Combining micro-scale site data with long-term satellite data improves the accuracy of solar resource estimates and financial planning confidence. Typical meteorological year (TMY) data alone is not sufficient, as it does not capture annual variability important for bankable assessments. The best approach combines ground station observations to correct satellite-derived data over many years, providing both short-term accuracy and long-term context missing from either method alone.

1. The Road to Bankability
Improving assessments for more accurate financial planning
Gwen Bender, Christian Gueymard
Francesca Davidson and, Solar Consulting
Scott Eichelberger Services
3TIER

2. » Combining micro-scale site information,
such as aerosol optical depth
measurements and local irradiance
observations, with long-term satellite
irradiance data significantly improves the
accuracy of solar resource estimations
and provides more confidence in financial
planning.

3. Understanding Long-Term Variability
170
180
190
200
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Wattspersq.m
Observations Satellite Data
Observations - Mean Satellite Data - Mean
-10
-5
0
5
10
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
%Anomalyfrom
Mean
Observations
Satellite Data

4. 2010 Irradiance Anomaly

5. 2010 Irradiance Anomaly

6. 2010 Irradiance Anomaly

7. Why Not Just Use TMY Data?
“Simple “TMY” (Typical Meteorological Year) formats are not
sufficient for building major projects.”
Venkataraman, S., D’Olier-Lees, T. "Key Credit Factors.” Standard and Poor’s
Solar Credit Weekly 29.42 (2009)
“The TMY should not be used to predict weather for a particular
period of time, nor is it an appropriate basis for evaluating real-
time energy production or efficiencies for building design
applications or solar conversion systems.”
NREL Technical Report 2008

8. Typical Meteorlogical Year
» To demonstrate the inherent
variability in GHI and DNI, a TMY
frequency distribution may not
always be appropriate to represent
the solar resource, particularly
during bad years.
» For this specific location, the TMY2
value corresponds quite exactly to
the P50 of the distribution.
» The P90 and P95 probabilities of
exceedance correspond to much
lower values, 11% and 16% below
the TMY2 value, respectively. This
is why TMY data should not be
used to derive bankable reports of
financial projections.

9. Aerosols: Volcanoes - Calculating 1-year
P90s

10. Solar Resource Assessments Can Be Done 3 Ways
» Satellite
Derived Data
» Ground Station
Observations
» Combination of
the Two
+

11. Ground Station Assessments
Cons
» Few on-ground
observations available and
most are short-term
» Inaccurate to interpolate
these measurements to
other locations
» Performance and prices
vary
Pros
» High accuracy if properly
maintained
» Critical part of solar
resource assessment,
necessary to sort out local
variability effects

12. Ground Station Assessments
» 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 an accurate resource
assessment
-30
-20
-10
0
10
20
1975 1980 1985 1990 1995 2000 2005 2010
Eugene, OR
1978–2009
DNI GHI
Anomaly(%)
Year
Convergence time
5%
13 years
Gueymard, C. (2010).
Temporal Solar Resource
Variability at Various Time
Scales and the Use of Typical
Meteorological Years
[PowerPoint Slides].
Retrieved from http://
www.3tier.com/en/about/
webinars/archive/solar-
assessment/

13. Satellite Derived Assessments
Cons
» Greater uncertainty than
observations (over the same
time period)
» Known issues with satellite
modeling include areas of
high albedo, turbidity
modeling, areas of snow
cover and satellite
degradation
Pros
» Consistent global approach
» Interannual variability
captured with multiple years
of data
» Satellite derived data is
known to be the most
accurate source of irradiance
information beyond 25 km of
a well-maintained ground
station (Zelenka et al., 1999)
» Modeled data based on actual
observations

14. Satellite Derived Assessments

15. High Resolution Solar Data (GHI)
NASA
Data
(~100km)
NREL
Data
(~40km)
3TIER
Data
(~3-­‐4km)
Increased resolution provides more detailed information.
Considering a central location on these maps…
GHI Band
At ~100km resolution (NASA) 4.0 - 4.5 kWh/m2/day
At ~40km resolution (NREL) 4.5 - 5.0 kWh/m2/day
At 3-4km resolution (3TIER) 6.0 - 6.5 kWh/m2/day
0.0-2.0
2.0-2.5
2.5-3.0
3.0-3.5
3.5-4.0
4.0-4.5
4.5-5.0
5.0-5.5
5.5-6.0
6.0-6.5
6.5-7.0
7.0-7.5
7.5+
GHI [kWh/m2/day]
Near Chengdu in
China

16. Satellite and Ground Stations Combined
Cons
» Algorithm for correcting
the satellite record to the
ground observations must
have skill but not over fit
» Corrections will only be as
accurate as the ground
station data
Pros
» Ground station
observations can be used
to improve the accuracy of
the satellite data
» Puts the short-term
observations into the
context of over a decade of
satellite data
+

17. Satellite and Ground Stations Combined
These observations include the lowest summer on record; without long-term
reference data for context the statistics for this site will be artificially low.

18. Case Study: Desert Rock, NV

19. Case Study: Desert Rock, NV
Units: kWh/m2/year P50 P75 P90 P95 P99 Long-term
Mean
MOS-corrected GHI 1-yr 2087 2058 2032 2017 1988 2087
MOS-corrected DNI 1-yr 2844 2778 2718 2683 2616 2843
Diffuse 1-yr 466.1 417.2 373.3 347.0 297.7 466.1
MOS-corrected GHI 10-yr 2087 2070 2055 2046 2029 2087
MOS-corrected DNI 10-yr 2844 2811 2781 2763 2730 2843
Diffuse 10-yr 466.1 418.7 376.1 350.7 302.8 366.1

20. Questions?
Gwen Bender
gbender@3tier.com
Christian Gueymard
Chris@SolarConsultingServices.com