A GSI-based, End-to-End cycled, Dual Resolution Hybrid Ensemble-Variational Data Assimilation System for HWRF: system description and experiment results

Xu Lu* and Xuguang Wang
School of Meteorology, University of Oklahoma

A Gridpoint Statistical Interpolation (GSI) based, continuously cycled, dual resolution hybrid ensemble Kalaman filter (EnKF)-variational data assimilation (DA) system was developed for the Hurricane Weather Research and Forecasting (HWRF) model to improve the high resolution analysis and predictions of tropical cyclones. In this hybrid system, a newly developed directed moving nest strategy was adopted to solve the issue of non-overlapped domains for cycled ensemble DA. In addition, both a dual-resolution and a four-dimentional (4D) capabilities were implemented in the ensemble-variational (EnVar) implementation. The performance of the system was investigated by conducting the end-to-end DA cycling and forecast experiments for hurricane Edouard (2014). All operational observations in addition to the Tail Doppler Radar data were assimilated. Experiments and diagnostics were designed to address various scientific questions using this newly extended hybrid DA system.

This study found that a) the dual resolution hybrid DA improved upon the coarser, single resolution hybrid; b) Vortex initialization and relocation in the control and relocation of the ensemble background on top of the DA improved the forecasts; c) Using 4DEnVar in the TDR-involved cycles improved the intensity forecasts for early lead times compared to 3DEnVar; and d) The hybrid system improved intensity forecasts relative to operational HWRF during the intensification period due to the alleviation of the “spin-down” issue resultant from better analyzed structures for an intensifying storm.

*email: luxu@ou.edu
*Preference: Oral