基于站点的机器学习天气预报模型在挪威北部的评估
A Station-Based Evaluation of Machine Learning-based Weather Forecasting Models in Northern Norway
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中文总结 AI 辅助
本研究在挪威北部利用站点观测评估了FCN3、GraphCast和HRES的风速预测性能，发现HRES略优，MLWP在训练期外保持稳定，但所有模型低估强风，表明MLWP已具竞争力但仍需改进。
中文摘要 AI 辅助
近期的机器学习天气预报（MLWP）模型在基于全球再分析的基准测试中展现出了显著的预测技能。然而，在挪威北部等具有挑战性的环境中，其性能仍不明确，这些地区狭窄的峡湾和快速变化的天气导致当地风况高度多变。在本案例研究中，我们利用挪威北部的多年站点观测数据，评估了FourCastNet3（FCN3）、GraphCast和ECMWF高分辨率预报（HRES）在风速预测方面的表现，重点关注它们的相对性能、在训练期之外的泛化能力以及在大风条件下的表现。我们的结果表明，HRES略优于FCN3和GraphCast，其总体均方根误差（RMSE）为2.89米/秒，而FCN3为2.96米/秒，GraphCast为2.94米/秒。值得注意的是，MLWP模型在各自训练期之外保持了相当的性能，没有明显退化的清晰证据。FCN3在大风条件下表现最佳，尽管所有模型都大幅低估了强风。我们的研究结果表明，MLWP在局部风预测方面已能与数值天气预报（NWP）相媲美，但仍需进一步改进以更好地捕捉复杂地形的影响。
英文摘要
Recent machine learning weather prediction (MLWP) models have demonstrated remarkable forecasting skill on global reanalysis-based benchmarks. However, their performance remains unclear in challenging environments such as Northern Norway, where narrow fjords and rapidly changing weather result in highly variable local wind conditions. In this case study, we evaluate FourCastNet3 (FCN3), GraphCast, and ECMWF High Resolution Forecast (HRES) for wind speed forecasting using multi-year station observations from Northern Norway, focusing on their relative performance, generalization beyond the training period, and performance under high-wind conditions. Our results show that HRES slightly outperforms FCN3 and GraphCast, with an overall RMSE of 2.89 $\mathrm{m\,s^{-1}}$, compared to 2.96 $\mathrm{m\,s^{-1}}$ for FCN3 and 2.94 $\mathrm{m\,s^{-1}}$ for GraphCast. Notably, the MLWP models maintain comparable performance beyond their respective training periods, with no clear evidence of noticeable degradation. FCN3 performs best under high-wind conditions, although all models substantially underestimate strong winds. Our findings suggest that MLWP has become competitive with NWP for local wind, but further refinements are still needed to capture complex terrain better.
发表机构
- UiT The Arctic University of Norway（挪威北极大学）
- The Norwegian Meteorological Institute（挪威气象研究所）
- Norwegian Computing Center（挪威计算中心）
- University of South Brittany（南布列塔尼大学）
- University of Copenhagen（哥本哈根大学）
机构由 AI 辅助整理，请以论文原文为准。
