Impact of FORMOSAT-3/COSMIC GPS radio occultation and dropwindsonde data on regional model predictions during the 2007 Mei-yu season

The Weather Research and Forecasting (WRF) model and its three-dimensional variation component, WRF-Var, are used for regional model predictions of heavy rainfall events in Taiwan. Without the use of GPS RO and dropwindsonde data, pressure and relative humidity are, in general, underestimated by the model; temperature predictions have a warm bias at the low level and a cold bias at the high level; and the east–west and north–south component winds have positive and negative biases, respectively.

Incorporating GPS RO data tends to improve the prediction for longer integration. The assimilation of dropwindsonde data improves the forecast at the earlier time and at higher levels, and the improvement decreases over time. The reason the dropwindsonde data produce a positive impact earlier and the GPS RO data later is that there are few GPS RO observations in the fine domain.