Impact of Model Error Representation in a Hybrid Ensemble-Variational Data Assimilation System for Track Forecast of Tropical Cyclone Hudhud (2014)

Uncertainties in Numerical Weather Prediction (NWP) models are generally not well represented in ensemble based data assimilation (DA) systems. The performance of ensemble DA system becomes suboptimal, if the sources of error are undersampled in the forecast system. The present study explores the effect of accounting model error in hybrid ensemble transform Kalman filter (ETKF) – three dimensional variational (3DVAR) DA system for the track and intensity forecast of a tropical cyclone, Hudhud, formed over Bay of Bengal.  This study investigated the effect of three different model error schemes in the hybrid DA system (i) multiphysics approach which uses different combination of cumulus, microphysics and planetary boundary layer schemes (ii) Stochastic Kinetic Energy Backscatter (SKEB) scheme which stochastically perturbs horizontal wind and potential temperature tendencies (iii) a combination of both multiphysics and SKEB scheme. Explicit model error representation is found to be beneficial in treating underdispersive spread in the ensemble DA system. Significant improvements are noticed in the track forecast of tropical cyclone initialized from a hybrid DA system with explicit model error representation. Among the three different model error schemes used in this study, combination of multiphysics and SKEB schemes has outperformed the other two schemes with improved the track and intensity forecast of the tropical cyclone.