1-2-NSRDB_status

The National Solar Radiation Database (NSRDB): Recent Updates, New Developments and Status in 2022 Presenter: Manajit Sengupta, NREL 2022 PV Performance Modeling and Monitoring Workshop in Salt Lake City, Utah USA (23 -24 August 2022) Aron Habte, Yu Xie , Grant Buster, Jaemo Yang, Haiku Sky, Brandon Benton, Galen Maclaurin (NREL) Michael Foster (Univ. of Wisc.), Andrew Heidinger (NOAA) NREL | 2 Outline • The Physical Solar Model (PSM) • What’s new in the National Solar Radiation Database (NSRDB) • Validation of the NSRDB • Data dissemination • Future workThe PSMNREL | 4 PSM Workflow Data Sources Model Inputs Radiative Transfer Model Solar Irradiance Time - Series Variables FARMS – Fast All -sky Radiation Model for Solar (FARMS) applications developed by NREL. This is a suite of radiative transfer models t hat represent how solar radiation interacts with the atmosphere and the Earth’s land cover as it reaches the surface. MERRA2 – Modern -Era Retrospective analysis for Research and Applications, Version 2 (MERRA -2) provides ancillary meteorological variables including aerosol optical depth (AOD) and the atmospheric profile. MODIS – Moderate Resolution Imaging Spectroradiometer (MODIS) provides satellite -derived aerosol optical depth (AOD) and albedo. IMS – Interactive Multisensor Snow and Ice Mapping System (IMS) provides daily snow coverage to represent snow albedo.What’s New in the NSRDBNREL | 6 Geostationary Satellites in the NSRDBNREL | 7 What Data Are Newly Available? Updated product to contain 2021 data. Products released in 2021.NREL | 8 Version Logs Version Effective Date Data Years* Notes 3.0.1 2018 2017+ Moved from timeshift of radiation to timeshift of cloud properties. 3.0.2 2/25/2019 1998-2017 Air temperature data recomputed from MERRA2 with elevation correction 3.0.3 2/25/2019 1998-2017 Wind data recomputed to fix corrupted data in western extent 3.0.4 3/29/2019 1998-2017 Aerosol optical depth patched with physical range from 0 to 3.2 3.0.5 4/8/2019 1998-2017 Cloud pressure attributes and scale/offset fixed for 2016 and 2017 3.0.6 4/23/2019 1998-2017 Missing data for all cloud properties gap filled using heuristics method 3.1.0 9/23/2019 2018+ Complete refactor of NSRDB processing code for NSRDB 2018 3.1.1 12/5/2019 2018+, TMY/TDY/TGY - 2018 Complete refactor of TMY processing code. 3.1.2 6/8/2020 2020 Added feature to adjust cloud coordinates based on solar position and shading geometry. 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)NREL | 9 Gap -Filling Cloud Properties Using Machine Learning • Each subsequent version improves the accuracy of the NSRDB irradiance data. • Improvement in cloudy gap -filled sky conditions using the MLClouds model (V3.2.0). https://doi.org/10.1016/j.solener.2022.01.004Near Future ImplementationNREL | 11 Updates in Fiscal Year 2022 Version Effective Date Data Years* Notes 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 clear -sky coordinates. This also fixes a strange pattern that was found in the long -term means generated from PC data. 3.2.2 2/25/2022 1998-2022 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. WalshNREL | 12 Parallax -Correction and Shading Remapping Original PC + Shading PC + Shading + Remapped Satellite Data Final NSRDB • Better algorithm for projecting clouds onto the NSRDB grid based on the cloud geometry -based parallax and shading corrections.NREL | 13 Albedo Adjustment 1 3 Updated surface albedo on March 1, 2020. Previous surface albedo on March 1, 2020. ! “# $%&’()*+ ,-.&/ $%.01$%$23-425+6’()*-.&/ ,+ ,-42/ $%.0’()*+ “-42/ Ross and Walsh (1987) suggested a parameterization that decreases the albedo linearly with temperature when it approaches the freezing point. The snow/ice albedo is updated according to Ross and Walsh (1987). Ross, Becky and John E. Walsh. “A comparison of simulated and observed fluctuations in summertime Arctic surface albedo.” Journal of Geophysical Research 92 (1987): 13115 -13125.Data Quality and ValidationNREL | 15 NSRDB Validation • The National Oceanic and Atmospheric Administration Surface Radiation Budget (SURFRAD) and Baseline Surface Radiation Network (BSRN) stations were used for the evaluation. • The NSRDB data based on a Geostationary Operational Environment Satellite (GOES) was evaluated using the hourly data from 4 -km by 4 -km pixels that encompass the ground measurement location. • In most cases, the NSRDB accuracy for annual total global horizontal irradiance is mean bias error within ± 5% and root mean square error 20 GOES Satellite Himawari -8 Satellite Meteosat IODC Satellite +/-5%Data DisseminationNREL | 17 Data Dissemination The data sets can be accessed: • By point location or a small area can be downloaded through the NSRDB Data Viewer (https://maps.nrel.gov/nsrdb -viewer/ ) • By application programming interface to access larger quantities of data through automated approaches ( https://nsrdb.nrel.gov/data -sets/api -instructions.html ) • Through the Highly Scalable Data Service hosted on Amazon Web Services (https://nsrdb.nrel.gov/data -sets/nsrdb -data -hsds -demo.html ). Announcement: Fully reprocessed data for the GOES extent using PSM V3.2.2 and covering 1998 -2021 will be released by the end of September 2022. This will replace all data that is currently available.NREL | 18 Future Development Implement the FARMS – DNI model. Implement machine learning/artificial intelligence - based derivation of cloud identification. Investigate the availability of aerosol data sets from GOES -16 and GOES -17 satellites. Custom Typical Meteorological Year in the plane -of -array. High -resolution cloud properties (500 m) to get cloud fraction and improved cloud optical depth. A 50-year projected solar radiation data set going out to 2070 from regional climate models.NREL | 19 The NSRDB paper: Primary reference Publication freely available on website (https://nsrdb.nrel.gov). Sengupta, Manajit, Yu Xie, Anthony Lopez, Aron Habte, Galen Maclaurin, and James Shelby. 2018. “The National Solar Radiation Database (NSRDB).” Renewable and Sustainable Energy Reviews 89: 51– 60. SSN 1364 -0321. https://doi.org/10.1016/j.rser.2018.03.003 . www.nrel.gov NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by th e Alliance for Sustainable Energy, LLC. Thank You! Contact: Manajit.Sengupta@nrel.gov Sengupta, Manajit, Yu Xie, Anthony Lopez, Aron Habte, Galen Maclaurin, and James Shelby. 2018. “The National Solar Radiation Dat a Base (NSRDB).” Renewable and Sustainable Energy Rev. 89: 51 – 60. https://doi.org/10.1016/j.rser.2018.03.003 . NSRDB: http://nsrdb.nrel.gov This work was authored by Alliance for Sustainable Energy, LLC, the manager and operator of the National Renewable Energy Laboratory for the U.S. Department of Energy (DOE) under Contract No. DE -AC36 -08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office. 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.