The Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) mission provides valuable three-dimensional precipitation structure data on a global scale and has been widely used in hydrometeorological research. However, due to its spatial resolution limitations and inherent algorithmic assumptions, the accuracy of GPM precipitation estimates can exhibit systematic biases, especially under complex terrain conditions or in the presence of variable precipitation structures, such as light stratiform rain or intense convective storms. In this study, we evaluated the near-surface precipitation rate estimates from the GPM-DPR Level 2A product using over 1440 min of disdrometer observations collected across China from 2021 to 2023. Based on three years of stable stratiform precipitation data from the Jinghe station, we developed a least squares linear correction model for radar reflectivity. Independent validation using national disdrometer data from 2023 demonstrated that the corrected reflectivity significantly improved rainfall estimates under light precipitation conditions, although improvements were limited for convective events or in complex terrain. To further enhance retrieval accuracy, we introduced a regionally adaptive R–Z relationship scheme stratified by precipitation type and terrain category. Applying these localized relationships to the corrected reflectivity yielded more consistent rainfall estimates across diverse conditions, highlighting the importance of incorporating regional microphysical characteristics into satellite retrieval algorithms. The results indicate that the accuracy of GPM precipitation retrievals is more significantly influenced by precipitation type than by terrain complexity. Under stratiform precipitation conditions, the GPM-estimated precipitation data demonstrate the highest reliability. The correction framework proposed in this study is grounded on ground-based observations and integrates regional precipitation types with terrain characteristics. It effectively enhances the applicability of GPM-DPR products across diverse environmental conditions in China and offers a methodological reference for correcting satellite precipitation biases in other regions.

This study utilizes Ka-band millimeter-wave cloud radar (MMCR), assisted by a precipitation phenomenon instrument, to conduct case studies and analyses of convective precipitation, cumulus precipitation, and stratus precipitation in the Xi’an region. Using the Doppler spectral data of the MMCR, dynamic parameters such as vertical air motion velocity (updraft and downdraft) and particle terminal fall velocity within these three types of cloud precipitation were retrieved. The results show that above the melting layer, the maximum updraft velocity in convective clouds reaches 15 m·s⁻¹, and the strong updraft drives cloud droplets to move upward at an average velocity of about 5 m·s⁻¹. The average updraft velocity in cumulus clouds is greater than that in stratus clouds, with updrafts in cumulus and stratus mainly distributed within 1.5–3 m·s⁻¹ and 1–2 m·s⁻¹, respectively. The reflectivity factor of precipitation particles (Ze) is used to correct the equivalent reflectivity factor (Ka-Ze) after attenuation correction below the MMCR melting layer. The accuracy of calculating the raindrop concentration using the Ka-Ze of MMCR was improved below the melting layer. Based on the relationship between terminal fall velocity and particle diameter and using the conversion between the MMCR power spectrum and raindrop spectrum, the concentration, fall velocity, and particle diameter of raindrops are calculated below the melting layer. The results show that the average reflectivity factor, average concentration, and average particle diameter of raindrops follow the order of convective precipitation > cumulus precipitation > stratiform precipitation. However, the average terminal fall velocity distribution of raindrop particles follows a different order: convective precipitation > stratiform precipitation > cumulus precipitation.