AOSC Seminar by Dr. Xuguang Wang, 4/22/2021
Dr. Xuguang Wang
Multiscale data Assimilation and Predictability (MAP) LabSchool of Meteorology, University of Oklahoma
Title: Multiscale data assimilation for numerical weather prediction
The emergence of new computing resources and technologies allows for a significant increase in the effective resolution of the next generation numerical models. Such computational resources also streamline the path for observations from a myriad of existing and new in-situ and remote sensing platforms, which observe a variety of scales, to be ingested in the numerical weather prediction (NWP) models. A next generation data assimilation system is therefore required to effectively analyze the state and its uncertainty across multiple scales, which hereafter is termed as “multiscale data assimilation (MDA)”. In this seminar, challenges associated with the MDA for NWP are first discussed. A novel multiscale data assimilation (DA) method, MLGETKF (Multiscale Local Gain Form Ensemble Transform Kalman Filter), is introduced. MLGETKF allows simultaneous update of multiple scales for both the mean and ensemble perturbations through assimilating all observations at once. Both statistical model and surface turbulence model experiments show that MLGETKF improves upon the scale unaware DA approach for both the analysis and the subsequent forecast. Methods to implement the MDA in ensemble-variational (EnVar) DA system are then introduced. Performance of implementing the MDA in the NOAA hybrid 4DEnVar and next generation FV3 global medium range weather modeling system is examined through one-month cycled data assimilation experiments. MDA is shown to improve global forecasts over scale unaware approach. Including cross band correlation during the MDA further improves the global forecast. Additionally, the MDA is further developed and implemented for convective scale weather prediction. Experiments are conducted for the 8 May 2003 OKC (Oklahoma City) tornadic supercell. It is found that the MDA enhances the simulated supercell during the analysis and subsequent prediction. Diagnostics suggests this improvement is due to the enhanced ambient wind convergence as a result of simultaneous proper update of both small (storm) and large (environment) scales.
Dr. Xuguang Wang obtained her B.S. in Atmospheric Science from Beijing University, China and her Ph.D. in Meteorology from the Pennsylvania State University. Dr. Wang is currently a Robert Lowry Chair Professor and Presidential Research Professor of School of Meteorology of University of Oklahoma (OU). She leads a Multiscale data Assimilation and Predictability (MAP) lab. Her research ranges from developing novel methodologies for data assimilation and ensemble prediction to applying these methods for global, hurricane, and convective-scale numerical weather prediction systems that assimilate a variety of in-situ and remote-sensing observations. The data assimilation research and development by Dr. Wang and her MAP team have been adopted by multiple NOAA NWS (National Weather Service) operational numerical weather prediction systems including e.g., GFS (Global Forecast System), HWRF (Hurricane WRF) and HRRR (High Resolution Rapid Refresh). Dr. Wang is also excited about cultivating the next generation in data assimilation. So far she has directly advised 25 graduate students and 19 postdocs during her tenure at OU. Dr. Wang also actively takes community scientific leadership role such as serving as NOAA Hurricane Forecast Improvement Program (HFIP) data assimilation team co-lead, UCAR Developmental Testbed Center (DTC) science advisory board and WMO WWRP Predictability, Dynamics and Ensemble Forecasting working group.
Contact: Jonathan Poterjoy
Pre-seminar refreshment: N/A
Seminar: 3:30-4:30pm, Zoom
Meet-the-Speaker: 4:30-5:00pm, Zoom [For AOSC Students only]