Unsupervised learning on U.S. weather forecast performance. (English) Zbl 07734156
Summary: Nowadays, climate events and weather predictions have a huge impact on human activities. To understand the accuracy of weather prediction, we applied the functional principal component analysis (FPCA) method to investigate the main pattern of variance within the U.S. weather prediction error over a period of 3 years. We further grouped the states in the U.S. based on their similarity in weather forecast performance using two types of functional clustering approaches: the filtering method and the model-based method. The strengths and weaknesses of each clustering method were detected through the simulation studies. Then, the clustering approaches were applied to U.S. weather data from 2014 to 2017. Through clustering, cluster-specific patterns were visually detected, and the cluster-to-cluster differences were quantified in order to identify the most and least predictable U.S. states.
MSC:
| 62-08 | Computational methods for problems pertaining to statistics |
Keywords:
weather forecast; functional principal component analysis; \(K\)-means clustering; discriminative functional mixture model; FunFEM model-based clusteringReferences:
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