Abstract

This thesis examines various methods for estimating the spatial distribution of solar radiation, and in particular its diffuse and direct components in mountainous regions. The study area is the Province of Bolzano (Italy). The motivation behind this work is that radiation components are an essential input for a series of applications, such as modeling various natural processes, assessing the effect of atmospheric pollutants on Earth's climate, and planning technological applications converting solar energy into electric power. The main mechanisms that should be considered when estimating solar radiation are: absorption and scattering by clouds and aerosols, and shading, reflections and sky obstructions by terrain. Ground-based measurements capture all these effects, but are unevenly distributed and poorly available in the Italian Alps. Consequently they are inadequate for assessing spatially distributed incoming radiation through interpolation. Furthermore conventional weather stations generally do not measure radiation components. As an alternative, decomposition methods can be applied for splitting global irradiance into the direct and diffuse components. In this study a logistic function was developed from the data measured at three alpine sites in Italy and Switzerland. The validation of this model gave MAB = 51 Wm^-2, and MBD = -17 Wm^-2 for the hourly averages of diffuse radiation. In addition, artificial intelligence methods, such as artificial neural networks (ANN), can be applied for reproducing the functional relationship between radiation components and meteorological and geometrical factors. Here a multilayer perceptron ANN model was implemented which derives diffuse irradiance from global irradiance and other predictors. Results show good accuracy (MAB in [32,43] Wm^-2, and MBD in [-7,-25] Wm^-2) suggesting that ANN are an interesting tool for decomposing solar radiation into direct and diffuse, and they can reach low error and high generality. On the other hand, radiative transfer models (RTM) can describe accurately the effect of aerosols and clouds. Indeed in this study the RTM libRadtran was exploited for calculating vertical profiles of direct aerosol radiative forcing, atmospheric absorption and heating rate from measurements of black carbon, aerosol number size distribution and chemical composition. This allowed to model the effect of aerosols on radiation and climate. However, despite their flexibility in including as much information as available on the atmosphere, RTM are computationally expensive, thus their operational application requires optimization strategies. Algorithms based on satellite data can overcome these limitations. They exploit RTM-based look up tables for modeling clear-sky radiation, and derive the radiative effect of clouds from remote observations of reflected radiation. However results strongly depend on the spatial resolution of satellite data and on the accuracy of the external input. In this thesis the algorithm HelioMont, developed by MeteoSwiss, was validated at three alpine locations. This algorithm exploits high temporal resolution METEOSAT satellite data (1 km at nadir). Results indicate that the algorithm is able to provide monthly climatologies of both global irradiance and its components over complex terrain with an error of 10 Wm^-2. However the estimation of the diffuse and direct components of irradiance on daily and hourly time scale is associated with an error exceeding 50 Wm^-2, especially under clear-sky conditions. This problem is attributable to the low spatial and temporal resolution of aerosol distribution in the atmosphere used in the clear-sky scheme. To quantify the potential improvement, daily averages of accurate aerosol and water vapor data were exploited at the AERONET stations of Bolzano and Davos. Clear-sky radiation was simulated by the RTM libRadtran, and low values of bias were found between RTM simulations and ground measurements. This confirmed that HelioMont performance would benefit from more accurate local-scale aerosol boundary conditions. In summary, the analysis of different methods demonstrates that algorithms based on geostationary satellite data are a suitable tool for reproducing both the temporal and the spatial variability of surface radiation at regional scale. However better performances are achievable with a more detailed characterization of the local-scale clear-sky atmospheric conditions. In contrast, for plot scale applications, either the logistic function or ANN can be used for retrieving solar radiation components.