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

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Abstract

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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Abbreviations

ANN:

Artificial neural network

AS:

Arabian Sea

BOB:

Bay of Bengal

BW:

Box Whisker

CS:

Cyclonic storm

DD:

Deep depression

EPTD:

Equivalent potential temperature difference

ESCS:

Extreme severe cyclonic storm

GRNN:

Generalized regression neural network

IMD:

Indian Meteorological Department

IQR:

Inter-quartile range

IWS:

Inverse of wind shear

K:

Kelvin

LLV:

Low-level vorticity

MAE:

Mean absolute error

MLP:

Multilayer Perceptron

MLR:

Multiple linear regression

MRH:

Mid-tropospheric relative humidity

MSWS:

Maximum Sustained Wind Speed

NIO:

North Indian Ocean

PC:

Principal component

PCA:

Principal component analysis

PD:

Pressure Drop

PE:

Prediction error

PI:

Potential intensity

RBFN:

Radial basis function network

RMSE:

Root mean-square error

SCS:

Severe cyclonic storm

SD:

Standard deviation

SST:

Sea surface temperature

TC:

Tropical cyclone

VSCS:

Very severe cyclonic storm

WRF:

Weather Research and Forecasting

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Acknowledgements

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

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Authors and Affiliations

  1. Department of Atmospheric Sciences, University of Calcutta, Kolkata, 700019, India

    Ishita Sarkar, Sutapa Chaudhuri & Jayanti Pal

  2. Department of Atmospheric Sciences, Central University of Rajasthan, Ajmer, Rajasthan, 305817, India

    Jayanti Pal

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  1. Ishita Sarkar
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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 133, 803–822 (2021). https://doi.org/10.1007/s00703-021-00777-2

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