Impact of background error statistics on forecasting of tropical cyclones over the north Indian Ocean

Abstract

[1] While the quality of background error statistics (BES) is recognized as one of the key components of assimilation, considerable uncertainties exist in prescribing BES, especially since the prescription and impact of BES can also depend on the weather regime and geographical location. In this backdrop, it is necessary to quantify the impact of different BES for particular weather systems; this is particularly true for cyclones over the north Indian Ocean which have characteristics different from those over the Atlantic and the Pacific. The objective of this work is to assess the relative improvement in forecasting tropical cyclone track and intensity due to different BES. We have used global BES (GBES), computed from global model forecasts for 357 cases distributed over a period of one year and regional BES (RBES), generated from short-range forecasts with Weather Research and Forecasting (WRF) model for a 30 day period. From a series of assimilation experiments using the WRF three-dimensional variational (3D-Var) data assimilation system with different BES, and a number of parameters to quantify the impact of BES, it is shown that the use of RBES in WRF 3D-Var significantly improves prediction of track as compared to simulations with no assimilation or GBES. Further, the skill with RBES is comparable with, or better than, many operational skills, although a strict comparison is difficult due to differences in the events and the basins. While parallel and significant efforts are needed for the formulation and incorporation of BES in assimilation systems in general, this study quantifies relative advantages of using RBES in forecasting cyclones over the Indian Ocean.