reanalysis datasets for solar resource assessment presented at ases 2014

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

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Solar Resource Assessment Options

Short-Term Ground Observations

Long-Term Satellite Observations

Long-Term Reanalysis Datasets?


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


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.


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)


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


How they differ:


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


How they differ:


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%


…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%


…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%


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


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


…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


…The Results for Desert Rock, USA

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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


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



…The Results for Solar Village, Saudi Arabia

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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




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


…The Results for Carpentras, France

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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




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


…The Results for Takamtsu, Japan

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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


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



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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reanalysis datasets for solar resource assessment presented at ases 2014

Environment

page vaisala internal

longterm data

updated da system

data assimilation da

older da system

da methoddoes

followup data sets

longterm mean7