Numerical weather prediction in Yangtze River Delta region with assimilation of AWS and GPS/PWV data

Verification is performed on the impacts of joint assimilation of the ground-based Global Positioning System (GPS) precipitable water vapor (PWV) and Automatic Weather Station (AWS) data in numerical weather prediction in the Yangtze River Delta region. It is found that the use of GPS/PWV and AWS observations in Short-range Numerical Model System (SSNS) with three-dimensional variational (3DVAR) scheme greatly improved the predictions of rainfall and air temperature, with significant reduction of model "spin-up" time (0.55 hours) and decrease of root mean square error (RMSE) for 24h temperature prediction (0.30°C). Preliminary analysis is also performed on the mechanisms of the impacts of assimilation. The numerical scheme designed in this study should be of potential use for operational numerical weather prediction and related applications (i.e., chemical weather predictions).