PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Evaluation of the Four-dimensional Ensemble-Variational Hybrid Data Assimilation with Self-consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts
Version 1
: Received: 11 August 2020 / Approved: 13 August 2020 / Online: 13 August 2020 (11:55:30 CEST)
How to cite:
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. Preprints2020, 2020080305. https://doi.org/10.20944/preprints202008.0305.v1
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. Preprints 2020, 2020080305. https://doi.org/10.20944/preprints202008.0305.v1
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. Preprints2020, 2020080305. https://doi.org/10.20944/preprints202008.0305.v1
APA Style
Zhang, S., & Pu, Z. (2020). Evaluation of the Four-dimensional Ensemble-Variational Hybrid Data Assimilation with Self-consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts. Preprints. https://doi.org/10.20944/preprints202008.0305.v1
Chicago/Turabian Style
Zhang, S. and Zhaoxia Pu. 2020 "Evaluation of the Four-dimensional Ensemble-Variational Hybrid Data Assimilation with Self-consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts" Preprints. https://doi.org/10.20944/preprints202008.0305.v1
Abstract
The feasibility of a hurricane initialization framework based on the GSI-4DEnVar data assimilation system for the 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-3DEnVar data assimilation system, similar to that used in the operational HWRF, which employs background error covariances provided by coarser-resolution global ensembles from the NCEP GFS ensemble Kalman filtering data assimilation system. 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, MSLP, and MSW forecast errors in all of the HWRF simulations, compared with the GSI-3DEnVar initialization framework. 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.
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
