Uncertainty of the Solargis solar radiation database

PV Performance Modeling and Monitoring WorkshopFreiburg, 24 Oct 2016

Marcel Suri, Tomas Cebecauer, Jose A. Ruiz-Arias and Juraj Betak

Solargis, Slovakia

PV Performance Modeling and Monitoring Workshop, Freiburg, 24 Oct 2016 2

About Solargis

Solar resource, meteorological and photovoltaic simulation data, software and expert services

• Prospection

• Project development

• Monitoring

• Forecasting

historical and recent

forecast NWP modelnowcast

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Solargis solar resource database

GHI

DNI

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Continuous geographical coverage

Spatial and temporal consistency

High resolution

High availability

History of 10 to 22+ years

Systematic update for monitoring and nowcasting

Historical and operational satellite-based data

Data inputs: JMA, ECMWF, NOAA, SRTM

Source: Solargis

Difference model - 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

*For validation, in major cases, we use high quality measurements

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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 ±15%

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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Solargis model

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Solargis satellite model

Atmospheric data Satellite dataTerrain data

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Solar radiation modelInputs and outputs

Solargis inputs(Americas)

Source of input data

Time representation

Original time step

Approx. grid resolution

Cloud indexGOES

(NOAA)1999 to date 30 minutes 3 km

Atmospheric Optical Depth (aerosols)

MACC-II(ECMWF)

2003 to date6 hours

(monthly until 2002)85 and 125 km

Water vaporCFSR/GFS(NOAA)

1999 to date 1 and 3 hours 35 and 55 km

Elevation and horizonSRTM-3(SRTM)

- - 250 m

Solargis primary data outputs (GHI and DNI)

- 1999 to date 30 minutes 250 m

Source: Solargis

Example region: Peru

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.

Methodology: Simplified framework

Total uncertainty σε2 is evaluated as:

• σm2 represents the background uncertainty of the model (constant)

• Model inputs xi are referred to as uncertainty drivers

• Φi(xi) are sensitivity functions determining how much each model input xi

contributes to the total uncertainty

Methodology: evaluation of the uncertainty driver

Satellite pixel distortion

For each driver the following is evaluated:

• Uncertainty model

• Distribution of values

Methodology: Uncertainty drivers

The uncertainty drivers:

• Clouds

• Aerosol optical depth

• 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 calculated

Uncertainty drivers: Clouds

Computed as as P99(GHI)/GHICS

Key issue: high variability and persistence of clouds in tropics

Uncertainty drivers: Aerosol optical depth

Computed from weather reanalysis as AOD550 x AODerror*

Key issue: High atmospheric turbidity and high variability

* Ruiz-Arias et al. 2013, doi: 10.5194/acp-13-675-2013

Uncertainty drivers: Water vapour

Precipitable water computed from weather reanalysis data

Key issue: high precipitable water in equatorial tropics

Uncertainty drivers: Snow coverage

Computed from snow depth water equivalent (weather reanalysis)

Key issue: Ability of satellites to identify snow/ice/clouds

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

Uncertainty drivers: Distance to water bodies

Computed as/from: GIS layers representing water bodies

Key issues: mixed pixels on the overlap of land and water vs. geometric instability of satellites

Uncertainty drivers: Anthropogenic pollution

Estimated from total SO2 emissions from EDGAR HTAP database

Key issue: Attenuation in high polluted areas

Uncertainty drivers: Satellite pixel distortion

Computed as change of pixel diagonal length

Shows magnitude of errors due to geometrical distortions in satellite data

Results: Total GHI uncertaintyLong-term value, in %

Background error = 3%

Comparing evidence with the uncertainty estimates

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

Ach

ieva

ble

un

ce

rta

inty

(%

)

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

Next steps

• 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