Solar and wind power are called to play a main role in the transition toward decarbonized electricity systems. However, their integration in the energy mix is highly compromised due to the intermittency of their production caused by weather and climate variability. To face the challenge, here we present research about actionable strategies for wind and solar photovoltaic facilities deployment that exploit their complementarity in order to minimize the volatility of their combined production while guaranteeing a certain supply. The developed methodology has been implemented in an open‐access step‐wise model called CLIMAX. It first identifies regions with homogeneous temporal variability of the resources, and then determines the optimal shares of each technology over such regions. In the simplistic application performed here, we customize the model to narrow the monthly deviations of the total wind‐plus‐solar electricity production from a given curve (here, the mean annual cycle of the total production) across five European domains. For the current shares of both technologies, the results show that an optimal siting of the power units would reduce the standard deviation of the monthly anomalies of the total wind‐plus‐solar power generation by up to 20% without loss in the mean capacity factor as compared to a baseline scenario with an evenly spatial distribution of the installations. This result further improves (up to 60% in specific regions) if the total shares of each technology are also optimized, thus encouraging the use of CLIMAX for practical guidance of next‐generation renewable energy scenarios.

<div><br>Air pollution forecasting can be used to alert about dangerous health effects caused by airborne pollutants and, in consequence, to take  actions to reduce pollutant concentrations (i.e reducing traffic, control industrial activities, etc..). Therefore, the development of reliable  air quality forecast systems is a of great interest.<br><br>The system consist of two main branchs. A statistical method based on  Neural Networks is used to forecast (10 days) several dayily air quality <br>index at the sites were historical data is available (i.e. pollution  measurement stations). A dynamical method based on WRF-CHEM to forecast hourly (48h) values of a large variety of species in a high resolution  domain (2km). Both subsystems use GFS and ECMWF forecasts as driving  conditions. The  dynamical subsystem incorporates 4DVAR data assimilation  of meteorological data (first 12 hours of forecast), and dynamical  emissions. The dynamical  emissions consist in changing the emissions of  large factories and trafficc. The emissions data are obtained by machine  learning methods based on historical series and meteorological conditions (mainly big energy factories). The WRF-CHEM configuration consist of several domains one way nested. The mother domain covers the entire Saharian desert in order to incorporante the dust transport contribution to particulate matter concentration. In addition, the base emission data is continuously updated.    The system also incorporates a module for automatic verification by comparing forecast with observed data, and analysis runs (in order to minimize meteorological forecast uncertainty). This verification process permit us to construct a MOS (Model Output statistics) in order to correct <br>possible model bias.</div>

<p>Solar and wind power curves typically exhibit inverted daily and annual cycles. However, their monthly anomalies show both positive and negative low correlation values across Europe, which compromises the effectiveness of their integration in the energy grid. This is because the well-known asymmetric response of the resources to the main large-scale teleconnection patterns vanishes and/or shows low synchronicity when the compound effect of these patterns is considered, as we show here. So we propose a step-wise method to help narrowing the monthly deviations of the total wind-plus-solar electricity production at the regional level from a given curve (here, the mean annual cycle of the total production), applied here across five continuous European regions but with straight application elsewhere and at other temporal scales. It detects the optimal shares of each power over previously identified sub-regions with homogeneous temporal variability of the monthly anomalies of the wind and solar capacity factors. Results show that, keeping the current total regional shares, just through a smart distribution of the power units, the standard deviation of the monthly anomalies of the total wind-plus-solar production is reduced up to 20% without loss in the mean capacity factor as compared to a base scenario with uniform distribution of the installations. This reduction grows above 50% if the total regional shares also came into the optimization game.</p><p> </p><p>Acknowledgments:</p><p>This study was supported by the Spanish Ministry of Science, Innovation and Universities – <em>Agencia Estatal de Investigación</em> and the European Regional Development Fund through the project EASE (RTI2018-100870-A-I00), and by the Fundación Séneca – <em>Agencia de Ciencia y Tecnología de la Región de Murcia</em> through the project CLIMAX (20642/JLI/18).</p>