用拉伸网格实现高分辨率气象概率预报，精准捕捉北欧天气变化。
High-Resolution Probabilistic Data-Driven Weather Modeling with a Stretched-Grid
- 采用拉伸网格，北欧区2.5公里分辨率，全球其余31公里，6小时更新。
- 在温度和海平面气压上比MEPS系统平均提升13%和10%的预报精度。
- 通过谱空间CRPS损失保证场的空间一致性，适合气候研究与灾害预警。
我们提出一种概率性数据驱动的气象模型，可生成任意数量和预报时长的87个变量高空间分辨率实况集合。该模型使用全局拉伸网格，在北欧区域提供2.5公里分辨率，其余地区为31公里，时间分辨率为6小时。通过随机模型架构生成独立的集合成员，并采用基于连续排名概率评分（CRPS）的损失函数，在格点与谱空间同时优化。结果表明，谱空间损失对生成空间一致性的场至关重要，仅用均方误差或格点空间的CRPS训练则无法实现。模型在地面气象站观测数据上评估，与高分辨率操作数值预报系统MEPS进行对比。对于2米气温和平均海平面气压，模型的CRPS低于MEPS，平均改进分别为13%和10%；风速和降水差异较小。针对风暴Dave，模型成功捕捉强风系统的结构与位置，但低估了峰值风速。
原文摘要 · Abstract (English)
We present a probabilistic data-driven weather model providing ensembles of high spatial resolution realizations of 87 variables at arbitrary ensemble size and forecast length. The model uses a global stretched grid, dedicating 2.5 km resolution to our Nordic region of interest and 31 km resolution elsewhere, with 6-hour temporal resolution. Unique ensemble members are generated by a stochastic model architecture, and we train it using a loss function based on the Continuous Ranked Probability Score (CRPS) evaluated in grid-point and spectral space. The spectral loss component is shown to be necessary to create fields that are spatially coherent, which is not the case when training with mean-squared error loss, nor CRPS in grid-point space only. We evaluate the forecasts against observations from surface weather stations and compare them to high-resolution operational numerical weather prediction forecasts from the MetCoOp Ensemble Prediction System (MEPS). The model shows lower CRPS than MEPS for 2 m temperature and mean sea-level pressure, with average improvements of 13\% and 10\%, respectively, while differences for wind speed and precipitation are smaller. For Storm Dave, the model captures the location and structure of strong-wind systems, but underestimates the peak winds.
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