发表机构
University of Ljubljana; ETH Zurich（卢布尔雅那大学; 苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个首创的一维理想化概率数据集，用于分析和比较概率预报验证方法，涵盖多种案例并支持定制，作为弥合差距项目的基础模块。
AI 中文摘要
概率天气预报的验证仍然是数值天气预报的关键方面，因为随着基于人工智能的新模型与持续开发和改进的传统基于物理的集合预报系统一起更广泛地使用，这一验证显得尤为重要。我们提出了一个首创的理想化概率数据集，由一维案例组成，旨在分析概率预报验证方法的行为和性质，并比较它们的行为。该数据集涵盖了广泛概率案例，如恒定事件、局地事件、梯度、锋面、噪声、双峰和极限情况。此外，与数据集相关的代码为定制其涵盖的实验提供了极大的灵活性。该数据集是“弥合差距”项目中更广泛比较数据集的第一个构建模块，该项目旨在促进概率预报空间验证方法的开发和比较。
英文摘要
Verification of probabilistic weather forecasts remains a crucial aspect of numerical weather prediction, as new AI-based models become more widely used alongside the more traditional physics-based ensemble forecasting systems that continue to be developed and improved. We present a first-of-its-kind idealized probabilistic dataset composed of one-dimensional cases aimed at analyzing the behavior and properties of verification methods for probabilistic forecasts and comparing their behavior. It covers a wide range of probabilistic cases, such as constant, localized events, gradients, fronts, noisy, bimodal, and limiting cases. Moreover, the code associated with the dataset provides great flexibility for customizing the experiments it covers. The dataset represents the first building block of the more extensive comparison dataset of the Bridging The Gap project, which aims to facilitate the development and comparison of spatial verification methods for probabilistic forecasts.
