Multiscale Data Assimilation and Modeling for Nowcasting and Beyond

The strategic plan is to improve storm nowcasting by using an advanced multiscale data assimilation and modeling system. This breaks down into three main objectives: 1) Developing a multiscale data assimilation and modeling system (MADAM) 2) Finding the optimal and efficient strategy of assimilating mesoscale and storm scale observations 3) Exploring the usefulness of uncertainty information from ensemble analysis and forecast in nowcasting The Rapid Refresh (RAP) system recently deployed in the National Center for Environment Prediction (NCEP) and National Oceanic and Atmospheric Administration (NOAA) are providing valuable guidance to US and Canadian forecasters. The analysis of the atmospheric state over North America is generated hourly using the 3-dimensional variational (3DVAR) data assimilation component in the Gridpoint Statistical Interpolation (GSI) system to assimilate various conventional and remotely sensed observations, including radar reflectivity and velocity-azimuth display (VAD) winds (Kleist et al. 2009). Then the Weather Research and Forecasting (WRF) model component is used to make a 18-h forecast. 

Zhan Li
Faculty Supervisor: 
Dr. Yongsheng Chen
Project Year: