Improving the Accuracy and Coverage of the NSRDB

The National Solar Radiation Database (NSRDB) provides global solar resource data at a high temporal and spatial resolution. This data is primarily used in solar energy modeling and is updated on a regular basis. The NSRDB uses a physical approach to satellite-based solar modeling. The underlying Physical Solar Model (PSM) computes cloud-properties using satellite remote sensing and subsequently solar radiation using radiative transfer models. The retrieved cloud properties include cloud-mask, cloud-type, cloud optical depth and cloud droplet size. The radiative transfer models require additional input parameters such as aerosol optical properties (AOD), preciptable water vapor, surface albedo, temperature and pressure to accurately model solar radiation. While cloud properties are obtained directly from the geostationary satellites other inputs are obtained from additional source such as the National Aeronautical and Space Administration (NASA) Modern Era Retrospective Analysis for Research and Applications version 2 (MERRA2), the Interactive Multisensor Snow and Ice Mapping System (IMS) model data from the U.S. National Ice Center and NASA’s polar orbiting satellites such as the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Aqua and Terra Platform.

 In 2022 the NSRDB was updated to include improved surface albedo and gap-filling of cloud properties. Further, significant new updates have been included in 2023. This includes the use of the new FARMS DNI model under cloudy sky situations which results in a more accurate decomposition of the GHI in direct and diffuse. With the expansion of the NSRDB to provide data from the region covered by Meteosat, the coverage is fully global at this point.

While standard data from the GOES continues to be served at an hourly 4km x 4km resolution, full resolution data has also been made available to the user. The user is provided significant flexibility for downloading data depending on the amount of data required. Data can be downloaded using either the web-interface, an Application Programming Interface or directly from the cloud using Amazon Web Services. Services such as spectral data use on-demand computation and delivery.

Evaluation of the NSRDB was conducted for 18 stations and the Mean Bias Error (MBE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were computed for both GHI and DNI. The evaluation was conducted for the 1998-2023 period. Generally, the MBE lies within plus or minus ±5% for GHI and ±7% for DNI. The RMSE is less than 25% for GHI and 35% for DNI. 

There are additional plans to include cloud fraction in cloudy sky situation to improve the accuracy of the NSRDB. This presentation will provide users with the latest information about the NSRDB as well as plans for future development and updates.