This study developed Fully Connected Neural Network (FCNN) models to retrieve the microphysical parameters of ice particles, specifically volume-weighted median diameter (Dm) and ice water content (IWC), using polarimetric radar measurements including reflectivity (ZH), differential reflectivity (ZDR), and specific differential phase (KDP). The performance and uncertainties of FCNN-based retrievals were evaluated through comparisons with conventional power-law (PL) estimators, where the effects of particle aspect ratio (AR), density (DC), and radar measurement errors were considered. For ice retrievals with a fixedDC, FCNN models outperformed PL, especially in the presence of measurement errors. Errors inZDRandKDPcan increase retrieval bias and reduce correlation coefficients in both kinds of methods. For datasets with varyingDCs,ZDR, even combined withZHandKDP, was unable to fully capture simultaneous changes inDCand AR, limiting the FCNNs ability to account for shape and density variations. Both the FCNN and PL methods were applied to analyze a snowstorm event and a mesoscale convective system (MCS). Both approaches successfully identified larger ice particles formed through riming and aggregation towards the ground. The FCNN exhibited higher retrieval accuracy and a smoother verticalIWCprofile, particularly in the dendritic growth layer. Additionally, evaluation using airborne in situ observations in the MCS demonstrated that both methods performed well in retrievingDm, with the FCNN providing more accurateIWCestimates.

Melting layers (MLs) contain complex microphysical processes that largely influence the precipitation systems. They usually manifest as bright bands in radar observations. Most radar forward operators embedded in numerical weather models showed a limited capacity to simulate MLs because bulk microphysics schemes (BMPs) neglect the melting state of particles to reduce the computational complexity. To present polarimetric simulations closer to observations, a new ML simulation algorithm was proposed for radar forward operators, which considered the evolutions of melting particle numbers and the coexistence of the liquid drops. The new simulator was tested using three double-moment schemes [i.e., Morrison, National Severe Storm Laboratory (NSSL), and WRF 6 class (WDM6)] for the simulations of Typhoon Hato (2017). Compared with polarimetric radar observations, it was found that the peak and thickness of the simulated bright bands were overestimated in the Morrison and NSSL scheme and underestimated in the WDM6 scheme using the simulating algorithm raised by past research. The new simulator significantly improved the simulation of MLs in the three schemes by simulating a more realistic evolution of particle size distribution of melting particles. The microphysical factors influencing polarimetric simulations are thoroughly examined, providing valuable insights for better understanding the polarimetric signatures of mixed-phase clouds. A forward operator with improved capability to represent melting particles will also help us develop better optimization-based radar retrieval methods for precipitation with mixed-phase processes.

The dual-frequency precipitation radar (DPR) onboard the global precipitation measurement (GPM) satellite provides valuable measurements of precipitation. In this study, the GPM DPR products (version 6) are validated against a ground-based S-band polarimetric radar in South China based on a volume-matching method. Good consistency is found for the reflectivity factor ($Z$) calibration of the two instruments. From the perspective of microphysics, the mass-weighted mean diameter ($D_{m}$) estimates correspond well with those of the ground-based radar in the inner swath of the normal scan (NS); however, underestimation is found for the raindrop number concentration, indicated by the generalized intercept parameter ($N_{w}$), especially for the intense echoes. Thus, the GPM DPR product may fail to depict the microphysical characteristics of small-to-medium raindrops in high concentration for heavy rainfall in South China. This is attributed to the negative$Z$bias of the DPR caused probably by insufficient correction of attenuation, which also leads to clear underestimation in the liquid water content ($W$) and the rainfall rate ($R$) products for intense echoes. In the outer swath where only single-frequency retrieval is available, overestimation in$D_{m}$exists regardless of echo intensity level, and more underestimation can be found in$N_{w}$,$W$, and$R$especially for intense echoes. In the selected typhoon and squall line cases, better capability in revealing microphysical properties is also found for the inner swath of the NS. After adjusting the scan mode, the performance of the precipitation products in the outer swath can be improved by dual-frequency retrievals in the future.

This study presents a variational approach for optimized estimation of specific differential phase ( KDP) for polarimetric radars using a linear forward operator. A cubic B-spline interpolating filter is included to mitigate the impact of measurement error in the total differential phase and ensure the spatial continuity of KDP. For rain, non-negative constraints are introduced to ensure that the KDPestimates are within the physical bounds. The variational approach is flexible to incorporate the background information constructed from the measurements of horizontal reflectivity factor ( ZH) and differential reflectivity ( ZDR) based on the self-consistent relationship of polarimetric variables. The variational approach is evaluated using simulated experiments, as well as real observations from an S-band operational weather radar. Without including background information, the variational approach has slightly better performance compared to the approach based on linear programming (LP), and the background information helps to further improve the performance. In addition, the linear forward operator makes this variational approach computationally efficient. It needs less than 3% computational power required by the approach based on LP, making it more suitable for real-time operational applications.

In this study, as part of an effort to study snowfall characteristics and quantify winter precipitation in East China, we investigated the microphysical properties of snowfall, including size, shape, density, and terminal velocity using a 2-D video disdrometer (2-DVD) and a weighing precipitation gauge in Nanjing (NJ), East China during the winters of 2015-2019. We obtained larger snow density and terminal velocity values than those reported in the literature for this region. Higher snow density could account for higher snowflake terminal velocity, after removing the effects of observation altitude and surface temperature. We then fit the snow particle size distributions (PSDs) to the gamma model and explored the interrelationships among the model parameters and snowfall rate (SR). The relationship between radar reflectivity factor (Ze) and SR was derived based on snow PSD measurements and the snow density relation. Using this Ze-SR relationship, the estimated liquid-equivalent SRs are obtained from S-band NJ radar data collected during several snowfall events. Radar-inferred SRs showed reasonable agreement with those measured on the ground, with a mean absolute error of 16% for the collected snowfall events in NJ.

A hybrid method of combining linear programming (LP) and physical constraints is developed to estimate specific differential phase (KDP) and to improve rain estimation. The hybrid KDPestimator and the existing estimators of LP, least squares fitting, and a self-consistent relation of polarimetric radar variables are evaluated and compared using simulated data. Simulation results indicate the new estimator's superiority, particularly in regions where backscattering phase (δhv) dominates. Furthermore, a quantitative comparison between auto-weather-station rain-gauge observations and KDP-based radar rain estimates for a Meiyu event also demonstrate the superiority of the hybrid KDPestimator over existing methods.