Yan, DD; Liu, TY; Dong, WJ; Liao, XH; Luo, SQ; Wu, K; Zhu, X; Zheng, ZY; Wen, XH (2020). Integrating remote sensing data with WRF model for improved 2-m temperature and humidity simulations in China. DYNAMICS OF ATMOSPHERES AND OCEANS, 89, 101127.

The default green vegetation fraction (GVF) in the Weather Research and Forecasting (WRF) Model version 3.7.1 was derived between 1985 and 1990 from the 1990s Normalized Difference Vegetation Index (NDVI) achieved from the NOAA Advanced Very High Resolution Radiometer (AVHRR), and its representation is deteriorating when used to simulate recent weather and climate events. In this study, we applied in WRF v3.7.1 the updated GVF estimated by the real-time NDVI of the Moderate Resolution Imaging Spectroradiometer (MODIS) data to provide a better representation of the prescribed surface GVF condition. A one-year simulation was carried out in China, and the simulated 2-m air temperature and specific humidity were compared between the WRF model control experiment that employs the default GVF data (WRF-CTL), the WRF simulations with updated GVF (WRF-MODIS), and the observations from 824 weather stations in China. Results are significantly improved for both the 2-m air temperature and the specific humidity by WRF-MODIS, which has effectively reproduced the observed pattern and increased the correlation coefficient between the model simulations and observations. The RMSE and bias of specific humidity are also reduced in WRF-MODIS. In general, the real-time MODIS-NDVI based GVF reflected the realistic increase of vegetation cover in China when comparing to the WRF default GVF, and also provided a more accurate seasonal variation for the simulated year of 2009. As a result, the WRF-MODIS simulation significantly improves its representation in the simulated 2-m air temperature and specific humidity, both in spatial distributions and seasonal variations, due to the GVF's great contribution in modulating the coupled land-atmosphere interactions.