用于多变量集合后处理的生成式机器学习方法
Generative machine learning methods for multivariate ensemble post-processing
AI总结：
针对传统多变量集合天气预报后处理两步法难以纳入额外预测变量的局限，提出基于生成式神经网络的非参数后处理方法，经德国气温与风速预报案例验证，其性能优于现有方法且提升了空间依赖表征效果。
摘要：
基于数值天气预报模型多次运行的集合天气预报通常存在系统误差，需要进行后处理以获得可靠的预报结果。在许多实际应用中，准确建模多变量依赖关系至关重要，目前已提出多种多变量后处理方法：这类方法先对每个边缘分布的集合预测分别进行后处理，再通过copula（连接函数）恢复多变量依赖关系。这些两步法存在共同的关键局限性，尤其是难以在依赖关系建模中纳入额外预测变量。我们提出一种基于生成式机器学习的新型多变量后处理方法以应对这些挑战。在这类新型非参数数据驱动分布回归模型中，多变量预报分布的样本可直接作为生成神经网络的输出获得。该生成模型通过优化一个恰当评分规则进行训练，该规则用于衡量在外生输入变量条件下，生成数据与观测数据之间的差异。我们的方法无需对单变量分布或多变量依赖关系做参数假设，且支持纳入任意预测变量。在两项针对德国境内气象站多变量气温和风速预报的案例研究中，我们的生成模型相比现有最优方法取得了显著提升，尤其改善了空间依赖关系的表征效果。
英文摘要：
Ensemble weather forecasts based on multiple runs of numerical weather prediction models typically show systematic errors and require post-processing to obtain reliable forecasts. Accurately modeling multivariate dependencies is crucial in many practical applications, and various approaches to multivariate post-processing have been proposed where ensemble predictions are first post-processed separately in each margin and multivariate dependencies are then restored via copulas. These two-step methods share common key limitations, in particular the difficulty to include additional predictors in modeling the dependencies. We propose a novel multivariate post-processing method based on generative machine learning to address these challenges. In this new class of nonparametric data-driven distributional regression models, samples from the multivariate forecast distribution are directly obtained as output of a generative neural network. The generative model is trained by optimizing a proper scoring rule which measures the discrepancy between the generated and observed data, conditional on exogenous input variables. Our method does not require parametric assumptions on univariate distributions or multivariate dependencies and allows for incorporating arbitrary predictors. In two case studies on multivariate temperature and wind speed forecasting at weather stations over Germany, our generative model shows significant improvements over state-of-the-art methods and particularly improves the representation of spatial dependencies.
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