Cluster Analysis for Verification of Precipitation Fields

Abstract

A statistical method referred to as cluster analysis is employed to identify features in forecast and observation fields. These features qualify as natural candidates for events or objects in terms of which verification can be performed. The methodology is introduced and illustrated on synthetic and real quantitative precipitation data. First, it is shown that the method correctly identifies clusters that are in agreement with what most experts might interpret as features or objects in the field. Then, it is shown that the verification of the forecasts can be performed within an event-based framework, with the events identified as the clusters. The number of clusters in a field is interpreted as a measure of scale, and the final ?product? of the methodology is an ?error surface? representing the error in the forecasts as a function of the number of clusters in the forecast and observation fields. This allows for the examination of forecast error as a function of scale.