什么是现实的预测？评估数据驱动的天气预报，从验证到证伪的旅程
"What is a realistic forecast?" Assessing data-driven weather forecasts, a journey from verification to falsification
AI总结：
本文针对数据驱动天气预报的“现实性”问题，提出功能、结构和物理三种现实性定义，并建立基于证伪的评估框架以补充传统验证方法。
摘要：
人工智能革命正在推动天气预报的范式转变：预测是通过在大型数据集上训练的机器学习模型生成的，而不是通过求解偏微分方程的基于物理的数值模型。这种新方法在提高预测性能方面取得了成功，如均方根误差等标准验证指标所衡量的那样。同时，数据驱动天气预报的现实性经常受到质疑，并被认为是机器学习模型的致命弱点。如何定义预测现实性以及如何评估这一预测属性是本文同时解决的两个问题。受Murphy（1993）关于预测优度定义的启发，我们确定了三种现实性：由评分函数衡量的功能现实性、与预测统计特征相关的结构现实性，以及通过科学知识视角理解的物理现实性。这一概念框架为设计数据驱动天气模型的新评估方法奠定了基础，其中证伪作为已建立的诊断和验证任务的补充过程出现。
英文摘要：
The artificial intelligence revolution is fuelling a paradigm shift in weather forecasting: forecasts are generated with machine learning models trained on large datasets rather than with physics-based numerical models that solve partial differential equations. This new approach proved successful in improving forecast performance as measured with standard verification metrics such as the root mean squared error. At the same time, the realism of data-driven weather forecasts is often questioned and considered an Achilles' heel of machine learning models. How forecast realism can be defined and how this forecast attribute can be assessed are the two questions simultaneously addressed here. Inspired by the seminal work of Murphy (1993) on the definition of forecast goodness, we identify 3 types of realism: a functional realism measured by scoring functions, a structural realism related to the statistical characteristics of the forecasts, and a physical realism that is apprehended through the lenses of our scientific knowledge. This conceptual setting serves as a basis for the design of a new framework for the evaluation of data-driven weather models where falsification arises as a complementary process to the well-established diagnostic and verification tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
