Detecting seasonal differences in the variations in diurnal precipitation using spaceborne Ku-band radars

In this study, we produced surface precipitation data at a high spatial resolution of 0.1° by integrating observations from two spaceborne radars, TRMM PR and GPM DPR KuPR, spanning a 25-year period. This dataset allowed us to analyze the seasonal variation in diurnal peaks, enhancing our understanding of spatiotemporal precipitation patterns and their detectability. The precipitation data were classified based on the horizontal scale for the individual precipitation systems, represented by the area-equivalent diameter of consecutive precipitation regions. Certain grid points in the equatorial and mid-latitude zones lacked sufficient long-term, time-resolved samples due to limited satellite overpasses. For example, in mid-latitude regions such as Europe, observations by DPR alone provided fewer than 10 passes under the current conditions for averaging time.
To address these limitations, we applied a running average technique and imputed missing values to minimize outlier impacts. Seasonal changes in the timing of maximum precipitation were then categorized into distinct clusters, revealing key spatiotemporal patterns. On the Tibetan Plateau, small-scale precipitation systems predominantly generate early afternoon peaks throughout the year, while in winter, morning rain frequently occurs in certain valleys. In the southern foothills of the Himalayas, precipitation peaks in the morning, whereas evening showers are observed in the southernmost regions during summer. In the southwestern part of Japan, which is heavily influenced by the ocean, large-scale precipitation dominates during the rainy season, with morning rainfall prevailing, while midsummer shows a shift toward afternoon peaks. Additionally, medium-scale precipitation systems tend to follow small-scale systems by a few hours, while large-scale systems exceeding 100 km in diameter exhibit distinct timing patterns. 
These findings underscore the diverse precipitation regimes shaped by geographical features and prevailing winds, highlighting the need to assess the value and challenges of leveraging high-resolution precipitation climate datasets.