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Assimilating INSAT‐3D Thermal Infrared Window Imager Observation With the Particle Filter: A Case Study for Vardah Cyclone
Author(s) -
Kumar Prashant,
Shukla Munn V.
Publication year - 2019
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1029/2018jd028827
Subject(s) - data assimilation , environmental science , meteorology , geostationary orbit , remote sensing , particle filter , atmospheric infrared sounder , satellite , filter (signal processing) , physics , computer science , troposphere , geography , astronomy , computer vision
A big challenge in the satellite data assimilation is the effective use of InfraRed (IR) window channel radiances in the high‐resolution weather model. A hybrid data‐assimilation method is used in this study for very severe cyclonic storm “Vardah,” in which three‐dimensional variational method is used to assimilate control observations, and particle filter method is used to assimilate Indian geostationary satellite INSAT‐3D data. In the context of imperfect weather model, various particles (or ensembles) are prepared with different combinations of model physics. To implement particle filter, INSAT‐3D thermal IR window channel 1 (TIR‐1; center wavelength 11 μm) measured brightness temperature (BT) and cloud mask product are used to assign appropriate weights to different particles to reduce model uncertainties. This step is followed by resampling step in which new particles are generated from high weight particles using stochastic kinetic‐energy backscatter scheme method and dynamical variables are perturbed into the model physics. Results suggest that simulated TIR‐1 BT analysis and forecasts from WPF (with INSAT‐3D data using particle filter experiments) are closer to INSAT‐3D measured TIR‐1 BT in comparison to WCNT (without INSAT‐3D data using particle filter) experiments. Furthermore, approximately 10% to 50% improvements are found in mean track error forecasts in WPF experiments. An improvement of ~10% is noticed in cyclone center position in analysis. Prediction of storm intensity is also improved after assimilation. Results also suggest that vertical structure of WPF simulated humidity, temperature, wind speed, and surface pressure is improved over WCNT runs.