多尺度损失提升天气模型对小尺度变化的捕捉能力。
A multi-scale loss formulation for learning a probabilistic model with proper score optimisation
- 采用多尺度损失函数优化概率预报模型
- 在不降低预报精度前提下增强小尺度细节建模
- 适合关注高分辨率气象模拟的研究者
我们评估了多尺度损失函数在训练概率性机器学习天气预报模型中的影响。该方法在欧洲中期天气预报中心（ECMWF）开发的AIFS-CRPS模型中进行了测试，该模型通过直接优化几乎公平连续排名概率评分（afCRPS）进行训练。结果表明，多尺度损失能在不损害预报技能的前提下，更有效地约束小尺度变异性，为未来尺度感知的模型训练提供了有前景的方向。
原文摘要 · Abstract (English)
We assess the impact of a multi-scale loss formulation for training probabilistic machine-learned weather forecasting models. The multi-scale loss is tested in AIFS-CRPS, a machine-learned weather forecasting model developed at the European Centre for Medium-Range Weather Forecasts (ECMWF). AIFS-CRPS is trained by directly optimising the almost fair continuous ranked probability score (afCRPS). The multi-scale loss better constrains small scale variability without negatively impacting forecast skill. This opens up promising directions for future work in scale-aware model training.
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