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Hawaii_CSP_High_Resolution.zip (.2MB Zip)

Hawaii_PV_High_Resolution.zip (1.6MB Zip)

Lower_48_DNI_High_Resolution.zip (8.9MB Zip)

Lower_48_GHI_High_Resolution.zip (9.9MB Zip)

Lower_48_LATTILT_High_Resolution.zip (8.9MB Zip)

Solar

Hawaii

Monthly and annual average renewable energy solar resource potential for the state of Hawaii. Provides information on the solar resource potential for the state of Hawaii. The insolation values represent the average solar energy available to a concentrating collector on a 2-axis tracker, such as a dish or a power tower. This data provides monthly average and annual average daily total solar resource averaged over surface cells of 0.1 degrees in both latitude and longitude, or about 10 km in size.

This data was developed using the State University of New York/Albany satellite radiation model. This model was developed by Dr. Richard Perez and collaborators at the National Renewable Energy Laboratory and other universities for the U.S. Department of Energy. Specific information about this model can be found in Perez, et al. (2002). This model uses hourly radiance images from geostationary weather satellites, daily snow cover data, and monthly averages of atmospheric water vapor, trace gases, and the amount of aerosols in the atmosphere to calculate the hourly total insolation (sun and sky) falling on a horizontal surface. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources.

A modified Bird model is used to calculate clear sky direct normal (DNI). This is then adjusted as a function of the ratio of clear sky global horizontal (GHI) and the model predicted GHI. Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalable at a 10km resolution.

As a result, it is believed that the modeled values are accurate to approximately 15% of a true measured value within the grid cell. Due to terrain effects and other microclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain.SUNY Albany and NRELMarch 24, 2007

Lower 48

This data provides monthly average and annual average daily total solar energy resource averaged over surface cells of 0.1 degrees in both latitude and longitude, or about 10 km in size. This data was developed using the State University of New York/Albany satellite radiation model. This model was developed by Dr. Richard Perez and collaborators at the National Renewable Energy Laboratory and other universities for the U.S. Department of Energy. Specific information about this model can be found in Perez, et al. (2002). This model uses hourly radiance images from geostationary weather satellites, daily snow cover data, and monthly averages of atmospheric water vapor, trace gases, and the amount of aerosols in the atmosphere to calculate the hourly total insolation (sun and sky) falling on a horizontal surface. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. A modified Bird model is used to calculate clear sky direct normal (DNI). This is then adjusted as a function of the ratio of clear sky global horizontal (GHI) and the model predicted GHI.

Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalable at a 10km resolution. As a result, it is believed that the modeled values are accurate to approximately 15% of a true measured value within the grid cell. Due to terrain effects and other microclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain.

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The SHP file contains the geospatial data. The SHX file contains the index of the geospatial data. The DBF file contains the attribute data in dBASEĀ® format. The PRJ file contains the coordinate system information (optional). The AVL file contains the legend information (optional). The SBN and SBX files contain the spatial index of the geospatial data (optional). The XML file (*.shp.xml) contains the metadata describing the data set (optional).

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