Artificial intelligence in forecasting central pressure drop and maximum sustained wind speed of cyclonic systems over Arabian Sea: skill comparison with conventional models


An attempt is made in this study to forecast the central pressure drop (PD) and maximum sustained wind speed (MSWS) associated with cyclonic systems at the stage of the highest intensity over Arabian Sea using artificial neural network models. The cyclonic systems include the phases from deep depression to extreme severe cyclones. The sea surface temperature, mid-tropospheric relative humidity, surface to middle tropospheric equivalent potential temperature gradient, inverse of wind shear and vertical wind velocity at 200 hPa level are evaluated as the most suitable predictors through principal component analysis. Various neural network models with different architectures have been trained with the data from 1990 to 2012 to select the best forecast model. The prediction skill of the intelligent models is evaluated by different accuracy measures. The results show that the multi-layer perceptron (MLP) model with five input layers, one hidden layer with four nodes and one output layer is the best model for forecasting PD with minimum prediction error of 0.14 at 36 h lead time, whereas the MLP model with five input layers, one hidden layer with five nodes and one output layer is found to be the best model for forecasting MSWS with minimum prediction error of 0.19 at 48 h (h) lead time. The results are well validated with the observations from 2013 to 2018. The forecast skill of MLP model is compared with multiple linear regression model and existing operational and numerical weather prediction models.

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Artificial neural network


Arabian Sea


Bay of Bengal


Box Whisker


Cyclonic storm


Deep depression


Equivalent potential temperature difference


Extreme severe cyclonic storm


Generalized regression neural network


Indian Meteorological Department


Inter-quartile range


Inverse of wind shear




Low-level vorticity


Mean absolute error


Multilayer Perceptron


Multiple linear regression


Mid-tropospheric relative humidity


Maximum Sustained Wind Speed


North Indian Ocean


Principal component


Principal component analysis


Pressure Drop


Prediction error


Potential intensity


Radial basis function network


Root mean-square error


Severe cyclonic storm


Standard deviation


Sea surface temperature


Tropical cyclone


Very severe cyclonic storm


Weather Research and Forecasting


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The corresponding author acknowledges the Space Application Centre, ISRO for providing the opportunity to work for SCATSAT 1 Application.

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Correspondence to Sutapa Chaudhuri.

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Sarkar, I., Chaudhuri, S. & Pal, J. Artificial intelligence in forecasting central pressure drop and maximum sustained wind speed of cyclonic systems over Arabian Sea: skill comparison with conventional models. Meteorol Atmos Phys (2021).

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