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Contract No. DE-AC36-08GO28308

The Impact of Indoor and 

Outdoor Radiometer Calibration 

on Solar Measurements

Preprint

Aron Habte, Manajit Sengupta, Afshin Andreas, 

and Ibrahim Reda

National Renewable Energy Laboratory

Justin Robinson

GroundWork Renewables, Inc.

Presented at the European PV Solar Conference and Exhibition 

(EU PVSEC)

Munich, Germany

June 20–24, 2016

Conference Paper

NREL/CP-5D00-66659

July 2016
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THE IMPACT OF INDOOR AND OUTDOOR RADIOMETER CALIBRATION ON SOLAR MEASUREMENTS

Aron Habte1

, Manajit Sengupta1, Afshin Andreas1, Ibrahim Reda1, 

Justin Robinson2

1

National Renewable Energy Laboratory, Golden, CO, 80401, USA 2

GroundWork Renewables Inc., Logan, UT, 84321, USA

ABSTRACT: Accurate solar radiation data sets are critical to reducing the expenses associated with mitigating performance 

risk for solar energy conversion systems, and they help utility planners and grid system operators understand the impacts of 

solar resource variability. The accuracy of solar radiation measured by radiometers depends on the instrument performance 

specification, installation method, calibration procedure, measurement conditions, maintenance practices, location, and 

environmental conditions. This study addresses the effect of calibration methodologies and the resulting calibration 

responsivities provided by radiometric calibration service providers such as the National Renewable Energy Laboratory 

(NREL) and manufacturers of radiometers. Some of these radiometers are calibrated indoors, and some are calibrated

outdoors. To establish or understand the differences in calibration methodology, we processed and analyzed field-measured 

data from these radiometers. This study investigates calibration responsivities provided by NREL’s broadband outdoor 

radiometer calibration (BORCAL) and a few prominent manufacturers. The BORCAL method provides outdoor calibration 

responsivity of pyranometers and pyrheliometers at a 45° solar zenith angle and responsivity as a function of solar zenith 

angle determined by clear-sky comparisons to reference irradiance. The BORCAL method also employs a thermal offset 

correction to the calibration responsivity of single-black thermopile detectors used in pyranometers. Indoor calibrations of 

radiometers by their manufacturers are performed using a stable artificial light source in a side-by-side comparison of the test 

radiometer under calibration to a reference radiometer of the same type. In both methods, the reference radiometer 

calibrations are traceable to the World Radiometric Reference. These different methods of calibration demonstrated 1% to 

2% differences in solar irradiance measurement. Analyzing these values will ultimately assist in determining the uncertainties 

of the radiometer data and will assist in developing consensus on a standard for calibration.

Keywords: Radiometer calibration, thermopile, photodiode, pyranometer, pyrheliometers, global horizontal 

irradiance, direct normal irradiance, diffuse global irradiance, responsivity, measurement uncertainty

1 INTRODUCTION

One of the critical components for a successful solar 

energy project is access to accurate solar resource data. 

Pyranometer and pyrheliometer radiometers are used to 

measure global horizontal, diffuse, plain-of-array, and 

direct normal solar irradiances. To acquire accurate solar 

resource information from these radiometers, the tools 

used to design, manufacture, calibrate, deploy, and 

maintain them must continuously improve to provide 

necessary assurances to solar system financiers, 

developers, and operators. Investors must understand the

quantitative terms of the risks of their investments based 

on the knowledge of the solar resource inputs, and they 

need to be assured that system operators can operate and 

maintain their solar energy conversion system assets with 

maximum efficiency and value.

This study assesses multiple radiometer calibration 

methodologies and the resulting variations of irradiance 

measurements. Calibration is one source of uncertainty in 

radiometric data. Addressing the irradiance differences in 

data resulting from the myriad calibration methodologies

helps quantify the radiometric measurement 

uncertainties. Further, the study serves to open a dialogue 

among radiometer manufacturers and calibration service 

providers to develop a consensus on a standard approach 

to calibration that will alleviate, reduce, or explain the 

differences in irradiance measurements due to the

differing calibration methodologies.

2 METHOD

The radiometric data used in the study is traceable to 

the System Internationale, or SI, units through the World 

Radiometric Reference scale [1]. Data was collected for 

two months through the Measurement Instrumentation 

Data Center at the National Renewable Energy 

Laboratory (NREL). Clear-sky data were selected for the 

analysis based on a clearness index (Kt) greater than 0.6 

(Eq.1).

KKKK = GGGGGG

EEEEEEEE ∗ Cos (SZA) (1)

where:

GHI = global horizontal irradiance (W/m2

)

ETRN = extraterrestrial direct normal radiation (W/m2

)

SZA = solar zenith angle (degree).

The 1-minute data from the unit under test (UUT) 

radiometers were compared to reference GHI or direct 

normal irradiance (DNI) data. The reference irradiance 

data were obtained using a Kipp and Zonen model CHP1 

pyrheliometer (serial number 140108), and reference 

diffuse horizontal irradiance (DHI) measurements were 

made by a shaded Kipp and Zonen model CM22 

pyranometer (serial number 010047). These two 

reference radiometers provide the lowest measurement 

uncertainty for GHI [2], [3], [4]. A component sum 

method (Eq. 2) was used to obtain reference GHI data 

using measured data from the above reference 

radiometers.

GGGGGG = DD ∗ CCCCCC(SSSSSS) + DD (2)

The DNI UUT radiometer data were directly 

compared to the reference DNI. All of the radiometers 

included in the study were calibrated by NREL’s

BORCAL process and the radiometer manufacturers 

using indoor calibration methods. The radiometers were

deployed soon after calibration by the two methods.
2

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Measurement degradation and/or sensitivity drift were 

not considered to affect the responsivity of the 

radiometers. Further, our analyses were well within the 

recommended calibration cycle of one to two years [5]. 

The two months of data in this study were collected from 

June 23, 2015, through July 19, 2015. The analyses are 

based on measurements under clear skies.

2.1 Background on Calibration Methodology

NREL provides the following calibration services 

through the BORCAL process;

1. Calibration responsivity at a 45° SZA with 

thermal offset correction (using Eq. 3) for 

thermopile pyranometers and no thermal offset 

correction for thermopile pyrheliometers and 

photodiode.

2. Calibration responsivity as a function of SZA 

with thermal offset correction (using Eq. 3) for 

thermopile radiometers and no thermal offset 

correction for thermopile pyrheliometers and 

photodiode.

= (VV − nnnnnn ∗ WWnnnnnn)

GGGGGG

GGGGGG = DD ∗ CCCCCC(SSSSSS) + DD

(3)

where:

R = the pyranometer’s responsivity, in μV/ (Wm-2

) 

V = the pyranometer’s sensor output voltage, in μV

DNI = the direct normal irradiance measured by a 

primary or standard reference pyrheliometer, measuring 

the beam irradiance directly from the sun’s disk, in Wm-2

SZA = the solar zenith angle, in degrees

DHI = the diffuse sky irradiance, without the beam 

irradiance from the sun’s disk, measured by a shaded 

pyranometer, in Wm−2

Rnet = the pyranometer’s net infrared responsivity, in 

μV/(Wm−2)

Wnet = the effective net infrared irradiance measured by 

a collocated pyrgeometer, in Wm−2.

Indoor calibration methods by manufacturers provide 

single calibration responsivity, typically at approximately 

normal incidence. In most cases, the indoor calibration 

method uses Eq. 4:

UUUUUU = VV tt

VVrr rr rr rr (4)

where:

Rtest = responsivity of the radiometer under calibration, 

μV/(Wm-2

)

Vref and Vtest = the voltages (μV), measured using the 

reference and the field radiometers, respectively

Rref = responsivity, μV/(Wm-2

) of the reference 

radiometer.

2.2 Measurement Analysis Method

To analyze the measured data, the radiometric data 

from the UUT radiometers were back-calculated to 

determine the raw voltage readings using Eq. 5 This 

equation excludes the thermal offset correction applied 

by NREL to the solar irradiance data used in the study. 

Then the different responsivity values were applied to the 

voltage readings to generate irradiances based on each 

responsivity. 

The measurement equation for generating raw 

voltages is:

VVrrrrrr = GGmm ∗ + nnnnnn ∗ WWnnnnnn (5)

where:

Vraw = raw voltage readings, in μV

Gm = measured irradiance, in W/m2

R = the responsivity, in μV/Wm-2 used to produce the 

initial GHI values (from BORCAL).

Note: Gm applies to both pyranometers and 

pyrheliometers UUT.

Five responsivity value cases (Table 1) were applied 

to the raw voltage readings. These values were obtained 

from the BORCAL and manufacturers’ indoor 

calibration methods—that is, users of the radiometric 

TABLE I

RESPONSIVITY VALUE CASES APPLIED IN THE STUDY. WHEN THERMAL OFFSET CORRECTION IS

APPLICABLE (YES), EQUATION (3) IS USED. IF NOT APPLICABLE (NO), EQUATION (4) IS USED.

Cases Calibration Method

Thermal Offset Correction Applicability

Thermopile 

Pyranometer

Thermopile 

Pyrheliometer

Silicon Photodiode 

Pyranometer

Case 1 BORCAL responsivity as a function of SZA Yes No No

Case 2 Manufacturer calibration responsivity at 

manufacturer-specified SZA in degrees N/A N/A N/A

Case 3 BORCAL responsivity at 45° Yes N/A N/A

Case 4 BORCAL responsivity at 45° No N/A N/A

Case 5

Manufacturer calibration responsivity at 

manufacturer-specified SZA in degrees with 

manufacturer-supplied measurement 

equation N/A Yes N/A
3

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data obtain calibration responsivity from one of the cases 

below either from NREL or manufacturers.

For thermal offset correction cases, the thermally 

corrected voltage reading was obtained using Eq. 6:

VVcccc = VVrrrrrr − WWnnnnnn ∗ nnnnnn (6)

where:

Vcor = corrected voltage, in μV

The thermal offset correction was applied using net 

long-wave responsivity (Rnet) estimated or determined by 

blackbody characterization of the pyranometer [6] and a 

collocated pyrgeometer of a body type similar to the 

thermopile radiometer under test for determining Wnet, 

using Eq.7 & 8 [7].

WWnnnnnn = WWii − WW oo (7)

WW oo = σσ ∗ cc

4 (8)

where:

Win = incoming infrared from the pyrgeometer, in W/m2

Wout = outgoing infrared from the pyrgeometer, in W/m2

Tc = case temperature of the pyrgeometer, in Kelvin (K)

Σ = 5.6704x10-8 W/(m2K4) (Stephan-Boltzmann's 

Constant).

The measured irradiance can now be determined for 

each case of responsivity under consideration:

GGnnnn = VVcccccc

RRccccccccii

or GGnnnn = VV rr

RRccccccccii

(9)

Gnew is the new GHI and DNI irradiance values 

obtained through implementing the various calibration 

cases. Rcase is the responsivity value for each case. Eq. 9

(left) was applicable for Case 1 and Case 3, and Eq. 9

(right) was used for Case 2 and Case 4; and for 

photodiodes and thermopile pyrheliometers, the thermal 

offset corrections (Rnet and Wnet) were not applicable.

BORCAL provides responsivity at 2° bins of SZA. 

For this case, irradiance values are calculated using a 

high-resolution responsivity and SZA. The highresolution function is obtained by linearly interpolating 

the morning and afternoon averaged 2° responsivity data 

[8]. The authors of [9] confirmed that the responsivity as 

a function of SZA significantly reduces the cosine 

response error and ultimately the overall uncertainty of

the radiometric data.

Note: The reference DNI and the DNI measured by 

the UUT were corrected for cosine response using the 

responsivity as a function of SZA method. 

A percentage difference calculation was applied to 

assess the performance of each UUT relative to the 

reference instruments (Eq. 10):

DDDDDDDDDDDDDDDDDDDD(%) = � (GGnnnn − GGRR )

GGRR

� ∗ 100 (10)

where GRef are the irradiance values for the reference 

instrument in W/m2

.

3 RESULTS AND DISCUSSION

Multiple radiometers were included in the study. Fig. 

1 and Fig. 3–5 show the results of the comparison of the 

UUT to the reference GHI and DNI data. To better 

understand the differences due to calibration 

methodology, the results of the comparison were

partitioned into 10° bins of SZA in which the center of 

the bin represented the midpoint of each 10° SZA bin. 

The information in the figures below is derived from 1-

minute irradiance data.

Figure 1: Indoor and outdoor calibration comparison of

the reference GHI irradiance to the CMP22 test 

radiometer irradiance.

Note: Each blue box represents a 10° bin as well as the 

upper quartile and the lower quartile (also called the 

interquartile range) of the data in each bin. The circle in 

each blue box is the mean, and the black line signifies the 

median value. Ninety-nine percent of the data set is 

within the whiskers; data beyond the whiskers are plotted 

with a symbol (dots).

The four calibration cases in Fig.1 show differences 

up to 2% in GHI measurement. However, the NREL 

responsivity as a function of SZA (Case 3) and the 

manufacturer calibration responsivity (Case 4) provide 

least differences compared to the NREL 45° SZA 

responsivity with and without thermal offset. One 

hypothetical explanation for the better agreement of the

manufacturer calibration responsivity result relative to 

the NREL methods is that the reference DHI used in the 

study is from the same manufacturer and same model as 

the UUT (CMP22). This condition could favor the 

CMP22 comparison to the reference data; however, this 

needs further investigation.

Overall, the NREL responsivity as a function of SZA 

performed relatively better compared to the GHI UUT 

(Fig. 3 and Fig. 4). However, this method demonstrated 

poor performance in higher SZA (Bin 80° to 90° SZA). 

The reason is that the NREL 2° responsivity bins extend 

only up to a maximum of 80° during the BORCAL, and 

the same maximum responsivity number gets repeated 

beyond the SZA limits (circled in red in Fig. 2) on the 

day of calibration.
4

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Figure 2: Example of responsivity as a function of SZA 

for Eppley Laboratory, Inc.’s Standard Precision 

Pyranometer (Model PSP) after averaging the a.m./p.m.

2° bins and interpolating among each set of bins. 

Responsivities for less than 26° SZA have the same 

value as the 26° SZA. The same is true for responsivities 

greater than 76° SZA, wherein they have the same value 

as the 76° SZA (modified from [8]).

However, the effect is minimal for the smaller SZA 

bin (Bin 10° to 20° SZA, Fig. 2—red circle) for three

reasons. First, the lowest SZA observed for the study 

location was approximately 17°; therefore, the bin range 

had an approximate 18.5° center point lowest SZA for 

this bin, which is on the higher end of Bin 10° to 20°. 

Second, the number of occasions when the SZA falls 

between 17° and 20° SZA during the study period is 

relatively small compared to Bin 80° to 90° SZA. Third, 

responsivity at a smaller SZA has much lower 

dependence on zenith angle compared to a high SZA

Figure 3: Mean percent differences for six thermopile pyranometers (first six) and four photodiode pyranometers (last four).

As shown in Fig. 3, some of the radiometers—such as 

MS-802, SPP, L200R, SP-110—in Case 1, which is 

NREL’s BORCAL responsivity as a function of SZA, 

appear to perform better than they do in the rest of the 

cases. Overall, comparing the calibration methodologies 

would provide a better understanding of the performance of 

the radiometers in relation to SZA.

DNI UUT radiometers were also compared. Fig. 3 

shows a comparison of the EKO Instruments model MS-56 

to the Hukseflux model DR02.

As stated in Case 5 of Table 1, the comparisons of the 

MS-56 and DR02 were carried out using manufacturersupplied equations (11 and 12, respectively).

GGmm MMMM−56 = VVrrrrrr + kk ∗ VV2

rrrrrr

tt

(11)

where:

Gman= DNI irradiance derived using manufacturer supplied 

equation, in W/m2

.

k = a multiplier coefficient supplied by the manufacturer, 

(1/ μV).

Figure 4: Mean percent differences for three thermopile pyrheliometers.
5

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GGmm DDDD02 = VVrrrrrr

tt ∗ (aa ∗ TTTT2 + bb ∗ TTTT + CC) (12)

where a, b, and c are the coefficients supplied by the

manufacturer, and Temp (degrees Celsius) is the

measured ambient temperature from a collocated 

temperature sensor.

For MS-56 in Fig. 4, for SZA greater than 30° and 

lower than 60°, the NREL 45° responsivity (Case 4) 

method provided a better comparison to the reference 

data than that of the manufacturer calibration 

responsivity (Fig. 4); however, the manufacturer 

calibration responsivity performed better than the NREL 

responsivity as a function of SZA in most SZA bins. This 

could be related to no SZA dependence of 

pyrheliometers.

For DR02, NREL’s BORCAL calibration 

responsivity provided a better result than that of the 

manufacturer-supplied responsivity; however, for the 

SPP, the manufacturer-supplied responsivity 

demonstrated relatively a better result than NREL’s

BORCAL methods.

4 SUMMARY AND RECOMMENDATION

The accuracy of solar radiation measurements 

depends on the radiometer calibration procedure. This 

study addressed the calibration methodology and the 

resulting calibration responsivity provided by the 

manufacturers and radiometric calibration service 

provider. Differences among the values from indoor 

manufacturers’ irradiance compared to outdoor NREL’s

BORCAL irradiance values are observed to be on the 

order of 1% to 2% (Fig. 1 and Fig. 3) for pyranometers. 

Differences for pyrheliometers are less than 1% (Fig. 4). 

The study is important for quantifying the overall 

radiometric data uncertainty estimation and to apprise 

radiometric data users and calibration service providers 

of the variations in solar resource measurements due to 

calibration. The results of our study suggest a need to 

develop a consensus on a standard approach to

calibration that will alleviate such differences in

irradiance measurements due to the differing calibration 

methodologies. Guidance from radiometer manufacturers 

and calibration service providers to the users of these 

specialized instruments will also aid in understanding the 

accuracy limitations (e.g., when and where to use the 

calibration responsivity). For example, radiometers used 

indoors, such as in accelerated weathering chambers,

should be calibrated indoors.

ACKNOWLEDGEMENTS

The coauthors would like to thank Dr. Andrew 

Clifton, manager of NREL’s Sensing, Measurement, and 

Forecasting group, for his leadership and critical review 

of this work. The coauthors also gratefully acknowledge 

Dr. Yu Xie, memeber of NREL’s Sensing, Measurement,

and Forecasting group; and retired NREL employees 

Thomas Stoffel, Daryl Myers, and Stephen Wilcox for 

their guidance and review of this work. We are grateful 

to NREL Solar Radiation Research Laboratory 

employees for maintaining the outdoor solar irradiance 

and surface meteorological instrumentation. This work 

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

Contract No. DE-AC36-08-GO28308 with the National 

Renewable Energy Laboratory.

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