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.

该论文回顾了大型模型，尤其是采用深度学习技术的模型在天气预报中的进展和应用，强调它们在改善传统预报方法方面的作用。这些模型利用卷积神经网络（CNN）、图神经网络（GNN）和 Transformer 等先进的神经网络架构来处理各种气象数据，提高了各种时间尺度和空间分辨率上的预测准确性。论文还讨论了该领域的挑战，包括数据获取和计算需求，并探索了模型优化和硬件进展的未来机会。它强调了人工智能与传统气象技术的整合，承诺提高天气预报准确性，并在应对与气候相关的挑战方面做出重要贡献。这种协同作用将大型模型置于气象预报不断发展的前沿地位。

未来技术对未来的预测：大型气象模型的进展

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

This work addresses the challenge of short-term precipitation forecasting by
applying Convolutional Long Short-Term Memory (ConvLSTM) neural networks to
weather radar data from the Royal Netherlands Meteorological Institute (KNMI).
The research exploits the combination of Convolutional Neural Networks (CNNs)
layers for spatial pattern recognition and LSTM network layers for modelling
temporal sequences, integrating these strengths into a ConvLSTM architecture.
The model was trained and validated on weather radar data from the Netherlands.
The model is an autoencoder consisting of nine layers, uniquely combining
convolutional operations with LSTMs temporal processing, enabling it to capture
the movement and intensity of precipitation systems. The training set comprised
of sequences of radar images, with the model being tasked to predict
precipitation patterns 1.5 hours ahead using the preceding data. Results
indicate high accuracy in predicting the direction and intensity of
precipitation movements. The findings of this study underscore the significant
potential of ConvLSTM networks in meteorological forecasting, particularly in
regions with complex weather patterns. It contributes to the field by offering
a more accurate, data-driven approach to weather prediction, highlighting the
broader applicability of ConvLSTM networks in meteorological tasks.

此研究利用 ConvLSTM 神经网络将天气雷达数据应用于短期降水预测，通过卷积神经网络层进行空间模式识别和 LSTM 网络层进行时间序列建模，构建了一个包含九层的自动编码器模型。结果表明，ConvLSTM 网络在气象预测中有着高准确性，尤其在天气复杂的地区具有显著潜力，为气象任务提供了一种更准确、数据驱动的方法。