Prediction of Convective Available Potential Energy and Equivalent Potential Temperature using a Coupled WRF and Deep Learning for Typhoon Identification

Abstract To predict typhoons in the western North Pacific Ocean, it is required to predict the determinants of typhoon activities. The formation of the typhoon can be controlled by Convective Available Potential Energy (CAPE) and Equivalent Potential Temperature (theta-e). To predict the variables, a mesoscale numerical model of Weather Research and Forecasting (WRF) can be used. However, the output of WRF needs to improve to obtain a more accurate CAPE and theta-e prediction. This study uses a coupled WRF model and Deep Learning (DL) Multilayer Perceptron Regressor approach to increase CAPE and theta-e prediction skills. Simulation with dataset scenarios with WRF outputs as predictors and sounding data as predictors are developed and tested to obtain the most appropriate package of deep learning simulation. The study found that coupled models provide increased mean accuracy of theta-e and CAPE, namely 16.6% and 32.0% higher than using original WRF, respectively. This study also shows the difference of skill scores in the spatial distribution of CAPE and theta-e of WRF result and its coupled model.