用真实地面观测数据构建天气预报新基准，更准捕捉极端和局部天气。
WeatherReal: A Benchmark Based on In-Situ Observations for Evaluating Weather Models
- 基于全球地面实测数据构建天气预报评估基准
- 实测数据在温、风、降水等变量上比再分析数据更贴近真实
- 适合关注实际应用与极端天气预测的研究者使用
近年来，基于人工智能的天气预报模型已达到甚至超越传统数值天气预报系统的水平。然而，这些模型大多在如ERA5这样的再分析数据集上训练和评估，而这些数据本身是数值模型的产物，在近地表温度、风速、降水和云量等关键变量上常与真实观测存在显著差异，而这些变量正是公众高度关注的内容。为解决这一偏差，我们提出了WeatherReal，一个基于全球近地表原位观测的新型天气预报基准数据集，并配套公开的质量控制与评估框架。本文详细说明了数据来源与处理方法，通过对比分析与案例研究，展示了原位观测在捕捉超本地化及极端天气方面的优势。利用WeatherReal，我们评估了多个数据驱动模型，并与领先的数值模型进行了比较。本研究旨在推动基于AI的天气预报研究向更注重应用与业务可用性的方向发展。
原文摘要 · Abstract (English)
In recent years, AI-based weather forecasting models have matched or even outperformed numerical weather prediction systems. However, most of these models have been trained and evaluated on reanalysis datasets like ERA5. These datasets, being products of numerical models, often diverge substantially from actual observations in some crucial variables like near-surface temperature, wind, precipitation and clouds - parameters that hold significant public interest. To address this divergence, we introduce WeatherReal, a novel benchmark dataset for weather forecasting, derived from global near-surface in-situ observations. WeatherReal also features a publicly accessible quality control and evaluation framework. This paper details the sources and processing methodologies underlying the dataset, and further illustrates the advantage of in-situ observations in capturing hyper-local and extreme weather through comparative analyses and case studies. Using WeatherReal, we evaluated several data-driven models and compared them with leading numerical models. Our work aims to advance the AI-based weather forecasting research towards a more application-focused and operation-ready approach.
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