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Contract No. DE-AC36-08GO28308

Assessment of the National 

Solar Radiation Database 

(NSRDB 1998–2016)

Preprint 

Aron Habte, Manajit Sengupta, Anthony Lopez, 

Yu Xie, and Galen Maclaurin

National Renewable Energy Laboratory

Presented at the 2018 World Conference on 

Photovoltaic Energy Conversion (WCPEC-7)

Waikoloa, Hawaii

June 10–15, 2018

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Conference Paper

NREL/CP-5D00-71607

August 2018
NOTICE

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provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy 

Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the 

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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.

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Assessment of the National Solar Radiation Database 

(NSRDB 1998-2016)

Aron Habte, Manajit Sengupta, Anthony Lopez, Yu Xie and Galen Maclaurin

National Renewable Energy Laboratory, Golden, CO, 80401, USA

Abstract — Applying traceable and standardized uncertainty 

characterization for solar resource data provides confidence in 

the dataset for use by financiers, developers and site operators of 

solar energy conversion systems, and ultimately reduces 

deployment cost. Performance guarantees of solar energy 

conversion systems are based on the available solar resource 

from measurement stations or modeled dataset such as the 

National Solar Radiation Database (NSRDB). In this study we 

implemented a comprehensive uncertainty determination 

approach [1]. The study also analyzed how the NSRDB (1998-

2016) – Version 3 compares with the previous NSRDB (1998-

2015) – Version 2. The study also attempted to estimate the 

uncertainty differences derived by comparing the NSRDB data 

to the seven measurement stations from the National Oceanic 

and Atmospheric Administration’s Surface Radiation Budget 

Network (SURFRAD) and University of Oregon Solar Radiation 

Monitoring Laboratory (SRML). The evaluation was conducted 

for hourly values, daily totals, monthly mean daily totals, and 

annual mean monthly mean daily totals and demonstrate the 

quality of the new datasets currently available from the NSRDB.

I. INTRODUCTION

The NSRDB has been updated over the years, to meet the 

demand of solar resource for solar energy conversion systems.

NREL released version 3, of the NSRDB (1998–2016) which 

contains gridded solar irradiance—global horizontal 

irradiance (GHI), direct normal irradiance (DNI), and diffuse 

horizontal irradiance at a 4-km by 4-km spatial resolution and 

half-hourly temporal resolution covering 19 years. Details of 

the dataset are available at https://nsrdb.nrel.gov. Additional 

details about the NSRDB are also described in [2], [3]. NREL 

implemented major meteorological input and processing 

changes in this update. MERRA-2 reanalysis data such as 

Aerosol Optical Depth and precipitable water vapor (PWV)

were used in the NREL radiative transfer model, the physical 

solar model (PSM). Some downscaling methodologies such as 

interpolation and extrapolation used to align the multiple 

datasets to the same grid were changed in the version 3. 

Understanding the impacts of these changes on the solar 

resource and quantifying the uncertainty are essential for solar 

energy conversion systems. This study attempts to provide a 

comprehensive uncertainty estimate that demonstrates the 

performance characteristics of the NSRDB (1998–2016) by

including all sources of uncertainty using observations at

various time-averaged periods.

II. METHOD

As reported in [4], [5], a comprehensive uncertainty 

determination approach was implemented by incorporating the 

mean bias error (MBE) and root mean square error (RMSE) as 

statistical measures, and ground-based solar measurement 

uncertainty.

The ground-based solar measurement uncertainty was 

estimated at 5% and this estimate is consistent with previous 

studies estimations [6], [7], [8], [9]. These studies reported 

the uncertainty of the ground-based solar measurement using 

pyranometers and/or pyrheliometer includes sources of 

uncertainties such as the specifications of the instrument, 

calibration procedure, measurement conditions and 

maintenance, and environmental conditions. The reports 

mentioned above show the steps required to calculate the 

overall ground-based uncertainty using the GUM method, by 

incorporating these specifications. 

To quantify the overall uncertainty of the modeled NSRDB 

data set, the approach applies RMSE, MBE, and the groundbased measurement uncertainties as sources of estimated 

combined uncertainty [4]. This approach provides uncertainty 

estimation on a 95% confidence interval representing two 

standard deviations (coverage factor of ~2.0) and assuming a 

Gaussian or normal distribution of the errors [4].

NSRDB 1998-2015 (version 2) and NSRDB 1998-2016 

(version 3) were compared. The approach used to determine 

the uncertainty in both datasets is the same. Data from 1998-

2015 from seven SURFRAD ground-based measurement were 

used to compare to the NSRDB version 2 and version 3. 

Further, four SRML measurement locations for 2015 were 

used to understand the differences between the GOES East 

and GOES West satellite irradiance values for the NSRDB 

version 3.

III. RESULTS AND DISCUSSION

A. NSRDB Version 2 and Version 3 Comparison and 

Uncertainty

Fig.1 shows uncertainties for varying time averages where 

the hourly dataset demonstrated higher uncertainty estimation 
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of the NSRDB and as time averaging increased, the 

uncertainty decreased and approached the measurement 

uncertainty. The bias of the NSRDB data does not change

from one-time average to another, on the other hand, the 

RMSE in the NSRDB (1998–2016) decreases significantly 

with the increase in the time average [4].

Further, as described in [4] the reason for the high 

uncertainty in the hourly dataset is due to the random error, 

RMSE and this in turn is due to the NSRDB pixel 

representing 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 higher differences.

Fig. 1. GHI estimated overall uncertainty for seven ground-based 

solar measurement locations for NSRDB version 2 and 3.

Compared to version 2 of the NSRDB, version 3 has 

improved in overall measurement uncertainty (Fig.2). The 

improvement was noticeable on the hourly basis. For instance,

at Desert Rock the uncertainty was ~18% in version 2; 

however, the uncertainty is reduced to about 12% in the 

NSRDB version 3. However, the monthly and annual 

averages for NSRDB version 2 appears to be better; this could 

be attributed to higher bias in NSRDB version 3 where the 

clear and cloudy condition biases are either both positive or 

negative. However, NSRDB version 2 appears to contain 

biases of opposing signs resulting in cancellation of biases 

(Fig. 2.).

Fig. 2. estimated biases for seven ground-based solar measurement 

locations for NSRDB version 2 and 3.

B. Comparison of GOES East and GOES West 

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 west 

longitude. GOES East data has time-shift applied to cloud 

properties instead of solar radiation. The solar radiation data 

from both satellites is 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 US as shown in Fig. 3.
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Fig. 3. NSRDB version 3 blended GHI dataset.

However, we noticed differences between the two version 

along the dividing line (105° longitude). To better understand 

these differences, we compared GHI estimates from the 

GOES-East and West satellites to four University of Oregon 

Solar Radiation Measurement Laboratory (SRML) ground 

measurement stations. Both satellites cover the state of 

Oregon where these stations are located. The result using 

2014 data shows the GOES East satellite performs better than 

the GOES west satellite for this region (Table 1 and Fig 4.). 

Note that the GOES West 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 condition and less than ±2% for clear and cloudy 

conditions for three out of four stations. However, the GOES 

West satellite shows higher biases in all, clear and cloudy 

skies, except for Ashland where all sky condition shows lower 

bias compared to GOES East; however, there is a cancellation

of error because clear and cloudy have opposite signs of equal 

magnitude.

TABLE I

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

Fig. 4. Comparison for Eugene, Oregon (left) using GOES West satellite and (right) using GOES East satellite.
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IV. CONCLUSION

Comparisons of the NSRDB (1998-2016) version 3 was 

assessed by comparing with selected ground-measured data 

under both clear- and cloudy-sky conditions and covered the 

period from 1998–2016. The result shows improvement 

compared to the previous version of NSRDB. The bias is ~ 

±5% and the overall uncertainty reduced significantly on the 

hourly biases. Further the study analyzed the differences 

between the GOES East and GOES West satellites as they 

relate to irradiances and the result shows GOES East satellite 

provides a better solar radiation result compared to ground 

measurement for locations in the Oregon state. However, this 

needs further study to better understand the cause of the 

outcome.

ACKNOWLEDGMENTS

This work was authored by Alliance for Sustainable Energy, 

LLC, the manager and operator of the National Renewable 

Energy Laboratory for the U.S. Department of Energy (DOE) 

under Contract No. DE-AC36-08GO28308. Funding provided 

by the U.S. Department of Energy Office of Energy 

Efficiency and Renewable Energy Solar Energy Technologies 

Office. The views expressed in the article do not necessarily 

represent the views of the DOE or the U.S. Government. The 

U.S. Government retains and the publisher, by accepting the

article for publication, acknowledges that the U.S. 

Government retains a nonexclusive, paid-up, irrevocable, 

worldwide license to publish or reproduce the published form 

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

purposes.

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