Assessment of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) Precipitation Products in Northwest China DOI Creative Commons
Wei Dong,

Wenjing Di,

Wenshou Tian

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(8), С. 1364 - 1364

Опубликована: Апрель 11, 2025

This study evaluates the applicability of IMERG satellite precipitation product in Northwest China using data from more than 6000 ground-level meteorological stations during warm season (April–September) 2016 to 2023. The evaluation spans climatological, annual, monthly, and daily time scales with different intensities. can well capture spatial temporal climatology, decreasing southeast China, peaking August. correlation coefficient (CC) between ground-observed is 0.69. However, systematically overestimates at monthly scales, especially areas relatively low climatology. At scale, represent events very well, southeastern part China. light rainfall while underestimating other While performs detecting rain events, its accuracy diminishes for heavier rainfall, highlighting limitations monitoring extreme precipitation. Probability Detection (POD) consistently above 0.9, Torrential Rainfall POD below 0.7. These findings provide insights into effective application forecasting

Язык: Английский

Assessment of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) Precipitation Products in Northwest China DOI Creative Commons
Wei Dong,

Wenjing Di,

Wenshou Tian

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(8), С. 1364 - 1364

Опубликована: Апрель 11, 2025

This study evaluates the applicability of IMERG satellite precipitation product in Northwest China using data from more than 6000 ground-level meteorological stations during warm season (April–September) 2016 to 2023. The evaluation spans climatological, annual, monthly, and daily time scales with different intensities. can well capture spatial temporal climatology, decreasing southeast China, peaking August. correlation coefficient (CC) between ground-observed is 0.69. However, systematically overestimates at monthly scales, especially areas relatively low climatology. At scale, represent events very well, southeastern part China. light rainfall while underestimating other While performs detecting rain events, its accuracy diminishes for heavier rainfall, highlighting limitations monitoring extreme precipitation. Probability Detection (POD) consistently above 0.9, Torrential Rainfall POD below 0.7. These findings provide insights into effective application forecasting

Язык: Английский

Процитировано

0