A Hurricane Initialization Scheme with 4DEnVAR Satellite Ozone and Bogus Data Assimilation (SOBDA) and Its Application: Case Study

The aim of this study is to joint assimilate the ozone product from satellite Atmospheric Infrared Sounder (AIRS) and bogus data using four-dimensional ensemble-variational (4DEnVar) method, demonstrate potential benefits initialization technique in improving hurricane forecasting through a case study. Firstly, quality control scheme employed enhance AIRS; sea level pressure (SLP) at center constructed simultaneously based on Fujita’s mathematical model for subsequent assimilation. Secondly, 4DEnVar assimilation (SOBDA) established, incorporating an observation operator that utilizes relationship between vorticity (PV) lower 400 hPa upper 50 hPa. Finally, several comparative experiments are performed assess influence assimilating and/or data, 4DEnVAR method variational (4D-Var) ensemble size prediction. It found with concurrently brings about significant alterations initial conditions (ICs) vortex, resulting more homogeneous deeper vortex larger, warmer, humid core as opposed only one type data. As duration integration increases, perturbations levels gradually propagate downwards, giving rise disparities prediction when information incorporated. results utilizing approach both leads maximum enhancement reducing track error central SLP simulation throughout entire 72 h period, compared single dataset. Furthermore, have indicated performance SOBDA influenced by size. Generally, selecting appropriate number members can not effectively improve accuracy but also significantly reduce demand computational resources relative 4D-Var method. This serve advantageous technical reference numerical applications products other satellites initialization.