Determining the sensitive parameters of WRF model for the prediction of Tropical cyclones in Bay of Bengal using Global sensitivity analysis and Machine learning

TL;DR

This study identifies the most influential WRF model parameters for tropical cyclone prediction in the Bay of Bengal using global sensitivity analysis and machine learning, leading to improved forecast accuracy.

Contribution

It applies three global sensitivity analysis methods to determine key parameters, and optimizes them to enhance WRF model predictions for tropical cyclones.

Findings

Eight parameters contribute 80-90% to sensitivity scores.

Sobol' method with Gaussian process regression is reliable with over 200 samples.

Optimal parameters improve wind prediction accuracy by 19.65%.

Abstract

The present study focuses on identifying the parameters from the Weather Research and Forecasting (WRF) model that strongly influence the prediction of tropical cyclones over the Bay of Bengal (BoB) region. Three global sensitivity analysis (SA) methods namely the Morris One-at-A-Time (MOAT), Multivariate Adaptive Regression Splines (MARS), and surrogate-based Sobol' are employed to identify the most sensitive parameters out of 24 tunable parameters corresponding to seven parameterization schemes of the WRF model. Ten tropical cyclones across different categories, such as cyclonic storms, severe cyclonic storms, and very severe cyclonic storms over BoB between 2011 and 2018, are selected in this study. The sensitivity scores of 24 parameters are evaluated for eight meteorological variables. The parameter sensitivity results are consistent across three SA methods for all the variables, and 8 out of the 24 parameters contribute 80%-90% to the overall sensitivity scores. It is found that the Sobol' method with Gaussian progress regression as a surrogate model can produce reliable sensitivity results when the available samples exceed 200. The parameters with which the model simulations have the least RMSE values when compared with the observations are considered as the optimal parameters. Comparing observations and model simulations with the default and optimal parameters shows that predictions with the optimal set of parameters yield a 19.65% improvement in surface wind, 6.5% in surface temperature, and 13.2% in precipitation predictions, compared to the default set of parameters.