Genesis and track prediction of pre-monsoon cyclonic storms over North Indian Ocean in a multi-model ensemble framework

Abstract

An attempt has been made in this study to evaluate the performance of a multi-model ensemble prediction system (MMEPS) in the extended range prediction of genesis and track of cyclonic storms (CS) over North Indian Ocean (NIO) during the onset phase of Indian summer monsoon. The MMEPS comprises of National Centres for Environmental Prediction Climate Forecast System version 2 and its atmospheric component, Global Forecast System version 2, at two different resolutions. In this study, we analyse the performance of this MMEPS in the prediction of six CS cases formed around the onset phase of the Indian summer monsoon over NIO. Along with this, we evaluate the usefulness of the genesis potential parameter used by India Meteorological Department (IMD) for the real-time cyclogenesis prediction. ERA-Interim re-analysis data sets are used to compare MME predictions from two initial conditions (IC) close to the genesis date of each storm. Results show that genesis forecasts from nearest available IC is capturing the vorticity and associated features of the storm better than far-IC. A modified version of vortex tracking scheme developed by Geophysical Fluid Dynamics Laboratory is used to obtain the mean track positions from MME outputs and compare with IMD-best tracks. Track verification is done by calculating average direct position error, cross-track error and along-track error against corresponding IMD-best tracks. It is found that track predictions from both near and far ICs have lower average errors initially with near-IC having lesser errors as compared to far-IC. Along-track error for pre-genesis track prediction from far-IC implies that the predicted tracks remarkably lag behind observed tracks for most of the cases. These errors could be contributed from the inability of the models in realistically capturing the region of evolution and intensification of both thermodynamic and dynamic parameters with increasing lead time.