Multiscale Data Assimilation and Modeling for Nowcasting and Beyond Year Two

Nowcasting encompasses a detailed description of the current weather along with forecasts during the next several hours. Most current nowcasting techniques, such as MAPLE (the McGill Algorithm for Prediction by Lagrangian Extrapolation) used in The Weather Network/Pelmorex Media Inc., are based on temporal extrapolation of radar and satellite imagery. Recent advances in observation networks, high-resolution numerical models, and in particular the data assimilation methods have great potentials to improve nowcasting accuracy. In this proposed study, we will develop a Multiscale Data Assimilation and Modeling (MADAM) system to assimilate O-Qnet wind profiler data and radar observations in addition to other conventional and remotely sensed satellite observations. Cases of summer convections and winter snowstorms will be tested. Nowcasting skills using MADAM and MAPLE will be evaluated. Uncertainty information provided by the ensemble forecasts will be explored for nowcasting.

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