Welcome to the National Solar Radiation Data Base (NSRDB)!

The National Solar Radiation Database (NSRDB) software includes all the methods for the irradiance data processing pipeline. To get started, check out the NSRDB command line interface (CLI). Refer to the NREL website and the original journal article for more information on the NSRDB. For details on NSRDB variable units, datatypes, interpolation methods, and other attributes, see the NSRDB variable meta data and NSRDB variable descriptions.

The PXS All-Sky Irradiance Model

The PXS All-Sky Irradiance Model is the main physics package that calculates surface irradiance variables.

The NSRDB Data Model

The NSRDB Data Model is the data aggregation framework that sources, processes, and prepares data for input to All-Sky.

The MLClouds Model

The MLClouds Model is used to predict missing cloud properties (a.k.a. Gap Fill). The NSRDB interface with MLClouds can be found here.

Installation

NOTE: The installation instructions below assume that you have python installed on your machine and are using either conda or pixi as your package/environment manager.

Option 1: Install from PIP (recommended for analysts):

1. Create a new environment: conda create --name nsrdb python=3.11

2. Activate environment: conda activate nsrdb

3. Install nsrdb: pip install NREL-nsrdb

Option 2: Clone repo (recommended for developers)

1. Run git clone git@github.com:NREL/nsrdb.git

2. cd nsrdb.

3. Make sure the branch is correct (install from main!)

4. If you are using conda, create and activate a new environment: conda create --name nsrdb python=3.11 and conda activate nsrdb

   4.1 Install nsrdb and its dependencies by running: pip install . (or pip install -e . for editable install)

5. Alternatively, run pixi install

6. Optional: Set up the pre-commit hooks with pip install pre-commit or pixi add pre-commit and pre-commit install

NSRDB Versions

Version	Effective Date	Data Years*	Notes
4.1.1	10/28/24	None	Integration with extended MLClouds models. Extended models can perform both cloud type and cloud property predictions.
4.1.0	7/9/24	None	Complete CLI refactor.
4.0.0	5/1/23	GOES 1998-2024, Meteosat 2005-2022.	Integrated an improved direct normal irradiance model (FARMS-DNI), described in the paper “Integration of a physics-based direct normal irradiance (DNI) model to enhance the National Solar Radiation Database (NSRDB)”
3.2.3	4/13/23	None	Fixed MERRA interpolation issue #51 and deprecated python 3.7/3.8. Added changes to accommodate pandas v2.0.0.
3.2.2	2/25/2022	1998-2021	Implemented a model for snowy albedo as a function of temperature from MERRA2 based on the paper “A comparison of simulated and observed fluctuations in summertime Arctic surface albedo” by Becky Ross and John E. Walsh
3.2.1	1/12/2021	2021	Implemented an algorithm to re-map the parallax and shading corrected cloud coordinates to the nominal GOES coordinate system. This fixes the issue of PC cloud coordinates conflicting with clearsky coordinates. This also fixes the strange pattern that was found in the long term means generated from PC data.
3.2.0	3/17/2021	2020	Enabled cloud solar shading coordinate adjustment by default, enabled MLClouds machine learning gap fill method for missing cloud properties (cloud fill flag #7)
3.1.2	6/8/2020	2020	Added feature to adjust cloud coordinates based on solar position and shading geometry.
3.1.1	12/5/2019	2018+, TMY/TDY/TGY-2018	Complete refactor of TMY processing code.
3.1.0	9/23/2019	2018+	Complete refactor of NSRDB processing code for NSRDB 2018
3.0.6	4/23/2019	1998-2017	Missing data for all cloud properties gap filled using heuristics method
3.0.5	4/8/2019	1998-2017	Cloud pressure attributes and scale/offset fixed for 2016 and 2017
3.0.4	3/29/2019	1998-2017	Aerosol optical depth patched with physical range from 0 to 3.2
3.0.3	2/25/2019	1998-2017	Wind data recomputed to fix corrupted data in western extent
3.0.2	2/25/2019	1998-2017	Air temperature data recomputed from MERRA2 with elevation correction
3.0.1	2018	2017+	Moved from timeshift of radiation to timeshift of cloud properties.
3.0.0	2018	1998-2017	Initial release of PSM v3 - Hourly AOD (1998-2016) from Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA2). Snow-free Surface Albedo from MODIS (2001-2015), (MCD43GF CMG Gap-Filled Snow-Free Products from University of Massachusetts, Boston). Snow cover from Integrated Multi-Sensor Snow and Ice Mapping System (IMS) daily snow cover product (National Snow and Ice Data Center). GOES-East time-shift applied to cloud properties instead of solar radiation. Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) is used for ancillary data (pressure, humidity, wind speed etc.)
2.0.0	2016	1998-2015	Initial release of PSM v2 (use of FARMS, downscaling of ancillary data introduced to account for elevation, NSRDB website distribution developed) - Clear sky: REST2, Cloudy sky: NREL FARMS model and DISC model - Climate Forecast System Reanalysis (CFSR) is used for ancillary data - Monthly 0.5º aerosol optical depth (AOD) for 1998-2014 using satellite and ground-based measurements. Monthly results interpolated to daily 4-km AOD data. Daily data calibrated using ground measurements to develop accurate AOD product.
1.0.0	2015	2005-2012	Initial release of PSM v1 (no FARMS) - Satellite Algorithm for Shortwave Radiation Budget (SASRAB) model - MMAC model for clear sky condition - The DNI for cloud scenes is then computed using the DISC model

Recommended Citation

Update with current version and DOI:

Grant Buster, Brandon Benton, Mike Bannister, Yu Xie, Aron Habte, Galen Maclaurin, Manajit Sengupta. National Solar Radiation Database (NSRDB). https://github.com/NREL/nsrdb (version v4.0.0), 2023. DOI: 10.5281/zenodo.10471523

Acknowledgments

This work (SWR-23-77) was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the DOE Grid Deployment Office (GDO), the DOE Advanced Scientific Computing Research (ASCR) program, the DOE Solar Energy Technologies Office (SETO), the DOE Wind Energy Technologies Office (WETO), the United States Agency for International Development (USAID), and the Laboratory Directed Research and Development (LDRD) program at the National Renewable Energy Laboratory. The research was performed using computational resources sponsored by the Department of Energy’s Office of Energy Efficiency and Renewable Energy and located at the National Renewable Energy Laboratory. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

*Note: The “Data Years” column shows which years of NSRDB data were updated at the time of version release. However, each NSRDB file should be checked for the version attribute, which should be a more accurate record of the actual data version.