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Operated by the Alliance for Sustainable Energy, LLC

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

Contract No. DE-AC36-08GO28308

Conference Paper

NREL/CP-5D00-72310

January 2019

The National Solar Radiation Database 

(NSRDB) for CSP Applications

Preprint

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

and Christian A. Gueymard2

1 National Renewable Energy Laboratory

2 Solar Consulting Services

Presented at the 2018 Solar Power and Chemical Energy Systems 

Conference (SolarPACES)

Casablanca, Morocco

October 2–5, 2018
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

Conference Paper

NREL/CP-5D00-72310

January 2019

The National Solar Radiation Database 

(NSRDB) for CSP Applications

Preprint

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

and Christian A. Gueymard2

1 National Renewable Energy Laboratory

2 Solar Consulting Services

Suggested Citation 

Sengupta, Manajit, Aron Habte, Yu Xie, Anthony Lopez, and Christian A. Gueymard.

2019. The National Solar Radiation Database (NSRDB) for CSP Applications: Preprint. 

Golden, CO: National Renewable Energy Laboratory. NREL/CP-5D00-72310.

https://www.nrel.gov/docs/fy19osti/72310.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. 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 

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NREL 46526.

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

The National Solar Radiation Database (NSRDB) for CSP 

Applications

Manajit Sengupta1, a), Aron Habte2, Yu Xie

2

, Anthony Lopez2

, and Christian

A. Gueymard3

1PhD., Chief Scientist, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden CO 80401, USA. 

2National Renewable Energy Laboratory, Golden, CO 80401. 

3Solar Consulting Services, Colebrook, NH, USA 

a)Corresponding author: Manajit.Sengupta@nrel.gov

Abstract. This study examines the differences in direct normal irradiance (DNI) in two versions of the National 

Solar Radiation Database (NSRDB) produced by the National Renewable Energy Laboratory (NREL). NSRDB 

V3 of the NSRDB includes significant changes to various parts of the radiative transfer model and inputs to the 

model compared to NSRDB V2. The changes in NSRDB V3 resulted in less uncertainty than NSRDB V2. The 

study quantified the uncertainty and the spatial and temporal variability under clear-sky conditions. The uncertainty 

estimation was performed using a standardized method, The Guide to the Expression of Uncertainty in 

Measurement (GUM). The method includes high-quality ground-measured data for seven National Oceanic and 

Atmospheric Administration Surface Radiation Budget Network (SURFRAD) stations for 1998–2015. 

INTRODUCTION

Measured and modeled solar resource data are essential for quantifying the available energy for

concentrating solar power (CSP) projects. The desired accuracy and spatiotemporal resolution of these data

sources depend on the requirement of various project phases. Although financing and policy decisions

mostly require annualized information, the required accuracy and resolution might increase significantly for

day-to-day operations. Generally, the availability of measured data is limited because deploying groundbased measurement stations are expensive; requires adequate knowledge of the devices used to measure solar

resources; and demands personnel for routine maintenance, data quality control, and analysis [1]. These 

requirements increase the time and financial investment required to maintain and operate ground-based 

measurement stations. On the other hand, satellite-derived models provide long-term solar resource data and

high spatial coverage. Well-maintained ground-based measurements are highly accurate and often can be 

used to validate satellite-based solar resource data or to correct long time series of such data through the

process referred to as “site adaptation” [2]. Therefore, the two sources of solar resource information

(modeled and measured) are complementary and important to delivering high-quality and high-resolution

data. The National Renewable Energy Laboratory (NREL) developed the gridded National Solar Radiation

Database (NSRDB) using a physics-based two-step model called the Physical Solar Model (PSM) [3]. The 

model uses cloud information physically retrieved from the Geostationary Operational Environment

Satellites (GOES) that cover North and South America. This 4-km, half-hourly database covers regions from

Canada in the north to Brazil in the south (latitudes 60°N to 21°S) and is freely available for the 1998–2016 

period from https://nsrdb.nrel.gov. Recently, NREL deployed NSRDB V3 of the NSRDB, which includes 

significant changes from NSRDB V2, both in various parts of the model and in the inputs it uses, most

importantly those related to clouds and aerosols. This study introduces the NSRDB, summarizes the

differences between the two model versions, particularly regarding direct normal irradiance (DNI), and 

discusses the resulting changes in the available solar resource.

METHODOLOGY

NSRDB Processing

Over the years, the NSRDB has been updated to meet the growing 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 

(1998–2016) based on NSRDB V3 of the PSM. The NSRDB data sets contain gridded solar irradiance—

DNI, global horizontal irradiance, and diffuse horizontal irradiance—at a 4-km by 4-km spatial resolution

and half-hourly temporal resolution covering 19 years. Details on the model and the data set are available at

the NSRDB website (https://nsrdb.nrel.gov). Additional details about the development of the NSRDB are

also available in [4], [5]. In the current version of the NSRDB (developed using PSM V3), NREL 
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implemented major changes in the meteorological input and processing. Hourly variables, such as aerosol 

optical depth (AOD) or precipitable water vapor (PWV), are now extracted from NASA’s Modern Era 

Retrospective Analysis for Research and Applications NSRDB V2 (MERRA-2) (Fig. 1) [6]. These variables 

are particularly important to correctly evaluate DNI under clear-sky conditions. Additionally, downscaling 

methodologies to match the lower-resolution MERRA-2 data (0.5x0.625°) to the high-resolution 4-km

NSRDB grid were redesigned to improve the accuracy in the ancillary variables such as PWV, 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 V2 (used in 

NSRDB V2) shifted the solar radiation directly using a parametric model, PSM V3 (used in NSRDB V3)

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 V2 used monthly averaged AOD, whereas 

NSRDB V3 uses hourly AOD from MERRA-2.

Figure 1. PSM flowchart for NSRDB.

NSRDB DNI Comparison and Uncertainty Analysis

As stated, both versions of the NSRDB contain some differences in ancillary input data and downscaling 

methodologies of the input parameters used in the NSRDB processing. Therefore, certain criteria needed to 

be implemented in the comparison and uncertainty estimation methodologies. Seven ground measurement 

stations from the National Oceanic and Atmospheric Administration’s Surface Radiation Budget Network 

(SURFRAD) stations (Fig. 2) were used for this comparison representing various climatic regions [7], [8].

The conditions used in the comparison include:

a) Solar zenith angles must be less than 80°.

b) Irradiance must be strictly greater than zero.

c) Data records with missing values from the surface measurements are excluded from both the surface 

measurements and NSRDB data sets.

d) A sky clarity index is determined to detect clear-sky periods from the surface measurement and the 

NSRDB data using this definition:

 SSSSSS cccccccccccc iiiiiiii = ssss ss −ss GGGGGG

NN NNNNNN cccc cccc−ss GGGGGG (1)

Approximately clear-sky situations are assumed when this clarity index is greater than 0.8. This 

identifies half-hourly clear periods using only NSRDB predictions and corresponding surface 

measurements.

e) Cloud masking from the satellite-based retrievals were used to determine cloudy-sky conditions.
3

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These selection criteria were applied to both versions of the NSRDB (V2 and V3) and to the surface 

measurements.

Figure 2. SURFRAD Network locations overlaid on U.S. climatic regions. Image modified from [4].

More information about the stations: https://www.esrl.noaa.gov/gmd/grad/surfrad/sitepage.html

As stated in [7], The Guide to the Expression of Uncertainty in Measurement (usually referred to as the

GUM method) is employed to estimate the uncertainty in the NSRDB data set. This uncertainty includes that 

in the surface measurement as well as the mean bias error (MBE) and root mean square error (RMSE) 

statistics of the modeled estimates. Overall, the following equation is used to calculate the NSRDB 

uncertainty for a 95% confidence interval (with coverage factor ~2, assuming a normal distribution):

UU9999 = 22 ∗ ± ��

UUmm

22 �

22

+ �

MM MM

22 �

22

+ � 22 �

22

(2)

RESULTS

Understanding the impacts of the changes from PSM V2 to V3 on the modeled solar resource and 

quantifying the uncertainty of these changes constitute essential information for accurately designing utilityscale CSP systems. An example, shown in Fig. 3, demonstrates that DNI, which is the essential resource 

information for CSP applications, presents significant differences between the two NSRDB versions, 

especially over the southeastern United States. A preliminary assessment attributes these differences to both 

the input data set and the interpolation methodologies. Because the increased resource predicted by NSRDB 

PSM V3 makes a significant difference in the economics of power generation from CSP technologies, it is 

important that these differences be carefully examined and explained. To assess these differences, a traceable 

and standardized uncertainty characterization method is applied. It is meant to provide confidence in the data

set for use by financiers, developers, and site operators of solar energy conversion systems and ultimately to 

reduce deployment costs. 
4

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The analysis in Fig. 4 is focused on clear-sky conditions, which are critical for CSP applications. Clearsky periods were selected using the selection criteria mentioned in Section 2. At almost all SURFRAD 

locations, the NSRDB V3 demonstrates significant improvement or reduction in uncertainty compared to V2, 

as shown in Fig. 4. These improvements are clearly noticeable for the eastern locations, such as Bondville

(BND), Goodwin Creek (GWN), Sioux Falls (SXF), and Penn State University (PSU). Further, the observed 

reduction in uncertainty occurs across all averaging timescales. On an annual basis, remarkably, the 

uncertainty is very close to the measurement uncertainty.

Conversely and unexpectedly, however, the two western locations of Desert Rock (DRA) and Fort Peck

(FPK) do not show reduction in uncertainty in the NSRDB V3; instead, they performed even worse—their 

uncertainty tends to increase for some averaging timescales. The Table Mountain (TBL) station in Boulder,

Colorado, shows some reduction in uncertainty in the NSRDB V3; however, this location also shows higher 

uncertainty in both NSRDB versions compared to the other locations. This could be attributed to the 

closeness of the site to the Rocky Mountains, resulting in shading in the observations during afternoon hours. 

The NSRDB does not include terrain impacts, so the DNI is significantly higher at all timescales. 

The detailed statistics in Fig. 5 of the sources of uncertainty, such as MBE and RMSE, provide some 

insights in explaining the overall uncertainty estimation described in Fig. 4. The MBE (Fig. 5 top panel) 

shows a negative bias at all locations in the NSRDB V2 but only at the three western locations in V3. In 

contrast, Bondville and Goodwin Creek in the East show very small negative and positive bias, respectively,

whereas Penn State and Sioux Falls demonstrate practically no bias in V3. A tentative explanation is that the 

changes implemented in V3 improved the clear-sky estimates.

Figure 3. DNI differences between NSRDB V3 (left) and NSRDB V2 (right).

Figure 4. DNI uncertainty from NSRDB V3 (left) and V2 (right). Years: 1998–2015.
5

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Figure 5. Top: Percentage MBE for NSRDB V3 (left) and V2 (right). Bottom: Same, but for the percentage RMSE. 

Years: 1998–2015.

Desert Rock and Fort Peck in the West show slightly higher MBE but similar RMSE (a measure of scatter or 

randomness) in the NSRDB V3, which is consistent with the results in Fig. 4. MBE does not change with 

timescale average; however, the RMSE or scatter decreases as the averaging time increases. That is why

these two locations did not demonstrate decreased uncertainty in Fig. 4 with the increase in averaging time. 

In other words, the MBE becomes dominant in the uncertainty estimation as the averaging time increases 

from hourly to annual.

Changes in DNI Prediction Caused by the Aerosol Data Timescale

The MERRA-2 AOD is a fairly new data set, so there is a need to further evaluate the accuracy of this 

data set, especially in conditions where the AOD is low, which generally occurs in locations in the West such 

as Desert Rock and Fort Peck. Another important challenge in DNI estimation occurs in situations where the 

cloud-masking algorithm misses subpixel cloudiness. Note that the cloud masking uses the visible channel of 

the GOES satellites, which have a 1-km resolution. In the case of the NSRDB, only one pixel (the Northwest

corner) is used for cloud detection.

It is well established (see, e.g., [9]) that the clear-sky DNI is a strong function of atmospheric turbidity. 

This is usually characterized by two main quantities: AOD at a specific wavelength (usually 550 nm) and the 

Ångström exponent, which represents the rate of change of AOD with wavelength. The NSRDB V2 used 

daily interpolations of monthly-mean aerosol information, as described in [10]. In contrast, V3 is now using 

hourly aerosol information from MERRA-2—with, however, some bias correction after topographic 

regridding [10]. This higher temporal resolution is expected to result in a much better frequency distribution 

of DNI at any site [11], which in turn can improve the evaluation of the potential of CSP plants [12]. This 

effect is caused by variations in the shape of AOD’s lognormal distribution over different timescales [13].

As an illustration, Fig. 6 shows the frequency distributions of AOD and clear-sky DNI at Boulder for 

three different cases of AOD input data: monthly (as used in the NSRDB V2), hourly from MERRA-2 (raw), 

and hourly MERRA-2 after bias correction and topographic regridding (as used in the NSRDB V3). The 

displacement of the DNI frequencies is uniquely caused by variations in AOD input. Figure 7 is similar to 

Fig. 6, but for Desert Rock, where AOD is generally lower than at Boulder but where a stronger bias 
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correction was necessary in the hourly data. At Boulder and various other sites, the distribution of the 

predicted DNI tends to shift to the right (higher frequency of high DNI) as a consequence of using hourly 

rather than monthly AOD data, which might be significant in CSP applications.

FIGURE 6. Top: Frequency distribution of AOD at Boulder using monthly data (NSRDB V2), raw hourly from 

MERRA-2, and hourly MERRA-2 after bias correction and topographic regridding (NSRDB V3)
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FIGURE 7. Top: Frequency distribution of AOD at Desert Rock using monthly data (NSRDB V2), raw hourly from 

MERRA-2, and hourly MERRA-2 after bias correction and topographic regridding (NSRDB V3)

CONCLUSION

NSRDB V3 demonstrated reduced uncertainties in DNI estimation compared to the previous version,

NSRDB V2, especially for the GOES-EAST satellite. The improvement is assumed to result from a 

combination of factors, including (i) better downscaling methodologies, particularly in the interpolation and 

extrapolation used to align the multiple data sets to the same grid, and (ii) the use of hourly values of aerosol

information (principally AOD), and surface albedo instead of interpolated monthly averages. Using hourly 

precipitable water vapor from MERRA-2 might have marginally contributed to the improvement too. The 

western regions do not show similar improvement, and this might be because of accuracies in the AOD from 

MERRA-2 in cleaner locations with significantly low AOD. Future improvements in the NSRDB include the 

use of next-generation GOES-16 data at a 2-km resolution with the cloud-masking algorithm making use of 

all visible pixels that are available at a 1-km resolution. It is also expected that cloud masking will improve 

in GOES-16 because the additional visible channels will enable cloud snow discrimination, which currently 

does not exist for the older four-channel GOES satellites.

Further validation efforts will look more closely into the delicate issue of precisely defining “clear-sky” 

periods because part of the uncertainty reported in clear-sky DNI might be caused by unsuspected cloud 

contamination in the data.

ACKNOWLEDGEMENTS

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. DEAC36-08GO28308. 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.
8

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

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