Abstract

GSI-based 3D and 4D ensemble-variational (EnVar) hybrid data assimilation (DA) system improved US NWS NOAA global forecast significantly since it was implemented operationally in 2012and 2016 respectively. This seminar will first discuss two new developments including the cost effective valid time shifting method to increase ensemble size and the multi-resolution ensemble 4DEnVar.Research and development have also been made to further develop the GSI-based hybrid DA system for convection-allowing regional modeling systems. In the second part of the seminar, the hybrid DA system is extended with the operational Hurricane Weather Research and Forecast (HWRF) modeling system to improve high-resolutiontropical cyclone prediction. Experiments have demonstrated that the new DA system was able to improve the hurricane intensity forecasts. The fully cycled and self-consistent hybrid DA system for HWRF was operationally implemented beginning summer 2017. In addition, recent research using the HWRF hybrid DA system to identify and diagnose a model error associated with the PBL physics scheme,and to best assimilate inner core data will be discussed.In the last part of the seminar, the GSI-based hybrid DA system is also extended for convective scale radar data assimilation. Issues associated with direct assimilation of radar reflective observations in EnVar are revealed. A new method that allows direct assimilation of radar reflectivity in EnVar is proposed and implemented. Experiments for a variety of convective scale weather phenomena including tornadic supercell and Mesoscale Convective Systems (MCS) were conducted.Experiments revealed that the new direct radar DA method for EnVar improved the tornadic supercell prediction, and also improved precipitation forecasts compared to the current operational method of assimilating the reflectivity, “the cloud analysis”. The new radar DA method is planned to be implemented operationally in 2020.

Presenter Profile

Prof. Wang is a Full Professor and Presidential Research Professor of University of Oklahoma of US. She currently directs the Multiscale data Assimilation and Predictability (MAP) Lab at University of Oklahoma. She earned her B.S. in Atmospheric Science from Beijing University and Ph.D. in Meteorology from the Pennsylvania State University. After holding a postdoctoral research scientist position at NOAA/University of Colorado, she joined School of Meteorology of University of Oklahoma. Her research interests include developing novel techniques and methodologies for data assimilation and ensemble prediction, implementing these techniques in global to convective scale modeling systems, and improving the understanding of atmospheric predictability and dynamics through data assimilation and ensemble approaches. Her research team is also excited about transitioning basic research into operations. She is a recipient of Innovative Research Award of University of Colorado, NASA New Investigator Award, Dean’s Award for Excellence in Research and Scholarship, and Presidential Research Professorship of University of Oklahoma.