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

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