用CRPS优化轨迹学习，提升集合预报精度。
Trajectory learning for ensemble forecasts via the continuous ranked probability score: a Lorenz '96 case study
- 以CRPS为损失函数，训练随机参数化模型生成预报
- 短时预报精度和尖锐性均优于传统方法
- 适合数据同化应用，易于校准
本文通过将连续排名概率评分（CRPS）作为损失函数，验证了基于轨迹学习的集合预报可行性。以双尺度Lorenz '96系统为例，开发并训练了加性和乘性随机参数化方法生成集合预测。结果表明，基于CRPS的轨迹学习所得到的参数化方案具有高准确性和高尖锐性。该方法参数化过程简单易校准，在短时预报中表现优于基于导数拟合的参数化方案。由于其在短预报时效内的高精度，该方法在数据同化应用中极具前景。
原文摘要 · Abstract (English)
This paper demonstrates the feasibility of trajectory learning for ensemble forecasts by employing the continuous ranked probability score (CRPS) as a loss function. Using the two-scale Lorenz '96 system as a case study, we develop and train both additive and multiplicative stochastic parametrizations to generate ensemble predictions. Results indicate that CRPS-based trajectory learning produces parametrizations that are both accurate and sharp. The resulting parametrizations are straightforward to calibrate and outperform derivative-fitting-based parametrizations in short-term forecasts. This approach is particularly promising for data assimilation applications due to its accuracy over short lead times.
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