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This study investigates the characteristics of the raindrop size distribution (DSD) for five typhoons that made landfall or passed by Zhejiang on the eastern coast of China, from 2019 to 2022. Additionally, it examines the raindrop shape–slope (µ-Λ) relationship, as well as the local Z-R relationship for these typhoons. The DSD datasets were collected by the DSG1 disdrometer located in Ningbo, Zhejiang Province. Based on rainfall rate (R), the DSD can be categorized into convective and stratiform rainfall types. Some rainfall parameters can also be derived from the DSDs to further analyze the specific characteristics of rainfall. The histograms of the generalized intercept parameter (log10Nw) exhibit negative skewness in both convective and stratiform rainfall, whereas the histograms of the mass-weighted mean diameter (Dm) of raindrops display positive skewness. During typhoon periods on the eastern coast of China, the DSD characteristic was composed of a lower number concentration of small and midsize raindrops (3.42 for log10Nw, 1.43 mm for Dm in the whole dataset) as compared to Jiangsu in eastern China, Tokyo, in Japan, Miryang, in South Korea, and Thiruvananthapuram in south India, respectively. At the same time, the scatter plots of Dm and log10Nw indicate that the convective rain during typhoon periods exhibits characteristics that are intermediate between "maritime-like" and "continental-like" clusters. Additionally, the raindrop spectra of convective rainfall and midsize raindrops in stratiform rainfall are well-represented by a three-parameter gamma distribution. The µ-Λ relation in this region is similar to Taiwan and Fujian, located along the southeastern coast of China. The Z-R relationship for eastern coastal China during typhoons based on filtered disdrometer data is Z = 175.04R1.53. These results could offer deeper insights into the microphysical characteristics of different rainfall types along the eastern coast of China and potentially improve the accuracy of precipitation estimates from weather radar observations. [ABSTRACT FROM AUTHOR]

Satellite-based precipitation products, especially those with high temporal and spatial resolution, constitute a potential alternative to sparse rain gauge networks for multidisciplinary research and applications. In this study, the validation of the 30-year Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) daily precipitation dataset was conducted over the Huai River Basin (HRB) of China. Based on daily precipitation data from 182 rain gauges, several continuous and categorical validation statistics combined with bias and error decomposition techniques were employed to quantitatively dissect the PERSIANN-CDR performance on daily, monthly, and annual scales. With and without consideration of non-rainfall data, this product reproduces adequate climatologic precipitation characteristics in the HRB, such as intra-annual cycles and spatial distributions. Bias analyses show that PERSIANN-CDR overestimates daily, monthly, and annual precipitation with a regional mean percent total bias of 11%. This is related closely to the larger positive false bias on the daily scale, while the negative non-false bias comes from a large underestimation of high percentile data despite overestimating lower percentile data. The systematic sub-component (error from high precipitation), which is independent of timescale, mainly leads to the PERSIANN-CDR total Mean-Square-Error (TMSE). Moreover, the daily TMSE is attributed to non-false error. The correlation coefficient (R) and Kling–Gupta Efficiency (KGE) respectively suggest that this product can well capture the temporal variability of precipitation and has a moderate-to-high overall performance skill in reproducing precipitation. The corresponding capabilities increase from the daily to annual scale, but decrease with the specified precipitation thresholds. Overall, the PERSIANN-CDR product has good (poor) performance in detecting daily low (high) rainfall events on the basis of Probability of Detection, and it has a False Alarm Ratio of above 50% for each precipitation threshold. The Equitable Threat Score and Heidke Skill Score both suggest that PERSIANN-CDR has a certain ability to detect precipitation between the second and eighth percentiles. According to the Hanssen–Kuipers Discriminant, this product can generally discriminate rainfall events between two thresholds. The Frequency Bias Index indicates an overestimation (underestimation) of precipitation totals in thresholds below (above) the seventh percentile. Also, continuous and categorical statistics for each month show evident intra-annual fluctuations. In brief, the comprehensive dissection of PERSIANN-CDR performance reported herein facilitates a valuable reference for decision-makers seeking to mitigate the adverse impacts of water deficit in the HRB and algorithm improvements in this product. [ABSTRACT FROM AUTHOR]