Three-Dimensional Variational Data Assimilation Experiments for a Heavy Rainfall Case in the Downstream Yangtze River Valley Using Automatic Weather Station and Global Positioning System Data in Southeastern Tibetan Plateau

摘要

 A severe rainfall event occurred in the downstream Yangtze River Valley (YRV) during 28-30 June 2009. This study focused on the role of the “key sensitive area” (KSA) in the southeastern edge of the Tibetan Plateau (TP) in transporting water vapor downstream. The characteristics of water-vapor transport from KSA and the relation with the summer rainfall event were first investigated using the National Centers for Environmental Prediction Final Operational Global Analysis data and conventional observations. The observations included temperature, specific humidity (q), and surface pressure that are all from the automatic weather stations (AWS) over TP; precipitable water vapor (PWV) from the Global Positioning System (GPS) stations over TP; and rainfall from rain gauge measurements in YRV. The results showed high correlations between variables (i.e., q and PWV) observed over KSA and the summer rainfall in YRV, with a lagged time of 48-72 h, suggesting that the former is a good early-warning signal for the latter. To confirm the importance of KSA and its impact on the rainfall in the downstream YRV, the observations from the AWS and GPS of the New Integrated Observational System over TP were assimilated into the Advanced Research Weather Research and Forecast model with 30-km mesh using three-dimensional variational method. A set of sensitivity experiments were also conducted during a different summer, namely, June 2008, and Threat Score is used to evaluate the rainfall forecast skill. The results showed that the assimilation of observations from AWS and GPS in KSA helped adjust the structures of moisture, temperature, and wind fields, which improved the rainfall forecast in YRV, especially the heavy rainfall event. Both data analysis and numerical experiments demonstrated that the observations in KSA improved the forecast of high-impact weather in YRV.