TY - GEN
T1 - Applicability analysis of GPM IMERG satellite precipitation data in cloudy and foggy regions
T2 - 2025 International Conference on Remote Sensing, Mapping, and Image Processing, RSMIP 2025
AU - Wu, Jianfeng
AU - Guo, Zhongyang
AU - Zhao, Yongyang
AU - Cao, Guangjie
AU - He, Chaofeng
N1 - Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
PY - 2025
Y1 - 2025
N2 - Guizhou Province, a typical karst region in Southwest China characterized by complex terrain and a cloudy and foggy climate, holds significant importance for the high-precision monitoring of satellite precipitation data. Based on observational data from 84 meteorological stations in Guizhou Province from 2000 to 2020, this study systematically evaluates the applicability of GPM IMERG satellite precipitation data across annual, seasonal, monthly, and station-level scales using analysis metrics such as linear regression, correlation coefficients, and relative deviations. The results indicate that: (1) Overall, GPM IMERG data exhibit good agreement with ground station data at the annual scale, with an average relative error of-2.59%; (2) At the seasonal scale, variations exist among the four seasons, with the highest correlation in summer (R = 0.72) and poorer performance in winter, necessitating further considerations during application; (3) At the monthly scale, GPM IMERG data demonstrate significant linear correlations (R = 0.86); (4) Station-level monthly scale analysis reveals that the correlation coefficients of all stations exceed 0.80, though some variability exists among stations, with 64 stations showing relative deviations between-10% and 10%. These findings provide a scientific basis for optimizing the application of precipitation data and improving water resource management in the Guizhou region.
AB - Guizhou Province, a typical karst region in Southwest China characterized by complex terrain and a cloudy and foggy climate, holds significant importance for the high-precision monitoring of satellite precipitation data. Based on observational data from 84 meteorological stations in Guizhou Province from 2000 to 2020, this study systematically evaluates the applicability of GPM IMERG satellite precipitation data across annual, seasonal, monthly, and station-level scales using analysis metrics such as linear regression, correlation coefficients, and relative deviations. The results indicate that: (1) Overall, GPM IMERG data exhibit good agreement with ground station data at the annual scale, with an average relative error of-2.59%; (2) At the seasonal scale, variations exist among the four seasons, with the highest correlation in summer (R = 0.72) and poorer performance in winter, necessitating further considerations during application; (3) At the monthly scale, GPM IMERG data demonstrate significant linear correlations (R = 0.86); (4) Station-level monthly scale analysis reveals that the correlation coefficients of all stations exceed 0.80, though some variability exists among stations, with 64 stations showing relative deviations between-10% and 10%. These findings provide a scientific basis for optimizing the application of precipitation data and improving water resource management in the Guizhou region.
KW - GPM IMERG
KW - Guizhou Province
KW - applicability analysis
KW - linear Regression
KW - precipitation data
UR - https://www.scopus.com/pages/publications/105008226671
U2 - 10.1117/12.3067674
DO - 10.1117/12.3067674
M3 - 会议稿件
AN - SCOPUS:105008226671
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - International Conference on Remote Sensing, Mapping, and Image Processing, RSMIP 2025
A2 - Tosti, Fabio
A2 - Alvarez, Roman
PB - SPIE
Y2 - 17 January 2025 through 19 January 2025
ER -