We discuss an approach to probabilistic forecasting based on two chained machine-learning steps: a dimensional reduction step that learns a reduction map of predictor information to a low-dimensional space in a manner designed to preserve information about forecast quantities; and a density estimation step that uses the probabilistic machine learning technique of normalizing flows to compute the joint probability density of reduced predictors and forecast quantities. This joint density is then renormalized to produce the conditional forecast distribution. In this method, probabilistic calibration testing plays the role of a regularization procedure, preventing overfitting in the second step, while effective dimensional reduction from the first step is the source of forecast sharpness. We verify the method using a 22-year 1-hour cadence time series of Weather Research and Forecasting (WRF) simulation data of surface wind on a grid.
翻译:我们讨论一种基于两个链式机器学习步骤的概率预测方法:一个以保存预测数量信息的方式,学习向低维空间缩小预测信息图的维度减少步骤,以保存关于预测数量的信息;一个密度估计步骤,使用正常流流的概率机器学习技术,以计算减少预测数据和预测数量的共同概率密度。然后对这种联合密度进行重新整顿,以产生有条件的预测分布。在这个方法中,概率校准测试起到正规化程序的作用,防止第二步过分调整,同时从第一步开始有效减少空间是预测锐性的来源。我们用22年1小时的天气研究和预报(WRF)电网地面风模拟数据来核查这种方法。