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

The green vegetation fraction (GVF) can greatly influence the partitioning of surface sensible and latent heat fluxes in numerical weather prediction (NWP) models. However, the multiyear averaged monthly GVF climatology-the most commonly used representation of the vegetation state in models-cannot capture the real-time vegetation state well. In 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 model. The annual variability of the GVF over North China is examined in detail. Many differences between the two GVF datasets are found over dryland, grassland, and cropland/grassland mosaic areas. Two experiments using different GVF datasets are performed to assess the impacts of GVF on forecasts of screen-level temperature and humidity. The results show that using NRT GVF can lead to a widespread reduction of 2-m temperature forecast errors from April to October. Evaluation against in-situ observations shows that the positive impact on 2-m temperature forecasts in the morning is more distinct than that in the afternoon. Our 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 China. Moreover, our study shows that the negative effect of using NRT GVF is closely related to the initial soil moisture.

Automated summary

By comparing different GVF datasets, the study found that using near real-time (NRT) GVF dataset can significantly reduce temperature forecast errors, especially in the morning. Real-time GVF data can provide a more realistic representation of vegetation state, thereby improving short-term weather forecast accuracy in arid and semiarid regions of North China.