Forecasting the Future with Future Technologies: Advancements in Large
  Meteorological Models
Hailong Shu Yue Wang Weiwei Song Huichuang Guo Zhen Song
The field of meteorological forecasting has undergone a significant
transformation with the integration of large models, especially those employing
deep learning techniques. This paper reviews the advancements and applications
of these models in weather prediction, emphasizing their role in transforming
traditional forecasting methods. Models like FourCastNet, Pangu-Weather,
GraphCast, ClimaX, and FengWu have made notable contributions by providing
accurate, high-resolution forecasts, surpassing the capabilities of traditional
Numerical Weather Prediction (NWP) models. These models utilize advanced neural
network architectures, such as Convolutional Neural Networks (CNNs), Graph
Neural Networks (GNNs), and Transformers, to process diverse meteorological
data, enhancing predictive accuracy across various time scales and spatial
resolutions. The paper addresses challenges in this domain, including data
acquisition and computational demands, and explores future opportunities for
model optimization and hardware advancements. It underscores the integration of
artificial intelligence with conventional meteorological techniques, promising
improved weather prediction accuracy and a significant contribution to
addressing climate-related challenges. This synergy positions large models as
pivotal in the evolving landscape of meteorological forecasting.