Assimilation of Multi-Sensor Synoptic and Mesoscale Datasets An Approach Based on Statistic, Dynamic, Physical and Synoptic Considerations.

摘要

Long-Term Goals: Our long-term goal is to contribute to our understanding of key elements for improving (i) the 2 to 3-day forecast of Pacific and Atlantic storms that strike the west coasts of the U. S. and Europe, and (ii) hurricane forecasts through analysis and assimilation of observations. Of particular interest to us are the combined effects of satellite data and in situ data (rawinsondes, dropsondes, buoys, ships) on the initialization of a numerical forecast model aimed at providing reliable objective forecast guidance. Objectives: To conduct data assimilation experiments using a mesoscale forecast model and observations available during NORPEX, hurricane Bonnie, and selected squall line events over the US. We focus on one technical and one scientific objective: 1. the best use of TOMS ozone, SSM/I microwave brightness temperature and rainfall data in data assimilation, 2. the role of the background auto-covariances and cross-covariances in mesoscale data assimilation (scientific goal).