Conference Paper

NREL/CP-550-45375

March 2009

Comparison of Historical SatelliteBased Estimates of Solar Radiation 

Resources with Recent Rotating 

Shadowband Radiometer

Measurements

Preprint 

D.R. Myers

National Renewable Energy Laboratory

To be presented at the American Solar Energy Society 

Annual Conference

Buffalo, New York

May 11–16, 2009
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COMPARISON OF HISTORICAL SATELLITE-BASED ESTIMATES OF SOLAR 

RADIATION RESOURCES WITH RECENT ROTATING SHADOWBAND RADIOMETER 

MEASUREMENTS

Daryl Myers

National Renewable Energy Laboratory

1617 Cole Boulevard

Golden, CO 80402

daryl.myers@nrel.gov

ABSTRACT

Satellite-based solar radiation estimates have recently been 

incorporated into the 1990–2005 update to the 1961–1990 

U.S. National Solar Radiation Database (NSRDB). The 

National Aeronautics and Space Administration (NASA) 

also supplies satellite-based estimates of solar radiation. The 

usefulness of such data with respect to solar resources for 

site selection and designing solar energy conversion systems 

is often questioned. The availability of rotating shadow band 

radiometer measurement data at several new stations 

provides an opportunity to compare historical satellite-based 

estimates of solar resources with measurements. We 

compare mean monthly daily total (MMDT) solar radiation 

data from eight years of NSRDB and 22 years of NASA 

hourly global horizontal and direct beam solar estimates 

with measured data from three stations, collected after the 

end of the available resource estimates. We compare the 

most recent shadowband radiometer MMDT with a 

complement of thermopile “first class” solar radiometers at 

one site. Quantitative analysis shows that in most cases, the 

long-term average MMDT and measured data are 

comparable, within 10% of each other for global, and 20% 

for direct-radiation MMDT. 

1.0 INTRODUCTION

In 2007, the National Renewable Energy Laboratory 

(NREL) released the 1991–2005 update to the 1961–1990 

National Solar Radiation Data Base (NSRDB), a 30-year 

data set of measured and modeled solar radiation and 

accompanying meteorological data. Solar estimates are 

based on the Meteorological/Statistical (METSTAT) solar 

radiation model developed at NREL [1,2]. From 1998 to 

2005, geostationary satellite imagery could be used to 

estimate solar radiation resources on a 10-km grid [3]. The 

10-km solar estimates were derived from the satellite model 

of Dr. R. Perez of the State University of New York 

(SUNY), Albany [4]. 

Since 2005, NREL has been developing a project to help the 

concentrating solar power industry deploy solar radiation

measurement stations at prospective locations for system 

installations. There are two remote locations in addition to 

the NREL Solar Radiation Laboratory (SRRL), where a 

model 2 rotating shadowband radiometer (RSR2) has been 

deployed. Data for these sites are accessible over the 

Internet from the SRRL Measurement and Instrumentation 

Data Center at http://www.nrel.gov/midc. Besides the SRRL 

Baseline Measurement System (at http://www.nrel.gov/ 

midc/srrl_bms), the other sites are the Lowery Range Solar 

Station (available at http://www.nrel.gov/midc/lrss/ and the 

Sunspot One, San Luis Valley (SS1) site, near Alamosa, 

Colorado, available at http://www.nrel.gov/midc/ss1. A 

wide complement of thermopile sensor instrumentation, 

considered to be of higher quality than the RSR2, is 

available at the SRRL. Figure 1 shows examples of the 

SRRL radiometers used in the study. 

Fig. 1: Types of 

radiometers used in the 

study. Top left: RSR2; 

top right: thermopile 

pyranometer (global); 

bottom left: thermopile 

pyrheliometers (direct) 

at the NREL SRRL.

1
2.0 STATION INFORMATION

2.1 Station distribution

Figure 2 shows the geography of the radiometer deployment 

in Colorado.

Fig. 2: Deployment sites for Irradiance RSR2 sensors in 

Colorado. The SSRL is located in Golden, Colorado. 

Table 1 summarizes the locations and periods of record used 

to compare RSR2 measured and historical satellite-based 

estimates for the solar radiation data. 

SITE

TABLE 1: LOCATIONS AND ANALYSIS PERIOD

 

START

DATE

END

DATE

LATN LONGW ELEV

METER 

SRRL 1/1/2008 12/31/2008 39.75 105.18 1800

7/1/2008 1/31/2009

Lowry 7/1/2008 1/31/2009 39.61 104.58 1860

Sunspot 7/1/2008 1/31/2009 37.56 106.08 2335

2.2 Solar Measurements

The RSR2 data are collected once per minute. The band 

rotates at a high angular velocity from out of the view of the 

pyranometer, over the pyranometer, and back out of view. 

The band sweeps a shadow across the pyranometer as it 

blocks the sun during the rotation. Data logging software 

computes the difference between the shaded and unshaded 

signals of the pyranometer (Li-cor LI 200 SB, sensed across 

a 100-ohm ±0.1% low-temperature coefficient precision 

resistor), which is proportional to the vertical component of 

the direct beam radiation. The manufacturer’s pyranometer 

calibration factor for global horizontal irradiance is used to 

compute “uncorrected” global and diffuse irradiance values 

[5]. 

From the local time and location coordinates, the zenith 

angle and the path length through the atmosphere (or air 

mass) are calculated. The uncorrected global and diffuse 

irradiances are corrected with functions that depend on the 

ambient temperature (also measured) and the air mass, as 

described elsewhere [6,7]. The corrected direct normal 

irradiance is then computed from the corrected global and 

diffuse data. All corrected and uncorrected irradiances are 

stored in the data logger memory for later download. 

The SRRL broadband shortwave thermopile instrumentation 

(Kipp and Zonen CM22 Pyranometer, and CH1 

Pyrheliometer) are estimated to have total uncertainties of 

±2% in the direct beam irradiance, and ±5% in the global

horizontal irradiance [8]. 

3.0 SATELLITE COMPARISON DATA

3.1 Perez/SUNY Satellite Estimates

Since 1992, NREL has made available and updated the 

NSRDB, which contains hourly solar radiation estimates 

derived from meteorological data. For 1961–1990, there are 

239 stations [9,10]; for 1991–2005, 1440 sites, [11]. For 

1998–2005, there is a 10-km gridded data set derived from 

geostationary satellite images for the entire United States 

(except Alaska) above 60° N latitude. The latter was 

developed by Perez [11]. 

The estimated uncertainty in the SUNY data under optimal 

conditions (e.g., clear skies, high view angles, and stable 

albedo conditions) is estimated at 8% for the global and 

diffuse irradiance, and 15% for the direct normal irradiance. 

These uncertainties grow depending on the need for time 

shifted corrections, high latitudes (grazing viewing angles), 

and influence of snow. Each hourly data point for the SUNY 

gridded data carries a source flag (1, K = satellite imagery; 

2, indicating time shifting, and 3, indicating time shifting 

and predetermined lower diffuse limit) and uncertainty 

magnitude (8% and up, depending on factors mentioned 

above). Analysis by NREL [12] indicates that the SUNY 

model estimates are 2%–5% more accurate than the 

METSTAT-based estimates in the 1991–2005 NSRDB 

update; this explains our attention to comparing RSR2 and 

historical SUNY data. 

3.2 NASA Surface Solar Energy Estimates

The National Aeronautics and Space Administration 

(NASA) has developed its own satellite-based estimates of 

solar radiation on a 1° × 1° latitude longitude grid, available 

through the NASA Surface Solar Energy website. [13]. 

Figure 3 shows the NASA SSE grid cells for the three sites.

Lowry RSR

SRRL RSR

Sunspot RSR

2
SRRL

Lowry

Sunspot

Fig. 3: NASA SSE grid cells for the three sites.

The SSE produces estimates of global horizontal, direct 

normal, and diffuse horizontal mean monthly daily total 

(MMDT) irradiances, as well as many other available 

parameters. The estimated accuracy of the SSE data sets is

shown in Table 2 [14]. 

TABLE 2: NASA SSE ACCURACY ESTIMATES

PARAMETER METHOD 

ROOTMEANSQUARE

(RMS)

MEAN 

BIAS

(MBE) 

Horizontal 

Insolation 

SSE satellite 

Pinker 13%–16% +0.7%– –2%

Direct Normal SSE/RETScreen 

SSE/Page 15%~24% –9%+2%

Thus, we will be comparing RSR data with:

• SUNY data with uncertainty of ±8% in global 

horizontal, ±15% in direct beam data 

• NASA SSE data with uncertainty of ±15% in global 

horizontal and ±20% in direct beam data

• SRRL thermopile data with uncertainty of ±5% in 

global horizontal and ±2% in direct beam data, as noted 

above.

The manufacturer’s calibrations of the RSR2 pyranometer 

are claimed to be accurate to ±5% for global horizontal data.

Uncorrected direct beam irradiance is expected to be 

accurate to ±10%, and corrected direct beam data slightly 

lower, perhaps ±8% after all corrections are applied. 

3.3 Computing the MMDT

In our analysis, we compare MMDT irradiances. This is the 

average of the daily total irradiances computed for each day 

of the month, or the sum of daily totals divided by the 

number of days in the month. Many random errors 

contributing to the RMS errors will cancel out in the process 

of averaging the time series data. 

The SUNY hourly data for the grid cells containing the 

three measurement sites were downloaded for the eight 

available years (1998–2005). An NREL Internet data access 

tool called the Solar Power Prospector (http://mercator.

nrel.gov/csp) makes available the eight years of 10-km 

gridded data for a user-selected cell, with the addition of a 

“Typical Direct Year” or TDY for the cell. The TDY is 

analogous to the Typical Meteorological Year (TMY), but is 

derived from only the eight years of gridded satellite data 

estimates for solar radiation in combination with 

meteorological data assimilated from the National Oceanic 

and Atmospheric Administration meteorological reanalysis 

data [15], resampled to the 10-km grid of the solar data (see 

http://www.cdc.noaa.gov/data/reanalysis). The selection 

criteria produce slightly different TDY MMDT than the 

average of the eight years of SUNY radiation data. 

The NASA SSE website produces MMDT representing the 

averages of the NASA 22-year period of record. Despite the 

fact that El Niño and La Niña year data can be selected or 

excluded, we used the all-inclusive 22-year average MMDT 

for each 1° × 1° latitude/longitude cell containing the 

measurement site in our analysis as the SSE MMDT.

4.0 RESULTS

4.1 SRRL One-Year Data Set Comparison

We begin with the most comprehensive comparison, that of 

the RSR2 data with the thermopile radiometers, long-term 

averaged SUNY and SSE for the SRRL location. Measured 

data for the year 2008 comprise the measurement sets. 

SUNY TDY and SSE data are averaged over 22 and 8 years, 

respectively. Tables 3 and 4 summarize the MMDT from

each data set for global horizontal and direct normal 

radiation, respectively. Table 5 shows the percent error 

between measured thermopile and the corrected (C) and 

uncorrected (U) RSR2 global and direct MMDT irradiances 

over the year. Applying corrections apparently reduces the 

MMDT percent error by about 50%. The RSR2 corrected 

MMDT data are on average within 1% for global data and 

within 1.5% for direct data.

3
TABLE 3: SRRL GLOBAL MMDT kWh/m2

/day

MONTH CM22 RSR2 C RSR2 UN SSE SUNY 

TDY

1 2.810 2.789 2.718 2.470 2.580

2 3.682 3.641 3.573 3.390 3.546

3 4.780 4.739 4.671 4.540 4.604

4 6.341 6.259 6.186 5.340 5.775

5 6.184 6.138 6.073 6.150 6.612

6 7.543 7.467 7.409 6.830 6.910

7 6.783 6.703 6.680 6.330 6.931

8 5.663 5.586 5.568 5.550 5.943

9 5.008 4.925 4.895 5.050 5.232

10 3.776 3.734 3.685 3.870 3.833

11 2.753 2.731 2.674 2.690 2.740

12 2.383 2.395 2.319 2.270 2.260

TABLE 4: SRRL DIRECT-NORMAL MMDT 

kWh/m2/day

MONTH CH1 RSR2

C

RSR2

UN SSE SUNY TDY

1 5.258 5.145 5.355 4.950 4.714

2 5.133 5.022 5.257 5.570 5.602

3 5.417 5.329 5.574 6.040 6.196

4 6.959 6.796 7.091 5.740 6.344

5 5.438 5.324 5.586 6.170 6.999

6 7.900 7.789 8.159 7.050 7.537

7 6.742 6.662 7.032 6.300 7.241

8 5.952 5.900 6.233 5.710 6.596

9 6.178 6.107 6.420 6.420 6.522

10 5.671 5.624 5.896 6.060 5.555

11 4.375 4.393 4.619 5.070 5.095

12 4.213 4.266 4.429 4.980 4.792

In Table 6 we compare the SRRL measured thermopile 

MMDT for 2008 with the long-term averages from SSE and 

SUNY TDY. Although there are large differences between 

the long-term means in particular months, especially during 

the spring and fall, the mean differences in global and direct 

MMDT over the year for both long-term data sets is –7% to 

+5%. The computation is for measured minus long-term 

mean divided by measured, so positive values imply 

measured data greater than long-term means, and negative 

values imply the reverse. Figure 4 plots the data in Table 6.

These results show the deviations from long-term means (up 

to 30%) that can occur for a particular month or year. 

TABLE 5: PERCENT DIFFERENCE BETWEEN 

THERMOPILE , CORRECTED, AND 

UNCORRECTED RSR2 MMDT SRRL 2008 DATA

MONTH % GLO 

C

%GLOR 

UN

% DIR 

C

%DIR

UN

1 0.7 3.3 2.2 –1.9

2 1.1 3.0 2.2 –2.4

3 0.8 2.3 1.6 –2.9

4 1.3 2.4 2.3 –1.9

5 0.7 1.8 2.1 –2.7

6 1.0 1.8 1.4 –3.3

7 1.2 1.5 1.2 –4.3

8 1.3 1.7 0.9 –4.7

9 1.7 2.3 1.1 –3.9

10 1.1 2.4 0.8 –4.0

11 0.8 2.9 –0.4 –5.6

12 –0.5 2.7 –1.3 –5.1

MEAN 0.9 2.3 1.2 –3.6

TABLE 6: PERCENT DIFFERENCES BETWEEN 

SSE, SUNY, AND 2008 SRRL REFERENCE DATA 

MMDT 

Month CM22 

SSE

CM22 

SUNY

CH1 

SSE

CH1 

SUNY

1 12.1 8.2 5.9 10.3

2 7.9 3.7 –8.5 –9.1

3 5.0 3.7 –11.5 –14.4

4 15.8 8.9 17.5 8.8

5 0.5 –6.9 –13.5 –28.7

6 9.5 8.4 10.8 4.6

7 6.7 –2.2 6.6 –7.4

8 2.0 –4.9 4.1 –10.8

9 –0.8 –4.5 –3.9 –5.6

10 –2.5 –1.5 –6.9 2.0

11 2.3 0.5 –15.9 –16.5

12 4.8 5.2 –18.2 –13.7

MEAN 5.3 1.5 –2.8 –6.7

The RSR2 and thermopile comparison data for 2008 show 

that the RSR2 corrected data and the SSE and SUNY longterm means would be within 1%–2% of these results. In the 

following sections we will compare both corrected and

uncorrected RSR2 data with the satellite-derived long-term 

means, but only for the seven months of available data (July 

2008 to January 2009). 

4
-30

-20

-10

0

10

20

1 2 3 4 5 6 7 8 9 10 11 12

Month

MMDT Percent Difference

% CM22 SSE %CM22 SUNY % CH1 SSE %CH1 SUNY

Fig. 4: Percent difference between SRRL 2008 thermopile 

radiometer measured global (CM22) and direct (CH1) 

MMDT with respect to long-term SSE and SUNY MMDT.

 

4.2 Lowry RSR2 Seven-Month Comparison 

 

Tables 7 and 8 show MMDT for the RSR2 global horizontal 

and direct normal, respectively, and the SUNY TDY and 

SSE long-term mean MMDT at the Lowry site for July 2008 

to January 2009.

TABLE 7: LOWRY GLOBAL MMDT COMPARISON

MTH 

TABLE 8. LOWRY DIRECT MMDT COMPARISON

RSR2 

CORRECTED 

RSR2

UNCORRECTED 

SUNY 

TDY SSE 

7 7.550 7.814 7.076 6.430

8 6.994 7.228 7.606 6.020

9 6.765 6.982 7.763 6.550

10 6.104 6.284 6.529 6.220

11 4.337 4.487 6.366 5.090

12 4.745 4.836 6.132 4.630 

13 4.699 4.812 5.004 4.780

Figure 5 plots the data in Table 7. Figure 6 shows the 

percent difference between global MMDT for the corrected 

and uncorrected RSR2 global data and the SUNY TDY and 

SSE long-term average MMDT. Figure 7 is for the direct.

Figure 5 indicates that the results for the measured and longterm means are relatively close, but Fig. 6 indicates that the 

percentage differences between the long-term means and 

measured data approach 10% for the SSE data set, 8% for 

the SUNY data set, measured data approach 10% for the 

SSE data set, and 8% for the SUNY data set. 

Fig. 5: RSR2 corrected and uncorrected and SUNY and SSE 

global horizontal MMDT for the Lowry site (from Table 7).

-6%

-4%

-2%

0%

2%

4%

6%

8%

10%

12%

7 8 9 10 11 12 13

Month July 08 - Jan 09

Percent Difference 

Global MMDT

RSR2 C- SUNY RSR2 U -SUNY RSR2 C- SSE RSR2 U- SSE

Fig. 6: Percent differences between RSR2 corrected and 

uncorrected and SUNY (solid) and SSE (hatch) global 

horizontal MMDT for Lowry site.

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

7 8 9 10 11 12 13

Month July 08 - Jan 09 

Percetn Difference

 Direct MMDT

RSR2 C- SUNY RSR2 U -SUNY RSR2 C- SSE RSR2 U- SSE

Fig. 7: Percent differences between RSR2 corrected and 

uncorrected and SUNY (solid) and SSE (hatch) direct beam

MMDT for Lowry site.

The largest percentage differences occur in winter months, 

with lower total radiation (by almost a factor of 3). This is 

well within the quoted uncertainties discussed in section 3.2.

This is excepting the direct beam in November 2008, when 

there were only about seven relatively clear days. 6.928 6.222 5.329 3.823 2.722 2.265

2.486

7.078

6.000

5.176

3.927

2.685

2.440

2.691

7.066

5.984

5.148

3.880

2.629

2.361

2.618

6.400

5.710

5.110

3.930

2.700

2.190

2.430

2.0

3.0

4.0

5.0

6.0

7.0

8.0

7 8 9 10 11 12 13

Month of 2008 (Jun - Oct)

Monthly Mean Daily Total kWm-2

TDY SUNY RSR2 CORRECTED RSR2 UNCORRECTED NASA SSE

Month

RSR2

Corrected

RSR2 

Uncorrected

SUNY 

TDY SSE 

7 7.078 7.066 6.928 6.400

8 6.000 5.984 6.222 5.710

9 5.176 5.148 5.329 5.110

10 3.927 3.880 3.823 3.930

11 2.685 2.629 2.722 2.700

12 2.440 2.361 2.265 2.190

13 2.691 2.618 2.486 2.430

5
7.658

6.839

7.930

7.204

5.796

4.923

6.462

7.775

6.960

8.054

7.292

5.914

4.924

6.430

7.861

7.161

7.430

6.608

5.624

5.969

5.561

6.150

5.600

6.440

6.190

5.410

5.390

5.480

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

7 8 9 10 11 12 13

Months Jul 08-Jan 09

Monthly Mean Dailty Tota 

kWh/m2/day

RSR2 C RSR2 U SUNY TDY SSE 

4.3 Sunspot RSR2, Alamosa, Colorado

 

As for the Lowry site, Tables 9 and 10 show measured 

global and direct MMDT with SUNY and SSE long-term 

average MMDT. 

MTH 

TABLE 9: SUNSPOT GLOBAL MMDT kWh/m2/day

RSR2

CORRECTED 

RSR2

UNCORRECTED

SUNY 

TDY SSE

7 7.234 7.165 7.180 6.310

8 6.216 6.166 6.537 5.590

9 5.881 5.830 5.643 5.200

10 4.492 4.420 4.400 4.090

11 3.268 3.183 3.568 2.970

12 2.854 2.747 2.720 2.560

13 3.416 3.292 2.816 2.800

Figure 8 plots the data in Table 10. Figures 9 and 10 show 

the percent difference between the Sunspot global and direct 

MMDT and long-term means, respectively.

MTH 

TABLE 10: SUNSPOT DIRECT MMDT kWh/m2/day

RSR2

CORRECTED

RSR2

UNCORRECTED

SUNY 

TDY SSE

7 7.658 7.775 7.861 6.150

8 6.839 6.960 7.161 5.600 

9 7.930 8.054 7.430 6.440

10 7.204 7.292 6.608 6.190 

11 5.796 5.914 5.624 5.410

12 4.923 4.924 5.969 5.390 

13 6.462 6.430 5.561 5.480 

Fig. 8: RSR2 corrected (C), uncorrected (U), SUNY, and 

SSE direct beam MMDT for Sunspot site (from Table 10).

For this site, differences between measured MMDT and the 

long-term SSE means are larger and rather more constant, 

around 10% for global and 20% for direct (measured greater 

than SEE). 

Fig. 9: Percent difference between RSR2 corrected and 

uncorrected and SUNY (solid) and SSE (hatch) global 

horizontal MMDT for Sunspot site.

Fig. 10: Percent differences between RSR2 corrected and 

uncorrected and SUNY (solid) and SSE (hatch) direct beam 

MMDT for Sunspot site.

5.0 DISCUSSION

We compare 7 months of RSR2 measured and long-term (8 

year and 22 year) estimated MMDT based on historical 

modeled estimates of MMDT. The long-term estimates are 

based on the NASA SSE (1983–2005) and SUNY (1998–

2005) data sets. We have three sites with measured data

available, all in Colorado, but in distinct NASA SSE (and 

SUNY) grid cells. Because the corrected RSR2 data are

slightly better than the uncorrected data (see Table 5), we 

summarize the results for the 7 months common to all of the 

RSR2 data (July 2008 to January 2009) in Table 11. The 

entries are percent differences between measured and longterm MMDT computed as 100* (Measured-Mean)/

Measured. Recall from section 3.2 the estimated 

uncertainties in the long-term means:

SUNY ±8% in global horizontal, ±15% in direct beam.

NASA SSE ±15% in global horizontal and ±20% in direct. 

-30%

-20%

-10%

0%

10%

20%

30%

7 8 9 10 11 12 13

Month July 08-Jan 09

Percent Difference

 Direct MMDT

RSR C - SUNY RSR U-SUNY RSR C - SSE RSR U -SSE

-15%

-10%

-5%

0%

5%

10%

15%

20%

7 8 9 10 11 12 13

Month July 08-Jan 09

Percent Difference 

Global MMDT

RSR C - SUNY RSR U-SUNY RSR C - SSE RSR U -SSE

6
SUNY GLOBAL ±8%

TABLE 11: PERCENT DIFFERENCES BETWEEN 

RSR2-C MEASURED AND LONG-TERM MEAN 

MMDT BY SITE AND LONG-TERM SOURCE 

MONTH LOWRY SUNSPOT SRRL AVG

7 2.1 0.7 –3.4 –0.2

8 –3.7 –5.2 –6.4 –5.1

9 –3.0 4.1 –6.2 –1.7

10 2.6 2.0 –2.7 0.7

11 –1.4 –9.2 –0.3 –3.6

12 7.1 4.7 5.6 5.8

13 7.6 17.6 7.5 10.9

MEAN 1.6 2.1 –0.8 1.0

SUNY Direct ±15%

7 6.3 –2.6 –8.7 –1.7

8 –8.8 –4.7 –11.8 –8.4

9 –14.8 6.3 –6.8 –5.1

10 –7.0 8.3 1.2 0.8 

11 –46.8 3.0 –16.0 –19.9

12 –29.2 –21.3 –12.3 –20.9

13 –6.5 13.9 8.4 5.3

MEAN –15.2 0.4 –6.6 –7.1

SSE Global ±15%

7 9.6 12.8 5.6 9.3

8 4.8 10.1 0.6 5.2

9 1.3 11.6 –2.5 3.4

10 –0.1 8.9 –3.6 1.7

11 –0.6 9.1 1.5 3.4

12 10.2 10.3 5.2 8.6

13 9.7 18.0 11.4 13.1

MEAN 5.0 11.5 2.6 6.4

SSE Direct ±20%

7 14.8 19.7 5.4 13.3

8 13.9 18.1 3.2 11.7

9 3.2 18.8 –5.1 5.6

10 –1.9 14.1 –7.8 1.5

11 –17.4 6.7 –15.4 –8.7

12 2.4 –9.5 –16.7 –7.9

13 –1.7 15.2 3.8 5.7

MEAN 1.9 11.9 –4.7 3.0

We examined how often the long-term means differ by an 

amount exceeding the estimated uncertainties in the longterm means, from the measured MMDT for the period. 

These cases are highlighted in Table 11 for various 

individual months and sites. For each long-term MMDT

(SSE, SUNY), there are 21 possible site-months of data. For 

the SUNY global estimate, in only 1 case of 21 (5%) was 

the 8% uncertainty exceeded; and that was in January, at the 

Sunspot site near Alamosa, Colorado, where the global 

irradiance is around 3 kWh/m2

/day. The 18% difference 

represents ~500 Wh/m2 in absolute terms. 

The differences with the SUNY direct estimates at all sites 

in November and December are larger, representing the 

sensitivity of the direct beam to cloud and storm passage 

differences in these specific months, with respect to the 

average pattern over the years 1998–2005. However, the 

maximum mean percentage difference over the seven-month 

period is just 0.2% larger (at Lowry) than the estimated 

SUNY direct uncertainty. The differences for particular 

months (again, winter months with lower irradiance values) 

occurred 21% of the time.

For the SSE data set, performance appears somewhat better. 

Only 1 of the 21 (5%) months (Sunspot, January) showed a 

difference between the estimated and measured global

MMDT greater than 15%. However, differences in 

individual months tend to be larger than those from the 

SUNY MMDT in most cases. In 11 of the 21 site-months,

or 52% of the sample, the difference exceeds the 8% 

estimated uncertainty in the SUNY data. Most of the larger 

differences are seen at the Sunspot site, where the SSE 1° × 

1°cell encloses a wide variety of terrain and elevations 

(mountains, valleys) that no doubt affect the model result 

(which averages about 12% low). 

SSE direct results are similar to the global findings, 

including the fact that most of the larger differences occur at 

Sunspot. However only 8 of the 21 (38%) site months 

actually exceed the SUNY estimated uncertainty of ±8%. 

Overall, in only 1 of the 12 (8%) estimates of overall mean 

differences (MEAN row in each table section) for both 

radiation components did the overall mean difference 

exceed the estimated uncertainty in the long-term mean 

(Lowry, direct MMDT mean), and then only by 0.2%. In all 

other cases, the mean difference between MMDT measured 

and estimated from the long-term means was half or less

than the estimated uncertainties of the long-term means. 

6.0 

 

CONCLUSION

On the basis of global and direct beam MMDT over one 

year, we compared performance of the RSR2 with 

thermopile-based sensors (pyranometers, pyrheliometers). 

The RSR2 MMDT were on average within 2% of the 

thermopile data over 2008. Differences in individual 

monthly MMDT varied about ±2% for the corrected data, 

and ±5% for the uncorrected data (Table 5).

7
Thus the RSR2 radiometer, with corrections applied, is a 

reasonable substitute for complex thermopile sensors for 

aggregated data on the scale of monthly averages, to an 

accuracy of about 2%. This is a relative performance figure 

of merit only, not a measure of the absolute accuracy of 

either the thermopile sensors or the RSR2 radiometer, which 

is typically about 2% for pyrheliometers and 4%–5% for 

pyranometers [8,16]. 

 

Comparison of long-term estimates of MMDT for three sites 

for a common seven-month period shows that the long-term 

estimates are reasonably accurate with respect to the 

estimated uncertainties associated with them and quoted for 

them. That is, ±8% and ±15% for SUNY and SSE global 

MMDT, respectively, and 15% and ±20% for SUNY and 

SSE direct MMDT, respectively. 

About 10% of the time for global, up to 25% of the time for 

direct in our sample of 21 site months, year-to-year 

variability in the weather will contribute to conditions 

resulting in “real” MMDT outside the bounds of the longterm estimate uncertainties. For our 21 site-month sample, 

the long-term estimates were within their estimated 

uncertainty limits about 85% of the time for the SUNY data.

The SSE performance was “better” because the uncertainties 

were larger (41 of the 42 months, or 98%, were “within 

tolerance”). 

Fifty-seven percent (24 of 42) of the SSE estimates for both 

global and direct MMDT were within 10% of the measured 

MMDT; so about 50% of the SSE estimates may be “off” 

by 10% or more for sites similar to those used here.

7.0 ACKNOWLEDGMENTS

Measurement capability at the NREL SRRL is maintained 

by A. Andreas, P. Gotseff, B. Kay, I. Reda, T. Stoffel, and 

S. Wilcox. A. Andreas maintains the data archive for the 

NREL Measurements and Instrumentation Data Center. This

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

under Contract No. DE-AC36-99GO10337 with the 

National Renewable Energy Laboratory.

8.0 REFERENCES

1. E. L. Maxwell, “METSTAT-The Solar Radiation 

Model Used in the Production of the National Solar 

Radiation Data Base (NSRDB).” Solar Energy 1998, 

62(4):263–279. 

2. E. L. Maxwell, “Producing a 1961–1990 Solar 

Radiation Data Base for the United States.” 

Proceedings American Solar Energy Society Austin 

TX, 1990, American Solar Energy Society, Boulder, 

CO. 

3. A. Zelenka, R. Perez, R. Seals, R. Renne, “Effective 

Accuracy of Satellite-Derived Hourly Irradiances.” 

Theoretical Applied Climatology 1999, 62:199–207. 4. R. Perez, P. Ineichen, et al., “A New Operational 

Satellite-to-Irradiance Model.” Solar Energy 2002,

73(5): 307–317.

5. J. Michalsky, J. L. Berndt, G. J. Schustere, “A 

Microprocessor Based Rotating Shadowband 

Radiometer.” Solar Energy 1986, 36(3):465–470.

6. F. Vignola, “Removing Systematic Errors from 

Rotating Shadowband Pyranometer Data.” Proceedings 

American Solar Energy Society 2006. Denver, CO; 

American Solar Energy Society, Boulder CO. 

7. D. L. King, D. R. Myers, (1997), “Silicon Photodiode

Pyranometers: Operational Characteristics, Historical 

Experiences, and New Calibration Procedures.” 26th 

IEEE Photovoltaic Specialists Conference, 1997.

8. D. R. Myers, T.L. Stoffel, S. Wilcox, et al., “Recent 

Progress in Reducing the Uncertainty in and Improving 

Pyranometer Calibrations.” Journal of Solar Energy 

Engineering 2002, 124: 44-50.

9. NSRDB Vol. 1, Users Manual National Solar Radiation 

Data Base (1961–1990). Golden CO, National 

Renewable Energy Laboratory, 1992. 

10. NSRDB Vol. 2, Final Technical Report- National Solar 

Radiation Data Base (1961–1990), NREL TP-463-

5784. Golden CO: National Renewable Energy 

Laboratory, 1995. 

11. S. Wilcox, M. Anderberg, et al., “Completing 

Production of the Updated National Solar Radiation 

Database for the United States.” Proceedings American 

Solar Energy Society 2007. Cleveland, OH; American 

Solar Energy Society, Boulder, CO.

12. NREL, National Solar Radiation Database 1991–2005 

Update: User’s Manual NREL/TP-581-41364. Golden 

CO: National Renewable Energy Laboratory, 2007. 

13. P.W. Stackhouse, T. Zhang, W. Chandler, et al., “Solar 

Renewable Energy Data Sets from NASA Satellites and 

Research.” Proceedings ISES 2005 Solar World 

Congress, Orlando, FL. American Solar Energy 

Society, Boulder, CO. 

14. NASA, “Surface Meteorology and Solar Energy: 

Accuracy.” http://eosweb.larc.nasa.gov/cgi-bin/sse/ 

print. cgi?accuracy.txt, 2008.

15. E. Kalnay, M. Kanamitsu, R. Kistler, et al., “The 

NCEP/NCAR 40-Year Reanalysis Project.” 

Bulletin of the American Meteorological Society 1996, 

77: 437–471. 

16. S. M. Wilcox, D. R. Myers, Evaluation of Radiometers 

in Full-Time Use at the National Renewable Energy 

Laboratory Solar Radiation Research Laboratory. 57 

pp.; NREL Report No. TP-550-44627, 2008.

8
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Comparison of Historical Satellite-Based Estimates of Solar 

Radiation Resources with Recent Rotating Shadowband 

Radiometer Measurements: Preprint

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14. ABSTRACT (Maximum 200 Words)

The availability of rotating shadow band radiometer measurement data at several new stations provides an 

opportunity to compare historical satellite-based estimates of solar resources with measurements. We compare 

mean monthly daily total (MMDT) solar radiation data from eight years of NSRDB and 22 years of NASA hourly 

global horizontal and direct beam solar estimates with measured data from three stations, collected after the end of 

the available resource estimates. 

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NREL; National Renewable Energy Laboratory;NREL Solar Radiation Laboratory; SRRL; National Solar Radiation 

Database; NSRDB; National Aeronautics and Space Administration; NASA; shadowband; radiometer

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