Weather forecasting has attracted increasing attention as climate change brought more heat waves, tropical cyclones, heavy rainfalls and other extreme weather events, affecting millions of people worldwide [1] [2]. Researchers have devised sophisticated numerical schemes and equations to capture complex weather dynamics to improve forecast accuracy[3]. However, its immense computational complexity often necessitates the use of large scale compute clusters, incurring notable energy cost and long processing time, making ensemble forecasts, critical for predicting such events[4], expensive or infeasible. Trained on European Centre for Medium-range Weather Forecast (ECMWF)'s reanalysis product ECMWF Reanalysis v5 (ERA5) dataset, deep learning based weather prediction (DLWP) models have shown promising performance, with FourCastNet[5] being the first data-driven model that directly competes with ECMWF's Integrate Forecasting System (IFS), followed by a series of other models[6][7][8][9] that outperforms IFS in various metrics [10], these models demonstrates excellent ability in capturing global weather trends, with a fraction of the computation requirement.