Ground Validation and Error Sources Identification for GPM IMERG Product over the Southeast Coastal Regions of China

Abstract

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement mission (IMERG) has been widely evaluated. However, most of these studies focus on the ultimate merged satellite-gauge precipitation estimate and neglect the valuable intermediate estimates which directly guide the improvement of the IMERG product. This research aims to identify the error sources of the latest IMERG version 6 by evaluating the intermediate and ultimate precipitation estimates, and further examine the influences of regional topography and surface type on these errors. Results show that among six passive microwave (PMW) sensors, the Microwave Humidity Sounder (MHS) has outstanding comprehensive behavior, and Special Sensor Microwave Imager/Sounder (SSMIS) operates advanced at precipitation detection, while the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometry (SAPHIR) has the worst performance. More precipitation events are detected with larger quantitative uncertainty in low-lying places than in highlands, in urban and water body areas than in other places, and more in coastal areas than in inland regions. Infrared (IR) estimate has worse performance than PMW, and the precipitation detectability of IR is more sensitive to the factors of elevation and the distance to the coast, as larger critical successful index (CSI) over lowlands and coastal areas. PMW morphing and the mixing of PMW and IR algorithms partly reverse the conservative feature of the precipitation detection of PMW and IR estimates, resulting in higher probability of detection (POD) and false alert ratio (FAR). Finally, monthly gauge calibration improves most of the statistical indicators and reduces the influence of elevation and surface type factor on these errors.

Comprehensive Evaluations on the Error Characteristics of the State‐of‐the‐Art Gridded Precipitation Products Over Jiangxi Province in 2019

“…Sui et al. (2020) demonstrated the systematic errors brought by the passive microwave sources of IMERG precipitation data from different sensors, and examined the influences of topography and surface type on these errors. Lu et al.…”

Systematic assessment of GPM IMERG V06 precipitation products with dense rain gauge observations over Zhejiang Province, China

“…Results showed that the IMERG performance in representing precipitation over ZJP was affected by the orographic conditions, which was consistent with the earlier studies over other regions (e.g., Milewski et al ., 2015; Navarro et al ., 2020; Sui et al ., 2020; Zhou et al ., 2020; Derin et al ., 2021; 2022; Ma et al ., 2021; Li et al ., 2022). For example, both IMERG‐L and IMERG‐F had larger underestimations over the mountainous areas of ZJP (Figure S1), accompanied by relatively poor categorical metrics‐based performance (i.e., lower POD and larger FAR).…”

Spatiotemporal characteristics of precipitation extremes based on reanalysis precipitation data during 1950–2020 over the Ganjiang River Basin and its surroundings, China

“…For instance, ERA5 precipitation data show highest performance over subregions of temperate monsoon climate and temperate continental climate over China mainland (Xu et al, 2022). Moreover, the performance of model‐based precipitation products significantly outperform satellite‐based products over high‐latitude regions in cold seasons (Gao et al, 2020; Ma et al, 2022; Sui et al, 2020; Zhu et al, 2021). Therefore, ERA5 could convincingly stand out among many state‐of‐the‐art global and regional precipitation products obtained through gauge‐based, satellite‐based, and atmospheric retrospective‐analysis models, as well as through the merging of their products (Hersbach et al, 2020; Chen et al, 2008; Harris et al, 2020; Huffman et al, 2007; Huffman et al, 2019; Kobayashi et al, 2015; Ma et al, 2020; Ma et al, 2022, shown in Table 1) in terms of long time series with high quality.…”