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Advances in Meteorology
Volume 2014 (2014), Article ID 237247, 11 pages
Research Article

A Hidden Markov Model Applied to the Daily Spring Precipitation over the Danube Basin

1National Institute of Hydrology and Water Management, 013686 Bucharest, Romania
2Hessian Centre on Climate Change, 65203 Wiesbaden, Germany
3University of Agronomic Sciences and Veterinary Medicine, 011464 Bucharest, Romania
4Institute of Meteorology, Free University Berlin, 12165 Berlin, Germany

Received 12 August 2013; Revised 20 November 2013; Accepted 26 December 2013; Published 19 February 2014

Academic Editor: Klaus Dethloff

Copyright © 2014 Constantin Mares 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.


The main goal of this study is to obtain an improvement of the spring precipitation estimation at local scale, taking into account the atmospheric circulation on the Atlantic-European region, by a statistical downscaling procedure. First we have fitted the precipitation amounts from the 19 stations with a HMM with 7 states. The stations are situated in localities crossed by the Danube or situated on the principal tributaries. The number of hidden states has been determined by means of BIC values. A NHMM has been applied then to precipitation occurrence associated with the information about atmospheric circulation over Atlantic-European region. The atmospheric circulation is quantified by the first 10 components of the decomposition in the EOFs or MEOFs. The predictors taking into account CWTs for SLP and the first summary variable from a SVD have also been tested. The atmospheric predictors are derived from SLP, geopotential, temperature, and specific and relative humidity at 850 hPa. As a result of analyzing the multitude of the predictors, a statistical method of selection based on the informational content has been achieved. The test of the NHMM performances has revealed that SLP and geopotential at 850 hPa are the best predictors for precipitation.