Probabilistic and deterministic results of the ANPAF analog model for Spanish wind field estimations

The purpose of this work is to obtain daily mean wind speed and wind gust estimations by means of the analog model. To do this, three data sets have been used: daily 1000 hPa geopotential height field over the North Atlantic, observational daily mean wind speed and observational daily gust wind speeds in Spain. Previously to the analog method, a Principal Component Analysis is used to reduce the dimensionality of the large-scale atmospheric database. Here, the analog method is based on finding in the historic geopotential height data base, a PC subset of large-scale atmospheric patterns that are the most similar to a large-scale atmospheric pattern used as input. In the algorithm process of the analog model, two Euclidean metrics in two different distances used as weighting functions are defined and analyzed for finding the best analogs. Thus, similar atmospheric situations to any particular atmospheric situation to be modeled have been determined and from them, several and different wind fields have been estimated, to characterize the wind fields in Spain. Several deterministic and probabilistic results are shown. Results of bias, spatial correlations and root mean squared errors between the estimated and observational wind fields are displayed, evaluating the model skilful for predicting mean wind and gusts. The probabilistic results are shown by means of Brier Skill Scores, rank histograms and reliability diagrams. The procedure used in this paper to find analogs, reducing previously the dimensionality of the large-scale atmospheric data and obtaining PCs, has been revealed as a good technique to find similar patterns to any particular one.