GSI-based, Continuously Cycled, Dual-Resolution Hybrid Ensemble-Variational Data Assimilation System for HWRF: System Description and Experiments with Edouard (2014)

GSI-based, Continuously Cycled, Dual-Resolution Hybrid Ensemble-Variational Data Assimilation... AbstractA Gridpoint Statistical Interpolation (GSI) based, continuously cycled, dual-resolution hybrid EnKF-Var (Ensemble Kalman filter-Variational) data assimilation (DA) system is developed for the Hurricane Weather Research and Forecasting (HWRF) model. In this system, a directed moving nest strategy is developed to solve the issue of non-overlapped domains for cycled ensemble DA. Additionally, both dual-resolution and four-dimensional ensemble-variational (4DEnVar) capabilities are implemented. Vortex modification (VM) and relocation (VR) are used in addition to hybrid DA.Several scientific questions are addressed using the new system for hurricane Edouard (2014). It is found that dual-resolution hybrid DA improves the analyzed storm structure and short-term Vmax (maximum wind speed) and MSLP (minimum sea-level pressure) forecasts compared to coarser, single-resolution hybrid DA, but track and RMW (radius of maximum wind) forecasts do not improve. Additionally, applying VR and VM on the control background before DA improves the analyzed storm, overall track, RMW, MSLP and Vmax forecasts. Further applying VR on the ensemble background improves the analyzed storm and forecast biases for MSLP and Vmax. Also, using 4DEnVar to assimilate tail doppler radar (TDR) data improves the analyzed storm and short-term MSLP and Vmax forecasts compared to 3DEnVar (three-dimensional ensemble-variational) although 4DEnVar slightly degrades the track forecast. Finally, the new system improves overall RMW, MSLP and Vmax forecasts upon the operational HWRF, while no improvement on track is found. The intensity forecast improvement during the intensifying period is due to the better analyzed structures for an intensifying storm. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Monthly Weather Review American Meteorological Society

GSI-based, Continuously Cycled, Dual-Resolution Hybrid Ensemble-Variational Data Assimilation System for HWRF: System Description and Experiments with Edouard (2014)

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0493
D.O.I.
10.1175/MWR-D-17-0068.1
Publisher site
See Article on Publisher Site

Abstract

AbstractA Gridpoint Statistical Interpolation (GSI) based, continuously cycled, dual-resolution hybrid EnKF-Var (Ensemble Kalman filter-Variational) data assimilation (DA) system is developed for the Hurricane Weather Research and Forecasting (HWRF) model. In this system, a directed moving nest strategy is developed to solve the issue of non-overlapped domains for cycled ensemble DA. Additionally, both dual-resolution and four-dimensional ensemble-variational (4DEnVar) capabilities are implemented. Vortex modification (VM) and relocation (VR) are used in addition to hybrid DA.Several scientific questions are addressed using the new system for hurricane Edouard (2014). It is found that dual-resolution hybrid DA improves the analyzed storm structure and short-term Vmax (maximum wind speed) and MSLP (minimum sea-level pressure) forecasts compared to coarser, single-resolution hybrid DA, but track and RMW (radius of maximum wind) forecasts do not improve. Additionally, applying VR and VM on the control background before DA improves the analyzed storm, overall track, RMW, MSLP and Vmax forecasts. Further applying VR on the ensemble background improves the analyzed storm and forecast biases for MSLP and Vmax. Also, using 4DEnVar to assimilate tail doppler radar (TDR) data improves the analyzed storm and short-term MSLP and Vmax forecasts compared to 3DEnVar (three-dimensional ensemble-variational) although 4DEnVar slightly degrades the track forecast. Finally, the new system improves overall RMW, MSLP and Vmax forecasts upon the operational HWRF, while no improvement on track is found. The intensity forecast improvement during the intensifying period is due to the better analyzed structures for an intensifying storm.

Journal

Monthly Weather ReviewAmerican Meteorological Society

Published: Oct 23, 2017

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