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Advances in Meteorology
Volume 2014, Article ID 498020, 11 pages
http://dx.doi.org/10.1155/2014/498020
Research Article

A Probabilistic Rain Diagnostic Model Based on Cyclone Statistical Analysis

1Department of Environmental Engineering, Technical University of Crete, 73100 Chania, Greece
2Department of Civil Engineering, McMaster University, Hamilton, ON, Canada L8S 4L7

Received 13 March 2014; Accepted 23 May 2014; Published 11 June 2014

Academic Editor: Hiroyuki Hashiguchi

Copyright © 2014 V. Iordanidou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Data from a dense network of 69 daily precipitation gauges over the island of Crete and cyclone climatological analysis over middle-eastern Mediterranean are combined in a statistical approach to develop a rain diagnostic model. Regarding the dataset, 0.5 × 0.5, 33-year (1979–2011) European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-Interim) is used. The cyclone tracks and their characteristics are identified with the aid of Melbourne University algorithm (MS scheme). The region of interest is divided into a grid mesh and for each grid the probability of rain occurrence from passing cyclones is estimated. Such probability maps are estimated for three rain intensity categories. The probability maps are evaluated for random partitions of the data as well as for selected rain periods. Cyclones passing south of Italy are found to have greater probability of producing light rain events in Crete in contrast to medium and heavy rain events which are mostly triggered by cyclones of southern trajectories. The performance of the probability maps is very satisfactory, recognizing the majority of “affecting” cyclones and rejecting most cyclones that do not trigger rain events. Statistical measures of sensitivity and specificity range between 0.5 and 0.8 resulting in effective forecasting potential.