Installation

---

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

1. Create a new environment: conda create --name nsrdb python=3.9

2. Activate environment: conda activate nsrdb

3. Install nsrdb: pip install NREL-nsrdb

Option 2: Clone repo (recommended for developers)

1. from home dir, git clone git@github.com:NREL/nsrdb.git

2. Create nsrdb environment and install package

   1. Create a conda env: conda create -n nsrdb

   2. Run the command: conda activate nsrdb

   3. cd into the repo cloned in 1.

   4. Prior to running pip below, make sure the branch is correct (install from main!)

   5. Install nsrdb and its dependencies by running: pip install . (or pip install -e . if running a dev branch or working on the source code)

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

NSRDB Versions

NSRDB Versions History

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: 2022-2023. Meteosat: 2005-2022.	Integrated new FARMS-DNI model.
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