Identification of inhomogeneities in precipitation time series using sur models and the Ellipse test

Abstract

The homogenization and analysis of long-term meteorological data sets are currently of unprecedented interest to the scientific community. If the monitoring station network is dense enough, many techniques use data from nearby stations ('reference' stations) in order to account for regional climate changes and to isolate the effects of irregularities in a 'candidate' station. We propose an extension of the method of cumulative residuals (Ellipse test) that takes into account the contemporaneous relationship between several candidate series from the same climatic area. The proposed technique uses the residuals from a Seemingly unrelated regression equations (SUR) model. This procedure (SUR+Ellipse test) was applied to a testing variable, with annual resolution, derived from the daily precipitation data from 27 stations located in the southern region of Portugal. Three well established statistical tests were also applied: the Standard normal homogeneity test (SNHT) for a single break, the Buishand range test and the Pettit test. The promising results from this case study indicate the proposed methodology as a valuable tool for homogeneity testing of climate time series if the station network is dense enough.