%0 Journal Article
%A ZHAO Ze-yu
%A QIN Fu-ying
%A NA Yin-tai
%A Narenmandula
%A GUO En-liang
%A BAO Yu-hai
%T Downscaling Simulation of TRMM Precipitation Data in the Mongolian Plateau Based on GWR
%D 2023
%R 10.3969/j.issn.1000-6362.2023.03.002
%J Chinese Journal of Agrometeorology
%P 182-192
%V 44
%N 03
%X The Mongolian plateau was divided into "vegetation area" and "non-vegetation area" using the threshold value of the multi-year monthly mean NDVI value above 0.1. Based on an examination of the time lagged response of vegetation to precipitation in the "vegetation area" and the association between various data values of land surface temperature and precipitation in the "non-vegetation area", a geographically weighted regression (GWR) model of TRMM 3B43 data with the elevation, slope, slope direction data, and normalized vegetation index (NDVI)/land surface temperature (LST) data was constructed to obtain monthly precipitation downscaling simulations at 1km spatial resolution from May to October 2006−2015 in the different areas. Utilizing information from 141 local meteorological stations, the accuracy of downscaled simulation data was validated. The results showed that, (1) there is a time lag in the response of vegetation to precipitation in the "vegetated area" of the Mongolian plateau, which is about one month. The most significant correlation between daytime and nighttime land surface temperature difference (LST_D_N) values and precipitation is found in the "non-vegetated area" in most months. (2) The descending-scale simulations agree with the meteorological station data, with a monthly-scale correlation coefficient of 0.83 and correlation coefficients ranging from 0.42 to 0.98 for each station. (3) On the growing season and monthly mean scales, the downscaled simulation data are highly accurate, with September and October data being more accurate than TRMM 3B43 data. The overall accuracy of the downscaled simulation data is higher, and together with the filling of areas not covered by the original data above 50°N and the increase in spatial resolution. It can provide essential data support for research on water cycle changes, agricultural and livestock production, and drought monitoring in the region.
%U https://zgnyqx.ieda.org.cn/EN/10.3969/j.issn.1000-6362.2023.03.002