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
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Agenda
§ Solar Resource Assessment Options § What are Reanalysis Datasets? § Comparison of Datasets
§ Reanalysis vs Satellite § Long-term Bias Adjustment
§ Applications
07/08/2014 2
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Solar Resource Assessment Options
Short-Term Ground Observations
Long-Term Satellite Observations
Long-Term Reanalysis Datasets?
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Why bother with long-term data if you have a year of ground-based observations?
4% above long-term mean
7% below long-term mean
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The Question
§ Could we use the second generation of reanalysis datasets for solar resource assessment?
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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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Reanalysis Dataset Assessments
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)
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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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How they differ:
Output format and
compatibility with NWP
downscaling
Temporal frequency
Grid resolution
Near-surface
wind variables provided
Date range, update
frequency
Types of observations
ingested
Data assimilation
method
Underlying NWP model,
land sfc scheme,
boundary layer physics
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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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…The Results for 3TIER Satellite Data
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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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…The Results for ERA-I
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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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…The Results for MERRA
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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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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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Long-term Data + Short-term Observations
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
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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
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* For more detail on MOS corrections see: “Evaluation of Procedures to Improve Solar Resource Assessments” from ASES proceedings 2012
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…The Results for Sde Boker, Israel
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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
Month2008 2009
240280320360400440480520560600640680720
W/m
2
GroundMOS−corrected MERRAMOS−corrected Satellite
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…The Results for Desert Rock, USA
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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
Month2010 2011
160200240280320360400440480520560600640680720
W/m
2
GroundMOS−corrected MERRAMOS−corrected Satellite
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…The Results for Solar Village, Saudi Arabia
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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
Month2000 2001
280320360400440480520560600640680720
W/m
2
GroundMOS−corrected MERRAMOS−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
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…The Results for Carpentras, France
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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
Month2009 2010
120160200240280320360400440480520560600640
W/m
2
GroundMOS−corrected MERRAMOS−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
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…The Results for Takamtsu, Japan
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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
Month2011 2012
160200240280320360400440480520
W/m
2
GroundMOS−corrected MERRAMOS−corrected Satellite
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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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Questions?
§ For follow up questions email: § Gwen Bender [email protected]
– LinkedIn: www.linkedin.com/pub/gwen-bender/0/498/57b/ – Twitter: @GwendalynBender
Or: § Bill Gustafson [email protected]
– LinkedIn: www.linkedin.com/pub/william-gustafson/7/b27/898 § Louise Leahy [email protected]
– LinkedIn: www.linkedin.com/pub/louise-leahy/27/496/a66 § Mark Stoelinga [email protected]
– LinkedIn: www.linkedin.com/pub/mark-stoelinga/36/b45/45a
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