Multivariate background error covariances in the assimilation of SAPHIR radiances in the simulation of three tropical cyclones over the Bay of Bengal using the WRF model

ABSTRACT

Assimilation of satellite observations in numerical weather models has helped in improving the prediction of tropical cyclones (TCs) in recent times. Utilizing observations from microwave sensors such as MeghaTropiques SAPHIR (Sounder for Probing Vertical Profiles of Humidity) which samples the tropical atmosphere more frequently can further improve the quality of analysis obtained. Further improved estimates of the atmospheric system can be obtained through the specification of better estimates of background error covariances (BECs or $\mathbf{B}$) in a variational system. An investigation on the impact of multivariate analysis of humidity variable on the simulation of three TCs over the Bay of Bengal region is presented here. Assimilation of SAPHIR radiances does improve the cyclone simulation in the Weather Research and Forecasting (WRF) model. The study indicates that utilizing the multivariate BEC does impact the simulation of TC features. The results of the three cyclones considered here indicate that inclusion of multivariate BEC leads to intensification of the TCs in terms on minimum sea level pressure as well as maximum wind speed. The rainfall simulated by the model for the three cyclones is also positively impacted by the use of multivariate BEC in WRF three-dimensional variational system.

Acknowledgements

The authors acknowledge the National Centre of Medium Range Weather Forecasting (NCMRWF), India for the SAPHIR radiance data. The authors also acknowledge National Center for Atmospheric Research (NCAR) for the WRF model and the WRF 3DVAR module, NASA for TRMM rainfall data, IMD and JTWC for the tropical cyclone observations. The authors also wish to acknowledge Dr V.S. Prasad, NCMRWF, India for his help and support.

Disclosure statement

No potential conflict of interest was reported by the authors.