Preliminary survey on site-adaptation techniques for satellite-derived and reanalysis solar radiation datasets

At any site, the bankability of a projected solar power plant largely depends on the accuracy and general quality of the solar radiation data generated during the solar resource assessment phase. The term “site adaptation” has recently started to be used in the framework of solar energy projects to refer to the improvement that can be achieved in satellite-derived solar irradiance and model data when short-term local ground measurements are used to correct systematic errors and bias in the original dataset. This contribution presents a preliminary survey of different possible techniques that can improve long-term satellite-derived and model-derived solar radiation data through the use of short-term on-site ground measurements. The possible approaches that are reported here may be applied in different ways, depending on the origin and characteristics of the uncertainties in the modeled data. This work, which is the first step of a forthcoming in-depth assessment of methodologies for site adaptation, has been done within the framework of the International Energy Agency Solar Heating and Cooling Programme Task 46 “Solar Resource Assessment and Forecasting”.

[1]  John Boland,et al.  Estimating the potential for solar energy utilization in Chile by satellite-derived data and ground station measurements , 2015 .

[2]  Ehrhard Raschke,et al.  Mapping of global radiation and cloudiness from Meteosat image data - Theory and ground truth comparisons , 1983 .

[3]  Stefan Wilbert,et al.  Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications , 2015 .

[4]  F. Antonanzas-Torres,et al.  Improving daily output of global to direct solar irradiance models with ground measurements , 2013 .

[5]  C. Gueymard,et al.  Do spaceborne aerosol observations limit the accuracy of modeled surface solar irradiance? , 2015 .

[6]  J. M. Vindel,et al.  Sensitivity of satellite-based methods for deriving solar radiation to different choice of aerosol input and models , 2014 .

[7]  Tomas Cebecauer,et al.  Site-adaptation of satellite-based DNI and GHI time series: Overview and SolarGIS approach , 2016 .

[8]  R. Perez,et al.  Satellite-to-irradiance modeling - a new version of the SUNY model , 2015, 2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC).

[9]  Rodrigo Escobar,et al.  Solar energy resource assessment in Chile: Satellite estimation and ground station measurements , 2014 .

[10]  L. Wald,et al.  The method Heliosat-2 for deriving shortwave solar radiation from satellite images , 2004 .

[11]  N. Geudera,et al.  Long-term behavior , accuracy and drift of LI-200 pyranometers as radiation sensors in Rotating Shadowband Irradiometers ( RSI ) , 2014 .

[12]  L. Alados-Arboledas,et al.  Interannual Variability of long time series of DNI and GHI at PSA, Spain. , 2011 .

[13]  Robert J. Davy,et al.  Improving the accuracy of hourly satellite-derived solar irradiance by combining with dynamically downscaled estimates using generalised additive models , 2016 .

[14]  Michel Journée,et al.  Solar resource assessment in the Benelux by merging Meteosat-derived climate data and ground measurements , 2012 .

[15]  Christian A. Gueymard,et al.  Temporal variability in direct and global irradiance at various time scales as affected by aerosols , 2012 .

[16]  C. Gueymard,et al.  Assessment of spatial and temporal variability in the US solar resource from radiometric measurements and predictions from models using ground-based or satellite data , 2011 .

[17]  Jesús Polo,et al.  Revision of ground albedo estimation in Heliosat scheme for deriving solar radiation from SEVIRI HRV channel of Meteosat satellite , 2012 .

[18]  Debra Lew,et al.  Creating the Dataset for the Western Wind and Solar Integration Study (U.S.A.) , 2008 .

[19]  Frank Vignola,et al.  Building a bankable solar radiation dataset , 2012 .

[20]  C. Gueymard.  Impact of on-site atmospheric water vapor estimation methods on the accuracy of local solar irradiance predictions , 2014 .

[21]  C. Gautier,et al.  A Simple Physical Model to Estimate Incident Solar Radiation at the Surface from GOES Satellite Data , 1980 .

[22]  R. Mueller,et al.  RETHINKING SATELLITE BASED SOLAR IRRADIANCE MODELLING , 2003 .

[23]  G. Powers,et al.  A Description of the Advanced Research WRF Version 3 , 2008 .

[24]  C. Gueymard.  REST2: High-performance solar radiation model for cloudless-sky irradiance, illuminance, and photosynthetically active radiation – Validation with a benchmark dataset , 2008 .

[25]  J. Dudhia,et al.  A geostatistical approach for producing daily Level-3 MODIS aerosol optical depth analyses , 2013 .

[26]  H. Guillard,et al.  A method for the determination of the global solar radiation from meteorological satellite data , 1986 .

[27]  R. Escobar,et al.  Solar Energy Resource Assessment in Chile: Satellite Estimation and Ground Station Measurement☆ , 2014 .

[28]  Eduardo F. Fernández,et al.  Optimal combination of gridded and ground-observed solar radiation data for regional solar resource assessment , 2015 .

[29]  Felix Ament,et al.  A New Method for Fusion of Measured and Model-derived Solar Radiation Time-series , 2014 .

[30]  Elke Lorenz,et al.  Short-Term Forecasting of Surface Solar Irradiance Based on Meteosat-SEVIRI Data Using a Nighttime Cloud Index , 2015, Remote. Sens..

[31]  B. Mayer,et al.  Long-Term Variability of Solar Radiation Derived from Satellite Data , 2004 .

[32]  C. Schillings,et al.  Operational method for deriving high resolution direct normal irradiance from satellite data , 2004 .

[33]  C. Gueymard,et al.  Improved Gridded Aerosol Data for India , 2013 .

[34]  Steven D. Miller,et al.  Chapter 3 – Physically Based Satellite Methods , 2013 .

[35]  R. Kuhlemann,et al.  Rethinking satellite-based solar irradiance modelling: The SOLIS clear-sky module , 2004 .

[36]  J. M. Vindel,et al.  Correcting satellite derived DNI with systematic and seasonal deviations: Application to India , 2015 .

[37]  Annette Hammer,et al.  A New Algorithm for the Satellite-Based Retrieval of Solar Surface Irradiance in Spectral Bands , 2012, Remote. Sens..

[38]  L. Mcarthur,et al.  World Climate Research Programme - Baseline Surface Radiation Network (BSRN) - Operations Manual Version 2.1 , 2005 .

[39]  H. Beyer,et al.  Solar energy assessment using remote sensing technologies , 2003 .

[40]  Christoph Schillings,et al.  Long-term variability of solar direct and global radiation derived from ISCCP data and comparison with reanalysis data , 2006 .

[41]  R. Perez,et al.  IMPROVING THE PERFORMANCE OF SATELLITE-TO-IRRADIANCE MODELS USING THE SATELLITE'S INFRARED SENSORS , 2010 .

[42]  M. Súri,et al.  SATELLITE-BASED SOLAR RESOURCE DATA: MODEL VALIDATION STATISTICS VERSUS USER'S UNCERTAINTY , 2014 .

[43]  P. Ineichen,et al.  A new operational model for satellite-derived irradiances: description and validation , 2002 .

[44]  Jean-Noël Thépaut,et al.  The MACC reanalysis: an 8 yr data set of atmospheric composition , 2012 .

[45]  Stefan Wilbert,et al.  Uncertainty of rotating shadowband irradiometers and Si-pyranometers including the spectral irradiance error , 2016 .

[46]  S. L. Abreu,et al.  Satellite-derived solar resource maps for Brazil under SWERA project , 2007 .

[47]  Jimy Dudhia,et al.  Assessment of the Level-3 MODIS daily aerosol optical depth in the context of surface solar radiation and numerical weather modeling , 2012 .

[48]  Jesús Polo,et al.  A new statistical approach for deriving global solar radiation from satellite images , 2009 .

[49]  John R. Mecikalski,et al.  Calibration of selected pyranometers and satellite derived solar radiation in Puerto Rico , 2014 .

[50]  Jan Kleissl,et al.  Solar Energy Forecasting and Resource Assessment , 2013 .

[51]  P. Ineichen.  Long Term Satellite Global, Beam and Diffuse Irradiance Validation , 2014 .

[52]  R. Perez,et al.  Chapter 2 – Semi-Empirical Satellite Models , 2013 .

[53]  P. Ineichen.  Five satellite products deriving beam and global irradiance validation on data from 23 ground stations , 2011 .

[54]  T. Ranchin,et al.  Increasing the spatial resolution of gridded data by fusion with other data sets , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[55]  L. Wald,et al.  Comparison between meteorological re-analyses from ERA-Interim and MERRA and measurements of daily solar irradiation at surface , 2015 .

[56]  Didier Thevenard,et al.  Monthly average clear-sky broadband irradiance database for worldwide solar heat gain and building cooling load calculations , 2009 .

[57]  J. A. Ruiz-Arias,et al.  Performance of Separation Models to Predict Direct Irradiance at High Frequency: Validation over Arid Areas , 2015 .

[58]  Peter Bogetoft,et al.  Estimating the Potential Gains from Mergers , 2005 .

[59]  L. Wald,et al.  Improving HelioClim-3 estimates of surface solar irradiance using the McClear clear-sky model and recent advances in atmosphere composition , 2014 .

[60]  S. Wilbert,et al.  Calibration methods for rotating shadowband irradiometers and evaluation of calibration duration , 2015 .

[61]  Thomas A. Huld,et al.  A New Database of Global and Direct Solar Radiation Using the Eastern Meteosat Satellite, Models and Validation , 2014, Remote. Sens..

[62]  Andreas Kazantzidis,et al.  Accuracy of ground surface broadband shortwave radiation monitoring , 2014 .

[63]  M. Nunez,et al.  Development of a method for generating operational solar radiation maps from satellite data for a tropical environment , 2005 .

[64]  F. R. Martins,et al.  Assessing the potential of concentrating solar photovoltaic generation in Brazil with satellite-derived direct normal irradiation , 2011 .

[65]  Richard Meyer,et al.  Improving Satellite-Derived Solar Resource Analysis with Parallel Ground-Based Measurements , 2011 .

[66]  J. Kleissl,et al.  Clear sky irradiances using REST2 and MODIS , 2015 .

[67]  Christophe Vernay,et al.  Characterizing measurements campaigns for an innovative calibration approach of the global horizontal irradiation estimated by HelioClim-3 , 2013 .

[68]  Birk Kraas,et al.  Task 46: Best Practices for Solar Irradiance Measurements with Rotating Shadowband Irradiometers , 2015 .

[69]  Christian A. Gueymard,et al.  Uncertainties in Modeled Direct Irradiance Around the Sahara as Affected by Aerosols: Are Current Datasets of Bankable Quality? , 2011 .

[70]  L. Wald,et al.  Optimizing the Heliosat-II Method for Surface Solar Irradiation Estimation with GOES Images , 2015 .

[71]  P. Blanc,et al.  Calibration of long-term global horizontal irradiation estimated by HelioClim-3 through short-term local measurement campaigns: extending of the results to European and African sites , 2012 .

[72]  Jesús Polo,et al.  Solar Radiation Derived from Satellite Images , 2008 .

[73]  J. A. Carta,et al.  A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site , 2013 .

[74]  C. Turchi,et al.  Concentrating Solar Power: Best Practices Handbook for the Collection and Use of Solar Resource Data (CSP) , 2010 .

[75]  Thomas Huld,et al.  Management and Exploitation of Solar Resource Knowledge , 2008 .