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Article

Evaluation of the Four-Dimensional Ensemble-Variational Hybrid Data Assimilation with Self-Consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts

by 1,2,*,† and 1,*,†
1
Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT 84112, USA
2
Pacific Northwest National Laboratory, Richland, WA 99354, USA
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Atmosphere 2020, 11(9), 1007; https://doi.org/10.3390/atmos11091007
Received: 12 August 2020 / Revised: 15 September 2020 / Accepted: 18 September 2020 / Published: 21 September 2020
(This article belongs to the Special Issue Modeling and Data Assimilation for Tropical Cyclone Forecasts)
The feasibility of a hurricane initialization framework based on the Gridpoint Statistical Interpolation (GSI)-based four-dimensional ensemble-variational (GSI-4DEnVar) hybrid data assimilation system for the Hurricane Weather Research and Forecasting model (HWRF) model is evaluated in this study. The system considers the temporal evolution of error covariances via the use of four-dimensional ensemble perturbations that are provided by high-resolution, self-consistent HWRF ensemble forecasts. It is different from the configuration of the GSI-based three-dimensional ensemble-variational (GSI-3DEnVar) hybrid data assimilation system, similar to that used in the operational HWRF, which employs background error covariances provided by coarser-resolution global ensembles from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) ensemble Kalman filtering data assimilation system. In addition, our proposed initialization framework discards the empirical intensity correction in the vortex initialization package that is employed by the GSI-3DEnVar initialization framework in operational HWRF. Data assimilation and numerical simulation experiments for Hurricanes Joaquin (2015), Patricia (2015), and Matthew (2016) are conducted during their intensity changes. The impacts of two initialization frameworks on the HWRF analyses and forecasts are compared. It is found that GSI-4DEnVar leads to a reduction in track, minimum sea level pressure (MSLP), and maximum surface wind (MSW) forecast errors in all of the HWRF simulations, compared with the GSI-3DEnVar initialization framework. With assimilating high-resolution observations within the hurricane inner-core region, GSI-4DEnVar can produce the initial hurricane intensity reasonably well without the empirical vortex intensity correction. Further diagnoses with Hurricane Joaquin indicate that GSI-4DEnVar can significantly alleviate the imbalances in the initial conditions and enhance the performance of the data assimilation and subsequent hurricane intensity and precipitation forecasts. View Full-Text
Keywords: GSI-3DEnVar; GSI-4DEnVar; HWRF; hurricane intensity changes; background error covariance GSI-3DEnVar; GSI-4DEnVar; HWRF; hurricane intensity changes; background error covariance
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MDPI and ACS Style

Zhang, S.; Pu, Z. Evaluation of the Four-Dimensional Ensemble-Variational Hybrid Data Assimilation with Self-Consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts. Atmosphere 2020, 11, 1007. https://doi.org/10.3390/atmos11091007

AMA Style

Zhang S, Pu Z. Evaluation of the Four-Dimensional Ensemble-Variational Hybrid Data Assimilation with Self-Consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts. Atmosphere. 2020; 11(9):1007. https://doi.org/10.3390/atmos11091007

Chicago/Turabian Style

Zhang, Shixuan; Pu, Zhaoxia. 2020. "Evaluation of the Four-Dimensional Ensemble-Variational Hybrid Data Assimilation with Self-Consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts" Atmosphere 11, no. 9: 1007. https://doi.org/10.3390/atmos11091007

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