NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Technical Report

NREL/TP-5D00-73667

May 2019

Solar Resource Calibration, 

Measurement, and Dissemination

Final Report FY 2016–FY 2018 

Manajit Sengupta,

1 Aron Habte,1 Yu Xie,1 Anthony Lopez,1

Mike Dooraghi,1 Mark Kutchenreiter,1 Afshin Andreas,1

Ibrahim Reda,

1 Galen Maclaurin,1 Michael J. Foster,2 

and Christian Gueymard3

1 National Renewable Energy Laboratory

2 Cooperative Institute for Meteorological Satellite Studies, 

University of Wisconsin

3 Solar Consulting Services 
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Technical Report

NREL/TP-5D00-73667

May 2019

Solar Resource Calibration, 

Measurement, and Dissemination

Final Report FY 2016–FY 2018 

Manajit Sengupta,1 Aron Habte,1 Yu Xie,1 Anthony Lopez,1

Mike Dooraghi,1 Mark Kutchenreiter,1 Afshin Andreas,1

Ibrahim Reda,

1 Galen Maclaurin,1 Michael J. Foster,2

and Christian Gueymard3

1 National Renewable Energy Laboratory

2 Cooperative Institute for Meteorological Satellite Studies, 

University of Wisconsin

3 National Renewable Energy Laboratory 

Suggested Citation 

Sengupta, Manajit, Aron Habte, Yu Xie, Anthony Lopez, Mike Dooraghi, Mark 

Kutchenreiter, Afshin Andreas, Ibrahim Reda, Michael J. Foster, and Christian Gueymard. 

2019. Solar Resource Calibration, Measurement, and Dissemination: Final Report FY 

2016–FY 2018. Golden, CO: National Renewable Energy Laboratory. 

NREL/TP-5D00-73667. https://www.nrel.gov/docs/fy19osti/73667.pdf.
NOTICE

This work was authored in part 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 U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy 

Technologies Office. The views expressed herein do not necessarily represent the views of the DOE or the U.S. 

Government.

This report is available at no cost from the National Renewable 

Energy Laboratory (NREL) at www.nrel.gov/publications.

U.S. Department of Energy (DOE) reports produced after 1991 

and a growing number of pre-1991 documents are available 

free via www.OSTI.gov.

Cover Photos by Dennis Schroeder: (clockwise, left to right) NREL 51934, NREL 45897, NREL 42160, NREL 45891, NREL 48097, 

NREL 46526.

NREL prints on paper that contains recycled content.
iii

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Acknowledgments 

We are grateful to the U.S. Department of Energy Solar Energy Technologies Office and the 

systems integration subprograms for supporting this project. Specifically, we acknowledge Dr. 

Tassos Golnas, Dr. Guohui Yuan, and Dr. Charlie Gay for their support and encouragement.
iv

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List of Acronyms

ACP Absolute cavity pyrgeometer

ACR Absolute cavity radiometer

AOD Aerosol optical depth 

ASTM American Society for Testing Materials

AVHRR Advanced very-high-resolution radiometer 

BMS Baseline Measurement System

BORCAL Broadband Outdoor Radiometer Calibration 

CLAVR-X Clouds from AVHRR-extended

CRADA Cooperative research-and-development agreement 

CSP Concentrating solar power 

DHI Diffuse horizontal irradiance

DNI Direct normal irradiance

DOE U.S. Department of Energy

FARMS Fast All-sky Radiation Model for Solar Applications

GHI Global horizontal irradiance 

GOES Geostationary Operational Environmental Satellite 

GUM Guide to the Expression of Uncertainty in Measurement

HF/AHF Automatic Hickey-Frieden 

IEA International Energy Agency

IEC International Electrotechnical Commission

IPC International Pyrheliometer Comparison

IRIS Infrared integrating sphere

ISO International Organization for Standardization 

MBD Mean bias difference 

MBE Mean bias error

MERRA Modern Era Retrospective analysis for Research Applications

MIDC Measurement and Instrumentation Data Center 

MODIS Moderate Resolution Imaging Spectroradiometer

NASA National Aeronautics and Space Administration 

NIST National Institute of Standards and Technology

NOAA National Oceanic and Atmospheric Administration

NPC NREL Pyrheliometer Comparisons

NREL National Renewable Energy Laboratory

NSRDB National Solar Radiation Database

PASS Pulse Analysis Spectroradiometer System

PMOD/WRC Physikalisch-Meteorologisches Observatorium Davos World Radiation 

Center

PSM Physical Solar Model

PV Photovoltaics

PVPS [IEA] Photovoltaic Power Systems Programme

R&D Research and development 

RMSE Root mean square error 

RReDC Renewable Resource Data Center

SETO Solar Energy Technologies Office

SMARTS Simple Model of the Atmospheric Radiative Transfer of Sunshine
v

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

SRRL Solar Radiation Research Laboratory 

SuNLaMP SunShot National Laboratory Multiyear Partnership

SURFRAD Surface Radiation Budget Network

UV Ultraviolet 

WISG World Infrared Standard Group

WRR World Radiometric Reference
vi

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Executive Summary 

The Solar Resource Calibration, Measurement, and Dissemination project supports the U.S. 

Department of Energy’s Solar Energy Technologies Office (SETO) initiative to make solar 

energy cost-competitive with other forms of electricity by improving the tools and methods to 

measure and model solar radiation and therefore reduce uncertainty in predicting solar output 

and improve the bankability of solar projects. This project has three tasks that conduct research 

on advancing solar resource measurements and modeling:

• Task 1: Applied solar Radiation Measurements

• Task 2: National Solar Radiation Data Base (NSRDB)

• Task 3: Knowledge Sharing.

The National Renewable Energy Laboratory’s (NREL’s) Solar Radiation Research Laboratory

(SRRL) activities ensure traceable solar measurements throughout the United States that are 

essential for project feasibility, due diligence, financing, and plant operations. The SRRL houses

NREL’s Baseline Measurement System (BMS), which provides a platform for improving the 

accuracy of solar measurements through research to improve the calibration and characterization 

of instruments and enable the development of new measurement technology and standards. The 

platform contains a variety of instruments measuring solar radiation and other meteorological 

parameters. Following best practices, BMS instruments are calibrated to ensure traceability to the 

World Radiometric Reference in compliance with ISO/IEC 170251 accreditation requirements. 

The BMS data sets are quality-controlled and made publicly available through the Measurement 

and Instrumentation Data Center website. 

The NSRDB is the most accessed public database of solar irradiance and associated weather 

parameters for use in energy modeling. It supports SETO’s goals of reducing barriers to high

penetrations of solar technologies by providing easy access to high-quality, foundational data 

essential for innovative product development and downstream modeling through the NSRDB 

website. Data are currently available for the contiguous United States and beyond for 20 years 

covering 1998–2017. The Physical Solar Model (PSM) that underpins the current NSRDB data

sets is a unique physics-based model that has been developed at NREL and has opened the door 

to the use of next-generation satellite data sets for solar resource assessment and forecasting.

Through the American Society for Testing Materials (ASTM) International and other 

international standards organizations, NREL continues to provide leadership in the development 

of standards that are relevant to the measurement and modeling of solar radiation for solar 

energy applications. Standards developed cover the calibration method of pyrheliometers (direct 

normal irradiance measurements), pyranometers (global horizontal irradiance measurements), 

spectroradiometers, ultraviolet radiometers, correct field deployment of these instruments, 

spectral distribution reference tables for photovoltaic modeling, and transfer of indoor 

calibrations. Further, NREL leads the International Energy Agency (IEA) Photovoltaic Power 

Systems Programme Task 16: Subtask 1, which focuses on the evaluation of current and 

emerging resource assessment methodologies. NREL and the IEA updated the Best Practices 

 

1 International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC) 

17025:2005: General Requirements for the Competence of Testing and Calibration Laboratories 
vii

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications,

2

which is widely used by the solar industry.

 

2 See https://www.nrel.gov/docs/fy15osti/63112.pdf. 
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Table of Contents

Background........................................................................................................................................... 1

Project Objectives ................................................................................................................................ 5

Project Results and Discussion.......................................................................................................... 7

3.1 Task 1: Applied Solar Radiation Measurements........................................................................... 7

3.1.1 National and International Pyrheliometer Comparison.................................................... 7

3.1.2 Development and Status of Absolute Cavity Pyrgeometer.............................................. 9

3.1.3 Broadband Outdoor Radiometer Calibration ................................................................. 10

3.1.4 Support NREL and Industry Partners on Providing Calibration Services of Spectral 

Measuring Devices......................................................................................................... 11

3.1.5 Cooperative Research and Development Agreement..................................................... 12

3.1.6 Data Quality Reporting .................................................................................................. 15

3.1.7 Data Dissemination ........................................................................................................ 16

3.1.8 Measurement Research and Publications....................................................................... 16

3.2 Task 2: National Solar Radiation Data Base ............................................................................... 22

3.2.1 Changes in National Solar Radiation Database from Version 2 to Version3................. 25

3.2.2 Improved PATMOS-x Algorithm for Retrieving Satellite Cloud Properties................. 25

3.2.3 National Solar Radiation Database Validation............................................................... 27

3.2.4 National Solar Radiation Database Users ...................................................................... 30

3.2.5 Research and Publications.............................................................................................. 31

3.2.6 MODIS-Derived Surface Albedo Data Set for NSRDB ................................................ 31

3.2.7 Use Case of NSRDB Data for the August 21, 2017, Total Solar Eclipse ...................... 32

3.3 Task 3: Knowledge Sharing ........................................................................................................ 34

3.3.1 International Energy Agency Photovoltaic Power Systems Programme Task 16.......... 34

3.3.2 American Society for Testing and Materials International ............................................ 35

Significant Accomplishments and Conclusions............................................................................. 41

4.1 Summary of Accomplishments................................................................................................... 41

4.2 Challenges................................................................................................................................... 41

4.2.1 Maintaining Traceability: Developing the Next-Generation Absolute Cavity Radiometer 

to Maintain Solar Measurement Accuracy..................................................................... 41

4.2.2 Changes in DNI Prediction Caused by the Aerosol Data Timescale ............................. 42

4.2.3 Differences Between GOES-East and GOES-West NSRDB Data ................................ 42

Path Forward....................................................................................................................................... 44

Bibliography: Inventions, Patents, Publications, and Other Results .................................................. 45

References................................................................................................................................................. 52
ix

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List of Figures

Figure 1. Activities enabling the development of standards, best practices, and national databases. Images 

by NREL................................................................................................................................... 4

Figure 2. WRR values for NREL reference cavities during IPC-XII ........................................................... 7

Figure 3. History of WRR reduction factors for NREL reference cavities. Figure from Reda et al. (2018) 8

Figure 4. Total number of radiometers calibrated during 2016–2018. The table shows the....................... 11

Figure 5. Total number of spectral devices calibrated during Fiscal Year 2016–2018............................... 12

Figure 6. Workflow for calibrating and characterizing Arable Labs’ low-cost sensor using the SRRL’s 

BMS for solar resource assessments. Photo by Dennis Schroeder, NREL 30542-C ............. 13

Figure 7. GHI MBE comparison under various sky conditions.................................................................. 14

Figure 8. GHI comparison using RMSE under various sky conditions...................................................... 14

Figure 9. Monthly MIDC data passing data quality test in percentages..................................................... 16

Figure 10. MBD in percentage for the hourly averaged GHI data under study: (A) all sky, (B) clear sky, 

(C) mostly cloudy sky, and (D) partly cloudy sky. The horizontal red line signifies the mean 

value of the MBD for the data that fall within the 95% confidence interval (black color box). 

Figure from Habte et al. (2016b)........................................................................................... 17

Figure 11. MBD in percentage for the hourly average for all DNI data under study: (A) all sky, (B) clear 

sky, (C) mostly cloudy sky, and (D) partly cloudy sky. The red line signifies the mean value 

of the MBD for the 95% confidence level. Figure from Habte et al. (2016b)....................... 18

Figure 12. Comparison between manufacturer’s supplied calibration responsivity and NREL’s calibration 

responsivity under clear skies, differences in both percentages and W/m2

. From Habte et al. 

(2016a)................................................................................................................................... 19

Figure 13. Top left: Plot of SpectraSun pyranometer dark offsets before and after switching to DC 

ventilation on Day 133. Top right: The gray histogram is for the offsets after Day 133 but 

before the correction; the red histogram is for the offsets after Day 133 after a correction 

based on a linear fit of offset versus instrument net infrared forced through zero at zero net 

infrared irradiance. From Michalsky, Kutchenreiter, and Long (2017)................................. 20

Figure 14. Setup of shaded and unshaded pyrgeometers to measure the longwave irradiance in solar 

beams. From Reda et al. (2017a), photo by Mark Kutchenreiter, NREL .............................. 21

Figure 15. Responsivity compared to wavelength for various angles of incidence. The responsivity is 

nearly constant as the angle of incidence changes. Only when the angle of incidence is 

increased to 75°is there a noticeable change in the responsivity compared to the other angles. 

A detailed view of the wavelengths from 300–400 nm is shown in the left panel. From 

Peterson et al. (2017)............................................................................................................. 22

Figure 16. A flowchart of the NSRDB PSM model ................................................................................... 23

Figure 17. MERRA-2 sample AOD and precipitable water data................................................................ 24

Figure 18. Comparison of NREL historical version of PATMOS-x (labeled NRELv2) and the latest stable 

release, ‘stable_release_2016B’ (labeled NRELv3). The first column is cloud fraction, the 

second is solar cloud transmission, and the third is cloud optical depth. The top row 

represents NRELv2, the second NRELv3, and the bottom row is the difference between the 

two. Data are taken from GOES-15 at 1500 UTC from May 19–25. .................................... 27

Figure 19. SURFRAD locations overlaid on U.S. climatic regions. Image modified from Habte, Sengupta, 

and Lopez (2017) ................................................................................................................... 28

Figure 20. GHI statistical result for seven SURFRAD locations for NSRDB Version 3 (data: 1998–2017)

................................................................................................................................................ 28

Figure 21. GHI distribution for the seven stations under varying sky conditions ...................................... 29

Figure 22. Overall uncertainty estimation of GHI for NSRDB PSM Version 2 (left) and PSM Version 3 

(right) using seven ground-based solar measurement locations. Data used in this figure are 

from 1998–2015 for Version 2 and 1998–2017 for Version 3............................................... 30
x

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Figure 23. Final data set for January 1, 2001, integrating MODIS-based surface albedo and Integrated

Multisensor Snow and Ice Mapping System snow cover. From Maclaurin et al. (2016)..... 32

Figure 24. Cloudless-sky probabilities for August 21, 2017, and the path of the total solar eclipse. Path of 

totality from “Eclipse Predictions by Fred Espenak, NASA/GSFC Emeritus.”Cartography 

by Billy J. Roberts, NREL ...................................................................................................... 33

Figure 25. Centerline profile of the path of the total solar eclipse (Wilcox et al. 2017). Path of totality 

from “Eclipse Predictions by Fred Espenak, NASA/GSFC Emeritus.” Cartography by Billy 

J. Roberts, NREL.................................................................................................................... 34

Figure 26. Participants in the third meeting of the IEA PVPS Task 16, held in Rapperswil, Switzerland. 

Photo by Jan Remund ............................................................................................................ 35

Figure 27. Modeled vs. measured global UV irradiance under all-sky conditions and zero tilt. RMSE and 

MBE values are in W/m2

. The black line is the 1:1 line, and the red line is the regression 

line. The dot colors refer to a “density scale” from blue to red, with red showing the highest 

density of points. From Habte et al. (2019)........................................................................... 40

Figure 28. DNI differences between NSRDB (left) Version 3 and (right) Version 2................................. 42

Figure 29. NSRDB Version 3 blended GHI data set .................................................................................. 42

Figure 30. Comparison for Eugene, Oregon, using (left) GOES-West satellite and (right) GOES-East 

satellite ................................................................................................................................... 43

List of Tables

Table 1. Summary of Milestones and Go/No-Go Decision Points............................................................... 6

Table 2. Results of the ACP, IRIS, and WISG from the First to Fifth International Pyrgeometer 

Comparisons (Reda et al. 2018)............................................................................................... 9

Table 3. Summary Result of the Number of Calibrations Performed During 2016–2018.......................... 11

Table 4. GOES-East and GOES-West Comparison with Surface Measurements (%MBE)....................... 43
1

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Background 

Accurate solar resource data are essential for reducing technical and nontechnical barriers to 

achieving the U.S. Department of Energy’s (DOE’s) Solar Energy Technologies Office (SETO) 

SunShot National Laboratory Multiyear Partnership (SuNLaMP) goals. Solar energy is a 

weather-driven renewable resource that poses unique challenges to researchers, developers, 

financiers, utilities, and system operators. Uncertainty in solar resource data can lead to

significant risks and increased costs for the design, installation, financing, and integration of 

renewable energy conversion systems. Since 1977, DOE has invested millions of dollars to 

develop unique capabilities at the National Renewable Energy Laboratory (NREL) for providing 

world-class solar resource measurements and model estimates. The primary objectives of this 

project are to provide improved solar resource data of known accuracy, traceable radiometer 

calibrations for bankable solar irradiance measurements, and advanced methods for resource 

information dissemination supporting foundational research and development (R&D) needed to 

address DOE’s SuNLaMP goals.

NREL’s Solar Radiation Research Laboratory (SRRL) is one of the few sources of high-quality 

long-term solar radiation data sets in the nation and probably the only source of a complete set of 

measurements for solar radiation modeling research. This includes the development and testing 

of industry-standard models, such as the Simple Model of the Atmospheric Radiative Transfer of 

Sunshine (SMARTS) and the testing of NREL’s Fast All-sky Radiation Model for Solar 

Applications (FARMS). FARMS is further used to develop the National Solar Radiation 

Database (NSRDB). The SRRL serves as a living laboratory that allows experience gathered 

from operating instruments, developing standards, and testing models to be shared quickly and 

easily across the solar energy industry. This experience is shared through the American Society 

for Testing Materials (ASTM) International and International Organization for Standardization 

(ISO) standards, conference presentations, technical reports, and journal articles. The data are

available in real time through the Measurement and Instrumentation Data Center (MIDC) 

portal.3

Measurement research is an important part of SRRL R&D. This includes research into sources of 

uncertainty in solar measurements, their quantification, and mitigation. Examples of recent work 

include investigation of differences in outdoor and indoor calibrations as well as instrument 

degradation as a result of long-term field deployment. New and improved instruments are 

deployed for validation and characterization against the Baseline Measurement System (BMS). 

The validation process provides an understanding of the quality of a new instrument when 

compared to peers. The characterization process provides an understanding of the variability 

between instruments of the same make and model and essentially provides an understanding of 

the manufacturing quality. These tests are carried out in collaboration with manufacturers,

including Kipp & Zonen, Hukseflux, EKO Instruments, Inc., Eppley Laboratory, Inc., Irradiance,

Inc., LI-COR, and Apogee. The data that are collected from these tests and published in reports

allow the solar energy industry to understand the performance of sensors and make informed 

decisions regarding the deployment and use of data collected by those sensors. Data collected by 

the instruments are used as inputs to U.S. and international standards. Examples are ASTM 

 

3 See http://www.nrel.gov/midc.
2

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E824: Standard Test Method for Transfer of Calibration from Reference to Field Radiometers 

and ASTM G183: Standard Practice for Field Use of Pyranometers, Pyrheliometers, and UV4

Radiometers. The standards are essential for acquiring traceable solar resource data sets for solar 

energy conversion applications and are described in more detail later in this document. 

Development of new types of instrumentation, advancement in measurement methods and 

techniques, and updates in measurement best practices resulting from SRRL R&D forms an 

essential part of regular updates to NREL’s Best Practices Handbook for solar measurement and 

modeling. Recent advances include understanding the use of photodiodes for solar measurement 

and the development of a standard to estimate uncertainty in field measurements.

Many solar irradiance models have been developed using empirical, semiempirical, or physical 

models (Pinker, Frouin, and Li 1995; Hammer et al. 2003). Empirical models develop regression 

functions relating long-term global horizontal irradiance (GHI) measurements at selected local 

stations to the simultaneous data recorded by satellites’ visible channels (Cano et al. 1986; Perez 

et al. 2002), which are then used to simulate GHI from global satellite observations. The GHI is 

combined with empirical relationships developed using modeled or observed solar radiation to 

retrieve direct normal irradiance (DNI) (Ineichen et al. 1992; Maxwell 1987). A well-known 

solar radiation data set developed by an empirical model is HelioClim, based on the observations 

of Meteosat geostationary satellites covering Europe, Africa, the Mediterranean Basin, the 

Atlantic Ocean, and part of the Indian Ocean (Cano et al. 1986; Rigollier, Lefèvre, and Wald

2004; Diabaté et al. 1987). Compared to empirical models, semiempirical models use a hybrid 

approach to derive solar radiation in which clear-sky background irradiance is solved from 

simple radiative transfer schemes (Rigollier, Lefèvre, and Wald 2004; Cebecauer, Šúri, and 

Perez 2010; Šúri, Cebecauer, and Skoczek 2011). A cloud index representing the proportion of 

radiation reflecting back to the satellite is converted to a clearness index that represents the 

proportion of incident radiation reaching the surface. The clearness index scales the clear-sky 

radiation to estimate the GHI, which is then partitioned to estimate DNI, similar to the empirical 

models. This semiempirical approach has been widely implemented in global solar radiation data

sets, including SolarGIS (Cebecauer, Šúri, and Perez 2010; Šúri, Cebecauer, and Skoczek 2011)

and SolarAnyWhere (Rigollier, Lefèvre, and Wald 2004). 

Physical models are conventionally categorized by single-step and two-step models according to 

the procedures in determining solar radiation (Cano et al. 1986). Single-step models directly 

solve for GHI using satellite observations and radiative transfer theory (Diak and Gautier 1983;

Gautier 1982). Two-step models intend to understand the complete physics affecting the 

transmission of solar radiation from the top of the atmosphere to the land surface. They retrieve 

aerosol, cloud, and other atmospheric properties from various satellite channels or modeling 

efforts and use the information to precisely simulate GHI by solving the radiative transfer 

equation (Pinker, Frouin, and Li 1995; Hammer et al. 2003). A typical product of a two-step 

model is the National Aeronautics and Space Administration’s (NASA’s) global surface 

radiation budget, where International Satellite Cloud Climatology Project pixel-level data and 

Goddard Earth Observing System Version 4 reanalysis products are used to infer atmospheric 

 

4 Ultraviolet 
3

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properties at a 250-km resolution for every 3 hours. The solar radiation is then derived using the 

atmospheric properties and a model developed by Pinker and Laszlo (Pinker and Laszlo 1992). 

Compared to empirical, semiempirical, and single-step physical models, most two-step physical 

models require significant computational capability because extensive information from satellite 

observations and other ancillary inputs need to be processed to estimate solar radiation. The 

multiple processes in the production chain require sufficient quality inputs to make full use of the 

advanced models for reducing uncertainties of GHI and DNI. During the years, rapid 

development of satellite technologies and modeling capabilities (e.g., the availability of 

Moderate Resolution Imaging Spectroradiometer [MODIS] spectral channels and multichannel 

geostationary satellites) have effectively increased the reliability and accuracy of the two-step 

physical models (Cano et al. 1986, Xie et al. 2012, Xie et al. 2012, Minnis et al. 2011). More 

recently, the expansion of spectral channels with improved temporal and spatial resolution on the 

third-generation Geostationary Operational Environmental Satellite-16 (GOES-16; previously 

GOES-R) is expected to lead to remarkable improvements in aerosol and cloud products (Schmit 

et al. 2005), which the two-step physical models are capable of exploiting. The improvements in 

reanalysis data such as NASA’s Modern Era Retrospective analysis for Research and 

Applications Version 2 (MERRA-2) bring observations and numerical models together in a 

unified standardized framework, resulting in high-quality ancillary information that significantly 

enhances the quality of the two-step physical models (Gelaro et al. 2017). In contrast, empirical, 

semiempirical, and single-step physical models are not expected to reap equivalent benefits from 

advances in satellite technology and reanalysis data sets because of inherent limitations in the 

underlying methods.

NREL has an extensive history of developing solar resource data over the United States and 

beyond using various sources of observations and modeling tools. NREL developed the Physical 

Solar Model (PSM), a physics-based solar irradiance model, in collaboration with the University 

of Wisconsin, the National Oceanic and Atmospheric Administration (NOAA), and Solar 

Consulting Services. PSM uses a two-step process where cloud properties are retrieved using the 

Clouds from AVHRR-extended (CLAVR-X)5 model and then used as inputs to the Fast All-sky 

Radiation Model for Solar applications (FARMS) model for clear-sky and cloudy-sky radiation 

calculations. The PSM is used for the current NSRDB covering North, Central, and parts of 

South America. Physical models such as the PSM can assimilate additional information content 

from future satellites (such as GOES-16) to improve solar radiation estimates. We will therefore 

use the PSM approach for future updates to the NSRDB.

 

5 AVHRR: advanced very-high-resolution radiometer 
4

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Figure 1. Activities enabling the development of standards, best practices, and national 

databases. Images by NREL
5

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Project Objectives 

The project supported and enabled DOE’s SuNLaMP goals in making solar energy fully costcompetitive with traditional energy sources. The work described in this project supported 

SuNLaMP goals as follows:

• Shorten the amount of time needed to move promising new solar photovoltaic (PV) and 

concentrating solar power (CSP) technologies from development to commercialization by 

providing the calibrated hardware, infrastructure, and software required for the design 

and testing of new technologies.

• Increase efficiency, reduce production costs, and open new markets for solar energy by 

providing high-resolution resource data for the United States and internationally that 

enable and give increased confidence for system design and deployment.

• Foster collaboration for utility-scale solutions and clear the way for high-penetration 

solar by increasing the accuracy of resource data to reduce financing costs and providing 

information about the variability of solar resources during shorter timescales to enable 

cost-effective grid integration and management.

• Strengthen the U.S. supply chain for solar manufacturing and commercialization of 

cutting-edge solar technologies by providing instrumentation and software to test 

generating technologies and providing test facilities for new sensor technologies.

• Invest in education, policy analysis, and technical assistance to remove critical barriers

through stakeholder engagement in measurement and modeling activities by 

disseminating accurate data and providing expert advice to industry and stakeholders 

through outreach and standards development.

• Develop a well-trained workforce to foster U.S. job creation in the solar industry by 

providing training to industry in the area of solar resource assessment.
6

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Table 1. Summary of Milestones and Go/No-Go Decision Points

FY Milestone Go/No-Go

2016 An ASTM standard for “integration of digital spectral weather 

data for weathering applications” will be developed, balloted,

and published.

Calibration certificates for all instruments deployed in the BMS 

are available, ensuring that data available via the MIDC is of 

known and traceable accuracy and uncertainty.

Produce regular updates to the 

existing NSRDB by adding the 

most currently completed full 

year of half-hourly solar and 

meteorological data (2015) for 

the spatially uniform 4-km x 

4-km grid of the continental 

United States and make the 

data publicly available through

the NSRDB website. 

2017 Update the Best Practices Handbook for the Collection and 

Use of Solar Resource Data for Solar Energy Applications and 

publish as a freely available report.

Calibration certificates for all instruments deployed in the BMS 

are available, ensuring that data available via the MIDC is of 

known and traceable accuracy and uncertainty.

Collect and analyze information on the use of SRRL facilities 

for research and its interdependencies, including information on 

instruments deployed in the BMS.

Collect and analyze information on the NSRDB process and 

usage as well as data production and dissemination by other 

government agencies.

Produce regular updates to the 

existing NSRDB by adding the 

most currently completed full 

year of half-hourly solar and 

meteorological data (2016) for

the spatially uniform 4-km x 

4-km grid of the continental 

United States and make the 

data publicly available through 

NSRDB website.

2018

Provide traceable, ISO-accredited fundamental measurement 

and calibration of solar resource measurement by conducting 

the NREL Pyrheliometer Comparison.

Complete development of ASTM standard on classification of 

radiometers, including balloting and addressing negative 

comments as part of the consensus process.

Calibration certificates for all instruments deployed in the BMS 

are available, ensuring that data available via the MIDC is of 

known and traceable accuracy and uncertainty.

Produce regular updates to the 

existing NSRDB by adding the 

most currently completed full 

year of half-hourly solar and 

meteorological data (2017) for 

the spatially uniform 4-km x 

4-km grid of the continental 

United States and make the 

data publicly available through 

NSRDB website.
7

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Project Results and Discussion

3.1 Task 1: Applied Solar Radiation Measurements

3.1.1 National and International Pyrheliometer Comparison

Every 5 years, the Physikalisch-Meteorologisches Observatorium Davos World Radiation Center 

(PMOD/WRC) in Davos, Switzerland, hosts an International Pyrheliometer Comparison (IPC) 

for transferring the World Radiometric Reference (WRR) to participating radiometers. 

Representing DOE, NREL staff participated in the 12th IPC (IPC-XII) from September 28, 

2015–October 16, 2015. During the event, 134 participants with 157 instruments from 32 

countries attended. NREL’s participation maintains the traceability to WRR for U.S. industry, 

academia, and other national and international organizations. The WRR is the internationally 

recognized standard for direct normal irradiance measurements of broadband solar radiation, 

essential for compliance with ISO/IEC 170256 accreditation requirements to demonstrate interlaboratory proficiency testing. NREL has been participating in the IPC events since 1980. Figure 

2 demonstrates that the 5 cavity radiometers in use at NREL have stable calibrations with an 

average change of 0.06% compared to the responsivities from the previous IPC, which was 

conducted in 2010. 

Figure 2. WRR values for NREL reference cavities during IPC-XII

 

6 International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC) 

17025:2005: General Requirements for the Competence of Testing and Calibration Laboratories 
8

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

To disseminate the capability to obtain and maintain high-quality solar radiation data sets, NREL 

organizes the NREL Pyrheliometer Comparisons (NPC) every year for the non-IPC years (Figure 

3). As a result, NREL has developed and maintained a select group of absolute cavity 

radiometers with direct calibration traceability to the WRR (Reda 2017a). These instruments are 

used by NREL to transfer WRR calibration to other radiometers used and owned by industries. 

During the NPC, participants from many national and international agencies participate to retain 

radiometer calibration traceability to the WRR. Some of these include NREL, Sandia National 

Laboratories, NOAA, NASA, DOE’s Atmospheric Radiation Measurement Program, 

Brookhaven National Laboratory, Lockheed Martin, Dset Laboratories, Directorate General Joint

Research Centre’s Institute for Energy and Transportation—Renewable Energy Unit, 

GroundWorks Renewables, Inc., and PMOD Technologies. After each NPC, NREL publishes 

the result of this comparison as a technical report.

7

Figure 3. History of WRR reduction factors for NREL reference cavities. Figure from Reda et al.

(2018)

 

7 See https://www.nrel.gov/docs/fy19osti/72607.pdf.
9

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

3.1.2 Development and Status of Absolute Cavity Pyrgeometer

The downwelling longwave irradiance is traceable to the World Infrared Standard Group 

(WISG) of pyrgeometers, which consists of four pyrgeometers. Since 2004, the WISG had been

providing uniformity to atmospheric downwelling longwave radiation measurements; however, 

additional work was needed to establish traceability to the International System of Units. This is

necessary to integrate longwave irradiance with broadband irradiance. NREL and other national 

and international organizations were working to develop a new downwelling longwave 

irradiance standard. The devices in the development include the absolute cavity pyrgeometer 

(ACP) by NREL, infrared integrating sphere (IRIS), and atmospheric emitted radiance 

interferometer.

NREL continues to upgrade the hardware and software of the ACP. This upgrade assists in 

having a fully automated system. When it was developed, it was run manually, and the data were

post-processed after measurement. The ACP is a patented instrument that was developed by 

NREL in an effort to establish a world reference for atmospheric longwave irradiance 

measurement and ultimately substitute the current WISG, which comprises a set of windowed 

pyrgeometers that have significantly higher uncertainty than instruments such as the ACP.

NREL participated in multiple pyrgeometer comparisons. Table 2 shows a comparison among 

the ACP, the WISG, and the IRIS. The Table shows that the WISG has significant differences 

when compared to the ACP and IRIS. Because the WISG consists of a group of windowed 

pyrgeometers, it is apparent that they are biased when compared to the absolute cavity 

instruments. 

Table 2. Results of the ACP, IRIS, and WISG from the First to Fifth International Pyrgeometer 

Comparisons (Reda et al. 2018)
10

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3.1.3 Broadband Outdoor Radiometer Calibration

The Broadband Outdoor Radiometer Calibrations (BORCAL) procedure is used to calibrate 

pyranometers and pyrheliometers outdoors using the sky and sunbeam irradiance as the reference 

source. This unique calibration procedure was developed by NREL staff using peer-reviewed 

reports and journal articles (Reda 1996; Reda et al. 2005; Reda, Stoffel , and Myers 2003; Reda, 

Myers, and Stoffel 2008; Reda and Andreas 2004; Stoffel and Reda 2009; Reda 1996; Reda et al. 

2008; Habte, Sengupta, and Reda 2015; JCGM 2008). NREL conducts active research to 

improve the BORCAL procedure. The BORCAL procedure is then used to provide calibration 

services both within NREL and outside the lab.

BORCAL assists in:

• Achieving consistent performance results, regardless of the sensor model or data 

processing scheme, with accurate and quantifiable uncertainties and risk 

• Engaging industry and sensor manufacturers on instrumentation requirements

• Quantifying uncertainties in measured data 

• Supporting national solar resource measurement networks through formal arrangements 

with other federal agencies, universities, and the private sector

• Without BORCAL:

o Traceability of radiometric data would not be maintained and reduced uncertainty 

of radiometric data would not be attained, which would then have a direct impact 

on SETO goals.

o It would cost DOE/NREL more than the current budget to calibrate/ship the 

radiometers and maintain the existing NREL-SRRL radiometric setup.

During the project period (2016–2018), BORCAL was carried out in compliance with ISO/IEC

17025 accreditation for 482 radiometers (Figure 4 and Table 3). These radiometers came from 

NREL, various labs, and industry. Further, during this period, NREL upgraded the radiometer 

calibration and characterization software to improve the BORCAL configuration, expand 

radiometer internal temperature sensor support, and add a longwave data export feature.
11

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 4. Total number of radiometers calibrated during 2016–2018. The table shows the 

Table 3. Summary Result of the Number of Calibrations Performed During 2016–2018 

3.1.4 Support NREL and Industry Partners on Providing Calibration Services of 

Spectral Measuring Devices

The NREL Optical Metrology Laboratory is responsible for spectral calibrations, field 

measurements, instrumentation troubleshooting/repair, and consultation. The laboratory has the 

ability to provide field measurements to non-NREL customers at their facility under the 

Metrology Services Agreement for a fee. In mid-2000, the laboratory developed an in-house,

unique, one-of-kind, spectroradiometer system called the Pulse Analysis Spectroradiometer 

4

30

13

4

20

50

8

12

1

24

13

6

34

66

17 16

1

29

10

5

35

58

10

16

PHOTODIODE THERMOPILE THERMOPILE THERMOPILE PHOTODIODE THERMOPILE THERMOPILE THERMOPILE

PYRANOMETER PYRGEOMETER PYRHELIOMETER PYRANOMETER PYRGEOMETER PYRHELIOMETER

NON-NREL NREL

2016 2017 2018
12

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

System (PASS), which can measure the spectrum from pulse solar simulators. Pulse simulators 

are commonly used today because they have much lower power requirements, do not increase 

the temperature of the PV test device, and can produce high-intensity flashes (several hundred 

times the intensity of the sun). Unfortunately, there is not an off-the-shelf spectroradiometer that 

can measure these light sources with all the needed functionality and accuracy as that provided 

by the PASS. As a result, industry has often been seeking NREL’s assistance for measurements 

of pulse solar simulators. Through the years, under technical services agreements, NREL has 

built two additional PASS instruments: one for Spire Solar and one for Newport Corporation.

For this project period, the laboratory has maintained the National Institute of Standards and 

Technology (NIST) traceable calibration with reduced uncertainty for many spectroradiometers 

to support optical solar simulator characterizations for NREL and industry, such as Sandia 

National Laboratories and the University of Oregon. A summary of the number of instruments 

calibrated is provided in Figure 5.

Figure 5. Total number of spectral devices calibrated during Fiscal Year 2016–2018

3.1.5 Cooperative Research and Development Agreement

1. NREL, through a cooperative research-and-development agreement (CRADA), has worked 

with Arable Labs to install, operate, calibrate, and maintain a low-cost sensor for solar 

resource assessments at the SRRL for demonstrating the ease of installation, system value to 

stakeholders, and validation of measurement accuracy by comparison with existing 

instruments in the BMS. 

3

9

3

17

2

13

9

17

4

26

14

13

SPECTRORADIOMETER RADIOMETER SOLAR SIMULATOR SPECTRORADIOMETER

NON-NREL NREL

2016 2017 2018

Total Number of Devices Calibrated
13

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 6. Workflow for calibrating and characterizing Arable Labs’ low-cost sensor using the

SRRL’s BMS for solar resource assessments. Photo by Dennis Schroeder, NREL 30542-C

2. Through a CRADA, NREL continues to work with EKO Instruments to test new 

instruments and evaluate radiometers deployed at the SRRL. Work conducted under this 

CRADA includes:

A. Evaluation of a fast-response pyranometer (MS-80)

B. A study of various approaches to grounding viewed through the effects observed 

with subsecond data on the MS-80

C. Study of rotating shadowband for an MS-711 spectroradiometer

D. Additionally, evaluation will be conducted with subsecond data collected from the 

MS-57 instruments.

Some preliminary results for the MS-80 compared with BMS instruments under various 

sky conditions (all sky, clear sky, and cloudy sky) show good agreement with highquality pyranometers. The cost of the MS-80 is about 50% less than the high-quality 

pyranometers. Figures 7 and 8 shows the comparison of multiple radiometers. The MS-80 

shows less than 2% bias compared to the reference data, which are the component sum of 

direct and diffuse measurements from our highest quality instrument set. The direct 

measurements are from an EKO Instruments CHP1, and the diffuse measurements are 

from an Eppley 8-48.
14

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 7. GHI MBE comparison under various sky conditions

Figure 8. GHI comparison using RMSE under various sky conditions

3. Kipp & Zonen has developed a new all-in-one solar monitoring instrument, the RaZON+.

The RaZON+ accurately measures both DNI and diffuse horizontal irradiance (DHI) 

from the sun and sky, enabling it to provide very reliable values of GHI and sunshine 

duration. Kipp & Zonen partnered with NREL under the NREL Commercialization 

Assistance Program to install the RaZON+ at NREL’s SRRL so that it is collocated with 

the reference instruments from the SRRL and can be operated under a controlled 

maintenance schedule. Under the agreement, the SRRL will monitor the performance of 

the RaZON+, particularly how its performance compares under difference weather 

conditions and under different maintenance regimes (e.g., daily cleaning, no cleaning, 

modified cleaning schedule). NREL is working with Kipp & Zonen to evaluate the data 

and produce a report describing how it performed from a data quality standpoint and an 
15

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

instrument performance standpoint. If we discover improvements that can be made, 

NREL’s SRRL staff will work with Kipp & Zonen to implement and test those 

improvements. Moreover, Kipp & Zonen would like to conduct additional calibrations at 

NREL and plans to use the south pad of the SRRL and a couple of our trackers to do 

these calibrations. This requires minimal NREL effort and is funded by Kipp & Zonen.

4. AccuFlux CRADA. Through this CRADA, NREL and AccuFlux developed a framework 

to conduct research and develop new radiometric devices and supporting equipment to 

provide more accurate, site-specific, long-term, continuous measurements of the solar 

resources needed by industry to reduce deployment costs and improve the operations of 

PV and CSP plants.

8

The deployment of the AccuFlux system was a success, having fully assessed the overall 

system and tracker design. The result showed an objective performance of the system:

A. 2AT solar tracker long-term reliability and tracking accuracy

B. Radiometer long-term stability

C. 2AT solar tracker system AC power year-round with DC power backup 

operational effectiveness for current battery and panel sizing

D. Accurate back-calculation of global and diffuse measurements relative to the

NREL SRRL BMS best radiometers (CM22 and 8-48 reference shaded/diffuse) 

data, via subtraction of the 2AT system temp-corrected DR03 pyrheliometer 

converted 1-minute average. DHI signal data from the SR20 temp- and directional 

response-corrected 1-minute average and GHI signal data.

3.1.6 Data Quality Reporting

Evaluating the performance of PV cells, modules, and arrays that form large solar deployments 

relies on accurate measurements of the available solar resource. Therefore, determining the 

accuracy of these solar radiation measurements provides a better understanding of investment 

risks. This becomes especially important as deployment size and investment costs increase to

hundreds of millions of dollars. The accuracy of measurements is also important for acceptance 

testing and operations. NREL maintains a suite of the highest quality solar radiation 

measurement systems that are maintained following the best practices for industry (Sengupta et 

al. 2017). These instruments provide a continuous stream of research quality data for baseline 

research through the continuous application of data-quality evaluation using NREL’s SERI QC 

software (Maxwell, Wilcox, and Rymes 1993). SERI QC defines ranges of acceptable data, 

depending on whether one, two, or all three GHI, DNI, and DHI elements are present. Ranges 

are defined based on dimensionless parameters normalized with respect to extraterrestrial 

radiation, where:

• Kt = Clearness index or global horizontal transmittance 

= Global horizontal radiation/extraterrestrial horizontal radiation 

• Kd = Diffuse horizontal transmittance 

= Diffuse horizontal radiation/extraterrestrial horizontal radiation 

 

8 See https://www.nrel.gov/docs/fy18osti/71837.pdf.
16

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

• Kn = Direct normal transmittance 

= Direct normal radiation/extraterrestrial direct normal radiation.

Overall, the quality assessment tool demonstrates less than 5% of the data with lower quality

(Figure 9). 

Figure 9. Monthly MIDC data passing data quality test in percentages

3.1.7 Data Dissemination 

NREL continued to disseminate solar resource information using the MIDC and Renewable 

Resource Data Center (RReDC) websites.

9,10 RReDC and the MIDC continue to be among the 

most visited sites at NREL, with nearly 200,000 users and more 120,000 unique users annually,

or more than 1 user per second. The data and models available from these portals enable all 

stakeholders to easily access information for improving the quality and reducing the cost of 

projects.

3.1.8 Measurement Research and Publications

1. NREL published an article in the Solar Energy journal about the performance of 51 

commercially available and prototype radiometers used for measuring GHI or DNI.

11 The 

performance and accuracy of these radiometers are essential for determining system 

efficiencies of solar energy conversion installations, developing improved satellite-based 

models for estimating solar resources, and validating solar radiation forecasts. The study 

included pyranometers, pyrheliometers, rotating shadowband radiometers, and a 

pyranometer with an internal shading mask deployed at the SRRL. Figures 10 and 11

show the results of the study under all-sky conditions. The statistical measure used here is 

mean bias difference (MBD), which is the same as mean bias error (MBE), and the two

are interchangeable in the document. The study assists in differentiating the similarities 

and differences among DNI and GHI measuring radiometers when compared to the 

 

9 See https://midcdmz.nrel.gov/. 10 See https://www.nrel.gov/grid/solar-resource/renewable-resource-data.html. 11 See http://www.sciencedirect.com/science/article/pii/S0038092X16300184.
17

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

reference data set with the lowest estimated measurement uncertainties of the 

corresponding measurement type. The study identified significant improvements in newer 

radiometers. Increasing numbers and sizes of solar energy conversion systems have

created more opportunities and demands for improvements to radiometry within the 

industry. Under these circumstances, various manufacturers of radiometers are working 

to improve instrument performance and harmonizing the methodology for acquiring 

radiometric data. These improvements are based on customer feedback; government 

research (NREL, NOAA, NIST, et al.); international standards development groups, such 

as ISO, ASTM International, and International Energy Agency (IEA) Task 46; and the 

needs/requirements of the solar and utility industries. The focus on improvement in 

radiometer design has been to identify the significant contributors to uncertainty and 

change instrument design and manufacturing accordingly.

Figure 10. MBD in percentage for the hourly averaged GHI data under study: (A) all sky, (B) clear 

sky, (C) mostly cloudy sky, and (D) partly cloudy sky. The horizontal red line signifies the mean 

value of the MBD for the data that fall within the 95% confidence interval (black color box). Figure 

from Habte et al. (2016b)

(A (B

(C (D
18

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 11. MBD in percentage for the hourly average for all DNI data under study: (A) all sky, (B) 

clear sky, (C) mostly cloudy sky, and (D) partly cloudy sky. The red line signifies the mean value 

of the MBD for the 95% confidence level. Figure from Habte et al. (2016b)

2. NREL published a journal article in Progress in Photovoltaics on “Radiometer 

Calibration Methods and Resulting Irradiance Differences.” This study addresses the 

effect of calibration methodologies and the resulting calibration responsivities provided 

by radiometric calibration service providers such as NREL and manufacturers of 

radiometers. Improved calibration of radiometers based on consensus methodology will 

help reduce the uncertainty of resource measurements for solar energy power plants, 

reducing the cost of financing those plants and thus potentially reducing the cost of 

electricity from such facilities.

(A (B

(C (D
19

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 12. Comparison between manufacturer’s supplied calibration responsivity and NREL’s 

calibration responsivity under clear skies, differences in both percentages and W/m2. From Habte 

et al. (2016a)

“Note: Each blue and orange box represents a 10-degree bin, and it also represents the upper quartile 

and the lower quartile (also called an “interquartile range”) of the data in each bin. The circle in each blue

box is a mean, and the black line signifies the median value. Ninety-nine percent of the data set is within 

the whiskers; beyond the whiskers is plotted with a symbol (dots)” (Habte et al. 2016a)

3. NREL published an article in Journal of Atmospheric and Oceanic Technology, 

“Significant Improvements in Pyranometer Nighttime Offsets Using High-Flow DC 

Ventilation,” in collaboration with the Cooperative Institute for Research in 

Environmental Sciences, University of Colorado Boulder, and NOAA/Earth System 

Research Laboratory, Boulder, Colorado. 

Ventilators are used to keep the domes of the pyranometers clean and dry, but they affect 

the nighttime offset as well. This paper examines different ventilation strategies. For the 

several commercial single-black-detector pyranometers with ventilators examined here, 

high-flow-rate (50-cfm and higher), 12-VDC fans reduce the offsets, reduce the scatter, 

and improve the predictability of the offsets during the night compared with lower-flowrate 35-cfm, 120-VAC fans operated in the same ventilator housings (example result is 

shown in Figure 13). Black-and-white pyranometers sometimes show improvement with 

DC ventilation, but in some cases DC ventilation makes the offsets slightly worse.
20

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 13. Top left: Plot of SpectraSun pyranometer dark offsets before and after switching to DC 

ventilation on Day 133. Top right: The gray histogram is for the offsets after Day 133 but before 

the correction; the red histogram is for the offsets after Day 133 after a correction based on a 

linear fit of offset versus instrument net infrared forced through zero at zero net infrared 

irradiance. From Michalsky, Kutchenreiter, and Long (2017)

4. NREL’s Metrology Laboratory and Sensing and Predictive Analytics group performed a 

study and published a paper in the Atmospheric and Climate Sciences journal on reducing 

calibration biases caused by longwave irradiance in the reference direct beam.

12

Shortwave radiometers—such as pyranometers, pyrheliometers, and PV cells—calibrated 

with traceability to a consensus reference, maintained by absolute cavity radiometers 

(ACRs). The ACR is an open cavity with no window that measures the extended 

broadband spectrum of the terrestrial direct solar beam irradiance, unlike shortwave 

radiometers that cover a limited range of the spectrum. The difference between the two 

spectral ranges might lead to calibration bias that can exceed 1%. This article describes a 

method to reduce the calibration bias resulting from using broadband ACRs to calibrate 

shortwave radiometers by using an ACR with Schott glass window to measure the 

reference broadband shortwave irradiance in the terrestrial direct solar beam from 0.3 μm 

to 3 μm. The experimental setup is shown in Figure 14.

 

12 See https://www.scirp.org/Journal/PaperInformation.aspx?PaperID=73462.
21

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 14. Setup of shaded and unshaded pyrgeometers to measure the longwave irradiance in 

solar beams. From Reda et al. (2017a), photo by Mark Kutchenreiter, NREL

5. NREL published a paper in collaboration with the University of Oregon in the Solar 

Energy journal that outlines a methodology to determine the uncertainty of a 

spectroradiometer that is consistent with the Guide to the Expression of Uncertainty in 

Measurement (GUM) method has been published. This methodology is applicable to all 

spectroradiometers and is not specific to a particular instrument. Moreover, the 

methodology aims to characterize the uncertainties of a spectroradiometer in the 

laboratory under conditions that resemble those in the field. Specifically, tests on the 

deviation from the true cosine response (Figure 15) and the temperature sensitivity of the 

instrument are added to the uncertainties typically considered when calibrating a 

spectroradiometer.

13

 

13 See https://www.sciencedirect.com/science/article/pii/S0038092X17302591. 
22

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Figure 15. Responsivity compared to wavelength for various angles of incidence. The responsivity 

is nearly constant as the angle of incidence changes. Only when the angle of incidence is 

increased to 75°is there a noticeable change in the responsivity compared to the other angles. A 

detailed view of the wavelengths from 300–400 nm is shown in the left panel. From Peterson et al. 

(2017)

3.2 Task 2: National Solar Radiation Data Base

With the increasing adoption of PV solar panels for energy production by both utility-scale and 

residential customers, in 2012 DOE chose to develop a gridded solar resource data set covering 

the entire United States for use by DOE researchers. It was also decided that this data set would 

be made available to the public for free, which would enable the widespread deployment of PV 

and CSP. This data set would become the latest version of NREL’s NSRDB. A physics-based 

approach was developed to produce the data set that paved pathways to scientific improvement 

and adoption of information from newer satellites to reduce uncertainty in the solar resource 

information.

The PSM is a physics-based solar irradiance model developed by NREL in collaboration with the 

University of Wisconsin, NOAA, and Solar Consulting Services. PSM uses a two-step process 

where cloud properties are retrieved using the clouds from the CLAVR-X model and then used 

as inputs to the FARMS model (Xie, Sengupta, and Dudhia 2016) for clear- and cloudy-sky 

radiation calculations. The PSM is used for the current NSRDB covering North America and 

Central America. Physical models such as the PSM can assimilate additional information content 

from future satellites (GOES-R) to improve solar radiation estimates. 
23

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 16. A flowchart of the NSRDB PSM model

NSRDB Version 3 is a spatially resolved time-series database of solar irradiance data for North 

America, Central America, and parts of South Asia.14

Following are the steps taken to process the NSRDB V3 data using the PSM:

1. Acquisition of calibrated and navigated geostationary cloud property data from two

satellites

NREL, in collaboration with NOAA and the University of Wisconsin, acquired the

GOES-West and GOES-East 1998–2017 data set. The data set provided by the University 

of Wisconsin includes cloud properties such as cloud optical depth, cloud type, and cloud 

mask at a 4-km x 4-km spatial resolution and half-hourly temporal resolution. 

2. An aerosol dataset and ancillary data sets were obtained from MERRA-2.

NREL, in collaboration with Solar Consulting Services, investigated the MERRA-2 

aerosol data set for the North and South American continents. The data set was used in 

the PSM model to estimate solar radiation in clear-sky situations. The data set was 

adjusted for elevation and interpolated to the NSRDB grid. Hourly aerosol optical depth 

(AOD) estimates are produced for each pixel.

3. NREL mapped cloud properties, aerosol, and meteorological data to the NSRDB grid at a

4-km half-hourly resolution through various physically consistent interpolation 

techniques.

 

14 See http://nsrdb.nrel.gov.
24

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

The AOD data set is being mapped to the static grid. The cloud properties delivered from 

University of Wisconsin in the native satellite grid had been mapped to the NSRDB grid. 

Figure 17. MERRA-2 sample AOD and precipitable water data

4. NREL implemented gridded cloud properties, aerosols, and meteorological information 

in REST2 (clear-sky radiative transfer model) and FARMS (cloudy-sky radiative transfer 

model) to calculate solar radiation (GHI and DNI). 

5. NREL filled missing values and merged data from two satellites to create seamless 30-

minute solar radiation updates.

The satellite data have gaps as a result of transmission errors, satellite/instrument ageing, 

malfunction, and the eclipse periods in the spring and fall, so NREL implemented highly 

parallelized algorithms to fill the existing time gaps in the satellite data. Further, NREL 

set up a framework to blend the satellite images. The satellite images originate from two 

satellites GOES-East and GOES-West, with the dividing line between 105o and 110o west 

longitude. 

6. NREL validated a new product using surface measurements.

And evaluation of the new NSRDB data set was carried out using high-quality ground 

stations. The spatial and temporal differences between the ground-measured and satellitederived data sets were analyzed. The ground measurements were averaged to hourly 

values centered at the satellite time stamp. This serves two purposes: (a) to convert a 

point measurement to a representation of a finite area covered by a satellite pixel and (b) 

to provide a half-hourly average estimate that the satellite data are meant to represent. 

Further, NREL developed a consensus uncertainty methodology to assess the differences 

between the surface measurement and the NSRDB data. Setting up a uniform benchmark 

is essential to improving the existing satellite-derived data or creating other satellitebased methods to improve the underlying uncertainties.

7. NREL developed summary products such as typical meteorological year using the 20-

year NSRDB record.
25

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

8. NREL updated the geographic information system-based NSRDB portal to deliver all 

time-series and statistical summary data including the development of new layers. NREL 

also updated the website documentation to reflect changes in the model/data.

9. NREL provides a response to users of the NSRDB in user forums and through email and 

disseminates and publicizes the NSRDB through webinars and conference publications.

10. NREL analyzed user data to understand the NSRDB’s user base (organization, frequency 

of use, volume of data transferred, geographic areas with highest number of downloads, 

etc.) as a function of time (quarterly or annually).

3.2.1 Changes in National Solar Radiation Database from Version 2 to Version3

During the years, the NSRDB has been updated to meet the increasing demand for solar resource 

data at a high spatiotemporal resolution for solar conversion systems. Such resource information 

is needed from the conceptual phase to routine solar power plant operation. Recently, NREL 

released the gridded NSRDB (1998–2017) based on NSRDB Version 3 of the PSM. The 

NSRDB data sets contain gridded solar irradiance—DNI, GHI, and DHI—at a 4-km by 4-km 

spatial resolution and half-hourly temporal resolution covering 20 years. Details on the model 

and the data set are available at the NSRDB website.

15 Additional details about the development 

of the NSRDB are also available in (Xie, Sengupta, and Dudhia et al. 2016; Sengupta et al. 2018. 

Relative to NSRDB Version 2, in the current version of the NSRDB (developed using PSM 

Version 3), NREL implemented major changes in the meteorological input and processing. 

Hourly variables, such as AOD or precipitable water vapor, are now extracted from NASA’s 

MERRA-2 (Gelaro et al. 2017). These variables are particularly important to correctly evaluate 

DNI under clear-sky conditions. Additionally, downscaling methodologies to match the lower

resolution MERRA-2 data (0.5 x 0.625°) to the high-resolution 4-km NSRDB grid were 

redesigned to improve the accuracy in the ancillary variables, such as precipitable water vapor, 

AOD, temperature, pressure, and relative humidity. In parallel, the information from GOES-East 

satellite data requires being shifted in time to produce data at the top and middle of the hour. 

Whereas PSM Version 2 (used in the NSRDB Version 2) shifted the solar radiation directly 

using a parametric model, PSM Version 3 (used in the NSRDB Version 3) shifts the cloud 

products derived from GOES-East and uses those properties to compute solar radiation at the 

correct time. More importantly for DNI calculations, NSRDB Version 2 used monthly averaged 

AOD, whereas NSRDB Version 3 uses hourly AOD from MERRA-2.

3.2.2 Improved PATMOS-x Algorithm for Retrieving Satellite Cloud Properties 

The current PATMOS-x algorithm version will retire because it is not capable of processing 

GOES-R data. GOES-R, which is the first of the next generation of GOES satellites, was

launched in November 2016. Upon successful launch, GOES-R was renamed GOES-16, and 

after a 12-month deployment and checkout period was moved to the East position and became

operational. In an effort to be immediately prepared for processing GOES-16 data, the 2016 data 

were processed with a newer GOES-R-ready version of PATMOS-x. The University of 

Wisconsin analyzed a week during May 2016 with both versions to inform algorithm impact 

changes, particularly in daytime cloud optical and microphysical properties.

 

15 See https://nsrdb.nrel.gov.
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Several differences between the two processed versions of PATMOS-x were noted, though most 

were small. Perhaps the largest difference is the solar zenith and sensor zenith angle criteria now 

applied to PATMOS-x optical retrievals. In the latest stable version of PATMOS-x, optical 

retrievals are not performed for solar zenith or sensor zenith angles above 75°. This is shown in 

Figure 18. The reason for this change is that it was determined that the quality of the retrievals 

degraded above 75°. A related change is that solar transmission is set to the missing value for 

clear skies. In the older version of PATMOS-x, solar transmission was set to 1 in these cases. 

Application of the cloud mask may be used to account for this change.

Cloud fraction in the newer version of PATMOS-x is slightly higher than that of the previous 

version. The average difference over the continental United States is on the order of 3%–4%. 

This corresponds to a decrease in the solar transmission through cloud of about 2%–3% and a 

decrease in cloud optical depth of around 1. This is likely caused by changes in cloud detection 

of thin cirrus, which produces more cloud, and which decreases transmission, but much of the 

additional cloud is optically thin cirrus, which decreases average optical depth. That said, the 

magnitude of these differences depends on the satellite (GOES-13 versus GOES-15) as well as 

the time of day the clouds are being observed.
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Figure 18. Comparison of NREL historical version of PATMOS-x (labeled NRELv2) and the latest 

stable release, ‘stable_release_2016B’ (labeled NRELv3). The first column is cloud fraction, the 

second is solar cloud transmission, and the third is cloud optical depth. The top row represents 

NRELv2, the second NRELv3, and the bottom row is the difference between the two. Data are

taken from GOES-15 at 1500 UTC from May 19–25.

3.2.3 National Solar Radiation Database Validation

A validation of the performance of NSRDB 1998–2017 Version 3 was conducted to quantify the 

accuracy and spatial and temporal variability of the solar radiation data. Comparisons of the 

NSRDB 1998–2017 Version 3 estimates with selected ground-measured data were conducted 

under both clear- and cloudy-sky conditions and covered the period from 1998–2017 for seven 

Surface Radiation Budget Network (SURFRAD) stations (Figure 19).
28

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 19. SURFRAD locations overlaid on U.S. climatic regions. Image modified from Habte, 

Sengupta, and Lopez (2017)16

The comparison demonstrates that the difference between the NSRDB and surface measurement 

is within + 5% (bias). There is significant reduction in the RMSE. The improvement is assumed 

to result from a combination of factors, including better downscaling methodologies, particularly 

in the interpolation and extrapolation used to align the multiple data sets to the same grid, the use 

of hourly values of aerosol information, and the use of surface albedo instead of interpolated 

monthly averages.

Figure 20. GHI statistical result for seven SURFRAD locations for NSRDB Version 3 (data: 1998–

2017)

 

16 For more information about the stations, see https://www.esrl.noaa.gov/gmd/grad/surfrad/sitepage.html.
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The distribution of irradiance from NSRDB 1998–2017 versus ground measurement under 

varying sky conditions (Figure 20) shows good agreement. The clear-sky condition demonstrates

a close relationship with the measurement.

Figure 21. GHI distribution for the seven stations under varying sky conditions

Further, NREL implemented a comprehensive uncertainty determination approach using GUM 

(JCGM 2008). The approach identifies the source of uncertainties and quantifies standard 

uncertainties using the measure of statistic for each source. The standard uncertainties are then 

combined using the root sum squared method (Figure 21). The statistics, such as bias and RMSE,

were derived by comparing the NSRDB data to the seven ground measurement stations. The 

evaluation was conducted for hourly values, daily totals, monthly mean daily totals, and annual 

mean monthly mean daily totals and demonstrates the quality of the new data sets currently 

available from the NSRDB (Figure 21).

The hourly data set demonstrated increased uncertainty; however, with increasing average 

timescales, the uncertainty approaches the measurement uncertainty, e.g., annual. As described 

in (Habte, Sengupta, and Lopez 2017), one reason for the high uncertainty in the hourly data set 

is because the NSRDB pixel represents a 4-km by 4-km area, whereas a ground-based solar 

measurement represents only a small area above the measuring station. The subpixel variability 

in clouds appears to contribute to increased differences.
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The NSRDB Version 3 demonstrates a significant reduction in hourly uncertainty estimations 

compared to Version 2, as shown in Figure 22. These improvements are clearly noticeable for all 

locations, especially Desert Rock, Nevada, where the uncertainty was reduced to approximately 

12% in NSRDB Version 3 from approximately 18% in NSRDB Version 2.

Figure 22. Overall uncertainty estimation of GHI for NSRDB PSM Version 2 (left) and PSM Version 

3 (right) using seven ground-based solar measurement locations. Data used in this figure are from 

1998–2015 for Version 2 and 1998–2017 for Version 3.

3.2.4 National Solar Radiation Database Users

Industry, labs, and academia outside of NREL also use the NSRDB. Some users include:

• Industry: EDP Renewables; First Solar; Southern Company; Suncore Photovoltaics; 

SolPEG GmbH; Iberdrola; Sono solar; DNV GL; Leidos, Strata Solar; OneEnergy 

Renewables; kWh Analytics; Lincoln Clean Energy; SunPower; Cypress Creek 

Renewables; Midwest Renewable Energy Association; GE Energy Consulting; LKS 

Energy; CFE Mexico; Arizona Public Service; Duke Energy; Sunpath Power & Electric;

ISRO; OST Energy; Sunshine Analytics; GRID Alternatives Colorado; Invenergy LLC;

Global Weather Corporation; Infinity Renewables; Oregon Department of 

Transportation; Sol Systems; NRG Energy; Carolina Solar Energy; New Energy Equity;

Radian Generation; DOE National Energy Technology Laboratory; American Cancer 

Society; Black & Veatch; Birdseye Renewable Energy and Academia; White Box 

Technologies.

• Academia and research labs: Lawrence Berkeley National Laboratory; U.S. Bureau of 

Reclamation; U.S. Air Force; U.S. Army Research Laboratory; National Energy Board 

Canada, Department of Agricultural Economics; Purdue University; European 

Commission, Joint Research Centre; VNIT, Nagpur, INDIA; Sandia National 
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Laboratories; U.S. Energy Information Administration; U.S. Environmental Protection 

Agency.

3.2.5 Research and Publications

NREL published an article on the NSRDB in Renewable and Sustainable Energy Reviews, a 

high-impact journal with an impact factor of 8.05 and a 5-year impact factor of 9.122. This paper 

briefly reviews the complete package of surface observations, models, and satellite data used for 

the latest version of the NSRDB as well as improvements in the measurement and modeling 

technologies deployed in the NSRDB during the years.

17

The journal paper discusses the evolution of the NSRDB and states that the data are a widely 

used public solar resource that have been developed and updated during more than 20 years to 

reflect advances in solar radiation measurement and modeling. The paper also discusses the 

current NSRDB model, PSM. Future updates of the NSRDB are expected annually. Advanced 

information in the planned data set will involve new satellite retrievals for the year 2017 and 

improved AOD data from MERRA-2. Future advancements in the PSM, however—e.g.,

identifying low clouds and fog in coastal areas; improving the discrimination of clouds from 

snow; providing specular reflection on bright surfaces; and reducing uncertainties of parallax,

especially under high-resolution conditions—are also desired to further increase the accuracy of 

the NSRDB.

3.2.6 MODIS-Derived Surface Albedo Data Set for NSRDB 

NREL developed an improved white-sky (bihemispherical reflectance) broadband (0.3–5.0 μm) 

surface albedo data set for use in NSRDB applications using two data sources: (1) the 

bidirectional reflectance distribution function/albedo product (designated MCD43D) from the 

MODIS18 and (2) the Integrated Multisensor Snow and Ice Mapping System (IMS) daily snow 

cover product.

19,20 The report found that the daily albedo data set provides a significant 

improvement from climatological products in spatial resolution and representation of the 

temporal variability of albedo. Although the gap-filled MODIS albedo product provides highquality estimates of surface albedo, the 8-day temporal resolution does not capture the ephemeral 

nature of snow cover. Changes in snow cover play a major role in the variability of surface 

albedo, particularly during months of snow accumulation and ablation (Déry and Brown 2007). 

Integration of the gap-filled MODIS albedo product and the Integrated Multisensor Snow and Ice 

Mapping System snow cover data set provide high-quality albedo estimates and fine temporal 

resolution of snow cover.

 

17 See https://www.sciencedirect.com/journal/renewable-and-sustainable-energy-reviews/vol/89/suppl/C.

18 NASA, Land Processes Distributed Active Archive Center (2015) 19 National Snow and Ice Data Center (2016) 20 See www.nrel.gov/docs/fy17osti/67306.pdf.
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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 23. Final data set for January 1, 2001, integrating MODIS-based surface albedo and 

Integrated Multisensor Snow and Ice Mapping System snow cover. From Maclaurin et al. (2016)

3.2.7 Use Case of NSRDB Data for the August 21, 2017, Total Solar Eclipse

NREL and collaborators have created a clear-sky probability analysis to help guide viewers to 

the August 21, 2017, total solar eclipse, the first such continent-spanning eclipse in nearly 100 

years for the United States (figure 24 and 25). Using cloud and solar data from the NSRDB, the 

analysis provides cloudless-sky probabilities specific to the date and time of the eclipse. 

Although not intended to be an eclipse weather forecast, the detailed maps helped guide eclipse 

enthusiasts to likely optimal viewing locations. Additionally, high-resolution data are presented 

for the centerline of the path of totality, representing the likelihood for cloudless skies and 

atmospheric clarity.
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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 24. Cloudless-sky probabilities for August 21, 2017, and the path of the total solar eclipse.

Path of totality from “Eclipse Predictions by Fred Espenak, NASA/GSFC Emeritus.” Cartography 

by Billy J. Roberts, NREL
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Figure 25. Centerline profile of the path of the total solar eclipse (Wilcox et al. 2017). Path of 

totality from “Eclipse Predictions by Fred Espenak, NASA/GSFC Emeritus.” Cartography by Billy 

J. Roberts, NREL

3.3 Task 3: Knowledge Sharing

This task developed and updated international consensus standards and best practices in solar 

measurement and modeling to represent the state-of-the-art knowledge through continuous 

formal engagement of various stakeholders.

3.3.1 International Energy Agency Photovoltaic Power Systems Programme Task

16

NREL leads the IEA Photovoltaic Power Systems Programme (PVPS) Task 16: Subtask 1,

which focuses on the evaluation of current and emerging resource assessment methodologies. 

The subtask covers three areas: ground-based methods, numerical weather prediction models,

and satellite-based methods. For each methodology, a separate activity is defined. The objective 

of this subtask is to contribute to the improvement of established and new measurement methods 

for solar resource assessment, analyze the numerical weather prediction models and 

improvements in cloud detection, and contribute to the evaluation and improvement of satellitederived data and their uncertainty and formulation of measurement best practices/standards. 

Figure 26 shows a group photo of the participants in the third IEA PVPS Task 16 meeting, which 

was held in Rapperswil, Switzerland, and attended by more than 40 participants representing 14 

countries. There are 56 institutions participating in the task.
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3.3.1.1 Publication of the Best Practices Handbook for the Collection and Use of Solar 

Resource Data for Solar Energy Applications

In collaboration with international partners within the IEA, the Best Practices Handbook for the 

Collection and Use of Solar Resource Data for Solar Energy Applications: Second Edition was 

published.

21 The handbook is assembled by scientists and engineers who have many decades of 

combined experience in state-of the-art atmospheric science, radiometry, meteorological data 

processing, and renewable energy technology development. The paper contains contributions 

from an international group of experts primarily from knowledge gained through participation in 

the IEA’s Solar Heating and Cooling Programme. The handbook discusses solar resource 

components, instrument types, measurement station design, and current best practices in 

modeling and forecasting solar radiation, historical radiation data, and methods to apply solar 

radiation data to solar energy projects. It also contains a summary of solar forecasting and its 

development throughout the last few years. Solar energy project developers; engineering, 

procurement, and construction firms; utility companies; electric power system operators; energy 

suppliers; financial investors; and others involved in solar energy systems planning and 

development will find this handbook to be a valuable resource for collecting and interpreting 

data that can be used as a reference during each project stage. 

Figure 26. Participants in the third meeting of the IEA PVPS Task 16, held in Rapperswil, 

Switzerland. Photo by Jan Remund 

3.3.2 American Society for Testing and Materials International 

NREL continued to provide leadership in the development and update of standards that are 

relevant to the measurement and modeling of solar radiation for solar energy applications. 

Standards developed cover calibration method of pyrheliometer (DNI measurements), 

 

21 See https://www.nrel.gov/docs/fy18osti/68886.pdf.
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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

pyranometer (GHI measurements), spectroradiometer, ultraviolet (UV) radiometers, correct field 

deployment of these instruments, spectral distribution reference tables for PV modeling, and 

transfer of indoor calibrations.

3.3.2.1 Existing American Society for Testing and Materials Standard Under 

Subcommittee Radiometry

Existing ASTM standards are subjected to revision at any time by the technical committee and 

must be reviewed every 5 years, and if not revised, either reapproved or withdrawn. The 

standards under ASTM Subcommittee G03.09 on Radiometry, which is led by NREL, are 

responsible for the following existing standards:

• E816: Standard Test Method for Calibration of Pyrheliometers by Comparison to 

Reference Pyrheliometers

• E824: Standard Test Method for Transfer of Calibration From Reference to Field 

Radiometers

• G130: Standard Test Method for Calibration of Narrow- and Broad-Band Ultraviolet 

Radiometers Using a Spectroradiometer

• G138: Standard Test Method for Calibration of a Spectroradiometer Using a Standard 

Source of Irradiance

• G167: Standard Test Method for Calibration of a Pyranometer Using a Pyrheliometer

• G173: Standard Tables for Reference Solar Spectral Irradiances: Direct Normal and 

Hemispherical on 37° Tilted Surface

• G177: Standard Tables for Reference Solar Ultraviolet Spectral Distributions: 

Hemispherical on 37° Tilted Surface

• G183: Standard Practice for Field Use of Pyranometers, Pyrheliometers and UV 

Radiometers

• G197: Standard Table for Reference Solar Spectral Distributions: Direct and Diffuse on 

20° Tilted and Vertical Surfaces

• G207- Standard Test Method for Indoor Transfer of Calibration from Reference to Field 

Pyranometers

• G213 - 17: Standard Guide for Evaluating Uncertainty in Calibration and Field 

Measurements of Broadband Irradiance with Pyranometers and Pyrheliometers

• G214 - 16: Standard Test Method for Integration of Digital Spectral Data for Weathering 

and Durability Applications

3.3.2.2 New Standards Developed During the Project Period

NREL led and shepherded the publication of new standards through ASTM.

• ASTM G213-17: Guide for Evaluating Uncertainty in Calibration and Field 

Measurements of Broadband Irradiance with Pyranometers and Pyrheliometers 22

As stated in this guide provides guidance and recommended practices for 

evaluating uncertainties when calibrating and performing outdoor measurements 

 

22 https://www.astm.org/Standards/G213.htm
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with pyranometers and pyrheliometers used to measure total hemispherical and 

direct solar irradiance. This guide quantifies the uncertainty in measuring the total 

(all angles of incidence) broadband (all wavelengths of light) irradiance 

experienced either indoors or outdoors.

The standard also included an interactive Excel spreadsheet as adjunct, 

ADJG021317. The intent is to provide users real-world examples and to illustrate 

the implementation of the GUM method.

• ASTM G214-16: Standard Test method for Integration of Digital Spectral Data for 

Weathering and Durability Application.

23

NREL led the development of a new standard that was published in 2016. This 

standard specifies a single relatively simple-to-implement common integration 

technique, the Modified Trapezoid Rule, to integrate digital or tabulated spectral 

data. The intent is to produce greater consistency and comparability of weathering 

and durability test results between various exposure regimes, calculation of 

materials properties, and laboratories with respect to numerical results that depend 

on the integration of spectral distribution data. Weathering and durability testing 

often require the computation of the effects of radiant exposure of materials to 

various optical radiation sources, including lamps with varying spectral power 

distributions and outdoor and simulated sunlight. Changes in the spectrally

dependent optical properties of materials, in combination with exposure source 

spectral data, are often used to evaluate the effect of exposure to radiant sources;

develop action spectra; and classify, evaluate, or rate sources with respect to 

reference or exposure source spectral distributions. Computation of these bulk 

optical properties requires the integration of measured wavelength-dependent 

digital data, sometimes in conjunction with tabulated wavelength-dependent 

reference or comparison data. Here the term integration refers to the numerical 

approximation to the true integral of continuous functions, represented by 

discrete, digital data. As stated in the standard, there are numerous mathematical 

techniques for performing numerical integration. Each method provides different 

levels of complexity, accuracy, ease of implementation and computational 

efficiency, and, of course, resultant magnitudes. Thus, the need for a standard 

integration technique is to simplify the comparison of results from different

laboratories, measurement instrumentation, or exposure regimes.

3.3.2.3 Standards Under Development

• Standard Performance Classification of Solar Radiometers

NREL provided input in the update of ISO 9060: Solar energy—Specification and 

Classification of Instruments for Measuring Hemispherical Solar and Direct Solar 

Radiation Standard. The international standard was accepted. 

• The ASTM subcommittee on radiometry has a draft standard on Guide for Performance 

 

23 https://www.astm.org/Standards/G214.htm
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Classification of Solar Radiometers. This guide describes available international 

specifications for the performance classification for instruments that measure 

hemispherical solar and direct solar radiation. The guide is limited to instruments 

optimized for measurement of the solar irradiance in W/m2. These instruments typically 

respond over the solar spectrum of wavelengths from 285 nm to 3,000 nm but are 

calibrated with respect to the internationally recognized WRR maintained by a group of 

absolute cavity pyrheliometers that respond to radiation out to the far infrared (~50 

micrometer) spectral region. The referenced classification schemes are based on a set of 

instrument specifications published in the referenced documents. The specifications are 

obtained through characterization experiments, involving indoor as well as outdoor 

testing. The objective of this guide is to reference the performance classification 

documents, provide background to the development of the classifications, and provide 

guidance to the user in using performance classification to select instruments appropriate 

for the user’s application. The user should realize that the measurement uncertainty as 

obtained during outdoor measurements is a function of (1) instrument class or 

classification, (2) calibration procedure, (3) measurement conditions and maintenance, 

and (4) environmental conditions. Therefore, statements about the overall measurement 

uncertainty can be made only on an individual basis, taking all these factors into account. 

Uncertainty evaluation is covered by ASTM Work Item WK 36479: Standard Practice for 

Uncertainty Evaluation of Calibration and Measurements with Pyranometers and 

Pyrheliometers.

• New Standard estimation of UV irradiance received by samples as a function of location, 

orientation, and tilt angle

Many companies and academic researchers use commercial or in-house UV calculators to 

estimate yearly UV dose received by samples as a function of location, orientation, and 

tilt angle. These calculations are used to roughly estimate service life based on dose to 

failure in laboratory or natural weathering experiments. The subcommittee recognizes the 

opportunity to standardize these practices and proposes to put forward: 

A. A set of equations used to calculate the direct and diffuse total solar radiation and 

total UV radiation as a function of position, orientation, and tilt angle 

B. A method to separate the total UV components of the GHI from the typical 

meteorological year data to be used as inputs in these equations 

C. An agreed-upon conversion method to estimate the equivalent accelerated 

weathering test duration in standard weathering cycles, based on UV dose. Other 

factors contribute to the acceleration of aging, such as temperature and moisture, 

which are not accounted for in such calculators. The standard will include the 

appropriate disclaimers. Future development might include these factors in a more 

comprehensive climate and acceleration factor calculation.

To support of this draft standard, NREL published a on “Estimating Ultraviolet Radiation

from Global Horizontal Irradiance” in the Journal of Photovoltaics. As stated in this 

paper, a model was developed to estimate the global UV irradiance contained in multiple 

wavebands (280–400 nm, 295–400 nm, 285–385 nm, and 295–385 nm) using the total 

broadband solar irradiance, as obtained from usual radiation models or measured with, 
39

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

e.g., a conventional pyranometer. The method is based on simulations of both global UV 

and total broadband solar irradiance obtained with the SMARTS spectral radiation model. 

Under clear-sky conditions, the airmass was found to be the primary driver of the global 

UV/total broadband solar irradiance ratio, at least under “typical” atmospheric conditions. 

Further, still using SMARTS, the model was extended to estimate the global UV/total 

broadband solar irradiance ratio for a variety of tilt angles. 

The proposed model can be applied to estimate the total UV irradiance from total solar 

irradiance (GHI or global tilted irradiance) under all-sky conditions. The necessary input 

data can be obtained from actual measurements, satellite-derived modeled time series, or 

usual typical meteorological year data files. 

The results (e.g. Figure 27) show that the modeled UV results are in good agreement with 

actual measurements at different timescales, multiple locations, and tilt angles, which 

provides confidence about the accuracy of the model. The model typically under- or 

overestimates the measured UV irradiance within only ±2 W/m2

. The RMSE appears 

slightly better for the horizontal than the tilt results. This situation could be related to the 

model’s limitation under tilted conditions or to data quality of the global tilted irradiance 

data (which are estimated from GHI with a transposition model, thus adding uncertainty). 

As usual, the quality of the model’s inputs largely conditions the UV predictions’ 

accuracy.

The model proposed here is meant to characterize the UV irradiance or irradiation at any 

location, and hence to help understand the degradation of various materials used in PV 

modules, and ultimately to provide a reliable assessment of their service life. In this 

perspective, the model demonstrated results within 2%–8% of the measured irradiance 

values on various tilts. 

Additionally, the model does not seem to be significantly affected by cloudiness (except 

possibly at Miami), but it appears sensitive to large excursions in AOD, precipitable 

water, or surface albedo, which will require further attention.
40

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Figure 27. Modeled vs. measured global UV irradiance under all-sky conditions and zero tilt. RMSE 

and MBE values are in W/m2. The black line is the 1:1 line, and the red line is the regression line. 

The dot colors refer to a “density scale” from blue to red, with red showing the highest density of 

points. From Habte et al. (2019)

• Standard Practice for Measurement and Calibration of the Specimen Irradiance in 

Accelerated Weathering Devices

• Weathering device manufacturers have their own approach to calibrating irradiance 

measuring radiometers. Lack of specific guidelines for the reliable determination of 

specimen irradiance in weathering devices is a significant factor in the variability of 

artificial weathering test results. This standard will propose a uniform set of best practices 

for the choice and calibration of radiometers used to measure the irradiance provided by 

the various types of weathering devices. It is intended for use by manufacturers, test 

laboratories, and end users of weathering devices. This practice covers the basic 

principles and procedures for measuring the irradiance incident upon specimens in a 

weathering device when operating a specified weathering method. This practice is 

applicable to all types of weathering devices, including fluorescent UV lamp devices, 

filtered xenon-arc devices, and metal halide devices.
41

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Significant Accomplishments and Conclusions

4.1 Summary of Accomplishments

• Continuous, real-time, high-quality 1-minute data from 60 instruments measuring GHI, 

DNI, DHI, spectrum, AOD, UV, water vapor, and other meteorological parameters that 

can be used for solar modeling

• Calibration of 342 NREL and 140 non-NREL radiometers using the BORCAL process to 

enable accurate solar measurements nationwide

• Transferred world radiation standard to more than 120 ACRs from national and 

international shareholders including calibration service providers

• Twenty years (1998–2017) of high-resolution (30-minute and 4-km temporal and spatial 

resolution) solar resource information provided free to users through the NSRDB 

website. The area covered is bordered by longitudes 25° W on the east and 175° W on the 

west and by latitudes -20° S on the south and 60° N on the north.

• Incorporated advanced NASA MERRA-2 data set in the NSRDB to improve aerosol 

characterization and other meteorological data sets

• Eighteen peer-reviewed journal publications, 10 technical reports, 15 conference papers, 

and 21 conference presentations and posters that demonstrate advances the state of the art 

in satellite-based resource estimation and reducing uncertainty in solar measurements

• Published the second edition of the Best Practices Handbook for the Collection and Use 

of Solar Resource Data for Solar Energy Applications in collaboration with worldleading experts through IEA’s Solar Heating and Cooling Programme Task 46

• Led technical contributions for the United States for two new ASTM standards and one 

ISO standard on measurement uncertainty and instrument classification 

• Disseminated research to 300,000 users per year through the MIDC, RReDC, and 

NSRDB websites.

4.2 Challenges

4.2.1 Maintaining Traceability: Developing the Next-Generation Absolute Cavity 

Radiometer to Maintain Solar Measurement Accuracy

The automatic Hickey-Frieden (HF/AHF) has been manufactured by Eppley Laboratory, Inc. 

since 1977 but was discontinued in 2015. The AHF is a critical part of the traceability chain for 

calibrating all radiometers in the United States and potentially worldwide. As the demand for 

solar energy grows, there will be an increased need for calibration of radiometers that are 

deployed at these solar generation sites (PV and CSP plants). In the absence of a replacement 

and with the nonavailability of the AHF, a critical break in the traceability chain might happen in 

the future. This could result in degradation of U.S. capabilities to provide accurate solar radiation 

measurements for resource assessments and plant operations, which would result in an increase 

in uncertainty and a corresponding increase in the both the development and cost of operations of 

PV and CSP plants. NREL, in collaboration with NIST and Eppley Laboratory, Inc., are 

attempting to redesign and manufacture AHF to ensure the traceability of all calibrated 

instruments that are deployed at PV and CSP plants around the United States.
42

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4.2.2 Changes in DNI Prediction Caused by the Aerosol Data Timescale

DNI data demonstarted differences between Version 2 and Version 3 of the NSRDB, as evident 

in Figure 28. A posible reason for the difference in the AOD between the two versions is that

Version 3 shows potential locations for solar development, whereas they were previously

excluded by the U.S. Bureau of Land Management Solar Energy Programmatic Environmental 

Impact Statement. Previously, the U.S. Bureau of Land Management developed a rule excluding 

lands from solar development with a solar insolation less than 6.5 kWh/m2/d.

Figure 28. DNI differences between NSRDB (left) Version 3 and (right) Version 2

4.2.3 Differences Between GOES-East and GOES-West NSRDB Data

The satellite information that is used to produce NSRDB data originates from two satellites:

GOES-East and GOES-West, with the dividing line between 105o and 110o W longitude. GOESEast data have time-shift applied to cloud properties instead of solar radiation. The solar 

radiation data from both satellites are available at the top and middle of every hour. The data 

from both satellites were joined to provide a complete uninterrupted satellite image every half

hour at a 4-km resolution for the United States, as shown in Figure 29.

Figure 29. NSRDB Version 3 blended GHI data set
43

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Table 4. GOES-East and GOES-West Comparison with Surface Measurements (%MBE)

Station Latitude Longitude

GOES-East GOES-West

All 

Sky

Clear 

Sky

Cloudy

Sky

All

Sky

Clear

Sky

Cloudy

Sky

Ashland 42.19 -122.7 4 5 2 1 6 -5

Burns 43.52 -119.02 0 2 -1 -1 2 -7

Eugene 44.05 -123.07 0 0 1 -1 2 -6

Silverlake 43.12 -121.06 0 0 -1 -2 1 -7

Figure 30. Comparison for Eugene, Oregon, using (left) GOES-West satellite and (right) GOESEast satellite

We noticed differences between the two versions, however, along the dividing line (105° 

longitude). To better understand these differences, we compared GHI estimates from the GOESEast and GOES-West satellites to four University of Oregon Solar Radiation Measurement 

Laboratory ground measurement stations. Both satellites cover the State of Oregon where these 

stations are located. The result using 2014 data shows that the GOES-East satellite performs 

better than the GOES-West satellite for this region (Table 4 and Figure 30). Note that the GOESWest satellite has a better viewing angle for the State of Oregon than the GOES-East satellite.

Overall, the GOES-East comparison shows zero bias for all-sky conditions and less than ±2% for 

clear and cloudy conditions for three out of four stations. The GOES-West satellite shows higher 

biases in all conditions, clear and cloudy skies, except for Ashland, where the all-sky condition 

shows less bias compared to GOES-East; however, there is a cancellation of error because clear 

and cloudy have opposite signs of equal magnitude. But the results obtained here need further 

study to better understand the cause of the outcome.
44

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Path Forward

NREL is internationally recognized for its continued expertise in solar resource measurements, 

calibration, and modeling. Developments in these areas and continued support from DOE and 

NREL will directly impact the outcomes of other R&D projects supported by SETO, including 

PV performance and characterization, numerous economic and performance models, policy 

analysis, geographic information system products, and grid integration systems.

Regular dissemination of NSRDB data containing GHI, DNI, DHI, and spectrally resolved 

irradiance will continue. Data will be delivered as time series or typical meteorological year data 

sets; however, currently NSRDB data do not include the effect of subgrid effects such as terrain 

or building shading on the irradiance and must be combined with other tools to estimate energy 

production. In the future, NREL will attempt to estimate the effects and incorporate the result in 

the data set. Moreover, NREL will seek to improve the quality and resolution of the NSRDB to 

represent the state of the art in the science of satellite remote sensing of clouds and radiation. 

With the deployment of the next-generation satellite GOES-16 (former GOES-R), there is an 

opportunity to provide grid awareness every 5 minutes at a 2-km resolution. Also, the addition of 

additional sensing channels on the satellite provides information that improves the detection of 

clouds and retrieval of their properties. This proposal seeks to enhance the methods used to 

develop the NSRDB to incorporate both the additional information from the new satellites as 

well as new science that uses this additional information to improve the quality of the products.

NREL will continue to make available the world-class continuous measurements of solar 

radiation and meteorological parameters from the BMS to enable R&D activities in support of 

SETO as follows. NREL through SRRL will continue to serve as a living laboratory that allows 

experience gathered from operating instruments, developing standards, and testing models to be 

shared quickly and easily across the solar energy industry. This experience will continue to be

shared through ASTM and ISO standards, conference presentations, technical reports, and 

journal articles. Measurement research will continue such as identifying sources of uncertainty in 

solar measurements, their quantification, and mitigation. New and improved instruments are 

continuously deployed for validation and characterization against the BMS. The validation 

process provides an understanding of the quality of a new instrument when compared to peers. 

The characterization process provides an understanding of the variability between instruments of 

the same make and model and essentially provides an understanding of the manufacturing 

quality. These tests will continue to be carried out in collaboration with manufacturers, including 

Kipp & Zonen, Hukseflux, EKO Instruments, and Eppley Labs. The result obtained will be 

available to allow the solar energy industry to understand the performance of sensors and make 

informed decisions regarding their deployment of the use of data collected by those sensors.

Development of new types of instrumentation, advancements in measurement methods and 

techniques, and updates in measurement best practices resulting from SRRL R&D forms an 

essential part of regular updates to NREL’s Best Practices Handbook for solar measurement and 

modeling. 

NREL will continue long-standing leadership roles and involvements in international standards 

and best practices with ASTM and IEA to advance radiometry, solar modeling, and support 

stakeholder needs for relevant standards addressing solar resource characterization. 
45

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Bibliography: Inventions, Patents, Publications, and 

Other Results

Fiscal Year 2016–Fiscal Year 2018 Publications

Book Chapters/Books

1. Manajit Sengupta, Aron Habte, Christian Gueymard, Stefan Wilbert, Dave Renne, and

Thomas Stoffel, Best Practices Handbook for the Collection and Use of Solar Resource 

Data for Solar Energy Applications: Second Edition (ISBN 978-0-692-08922-4).

(Golden, CO: National Renewable Energy Laboratory, 2017).

https://www.nrel.gov/docs/fy18osti/68886.pdf.

Journal Articles

2018

1. M. Sengupta, Y. Xie, A. Lopez, A. Habte, G. Maclaurin, and J. Shelby, “The National 

Solar Radiation Data Base (NSRDB),” Renewable and Sustainable Energy Reviews 89

(2018): 51–60, https://doi.org/10.1016/j.rser.2018.03.003.

2. I Garcia, W.E. McMahon, A. Habte et al., “Spectral Binning for Energy Production 

Calculations and Multijunction Solar Cell Design,” Progress in Photovoltaics: Research 

and Applications 26 (2018): 48–54, https://doi.org/10.1002/pip.2943.

3. Andrew Clifton, Bri-Mathias Hodge, Caroline Draxl, Jake Badger, and Aron Habte,

“Wind and Solar Resource Data Sets,” WIREs Energy and Environment 7 (2018), doi: 

10.1002/wene.276, http://wires.wiley.com/WileyCDA/WiresArticle/wisIdWENE276.html.

4. S.E. Haupt, B. Kosović, T. Jensen, J.K. Lazo, J.A. Lee, P.A. Jiménez, J. Cowie, G. 

Wiener, T.C. McCandless, M. Rogers, S. Miller, M. Sengupta, Y. Xie, L. Hinkelman, P. 

Kalb, and J. Heiser, “Building the Sun4Cast System: Improvements in Solar Power 

Forecasting,” Bulletin of the American Meteorological Society 99 (2018) 121–

136, https://doi.org/10.1175/BAMS-D-16-0221.1.

5. S.D. Miller, M.A. Rogers, J.M. Haynes, M. Sengupta, and A.K. Heidinger, “Short-Term 

Solar Irradiance Forecasting via Satellite/Model Coupling,” Solar Energy (2018) 168:

102–117, https://doi.org/10.1016/j.solener.2017.11.049.

6. Wilko Jessen, Stefan Wilbert, Christian A. Gueymard, Jesús Polo, Zeqiang Bian, Anton 

Driesse, Aron Habte, Aitor Marzo, Peter R. Armstrong, Frank Vignola, and Lourdes 

Ramírez, “Proposal and Evaluation of Subordinate Standard Solar Irradiance Spectra for 

Applications in Solar Energy Systems,” Solar Energy 168 (2018): 30 –43, ISSN 0038-

092X, https://doi.org/10.1016/j.solener.2018.03.043.

7. C.A. Gueymard, A. Habte, and M. Sengupta, “Reducing Uncertainties in Large-Scale 

Solar Resource Data: The Impact of Aerosols,” IEEE Journal of Photovoltaics 8, no. 6

(2018): 1732–1737, doi: 10.1109/JPHOTOV.2018.2869554. 

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8478427&isnumber=85

11079.

8. A. Habte , C.A. Gueymard, and M. Sengupta, “Estimating Ultraviolet Radiation from 

Global Horizontal Irradiance,” doi: 10.1109/JPHOTOV.2018.2871780.
46

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

2017

9. Ibrahim Reda, Afshin Andreas, Mike Dooraghi, Manajit Sengupta, Aron Habte, and

Mark Kutchenreiter, “Reducing Broadband Shortwave Radiometer Calibration-Bias 

Caused by Longwave Irradiance in the Reference Direct Beam,” Atmospheric and 

Climate Sciences 7 (2017): 36–47, http://dx.doi.org/10.4236/acs.2017.71004,

http://www.nrel.gov/docs/fy17osti/67048.pdf.

10. Aron Habte, Manajit Sengupta, Afshin Andreas, Ibrahim Reda, and Justin Robinson,

“Radiometer Calibration Methods and Resulting Irradiance Differences,” Progress in 

Photovoltaics: Research and Applications, 25 (2017): 614–622,

http://dx.doi.org/10.1002/pip.2812.

11. Josh Peterson, Frank Vignola, Aron Habte, and Manajit Sengupta, “Developing a 

Spectroradiometer Data Uncertainty Methodology,” Solar Energy 149 (2017): 60–76,

https://doi.org/10.1016/j.solener.2017.03.075.

12. Joseph J. Michalsky, Mark Kutchenreiter, and Charles N. Long, “Significant 

Improvements in Pyranometer Nighttime Offsets Using High-Flow DC Ventilation,”

Journal of Atmospheric and Oceanic Technology 34 (2017): 1323–1332,

http://dx.doi.org/10.1175/JTECH-D-16-0224.1.

2016

13. Aron Habte, Manajit Sengupta, Afshin Andreas, Stephen Wilcox, and Thomas Stoffel. 

“Intercomparison of 51 Radiometers for Determining Global Horizontal Irradiance and 

Direct Normal Irradiance Measurements,” Solar Energy 133 (2016): 372–393,

http://dx.doi.org/10.1016/j.solener.2016.03.065.

14. Yu Xie, Manajit Sengupta, and Jimy Dudhia, “Fast All-Sky Radiation Model for Solar 

applications (FARMS): Algorithm and Performance Evaluation,” Solar Energy 135 

(2016): 435–445, http://dx.doi.org/10.1016/j.solener.2016.06.003.

15. N. Hanrieder, Manajit Sengupta, Yu Xie, S. Wilbert, and R. Pitz-Paal, “Modeling Beam 

Attenuation in Solar Tower Plants using Common DNI Measurements,” Solar Energy

129 (2016): 244–255, http://dx.doi.org/10.1016/j.solener.2016.01.051.

16. J. Polo, S. Wilbert, J.A. Ruiz-Arias, R. Meyer, C. Gueymard, M. Suri, L. Martin, T. 

Mieslinger, P. Blanc, I. Grant, J. Boland, P. Ineichen, J. Remund, R. Escobar, A. 

Troccoli, M. Sengupta, K.P. Nielsen, D. Renne, N. Geuder, and T. Cebecauer,

“Preliminary Survey on Site-Adaptation Techniques for Satellite-Derived and Reanalysis 

Solar Radiation Datasets,” Solar Energy 132 (2016): 25–37,

https://doi.org/10.1016/j.solener.2016.03.001.

17. F.A. Mejia, B. Kurtz, K. Murray, L.M. Hinkelman, M. Sengupta, Y. Xie, and J. Klessl, 

“Coupling Sky Images with Radiative Transfer Models,” Atmospheric Measurement 

Technology 9 (2016): 4151–4165, 10.5194/amt-9-4151-2016, https://www.atmos-meastech.net/9/4151/2016/.

18. J. Peterson, F. Vignola, A. Habte, and M. Sengupta, “Developing a Spectroradiometer 

Data Uncertainty Methodology,” Solar Energy 149 (2016): 60–76,

https://doi.org/10.1016/j.solener.2017.03.075.
47

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Technical Reports

2018

1. Aron Habte, Improved Instrumentation for Solar Resource Measurements and 

Forecasting: Cooperative Research and Development Final Report, CRADA Number 

CRD-09-349 (NREL/TP-5D00-71837) (Golden, CO: National Renewable Energy 

Laboratory, 2018). https://www.nrel.gov/docs/fy18osti/71837.pdf.

2017

2. Manajit Sengupta, Aron Habte, Christian Gueymard, Stefan Wilbert, Dave Renne, and

Thomas Stoffel, Best Practices Handbook for the Collection and Use of Solar Resource 

Data for Solar Energy Applications: Second Edition (NREL/TP-5D00-68886, ISBN 978-

0-692-08922-4) (Golden, CO: National Renewable Energy Laboratory, 2017),

https://www.nrel.gov/docs/fy18osti/68886.pdf.

3. Ibrahim Reda, Mike Dooraghi, Afshin Andreas, and Aron Habte, NREL Pyrheliometer 

Comparisons: September 25–October 6, 2017 (NPC-2017) (NREL/TP-1900-70436) 

(Golden, CO: National Renewable Energy Laboratory, 2017),

https://www.nrel.gov/docs/fy17osti/67311.pdf. 

4. Aron Habte, Manajit Sengupta, and Anthony Lopez. Evaluation of the National Solar 

Radiation Database (NSRDB): 1998–2015 (NREL/TP-5D00-67722) (Golden, CO: 

National Renewable Energy Laboratory, 2017),

https://www.nrel.gov/docs/fy17osti/67722.pdf.

5. Ibrahim Reda and Afshin Andreas, Calibration Procedure of Hukseflux SR25 to Establish 

the Diffuse Reference for the Outdoor Broadband Radiometer Calibration (NREL/TP1900-68999) (Golden, CO: National Renewable Energy Laboratory, 2017),

https://www.nrel.gov/docs/fy17osti/68999.pdf.

6. Steve Wilcox, Aron Habte, Billy Roberts, Tom Stoffel, Mark Kutchenreiter, and Manajit 

Sengupta, Clear-Sky Probability for the August 21, 2017, Total Solar Eclipse Using the 

NREL National Solar Radiation Database (NREL/TP-5D00-68885) (Golden, CO: 

National Renewable Energy Laboratory, 2017),

http://www.nrel.gov/docs/fy17osti/68885.pdf.

7. Anthony Lopez, Galen Maclaurin, Billy Roberts, and Evan Rosenlieb, Capturing InterAnnual Variability of PV Energy Production in South Asia (NREL/ TP-6A20-68955) 

(Golden, CO: National Renewable Energy Laboratory, 2017),

http://www.nrel.gov/docs/fy17osti/68955.pdf.

2016

8. Ibrahim Reda, Mike Dooraghi, Afshin Andreas, and Aron Habte, NREL Pyrheliometer 

Comparisons: September 26–October 7, 2016 (NPC-2016) (NREL/TP-3B10-67311)

(Golden, CO: National Renewable Energy Laboratory, 2016),

http://www.nrel.gov/docs/fy17osti/67311.pdf.

9. Galen Maclaurin, Manajit Sengupta, Yu Xie, and Nicholas Gilroy, Development of a 

MODIS-Derived Surface Albedo Data Set: An Improved Model Input for Processing the 
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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

NSRDB (NREL/TP-6A20-67306) (Golden, CO: National Renewable Energy Laboratory, 

2016), http://www.nrel.gov/docs/fy17osti/67306.pdf.

10. Afshin M. Andreas and Stephen M. Wilcox, Radiometer Calibration and 

Characterization (RCC) User's Manual: Windows Version 4.0 (NREL/TP-3B10-65844) 

(Golden, CO: National Renewable Energy Laboratory, 2016),

http://www.nrel.gov/docs/fy16osti/65844.pdf.

Conference Papers

2018

1. Yu Xie and Manajit Sengupta, “Assessing the Performance of the Fast All-sky Radiation 

Model for Solar Applications with Narrowband Irradiances on Tilted Surfaces (FARMSNIT): Preprint,” 2018, https://www.nrel.gov/docs/fy18osti/71595.pdf. 

2. Yu Xie and Manajit Sengupta, “Assessment of Error Sources in the Modeling of POA 

Irradiance Under Clear-Sky Conditions: Preprint,” 2018,

https://www.nrel.gov/docs/fy18osti/71104.pdf.

3. Aron Habte, Manajit Sengupta, Afshin Andreas, Ranganath Narasappa, Taylor Thomas, 

Adam Wolf, and Christian A. Gueymard, “Characterization of a Low-Cost MultiParameter Sensor for Resource Applications: Preprint” (NREL/CP-5D00-71585), 

presented at the 2018 IEEE PVSC, https://www.nrel.gov/docs/fy18osti/71585.pdf.

4. Frank Vignola, Josh Peterson, Rich Kessler, Mike Dooraghi, Manajit Sengupta, and Fotis 

Mavromatakis, “Evaluation of Photodiode-based Pyranometers and Reference Solar Cells 

on a Two-Axis Tracking System,” presented at the 2018 World Conference on 

Photovoltaic Energy Conversion (WCPEC-7), Waikoloa, Hawaii, June10–15, 2018,

https://www.nrel.gov/docs/fy18osti/71354.pdf.

5. Aron Habte, Manajit Sengupta, Anthony Lopez, Yu Xie, and Galen Maclaurin,

“Assessment of the National Solar Radiation Database (NSRDB 1998–2016): Preprint,”

2018, https://www.nrel.gov/docs/fy18osti/71607.pdf.

6. Stefan Wilbert, Wilko Jessen, Chris Gueymard, Jesus Polo, Zeqiang Bian, Anton Driesse, 

Aron Habte, Aitor Marzo, Peter Armstrong, Frank Vignola, and Lourdes Ramirez,

“Proposal and Evaluation of Subordinate Standard Solar Irradiance Spectra: Preprint,”

2018, https://www.nrel.gov/docs/fy18osti/70318.pdf.

2017

7. Aron Habte and Manajit Sengupta, “Best Practices of Uncertainty Estimation for the 

National Solar Radiation Database (NSRDB 1998–2015): Preprint” (NREL/CP-5D00-

70165), https://www.nrel.gov/docs/fy18osti/70165.pdf. 

8. F. Vignola, Josh Peterson, Chun-Yu Chiu, Michael Dooraghi, Manajit Sengupta, and

Fotis Mavromatakis, “Comparison of Pyranometers and Reference Cells on Fixed and 

One-axis Tracking Surfaces” (NREL/CP-5D00-70285), presented at the 2017 ASES 

National Solar Conference, https://dx.doi.org/10.18086/solar.2017.07.01.

9. Frank Vignola, Chun-Yun Chiu, Josh Peterson, Mike Dooraghi, and Manajit Sengupta,

“Comparison and Analysis of Instruments Measuring Plane-of-Array Irradiance for OneAxis Tracking Systems: Preprint,” 2018, https://www.nrel.gov/docs/fy18osti/67882.pdf.
49

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

2016

10. Aron Habte, Manajit Sengupta, Afshin Andreas, Ibrahim Reda, and Justin Robinson,

“Shortwave Radiometer Calibration Methods Comparison and Resulting Solar Irradiance 

Measurement Differences: A User Perspective,” Proceedings of the 2016 IEEE 43rd 

Photovoltaic Specialists Conference (PVSC), June 5–10 2016, Portland, Oregon 

(Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2016),

https://doi.org/10.1109/PVSC.2016.7749561.

11. Aron Habte, Manajit Sengupta, Afshin Andreas, Ibrahim Reda, and Justin Robinson,

“The Impact of Indoor and Outdoor Radiometer Calibration on Solar Measurements: 

Preprint,” 2016, https://www.nrel.gov/docs/fy16osti/66659.pdf.

12. Jorgen Konings and Aron Habte, “Uncertainty Evaluation of Measurements with 

Pyranometers and Pyrheliometers,” Proceedings of SWC 2015: ISES Solar World 

Congress, November 8–12, 2015, Daegu, Korea (Freiburg, Germany: International Solar 

Energy Society (ISES), 2016), https://proceedings.ises.org/paper/swc2015/swc2015-

0030-Konings.pdf.

13. Stefan Wilbert, Stefan Kleindiek, Bijan Nouri, Norbert Geuder, Aron Habte, Marko 

Schwandt, and Frank Vignola, “Uncertainty of Rotating Shadowband Irradiometers and 

Si-Pyranometers Including the Spectral Irradiance Error. Paper No. 150009,”

Proceedings of SOLARPACES 2015: International Conference on Concentrating Solar 

Power and Chemical Energy Systems, October 13–16, 2015, Cape Town, South Africa 

(Melville, NY: AIP Publishing, 2016), https://dx.doi.org/10.1063/1.4949241.

14. Frank Vignola, Josh Peterson, Rich Kessler, Fotis Mavromatakis, Mike Dooraghi, and

Manajit Sengupta, “Use of Pyranometers to Estimate PV Module Degradation Rates in 

the Field: Preprint,” 2016, https://www.nrel.gov/docs/fy16osti/66498.pdf. 

15. Paul W. Stackhouse Jr., Patrick Minnis, Richard Perez, Manajit Sengupta, Kenneth 

Knapp, J. Colleen Mikovitz, James Schlemmer, Benjamin Scarino, Taiping Zhang, and

Stephen J. Cox, “An Assessment of New Satellite Data Products for the Development of 

a Long-Term Global Solar Resource at 10-100 km,” Proceedings of Solar 2016: ASES 

National Solar Conference, July 10–14, 2016, San Francisco, California (Boulder, CO: 

American Solar Energy Society (ASES), 2016), https://www.osti.gov/biblio/1350021.

Conference Presentation/Poster

2018

1. Ibrahim Reda, Juian Grobner, Mike Dooraghi, Christian Thomann, Chuck Long, Allison 

McComiskey, Manajit Sengupta, Emiel Hall, and Stefan Wacker, “Results of Absolute 

Cavity Pyrgeometer and Infrared Integrating Sphere Comparisons” (NREL/PO-5D00-

70955), 2018, https://www.nrel.gov/docs/fy18osti/70955.pdf.

2. Ibrahim Reda, Julian Grobner, Stefan Wacker, Dave Turner, Mike Dooraghi, Christian 

Thomann, Chuck Long, Allison McComiskey, Manajit Sengupta, Isabelle Ruedi, and

Bruce Forgan, “Results of Absolute Cavity Pyrgeometer (ACP), InfraRed Integrating 

Sphere (IRIS), and Atmospheric Emitted Radiance Interferometer (AERI) Comparisons 

and CIMO Recommendations” (NREL/PR-1900-71041), 2018,

https://www.nrel.gov/docs/fy18osti/71041.pdf.
50

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

3. Yu Xie, Manajit Sengupta, and Aron Habte, “Advancing Radiative Transfer Models for 

Solar Energy” (NREL/PR-5D00-70751, (NREL Internal Use Only), 2018,

https://www.nrel.gov/docx/gen/fy18/70751.pdf. 

4. Yu Xie, Manajit Sengupta, and Aron Habte, “Advancing Radiative Transfer Models for 

Solar Energy” (NREL/PR-5D00-707510, NREL Internal Use Only), 2018,

https://www.nrel.gov/docx/gen/fy18/70751.pdf. 

5. Aron Habte, Manajit Sengupta, Anthony Lopez, Yu Xie, Michael J. Foster, and Andrew 

Heidinger, “Assessing the Accuracy of NREL's National Solar Radiation Database 

(NSRDB)” (NREL/PR-5D00-70750, NREL Internal Use Only), 2018,

https://www.nrel.gov/docx/gen/fy18/70750.pdf. 

6. Manajit Sengupta, Aron Habte, Anthony Lopez, Yu Xie, and Galen Maclaurin, “Update 

of the National Solar Radiation Database (1998–2016): Version 3 (NREL/PR-5D00-

71564, NREL Internal Use Only), 2018, https://www.nrel.gov/docx/gen/fy18/71564.pdf. 

7. Aron Habte, Manajit Sengupta, Christian A. Gueymard, and Ranganath Narasappa, “A 

Data-Driven Model to Estimate Ultraviolet Radiation from Total Irradiance (NREL/PO5D00-71552, NREL Internal Use Only), 2018,

https://highpoint.nrel.gov/sites/iop/Documents/gen/fy18/71552.pdf.

8. Ibrahim Reda, Julian Grobner, and Dave Turner, “Recent Updates about the Absolute 

Cavity Pyrgeometer (ACP), InfraRed Integrating Sphere (IRIS), and Atmospheric 

Emitted Radiance Interferometer (AERI) Comparisons” (NREL/PR-1900-71819), 2018,

https://www.nrel.gov/docs/fy18osti/71819.pdf. 

2017

9. Manajit Sengupta, Aron Habte, Anthony Lopez, Yu Xie, Christine Molling, and Christian 

Gueymard, “The National Solar Radiation Database (NSRDB)” (NREL/PR-5D00-

67746), 2017, https://www.nrel.gov/docs/fy17osti/67746.pdf. 

10. Aron Habte and Manajit Sengupta, “Evaluation of Sources of Uncertainties in Solar 

Resource Measurement” (NREL/PO-5D00-68917), 2017,

https://www.nrel.gov/docs/fy17osti/68917.pdf.

11. Ibrahim Reda, Afshin Andreas, Mike Dooraghi, Aron Habte, Manajit Sengupta, and

Mark Kutchenreiter, “Reducing Broadband Shortwave Radiometer Calibration Bias 

Caused by Longwave Irradiance in the Reference Direct Beam”.( NREL/PO-5D00-

68066), 2017, https://www.nrel.gov/docs/fy17osti/68066.pdf.

12. Aron Habte, Manajit Sengupta, Afshin Andreas, Mike Dooraghi, Ibrahim Reda, and

Mark Kutchenreiter, “Evaluating the Sources of Uncertainties in the Measurements from 

Multiple Pyranometers and Pyrheliometers” (NREL/PO-5D00-68065), 2017,

https://www.nrel.gov/docs/fy17osti/68065.pdf.

13. Mike Dooraghi, Mark Kutchenreiter, Ibrahim Reda, Aron Habte, Manajit Sengupta, 

Afshin Andreas, Martina Newman, and Craig Webb, “ARM Shortwave and Longwave 

Radiometer Calibrations” (NREL/PR-5D00-68110), 2017,

https://www.nrel.gov/docs/fy17osti/68110.pdf.

14. Chuck Long, Afshin Andreas, John Augustine, Mike Dooraghi, Aron Habte, Emiel Hall, 

Mark Kutchenreiter, Allison McComiskey, Ibrahim Reda, and Manajit Sengupta, “A 

Study of IR Loss Correction Methodologies for Commercially Available Pyranometers”

(NREL/PO-5D00-68117), 2017, https://www.nrel.gov/docs/fy17osti/68117.pdf.
51

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

15. M. Kutchenreiter, J.J. Michalski, C.N. Long, and A. Habte, “Significant Improvements in 

Pyranometer Nighttime Offsets Using High-Flow DC Ventilation” (NREL/PO-5D00-

68517), 2017, https://www.nrel.gov/docs/fy17osti/68517.pdf.

16. Ibrahim Reda, Afshin Andreas, Aron Habte, and Manajit Sengupta, “Impact of the 2017 

Solar Eclipse on the Smart Grid” (NREL/PO-5D00-70608), 2017,

https://www.nrel.gov/docs/fy18osti/70608.pdf.

17. Manajit Sengupta, Aron Habte, Anthony Lopez, and Yu Xie, “The National Solar 

Radiation Database (NSRDB): A Brief Overview” (NREL/PO-5D00-68916), 2017,

https://www.nrel.gov/docs/fy17osti/68916.pdf.

18. Frank Vignola, Chun-Yu Chiu, Josh Peterson, Michael Dooraghi, and Manajit Sengupta, 

“Comparison and Analysis of Instruments Measuring Plane of Array Irradiance for OneAxis Tracking PV Systems” (NREL/PO-5D00-68708), presented at the IEEE 

Photovoltaic Specialist Conference (IEEE PVSC), 2017,

https://www.nrel.gov/docs/fy17osti/68708.pdf.

2016

19. I. Reda, A. Andreas, M. Dooraghi, A. Habte, M. Sengupta, and M. Kutchenreiter,

“Measuring Broadband IR Irradiance in the Direct Solar Beam and Recent 

Developments,” presented at the American Geophysical Union (AGU) Fall Meeting, 

December 12–16, 2016, San Francisco, California,

https://www.nrel.gov/docs/fy17osti/67361.pdf.

20. Frank Vignola, Josh Peterson, Rich Kessler, Fotis Mavromatakis, Mike Dooraghi, and 

Manajit Sengupta, “Use of Pyranometers to Estimate PV Module Degradation Rates in 

the Field” (NREL/PO-5D00-66499), presented at the IEEE Photovoltaic Specialists 

Conference (IEEE PVSC), 2016, https://www.nrel.gov/docs/fy16osti/66499.pdf.

21. Mike Dooraghi, Mark Kutchenreiter, Ibrahim Reda, Aron, Habte, Manajit Sengupta, 

Afshin Andreas, Martina Newman, and Craig Webb, “Traceable Pyrgeometer 

Calibrations” (NREL/PR-5D00-66353), presented at the 2016 ARM/ASR PI Meeting, 

Vienna, Virginia, 2016, https://www.nrel.gov/docs/fy16osti/66353.pdf. 
52

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

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