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Contract No. DE-AC36-08GO28308

Validation of the National Solar 

Radiation Database (NSRDB) 

Preprint

Manajit Sengupta, Andrew Weekley, 

Aron Habte, and Anthony Lopez

National Renewable Energy Laboratory

Christine Molling

University of Wisconsin

To be presented at the European PV Solar Energy Conference 

and Exhibition

Hamburg, Germany

Conference Paper

NREL/CP-5D00-64981

September 2015
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Validation of the National Solar Radiation Database (NSRDB) (2005 2012)

Manajit Sengupta, Andrew Weekley, Aron Habte, Anthony 

Lopez

National Renewable Energy Laboratory

15013 Denver West Parkway, Golden, CO 80401, USA

manajit.sengupta@nrel.gov

andrew.weekley@nrel.gov

aron.habte@nrel.gov

anthony.lopez@nrel.gov

Christine Molling

University of Wisconsin

SSEC, 1225 W. Dayton Street, Madison, WI 53706, USA

chrisitine.molling@ssec.wisc.edu

Andrew Heidinger

National Oceanic and Atmospheric Administration

SSEC, 1225 W. Dayton Street, Madison, WI 53706, USA

andrew.heidinger@noaa.gov

ABSTRACT: Publicly accessible, high-quality, long-term, satellite-based solar resource data is foundational and critical to solar 

technologies to quantify system output predictions and deploy solar energy technologies in grid-tied systems. Solar radiation 

models have been in development for more than three decades. For many years, the National Renewable Energy Laboratory

(NREL) developed and/or updated such models through the National Solar Radiation Data Base (NSRDB). There are two widely 

used approaches to derive solar resource data from models: (a) an empirical approach that relates ground-based observations to

satellite measurements and (b) a physics-based approach that considers the radiation received at the satellite and creates retrievals 

to estimate clouds and surface radiation. Although empirical methods have been traditionally used for computing surface 

radiation, the advent of faster computing has made operational physical models viable. The Physical Solar Model (PSM) 

developed by NREL in collaboration with the University of Wisconsin and the National Oceanic and Atmospheric 

Administration (NOAA) computes global horizontal irradiance (GHI) using the visible and infrared channel measurements from 

the Geostationary Operational Environmental Satellites (GOES) system. PSM uses a two-stage scheme that first retrieves cloud 

properties and then uses those properties to calculate surface radiation. The cloud properties in PSM are generated using the 

AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) algorithms [5]. Using the cloud mask from PATMOS-x, and aerosol 

optical depth (AOD) and precipitable water vapor (PWV) from ancillary sources, the direct normal irradiance (DNI) and GHI are

computed for clear-sky conditions using the MMAC model. For cloud scenes identified by the cloud mask, the Satellite 

Algorithm for Surface Radiation Budget (SASRAB) is used to compute the GHI. The DNI for cloud scenes is then computed 

using the DISC model [6]. The current NSRDB update has a 4-km x 4-km, 30-minute resolution covering 2005 2012. This 

paper evaluates the PSM-based NSRDB data set using ground-measured data and provides detailed evaluation statistics. The 

result of the comparison shows a good correlation between the NSRDB and ground data. Further, an outline of the next version 

of the NSRDB and future plans for enhancement and improvement are provided. This version is expected to be released in 

September 2015 and will contain data from 1998 2014.

1 INTRODUCTION

Photovoltaic system performance and concentrating solar 

power (CSP) rely on accurate measurements of the solar 

radiation resources available for power conversion. 

Measuring solar resources accurately can lead to a 

reduction in the investment risks associated with installing 

and operating solar energy systems. Further, understanding 

the impact of parameters such as clouds, aerosols, water 

vapor, etc., on the incoming solar resources is essential to 

accurately design renewable energy systems. Solar 

radiation resources are acquired using ground-measured

and/or modeled data, and they are complementary to each 

other. Ground-measured data is inadequate because there 

are a very limited number of measurement stations in longterm operation, yet the data are essential to modeled solar 

data validation. To fill the gap, modeled data, such as that 

derived from satellites, provides measurements for creating 

solar resource assessment maps on a global scale. In this 

paper, we discuss and analyze the PSM-based National 

Solar Radiation Data Base (NSRDB) data set. PSM is a

physics-based, satellite-derived solar radiation data set that 

uses cloud properties from the PATMOS-X algorithms in 

radiative transfer models to calculate surface radiation. 

Using the cloud mask to identify clear-sky scenes, the 

MMAC model is used to compute global horizontal 

irradiance (GHI) and direct normal irradiance (DNI) for 

clear skies. To improve the accuracy of clear-sky modeling,

the AOD and PWV, which are critical inputs to MMAC,

are derived from ancillary information. The AOD is 

derived using a combination of the MODIS- and MISRbased AOD product that is scaled to ground-based 

AERONET data. The PWV is derived from the CFSR 

model through an interpolation to the NSRDB grid. For 

cloudy skies identified by the cloud mask, the Satellite 

Algorithm for Surface Radiation Budget (SASRAB) model
 

 

௡

1

݊

is used to calculate the GHI. The DNI is then calculated 

from the GHI using the DISC model [6]. Data gaps in the ܧܤܯ)ܹ⁄݉ଶ)

ଶ

= ܫ ௧௘௜௟௦௔௧௘௟ − ܫெ௘௔௦௨௥௘ௗ)

|

෍( (1) 

௜ୀଵ

௡

|ܫ௦௔௧௘௟

satellite-based product due to various reasons, including 

issues with the raw satellite data, were filled using a gapfilling algorithm. We used data from two satellites, GOESEast and GOES-West, to cover the whole United States. 

GOES-West provides observations at the top and middle of 

the hour. GOES-East, on the other hand, provides 

observations at 15 minutes and 45 minutes past the hour. 

The GOES-East solar radiation product was time-shifted to 

match the GOES-West times. 

Previously, SASRAB was used for both clear- and cloudy 

sky computations. SASRAB demonstrated a low bias under 

clear-sky conditions, and to correct the bias we 

implemented a newer generation fast clear-sky radiative 

transfer model. Three clear-sky models were investigated 

[1], [2], and [3], and MMAC was chosen as the clear-sky 

radiative transfer model [7]. The results in this paper were 

validated using a high-quality, ground-based solar data set 

obtained from the National Oceanic and Atmospheric 

Administration’s (NOAA’s) Surface Radiation 

(SURFRAD)1

. Prior to using the ground-based solar data, 

data quality schemes were applied to detect any data issues 

due to equipment and operational related errors, unclean 

instruments, instrument limitations provided by 

manufacturer specifications, etc. 

2 METHOD 

Seven locations were selected for the validation study 

(Figure 1). The evaluation was made by comparing the 

ground-based data to the satellite-derived data on a halfhourly time interval. In this report, we illustrate the 

comparison results and any presence of systematic (bias) or 

random (scatter) tendencies in the satellite-derived data. 

For our comparisons we screened out high solar zenith 

angles above 75 degrees. To evaluate the performance of 

the satellite data relative to the ground-measured data, 

mean bias error (MBE), mean absolute error (MAE), and 

root mean square error (RMSE) were used as a basic 

performance set of measures (eq. 1‒3). Clear, cloudy-sky, 

and all sky conditions were compared. The sky conditions 

were categorized using the cloud type information derived 

from the satellite retrievals. 

The ground-based observations were available at a 1-

minute resolution, whereas the satellite-based results were 

every 30 minutes for 4-km resolution pixels [8]. The 

ground data was averaged to 30 minutes centered at the 

satellite time stamp to account for spatial scales of the 

satellite. This particular averaging time was chosen to 

match the satellite time interval. 

1 www.srrb.noaa.gov/surfrad/sitepage.html 

1

( ܹ⁄݉)ܧܣܯ ݊

௡

ଶ )ଶ൱ (3) ܫ ௧௘௜௟௦௔௧௘௟ − ܫ ௨௥௘ௗ௦ெ௘௔ ෍ ( ݊

1

=ඩ൭ ) ܹ⁄݉(ܧܵܯܴ

௜ୀଵ

෍

௜ୀଵ

= ௟௜௧௘ − ܫெ௘௔௦௨௥௘ௗ (2) 

Figure 1: SURFRAD stations included in the analysis 

3 RESULTS AND DISCUSSION 

The spatial and temporal differences between the groundmeasured and satellite-derived data sets were analyzed 

(Table 1). The ground measurements were averaged to 

half-hourly values centered at the satellite time stamp. This 

served 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 is meant to represent. 

Further, investigating the differences and setting a uniform 

benchmark is essential to improving the existing satellitederived data or creating other satellite-based methods to 

improve the underlying uncertainties. 

The following figures and Table 1 show the bias and scatter 

of the differences when the ground-measured data was 

compared to the NSRDB satellite-derived data. The results 

demonstrate that the clear-sky condition showed better 

statistical correlation and less bias than the cloudy 

condition. However, the direct normal irradiance (DNI) 

difference between the measured and NSRDB data set was 

much higher than the global horizontal irradiance (GHI), 

especially under cloudy-sky conditions. 

2 

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Table 1: Average statistical differences (2005 2012) of correlation (R), MBE (W/m2), MAE (W/m2), and RMSE (W/m2) between

the measured and satellite irradiance data under various sky conditions.

Sky Condition Statistics

TBL DRA GCM PSU BON FPK SXF

GHI DNI GHI DNI GHI DNI GHI DNI GHI DNI GHI DNI GHI DNI

All

MBE -21 -53 2 -7 27 39 14 36 8 16 -4 5 4 18

MAE 85 174 48 100 67 120 78 139 63 122 63 127 59 113

RMSE 133 261 84 182 105 181 120 209 98 183 97 197 93 176

R square 0.76 0.56 0.9 0.67 0.86 0.73 0.79 0.65 0.86 0.72 0.84 0.69 0.87 0.76

Clear

MBE 18 3 19 14 38 37 31 31 22 3 26 29 22 15

MAE 23 55 23 41 42 104 39 118 31 107 29 71 29 87

RMSE 40 96 37 77 71 155 68 170 53 148 45 116 47 132

R square 0.97 0.37 0.98 0.56 0.93 0.38 0.93 0.3 0.96 0.3 0.97 0.37 0.96 0.3

Cloudy

MBE -37 -76 -34 -53 14 40 5 39 -2 26 -22 -9 -10 20

MAE 112 223 102 227 94 139 98 150 86 133 84 161 83 134

RMSE 156 305 139 302 133 205 139 227 121 204 117 233 117 205

R square 0.66 0.34 0.7 0.23 0.74 0.45 0.68 0.36 0.75 0.47 0.72 0.41 0.74 0.5

As shown in Figure 3, under clear-sky conditions in all 

seven SURFRAD stations, the GHI comparison appeared to 

be close to the 1:1 line; however, the NSRDB data set 

demonstrated a slight positive bias, which indicates that the 

surface measurements were slightly lower than the NSRDB 

data set. Under cloudy conditions, as might be expected,

the GHI comparison demonstrated relatively higher scatter 

and lower correlation, and this is mainly because a satellite 

pixel represents a nominal 4-km by 4-km area, whereas a 

ground-based station represents only a small area above the 

measuring station. As clouds pass by, it appears that these 

spatial differences contributed to the higher differences. 

Further, for the DNI comparison, the spatial differences 

under cloudy conditions were exacerbated, because the 

ground-measuring radiometer has an approximate fivedegree field of view, but the satellite DNI represents the 4-

km by 4-km area.
4

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Figure 3: Half-hourly irradiance comparison in W/m2 for all SURFRAD stations for years 2005 2012. (Left) DNI comparison of 

the NSRDB to the ground-measured data set. (Right) The same comparison but for GHI.

4 FUTURE PLANS AND OUTLINE OF THE 

NEW NSRDB RELEASE

The National Renewable Energy Laboratory (NREL) has 

developed a new radiative transfer model to replace 

SASRAB and further improve the accuracy and efficiency 

of the simulation of solar radiation under cloudy-sky 

conditions. This new parametrized radiative transfer model 

will be implemented in the upcoming NSRDB data set

release, which will cover the years from 1998 to 2014. 

NREL also developed a new website (http://nsrdb.nrel.gov) 

to disseminate the resource data. The website has improved 

user interface with functionality to host the new gridded 

and historical data set. Users have an option to download a 

point or an area (polygon) with specific attributes and a 

time range. The data set will be released by the end of 

September 2015. The NSRDB covers the United States, 

part of Canada, Central America, and part of South 

America (Figure 4). The three solar resources attributes—

GHI, DNI and DHI—are included in the data set along with 

many other meteorological parameters, such as 

temperature, relative humidity, and wind speed.
Figure 4: Example of current gridded NSRDB product for 2010. (Left) DNI. (Right) GHI. 

4 SUMMARY 

Improved satellite-based models are essential to understand 

system efficiencies of solar renewable installations. 

Further, to finance these installations through a financial 

system, banks will need assurances that these systems will 

produce the energy predicted through performance models. 

Failing to meet the minimum energy performance 

requirements can result in large financial penalties that 

require expensive risk-mitigation measures. Therefore,
 

refining the NSRDB satellite-derived data will benefit 

renewable energy installations and reduce the expense 

associated with mitigating performance risks. This paper
 

validated the improvement of the PSM model, which 

applied the MMAC model with accurate input parameters
 

such as AOD and PWV to compute GHI and DNI for clear 

skies. Under cloudy conditions, the DISC model was used 

to compute DNI. Overall, significant improvement in GHI 

and DNI was seen compared to the original SASRAB 

product for all ground-based stations [4], [7], [9]. This data 

set will deliver a location-specific and reliable time-series 

solar resource data set that will benefit CSP and
 

photovoltaic projects. 

5 ACKNOWLEDGEMENTS AND DISCLAIMER 

This work was supported by the U.S. Department of 

Energy under Contract No. DE-AC36-08GO28308 with the 

National Renewable Energy Laboratory. The U.S.
 

Government retains and the publisher, by accepting the 

article for publication, acknowledges that the U.S. 

Government retains a nonexclusive, paid-up, irrevocable, 

worldwide license to publish or reproduce the published 

form of this work, or allow others to do so, for U.S.
 

Government purposes. 

6 REFERENCES 

[1] Bird, R.E.; Hulstrom, R.L. (1981). A Simplified Clear 

Sky Model for Direct and Diffuse Insolation on Horizontal 

Surfaces. SERI/TR-642-761. Golden, CO: Solar Energy 

Research Institute. 

[2] Davies, J.A; McKay, D.C.; Luciani, G., Abdel-Wahab, 

M., Validation of Models for Estimating Solar Radiation on 

Horizontal Surfaces, IEA Task IX Final Report. 

Downsview, OT: Atmospheric Environment Service, 1988. 

[3] Gueymard, C. (2012) “Clear-sky irradiance predictions 

for solar resource mapping and large-scale applications: 

Improved validation methodology and detailed 

performance analysis of 18 broadband radiative models.” 

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[4] Habte, A.; Sengupta, M.; Wilcox, S. (2013). Validation 

of GOES-Derived Surface Radiation Using NOAA's 

Physical Retrieval Method. 30 pp. NREL/TP-5500-57442. 

[5] Heidinger, A. K., Foster, M. J., Walther, A., Zhao, X., 

(2014) The Pathfinder Atmospheres Extended (PATMOSx) AVHRR Climate Data Set, Bull. Amer. Meteor. Soc., 

doi: 10.1175/BAMS-D-12-00246.1 

[6] Maxwell, E. L., A Quasi-Physical Model for Converting 

Hourly Global Horizontal to Direct Normal Insolation, 

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Solar Energy Research Institute, 1987. 

 [7] Sengupta, M.; Habte, A.; Gotseff, P.; Weekley, A.; 

Lopez, A.; Molling, C.; Heidinger, A. (2014). PhysicsBased GOES Satellite Product for Use in NREL's National 

Solar Radiation Database: Preprint. 9 pp.; NREL Report 

No. CP-5D00-62237. 

[8] Sengupta, M.; Heidinger, A.; Miller, S. “Validating an 

Operational Physical Method to Compute Surface 

Radiation From Geostationary Satellites.” SPIE Conference 

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[9] Sengupta, M.; Gotseff, P., “Evaluation of Clear Sky 

Models for Satellite-Based Irradiance Estimates”, pp. 42, 

2013, NREL Report No. TP-5D00-60735. 

6 

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