Direct Assimilation of Satellite-Derived AMVs into HWRF: First Results

Thursday, 21 April 2016: 9:45 AM
Ponce de Leon C (The Condado Hilton Plaza)
William E. Lewis, Univ. of Wisconsin, Madison, WI; and C. S. Velden, V. Tallapragada, and J. M. Daniels

Atmospheric motion vectors (AMVs) derived from sequences of visible and infrared satellite images are a longstanding and valuable source of tropospheric wind information at synoptic scales, especially in data-sparse regions such as the open oceans. The Advanced Baseline Imager (ABI) aboard GOES-R (to be launched in 2016) will provide increased temporal and spatial sampling relative to current instruments in geosynchronous orbit. These advances will in turn allow the production of enhanced AMV datasets which are expected to provide important insight into mesoscale processes. In particular, one of the impediments to advancement in numerical prediction of tropical cyclones (TCs) has been difficultly in initializing the wind structures associated with the vortex and its immediate environment. It is posed that the enhanced AMVs may offer an excellent opportunity for substantive progress in this area. As part of an effort supported by the Hurricane Forecast Improvement Project (HFIP), we demonstrate the promise of these data for the purpose of TC analysis and prediction. Proxy GOES-R AMV datasets are constructed from GOES-13 and GOES-14 super-rapid scan (1-min. frequency) imagery for selected cases of Atlantic hurricanes: Sandy (2012), Edouard (2014) and Gonzalo (2014). These data are then assimilated using the hybrid Gridpoint Statistitcal Interpolation (GSI) package within NOAA's operational Hurricane Weather Research and Forecasting (HWRF) modeling system. Initial results indicate that, given proper quality control, the enhanced AMVs can have a beneficial impact on both track and intensity at all lead times relative to a control that assimilates only radiosonde observations (RAOBs). The impact on the intensity bias is likewise robust and positive at all lead times. Details of the proxy dataset construction and data assimilation methodologies will be presented, and model impact will be addressed in terms of both traditional track/intensity metrics as well as analysis of structural changes at the vortex scale.
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