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Studying the characteristics of precipitation diurnal variation is beneficial for understanding precipitation formation and underlying mechanisms. In this study, using hourly rain gauge data from 108 stations in the Yangtze River Delta Urban Agglomeration (YRDUA) from 1980–2021, the diurnal variations of the precipitation amount (PA), precipitation frequency (PF), precipitation duration (PD), and precipitation intensity (PI) were analyzed. The effects of elevation, distance of the station from the east coastline, and urbanization on the characteristics of different precipitation duration events were determined. The results indicated that (1) the spatial distributions of PA, PD, and PF were similar in short-duration (SD), long-duration (LD), and ultra-long-duration (ULD), with high values in the south and low values in the north. Most of PA, PD, and PF showed an increasing trend after breakpoint in LD and ULD, but precipitation characteristics in SD showed a decreasing trend before and after breakpoint; (2) the diurnal cycles of PA presented two comparable peaks in the late afternoon and early morning, which occurred SD and ULD precipitation events, respectively. A single peak in the late afternoon (15:00 local solar time [LST]) occurred during the diurnal cycle of PI. The start and peak times occurred mainly in the afternoon for SD and LD. In contrast, the peak time of ULD mainly occurred in the early morning, accounting for 63% of the stations. The start and peak times of LD and ULD occurred in the early morning mainly along the Yangtze River; (3) from the plains to the mountains, the diurnal peaks of PA and PI had gradual variations from noon to afternoon. In addition, dominant diurnal peak values of PA and PI, which are affected by the distance from the east coast, were observed in the early morning in ULD. The effect of urbanization on the difference between urban and rural areas changed from negative to positive after 2000. In addition, urbanization had a significant impact on SD. After 2000, the increase of PA in urban areas was mainly due to the obvious increase of PD and PF in SD, while the increasing trend of LD and ULD in urban areas was smaller than that in rural areas. [ABSTRACT FROM AUTHOR]

Herein, the raindrop size distribution (DSD) and rainfall microphysics characteristics are investigated at multiple stations within the Yangtze River Delta (YRD) under two air pollution conditions. Raindrop data from 14 Thies Clima Laser Precipitation Monitor disdrometers spanning approximately 2 years were employed together with corresponding air pollution data. Rain gauge data and several types of meteorological data were also used. The DSD characteristics were found to vary across the YRD; thus, we compiled DSD‐based demarcations between convective rain and stratiform rain at each site. During summer, the average mass‐weighted mean drop diameter parameter value was higher by a mean 20%, and the average normalized intercept parameter value was lower by a mean 12% in polluted atmosphere conditions for both convective rain and stratiform rain, as well as for different rainfall intensity classes. The differences in atmospheric conditions between clear and polluted air conditions partially account for the uncertainties in the DSD characteristics. Air pollution resulted in uncertainty in the shape parameter‐slope parameter relationship and in the rainfall kinetic energy‐rainfall intensity (KE‐R) relationship. The air pollution index can be integrated into the KE‐R relationship to improve the KE simulation accuracy. In addition, air pollution causes some uncertainty in the radar reflectivity‐rainfall intensity (Z‐R) relationship, while the acceptable deviation of the Z‐R relationship did not significantly impact the accuracy of rainfall simulations. This study improves our understanding of DSD and provides a comprehensive overview of the relationship between air pollution and rainfall microphysics for a typical urban agglomeration area. Plain Language Summary: This study investigates the relationship between air pollution and rainfall microphysics at several stations within the Yangtze River Delta (YRD). Rainfall microphysics were found to vary across the YRD. Moreover, summer raindrop characteristics differ between low and high air pollution conditions. That is, compared with a clean atmosphere, a polluted atmosphere results in larger average raindrop size and lower average raindrop concentration. Air pollution results in slight uncertainty in radar rainfall estimation, while causing significant uncertainty when calculating rainfall erosivity. This study provides a comprehensive understanding of rainfall microphysics in a typical urban agglomeration area. Key Points: The characteristics of rainfall microphysics across the Yangtze River Delta are relatively uncertainLarger average raindrops and lower average number concentrations are observed during high pollution in summerAir pollution causes high uncertainty of rainfall kinetic energy calculations and low uncertainty of radar rainfall simulations [ABSTRACT FROM AUTHOR]

In modeling the radar rainfall uncertainty, rain gauge measurement is generally regarded as the areal "true" rainfall. However, the inconsistent scales between radar and gauge may introduce a new uncertainty (also known as gauge representative uncertainty), which is erroneously identified as radar rainfall uncertainty and therefore called pseudouncertainty. It is crucial to comprehend what percentage of the estimated radar rainfall uncertainty actually stems from such pseudouncertainty rather than radar rainfall itself. For this reason, based on a fully formulated radar rainfall uncertainty model, this study aims to explore how the gauge representative error affects the distribution, spatial dependence, and temporal dependence of hourly accumulated radar rainfall uncertainty, and consequently affects the produced radar rainfall uncertainty band. Three group scenarios that delineate various degrees of gauge representative errors were designed to configure and run the uncertainty model. In the setting of a long-term analysis (almost 7 years) of the Brue catchment in the United Kingdom, we found that the gauge representative error affected the simulation of the marginal distribution of radar rainfall error, and had a considerable effect on temporal dependence estimation of radar rainfall uncertainty. The spread of the rainfall uncertainty band decreased with the growth of the gauge density in a radar pixel. The scenario with the lowest representative error only had 78% uncertainty spread of the scenario that has the largest error. This indicated there was a large impact of the representative error on radar rainfall uncertainty models. It is hoped that more catchments with diverse climate and geographical conditions and more radar data with various spatial scales could be explored by the research community to further investigate this crucial issue. [ABSTRACT FROM AUTHOR]

Raindrop size distribution (RSD) is a key parameter in the Weather Research and Forecasting (WRF) model for rainfall estimation, with gamma distribution models commonly used to describe RSD under WRF microphysical parameterizations. The RSD model sets the shape parameter (μ) as a constant of gamma distribution in WRF double-moment bulk microphysics schemes. Here, we propose to improve the gamma RSD model with an adaptive value of μ based on the rainfall intensity and season, designed using a genetic algorithm (GA) and the linear least-squares method. The model can be described as a piecewise post-processing function that is constant when rainfall intensity is <1.5 mm/h and linear otherwise. Our numerical simulation uses the WRF driven by an ERA-interim dataset with three distinct double-moment bulk microphysical parameterizations, namely, the Morrison, WDM6, and Thompson aerosol-aware schemes for the period of 2013–2017 over the United Kingdom at a 5 km resolution. Observations were made using a disdrometer and 241 rain gauges, which were used for calibration and validation. The results show that the adaptive-μ model of the gamma distribution was more accurate than the gamma RSD model with a constant shape parameter, with the root-mean-square error decreasing by averages of 23.62%, 11.33%, and 22.21% for the Morrison, WDM6, and Thompson aerosol-aware schemes, respectively. This model improves the accuracy of WRF rainfall simulation by applying adaptive RSD parameterization and can be integrated into the simulation of WRF double-moment microphysics schemes. The physical mechanism of the RSD model remains to be determined to improve its performance in WRF bulk microphysics schemes. [ABSTRACT FROM AUTHOR]

• The correlations between the radar rainfall uncertainty and atmospheric fields are strong. • Three radar rainfall uncertainty adjustment schemes are proposed using a LSTM network. • The schemes can decrease the radar–gauge rainfall discrepancy by 22.52%, averagely. The unstable performance of radar rainfall measurement hinders its application in hydrology. Radar rainfall uncertainties induced by atmospheric conditions as raindrops fall from the radar sampling altitude to the ground are critical to radar-based quantitative precipitation estimation. However, these have rarely been considered in previous radar rainfall correction procedures. This study first demonstrates that the correlations between the radar–gauge rainfall discrepancy (RGD) and atmospheric fields are strong. A systematic radar-rainfall adjustment method is then proposed to decrease the discrepancies originating from changes in atmospheric conditions using a long short-term memory (LSTM) network. Three RGD adjustment models were established using a mass-variation scheme, a wind-drift scheme, and a combined mass-variation and wind-drift scheme, based on long-term (2013–2017) data covering most of the United Kingdom. The evaluation results demonstrate that all of the designed models performed well from overall, single-site, and event perspectives. Overall, the combined model, which exhibited the best performance, decreased the root-mean-square error between the rainfall levels measured by radar and gauges by 23.84%, increased Pearson's correlation coefficient from 0.23 to 0.53, and improved the critical success index of the radar rainfall estimation from 0.56 to 0.92. The results indicate that the performances of the proposed models improved with an increase in average relative humidity or wind speed, which demonstrates that they can correct rainfall under high levels of relative humidity and wind speed. This study establishes a highly capable and comprehensive adjustment framework for radar rainfall estimation uncertainty induced by atmospheric conditions, which is essential for achieving high-quality radar products. [ABSTRACT FROM AUTHOR]