Forecasting the Future with Future Technologies: Advancements in Large Meteorological Models

TL;DR

This paper reviews recent advancements in large deep learning models for meteorological forecasting, highlighting their improved accuracy, novel neural architectures, and potential to transform traditional weather prediction methods.

Contribution

It provides a comprehensive overview of state-of-the-art large models like FourCastNet and GraphCast, emphasizing their innovative neural architectures and applications in weather prediction.

Findings

Large models outperform traditional NWP in accuracy

Neural architectures like CNNs, GNNs, Transformers enhance predictions

Challenges include data acquisition and computational costs

Abstract

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.