3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

Sabrina Gentile,Saverio T Nilo,Ermann Ripepi,Edoardo Geraldi,Donatello Gallucci,Mariassunta Viggiano,Francesco Di Paola,Salvatore Larosa,Elisabetta Ricciardelli,Domenico Cimini,Filomena Romano

doi:10.3390/rs12060920

Abstract

Solar power generation is highly fluctuating due to its dependence on atmospheric conditions. The integration of this variable resource into the energy supply system requires reliable predictions of the expected power production as a basis for management and operation strategies. This is one of the goals of the Solar Cloud project, funded by the Italian Ministry of Economic Development (MISE)—to provide detailed forecasts of solar irradiance variables to operators and organizations operating in the solar energy industry. The Institute of Methodologies for Environmental Analysis of the National Research Council (IMAA-CNR), participating to the project, implemented an operational chain that provides forecasts of all the solar irradiance variables at high temporal and horizontal resolution using the numerical weather prediction Advanced Research Weather Research and Forecasting (WRF-ARW) Solar version 3.8.1 released by the National Center for Atmospheric Research (NCAR) in August 2016. With the aim of improving the forecast of solar irradiance, the three-dimensional (3D-Var) data assimilation was tested to assimilate radiances from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) geostationary satellite into WRF Solar. To quantify the impact, the model output is compared against observational data. Hourly Global Horizontal Irradiance (GHI) is compared with ground-based observations from Regional Agency for the Protection of the Environment (ARPA) and with MSG Shortwave Solar Irradiance estimations, while WRF Solar cloud coverage is compared with Cloud Mask by MSG. A preliminary test has been performed in clear sky conditions to assess the capability of the model to reproduce the diurnal cycle of the solar irradiance. The statistical scores for clear sky conditions show a positive performance of the model with values comparable to the instrument uncertainty and a correlation of 0.995. For cloudy sky, the solar irradiance and the cloud cover are better simulated when the SEVIRI radiances are assimilated, especially in the short range of the simulation. For the cloud cover, the Mean Bias Error one hour after the assimilation time is reduced from 41.62 to 20.29 W/m2 when the assimilation is activated. Although only two case studies are considered here, the results indicate that the assimilation of SEVIRI radiance improves the performance of WRF Solar especially in the first 3 hour forecast.

Highlights

The rise of renewable energy in global energy production requires the development of procedure to better manage these highly variable sources
Two studies analyzed the impact of the assimilation of radiance data from the Advanced Microwave Sounding Unit-A (AMSU-A) [24] and from the Advanced Microwave Scanning Radiometer 2 (AMSR2) [25] for the hurricane Sandy forecasts and the results proved that the radiance assimilation improved the short and medium range forecast [24] and the hurricane structure and cloud distributions [25]
The ones located in urban areas (e.g. Milano, Milano Lambrate and Cinisello Balsamo), the model shows positive Mean Bias Error (MBE) > 5 W/m2, indicating an overestimation of the Global Horizontal Irradiance (GHI) probably due to an underestimation of GHI extinction caused by pollution aerosols or to excessively high albedo associated to improper land use category (Table 3, clear sky)

Summary

Introduction

The rise of renewable energy in global energy production requires the development of procedure to better manage these highly variable sources. Contrary to the conventional energy sources (fossil and nuclear), solar energy is considered a variable source because the energy production is dependent on the intensity of solar irradiance that is mainly attenuated by atmospheric aerosols and clouds passing between the sun and the solar-powered plants. Given this variability in the generation of solar power, nowadays it becomes imperative to focus on a realistic modelling and accurate prediction of this variable, which is essential for management, strategies operations and regulation of power supplies

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