19B.1 Test of a Weather-Adaptive Hybrid 3DEnVAR and WRF-DART Analysis and Forecast System During the HWT Spring Experiments in 2017

Wednesday, 30 August 2017: 10:30 AM
St. Gallen 1&2 (Swissotel Chicago)
Jidong Gao, NOAA/NSSL, Norman, OK; and Y. Wang, D. M. Wheatley, K. H. Knopfmeier, T. A. Jones, and G. Creager

A real-time, weather adaptive hybrid 3DVAR and WRF-DART analysis and forecast system with the WRF-ARW forecast model have been developed recently for the NOAA supported Warn-on-Forecast project (WoF). The goal is to provide ensemble-based physically-consistent gridded analysis and forecast products to forecasters for making warning decisions in a timely manner. First, the storm position is determined based on NOAA/Storm Prediction Center’s convective outlook each day so that the analysis and forecast can be performed with on-demand capability in which end users (e.g., forecasters or scientists) can set up the location of the analysis and forecast domain in real time based on the current weather situation. Second, both the WRF-DART with 36 ensemble members and the 3DEnVAR system incorporate available mesoscale forecasts, radar data, satellite retrieved cloud water path, and traditional observations to perform two separate 15-minute data assimilation cycles with dual-resolution capability. The WRF-DART analysis is performed in 3 km resolution, and the 3DEnVAR analysis is performed with 1.5 km resolution. The ensemble covariance derived from 36 members of cycled WRF-DART ensemble forecasts is used in the 3DEnVAR system. Then 36 ensemble members and one deterministic forecast with 90 minutes forecast length are launched every 30 minutes from these high frequency dual-resolution data assimilation cycles. This enhanced system will be tested during the 2017 Hazardous Weather Testbed (HWT) Spring Experiment period. Two types of experiments will be conducted. The first experiment uses the 3DVAR data assimilation analysis and forecast cycles only; and the second one uses the hybrid method mentioned above. Our hope is that the hybrid system should outperform the 3DVAR-only system because the 3DVAR-only method does not use the ensemble error covariance. The performance of the system during the 2017 Spring Experiment period will be reported in the conference.

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