Seasonal forecasting of tropical cyclones over the Bay of Bengal using a hybrid statistical/dynamical model. (26th April 2022)

• Record Type: Journal Article
• Authors: Sabeerali, C. T.; Sreejith, O. P.; Acharya, Nachiketa; Surendran, Divya E.; Pai, D. S.
• Is Part Of: International journal of climatology. Volume 42:Number 14(2022)
• Page Start: 7383
• Page End: 7396
• Publication Date: 2022-04-26
• Subjects: CFSv2 -- PCR model -- seasonal prediction -- tropical cyclone; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.7651
• Languages: English
• ISSNs: 0899-8418

Abstract

Abstract: The post‐monsoon (October–November–December) tropical cyclone (TC) over the Bay of Bengal is one of the most devastating natural disasters causing economic and human losses over India and its neighbouring countries. This study discusses a hybrid statistical/dynamical model developed to forecast the post‐monsoon cyclone activities over the Bay of Bengal, where 80% of the TCs of the North Indian Ocean are originated. In the hybrid model, the coupled model CFSv2 predicts the large‐scale climate indices, and the principal component regression (PCR) model is used to relate these indices with the TC frequency. A solid concurrent relation between the cyclonic disturbance frequencies and various large‐scale variables is noted. The dynamical variable, for example, the zonal wind, acts as a precursor variable. We identified three concurrent predictors (ocean heat content over the Bay of Bengal, sea surface temperature (SST) over the Indian Ocean, and SST over the tropical central Pacific regions) and two precursor predictors (low‐level wind at equatorial Indian ocean and strength of upper‐level easterly jet over African coast) influencing the cyclonic disturbance frequencies over the Bay of Bengal. The concurrent predictors are calculated from the CFSv2 hindcast/forecast output and the precursor predictors are calculated from the reanalysis data. The predictors influencing the cyclonic disturbance over the Bay of Bengal are also influencing the cyclonic storms. Hence, the same predictors are used for developing a hybrid model for cyclonic disturbance and storm frequencies. A significant inter‐correlation among different predictors is observed and the PCR model avoids these inter‐correlations and, in this method, PCs are estimated on the predictors to make them orthogonal to each other. The hybrid model achieved a significant skill for seasonal cyclone forecast over the Bay of Bengal. Results suggest the potential for using the hybrid model for the operational seasonal forecasting of post‐monsoon cyclone activity over the Bay of Bengal. Abstract : (a) Verification of forecasted cyclonic disturbance frequency over the Bay of Bengal during post‐monsoon season by the hybrid model along with corresponding observed cyclonic disturbance frequency over the Bay of Bengal for the training period (1982–2016). (b) Same as (a) but for cyclonic storm frequencies. The hybrid model achieved a significant skill for seasonal cyclone forecast over the Bay of Bengal.