1. Christian A. Gueymard

2. Overview
• DNI: Definitions and general considerations
• DNI measurement: instruments, calibration, maintenance,
spectral corrections and accuracies
• DNI prediction: various types of radiative models
• Sources of modeled DNI data for the world: Why do they
differ so much, what accuracy can we expect?
• Short-term, interannual and long-term variability in DNI
• Frequency distributions as a function of climate
• Can DNI data from TMY time series be trusted?
• Resource assessment for large projects: local
measurements are important!

3. DNI: Definition and General Considerations
• The “fuel” of CSP/CPV plants is DNI: Direct Normal Irradiance. Two
possible definitions for DNI:
• Irradiance received from the sun’s disc only (theoretical def.)
• Direct irradiance from the sun’s disc plus some circumsolar diffuse
irradiance within a cone of 2.5° around the sun center (practical def.)
• DNI is what 2-axis tracking concentrators can utilize fully; 1-axis
trackers (e.g., parabolic troughs) get somewhat less.
• CSP systems use the complete solar spectrum, so only the broadband
DNI is of interest.
• CPV systems use solar cells that have
pronounced spectral sensitivity. DNI is still
what matters most, but spectral effects also
come into play.
• Specialized topics (e.g., spectral effects
and circumsolar radiation) will be covered
in the next webinar…

4. DNI Measurement (1)
• DNI can be measured directly or indirectly
• Direct measurements
• Active cavity (reference “lab” instrument; not for continuous monitoring).
• Thermopile pyrheliometer (robust field instrument, mounted on a
tracker; most common models: Eppley NIP and Kipp & Zonen CHP1).
• Rotating shadowband pyranometer [RSP] (field instrument, fast
response, does not need tracker, nor as much electricity or maintenance
as thermopiles; but needs corrections for temperature, cosine errors and
spectral sensitivity).
CAVITY THERMOPILES SILICON SENSORS
NIP
Active cavity NIP CHP1 RSR2 RSP
(Eppley) (Eppley) (Kipp & Zonen) (Irradiance Inc.) (Solar Millenium)

5. DNI Measurement (2)
• Indirect measurement
Consists in using one pyranometer for Global Horizontal Irradiance
(GHI) and another one (with fixed shadowring or tracking shade-disc) for
Diffuse (DIF), and applying the fundamental closure equation
Instantaneous DNI = (GHI – DIF)/cosZ [Z: Zenith angle]
This method was very common in the past, and may still be in some
countries, but has typically much higher uncertainties than direct
measurements, depending on the type of pyranometer and sun
shade. Ref.: C.A. Gueymard & D.R. Myers, Solar Energy 83, 171-185, 2009.
Pyranometers PSP + shadowband 8-48 & PSP + shadeball CM22 + shadeball
(Eppley) (Eppley) (Eppley) (Kipp & Zonen)

6. DNI Measurement (3)
• Calibration—Modern methods of calibration of pyrheliometers and pyranometers
against the WRR are explained in:
C.A. Gueymard & D.R. Myers, Solar Radiation Measurement: Progress in Radiometry for Improved
Modeling. In Modeling Solar Radiation at the Earth Surface, Springer 2008.
• Performance issues—The NIP appears sensitive to a small daytime bias and/or
thermal effects, which make its response vary during the day, with higher relative
errors early AM and late PM.
J. Michalsky et al., An extensive comparison of commercial pyrheliometers under a wide range of routine
observing conditions. Submitted to J. Atmos. Ocean. Tech., 2010.
DNI measured with RSP must be corrected for various shortcomings
• F. Vignola, Removing systematic errors from Rotating Shadowband Pyranometer data. ASES Conf.,
2006.
• N. Geuder et al., Validation of direct beam irradiance measurements from rotating shadowband
pyranometers in a different climate. SolarPACES Conf., 2010.
• Measurement uncertainties—Typically, 2 to 5%
uncertainty under field conditions, if well WRR
maintained. Significantly larger uncertainty for
indirect measurement with conventional setup
(shadowband for diffuse and GHI uncorrected for
thermal imbalance).

7. DNI Measurement (4)
• Sources of measured data
Unfortunately, not many sites measuring DNI with high-quality
instrumentation provide publicly available data.
The only international network of research-class stations is that of BSRN,
http://www.bsrn.awi.de/en/home/bsrn/
In the U.S., only 4 high-quality stations have accumulated more than 25
years of DNI data: Hermiston, OR (1979); Eugene, OR (1977); Burns,
OR (1979); and Golden, CO (1981).
Data from national
weather services
are usually not in
the public domain.

8. DNI Prediction with Radiative Models (1)
• Since DNI measurements are much too scarce on a global scale,
modeling is necessary!
• Various types of radiative models exist. See general typology in:
C.A. Gueymard & D.R. Myers, Validation and Ranking Methodologies for Solar Radiation
Models. In Modeling Solar Radiation at the Earth Surface, Springer 2008.
This reference also proposes various quality criteria, validation
methods, and performance/ranking metrics.
• To calculate irradiances, atmospheric scientists use radiative
transfer models that evaluate fluxes wavelength by wavelength.
These are too cumbersome for general use; thus, only
“engineering-type” broadband models are used in practice,
unless specific spectral effects (on PV/CPV) need be evaluated
[next webinar…]
• Some simple models calculate DNI with a daily or monthly time
step. This is good only for rough design purposes. For serious
resource assessment, hourly or sub-hourly data are necessary.

9. DNI Prediction with Radiative Models (2)
• To obtain realistic DNI time series with hourly or sub-hourly time
steps, two possible methods are currently used.
1. Physical method 2. Semi-physical method

10. DNI Prediction with Radiative Models (3)
• Examples of semi-physical or physical models:
• MAC3 (Canada) and METSTAT (USA), using hourly human cloud obs (now
discontinued in North America)
• GSIP (USA), using satellite cloud retrievals
(in progress)
• Examples of semi-physical/empirical models:
• Perez/SUNY (USA) • DLR (Germany)
• 3Tier (USA) • HelioSat (Europe)
• A weak point of semi-physical models is the
empirical derivation of DNI from GHI. 50 years
after the pioneering work of Liu & Jordan, there
is still no accurate or universal method to do this.
Ref.: C.A. Gueymard, Progress in direct irradiance
modeling and validation. ASES Conf., 2010.

11. DNI Prediction with Radiative Models (4)
• Accuracy of modeled clear-sky DNI
The REST2 model’s performance is currently unsurpassed. Assuming
good input data is available, it can predict DNI within the uncertainty of
high-quality irradiance measurements.
• C.A. Gueymard, Solar Energy 82, 272–285, 2008.
• C.A. Gueymard, Progress in direct irradiance modeling and validation. ASES Conf., 2010.
1200
REST2 Model Predictions 100 DNI Cumulative
Air Mass 1.50 ± 0.05 Frequency Distribution
1100 90
Measured
1000 80 Bird
Cumulated Frequency (%)
Ineichen
DNI Predicted (W/m )
2
900 70 METSTAT
REST2
60 Yang
800
50
700
40
600
Bondville 30
Golden
500
Mauna Loa 20
±5% Solar Village
400 1-Min Clear-Sky DNI
10 Mauna Loa, HI
(2008)
300 0
300 400 500 600 700 800 900 1000 1100 1200 800 900 1000 1100 1200
2 2
DNI Measured (W/m ) Irradiance (W/m )

12. DNI Prediction with Radiative Models (5)
To obtain accurate modeled DNI predictions, the chosen model must be as physical
as possible. Beware of too conveniently simple algorithms.
Example—ASHRAE (1972) model: DNI = A exp(-B/cosZ) [A and B: monthly constants]
vs. REST2 (2008)

13. DNI Prediction with Radiative Models (6)
• In validation tests, do not dismiss apparent “outliers” too fast: they might
reveal a problem in a part of the algorithm.
Revisiting and expanding the validation data is how the “Eugene
syndrome” affecting the Perez/SUNY model was eventually discovered
and explained.
C.A. Gueymard and S.M. Wilcox, Spatial and temporal variability in the solar resource:
Assessing the value of short-term measurements at potential solar power plant sites. ASES
Conf., 2009.
100
80 Global
Direct
Monthly Bias Error (%)
60
40
The Eugene
20
syndrome
0
-20
Eugene, OR Bias in monthly SUNY-modeled irradiations
-40
1998 1999 2000 2001 2002 2003 2004 2005 2006
Date

14. DNI Prediction with Radiative Models (7)
• Availability of high-quality inputs is crucial, with as little spatial or temporal
interpolation as possible for the most important atmospheric variables,
particularly clouds and aerosols. Use of long-term monthly-average
aerosol data leads to significant errors in modeled DNI, and incorrect
frequency distributions.
• R. George et al., National solar radiation database (NSRDB)—10 km gridded hourly solar
database. ASES Conf., 2007.
• C.A. Gueymard, Variability in direct irradiance around the Sahara: Are the modeled
datasets of bankable quality? SolarPACES Conf., 9 100
Long-term mean DNI
2010.
DNI (kWh/m )
8 Tamanrasset 90
2
7 80
6 70
DN
5 3Tier 60
ISIS
4 GeoModel 50
Meteonorm
3 SSE 40
SWERA
2 30
% Difference
1 20
0 10
-1 0
-2 -10
-3 -20
-4 -30
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Yr
Month

15. Factors Affecting DNI
Atmospheric factors:
• Clouds 1100 SMARTS Model
Precipitable
• Aerosols (AOD, etc.) Direct normal irradiance
Rural Aerosol water (cm)
Elevation Elevation
• Water vapor (PW) 1000
0m PW 1500 m
• Ozone, pressure, NO2… 0.470
• Air mass (m) 900 1.416
4.250
Aerosols: 800
Irradiance (W m-2)
m
• Main cause of DNI extinction m=1
700 DUSTY
under cloudless skies.
• DNI strongly decreases from 600
clean (850 W/m2) to dust-storm ASTM G173
conditions (300 W/m2), for 500
m = 1.25. m = 1.5
• DNI is 3–5 times more 400
sensitive to AOD than GHI. CLEAN
300 m=2
200
0 0.2 0.4 0.6 0.8 1 1.2
AOD Aerosol Optical Depth at 500 nm

16. Importance of Aerosols
Aerosol sources are highly variable:
• Vegetation • Ground dust
• Sea spray • Sand storms
• Smoke (fires) • Industrial pollution
• Volcanic plumes • Urban pollution
Sarychev volcano eruption, June ‘09 Sahara dust storm

17. Optimal Siting for CSP/CPV plants
Optimal siting of a CSP/CPV plant results from a
compromise between many technical, environmental and
solar resource constraints. Maximum solar resource (DNI)
requires five Minimums:
1. Sustained clear skies (i.e., minimum cloudiness)
2. Absence of haze (i.e., minimum atmospheric turbidity)
3. Dry atmosphere (i.e., minimum water vapor)
4. Minimal air mass (i.e., minimum latitude)
5. High site elevation (i.e., minimum pressure)

18. The Sun Belt—Where CSP/CPV is advisable
Approximate evaluation of DNI is possible with free datasets such as
NASA-SSE. Sun Belt: mean daily DNI > 5.5 kWh/m2 or mean annual
DNI > 2000 kWh/m2. Maps and data of much higher spatial resolution
are needed for serious resource assessment.
> 5.5

19. Sources of Modeled DNI Data
Free data sources Commercial data sources
• NASA-SSE (world) • 3Tier
• DLR-ISIS (world) • AWS Truepower
• NREL-NSRDB (USA) • Clean Power Research
• UNEP-SWERA (various countries) • DLR-SOLEMI
• European Solar Radiation Atlas
Free solar resource maps and • Focus Solar
geospatial toolkits • GeoModel-SolarGIS
• NREL (for various countries) • Meteonorm
http://www.nrel.gov/international/ • SoDa-HelioClim3
global_energy.html • Univ. Oldenburg-EnMetSol
• NREL-NSRDB (USA)
http://rredc.nrel.gov/solar/old_data/nsrdb/
• UNEP-SWERA (various countries) Solar resource for CSP: handbook
http://swera.unep.net http://www.nrel.gov/docs/fy10osti/
47465.pdf

20. Example of DNI Resource Map
NREL’s DNI map for India (2010)
• Based on satellite data for aerosols (SCS),
clouds (Meteosat), and SUNY model.
• High resource in the Himalayas
• Elsewhere, the DNI resource is limited by
strong haze and monsoon cloudiness.
http://www.nrel.gov/international/ra_india.html

21. Differences in Resource Maps (1)
Differences in DNI resource maps are much larger than those in GHI maps.
Typically, ±5–10% differences over regions with good density of weather stations,
±30% or more elsewhere. Extreme differences have been found over parts of
Africa, in particular. Such uncertainties can slow down the development of large
CSP projects, which has actually happened recently in Abu Dhabi.
NREL-SWERA NASA-SSE
Kenya:
NREL vs. DLR
NREL
map

22. Differences in Resource Maps (2)
Q: What can explain inconsistencies and large disagreement between
resource maps?
• Cloud data obtained from different sources or different periods
• Widely different aerosol data
• Use of long-term monthly-average vs. mean daily aerosol data
• Use of empirical algorithms, with degraded performance in some areas
• Lack of validation against ground-truth DNI (since such data is rare)
• Use of validation data (measured DNI) of low quality
• Undocumented tweaking of some models or input data
• Lack of scientific consensus on various modeling techniques and quality
control methods
• Lack of transparency from some commercial vendors, since their
methods are proprietary, at least in part.
To remedy this situation, the International Energy Agency (Task 36) and
SolarPACES have launched research projects to validate or benchmark
various datasets. A preliminary task is to identify and obtain high-quality
DNI measurement data for the whole world, to be used as ground truth.

23. Solar Resource Variability vs. Financing
• Solar resource is variable, and therefore so are the produced power
and the revenues it generates. This directly affects cash flow.
• To account for such variability in revenue, reserve accounts are
generally necessary for debt service and to limit cash flow
fluctuations.
• Financing is often offered based on a restrictive revenue model,
conservatively using a high probability (90, 95 or 99%) to exceed
some minimum power production and revenue.
• Lenders need to assess risks due to failure or bad years.
• Incorrect evaluation of risk mitigation may lead to rejection of good
projects, or to financial distress of risky projects.
• Financing projects based on the nth percentile production is traditional
and appears to work well, provided the production variability, and
hence the solar resource, can be quantified probabilistically.
Q: How can percentiles be calculated accurately?

24. Short-term DNI Variability (1)
DNI varies smoothly under clear skies, but can vary extremely fast under
partly cloudy skies, e.g., from 0 to 1000 W/m2 in a second, and vice versa.
These fast transient conditions did not show easily in the past, when only
hourly data were available. Time steps of 1-min become the norm for first-
class stations. Some research stations use 1-sec to 3-sec time steps.
Example Oahu, Hawaii
2
1654 W/m
“Typical” partly-cloudy day 1600 5 July 2010
3-sec data ETHI
for Oahu, HI
GHI
DNI
Irradiance (W/m )
2
1200
• GHI up to 25% more than
ETHI* during lensing
effect peaks, around noon. 800
• DNI also increases by a
few %, due to large transient
circumsolar diffuse. 400
* Extraterrestrial horizontal
irradiance 0
6 7 8 9 10 11 12 14 15 16 17 18 19
LST

25. Short-term DNI Variability (2)
Short-term DNI variability is no problem if the plant’s operation tolerates DNI
fluctuations, incl. no DNI. But many CSP plants can’t operate below some
threshold DNI. Q: Can these transient effects be correctly accounted in the
daily, monthly or annual solar resource if DNI is not measured fast enough?
Example
“Typical” day in Oahu, HI 1 0
100
• Various thresholds: 200
0, 100, 200 and 300 W/m2 Relative daily irradiation
300
• Various measurement 0.95
time steps considered:
3 sec, 1 min, 15 min, 1 hr Threshold (W/m )
2
• Some commercial data
vendors provide data at 0.9
15-min intervals Oahu, Hawaii
• Hourly time step may be 5 July 2010
2
Total DNI: 7 kWh/m
too coarse for accurate
0.85
system simulation 1 100
• No gain in accuracy 3 sec 1 min 15 min 1 hr
likely for steps < 1 min Time step (sec)
• This topic needs further research, so an optimum data time step can be defined.

26. Interannual DNI Variability (1)
There are good years and bad years in everything, particularly in DNI, due to:
Climate cycles (El Niño, La Niña…), changes in release of natural aerosols,
increase or decrease in pollution, volcanic eruptions, climate change…
For GHI, it might take only 2–3 years of measurement to be within ±5% of the long-
term mean. For DNI, it takes much longer, up to 5–15 years.
Short measurement periods (e.g. 1 year) are not sufficient for serious DNI resource
assessment! 20
Annual Resource
Special techniques must be used to
correct long-term modeled data using 10
short-term measured data.
Anomaly (%)
0
20 -10
Convergence time
10 -20 Eugene, OR
1978–2009
5%
Anomaly (%)
0 DNI GHI
-30
-10 1975 1980 1985 1990 1995 2000 2005 2010
Eugene, OR Year
-20
1978–2009
-30 13 years DNI GHI
Eugene data: http://solardat.uoregon.edu/
1975 1980 1985 1990 1995 2000 2005 2010
Year

27. Interannual DNI Variability (2)
Interannual variability in DNI is much higher (at least double) than that in
GHI. This variability is higher in cloudier climates (low Kn), but still
significant in clearer regions (high Kn), which are targeted by CSP/CPV.
Plots and maps provide this variability in terms of Coefficient of Variation
(COV): COV = St. Dev. / Mean
14 Annual Resource
This is significant at only a 66% Interannual Variability
12 NSRDB Data, 1961–1990
probability level. For a “bankable”
Coefficient of Variation (%)
DNI GHI
95% probability, double the COV 10
results. 8
6
4
2
0
0.05 0.15 0.25 0.35 0.45
Kn
C.A. Gueymard, Fixed or tracking solar collectors?
Helping the decision process with the Solar Resource
Enhancement Factor. SPIE Conf. #7046, 2008.
S. Wilcox and C.A. Gueymard, Spatial and temporal
variability in the solar resource in the United States.
ASES Conf., 2010.
http://rredc.nrel.gov/solar/new_data/variability