective scale weather forecasts, a three‐dimensional variational data assimilation (DA) package developed by the National Severe Storm Laboratory was employed to assimilate these data. To mimic operational settings, the Weather Research and Forecasting Model configuration used in NOAA Global Systems Laboratory's High‐Resolution Rapid Refresh Model version 4 was utilized. During the 2020 NOAA Hazardous Weather Testbed Spring Forecasting Experiment, four experiments were run in real time during a 29‐day period over CONUS and surrounding territories to assess the added value of GOES‐16 lightning over conventional radar data. Overall, the lightning DA (LDA) showed benefit in improving QPFs up to 6 hr, with the best improvements seen during the first 3 hr. Owing to notably larger lightning activity over the eastern CONUS, the most noticeable impacts from the LDA were seen there. Case‐by‐case analysis revealed that positive impacts from the LDA were seen over areas characterized by both good (eastern two thirds of CONUS) and poor radar coverage (western one third of CONUS). Highlighting the inherent difficulties in developing an optimal observation operator based on moisture for LDA applications, the GLM‐based DA experiments systematically overestimated rainfall over the eastern two thirds of CONUS while underestimating it over the western one third of CONUS.

A time–space shift method was recently developed for relocating the best ensemble member predicted tornado vortex to the radar-observed location, aiming to improve the model’s initial condition and subsequent prediction of tornadoes. To further improve tornado prediction, a variational method for analyzing vortex flows, referred to as VF-Var, is used in this paper to retrieve high-resolution vortex winds from the earliest radar volume scan of tornado and the retrieved vortex winds are then assimilated as “observations” after the vortex relocation. The previous three-step method is also adaptively modified to estimate the tornado vortex center location, denoted by x_c ≡ (x_c, y_c) as a continuous function of height z and time t, from the earliest two consecutive radar volume scans of the tornado, so the estimated x_c(z, t) can have the VF-Var required accuracy for retrieving high-resolution vortex winds and the retrieved vortex winds can be assimilated as “observations” with a minimized observation latency. This approach, combined with vortex relocation, is applied to the 20 May 2013 Oklahoma Newcastle–Moore tornado, and is shown to be very effective in further improving the tornado intensity prediction and the continuity of predicted tornado track. Although assimilating the retrieved high-resolution vortex winds after the vortex relocation does not greatly affect the overall trajectory of the predicted tornado track, it proves highly beneficial.