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Conference Paper 

NREL/CP-5D00-76858 

July 2020 

Annual Solar Irradiance Anomaly 

Features Over the USA During 1998–

2017 Using NSRDB V3

Preprint

Aron Habte,

1 Manajit Sengupta,1 Peter Gotseff,1

and Christian A. Gueymard2

1 National Renewable Energy Laboratory

2 Solar Consulting Services 

Presented at the 47th IEEE Photovoltaic Specialists Conference (PVSC 47)

June 15–August 21, 2020 
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

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

Contract No. DE-AC36-08GO28308

Conference Paper

NREL/CP-5D00-76858 

July 2020 

National Renewable Energy Laboratory

15013 Denver West Parkway

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303-275-3000 • www.nrel.gov

Annual Solar Irradiance Anomaly 

Features Over the USA During 1998–

2017 Using NSRDB V3

Preprint

Aron Habte,1 Manajit Sengupta,1 Peter Gotseff,1

and Christian A. Gueymard2

1 National Renewable Energy Laboratory

2 Solar Consulting Services 

Suggested Citation 

Habte, Aron, Manajit Sengupta, Peter Gotseff, and Christian A. Gueymard. 2020. Annual 

Solar Irradiance Anomaly Features Over the USA During 1998–2017 Using NSRDB V3: 

Preprint. Golden, CO: National Renewable Energy Laboratory. 

NREL/CP-5D00-76858. https://www.nrel.gov/docs/fy20osti/76858.pdf. 

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1 

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

Annual Solar Irradiance Anomaly Features Over the 

USA During 1998–2017 Using NSRDB V3 

Aron Habte, Manajit Sengupta, and Peter Gotseff

National Renewable Energy Laboratory 

Golden, Colorado 

Aron.Habte@nrel.gov; Manajit.Sengupta@nrel.gov; 

Peter.Gotseff@nrel.gov

Christian A. Gueymard

Solar Consulting Services

P.O. Box 392

Colebrook, NH

Chris@SolarConsultingServices.com

Abstract—Annual solar irradiance anomalies (or departures 

from the long-term mean annual value) have a direct impact on 

various phases of solar energy projects, from prefeasibility studies to technical deployment decisions. Anomalies can happen because of normal climate variability or exceptional weather patterns. This study investigates such anomalies for both global 

horizontal irradiance (GHI) and direct normal irradiance (DNI) 

using Version 3 of the National Solar Radiation Database 

(NSRDB V3) and surface irradiance measurements at eight U.S.

locations. At each site, the annual anomaly is analyzed here by 

evaluating the irradiance deviation from the long-term average

for each specific year from 1998–2017. A positive/negative

anomaly indicates that the solar resource was higher/lower than 

the long-term average during that specific year. The results 

show that in most cases the anomaly is within ±5% for GHI and 

±10% for DNI using either ground-based irradiance measurements or modeled data from the NSRDB.

Keywords—GHI, DNI, Solar Radiation, PV Resource, Anomaly

I. INTRODUCTION

Interannual variability in solar irradiance is a challenging 

issue for the proper design, financing, and operation of solar 

energy conversion projects. With reference to the long-term 

annual solar resource, which is used as the basis for all project 

calculations, any annual deviation (or anomaly) during a specific year can result in generation variability and financial 

challenges. Hence, understanding and quantifying the time series of annual anomalies at any site is essential for reducing 

the investment risks for such projects[1], [2], [3]. Solar energy 

projects tend to depend on a limited solar irradiance data set, 

such as a typical meteorological year (TMY) and/or a data set 

that constitutes 1 year to a few years of solar irradiance measurements or modeled information. However, these limited 

data sets are prone to bias and might underestimate or overestimate the solar resource. This is in part because all weather 

patterns deviating from the median are excluded by the very 

design of TMYs [4]. Short measurement time series of, e.g., 1 

year could, by chance, correspond to a normal year or an exceptional year, which complicates solar resource assessments. 

Conversely, extreme weather situations are typically included 

in longer time series of, e.g., 15–30 years. Therefore, it is imperative to understand the long-term and interannual variability in solar irradiance. 

Multiple causes can trigger long-term changes in solar irradiance. Beside the normal stochastic variability in weather 

patterns, there are relatively regular climate cycles (e.g., the El 

Niño Southern Oscillation) as well as exceptional events, such 

as major volcanic eruptions, extended wildfires, or hurricanes

[5], [6]. 

In this study, we investigate the solar irradiance anomalies 

from ground-based measurement stations and concurrent 

model predictions from the 20-year National Solar Radiation 

Database (NSRDB) Version 3 developed by the National Renewable Energy Laboratory [7]. Such 20-year time series can 

be assumed representative of the “true” local climatology 

(which is normally considered to be based on 30 years of data

[1]). 

The NSRDB is a widely used public data set that provides 

predictions of the three-component solar irradiance data set—

i.e., global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI)—as well 

as related parameters [7]. The operational model used to produce the NSRDB is called the Physical Solar Model. It uses 

satellite-derived cloud properties and some meteorological inputs to operate the Fast Radiative Model for Satellite Applications (FARMS) [8] and derive irradiance values. The NSRDB 

disseminates solar irradiance and meteorological data on a 4-

km x 4-km and half-hourly spatiotemporal resolution over the 

Americas between 60°N and 21°S. 

II. METHOD

In this study, the annual anomalies for the 1998‒2017 period at seven National Oceanic and Atmospheric Administration Surface Radiation Budget Network (SURFRAD) stations and one station from the University of Oregon’s Solar 

Radiation Measurement Laboratory are compared to those of 

the corresponding NSRDB pixels (Table 1). Analyzing solar 

irradiance anomalies using ground observations in conjunction with modeled data from the NSRDB is beneficial to, e.g., 

validate the reliability of the NSRDB in capturing the “true” 

annual or long-term anomalies under varied solar climates. 

For any specific year, the anomalies are evaluated by calculating the percentage irradiance deviation from the long-term 

average—which constitutes the main characteristic of the solar resource at any solar project site. This method is consistent 

with what was used in earlier studies [2], [3]. 
2 

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Fig. 1. Percentage annual anomaly of ground measurements (yellow bar) and NSRDB (green bar) for GHI (top row) and DNI (bottom row) at eight locations

during the period from 1998–2017 (X-axis).
3 

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III. RESULTS

Fig. 1 shows the anomaly time series obtained for each 

year relative to the 1998–2017 long-term average at the eight 

test locations (except 2003–2017 at Sioux Falls, SD). In general, the NSRDB and surface measurements capture similar 

positive or negative deviations as well as their magnitudes. 

This gives confidence that the NSRDB data are able to accurately predict long-term variability, even though exceptions 

do occur during some years, when the anomaly magnitudes 

differ for reasons still unclear. It is anticipated that the

NSRDB Version 4, to be released later in 2020, will address 

some of the remaining sources of discrepancy.

TABLE I. LOCATIONS USED IN THIS STUDY DURING 1998–2017

(EXCEPT 2003–2017 AT SIOUX FALLS) 

Station State Code Latitude (°) Longitude (°)

Bondville IL BON 40.052 -88.373

Boulder CO TBL 40.125 -105.237

Desert Rock NV DRA 36.624 -116.019

Fort Peck MT FPK 48.308 -105.102

Goodwin Creek MS GWN 34.255 -89.873

Penn. State Univ. PA PSU 40.720 -77.931

Sioux Falls SD SXF 43.734 -96.623

Burns OR BURNS 43.520 -119.020

Interestingly, the 1998–2017 period was not affected by 

major volcanic eruptions, contrary to earlier periods when

such eruptions (from, e.g., El Chichon or Pinatubo) had a profound impact [9]. 

Another observation is that the anomaly values are higher 

for DNI than for GHI, which could be expected because DNI 

is more sensitive to cloudiness and aerosols than GHI. This 

also substantiates previous studies [9], [10]. Remarkably, 

some years are characterized by DNI anomalies reaching or 

even exceeding 10% (Fig.1), despite the absence of significant interference from volcanic activity. Although it can be 

hypothesized that annual variations in cloudiness are the 

prominent factor here, large-scale variations in atmospheric 

aerosol load also constitute a known source of interannual 

variability [9]. 

Desert Rock, Nevada, demonstrates relatively less anomaly for most years than other locations. This can be explained 

by the low cloudiness and aerosol load. Conversely, stations 

with larger impacts from both clouds and aerosols—such as

Bondville, Illinois, and Goodwin Creek, Mississippi—are 

characterized by higher DNI anomalies. 

In Fig. 2, a hypothetical time series is constructed in a 

non-chronological order to identify the nominal period of record required to estimate the long-term “climatological” average at a given location. The method used in Fig. 2 is similar 

to that in [2], except for the number of years and the period 

considered. Fig. 2 is based on a 20-year period (1998–2017), 

whereas Figs. 7–10 in [2] covered 23–28 years (depending on 

station), including years affected by the two major volcanic 

eruptions of El Chichon and Pinatubo. To construct Fig. 2, 

the observations and model predictions are sorted from the 

worst years showing the largest anomalies to the best years 

with smaller anomalies. This exemplifies the extreme cases 

when the period of irradiance record could, by chance, start 

during a very good year (maximum positive deviation) or 

during a very bad year (maximum negative deviation) relative 

to the long-term “climatological” average. 

As shown in Fig. 2, the selected 20-year period appears 

stable compared to the longer and different period used in [2], 

even though the latter’s extremes were severely impacted by

volcanic eruptions—especially in the case of the DNI’s 

anomalies. This result suggests that each year of irradiance 

data is essentially independent from previous years, as stated 

in [11] and that the anomalies could also be dependent on location in relation to the area’s climate conditions. At Desert 

Rock, for example, clear-sky conditions are most frequent.

The low overall cloudiness and aerosol loads induce less variability than at the Penn State location, which is significantly 

cloudier and hazier. 

During the due-diligence phase of the deployment of photovoltaic (PV) plants or other solar technologies, these findings provide an estimate of the minimum length of irradiance 

records that is required to accurately assess their performance. Based on the limited number of sites investigated 

here, it is possible to recommend a minimum period of 5 

years to correctly characterize the solar resource of PV plants, 

considering a ±5% possible error in the long-term average 

GHI. To reach a more conservative risk of error of only ±1%, 

a 10-year period would be necessary.
4 

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Fig. 2. Number of years required to stabilize GHI and DNI for the eight locations using NSRDB (orange) and observation data (black). The blue line represents the ±5% range. A period of 20 years of the NSRDB and observation data is assumed to represent the “true” climatological average. 
5 

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IV. CONCLUSION

Solar irradiance anomalies from one year to the next is an 

important source of uncertainty for solar energy projects, 

which in turn can cause financial risks for these projects. This 

study used the latest version of the NSRDB and surface measurements at eight U.S. stations to capture the solar irradiance 

anomalies during the period from 1998–2017. In most years 

at all locations, the results show deviations of up to approximately ±5% for GHI and ±10% for DNI. 

The present findings have clarified what the minimum period of irradiance records should be to correctly characterize 

the solar resource of PV plants. Assuming that the eight stations considered here are representative of the general situation, at least over the United States, a minimum period of 5 

years is recommended to approach the long-term average 

GHI within ±5%. In contrast, a 10-year period appears necessary to reach a more conservative risk of error of only ±1%. 

The results in this study demonstrate the importance of 

analyzing and understanding the long-term variability in solar 

irradiance, which has a direct impact on solar energy projects, 

such as prefeasibility studies or estimating the system performance of PV power plants. 

ACKNOWLEDGMENTS

This work was authored in part by Alliance for Sustainable Energy, LLC, the manager and operator of the National 

Renewable Energy Laboratory for the U.S. Department of 

Energy (DOE) under Contract No. DE-AC36-08GO28308. 

Funding provided by the U.S. Department of Energy Office 

of Energy Efficiency and Renewable Energy Solar Energy 

Technologies Office. The views expressed in the article do 

not necessarily represent the views of the DOE or the U.S. 

Government. The U.S. Government retains and the publisher, 

by accepting the article for publication, acknowledges that 

the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. 

Government purposes. 

We are grateful to the U.S. Department of Energy Solar 

Energy Technologies Office and the systems integration subprograms for supporting this project. Specifically, we 

acknowledge Dr. Tassos Golnas and Dr. Guohui Yuan for 

their support and encouragement.

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