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The mechanisms linking raindrop size distributions (DSDs) to environmental conditions remain poorly understood, limiting their practical application. We develop a unique fine‐scale vertical in situ data set to reveal the evolution of near‐surface DSDs and quantify how environmental factors modulate raindrop microphysics. Near‐surface raindrop breakup is identified as a common feature during the East Asian summer, with an average threshold diameter of 1.16 mm for breakup initialization. Further analysis reveals that relative humidity and wind speed exert opposing influences on raindrop microphysical processes, with coalescence favored in humid monsoon environments and breakup intensified within typhoon outer rainbands. By incorporating empirical relationships between these two environmental factors and microphysical processes, we derive observational constraints that significantly reduce biases in near‐surface rainfall estimates. For heavy rainfall cases the bias is reduced by up to 75%. These findings improve understanding of raindrop microphysics in boundary layer and help improve quantitative precipitation estimation.

Dual-polarization (dual-pol) radar can measure additional parameters that provide more microphysical information of precipitation systems than those provided by conventional Doppler radar. The dual-pol parameters have been successfully utilized to investigate precipitation microphysics and improve radar quantitative precipitation estimation (QPE). The recent progress in dual-pol radar research and applications in China is summarized in four aspects. Firstly, the characteristics of several representative dual-pol radars are reviewed. Various approaches have been developed for radar data quality control, including calibration, attenuation correction, calculation of specific differential phase shift, and identification and removal of non-meteorological echoes. Using dual-pol radar measurements, the microphysical characteristics derived from raindrop size distribution retrieval, hydrometeor classification, and QPE is better understood in China. The limited number of studies in China that have sought to use dual-pol radar data to validate the microphysical parameterization and initialization of numerical models and assimilate dual-pol data into numerical models are summarized. The challenges of applying dual-pol data in numerical models and emerging technologies that may make significant impacts on the field of radar meteorology are discussed.

The Weather Surveillance Radar–1988 Dopplers (WSR‐88Ds) are an operational network of S‐band, dual‐polarization radars in the United States. Currently, replacement options are being considered for the next generation of weather radars. One option is dual‐polarization phased array radar (PAR), which employs electronic beam steering to provide faster volumetric updates. The Advanced Technology Demonstrator (ATD) is a research PAR that is being used to evaluate the feasibility of PAR technology. Presumably, the improved temporal resolution of the PARs will lead to more accurate radar‐based quantitative precipitation estimation (QPE). However, PARs require more complex calibration, especially for dual‐polarization observations. To test this hypothesis, the ATD and KOUN are used. From radar data over rain gauge locations, rainfall accumulations are calculated and compared to rain gauge observations. We show that, overall, PAR‐based QPE can perform 16.1% better than current WSR‐88Ds, in part due to the <60‐s low‐level sampling rate of the ATD.

Accurate radar quantitative precipitation estimation (QPE) plays an essential role in disaster prevention and mitigation. In this paper, two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are designed. Meanwhile, a self-defined loss function (SLF) is proposed during modeling. The dataset includes Shijiazhuang S-band dual polarimetric radar (CINRAD/SAD) data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North China. Considering that the specific propagation phase shift ( K DP ) has a roughly linear relationship with the precipitation intensity, K DP is set to 0.5° km −1 as a threshold value to divide all the rain data (AR) into a heavy rain (HR) and light rain (LR) dataset. Subsequently, 12 deep learning-based QPE models are trained according to the input radar parameters, the precipitation datasets, and whether an SLF was adopted, respectively. The results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing, and the effects of using SLF are better than those that used MSE as a loss function. A Z - R relationship and a Z H - K DP - R synthesis method are compared with deep learning-based QPE. The mean relative errors (MRE) of AR models using SLF are improved by 61.90%, 51.21%, and 56.34% compared with the Z - R relational method, and by 38.63%, 42.55%, and 47.49% compared with the synthesis method. Finally, the models are further evaluated in three precipitation processes, which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods.

The Gated Recurrent Unit (GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity (Z), radar echo-top height (ET) is also a good indicator of rainfall rate ( R ). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z - R relationship ( Z =300 R 1.4 ), the optimal Z - R relationship ( Z =79 R 1.68 ) and the GRU neural network with only Z as the independent input variable (GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z - R relationship performs the worst. The performances of the rest two methods are similar. To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z - R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z - R relationship. Thus, it can be concluded that the performance of the GRU_Z-ET is the best in the four methods for the quantitative precipitation estimation.

In addressing the challenges of quantitative precipitation estimation (QPE) using weather radar, the importance of enhancing the rainfall estimates for applications such as flash flood forecasting and hydropower generation management is recognized. This study employed dual-polarization weather radar data to refine the traditional Z–R relationship, which often needs higher accuracy in areas with complex meteorological phenomena. Utilizing tree-based machine learning algorithms, such as random forest and gradient boosting, this research analyzed polarimetric variables to capture the intricate patterns within the Z–R relationship. The results highlight machine learning’s potential to improve the precision of precipitation estimation, especially under challenging weather conditions. Integrating meteorological insights with advanced machine learning techniques is a remarkable achievement toward a more precise and adaptable precipitation estimation method.

Satellite precipitation estimation provides crucial information for those places lacking rainfall observations from ground–based sensors, especially in terrestrial or marine areas with complex climatic or topographic conditions. This is the case over much of Western China, including Upper and Middle Lancang River Basin (UMLRB), an extremely important transnational river system in Asia (the Lancang–Mekong River Basin) with complex climate and topography that has limited long–term precipitation records and high–elevation data, and no operational weather radars. In this study, we evaluated three GPM IMERG satellite precipitation estimation (IMERG E, IMERG L and IMERG F) over UMLRB in terms of multi–year average precipitation distribution, amplitude consistency, occurrence consistency, and elevation–dependence in both dry and wet seasons. Results demonstrated that monsoon and solid precipitation mainly affected amplitude consistency of precipitation, aerosol affected occurrence consistency of precipitation, and topography and wind–induced errors affected elevation dependence. The amplitude and occurrence consistency of precipitation were best in wet seasons in the Climate Transition Zone and worst in dry seasons in the same zone. Regardless of the elevation–dependence of amplitude or occurrence in dry and wet seasons, the dry season in the Alpine Canyon Area was most positively dependent and most significant. More significant elevation–dependence was correlated with worse IMERG performance. The Local Weighted Regression (LOWERG) model showed a nonlinear relationship between precipitation and elevation in both seasons. The amplitude consistency and occurrence consistency of both seasons worsened with increasing precipitation intensity and was worst for extreme precipitation cases. IMERG F had great potential for application to hydroclimatic research and water resources assessment in the study area. Further research should assess how the dependence of IMERG’s spatial performance on climate and topography could guide improvements in global precipitation assessment algorithms and the study of mountain landslides, floods, and other natural disasters during the monsoon period.

Near-real-time satellite precipitation estimation is indispensable in areas where ground-based measurements are not available. In this study, an evaluation of two near-real-time products from the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine—PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Cloud Classification System) and PDIR-Now (PERSIANN-Dynamic Infrared Rain Rate near-real-time)—were compared to each other and evaluated against IMERG Final (Integrated Multi-satellite Retrievals for Global Precipitation Measurement—Final Run) from 2015 to 2020 over the Mekong River Basin and Delta (MRB) using a spatial resolution of 0.1∘ by 0.1∘ and at a daily scale. PERSIANN-CDR (PERSIANN-Climate Data Record) was also included in the evaluation but was not compared against the real-time products. In this evaluation, PDIR-Now exhibited a superior performance to that of PERSIANN-CCS, and the performance of PERSIANN-CDR was deemed satisfactory. The second part of the study entailed performing a Mann–Kendall trend test of extreme precipitation indices using 38 years of PERSIANN-CDR data over the MRB. This annual trend analysis showed that extreme precipitation over the 95th and 99th percentiles has decreased over the Upper Mekong River Basin, and the consecutive number of wet days has increased over the Lower Mekong River Basin.

Polarimetric radar data are an important tool for quantitative precipitation estimation (QPE), which is essential for monitoring and forecasting precipitation. Previous studies have shown that the drop size distribution (DSD) and polarimetric radar parameters of typhoon-induced precipitation differ significantly from those of other types of rainfall. South China is a region that frequently experiences typhoons and heavy rainfall, which can cause serious disasters. Therefore, it is critical to develop a QPE algorithm that is suitable for typhoon precipitation over South China. In this study, we constructed four simple QPE estimators, R(ZH), R(ZH, ZDR), R(KDP) and R(KDP, ZDR) based on two-dimensional video disdrometer (2DVD) DSD observations of typhoon-induced precipitation over South China in 2017–2018. We analyzed the DSD characteristics and the estimation accuracy of these four QPE estimators in the reflectivity–differential reflectivity (ZH–ZDR) space, as well as the S-band polarimetric radar (S-POL) data of seven typhoon-induced precipitation events that affected South China in 2017–2019. We used these data to quantitatively determine the optimal ranges of the estimators and establish a typhoon precipitation QPE algorithm for typhoon-induced precipitation over South China (2DVD-Typhoon). The evaluation results showed that: (1) compared to R(ZH) and R(KDP), R(ZH, ZDR) and R(KDP, ZDR) had lower performance in estimating typhoon-induced rainfall after incorporating the polarimetric parameter ZDR, as strong crosswind of the typhoon caused some bias in the raindrop-induced ZDR; (2) the 2DVD-Typhoon algorithm utilizes the respective advantages of the individual estimators to generate the best QPE results; (3) the QPE performance of 2DVD-Typhoon and the Colorado State University–Hydrometeor Identification Rainfall Optimization (CSU-HIDRO) is used as a comparison for hourly rainfall, cumulative rainfall and different rainfall intensity. The comparison shows that 2DVD-Typhoon gives a better normalized error (NE), root mean square error (RMSE) and correlation coefficient (CC), indicating its strength in rainfall estimation for typhoons over South China. The above results provide theoretical support for improving typhoon-induced rainfall monitoring and numerical weather forecasting models in South China.

In this study, we applied an explainable machine learning technique based on the LightGBM method, a category of gradient boosting decision tree algorithm, to conduct a quantitative radar precipitation estimation and move to understand the underlying reasons for excellent estimations. By introducing 3D grid radar reflectivity data into the LightGBM algorithm, we constructed three LightGBM models, including 2D and 3D LightGBM models. Ten groups of experiments were carried out to compare the performances of the LightGBM models with traditional Z–R relationship methods. To further assess the performances of the LightGBM models, rainfall events with 11,483 total samples during August-September of 2022 were used for statistical analysis, and two heavy rainfall events were specifically chosen for the spatial distribution evaluation. The results from both the statistical analysis and spatial distribution demonstrate that the performance of the LightGBM 3D model with nine points is the best method for quantitative precipitation estimation in this study. Through analyzing the explainability of the LightGBM models from Shapley additive explanations (SHAP) regression values, it can be inferred that the superior performance of the LightGBM 3D model is mainly attributed to its consideration of the rain gauge station attributes, diurnal variation characteristics, and the influence of spatial offset.