Forecasting pressure drop and maximum sustained wind speed associated with cyclonic systems over Bay of Bengal with neuro-computing

Abstract

The current research anticipates developing a model based on adaptive neuro-computation to foresee the minimum pressure drop (PD) at the centre as well as the maximum sustained wind speed (MSWS) accompanying with cyclonic systems over Bay of Bengal (BOB). The cyclonic systems taken in this work contain systems of different ranges starting from deep depression to extreme severe cyclones. For predicting PD and MSWS, suitable predictors have been sorted using factor analysis and it is observed that low-level vorticity (LLV), mid-tropospheric relative humidity (MRH) and vertical wind velocity at 850, 500 and 200 hPa pressure levels are appropriate parameters to create input matrix of neural network (NN). The adaptive NN representations are skilled with the data from 1990 to 2015 to estimate the PD as well as MSWS over BOB for 47 cyclonic systems. The outcome divulges that the multi-layer perceptron (MLP) NN model delivers decent precision at 6- and 30-h lead time in foretelling the PD. But the lowest error has been found at 6-h lead time in forecasting the central PD during mature stage of cyclonic systems. The result also illustrates that the MLP model is the utmost capable in forecasting the MSWS during mature stage of cyclonic structures with the lowest prediction error at 60-h lead time. The model results were validated and compared with the operational forecast by IMD for the 10 cyclonic systems from 2016 to 2019.