This document provides an update on Solargis' uncertainty map for its solar radiation database. It summarizes Solargis' methodology for developing the map, which considers various uncertainty drivers like clouds, aerosols, water vapor, terrain variability and more. Preliminary results estimate the total GHI uncertainty to be within ±4% to ±8% for a long-term yearly value, representing an 80% probability of occurrence. Next steps include further refining the uncertainty model with new empirical evidence and developing an uncertainty map for DNI values.

1. Update on the uncertainty map
of Solargis solar radiation database
PV Performance Modeling and Monitoring Workshop
Albuquerque, 9 - 10 May 2017
Marcel Suri, Tomas Cebecauer, Jose A. Ruiz-Arias, Juraj Betak and Artur Skoczek
Solargis, Slovakia

2. PV Performance Modeling and Monitoring Workshop, Albuquerque, 9 - 10 May 2017
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About Solargis
Solar resource, meteorological and photovoltaic simulation data,
software and expert services
• Prospection
• Project development
• Monitoring
• Forecasting
historical and
recent
forecast NWP modelnowcast

3. PV Performance Modeling and Monitoring Workshop, Albuquerque, 9 - 10 May 2017
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Customers: Commercial organizations
• International technical consultancy advisors
• Project developers
• Finance
• Asset management and monitoring companies
• EPC and manufacturers
• Utilities
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Data delivered for commercial projects - last 12 months:
• 2500+ sites served on-request
• 1500+ sites regularly served via API

4. PV Performance Modeling and Monitoring Workshop, Albuquerque, 9 - 10 May 2017
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Customers: Public organizations
• World Bank Group
• GIZ: India, South Africa, Nigeria, Pakistan, etc.
• MASEN: Moroccan Agency for Sustainable Energy
• KACARE Saudi Arabia
• Ministry of energy of the UAE
• Palestinian energy and natural resources authority
• Secretariat of the Pacific Community, Micronesia
• ESKOM South Africa
• NamPower Namibia
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Saudi Arabia Morocco UAE The World Bank Group
Solargis data integrated in online solar atlases

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What we do
Solargis solar and meteo database
• Historical data (project evaluation)
• Real-time data (monitoring and forecasting)
Solargis online software services
• Online apps
• API
Expert consultancy
• Regional evaluation for countries
• Site specific evaluation for solar project development
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How to measure/model solar radiation
Meteorological measurements Satellite-based solar models
(historical and recent data)
Numerical Weather-Prediction models
(Forecasts)
(forecast data)
Source: JMA
Source: NOAA
Source: CSP Services

7. PV Performance Modeling and Monitoring Workshop, Albuquerque, 9 - 10 May 2017
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We focus on models
Meteorological measurements Satellite-based solar models
Numerical Weather-Prediction models
Source: NOAA
Source: CSP Services

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Solargis model

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Solargis:solar resource data
Atmospheric models (aerosols, water vapor)
Meteorological
satellitesTerrain and other data
GTI
Global Tilted
Irradiance
(PV power plants)
GHI
DNI
DIF
DNI
Direct Normal
Irradiance
(CSP power plants)
Validation by solar
measurements
Source:: EUMETSAT, ECMWF, NOAA, SRTM, Solargis

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Data inputs: Solargis
Optimised models
Adapted to all geographies
Output data: time series
• Time step 10/15/30 minutes (depends on the satellite)
• Spatial resolution: up to 250 m
• History of last 10/18/23+ years
• Real time update
• Global coverage (latitudes 60N to 55S)
• High accuracy (validated)
Solargis:solar resource data
Continuity
• Historical data
• Real-time data for monitoring,
nowcasting and forecasting

11. Factors that determine the difference between the model and measurements
Models
• Mathematical and algorithmic formulation of models
• Input data sets (satellite, weather models, etc.)
Solar monitoring instruments*
• Accuracy of sensors
• Maintenance and calibration of the instruments
• Quality control of the measured data
*For validation, in major cases, we use high quality
measurements
Differencemodel - measurements

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Solargis GHI uncertainty map
• Uncertainty of yearly GHI value is considered only
• Uncertainty = 1.28155 * STDEV of yearly biases
• Uncertainty map is based on:
• 220+ GHI validation sites
• 200+ AERONET sites
• Experience and knowledge based on solar resource assessment expert
studies at 600+ commercial assignments, worldwide
• Analysis of multiple drivers prepared as map layers
• First version in 10/2016
• New (second) version includes better evaluation of cloud effects
• Final version to be published in Q3

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Solargis uncertainty of yearly estimates
GHI: ±4% to ±8%
±3.9%**
±7.6%**
* 68.27% occurrence: standard deviation (STDEV) assuming simplified assumption of normal distribution
** 80% occurrence: calculated as 1.28155 STDEV − can be used for an estimate of P90 values
DNI: ±8% to ±16%
Source: SolarGIS

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Solargis uncertainty: focus on yearly GHI
Model deviation
yearly GHI
Distance to
validation sites
GHI: ±4% to ±8%

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Solar radiation model
Inputsandoutputs
Solargis inputs
(Americas)
Source
of input data
Time
representation
Original
time step
Approx. grid
resolution
Cloud index
GOES
(NOAA)
1999 to date 30 minutes 3 km
Atmospheric Optical
Depth (aerosols)
MACC-II
(ECMWF)
2003 to date
6 hours
(monthly until 2002)
85 and 125 km
Water vapor
CFSR/GFS
(NOAA)
1999 to date 1 and 3 hours 35 and 55 km
Elevation and horizon
SRTM-3
(SRTM)
- - 250 m
Solargis primary data
outputs (GHI and DNI)
- 1999 to date 30 minutes 250 m
Source: Solargis
Example region: Peru

16. Errors of model values
The total error of the model values is a combination of:
1.Errors associated to the representation of solar radiation with physical
formulation (the model)
2.Errors related to the model inputs: satellite data features, clouds, aerosols,
water vapor, etc.

17. Methodology: Uncertainty drivers
The uncertainty drivers:
• Clouds persistence
• Clouds variability
• Atmospheric Optical Depth (Aerosols)
• Total water vapor
• Snow coverage
• Terrain variability
• Distance to water surface
• Anthropogenic pollution
• Satellite pixel distortion
For each uncertainty driver, the corresponding sensitivity function has been
empirically determined

18. Methodology: Simplified framework
Total uncertainty σε
2 is considered the sum of individual contributions by
several environmental factors (hereinafter referred to as uncertainty drivers)
that add up to total uncertainty.
Mathematically:
• σm
2 represents the background uncertainty of the model (constant)
• Φi(xi) are sensitivity functions which determine the individual contribution
of each uncertainty driver xi. Note, it is assumed that φi is only a function of
the uncertainty driver xi.

19. Methodology: evaluation of the uncertainty driver
Satellite pixel distortion
For each driver the following
is evaluated:
• Uncertainty model
• Distribution of values
sensitivity function global distribution

20. Uncertainty drivers: Clouds (persistenceand variability)
Computed as as P99(GHI)/GHICS
Key issue: high variability and
persistence of clouds in tropics

21. Uncertainty drivers: Aerosol optical depth
Computed from weather reanalysis as AOD550nm x AODerror (normalized)*
Key issue: High atmospheric turbidity and high variability
* Ruiz-Arias et al. 2013, doi: 10.5194/acp-13-675-2013

22. Uncertainty drivers: Water vapour
Precipitable water computed from weather reanalysis data
Key issue: high precipitable water in equatorial tropics

23. Uncertainty drivers: Snow coverage
Computed from snow depth water equivalent (weather reanalysis)
Key issue: Ability of satellites to discern snow/ice from clouds

24. Uncertainty drivers: Terrain
Terrain variability computed as standard deviation of elevation from DEM
Key issue: Mountains with complex and fast changing patterns of clouds, shadows,
elevation and albedo

25. Uncertainty drivers: Distance to water surfaces
Computed as/from: GIS layers representing water surfaces
Key issues: mixed pixels on the overlap of land and water vs. geometric instability
of satellites

26. Uncertainty drivers: Anthropogenic pollution
Estimated from total SO2 emissions from EDGAR HTAP database
Key issue: Solar radiation attenuation in high polluted areas

27. Uncertainty drivers: Satellite pixel distortion
Computed as change of pixel diagonal length
Shows magnitude of errors due to geometrical distortions in satellite data

28. Results (preliminary): Total GHI uncertainty
Long-termvalue,in%
Background error ≈ 3.5%
• Compared to high quality measurements only
• The value represents 80% probability of occurrence, i.e.
90% probability of exceedance

29. Comparing evidencewith the uncertainty estimates

30. Comparing model with measurements
Factors that determine the difference between the model and measurements
Models
• Mathematical and algorithmic formulation of models
• Input data sets (satellite, weather models, etc.)
Solar monitoring instruments*
• Accuracy of sensors
• Maintenance and calibration of the instruments
• Quality control of the measured data

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Reducing uncertainty of yearly estimates
by ground measurements
DNI: ±3.5%
GHI: ±2.5%
Best
achievable
uncertainty
0
2
4
6
8
10
12
14
16
0 12 24 36 48
Achievableuncertainty(%)
Period of ground measurements (months)
DNI P99.5
DNI P90
GHI P99.5
GHI P90
Running ground-monitoring campaign and combining measurements with satellite data
is the way to maintain low uncertainty of solar resource data in a longterm
Values are indicative

32. Next steps
• This is improved version of the Solargis GHI uncertainty map
• Further reduction of uncertainty expected with new empirical evidence
• Results will be incorporated into the uncertainty processing chain
• Uncertainty map for DNI under development
• New uncertainty drivers and time aggregations will be considered
Thank you!
Artur Skoczek
solargis.com
contact@solargis.com