The Global Precipitation Measurement (GPM) mission has generated global precipitation products of improved accuracy and coverage that are promising for advanced hydrological and meteorological studies. This study evaluates three Integrated Multi-satellitE Retrievals for GPM (IMERG) Hourly products, including the Early-, Late-, and Final-run products (IMERG-HE, IMERG-HL, and IMERG-HF, respectively), over Sichuan Basin of China. This highly complex terrain of the steep mountainous region offers further scrutiny on the quality and applicability of the data. The China Meteorological Precipitation Analysis (CMPA) data from January 2016 to December 2018 are used as the reference for the evaluation. Results show that: (1) At grid scale, IMERG-HL and IMERG-HF outperform IMERG-HE in terms of correlation coefficient (CC) and root-mean-square error (RMSE), but IMERG-HL has smaller relative bias (RB) than that of the IMERG-HF (by 21.16%). IMERG-HF presents the highest probability of detection (POD = 0.52) and critical success index (CSI = 0.32), except for high false alarm ratio (FAR) for light precipitation. (2) At regional scale, IMERG-HF outperforms IMERG-HE and IMERG-HL in annual evaluation in all the metrics except for the serious overestimation as shown in RB (20.18%, 3.84%, and 4.97%, respectively). Its accumulative precipitation deviation mainly comes from moderate precipitation events (1–10 mm/h), while better detection capability is seen in light precipitation (<1 mm/h). Seasonally, IMERG-HF performs the best in winter, while IMERG-HL performs the best in the other seasons. (3) IMERG-HF captures the peak precipitation more accurately in all seasons. In reproducing the diurnal cycle, IMERG-HF performs better in winter, while IMERG-HL performs better in summer and autumn, and IMERG-HE in spring. However, all three products overestimate the early morning precipitation (01:00–08:00 local standard time) of the diurnal cycle in spring, summer, and autumn.

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) is widely used in hydrological and meteorological studies, owing to its high spatial resolution and accuracy. The quantification of systematic and random errors for the IMERG estimates at different temporal resolutions is beneficial to the calibration of observation instruments and correction of estimates, especially for regions such as the Sichuan Basin where frequent geological disasters occur in the summer. At present, there are two most commonly used models, namely the additive and multiplicative models, for modeling the systematic and random errors of precipitation. However, it is unknown which model is more suitable for the IMERG summer hourly, daily, and monthly precipitation estimates. Therefore, in this study, two models’ separative capability of the systematic and random errors and predictive capability in the IMERG estimates are investigated, upon the evaluation of the models’ applicability. Results show that for the hourly and daily precipitation estimates, the multiplicative error model has better separative and predictive capabilities than the additive error model and is recommended to quantify the systematic and random errors. Conversely, as for the monthly precipitation estimates, the additive error model is a relatively better choice by comparing the overall performance of both models. However, it still has some weaknesses for heavy monthly precipitation, such as nonconstant fluctuations and reduced predictive capability. So, the additive model should be used with caution in analyzing the systematic and random errors of the heavy monthly precipitation.

Surface clutter interference will be one of the important problems for the future of geostationary spaceborne weather radar (GSWR). The aim of this work is to provide some numerical analyses on surface clutter interference and part of the performance evaluation for the future implementation of GSWR. The received powers of rain echoes, land and sea surfaces from a radar scattering volume are calculated numerically based on the derived radar equations, assuming a uniform rain layer and appropriate land and sea surface scattering models. An antenna pattern function based on a Bessel curve and Taylor weighting is considered to approximate the realistic spherical antenna of a GSWR. The power ratio of the rain echo signal to clutter (SCR) is then used to evaluate the extension of surface clutter interference. The study demonstrates that the entire region of surface clutter interference in GSWR will be wider than those in tropical rainfall measuring mission precipitation radar (TRMM PR). Most strong surface clutter comes from the antenna mainlobe, and the decrease of clutter contamination through reducing the level of the antenna sidelobe and range sidelobe are not obvious. In addition, the clutter interference is easily affected by rain attenuation in the Ka-band. When rain intensity is greater than 10 mm/h, most of rain echoes at off-nadir scanning angles will not be interfered by surface clutter.

An error decomposition method is used to analyze and track seasonal error sources of the Integrated Multi‐satellitE Retrievals for the Global Precipitation Measurement (IMERG) Version 05 daily precipitation estimations, including Early‐, Late‐, and Final‐run products (IMERG‐DE, IMERG‐DL, and IMERG‐DF, respectively) over the Sichuan Basin of China from January 2016 to December 2018. The method decomposes the total errors into hit bias, missed precipitation, and false precipitation, which can be attributed to the satellite retrieval processes. Results show that in spring, all the estimations had serious overestimation in the northwest of the basin. The major error source is the hit bias of the intensity over 25 mm/day. In summer, the three IMERG estimations overestimated the precipitation in almost the entire Sichuan Basin, especially in the south and southwest. The principal determinants of the error sources are missed and false light precipitation (<25 mm/day), while hit bias and false precipitation are the leading error sources for the moderate precipitation (25–54 mm/day). In autumn, the IMERG‐DF's overestimation mainly comes from hit and false moderate precipitation (15–64 mm/day), while the underestimation of the IMERG‐DE and DL is mainly determined by missed and false precipitation of the precipitation less than 15 mm/day. In winter, the main contributors to the underestimation of the IMERG‐DE and DL estimations are missed and false lighter precipitation (<8 mm/day). However, the total errors of the IMERG‐DF are resulting from the three components canceling one another. Additionally, missed precipitation often takes place in the autumn and winter and false precipitation in summer and winter. The IMERG‐DL shows better error control of positive hit bias and false precipitation than do the other estimations in summer.

Error evaluation is essential for the improvement and application of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) Version 06 daily precipitation estimates, including early-, late-, and final-run products (IMERG-DE, IMERG-DL, and IMERG-DF, respectively), especially for summer precipitation in complex topographical areas. However, many existing works mainly focus on comparing the error statistical metrics of precipitation estimates, but few further analyze the internal relationships between these error statistics and geographical features. Therefore, taking Sichuan Province of China as a case study of the complex topographic and mountainous area, we adopt statistical metrics, error decomposition schemes, systematic and random error separation models, and regression methods to analyze the relationships between the spatial distribution of IMERG summer precipitation error metrics and geographical features. These features include longitude, latitude, distance from Sichuan Basin edge (DFBE), digital elevation model (DEM), normalized difference vegetation index (NDVI), slope, aspect, and topographic position index (TPI). The results show that: (1) DEM and DFBE are the two most important geographical features affecting the spatial distribution of error metrics, while both aspect and TPI have negligible effects on these metrics; (2) the variations in DEM, DFBE, and latitude have the negative relationships with error metrics; (3) longitude and DFBE do not have a direct impact on the errors, but indirectly affect the precipitation errors through the changing DEM; (4) slope shows a strong negative correlation with hit bias, and its increase significantly amplifies the sensitivity of systematic errors of hit bias from IMERG-DE and DL; and (5) the high detection probability and small missed precipitation error of the three IMERG estimates are virtually unaffected by changes in geographical features.