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Influence of Near Real-Time Green Vegetation Fraction Data on Numerical Weather Prediction by WRF over North China

Journal of meteorological Research 2021年第3期35卷 505-520页

作者： Bing LU Jiqin ZHONG Wei WANG Shihao TANG Zhaojun ZHENG Institute of Urban meteorology China Meteorological AdministrationBeijing 100089China National Center for Atmospheric Research BoulderCO 80301USA Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites National Satellite Meteorological CenterChina Meteorological AdministrationBeijing 100089China

The green vegetation fraction(GVF)can greatly influence the partitioning of surface sensible and latent heat fluxes in numerical weather prediction(NWP)***,the multiyear averaged monthly GVF climatology—the most commonly used representation of the vegetation state in models—cannot capture the real-time vegetation state *** this study,a near real-time(NRT)GVF dataset generated from an 8-day composite of the normalized difference vegetation index is compared with the 10-yr averaged monthly GVF provided by the WRF *** annual variability of the GVF over North China is examined in *** differences between the two GVF datasets are found over dryland,grassland,and cropland/grassland mosaic *** experiments using different GVF datasets are performed to assess the impacts of GVF on forecasts of screen-level temperature and *** results show that using NRT GVF can lead to a widespread reduction of 2-m temperature forecast errors from April to *** against in-situ observations shows that the positive impact on 2-m temperature forecasts in the morning is more distinct than that in the *** study demonstrates that NRT GVF can provide a more realistic representation of the vegetation state,which in turn helps to improve short-range forecasts in arid and semiarid regions of North ***,our study shows that the negative effect of using NRT GVF is closely related to the initial soil moisture.

关键词： green vegetation fraction(GVF) near real-time(NRT) soil moisture 2-m temperature and humidity warm season

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