King Abdullah City for Atomic and Renewable Energy
Renewable Resource Atlas
Services
Solar Ground Stations is collecting a high-quality dataset of solar resources at solar monitoring stations throughout Saudi Arabia.Collectively, the stations characterize the three components of solar radiation (Direct Normal Irradiance [DNI], Global Horizontal Irradiance [GHI], and Diffuse Horizontal Irradiance [DHI]), in addition to related meteorological parameters.
Wind ground stations measure wind energy resources intended for estimating wind power potentials for utility scale projects.
The network stations encompass cup anemometers, wind vanes, temperature, relative humidity and barometer sensors.
Description: Solar Irradiance Estimation with Neural Network Algorithm (SIENNA) is a satellite-based solar resource model calibrated by high accuracy ground data that have been developed by K.A.CARE, offering comprehensive data for solar stakeholders throughout the Kingdom of Saudi Arabia.
Source: SIENNA makes use of artificial neural networks to model the solar irradiance using imagery from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on-board the European Union’s MeteoSat Second Generation (MSG) satellite.
Description: Long-term time series have been analyzed to construct typical meteorological year (TMY) time series, which are single-year solar and weather time series representing typical conditions.
TMY time series have been constructed for the 50th percentile (average) conditions as well as for 90th percentile low-irradiance conditions, often demanded for project financial bankability analyses.
Source: SIENNA model from K.A.CARE