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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 FROM AUTHOR]

ABSTRACT 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. [ABSTRACT FROM AUTHOR]

Snowfall in elevated areas of the mid-latitudes has a strong impact on infrastructure, freshwater availability, and the climate system. The Cantabrian Mountains of the northwestern Iberian Peninsula are very vulnerable to climate change because of their moderate altitudes, which limits their snowfall. Monitoring snow events is essential for the evaluation of weather and climate prediction models. However, measurement networks are scarce in mountainous areas and have great uncertainties because of blizzards. In this study, a multiphysics ensemble of the Weather Research and Forecasting (WRF) model was designed using three microphysics and two planetary boundary layer (PBL) schemes to simulate nine snowfall events in the Cantabrian Mountains during autumn and winter 2021–2022. The WRF was validated using several snow characteristics, such as liquid water equivalent, snow cover, and snow depth. Liquid water equivalent was evaluated using snow-gauge networks and satellite products in an assessment of snow cover. In addition, a monitoring network of webcams and snow poles was implemented, improving the low density of snow observations in the mountains. The results showed good model performance for detection of snow cover and slight overestimation of liquid water equivalent and snow thickness, which may have been caused by under-catchment that is generally an effect of wind on the measurement systems and by snow compaction, respectively. Morrison microphysics and Mellor-Yamada-Nakanishi-Niino (MYNN PBL) yielded better results for liquid water equivalent at higher altitudes and output greater snow cover. The results help determine the best configurations for snow modelling in the study area to develop future studies of the spatiotemporal patterns of snow distribution. • Monitoring system of snow events in Cantabrian Mountains has been developed. • We selected nine snowfall events during autumn and winter 2021–2022. • Three microphysics and two PBL parameterizations were tested. • Morrison microphysics and MYNN PBL yielded better results at higher altitudes. [ABSTRACT FROM AUTHOR]

The Iberian Peninsula (IP), located in a transition area between temperate and subtropical latitudes, has marked temporal and spatial variability of precipitation between its various climatic zones. Therefore, the peninsula is particularly sensitive to climate change owing to frequent periods of precipitation deficit together with societal dependence on water. In this study, we address meteorological droughts of the peninsula between 1901 and 2010, using the Global Precipitation Climatology Center database. First, we carried out a statistical classification of the peninsula according to annual precipitation cycle. The spatiotemporal variability of droughts was evaluated using the Standardized Precipitation Index (SPI) by determining the evolution of areas affected by severe and extreme drought. Subsequently, we calculated the overall trend of precipitation and anomalies to investigate whether an observed drought increase is attributable to a decrease of precipitation or an increase of precipitation extremes. Finally, we examined periodicities of precipitation anomalies via wavelet analysis, relating these to large-scale modes such as the North Atlantic Oscillation (NAO). The results show an increase in the area affected by severe and extreme drought, particularly on Atlantic-facing slopes. The mainland areas have longer and more intense periods of drought, with an average SPI of − 0.52 and 4-month duration. Over the 110 years analyzed, we observed weak trends of annual precipitation in the extreme northwest and southern IP. However, in the last decades of the series, long periods of strong negative anomalies are seen, interspersed with years of strong positive anomalies, suggesting an increase in precipitation pattern extremes. Finally, the relationship between the NAO index and precipitation anomalies on the Atlantic seaboard is highlighted. Thus, in both series we found a 5–6-year periodicity in their first years, with a diminishing cycle period reaching a periodicity of 2–3 years in the final years. This confirms the existence of regular temporal cycles in which droughts alternate with wet periods modulated by large-scale modes. [ABSTRACT FROM AUTHOR]