Deep learning has gained immense popularity in the Earth sciences as it enables us to formulate purely data-driven models of complex Earth system processes. Deep learning-based weather prediction (DLWP) models have made significant progress in the last few years, achieving forecast skills comparable to established numerical weather prediction models with comparatively lesser computational costs. In order to train accurate, reliable, and tractable DLWP models with several millions of parameters, the model design needs to incorporate suitable inductive biases that encode structural assumptions about the data and the modelled processes. When chosen appropriately, these biases enable faster learning and better generalisation to unseen data. Although inductive biases play a crucial role in successful DLWP models, they are often not stated explicitly and their contribution to model performance remains unclear. Here, we review and analyse the inductive biases of state-of-the-art DLWP models with respect to five key design elements: data selection, learning objective, loss function, architecture, and optimisation method. We identify the most important inductive biases and highlight potential avenues towards more efficient and probabilistic DLWP models.

翻译：深度学习在地球科学领域广受欢迎，因为它使我们能够构建纯粹数据驱动的复杂地球系统过程模型。基于深度学习的天气预报（DLWP）模型在过去几年取得了显著进展，其预测技能可与传统数值天气预报模型相媲美，且计算成本相对较低。为了训练具有数百万参数、准确、可靠且易于处理的DLWP模型，模型设计需要融入合适的归纳偏差，这些偏差编码了关于数据和建模过程的结构性假设。当选择恰当时，这些偏差能够加速学习过程，并提升对未见数据的泛化能力。尽管归纳偏差在成功的DLWP模型中扮演着关键角色，但它们往往未被明确阐述，且对模型性能的贡献仍不清晰。本文针对五个关键设计要素：数据选择、学习目标、损失函数、架构和优化方法，回顾并分析了最先进的DLWP模型中的归纳偏差。我们识别了最重要的归纳偏差，并指出了构建更高效、概率型DLWP模型的潜在途径。