Version 1
: Received: 2 November 2023 / Approved: 2 November 2023 / Online: 3 November 2023 (06:32:43 CET)
How to cite:
Wang, Z.; Li, Q. Validation of GSMaP Data in Depicting Precipitation Climatology and Climate Variability in China. Preprints2023, 2023110169. https://doi.org/10.20944/preprints202311.0169.v1
Wang, Z.; Li, Q. Validation of GSMaP Data in Depicting Precipitation Climatology and Climate Variability in China. Preprints 2023, 2023110169. https://doi.org/10.20944/preprints202311.0169.v1
Wang, Z.; Li, Q. Validation of GSMaP Data in Depicting Precipitation Climatology and Climate Variability in China. Preprints2023, 2023110169. https://doi.org/10.20944/preprints202311.0169.v1
APA Style
Wang, Z., & Li, Q. (2023). Validation of GSMaP Data in Depicting Precipitation Climatology and Climate Variability in China. Preprints. https://doi.org/10.20944/preprints202311.0169.v1
Chicago/Turabian Style
Wang, Z. and Qingquan Li. 2023 "Validation of GSMaP Data in Depicting Precipitation Climatology and Climate Variability in China" Preprints. https://doi.org/10.20944/preprints202311.0169.v1
Abstract
In this analysis, we assess the performance of GSMaP-GNRT6 data in capturing precipitation climatology and climate variability across China from 2001 to 2020. The evaluation involves comparing four precipitation indices of accumulated precipitation, the number of rainy days, rainstorm days, and precipitation maximum with the daily precipitation data from 2419 stations in China. The findings reveal that the GSMaP data effectively captures the overall spatial distribution of annual, summer, and monthly precipitation. However, it exhibits minor limitations in accurately depicting the spatial distribution of the number of rainy days from July to September and the precipitation maximum during wintertime in eastern China. While the GSMaP data depicts a coherent annual cycle consistent with the station observations, a general underestimation is observed. Notably, the GSMaP data presents a much smoother annual cycle in precipitation maximum. Regarding accumulated precipitation, the number of rainstorm days, and precipitation maximum, the GSMaP data shows an almost consistent interannual variation and increasing trends, aligning with station observations. However, it is noteworthy that the magnitude of the increasing trend based on the GSMaP data is greater, especially entering the early 21st century. Conversely, a significant discrepancy in the annual variation and an almost opposite changing trend are noticed in the number of rainy days between the GSMaP data and station observations.
Keywords
GSMaP; station observations; validation; climatology; climate variability
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
