TJ11.1 A Weather-Adaptive Hybrid Gain WRF-DART and 3DVAR Analysis and Forecast System with Automatic Storm Positioning and On-demand Capability

Tuesday, 24 January 2017: 4:00 PM
Conference Center: Skagit 5 (Washington State Convention Center )
Jidong Gao, NSSL/NOAA, Norman, OK; and Y. Wang, D. M. Wheatley, K. H. Knopfmeier, T. A. Jones, G. J. Creager, L. J. Wicker, and J. S. Kain

A real-time, weather adaptive hybrid Gain WRF-DART and 3DVAR analysis and forecast system with WRF model have been developed recently for the NOAA supported Warn-on-Forecast project (WoF). The goal for this work is to provide ensemble-based physically-consistent gridded analysis and forecast products to forecasters to help make their warning decisions in a timely manner. First, a storm positioning program is implemented based on NSSL WDSS-II two-dimensional composite reflectivity product. So it has the ability to automatically detect severe local hazardous weather events. Furthermore, the analysis and forecast can also be performed with on-demand capability in which end-users (e.g., forecasters or scientists) set up the location of the analysis and forecast domain in real time based on the current weather situation. Second, both the WRF-DART and 3DVAR system incorporates available mesoscale forecasts, radar, satellite retrieved cloud water path, and traditional observations to perform two separate rapid analyses. Then the ensemble mean analysis from the WRF-DART and the 3DVAR analysis are combined to form a new analysis which is used to update the WRF-DART ensemble mean analysis. This implementation is equivalent to combine the gain matrices of the ensemble and variational methods and may help reduce imbalances between different model variables (Penny 2014). This enhanced system will be tested with several cases collected during the 2016 Hazardous Weather Testbed (HWT) Spring Experiment period. Three type of experiments will be conducted. The first experiment uses the 3DVAR data assimilation analysis and forecast cycles only; the second one uses WRF-DART analysis and forecast cycles only, and the third one uses hybrid-gain method that combines both WRF DART and 3DVAR system. Our hope is that hybrid gain system is capable of out-performing both component systems. The performance of the systems with several cases during the 2016 Spring Experiment period will be reported during the conference.
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