The document compares the accuracy of two second generation reanalysis datasets (MERRA and ERA-Interim) to satellite-derived solar data for solar resource assessment. It finds that MERRA has less error than ERA-Interim or satellite data alone. A bias correction method is applied to improve MERRA, reducing errors further. However, in cloudier locations, one year of training data is insufficient for the correction. While reanalysis datasets provide long-term global data, satellite observations currently provide more accurate short-term solar estimates.

1. How good are the second
generation reanalysis
datasets?
Presented at ASES July 8, 2014 by Gwendalyn Bender
Co-authors: William Gustafson, Louise Leahy P.hD, and
Mark Stoelinga P.hD

2. Agenda
§ Solar Resource Assessment Options
§ What are Reanalysis Datasets?
§ Comparison of Datasets
§ Reanalysis vs Satellite
§ Long-term Bias Adjustment
§ Applications
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3. Solar Resource Assessment Options
Short-Term
Ground Observations
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Long-Term
Satellite Observations
Long-Term Reanalysis
Datasets?

4. Why bother with long-term data if you
have a year of ground-based
observations?
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4% above long-term mean
7% below long-term mean

5. The Question
§ Could we use the second generation of reanalysis datasets
for solar resource assessment?
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6. Reanalysis Datasets – What are they?
§ A global, gridded, 3-dimensional description of all weather
variables at sub-daily time resolution over a period of several
decades.
§ It is produced by feeding all available observations (ground
and satellite) into a data assimilation (DA) system, which
uses a global numerical weather prediction model to “fill in
the gaps” while retaining fidelity to the available
observations.
§ The modeling and DA are performed consistently over the
entire period of record, to ensure that at least the DA method
does not introduce discontinuities in the data.
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7. Reanalysis Dataset Assessments
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Cons
§ While they provide
parameters that can be used
to derive GHI they can not be
directly used to derive DNI or
Diffuse information
§ Historically known for not
resolving clouds well enough
for solar resource assessment
(Perez et al., 2013)
§ Their focus is on non-solar
applications
Pros
§ Globally consistent in
methodology, resolution, time
steps and parameters provided
§ Updated regularly. Consistently
produced back to 1979
§ Solar resource and weather
data derived from the same
source
§ Can be used as a site specific
time series or to generate large
scale maps (including anomaly
maps)

8. A brief history of reanalysis data sets
§ First-Generation:
§ NCAR/NCEP Reanalysis Project (NNRP)
§ Produced in mid 1990s with a coarse resolution of underlying model (2.5
deg) and goes back to 1948. Updated within a few days. Older DA system.
§ Nevertheless, NNRP has been a workhorse!
§ Other follow-up data sets, with slight improvements: ERA-15, ERA-40,
JRA-25, R2
§ Second-Generation:
§ CFSR (NOAA / National Weather Service / NCEP)
§ ERA-Interim (European Centre for Med. Range Weather Forecasts)
§ MERRA (NASA)
§ Produced in mid to late 2000s with a 34-year record with a high-resolution
of the underlying model (~0.5 degree). Updated DA system. Updates lag a
few weeks.
§ More output variables, vertical levels, and temporal frequency
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9. How they differ:
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10. How they differ:
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Output
format and
compatibility
with NWP
downscaling
Temporal
frequency
Grid
resolution
Near-surface
wind
variables
provided
Types of
observations
ingested
Date range,
update
frequency
Data
assimilation
method
Underlying
NWP model,
land sfc
scheme,
boundary layer
physics

11. How they differ:
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12. The Set-up….
§ Used the “short wave down welling” fields from the MERRA and
ERA-I reanalysis datasets
§ This is not exactly the same as but can be used as a substitute for
Global Horizontal Irradiance (GHI)
§ Didn’t use NNRP because the first generation data is already known to
be insufficient for solar resource assessment
§ Didn’t use CFSR because the change in methodology in 2011 reduces
its usefulness as a long-term record
§ Interpolated to hourly time series from ~6hr for the purposes of this study
§ Used the GHI values from the satellite derived 3TIER Services
global solar dataset
§ Compared all 3 datasets to actual ground station observations of
GHI at ~165 stations globally distributed
§ Some variation in number of stations based on overlap in years available
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13. …The Results for 3TIER Satellite Data
Out of 163 stations: Mean Bias Error in W/m2 is 4.19, Mean Bias Error in Percent is 2.05%
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14. …The Results for ERA-I
Out of 140 stations: Mean Bias Error in W/m2 is -24.93, Mean Bias Error in Percent is -12.81%
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15. …The Results for MERRA
Out of 165 stations: Mean Bias Error in W/m2 is 18.49, Mean Bias Error in Percent is 9.33%
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16. The Question
§ Can we use a correction to ground observations to adjust for
the reanalysis data’s inability to resolve clouds properly?
§ Why bother?
§ A solar project is a 20+ year investment and currently even the
best satellite records have only ~15 years of historical data
available
§ If we can have confidence in a bias corrected time series based
on reanalysis data we would have a 30+ year record to make our
energy estimates from.
§ The satellite algorithms currently in use don’t always resolve all
climates equally well, notably desert environments are
challenging. Having another option could be useful.
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17. Long-term Data + Short-term
Observations
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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
§ Limited distances between
project site and and
observational site can be used
Pros
§ Ground station observations
can be used to improve the
accuracy of the long-term data
§ Puts the short-term
observations into the context of
over a decade of resource data
§ Accepted, and sometimes
required, methodology for
finance providers

18. The Set-up….
§ At 5 sample sites we have multiple years of high quality publicly
available GHI observations
§ Sites chosen represent a variety of climates, some of which the satellite data
does well in and some that provide challenges
§ Using Model Output Statistics (MOS)* as the method for bias correction
we
§ Used 1 year of the observational data for training and compared the results to
the ~2 years outside the training period not used in the correction process
§ Corrected the satellite data as a baseline for what we were trying to achieve
§ Corrected the MERRA data as it had performed the best previously
§ All results shown in the next slides are for the years outside the training
period
* For more detail on MOS corrections see: “Evaluation of Procedures to Improve Solar Resource Assessments”
from ASES proceedings 2012
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19. …The Results for Sde Boker, Israel
2008 2009
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Satellite Data MERRA Data
Raw MOS-Corrected
Raw MOS-Corrected
Daily
Correlation 0.97 0.97 0.94 0.94
RMS (W m-2) 30.43 25.62 40.57 38.32
Monthly
Correlation 0.99 0.99 0.99 0.99
RMS (W m-2) 17.81 7.74 17.56 11.5
Global Horizontal Irradiance
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
Month
240
W/m2
Ground
MOS−corrected MERRA
MOS−corrected Satellite

20. …The Results for Desert Rock, USA
2010 2011
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Satellite Data MERRA Data
Raw MOS-Corrected
Raw MOS-Corrected
Daily
Correlation 0.98 0.99 0.93 0.92
RMS (W m-2) 32.43 25.59 55.07 53.54
Monthly
Correlation 0.99 1 0.99 0.99
RMS (W m-2) 20.91 12.22 24.54 17.85
Global Horizontal Irradiance
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
Month
160
W/m2
Ground
MOS−corrected MERRA
MOS−corrected Satellite

21. …The Results for Solar Village, Saudi
Arabia
2000 2001
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Global Horizontal Irradiance
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
Month
280
W/m2
Ground
MOS−corrected MERRA
MOS−corrected Satellite
Satellite Data MERRA Data
Raw MOS-Corrected
Raw MOS-Corrected
Daily
Correlation 0.94
0.95
0.86
0.83
RMS (W m-2)
36.88
33.11
51.35
55.30
Monthly
Correlation 0.97
0.97
0.95
0.94
RMS (W m-2) 24.88
21.54
23.44
28.64

22. …The Results for Carpentras, France
2009 2010
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Global Horizontal Irradiance
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
Month
120
W/m2
Ground
MOS−corrected MERRA
MOS−corrected Satellite
Satellite Data MERRA Data
Raw MOS-Corrected
Raw MOS-Corrected
Daily
Correlation 0.99 1 0.93 0.93
RMS (W m-2) 24.54 20.83 56.01 55.97
Monthly
Correlation 1 1 0.99 0.99
RMS (W m-2) 9.27 4.8 18.35 13.92

23. …The Results for Takamtsu, Japan
2011 2012
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Satellite Data MERRA Data
Raw MOS-Corrected
Raw MOS-Corrected
Daily
Correlation 0.97 0.98 0.81 0.80
RMS (W m-2) 37.68 28.40 91.68 77.81
Monthly
Correlation 0.99 1 0.96 0.94
RMS (W m-2) 22.61 8.35 54.22 22.31
Global Horizontal Irradiance
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
Month
160
W/m2
Ground
MOS−corrected MERRA
MOS−corrected Satellite

24. Conclusions
§ In cloudy locations one year of training data is insufficient to make
up for the reanalysis data not being able to resolve cloud cover
well (see Takamatsu Japan results)
§ In typically sunny locations the reanalysis data shows promise
with a year of correction but does not meet or improve upon what
we can already do with satellite data (see Sde Boker Israel results)
§ More work could be done in the pursuit of a record extension to
investigate better ways to improve the reanalysis data +
correction results. Such as:
§ Correcting with long-term satellite data
§ Using more variables derived from the reanalysis data to seed the MOS
algorithm
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25. Questions?
§ For follow up questions email:
§ Gwen Bender gwendalyn.bender@vaisala.com
– LinkedIn: www.linkedin.com/pub/gwen-bender/0/498/57b/
– Twitter: @GwendalynBender
Or:
§ Bill Gustafson william.gustafson@vaisala.com
– LinkedIn: www.linkedin.com/pub/william-gustafson/7/b27/898
§ Louise Leahy louise.leahy@vaisala.com
– LinkedIn: www.linkedin.com/pub/louise-leahy/27/496/a66
§ Mark Stoelinga mark.stoelinga@vaisala.com
– LinkedIn: www.linkedin.com/pub/mark-stoelinga/36/b45/45a
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