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Contract No. DE-AC36-08GO28308

A Physics-Based GOES Satellite 

Product for Use in NREL’s 

National Solar Radiation 

Database

Preprint 

M. Sengupta, A. Habte, P. Gotseff, A. Weekley, 

and A. Lopez

National Renewable Energy Laboratory 

C. Molling

University of Wisconsin

A. Heidinger

National Oceanic Atmospheric Administration

To be presented at Solar 2014 

San Francisco, California

July 6–10, 2014

Conference Paper

NREL/CP-5D00-62237

July 2014 
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Cover Photos: (left to right) photo by Pat Corkery, NREL 16416, photo from SunEdison, NREL 17423, photo by Pat Corkery, NREL 

16560, photo by Dennis Schroeder, NREL 17613, photo by Dean Armstrong, NREL 17436, photo by Pat Corkery, NREL 17721.
A Physics-Based GOES Satellite Product for Use in 

NREL’s National Solar Radiation Database 

Manajit Sengupta, Aron Habte, Peter Gotseff, 

Andrew Weekley, and Anthony Lopez

15013 Denver West Pkwy

National Renewable Energy Laboratory

Golden, CO

Christine Molling

University of Wisconsin

Andrew Heidinger

National Oceanic Atmospheric Administration

ABSTRACT

The National Renewable Energy Laboratory (NREL), 

University of Wisconsin, and National Oceanic Atmospheric 

Administration are collaborating to investigate the integration 

of the Satellite Algorithm for Shortwave Radiation Budget 

(SASRAB) products into future versions of NREL’s 4-km by 

4-km gridded National Solar Radiation Database (NSRDB). 

SASRAB product comparison with ground measurement sites 

revealed that the satellite-based surface radiation suffered 

from a significant low bias under clear-sky conditions. NREL 

researchers evaluated and ranked three reduced-order clearsky radiative transfer algorithms that could be used to replace 

the current biased satellite-based clear-sky irradiance. These 

models use aerosol optical depth (AOD) and precipitable 

water vapor (PWV) as primary inputs. During initial model 

evaluation, surface measurements of AOD were used for 

model ranking, resulting in the selection of the spectral-based 

AOD Bird model [1] for production work. During final 

production, the selected model was fed satellite-based AOD 

and PWV, but during validation the model DNI output was 

found to be biased high due to underestimated satellite-derived 

AOD. For final implementation, a solution was found to

incorporate an empirical weighting factor to the satellite AOD 

and apply it to the Modified MAC model: MMAC [3], a 

broadband AOD-based clear-sky model. Final validation 

showed improved clear-sky irradiance values when compared 

to surface measurements from the original SASRAB product. 

 

Keywords: satellite solar resource data; National Solar 

Radiation Database; NSRDB; radiative transfer algorithms; 

clear-sky models; aerosol optical depth; AOD 

1. INTRODUCTION 

Providing publicly accessible high-quality and long-term 

satellite-based solar resource data sets of the United States

reduces barriers to solar grid penetration. Two widely used 

approaches derive solar resource from satellites: (a) an 

empirical approach that relates ground-based observations 

using 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 used traditionally for computing 

surface radiation, the advent of faster computing has made 

operational physical models viable. 

The Global Solar Insolation Project (GSIP) is an operational 

physical model from the National Oceanic Atmospheric 

Administration (NOAA) that computes global horizontal 

irradiance (GHI) using the visible and infrared channel 

measurements from the GOES satellites. GSIP uses a twostage scheme that first retrieves cloud properties and then uses 

those properties in the Satellite Algorithm for Shortwave

Radiation Budget (SASRAB) model to calculate surface 

radiation. 

The National Renewable Energy Laboratory (NREL), 

University of Wisconsin, and NOAA are collaborating to 

adapt GSIP to create a data set with high temporal, hourly, and 

spatial resolution that matches the GOES 4-km by 4-km image 

pixels. The product initially generates the cloud properties 

using the AVHRR Pathfinder Atmospheres-Extended 

(PATMOS-x) algorithms, whereas GHI and diffuse horizontal 

radiation (DHI) are calculated using SASRAB. A comparison 

to ground sites resulted in the finding that the satellite-based 

surface radiation suffered from a significant low bias under 

clear-sky conditions. To remove this bias, we evaluated 

reduced-order clear-sky radiative transfer algorithms that 

require aerosol optical depth (AOD) and precipitable water 

vapor (PWV) as primary inputs. Using surface-derived AOD 

and precipitable water as model inputs, new calculated 

irradiance showed significant improvement in the clear-sky 

radiation compared to surface measurements from the original 

SASRAB product. These models were used as a starting point 

to develop replacement clear-sky irradiance (direct normal 

irradiance, or DNI, and GHI) in the 1998 to 2012 SASRAB 

data set for eventual use in NREL’s future National Solar 

Radiation Database (NSRDB). 

The remainder of this paper is organized as follows. Section 2

describes the initial clear-sky model performance and 

selection based on surface measurements. Section 3 describes 

the bias problem that is encountered when using the satellitebased AOD input to the clear-sky models along with the

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resolution to implement an empirically trained weighted AOD. 

The last part of Section 3 presents final results that quantify 

the improvements in the satellite-based clear-sky irradiance 

estimates using MMAC compared to the original SASRAB 

product. 

2. CLEAR-SKY MODEL PERFORMANCE

Three clear-sky models [1], [2], [3] were converted from their 

original sources into a MATLAB script format and used for 

the initial performance evaluation.

The relative improvement in performance of the three

converted clear-sky models compared to the current SASRAB 

clear-sky algorithm was quantified using the seven continental 

NOAA SURFRAD ground measurement sites for an 

arbitrarily chosen validation year, 2009. These seven sites 

represent varying mid-latitude continental atmospheric sky 

conditions from the desert Southwest (Desert Rock, Nevada) 

to the high-humidity South (Goodwin Creek, Mississippi). 

NOAA also has nine Integrated Surface Irradiance Study 

(ISIS) measurement stations that are well distributed 

throughout the United States, but their use for initial 

performance evaluation was rejected because of a lack of 

ground-based AOD measurements required for clear-sky 

model input.

2.1 Surface Measurement Inputs

The seven NOAA SURFRAD sites supplying broadband 

radiation (GHI and DNI) at 1-minute averaged intervals were

used to quantify the clear-sky model performance. In addition, 

the colocated surface meteorological measurements (surface 

pressure, temperature, relative humidity, wind speed, and 

direction) as well as AOD measurements were used to create 

more accurate site-specific model inputs to the clear-sky 

models. This allowed us to evaluate the models with the best 

available surface data to determine local atmospheric 

conditions as model inputs. 

The SURFRAD GHI and DNI were preprocessed to align 

properly with the instantaneous GOES images (used as the 

original source of the SASRAB products) that are taken twice 

hourly at :00 and :30 past the hour for GOES West and at :15 

and :45 past the hour for GOES East. That is, the 1-minute 

SURFRAD irradiance was 30-minute time-averaged on the 

center of the corresponding GOES image time stamp.

AOD measurements were determined from the colocated 

Multi-Filter Rotating Shadowband Radiometer (MFRSR) [4]. 

Because each clear-sky model requires AOD at different 

wavelengths, the following approach was used for this 

analysis: The MFRSR measured spectral AOD wavelengths at 

415 nm, 501 nm, 616 nm, 672 nm, and 868 nm. In addition, 

two clear-sky models used spectral-based AOD—the Bird 

model used AOD at 380 nm and 500 nm, and REST2 used the

Angstrom turbidity coefficient α and Angstrom wavelength 

exponent, β, to describe atmospheric turbidity. Finally, 

MMAC used broadband AOD (BAOD). For this analysis, the 

Angstrom turbidity values were calculated for the REST2 

model, and using those values, the spectral AOD for Bird and 

BAOD for MMAC were back calculated to ensure that all 

AODs were internally consistent and described the same 

atmospheric turbidity conditions. For the spectral-based 

models (Bird and REST2), this was performed in the 

following manner: 

In general, AOD is generally a smooth extinction function 

dependent on wavelength. 

τ(λ) = βλ−α (1) 

The natural log of each side can be taken, resulting in

ln(τλ) = ln(β) − α ∙ ln(λ) (2) 

This equation has the form of a linear equation for λ measured 

in microns. In a stable atmosphere, this results in a straight 

line with an intercept of β = τ (λ = 1 μm) and slope α. α and β

are then determined using the linear fit of MFRSR spectral 

AOD data to Equation (2). Thus, the AOD at any specified 

wavelength can be calculated from Equation (1). 

The MMAC model AOD is slightly different because it is 

based on BAOD. Here, BAOD was computed using (1), but 

an effective wavelength, λeff, was used that is based on an 

empirical relationship that is a function of AOD at 700 nm and 

relative air mass [5]. This compensated for the nonlinear 

spectral shift in BAOD with air mass.

Additionally, colocated SURFRAD temperature and relative 

humidity were used to determine the PWV based on a method 

by Gueymard [6]. This data was preprocessed on the same 

time scale as the irradiance data to coincide with the satellite 

image. 

2.2 Satellite Measurement Inputs

The GOES-based PATMOS-x/SASRAB product makes more 

than 50 measured atmospheric parameters available in selfcontained Hierarchical Data Format files. However, for this 

analysis, only the calculated insolation (GHI), 

insulation_diffuse (DHI), and cloud_type were used. Further, 

this analysis was restricted to clear skies as determined by 

SASRAB. This is equivalent to satellite image pixels in which

cloud_type is identified as “0.”

The nearest GOES East or GOES West satellite pixel 

corresponding to a SURFRAD location was used to extract the 

calculated insolation and cloud type for analysis. 

The satellite cloud_type was the primary filter for identifying 

clear skies with a secondary requirement that the satellite 

reported zenith angle is less than 85 degrees. This threshold 

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was intended to eliminate the highest zenith angles (sunrise 

and sunset). The times of all half-hour SASRAB data that 

passed these criteria were then intersected with the 

corresponding half-hour averaged SURFRAD data for 

subsequent processing of the 2009 analysis year. 

2.3 Initial Model Comparison

Performance was quantified for each of three models for two 

input parameter cases based on surface measurements: (1) 

annual climatic values for the AOD and PWV model inputs 

and (2) average daily values for the AOD and half-hour PWV

estimates using the method in [5]. 

Clear-sky models are generally very sensitive to AOD and 

PWV; hence, the more accurate these terms are (both spatially 

and temporally), the better the clear-sky models will perform. 

For this work, other lower-order atmospheric model inputs 

were set to annual climatic values: pressure, temperature, 

ground albedo, single scattering albedo, and ozone. The 

scatter plots in Fig. 1 and 2 (GHI) and Fig. 3 and 4 (DNI) 

graphically demonstrate the use of climatic versus daily 

surface-based AOD and PWV for the Desert Rock SURFRAD 

site using the clear-sky models and the original SASRAB 

values. For the Desert Rock location, using daily surfacebased AOD and PWV resulted in mild improvement in 

modeled GHI in all three models—improving the average 

RMSE to 27.9 W/m2 from 29.1 W/m2. DNI improvement was 

more significant—improving the model average value to 62.3 

W/m2 from 90.1 W/m2, which shows that the quality of the 

AOD measurement drives the accuracy of all the clear-sky 

models. 

Fig. 1: Desert Rock modeled vs. measured GHI using climatologic 

AOD. 

Fig. 2: Desert Rock modeled vs. measured GHI using daily AOD. 

Fig. 3: Desert Rock modeled vs. measured DNI using climatologic 

AOD. 

Fig. 4: Desert Rock modeled vs. measured DNI using daily AOD. 

The relative performance of each clear-sky model was then 

ranked based on summary statistics for the 2009 analysis year 

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using the refined daily AOD model inputs. The bar charts in 

Fig. 5 and 6 show the statistical performance of each model 

with respect to the other for each of the seven SURFRAD sites 

for GHI and DNI, respectively. Note the different axis limits 

for both the GHI and DNI statistics. These show that GHI had 

similar errors irrespective of the model, whereas DNI was

more strongly influenced by AOD and water column, and its 

degree of “correctness” was more strongly dependent on the 

model being used. Table 1 shows the summary statistics for all 

stations combined. The data in this table was used for ranking 

model performance in matching surface measurements. The 

final model selection is described in Section 2.4. 

Fig. 5: Model comparison of GHI for seven SURFRAD sites. 

Fig. 6: Model comparison of DNI for seven SURFRAD sites. 

All three clear-sky models compared well to each other, with 

REST2 slightly outperforming Bird and MMAC for most 

sites. The REST2 RMSE was typically less than Bird by 3 

W/m2 to 10 W/m2 for GHI, but DNI was not significantly 

different, with typical site-to-site differences of less than 2 

W/m2. 

Combined statistics would indicate that, for all sites, Bird and 

REST2 models have nearly identical performance followed by 

the MMAC model.

TABLE 1. COMBINED CLEAR-SKY MODEL STATISTICS

GHI [W/m2] DNI [W/m2]

MMAC Bird REST2 MMAC Bird REST2

RMSE 38.68 36.50 33.31 100.74 96.42 97.11

MBE 19.89 9.39 -1.35 39.76 13.79 21.67

R 0.992 0.991 0.991 0.791 0.777 0.784

2.4 Model Run Times and Final Selection

All three clear-sky models were evaluated for runtime 

performance. Results for per-point runtime were 3.77 us, 4.18 

us, and 5.48 us for MMAC, Bird, and REST2, respectively. 

Because the models were vectorized in MATLAB, vectors of

time, location, and model parameters were passed to each 

clear-sky model. The per-point time is the total function 

execution time divided by the number of elements in the 

vectors, excluding the first call to the function, which was

found to have considerable overhead in terms of execution 

time for all the models. In practice, it has been found that the 

lengths of the model input parameter vectors passed to the 

clear-sky models make negligible differences in per-point 

timing. Thus, this level of performance is expected for a 

production version of these models that will consist of 

multiple years of data for each pixel used to correct the 

SASRAB clear-sky GHI and DNI values.

Considering both the relative accuracy of the Bird model and 

its fast execution speed led to its selection as a replacement 

clear-sky irradiance model for the current SASRAB product. 

The next section discusses the difficulties with implementing 

the clear-sky model in a production environment using 

monthly satellite-based AOD as inputs. 

3. PRODUCTION MODEL AND BIAS REMOVAL

Monthly satellite-based AOD developed from the National 

Aeronautics and Space Administration Moderate Resolution 

Imaging Spectroradiometer (MODIS) data products was to be 

input to the Bird model to correct the SASRAB irradiance. 

The MODIS data was preprocessed to develop monthly 

Angstrom turbidity coefficients, mean α and mean AOD550, 

both on a 1⁄2-degree by 1⁄2-degree grid overlapping the GOES 

imagery extents [7]. The effective AOD550 and effective β

were then computed from the mean Angstrom turbidity 

coefficient, α, and mean AOD550 values. Mean α and 

effective β were then interpolated to the GOES/PATMOS-x 

4-km by 4-km grid and interpolated from monthly to daily 

values. Finally, using equation (1), the AOD at 380 nm and 

500 nm were calculated as inputs to the Bird model along 

with PWV extracted from the GSIP data stream (i.e., modeled 

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water vapor estimated from numerical weather prediction 

models) to replace the PATMOS-x-designed clear-sky GOES 

East and West pixels for the data period from 1998 to 2012. 

However, during this production development activity, it was 

discovered that the MODIS AOD-based mean α term did not 

accurately represent α calculated from surface measurements

for the SURFRAD validation sites. Because the effective β

term was also dependent on mean α, its quality was also 

suspect. However, MODIS-based mean AOD550 was found 

to consistently compare well to SURFRAD surface 

measurements. 

With only one reliable satellite-based AOD measurement

from the broadband AOD-based clear-sky model, MMAC 

became the de facto go-to model for this work since this 

model is not dependent on α. MMAC uses the UnsworthMonteith broadband aerosol turbidity coefficient, BAOD, 

which is a parameterized unitless coefficient representing the 

aerosol transmittance attenuation across the spectrum from .2 

to 4 microns. No simple method exists for converting mean 

AOD550 to BAOD without the aerosol spectral information 

contained in α [8], but because the goal was to determine a 

model input that minimized the error between measured and 

modeled irradiance, we created our own empirically trained 

BAOD based on mean ADO550 to minimize the mean bias 

error (MBE) between measured and model DNI. Using a 

training data set of surface data from 1998 to 2012, a 

weighting factor binned by AOD was created with the goal of 

reducing the MBE in the modeled DNI. DNI was chosen 

instead of GHI because it is most sensitive to AOD, and if the 

error in modeled DNI can be removed, the GHI typically 

improves as well.

The training set used was NOAA’s SURFRAD and ISIS 

site’s DNI irradiance from 2005 to 2012. Simple robust 

statistics were applied to remove the largest outliers in this 

broad data set consisting of more than 400,000 points. Here, 

any measured DNI greater than three standard deviations 

from the measured mean or less than 50 W/m2 were excluded 

from the training set. This screened training data set was then 

used to develop AOD weighting factors that minimized the 

MBE with respect to modeled and measured DNI. The 

weighting factors are shown in Fig. 7 below.

Fig. 7: Weights applied to the original mean AOD550 and associated 

improvement in MBE. 

The creation of a weighted mean AOD550 and application of 

this weighted AOD as input to the MMAC model for the 

entire training data set resulted in general improvement of 

DNI irradiance compared to the unweighted AOD. Fig. 7 

graphically shows the improvement in the DNI MBE with 

and without the weighted AOD applied to the MMAC clearsky model.

Fig. 8: Mean bias error improvement in the training data set using 

weighted AOD. 

The overall improvement for the entire DNI data set 

compared to surface measurements is shown in the figure

below. The contour plot of density allows a better view of the 

overall improvement because the scatter obscures a majority 

of the data. Note that the original SASRAB product showed

very poor performance. Additional testing excluded 

individual SURFRAD and ISIS stations to ensure that no one 

station was biasing the weights. 

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The final MBE improvement in the model of DNI surface 

measurements using the training data set is shown in Table 4 

below, which demonstrates the significant improvement of 

the weighted AOD MMAC clear-sky model compared to the 

original SASRAB irradiance. 

TABLE 2. FINAL DNI MBE IMPROVEMENT

Model DNI MBE (W/m2)

SASRAB -167.01

Un-weighted AOD MMAC -12.921

Weighted AOD MMAC 6.3453

In final production, the weighted satellite-based AOD was

applied to the MMAC model and clear-sky irradiance DNI, 

GHI, and DHI produced for all GOES East and West pixels 

from 1998 to 2012 on a time stamp associated with the center 

of the 30-minute GOES East (:00 and :30) and West images 

(:15 and :45). The SASRAB clear-sky identified pixels were

then replaced with MMAC model data. From that point, the 

time series irradiance data for each pixel were qualitychecked to ensure that they were within acceptable physical 

limits, any gaps were filled, and the UTC time stamp was 

shifted to local standard time. Finally, the East and West data

sets were blended to create a contiguous national data set of 

irradiance data for the period from 1998 to 2013. 

4. SUMMARY

In this paper, a method was described to select an improved 

clear-sky model that could replace the current SASRAB GHI 

and DNI irradiances reported during clear-sky conditions. 

Clear-sky models and especially the DNI irradiance 

component were shown to be very sensitive to AOD and 

hence require accurate AOD estimates to achieve modeled 

irradiances that compare well to surface measurements. Using 

surface-based AOD and precipitable water vapor as the key 

inputs to three clear-sky models, the Bird clear-sky model 

was shown to have excellent performance both in 

computational time and model prediction when compared to 

SURFRAD surface measurements. 

Next, the problems encountered with implementing the new 

model using satellite-based AOD used as model input were 

described. The specific difficulty with the estimate for the 

Angstrom’s turbidity coefficient was detailed. The 

broadband-based AOD model, MMAC, was proposed for 

final production work using a weighted mean AOD550. The 

weights were determined using a large training data set of 

quality-checked surface DNI measurements from SURFRAD 

and ISIS stations. 

Future work will evaluate improved MODIS processing 

methods to provide the more accurate aerosol data required by 

clear-sky models. Additional work will evaluate the 

performance of reduced-order radiative transfer models that

can model cloudy skies. Investigations are beginning into 

AER’s Rapid Radiative Transfer Model (RRTM) and 

functions within libRadtran, maintained by the University of 

Munich. If these models are shown to outperform SASRAB 

irradiances using the same surface comparison performance 

benchmarking methods described here, they could potentially 

improve on the irradiance that is reported during those times 

when the PATMOS-x reports cloudy or partly cloudy skies.

5. ACKNOWLEDGMENT

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

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

Renewable Energy Laboratory. 

6. REFERENCES

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for Direct and Diffuse Insolation on Horizontal Surfaces, 

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Institute, 1981.

(2) Gueymard, C., Clear-Sky Irradiance Predictions for Solar 

Resource Mapping and Large-Scale Applications: Improved 

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2012; pp. 2,145–2,169. 

(3) Davies, J.A.; McKay, D.C.; Luciani, G., Abdel-Wahab, 

M., Validation of Models for Estimating Solar Radiation on 

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(4) Harrison, L. C.J. J. MichalskyJ. Berndt, Automated MultiFilter Rotating Shadow-band Radiometer: An Instrument for 

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(33:22),1994; pp 5,188-5,125. 

(5) Gueymard, C. Private communication.

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(6) Gueymard, C., Analysis of Monthly Average 

Atmospheric Precipitable Water and Turbidity in Canada and 

Northern United States, Solar Energy (53:1), 1994; pp. 57–

71. 

(7) Gueymard , C.; George, R., Gridded Aerosol Optical 

Depth Climatological Datasets over Continents for Solar 

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(8) Gueymard , C., Turbidity Determination from Broadband 

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