AI- Enhanced Dynamic System Analysis: Modified Deep Learning for Global Weather Prediction

Dr. Rajendra Singh

Vol. 8, Issue 1, Jan-Dec 2022

Page Number: 118 - 126

Abstract:

Deep learning technology, specifically DLWP-CS, has been proposed for weather prediction using cubed spheres in data-driven simulations of global weather fields. For basic fields like temperature and geopotential height, DLWP-CS performs admirably, but for complex, non-linear fields like precipitation, it is computationally demanding. Precipitation precursors are the input for the modified DLWP-CS (MDLWP-CS) technique, which changes the architecture from temporal to spatio-temporal mapping. The technique predicts precipitation using a 2-m surface air temperature as a proof of concept. In comparison to the GFS output with a one-day lag, the hourly ERA-5 reanalysis used to train the MDLWP-CS model outperforms both linear regression and the Global Forecast System (GFS) in daily precipitation prediction with a one-day lag. This provides an effective DT framework for quick, high-fidelity precipitation predictions.

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