Improving Global Weather and Ocean Wave Forecast with Large Artificial Intelligence Models

TL;DR

This paper reviews the development of large AI models for weather and ocean wave forecasting, highlighting their advantages, challenges, and the potential for integrating them with traditional models for improved accuracy and efficiency.

Contribution

It introduces the 'Three Large Rules' for evaluating AI forecast models and discusses a framework for combining AI with traditional methods for better atmosphere-ocean predictions.

Findings

Large AI models outperform traditional models in accuracy and efficiency.

Integration of AI and numerical models enhances forecast reliability.

Example of a large AI model for global ocean wave prediction provided.

Abstract

The rapid advancement of artificial intelligence technologies, particularly in recent years, has led to the emergence of several large parameter artificial intelligence weather forecast models. These models represent a significant breakthrough, overcoming the limitations of traditional numerical weather prediction models and indicating the emergence of profound potential tools for atmosphere-ocean forecasts. This study explores the evolution of these advanced artificial intelligence forecast models, and based on the identified commonalities, proposes the "Three Large Rules" to measure their development. We discuss the potential of artificial intelligence in revolutionizing numerical weather prediction, and briefly outlining the underlying reasons for its great potential. While acknowledging the high accuracy, computational efficiency, and ease of deployment of large artificial intelligence forecast models, we also emphasize the irreplaceable values of traditional numerical forecasts and explore the challenges in the future development of large-scale artificial intelligence atmosphere-ocean forecast models. We believe that the optimal future of atmosphere-ocean weather forecast lies in achieving a seamless integration of artificial intelligence and traditional numerical models. Such a synthesis is anticipated to offer a more advanced and reliable approach for improved atmosphere-ocean forecasts. Additionally, we illustrate how forecasters can adapt and leverage the advanced artificial intelligence model through an example by building a large artificial intelligence model for global ocean wave forecast.