Systematical Evaluation of GPM IMERG and TRMM 3B42V7 Precipitation Products in the Huang-Huai-Hai Plain, China

Abstract

Accurate estimation of high-resolution satellite precipitation products like Global Precipitation Measurement (GPM) and Tropical Rainfall Measuring Mission (TRMM) is critical for hydrological and meteorological research, providing a benchmark for the continued development and future improvement of these products. This study aims to comprehensively evaluate the Integrated Multi-Satellite Retrievals for GPM (IMERG) and TRMM 3B42V7 products at multiple temporal scales from 1 January 2015 to 31 December 2017 over the Huang-Huai-Hai Plain in China, using daily precipitation data from 59 meteorological stations. Three commonly used statistical metrics (CC, RB, and RMSE) are adopted to quantitatively verify the accuracy of two satellite precipitation products. The assessment also takes into account the precipitation detection capability (POD, FAR, CSI, and ACC) and frequency of different precipitation intensities. The results show that the IMERG and 3B42V7 present strong correlation with meteorological stations observations at annual and monthly scales (CC > 0.90), whereas moderate at the daily scale (CC = 0.76 and 0.69 for IMERG and 3B42V7, respectively). The spatial variability of the annual and seasonal precipitation is well captured by these two satellite products. And spatial patterns of precipitation gradually decrease from south to north over the Huang-Huai-Hai Plain. Both IMERG and 3B42V7 products overestimate precipitation compared with the station observations, of which 3B42V7 has a lower degree of overestimation. Relative to the IMERG, annual precipitation estimates from 3B42V7 show lower RMSE (118.96 mm and 142.67 mm, respectively), but opposite at the daily, monthly, and seasonal scales. IMERG has a better precipitation detection capability than 3B42V7 (POD = 0.83 and 0.67, respectively), especially when detecting trace and solid precipitation. The two precipitation products tend to overestimate moderate (2–10 mm/d) and heavy (10–50 mm/d) precipitation events, but underestimate violent (>50 mm/d) precipitation events. The IMERG is not found capable to detecting precipitation events of different frequencies more precisely. In general, the accuracy of IMERG is better than 3B42V7 product in the Huang-Huai-Hai Plain. The IMERG satellite precipitation product with higher temporal and spatial resolutions can be regarded a reliable data sources in studying hydrological and climatic research.

Nine-Year Systematic Evaluation of the GPM and TRMM Precipitation Products in the Shuaishui River Basin in East-Central China

“…Similar to our study, Xu et al (2019) [48] concludes that IMERG_F performs better than 3B42 in detecting precipitation events in the relatively flat Huang-Huai-Hai Plain of East Coastal China, with an annual POD of 0.83 and CSI of 0.52. The PODs and CSIs of IMERG_F surpass those of 3B42 in all seasons, especially in winter.…”

A Preliminary Assessment of the Gauge-Adjusted Near-Real-Time GSMaP Precipitation Estimate over Mainland China

“…where H, M, and F are the numbers of different precipitation events: Hit (both satellite estimates and gauge observations detect rain), miss (observed rain that is not detected by satellite), false (rain detected but not observed). Here, a commonly used threshold of 1.0 mm/day was set to define the rain/no rain event, as suggested by many previous studies [47][48][49][50]. For more detailed description of above continuous statistical metrics and contingency table metrics, readers can refer to Yong et al [25] and Lu et al [30].…”

Comparative Analysis of the Performance of Satellite‐Based Rainfall Products Over Various Topographical Unities in Central East Africa: Case of Burundi

“…The mean, the standard deviation (Std), the bias, the standard error, the root‐mean‐square error (RMSE), the mean absolute error (MAE), and the correlation coefficient (CC) are the main statistical metrics used to evaluate the performance and efficacy of PERSIANN‐CDR, CHIRPS, and CRU for the studied period ranging from 1983 to 2016. These statistical metrics are chosen because they have been widely used to evaluate the performance of satellite‐based rainfall products using ground observation as reference data and are found to provide a consistent and trustful products' evaluation (Ayugi et al, ; Deng et al, ; Dinku et al, ; Sun et al, ; Ullah et al, ; Xu et al, ).…”

“…Furthermore, this study used the probability of detection (POD), the false alarm ration (FAR), and the critical success index (CSI) to evaluate the daily precipitation detection capability for different thresholds. These categorical statistical metrics are chosen in this work because they enable to evaluate the rainfall detection and deliver reliable results as demonstrated by many scholars (Diem et al, ; Schaefer, ; Tan & Santo, ; Xu et al, ). POD is mainly used to describe the ratio of the occurrence of the rainfall that is correctly detected by the satellite‐based rainfall product among all the rainfall events recorded (equation ).…”

“…Therefore, employing suitable measurements presents paramount guarantee for consistent results in assessing hydrometeorological hazards, water resources management, and climate variability (Hong et al, ; Nikulin et al, ; Sahoo et al, ; Sun et al, ; Wang et al, ). As a result, recent studies focusing on monitoring extreme weather events such as floods and droughts across many regions have resorted to using of satellite‐based rainfall estimate products (Nicholson et al, ; Serrat‐Capdevila et al, ; Tan & Santo, ; Xu et al, ) These products are preferred due their capability in monitoring precipitation over spatial and temporal scales unlike in situ rain gauge data (Maggioni et al, ; Ongoma et al, ; Peña‐Arancibia et al, ). Numerous studies have been conducted to examine the role of satellite‐based rainfall estimate products, regional climate models, and reanalysis data to quantify rainfall and establish reliable forecast systems (Ayehu et al, ; Ayugi et al, ; Dinku et al, ; Ongoma et al, ).…”