SRID 900913 of two available coordinate systems. Annual average of the aeolic potential at 50m. Content: wind speed in m/s, power class (7 classes), power density in W/m2 and Weibull k value organized into cells with 40km x 40km Source: CEPEL (Electric Energy Research Center/Federal University of Rio de Janeiro) - Brazil and INPE (National Institute for Space Research)

Monthly Average Solar Resource for horizontal and tilted flat-plates, and 2-axis tracking concentrating collectors. These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. Countries included in dataset include: Africa, Bangladesh, Brazil, Caribbean, Central America, China, East Asia, Ethiopia, Ghana, Kenya, Mexico, Nepal, South America, Sri Lanka, and the United States. Units: kWh/m^2/day Source: U.S. National Renewable Energy Laboratory (NREL)

SRID 4326 of two available coordinate systems. Annual average of the aeolic potential at 50m. Content: wind speed in m/s, power class (7 classes), power density in W/m2 and Weibull k value organized into cells with 40km x 40km Source: CEPEL (Electric Energy Research Center/Federal University of Rio de Janeiro) - Brazil and INPE (National Institute for Space Research)

SRID 4326 of two available coordinate systems. Normal direct solar radiation in kWh/m2/day for 1 year organized into cells with 40km x 40km units:KWh/m sq. per day, Source: INPE (National Institute for Space Research) and LABSOLAR (Laboratory of Solar Energy/Federal University of Santa Catarina) - Brazil

SRID 900913 of two available coordinate systems. Normal direct solar radiation in kWh/m2/day for 1 year organized into cells with 40km x 40km Source: INPE (National Institute for Space Research) and LABSOLAR (Laboratory of Solar Energy/Federal University of Santa Catarina) - Brazil

SRID 900913 of two available coordinate systems. Photosynthetically active radiation in kWh/m2/day for 1 year organized into cells with 40km x 40km. (Purpose): The BRASIL-SR model and the SPRING software (both developed by INPE - National Institute for Space Research) were used to produce the dataset and SHAPE files (Supplemental Information): The assessment of reliability levels of the BRASIL-SR model were performed through the evaluation of the deviations shown by the estimated values for solar radiation flux vis-à-vis the values measured at the surface (ground truth). This evaluation was done in two phases. The first phase consisted in an inter-comparison between the core radiation transfer models adopted by the SWERA Project to map the solar energy in the various countries participating in the project. The HELIOSAT model took part in this phase like benchmark due to its employment to map solar energy resources in countries from European Union. In the second phase, the solar flux estimates provided by the BRASIL-SR model were compared with measured values acquired at several solarimetric stations spread along the Brazilian territory.

SRID 900913 of two available coordinate systems. These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. Existing ground measurement stations are used to validate the data where possible. The modeled values are accurate to approximately 10% of a true measured value within the grid cell due to the uncertainties associated with meteorological input to the model. The local cloud cover can vary significantly even within a single grid cell as a result of terrain effects and other microclimate influences. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain.