The study of weather extremes is critical because of their great impact on the environment, economy and society. The identification of areas at greater risk of extreme conditions, and of meteorological situations that give rise to such conditions, enhances the understanding of climate risks and helps establish measures to reduce adverse impacts. In the current paper, precipitation extreme events (PEEs) in Spain between 1960 and 2011 were analysed. Thresholds for determining event severity were defined using 99th percentiles. First, regions of extreme weather risk were identified and then trends of extreme precipitation index were analysed using the Mann–Kendall test. To better understand atmospheric processes associated with extreme weather events in each season, synoptic‐scale fields of events exceeding the 99th percentile were analysed. By applying non‐hierarchical K‐means clustering, we defined six large‐scale atmospheric patterns that largely explain the spatiotemporal distribution of PEEs in the study area. PEEs on the western Iberian Peninsula mainly occurred with zonal flow, with a long Atlantic fetch generating moisture advection towards that area. On the eastern peninsula, the most important pattern for PEE production is characterized by a cutoff low at mid‐levels together with easterly moisture flow. The relationship of PEEs with teleconnection patterns, such as the North Atlantic Oscillation (NAO), Mediterranean Oscillation (MO) and Western Mediterranean Oscillation (WeMO), showed that nearly all the events over the southwestern peninsula were during the NAO‐ and MO‐negative phases. However, on the Mediterranean coast, the negative WeMO phase had greater influence. By contrast, the northwestern peninsula and eastern Cantabrian coast showed weaker relationships between these indices and PEEs. The results show a clear ability to identify regions exposed to extreme precipitation hazards. The correct identification of synoptic patterns associated with each type of weather extreme will assist the prediction of such events, thereby providing useful information for decision making and warning systems.

Wind energy requires accurate forecasts for adequate integration into the electric grid system. In addition, global atmospheric models are not able to simulate local winds in complex terrain, where wind farms are sometimes placed. For this reason, the use of mesoscale models is vital for estimating wind speed at wind turbine hub height. In this regard, the Weather Research and Forecasting (WRF) Model allows a user to apply different initial and boundary conditions as well as physical parameterizations. In this research, a sensitivity analysis of several physical schemes and initial and boundary conditions was performed for the Alaiz mountain range in the northern Iberian Peninsula, where several wind farms are located. Model performance was evaluated under various atmospheric stabilities and wind speeds. For validation purposes, a mast with anemometers installed at 40, 78, 90, and 118 m above ground level was used. The results indicate that performance of the Global Forecast System analysis and European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) as initial and boundary conditions was similar, although each performed better under certain meteorological conditions. With regard to physical schemes, there is no single combination of parameterizations that performs best during all weather conditions. Nevertheless, some combinations have been identified as inefficient, and therefore their use is discouraged. As a result, the validation of an ensemble prediction system composed of the best 12 deterministic simulations shows the most accurate results, obtaining relative errors in wind speed forecasts that are <15%.

ABSTRACT:The variability of winter precipitation in the west of the Iberian Peninsula is strongly affected by the North Atlantic Oscillation (NAO). This study focuses on identifying the relationship that exists between precipitation registered in the city of León (in northwestern Spain), the NAO index and the associated weather type during the same period. In order to achieve this objective, the prevailing weather type has been calculated for each day in León from January 1948 to March 2009, using the objective Lamb Weather Types classification method. The most significant results appear in winter (from December to March). During these months an increase has been observed in the frequency of 'anticyclonic' weather type (A) (very dry), and a decrease in the 'cyclonic' (C), 'south-westerly' (SW) and 'westerly' (W) types (the three rainiest weather types). The positive trend in the NAO index could be the main cause of the decrease in the frequency of the three rainiest weather types (C, SW and W) and therefore, responsible for the relevant decline in winter precipitation observed in the city of León. The high correlation coefficients between the NAO index, the frequency of the three rainiest weather types and winter precipitation suggests an interesting method to forecast rainfall. Using a binary logistic regression model, a downscaling model for daily precipitation has been obtained based on the weather types and the NAO index. The daily results obtained for the winter months are good (TSS = 0.64) bearing in mind that only sea level pressure data were used in the logistic model.

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