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Assimilating radar reflectivity into convective-scale NWP models remains a challenging topic in radar data assimilation. A primary reason is that the reflectivity forward observation operator is highly nonlinear. To address this challenge, a power transformation function is applied to the WRF Model's hydrometeor and water vapor mixing ratio variables in this study. Three 3D variational data assimilation experiments are performed and compared for five high-impact weather events that occurred in 2019: (i) a control experiment that assimilates reflectivity using the original hydrometeor mixing ratios as control variables, (ii) an experiment that assimilates reflectivity using power-transformed hydrometeor mixing ratios as control variables, and (iii) an experiment that assimilates reflectivity and retrieved pseudo–water vapor observations using power-transformed hydrometeor and water vapor mixing ratios (qυ) as control variables. Both qualitative and quantitative evaluations are performed for 0–3-h forecasts from the five cases. The analysis and forecast performance in the two experiments with power-transformed mixing ratios is better than the control experiment. Notably, the assimilation of pseudo–water vapor with power-transformed qυ as an additional control variable is found to improve the performance of the analysis and short-term forecasts for all cases. In addition, the convergence rate of the cost function minimization for the two experiments that use the power transformation is faster than that of the control experiments. Significance Statement: The effective use of radar reflectivity observations in any data assimilation scheme remains an important research topic because reflectivity observations explicitly include information about hydrometeors and also implicitly include information about the distribution of moisture within storms. However, it is difficult to assimilate reflectivity because the reflectivity forward observation operator is highly nonlinear. This study seeks to identify a more effective way to assimilate reflectivity into a convective-scale NWP model to improve the accuracy of predictions of high-impact weather events. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The assimilation of radar data into storm-scale numerical weather prediction models has been shown to be beneficial for successfully modeling convective storms. Because of the difficulty of directly assimilating reflectivity ( Z), hydrometeor mixing ratios, and sometimes rainfall rate, are often retrieved from Z observations using retrieval relations, and are assimilated as state variables. The most limiting (although widely employed) cases of these relations are derived, and their assumptions and limitations are discussed. To investigate the utility of these retrieval relations for liquid water content (LWC) and ice water content (IWC) in rain and hail as well as the potential for improvement using polarimetric variables, two models with spectral bin microphysics coupled with a polarimetric radar operator are used: a one-dimensional melting hail model and the two-dimensional Hebrew University Cloud Model. The relationship between LWC and Z in pure rain varies spatially and temporally, with biases clearly seen using the normalized number concentration. Retrievals using Z perform the poorest while specific attenuation and specific differential phase shift ( KDP) perform much better. Within rain-hail mixtures, separate estimation of LWC and IWC is necessary. Prohibitively large errors in the retrieved LWC may result when using Z. The quantity KDP can be used to effectively retrieve the LWC and to isolate the contribution of IWC to Z. It is found that the relationship between Z and IWC is a function of radar wavelength, maximum hail diameter, and principally the height below the melting layer, which must be accounted for in order to achieve accurate retrievals. [ABSTRACT FROM AUTHOR]

Radar reflectivity contains information about hydrometeors and plays an important role in the initialization of convective-scale numerical weather prediction (NWP). In this study, a new background-dependent hydrometeor retrieval method is proposed and retrieved hydrometeors are assimilated into the Weather Research and Forecasting model (WRF), with the aim of improving short-term severe weather forecasts. Compared to traditional approaches that are mostly empirical and static, the retrieval parameters for hydrometeor identification and reflectivity partitioning in the new scheme are extracted in real-time based on the background hydrometeor fields and observed radar reflectivity. It was found that the contributions of hydrometeors to reflectivity change a lot in different reflectivity ranges and heights, indicating that adaptive parameters are necessary for reflectivity partitioning and hydrometeor retrieval. The accuracy of the background-dependent hydrometeor retrieval method and its impact on the subsequent assimilation and forecast were examined through observing system simulation experiments (OSSEs). Results show that by incorporating the background information, the retrieval accuracy was greatly improved, especially in mixed-hydrometeor regions. The assimilation of retrieved hydrometeors helped improve both the hydrometeor analyses and forecasts. With an hourly update cycling configuration, more accurate hydrometeor information was properly transferred to other model variables, such as temperature and humidity fields through the model integration, leading to an improvement of the short-term (0−3 h) precipitation forecasts. • Adaptive hydrometeor retrieval method from radar reflectivity was proposed by incorporating NWP background information. • Accuracy of the retrieved hydrometeors from the proposed scheme was greatly improved, especially in regions of mixed species. • Assimilation of retrieved hydrometeors improved the T and Q fields in forecasts, leading to better precipitation forecasts. [ABSTRACT FROM AUTHOR]