Progress on an Updated 

National Solar Radiation 

Data Base

Preprint 

March 2004 • NREL/CP-560-36038 

S. Wilcox, M. Anderberg, R. George, W. Marion, D. Myers, and 

D. Renne 

National Renewable Energy Laboratory 

W. Beckman 

University of Wisconsin 

A. DeGaetano 

Northeast Regional Climate Center 

C. Gueymard 

Solar Consulting Services 

R. Perez 

State University of New York at Albany 

M. Plantico 

National Climatic Data Center 

P. Stackhouse 

National Aeronautics and Space Administration 

F. Vignola 

University of Oregon 

To be presented at the American Solar Energy Society Annual Meeting

Portland, Oregon 

July 11–14, 2004 

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Progress on an Updated National Solar Radiation Data Base 

Stephen Wilcox

Mary Anderberg 

Ray George 

William Marion

Daryl Myers 

David Renné 

National Renewable Energy 

Laboratory 

1617 Cole Boulevard 

Golden, CO 80402 

Email: Stephen_wilcox@nrel.gov 

William Beckman 

University of Wisconsin 

Arthur DeGaetano 

Northeast Regional Climate Center 

Christian Gueymard

Solar Consulting Services 

Richard Perez 

State University of New York at 

Albany 

Marc Plantico 

National Climatic Data Center 

Paul Stackhouse 

National Aeronautics and Space 

Administration 

Frank Vignola 

University of Oregon 

ABSTRACT

In 1992, The National Renewable Energy Laboratory 

(NREL) released the 1961-1990 National Solar Radiation 

Data Base (NSRDB), a 30-year set of hourly solar radiation 

data. In April 2003, NREL convened a meeting of experts to 

investigate issues concerning a proposed update of the 

NSRDB. The panel determined that an important difficulty 

posed by the update was the shift from manual to automated 

cloud observations at National Weather Service stations in 

the United States. The solar model used in the original 

NSRDB relied heavily on the methodology and resolution 

of the manual cloud observations. The meeting participants 

recommended that NREL produce a plan for creating an 

update using currently available meteorological 

observations and satellite imagery. This paper describes 

current progress toward a plan for an updated NSRDB. 

1. INTRODUCTION

In 1992, the National Renewable Energy Laboratory 

(NREL) released the 1961-1990 National Solar Radiation 

Data Base (NSRDB), a 30-year data set of measured and 

modeled solar radiation and accompanying meteorological 

data, with solar estimates based on the 

Meteorological/Statistical (METSTAT) solar radiation 

model developed at NREL (1). In recent years, interest has 

grown for updating the NSRDB to include the decade of the 

1990s or possibly beyond. In April 2003, NREL convened a 

meeting of experts to investigate the feasibility and 

desirability of an NSRDB update. Meeting participants 

concluded that the greatest obstacle to an update was a 

change in the measurements of cloud cover by the National 

Weather Service (NWS). During the 1990s, the NWS 

migrated from a manual (human) system of full-sky cloud 

observations to the Automated Surface Observing System 

(ASOS) method, which derives cloud cover from laser 

ceilometer observations at the zenith. The ASOS cloud 

observations are not compatible with the necessary inputs 

for the METSTAT model. This makes it impossible to 

create an updated NSRDB by simple METSTAT model 

runs using currently available meteorological data. 

Meeting participants agreed that an update was desirable to 

provide constituents with the most recent climatology and 

possibly a data set with enhanced spatial resolution. 

Additionally, recent advances in satellite-based techniques 

for modeling solar irradiance and cloud algorithms may 

provide an effective solution to circumvent the change from 

manual to automated cloud observations. 

From the meeting came a multi-stage plan for NREL fiscal 

year October 2003 to September 2004. This effort will 

produce a small-scale evaluation database, enabling us to 

investigate database production issues, assess input data 

availability and quality, and develop modeling alternatives. 

This paper discusses plans and preliminary results for tasks 

identified to produce the test-case database: 

• Acquire and quality assess available measurements 

of solar irradiance data for validation 

• Research data filling methods (for missing periods 

of input meteorological data) 

• Modify the METSTAT solar model inputs for 

ASOS and satellite cloud product 

1
• Evaluate a new American Society of Heating, 

Cooling, and Refrigeration Engineers (ASHRAE) 

model (using ASOS and satellite cloud inputs) 

• Produce a gridded solar product using State 

University of New York at Albany (SUNYA) 

satellite model 

• Investigate feasibility of new clear sky algorithms 

for solar models 

• Develop improved atmospheric aerosol and water 

vapor estimates 

• Acquire a comparison satellite-derived irradiance 

product from the National Aeronautics and Space 

Administration

• Quantify differences between the original NSRDB 

methods and update methods. 

The year's effort will culminate with a report of 

recommendations on the feasibility of and preferred 

methods for producing an updated database. That report will 

provide the Department of Energy and other interests with 

the information necessary to allocate resources for a fullscale NSRDB update. 

2. PROJECT STATUS

This section describes significant issues or activities that are 

either underway or planned. 

2.1 Cloud Observations

The difficulty of changing from manual to automated cloud 

observations in the 1990s was exacerbated by significant 

changes in the operation of several solar radiation networks 

during the same period of time, removing a consistent 

reference for studying the effects of changing solar models. 

Although the changes from manual to automated cloud 

observations were well supported by cost savings and 

exploitation of new technology, the effect on solar 

modelers—particularly in this project—has been to insert a 

large discontinuity in the cloud observation data record and 

consistency. (In addition, cloud reporting since the ASOS 

deployment has been found to be inconsistent from station 

to station due to the use of augmented human-based 

observation at several sites.) (2). The cloud observation data 

applicability from past to present is not clearly delineated. 

We expect that one result will be difficulty in comparing 

the methodologies used in the old and new NSRDB data 

sets. 

2.2 Solar Measurements

Solar model validation relies on high quality solar radiation 

measurements to evaluate model performance. A 

significant part of the effort this year was to find, acquire, 

and quality assess surface solar measurements to form an 

evaluation data set. Approximately 35 measurement sites 

were considered for the project. Most data were available 

from data distribution web sites developed by network 

operators. Data were downloaded to NREL computers, 

imported to a database, and then evaluated with several 

quality assessment tools including SERI QC (3). 

A data analyst used a rough scoring technique to evaluate 

data quality for each station-month where 0 = Missing or 

Unacceptable, 1 = Conditionally Acceptable, and 2 = 

Acceptable. A monthly mean score across all stations was 

calculated, and figure 1 below shows the moving 12-month 

average of these monthly means for the period of record 

(1995-2003). A peak in data quality/availability is evident 

between 1999 and 2001, which will provide input for 

choosing the test-case year. 

Data Quality by Year

0.0

0.5

1.0

1.5

2.0

1996

1997

1998

1999

2000

2001

2002

2003

Y ear

Quality (Low ----> High)

Fig 1: Solar radiation data quality by year (12 month 

moving average). 

2.3 Other Comparison Data

NREL and the National Aeronautics and Space 

Administration (NASA) have a Memorandum of 

Understanding, which provides an opportunity to 

collaborate on the NSRDB project. NASA has offered to 

provide satellite-derived validation solar radiation data sets 

for the United States, derived from 

• The Cloud and Earth's Radiant Energy System 

(CERES) satellite using an iterative radiative 

transfer algorithm, and 

• The International Satellite Cloud Climatology 

Project (ISCCP) using the Pinker/Laszlo method 

Data availability and processing considerations will 

probably dictate that we use the ISCCP data for our project 

this year. These data sets will be used as another point of 

reference for comparison to modeled values. 

2
2.4 Choosing the Test Year

This year's effort centers on creating a test-year data set that 

exercises existing or newly modified models. The test-year 

will provide insight into model performance, data 

availability, and production procedures. Using this 

information, NREL expects to develop a plan for a full-scale 

NSRDB update and evaluate the availability of required 

resources to determine if the project is feasible. 

The test year model evaluations will further illuminate the 

issue of comparability between data produced using the new 

methods and input data versus values in the 1961-1990 

NSRDB. Such comparability may be important for 

applications sensitive to trends in solar radiation due to 

climate change, but because of a lack of suitable data 

spanning both periods, a direct and comprehensive 

comparison of the methods will not possible. Thus, we must 

rely on inferences drawn by independent evaluations of old 

and new models based on comparisons of the best validation 

data available for each time period. From these 

comparisons, we may be able to deduce some measure of 

comparability between the methods. 

Our original intent was to determine a test year that 

provided a cross section of sites and modeling methods that 

would allow us to compare the 1961-1990 NSRDB 

techniques with newer methods under consideration. 

Ideally, this would have been a year from the mid-1990s for 

which manual cloud observations were available for some 

sites and automated observations available for other sites. 

Such a mix would allow a comparison of old methods with 

new methods, including a high-resolution satellite model 

developed at the SUNYA. However, historical 

Geostationary Operational Environmental Satellite (GOES) 

data necessary for the satellite model were not easily 

available for this project. Thus, our options for a test year 

were limited by the period of record of GOES data archived 

at SUNYA (which began in mid-year 1998), solar surface 

observations, and the availability of other validation data 

from NASA. The selection of the test year was subject to 

these constraints: 

• GOES data available from 1999 forward 

• ISCCP data available from 2001 back 

• Optimal solar measurement data from 1999 – 2001 

These limitations narrowed the choice to 1999-2001. Given 

that a significant drought affecting the western United States 

intensified in 2001, we decided to choose 1999. We also 

decided to include the year 2000 to include some 

interannual variability in our analysis. 

2.5 New Clear Sky Algorithms

With many solar models, the starting point for estimating 

solar radiation from meteorological and satellite data is a 

clear sky model that is later modified to include the effect of 

clouds. The clear sky model can produce estimates of any 

combination of the three solar components (direct normal, 

global horizontal, and diffuse horizontal). In the case of the 

1961-1990 NSRDB, the METSTAT model used a modified 

version of the clear sky component direct model of Bird (4). 

The influence of aerosols on scattering and absorption of the 

direct beam presents a critical issue for modeling clear 

skies. The properties of aerosols, such as size distribution, 

composition, and optical properties define aerosol optical 

depth and aerosol optical transmittance. Measurements of 

any of these properties, including optical depth (AOD) or 

transmittance, are difficult, sometimes unreliable, and 

relatively rare. As a result, estimates of AOD from 

broadband measurements are attempted, as in the 1961-1990 

NSRDB (1). Empirical parameterizations of AOD such as 

that used in the new ASHRAE model (see below) are 

essentially deriving aerosol transmittances from measured 

broadband data, as well. Many direct beam models are 

evaluated by Gueymard (5), especially with regard to their 

performance vis-à-vis varying AOD. 

One of the authors has developed an improved clear sky 

model, REST2 (6), based on using parameterizations of the 

parameters alpha and beta in the Angstrom turbidity 

equation, τ = β λ −α . Besides alpha and beta, the model 

requires date, time, and location, as well as estimates of 

water vapor (from surface dew point or relative humidity), 

pressure, temperature, ozone and nitrogen dioxide 

concentration. The model, as well as algorithms to 

empirically derive or otherwise establish input values for 

alpha and beta, will be included in the test year evaluation 

of results along with the METSTAT Bird algorithm and the 

ASHRAE clear sky component. Table 1 summarizes 

required model inputs. Pressure, water vapor, ozone are 

readily available. Broadband AOD requires direct normal 

data or parameterization, and nitrogen dioxide, α, and β

require specialized sources or parameterizations. 

TABLE 1. INPUT REQUIREMENTS FOR CLEAR SKY 

MODELS 

Model P W †

 O N τb β α 

Bird z z z z 

REST2 z z z z z z

METSTAT z z z z 

ASHRAE z z z z 

P= Atm pressure; W = Water vapor †(From Temp & RH) 

O=Ozone;N=Nitrogen Dioxide; τb=broadband AOD 

β=Ångstrom's beta, α= Ångstrom's alpha

3
2.6 METSTAT Model

As previously discussed, the recent switch to automatic 

weather stations eliminated the human observed total and 

opaque sky cover amounts used for inputs to the METSTAT 

model. The approach we are evaluating for this year's work 

is to derive equivalent sky cover inputs (total and opaque 

cloud cover) for use with METSTAT from a combination of 

ASOS and ASOS supplemental cloud measurements. ASOS 

detects clouds to 12,000 feet, while the ASOS supplemental 

cloud measurements provide information about clouds for 

height above 12,000 feet for a 50 km x 50 km area centered 

upon the ASOS station. The ASOS supplemental cloud 

measurements are derived from GOES satellite data. 

Output from METSTAT using the cloud cover derived from 

ASOS and the supplemental product cloud inputs will be 

part of the evaluation data set. 

2.7 ASHRAE/NRCC Model

Recently the American Society of Heating and Refrigeration 

Engineers (ASHRAE) and the Northeast Regional Climate 

Center have made a concerted effort to produce a solar 

radiation model for applications in the architectural 

community (7). Their approach was to modify a model 

developed for estimating global horizontal radiation for the 

northeast region of the US. (8). That model computes global 

horizontal irradiance from the product of extraterrestrial 

direct beam, modified by the cosine of the zenith angle, and 

transmittance functions for typical atmospheric attenuation, 

Rayleigh scattering, gas and water vapor absorption, aerosol 

absorption, and cloud absorption. 

Aerosol transmission (Ta) functions were based on the form 

Ta = Xm, derived by Houghton (9), where X was empirically 

derived from broadband solar radiation data, and m is air 

mass. Cloud transmission was based on four cloud fraction 

categories (clear, scattered, broken, overcast) and six height 

ranges. The cloud parameter inputs were derived from 

human observations. 

The new, revised model uses new algorithms for cloud 

transmittances for cloud input data from either human 

observations, the new ASOS, or ASOS data augmented with 

additional data from a "carbon dioxide slicing" (CDS) 

satellite derived product. Fifty-three automated (ASOS) 

ground measurement stations and 17 "manual" or human 

based observation stations were used to develop and verify 

the model. 

Essentially, the model consists of closed form transmittance 

equations for Rayleigh, gas, and water vapor transmission 

(estimated from dew point), and a look-up table of empirical 

expressions for aerosol transmission and cloud 

transmissions to computed global and direct irradiance. 

Diffuse irradiance is computed from these two estimated 

components. 

The model was evaluated with respect to estimates of daily 

and hourly radiation results by computing Root-MeanSquare Error and Mean Average Error statistics for each of 

the stations used in the model development. Daily model 

output errors were on the order of 10% for human based 

inputs and 14% for automated inputs. Augmenting the 

ASOS with CDS data reduces the errors to about 12%. 

Errors were largest in the winter and smallest in the 

summer. Errors tended to be smaller for clear conditions, 

and largest for overcast conditions. The human based results 

are comparable with the estimates of errors in the 

METSTAT model (1), which also depended upon human 

observations. For this reason, the ASHRAE model will be 

compared with a modified METSTAT model during the test 

year. 

2.8 SUNYA Satellite Model

One goal of the updated NSRDB is a spatial resolution 

greater than the ancillary interpolated products that were 

later produced based on the 239 NSRDB meteorological 

stations. Toward that end, we are considering using a model 

that estimates solar radiation from satellite imagery. The 

Atmospheric Sciences Research Center (ASRC) at SUNYA 

has been developing such a model. It derives 10 km pixel 

solar estimates based on differences between a pixel’s clearsky reflectance as seen by the satellite and the brighter 

values that occur with increasing cloud reflectance of 

incoming solar radiation. 

The SUNYA model has been refined to take into account 

anomalous ground conditions that occur either 

geographically (specular reflections from bright sand or 

water) or with time (snow cover). Perez, et al. (10) have 

shown that the refined model when compared with 13 

ground measurement stations has an average MBE in global 

irradiance of 3 W/m2

, (less than 1% of the average 

irradiance) and an average RMSE of 54 W/m2

 (14%). The 

errors for direct normal estimates are an MBE of 4 W/m2

(1%) and RMSE of 137 W/m2

 (30%). 

2.9 Improved Aerosol and Water Vapor estimates

The 1961-1990 NSRDB used only direct normal irradiance 

(DNI) measurements to estimate broadband Aerosol Optical 

Depth (BAOD). Today there is much less measured DNI 

data available, but much more aerosol data from other 

sources, including sun photometers and satellite-based 

estimates. For the southwest U.S., NREL recently created 

new BAOD estimates based largely on sun photometry. 

This includes a method of adjusting the BAOD for site 

4
elevation for relatively unpolluted sites in the interior 

Southwest. As shown in figure 2, the BAOD decreases 

exponentially, such that it is reduced by 50% at an elevation 

of 2 km above sea level. Satellite based techniques will be 

used to complete the map of the U. S. in the current phase of 

the NSRDB project. 

Annual AOD vs Elevation

y = 0.0718e-0.3259x

R2 = 0.9424

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0 0.5 1 1.5 2 2.5

Elevation Above MSL (km)

Annual Average AOD

 Fig. 2: Measured BAOD plotted against elevation for 5 

stations, Maricopa, Rogers Lake, Desert Rock, Sevilleta, 

and Flagstaff. 

For water vapor, NREL uses the NASA Water Vapor 

Project (NVAP) dataset for daily estimates of water vapor 

on a 1x1° grid. NVAP integrates sounding data with 

satellite measurements of atmospheric water vapor. For 

selected areas near National Weather Service upper air 

sounding locations, NREL may choose to directly use water 

vapor estimates from available twice-daily soundings. 

2.10 Data Filling Methods

When designing the 1961-1990 NSRDB, serially complete 

solar data was a primary goal of the project. Rather than 

attempting to fill gaps in the measured or modeled solar 

radiation data, we chose to fill the solar model input 

parameters, and model the solar irradiance based on the 

filled input parameters. This approach allowed the statistical 

algorithms of the model to operate, producing more realistic 

model output. Other meteorological parameters were also 

filled at the same time in an effort to produce a more 

complete output data set. 

A criticism of the 1961-1990 NSRDB, was that missing 

nighttime data were not filled (nighttime input data were not 

necessary for solar model operation). This approach had the 

benefit of increasing the population of sites in the NSRDB 

because many weather service sites routinely closed down at 

night, an operational constraint that would have eliminated 

them from a serially complete database. However, 

applications that required serially complete meteorological 

data were hampered by this approach. 

In the context of the above discussion, significant value 

would be derived from an updated NSRDB with both 

serially complete solar and meteorological data. However, 

stricter requirements for serially complete data will increase 

the probability of a site's exclusion from the data set (even 

at full-time sites, there can be gaps of days, weeks, or 

months). To determine if the design goal of serial 

completeness is reasonable, we have investigated other data 

filling methods. 

The following methods were used in the 1961-1990 

NSRDB: 

• Short-term filling – for gaps of 5 hours or less, 

linear interpolation 

• Medium-term filling – for gaps of 6 – 47 hours, 

substitution of data from the same hours of 

adjacent days 

• Long-term filling – for gaps of 2 days to one year, 

substitution of data from the same calendar days 

from another similar year. 

Additional techniques were employed to fill missing data 

when NREL developed the Typical Meteorological Year 2 

(TMY2) data sets (for those data gaps that still existed after 

NSRDB methods were applied): 

• Cloud cover was linearly interpolated over a 

nighttime-only gap 

• Dry bulb temperature was linearly interpolated 

over a nighttime-only gap and modified for 

expected non-linearities, such as more rapid 

changes near sunrise or sunset 

• Dew point temperature (which was not filled by 

NSRDB methods) was filed using psychometric 

relationships with dry bulb and relative humidity 

values (which were filled) (11) 

• Missing nighttime relative humidity was filled 

using psychometric relationships with dry bulb and 

dew point temperatures (11) 

• Wind speed and direction (up to 47 hours) were 

filled with linear interpolation and (wind speed) 

adjusted for expected diurnal non-linearities 

• Precipitable water (up to 47 hours) was filled with 

linear interpolation. 

Another technique used for other projects is the Linacre 

method, which characterizes measurements from a nearby 

station to form a constant relationship with like 

measurements from the target site (12). Data at the target 

site are then filled with data from the nearby station, 

adjusted according to the relationship. 

5
One possible outcome of the data filling analysis may be a 

relaxed requirement of serially complete data by allowing 

the inclusion of stations with a short period of record. We 

will investigate the pros and cons of a larger data set (more 

stations) that allows large gaps of data. For example, TMY 

data sets from stations of differing periods of record may 

not be comparable because of the smaller pool of years from 

which to typify the station’s climate. 

2.11 Planned Analysis of Models and Methods

Our goal is to create a database of (some) measured and 

(mostly) modeled data that reproduce the principle statistics 

of measured data for monthly and annual periods. For the 

test year chosen, we will investigate how well the 

distributions of modeled and measured data match on 

various time scales. 

The usual hour-by-hour and daily comparison of measured 

and modeled solar radiation data will be evaluated and 

reported, but "correct" statistics for the overall data set over 

the monthly and annual periods will be our target. Besides 

mean and standard deviation, the range, median, and 2nd 

moments of the distributions (kurtosis and skewness) will be 

compared, and the cumulative frequency distributions of 

modeled and measured data will be examined. Based on our 

results, recommendations for the modification, inclusion, or 

exclusion of a particular model will be made. 

3. CONCLUSION

We are tasked this year with determining the feasibility of 

producing an updated NSRDB, by examining tools, input 

data availability, processing constraints, and uncertainties of 

the output product. The tools described in this paper are 

available to do the job, and we hope to show that the 

uncertainties due to changes in cloud observations or 

different models can be minimized to produce a product 

with more recent climate data, improved solar modeling, 

and increased spatial resolution. 

4. References

(1) Maxwell, E. L. (1998). "METSTAT-The Solar Radiation 

Model Used in the Production of the National Solar 

Radiation Data Base (NSRDB)." Solar Energy 62(4): 263-

279 

(2) Perez, R., Bonaventura-Sparagna, J., George, R., Cloud 

cover reporting bias as major airports. 2001 Annual 

conference of the American Solar Energy Society, 

American Solar Energy Society, 2001 

(3) Maxwell, E. L. (1993). "Users Manual for Quality 

Assessment of Solar Radiation Data Using SERI QC 

Software" NREL TP-463-5608. National Renewable Energy 

Laboratory July 1993 

(4) Bird, R. E.. R. L. Hulstrom (1981). "Review, 

evaluation, and improvement of direct irradiance models." 

Trans. ASME, J. Sol. Eng. 103: 182-192 

(5) Gueymard, C. (2003). "Direct solar transmittance and 

irradiance predictions with broadband models. Part II: 

validation with high quality measurements." Solar Energy

74(5): 381-395 

(6) Gueymard, C., High performance model for clear-sky 

irradiance and illuminance. 2004 Annual Conference of 

American Solar Energy Society American Solar Energy 

Society, 2004 

(7) Belcher, B.N., DeGaetano, A.T. (2004). Integration of 

ASOS Weather Data into Building Energy Calculations 

with Emphasis on Model Derived Solar Radiation. Ithaca 

NY, Northeast Regional Climate Center Pub. ASHRAE 

1226-RP 

(8) DeGaetano, A.T., Eggleston, K. L., Knapp. W. W. 

(1993). Daily solar radiation estimates for the northeastern 

United States. Ithaca, NY, Northeast Regional Climate Data 

Center Pub. No. RR 93-4 

(9) Houghton, H. G. (1954). "On the annual heat balance of 

the Northern Hemisphere." Journal of Meteorology 11: 9-11 

(10) Perez, R., Moore, K., Kmiecik, M., Chain, C., 

Ineichen, P., George, R., Vignola, F., . A new satellite-toirradiance model description and validation. 2002 Annual 

Conference of the American Solar Energy Society, 

American Solar Energy Society, 2002 

(11) ASHRAE (1993). 1993 ASHRAE Handbook: 

Fundamentals. Atlanta, GA: American Society of Heating, 

Refrigerating and Air-Conditioning Engineers, Inc. 

(12) Linacre, E. (1992) Climate Data and Resources – A 

Reference and Guide, pp. 66-67. Routledge, London 

6
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13. ABSTRACT (Maximum 200 words)

In 1992, The National Renewable Energy Laboratory (NREL) released the 1961-1990 National Solar Radiation Data Base 

(NSRDB), a 30-year set of hourly solar radiation data. In April 2003, NREL convened a meeting of experts to investigate 

issues concerning a proposed update of the NSRDB. The panel determined that an important difficulty posed by the update 

was the shift from manual to automated cloud observations at National Weather Service stations in the United States. The 

solar model used in the original NSRDB relied heavily on the methodology and resolution of the manual cloud observations. 

The meeting participants recommended that NREL produce a plan for creating an update using currently available 

meteorological observations and satellite imagery. This paper describes current progress toward a plan for an updated 

NSRDB. 

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National Solar Radiation Data Base (NSRDB); NSRDB update; automated cloud 

observations 16. PRICE CODE

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