Abstract
Satellite-based precipitation estimates with high quality and spatial-temporal resolutions play a vital role in forcing global or regional meteorological, hydrological, and agricultural models, which are especially useful over large poorly gauged regions. In this study, we apply various statistical indicators to comprehensively analyze the quality and compare the performance of five newly released satellite and reanalysis precipitation products against China Merged Precipitation Analysis (CMPA) rain gauge data, respectively, with 0.1° × 0.1° spatial resolution and two temporal scales (daily and hourly) over southern China from June to August in 2019. These include Precipitation Estimates from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS), European Center for Medium-Range Weather Forecasts Reanalysis v5 (ERA5-Land), Fengyun-4 (FY-4A), Global Satellite Mapping of Precipitation (GSMaP), and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). Results indicate that: (1) all five products overestimate the accumulated rainfall in the summer, with FY-4A being the most severe; additionally, FY-4A cannot capture the spatial and temporal distribution characteristics of precipitation over southern China. (2) IMERG and GSMaP perform better than the other three datasets at both daily and hourly scales; IMERG correlates slightly better than GSMaP against CMPA data, while it performs worse than GSMaP in terms of probability of detection (POD). (3) ERA5-Land performs better than PERSIANN-CCS and FY-4A at daily scale but shows the worst correlation coefficient (CC), false alarm ratio (FAR), and equitable threat score (ETS) of all precipitation products at hourly scale. (4) The rankings of overall performance on precipitation estimations for this region are IMERG, GSMaP, ERA5-Land, PERSIANN-CCS, and FY-4A at daily scale; and IMERG, GSMaP, PERSIANN-CCS, FY-4A, and ERA5-Land at hourly scale. These findings will provide valuable feedback for improving the current satellite-based precipitation retrieval algorithms and also provide preliminary references for flood forecasting and natural disaster early warning.
Highlights
Precipitation is among the most important meteorological variables in global climate models and terrestrial hydrological cycles [1,2]
(2) Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) perform better than the other three datasets at both daily and hourly scales; IMERG correlates slightly better than GSMaP against China Merged Precipitation Analysis (CMPA) data, while it performs worse than GSMaP in terms of probability of detection (POD). (3) ERA5-Land performs better than PERSIANN-CCS and FY-4A at daily scale but shows the worst correlation coefficient (CC), false alarm ratio (FAR), and equitable threat score (ETS) of all precipitation products at hourly scale
This study aimed to provide a comprehensive evaluation for the main current satellite and reanalysis precipitation products over southern China in 2019
Summary
Precipitation is among the most important meteorological variables in global climate models and terrestrial hydrological cycles [1,2]. Global warming-induced extreme climatic changes have increased the frequency of heavy rainfalls brought by typhoons in the south coast regions of China, which will cause geological disasters, such as landslides, urban floods, soil erosion, and severe storms [3]. Accurate precipitation inputs with high spatial and temporal resolutions are crucial for reliable weather forecasting, hydrologic modeling, and agricultural studies [4]. Ground weather radars, and ocean-based buoys are more accurate at the point or local scales, they cannot observe the whole globe [8]. Compared to in situ precipitation measurements, satellite-based precipitation products have more advantages in terms of markedly increased spatial coverage with high resolutions, and their data is publicly available
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