  Modern deep learning techniques, which mimic traditional numerical weather
prediction (NWP) models and are derived from global atmospheric reanalysis
data, have caused a significant revolution within a few years. In this new
paradigm, our research introduces a novel strategy that deviates from the
common dependence on high-resolution data, which is often constrained by
computational resources, and instead utilizes low-resolution data (2.5 degrees)
for global weather prediction and climate data analysis. Our main focus is
evaluating data-driven weather prediction (DDWP) frameworks, specifically
addressing sample size adequacy, structural improvements to the model, and the
ability of climate data to represent current climatic trends. By using the
Adaptive Fourier Neural Operator (AFNO) model via FourCastNet and a proposed
time-sliding method to inflate the dataset of the ECMWF Reanalysis v5 (ERA5),
this paper improves on conventional approaches by adding more variables and a
novel approach to data augmentation and processing. Our findings reveal that
despite the lower resolution, the proposed approach demonstrates considerable
accuracy in predicting atmospheric conditions, effectively rivaling
higher-resolution models. Furthermore, the study confirms the model's
proficiency in reflecting current climate trends and its potential in
predicting future climatic events, underscoring its utility in climate change
strategies. This research marks a pivotal step in the realm of meteorological
forecasting, showcasing the feasibility of lower-resolution data in producing
reliable predictions and opening avenues for more accessible and inclusive
climate modeling. The insights gleaned from this study not only contribute to
the advancement of climate science but also lay the groundwork for future
innovations in the field.
