This study was motivated by the need to improve densification of Global Horizontal Irradiance (GHI) observations, increasing the number of surface weather stations that observe it, using sensors with a sub-hour periodicity and examining the methods of spatial GHI estimation (by interpolation) with that periodicity in other locations. The aim of the present research project is to analyze the goodness of 15-minute GHI spatial estimations for five methods in the territory of Spain (three geo-statistical interpolation methods, one deterministic method and the HelioSat2 method, which is based on satellite images). The research concludes that, when the work area has adequate station density, the best method for estimating GHI every 15 min is Regression Kriging interpolation using GHI estimated from satellite images as one of the input variables. On the contrary, when station density is low, the best method is estimating GHI directly from satellite images. A comparison between the GHI observed by volunteer stations and the estimation model applied concludes that 67% of the volunteer stations analyzed present values within the margin of error (average of ±2 standard deviations).

Predicting the consequences of large earthquakes to the built environment is of high importance for disaster control, civil protection and emergency planning. A number of software tools are now available to estimate physical building damage and associated losses in terms of casualties and economic losses. In recent years, SELENA, a seismic risk and loss assessment software which makes use of the capacity spectrum method (CSM), has been developed into a widely applicable tool. Since SELENA functions independently from a Geographic Information System, we developed RISe ( R isk I llustrator for Se lena), a stand‐alone tool that illustrates SELENA files in Google ^™ Earth. RISe is customized to the SELENA file structure and allows easy conversion of all geographically referenced files such as building inventory data, soil conditions, ground motion values, as well as final risk and loss results. RISe is distributed as public domain open‐source software that allows the user to take full advantage of Google ^™ Earth's features including high‐resolution satellite images from nearly every built environment worldwide.

The enrichment of knowledge about solar irradiance (SI) on the Earth's surface and its prediction (forecast) has a great interest in renewable energy (RE), such as systems based on solar energy (SE) and for different applications industrial and environmental. At the present research it has been investigated five techniques of spatial estimation of the SI in 15 minutes of temporal resolutions for the Spanish mainland, with several spatial configurations. It’s been found that the Geostatistics through Regression Kriging, using auxiliary variables -one of this: the SI estimated from Satellite Images-, allows spatially estimates the SI beyond the 25 km identified by the related researches as the maximum distance limit to the estimation point. It has been experimented with the Artificial Neural Networks (ANN) modeling for the short-term forecasting of the SI, using close observations (spatial component) as part of its inputs, and the results are promising. In this way the error levels diminish, regarding to the related researches, under the following conditions: when the temporal horizons of the forecast is lower or equal to 3 hours, the neighbors stations to be included as input to the models should be at a 55 km of maximum distance. Mejorar el conocimiento de la Irradiancia Solar (IS) sobre la superficie terrestre, así como su predicción (pronóstico), cobra especial interés por su importancia para las energías renovables, a como lo son los sistemas basados en Energía Solar (ES), y para distintas aplicaciones industriales o ecológicas. En la presente investigación se ha experimentado con cinco técnicas de estimación espacial de la IS a intervalos de 15 minutos, en el territorio peninsular español, con distintas configuraciones espaciales. Encontrándose que la geoestadística mediante el Kriging con Regresión, usando variables auxiliares -una de ellas la IS estimada a partir de imágenes satelitales- permite estimar espacialmente la IS más allá de los 25 km, identificados en las investigación científicas previas, como límite de distancia máxima al punto de estimación. Se ha experimentado con el modelado de Redes Neuronales Artificiales (RNA) para la predicción en tiempo futuro (temporal) -a corto plazo- de la IS utilizando observaciones próximas (componentes espaciales) en sus entradas y los resultados son prometedores. Así los niveles de errores disminuyen, en relación a investigaciones relacionadas, bajo las siguientes condiciones: cuando el horizonte temporal de predicción es inferior o igual a 3 horas, las estaciones vecinas que se incluyen en los modelos deben encentrarse a una distancia máxima aproximada de 55 km.