Weather regimes designed for local precipitation modeling: Application to the Mediterranean basin

Although weather regimes are often used as a primary step in many statistical downscaling processes, they are usually defined solely in terms of atmospheric variables and seldom to maximize their correlation to observed local meteorological phenomena. This paper compares different clustering methods to perform such a task. The correlation clustering model is introduced to define regimes that are well correlated to local-scale precipitation observed on seven French Mediterranean rain gauges. This clustering method is compared to other approaches such as the k-means and expectation-maximization (EM) algorithms. The two latter are applied either to the main principal components of large-scale reanalysis data(geopotential height at 500 mbar and sea level pressure) covering the Mediterranean basin or to the canonical variates associated with large scale and resulting from a canonical correlation analysis performed on reanalyses and local precipitation. The weather regimes obtained by the different approaches are compared, with a focus on the extreme content captured within the regimes. Then, cost functions are developed to quantify the errors due to misclassification, in terms of local precipitation. The different clustering approaches show different misclassification and costs. EM applied to canonical variates appears as a good compromise between the other approaches, with high discrimination, overall for extreme precipitation, while the precipitation costs due to bad classification are acceptable. This paper provides tools to help the users choose the clustering method to be used according to the expected goal and the use of the weather regimes.