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Abstract The assimilation of radar reflectivity requires an accurate and efficient forward operator that links the model state variables to radar observations. In this study, newly developed parameterized forward operators (PFO) for radar reflectivity with a new continuous melting model are implemented to assimilate observed radar data. To assess the impact of the novel parameterized reflectivity forward operators on convective storm analysis and forecasting, two distinct sets of cycled assimilation and forecast experiments are conducted. One set of experiments (ExpRFO) uses a conventional Rayleigh‐scattering‐approximation‐based forward operator (RFO) with hydrometeor classification, while the other uses the PFO (ExpPFO_New) for radar reflectivity with a new continuous melting model. Eight high‐impact severe convective weather events from the Hazardous Weather Testbed (HWT) 2019 Spring Experiments are selected for this study. The analysis and forecast results are first examined in detail for a classic tornadic supercell case on 24 May 2019, with the potential benefits provided by the PFO then evaluated for all eight cases. It is demonstrated that ExpPFO_New provides more robust results in terms of improving the short‐term severe weather forecasts. Compared to ExpRFO, ExpPFO_New better reproduces all observed supercells in the analysis field, yields a more continuous and reasonable reflectivity distribution near the melting layer, and improves the strength of the cold pool compared to observations. Overall, ExpPFO_New, initialized from the more accurate analysis fields, produces better forecasts of reflectivity and hourly precipitation with smaller biases, especially at heavy precipitation thresholds. 

Abstract To improve short‐term severe weather forecasts through assimilation of polarimetric radar data (PRD), the use of accurate and efficient forward operators for polarimetric radar variables is required. In this study, a new melting model is proposed to estimate the mixing ratio and number concentration of melting hydrometeor species and incorporated in a set of parameterized polarimetric radar forward operators. The new melting model depends only on the mixing ratio and number concentration of rain and ice species and is characterized by its independence from ambient temperature and its simplicity and ease of linearization. To assess the impact of this newly proposed melting model on the simulated polarimetric radar variables, a real mesoscale convective system is simulated using three double‐moment microphysics schemes. Compared with the output of the original implementation of the parameterized forward operators (PFO_Old) that rely on an “old” melting model which only estimates the mixing ratio of the melting species, the updated implementation with the new melting model (PFO_New) that estimates both the mixing ratio and number concentration of melting species eliminates the very large mass/volume‐weighted mean diameter (D_m) at the bottom of the melting layer and produces more reasonable melting layer signatures for all three double‐moment microphysics schemes that more closely match the corresponding radar observations. This suggests that the new melting model has more reasonable implicit estimates of mixing ratios and number concentrations of melting hydrometeor species than the “old” melting model. 

Abstract Polarimetric variables such as differential phase Φ_DPand its range derivative, specific differential phaseK_DP, contain useful information for improving quantitative precipitation estimation (QPE) and microphysics retrieval. However, the usefulness of the current operationally utilized estimation method ofK_DPis limited by measurement error and artifacts resulting from the differential backscattering phaseδ. The contribution ofδcan significantly influence the Φ_DPmeasurements and therefore negatively affect theK_DPestimates. Neglecting the presence ofδwithin non-Rayleigh scattering regimes has also led to the adoption of incorrect terminology regarding signatures seen within current operationalK_DPestimates implying associated regions of unrealistic liquid water content. A new processing method is proposed and developed to estimate bothK_DPandδusing classification and linear programming (LP) to reduce bias inK_DPestimates caused by theδcomponent. It is shown that by applying the LP technique specifically to the rain regions of Rayleigh scattering along a radial profile, accurate estimates of differential propagation phase, specific differential phase, and differential backscattering phase can be retrieved within regions of both Rayleigh and non-Rayleigh scattering. This new estimation method is applied to cases of reported hail and tornado debris, and the LP results are compared to the operationally utilized least squares fit (LSF) estimates. The results show the potential use of the differential backscattering phase signature in the detection of hail and tornado debris. 

Abstract 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 (q_υ) 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-transformedq_υas 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 StatementThe 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 Raindrop size distributions (DSD) and rain rate have been estimated from polarimetric radar data using different approaches with the accuracy depending on the errors both in the radar measurements and the estimation methods. Herein, a deep neural network (DNN) technique was utilized to improve the estimation of the DSD and rain rate by mitigating these errors. The performance of this approach was evaluated using measurements from a two-dimensional video disdrometer (2DVD) at the Kessler Atmospheric and Ecological Field Station in Oklahoma as ground truth with the results compared against conventional estimation methods for the period 2006–17. Physical parameters (mass-/volume-weighted diameter and liquid water content), rain rate, and polarimetric radar variables (including radar reflectivity and differential reflectivity) were obtained from the DSD data. Three methods—physics-based inversion, empirical formula, and DNN—were applied to two different temporal domains (instantaneous and rain-event average) with three diverse error assumptions (fitting, measurement, and model errors). The DSD retrievals and rain estimates from 18 cases were evaluated by calculating the bias and root-mean-squared error (RMSE). DNN produced the best performance for most cases, with up to a 5% reduction in RMSE when model errors existed. DSD and rain estimated from a nearby polarimetric radar using the empirical and DNN methods were well correlated with the disdrometer observations; the rain-rate estimate bias of the DNN was significantly reduced (3.3% in DNN vs 50.1% in empirical). These results suggest that DNN has advantages over the physics-based and empirical methods in retrieving rain microphysics from radar observations. 

This paper presents clutter detection and mitigation for polarimetric phased array weather radar measurements using machine learning. The following three approaches are analyzed for clutter detection in the cylindrical polarimetric phased array radar measurements, including naive Bayes classifier (NBC), multilayer perceptron (MLP), and convolutional neural network (CNN). Results show that CNN achieves the best performance in clutter detection, followed by MLP and NBC. This is because CNN utilizes spatial information of the input images, which has different features for clutter from that for weather. It is also shown that the combination of physics-based discriminants of power ratio and raw radar measurements is more effective in clutter detection than the direct use of raw radar measurements. In addition, CNN is employed for clutter mitigation and its performance is compared with the traditional speckle filter technique. It is demonstrated that CNN outperforms the speckle filter and incorporation of power ratio in the training process could further improve CNN’s performance in clutter mitigation.