AI 中文总结
研究比较全球天气预报模型AIFS - CRPS不同版本，用不同单变量和多变量评分规则训练以明确尺度感知。通过对比不同评分规则，发现多变量评分是基于CRPS训练的可行替代，且明确尺度感知能提高预测场逼真度。
AI 中文摘要
概率预测模型可通过基于评分规则（如连续排序概率评分（CRPS））的损失函数从数据中进行机器学习。本笔记总结了一项初步研究，比较了全球天气预报模型AIFS - CRPS的不同版本，这些版本使用不同单变量和多变量评分规则训练，旨在在损失函数中明确表示尺度感知。第一部分比较了（几乎）公平CRPS、公平全局能量评分和基于节点邻域的图能量评分，跨标准验证指标，预测技能大致相似，在热带地区图能量评分设置表现稍好，全局能量评分有所下降。第二部分分析不同评分规则和尺度感知损失约束如何塑造预测场的频谱，任何形式的明确尺度感知都能提高逼真度。
英文摘要
Probabilistic forecast models can be machine-learned from data using loss functions based on scoring rules such as the Continuous Ranked Probability Score (CRPS). This note summarises a preliminary study comparing versions of AIFS-CRPS, a global weather forecast model, trained with different univariate and multivariate scoring rules that aim to explicitly represent scale-awareness in the loss function. In the first part, we compare the (almost) fair CRPS, a fair global energy score, and a graph energy score based on node neighbourhoods. Across standard verification metrics, forecast skill is broadly similar. In the extratropics we find only small differences, while in the tropics the graph energy score setup performs somewhat better and the global energy score shows some degradation. These results suggest that multivariate scores are a viable alternative to CRPS-based training for global machine-learned weather forecasting. In the second part of the study, we analyse how different scoring rules and scale-aware loss constraints shape the spectra of forecast fields. It is apparent that any form of explicit scale-awareness improves realism. Here, the largest differences are likely associated with different effective weights per scale.
