Comparison of the Performance of Different Analog-Based Bayesian Probabilistic Precipitation Forecasts over Bilbao, Spain

• Published in: Monthly weather review Vol. 138; no. 8; pp. 3107 - 3119
• Main Authors: Fernandez-Ferrero, Alejandro, Saenz, Jon, Ibarra-Berastegi, Gabriel
• Format: Journal Article
• Language: English
• Published: Washington American Meteorological Society 01.08.2010
• Subjects: Analogs; Bayesian analysis; Climatology; Mathematical models; Meteorology; Metropolitan areas; Neural networks; Precipitation; Principal components analysis; Probabilistic methods; Probability theory; Rain; Statistical models; Studies; Weather; Weather forecasting
• ISSN: 0027-0644; 1520-0493
• DOI: 10.1175/2010MWR3284.1

Abstract This study evaluates the performance of different analog-based downscaling models for probabilistic quantitative precipitation forecasts over the metropolitan area of Bilbao, Spain. The analog-based statistical downscaling models used are a probability of the exceedance models in which the probability is derived from the probabilities given by a set of analogs and three Bayesian models. Results show that the differences in the performance of the models are subtle. The simplest model, which makes no use of Bayesian methods, performs better than the other models in the forecast of very low precipitation events, very likely due to the quality of the found analogs, since these categories are very populated in the phase space and forecast is very easy. However, as precipitation rates increase, Bayesian models perform better than the simple one based on probability of exceedance from individual analogs. The Bayesian models involving precipitation in the computation of the likelihood show in general the smallest biases in the forecast versus observed probabilities.