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Reanalysis Datasets for Solar Resource Assessment Presented at ASES 2014
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.
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Reanalysis Datasets for Solar Resource Assessment Presented at ASES 2014
- 1. How good arethe 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 § SolarResource Assessment Options § What are Reanalysis Datasets? § Comparison of Datasets § Reanalysis vs Satellite § Long-term Bias Adjustment § Applications Page 2 © Vaisala 07/08/2014 Internal use
- 3. Solar Resource AssessmentOptions Short-Term Ground Observations Page © Vaisala Internal use Long-Term Satellite Observations Long-Term Reanalysis Datasets?
- 4. Why bother withlong-term data if you have a year of ground-based observations? Page © Vaisala Internal use 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? Page 5 © Vaisala Internal use
- 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. Page © Vaisala Internal use
- 7. Reanalysis Dataset Assessments Page © Vaisala Internal use 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 historyof 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 Page © Vaisala Internal use
- 9. How they differ: Page © Vaisala Internal use
- 10. How they differ: Page © Vaisala Internal use 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: Page © Vaisala Internal use
- 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 Page 12 © Vaisala Internal use
- 13. …The Results for3TIER Satellite Data Out of 163 stations: Mean Bias Error in W/m2 is 4.19, Mean Bias Error in Percent is 2.05% Page 13 © Vaisala Internal use
- 14. …The Results forERA-I Out of 140 stations: Mean Bias Error in W/m2 is -24.93, Mean Bias Error in Percent is -12.81% Page 14 © Vaisala Internal use
- 15. …The Results forMERRA Out of 165 stations: Mean Bias Error in W/m2 is 18.49, Mean Bias Error in Percent is 9.33% Page 15 © Vaisala Internal use
- 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. Page 16 © Vaisala Internal use
- 17. Long-term Data +Short-term Observations Page © Vaisala Internal use 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 Page 18 © Vaisala Internal use
- 19. …The Results forSde Boker, Israel 2008 2009 720 680 640 600 560 520 480 440 400 360 320 280 Page 19 © Vaisala Internal use 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 forDesert Rock, USA 2010 2011 720 680 640 600 560 520 480 440 400 360 320 280 240 200 Page 20 © Vaisala Internal use 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 forSolar Village, Saudi Arabia 2000 2001 720 680 640 600 560 520 480 440 400 360 320 Page 21 © Vaisala Internal use 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 forCarpentras, France 2009 2010 640 600 560 520 480 440 400 360 320 280 240 200 160 Page 22 © Vaisala Internal use 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 forTakamtsu, Japan 2011 2012 520 480 440 400 360 320 280 240 200 Page 23 © Vaisala Internal use 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 § Incloudy 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 Page 24 © Vaisala Internal use
- 25. Questions? § Forfollow 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 Page 25 © Vaisala Internal use