Traditional weather forecasting relies on domain expertise and computationally intensive numerical simulation systems. Recently, with the development of a data-driven approach, weather forecasting based on deep learning has been receiving attention. Deep learning-based weather forecasting has made stunning progress, from various backbone studies using CNN, RNN, and Transformer to training strategies using weather observations datasets with auxiliary inputs. All of this progress has contributed to the field of weather forecasting; however, many elements and complex structures of deep learning models prevent us from reaching physical interpretations. This paper proposes a SImple baseline with a spatiotemporal context Aggregation Network (SIANet) that achieved state-of-the-art in 4 parts of 5 benchmarks of W4C22. This simple but efficient structure uses only satellite images and CNNs in an end-to-end fashion without using a multi-model ensemble or fine-tuning. This simplicity of SIANet can be used as a solid baseline that can be easily applied in weather forecasting using deep learning.
翻译:最近,随着数据驱动方法的开发,基于深层次学习的天气预报一直受到关注。深层次的基于学习的天气预报取得了惊人的进展,从使用CNN、RNN和变异器进行的各种主干研究到使用气象观测数据集和辅助投入的培训战略,所有这些进展都有助于天气预报领域;然而,许多要素和深层次学习模型的复杂结构使我们无法达到物理解释。本文建议采用一个SIMPle基线,并配有简易时空环境聚合网络(SIANet),在W4C22的5个基准的4个部分达到最新水平。这一简单而有效的结构仅使用卫星图像和CNN,在终端到终端时使用,而不使用多模型的合用或微调。可以将SIANet的这种简单性作为可靠的基准,通过深层学习很容易用于天气预报。