The document introduces solar resource assessments through satellite data. It discusses how satellites like Meteosat scan the Earth's surface to measure solar irradiance, and how these measurements are used along with atmospheric models and ground data to estimate solar resources. Key factors that influence solar radiation reaching the surface like aerosols, clouds, and water vapor are also summarized. The document then shows how satellite data can be used to analyze solar potentials and identify prime areas for solar technologies.

1. Introduction to Solar Resource Assessments
Carsten Hoyer-Klick
German Aerospace Center
Institute of Technical Thermodynamics
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2. Transfer of Solar Radiation through the
Atmosphere
Radiation at the top of atmosphere
Absorption (ca. 1%)
Ozone.……….…....
Rayleigh scattering and absorption (ca. 15%)
Air molecules..……
Scatter and Absorption ( ca. 15%, max. 100%)
Aerosol…….………..…...……
Clouds………….……….. Reflection, Scatter, Absorption (max. 100%)
Water Vapor…….……...……… Absorption (ca. 15%)
Direct normal irradiance at ground
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3. Direct Normal Irradiation (DNI)
direct

Direct-Normal- Irradiation (DNI)
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4. Diffuse Horizontal Radiation
diffuse
diffuse
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5. Global Horizontal Irradiation (GHI)
direct
diffuse
diffuse
Global-Horizontal-Irradiation (GHI)
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6. Solar Spectrum and Atmospheric influence
1 Planck curve T=5780 K at mean sun-
earth distance
2 extraterrestrial solar spectrum with
additional
3 absorption by 03
4 scattering by 02 und N
5 scattering by aerosols
6 absorption by H2O vapor
7 absorption by aerosols
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Introduction to Solar Resources > Carsten Hoyer-Klick > Feb. 28th 2012> Sultan Qaboos University

7. Atmospheric Extinction Processes
Reflected back
to space
Scattered in
any direction
Absorbed
Direct transmitted
to surface
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Introduction to Solar Resources > Carsten Hoyer-Klick > Feb. 28th 2012> Sultan Qaboos University

8. Rayleigh and Mie Scattering
Rayleigh scattering Mie scattering
particle size ≥ wavelength
particle size << wavelength
~ λ-4 ~ λ-1.3
directionality:
directionality: (1 + cos² α) very strong forward scattering
Large variability by
non-uniform particles
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Introduction to Solar Resources > Carsten Hoyer-Klick > Feb. 28th 2012> Sultan Qaboos University

9. Rayleigh and Mie Scattering
Rayleigh scattering Mie scttering Mie Scattering,
larger particles
Direction of incident light
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Introduction to Solar Resources > Carsten Hoyer-Klick > Feb. 28th 2012> Sultan Qaboos University

10. Scattering Regimes
Dimensionless size parameter Χ=2πr/λ as a function of wavelength (λ) of the incident radiation
and particle radius r.
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Introduction to Solar Resources > Carsten Hoyer-Klick > Feb. 28th 2012> Sultan Qaboos University

11. How two derive irradiance data
from satellites
The Meteosat
satellite is located in
a geostationary orbit
The satellite scans
the earth line by line
every half hour
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12. Das Meteosat System – Image recording
• The satellite rotates at 100 rpm
• Line by line scanning of the
earth from south to north
• Pixels by sampling of the analog
sensor signal
• Field of view of the sensor e.g. in
Europe 3 x 4 km due to
geometric distortion
Scan
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13. How two derive irradiance data
from satellites
The Meteosat
satellite is located in
a geostationary orbit
The satellite scans
the earth line by line
every half hour
The earth is scanned
in the visible …
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14. How two derive irradiance data
from satellites
The Meteosat
satellite is located in
a geostationary orbit
The satellite scans
the earth line by line
every half hour
The earth is scanned
in the visible and
infra red spectrum
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15. How two derive irradiance data
from satellites
The Meteosat
satellite is located in
a geostationary orbit
The satellite scans
the earth line by line
every half hour
The earth is scanned
in the visible and
infra red spectrum
A cloud index is
composed from the
two channels
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16. Clear sky Model input data
Aerosol optical thickness
GACP Resolution 4°x5°, monthly climatology
MATCH Resolution 1.9°x1.9°, daily climatology
Water Vapor: NCAR/NCEP Reanalysis
Resolution 1.125°x1.125°, daily values
Ozone: TOMS sensor
Resolution 1.25°x1.25°, monthly values
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17. Remote Sensing of Aerosols
Usually split channel / dark target approach:
A dark target is searched in a long-wave channel
The reflectivity is observed in a short wave channel (usually
aerosol backscatter increases with frequency)
The difference is translated into a AOD
Problems:
Dark targets a land: Forrests, lakes – only a very few available
almost none in deserts
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18. Chemistry Transport Models for
Aerosol Data
Modeling of aerosol uptake, transport, chemical change and deposition in a numerical model
Input:
Surface properties
Emission data bases
Numerical weather models (especially wind fields, rain).
Modeling:
Aerosol uptake
Aerosol transport with wind
Aerosol outfall
Aerosol chemistry, change in properties with time
Output:
Mass concentrations
converted to optical properties
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19. Chemistry Transport Models –
Emission data bases
Emission data
bases, e.g.
SOx emissions
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20. Chemistry Transport Models - Dust
Mineral dust mobilisation is initiated by strong winds blowing over bare
ground and erodible particles.
Medium range sand-sized particles above 60 μm particle diameter are
lifted up, but also fall quickly down again to the ground.
The momentum of the landing particles results in the loosening of small
and fast moving particles which are known as sandblasting particles.
Dust particles are mixed into higher atmospheric layers by turbulent
processes and transported in the atmospheric flow over larger distances.
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21. Uncertainty in Aerosols
All graphs are for July
Scales are the same!
(0 – 1.5)
Large differences in
Aerosol values and
GADS NASA GISS v1 / GACP distribution
Toms NASA GISS v2 1990 Linke Turbidity
GOCART AeroCom MATCH
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22. Calculation of solar radiation from
remote sensing
METEOSAT
Gext image
Atmospheric Model Images Ground
Corrected image
of one albedo
Gdirect, clear Gdiffuse, clear month
cloud-index
(    min )
n
+ (  max   min )
Gclear sky
clear sky index
k *  f (n)  1  n
Methods used:
• Heliosat-2 for the
· visible channel
• IR brightness
Gsurface temperature as
indicator for high
cirrus clouds
(T < -30°C, DNI = 0)
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23. Cloud Transmission for GHI
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24. Cloud Transmission for DNI
Sun-satellite angle 60-80
1.2 1.2
-26 °C
-16 °C
1 1 -6 °C
4 °C
14 °C
0.8 0.8 -30°C - -20°C
cloud transmission
-20°C - -10°C
0.6 0.6 -10 °C - 0°C
0°C - 10 °C
>10°C
0.4 0.4
0.2 0.2
0 0
-0.2 -0.2
-0.2 0 0.2 0.4 0.6 0.8 1 1.2 -0.2 0 0.2 0.4 0.6 0.8 1 1.2
cloud index
Complex functions:
Simple function τ = e-10*ci Different exp. function for
various viewing angles and
brightness temperatures
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25. Comparing ground and satellite data:
time scales
12:45 13:00 13:15 13:30 13:45 14:00 14:15
Hourly average Meteosat image Measurement
Ground measurements are typically
pin point measurements which are
temporally integrated
Satellite measurements are
Hi-res satellite pixel in Europe instantaneous spatial averages
Hourly values are calculated from
temporal and spatial averaging
(cloud movement)
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26. Comparing ground and satellite data:
“sensor size”
solar thermal
power plant satellite pixel
(200MW  2x2 ( 3x4 km²)
km²
ground
measurement
instrument
(2x2 cm²)
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27. Comparison with ground measurements and accuracy
general difficulties: point versus area and
time integrated versus area integrated
1200
ground
1000 satellite
800
W/m²
600
400
200
0
DNI time serie for 1.11.2001, Almería 0 6 12 Folie 27 18 24
Partially cloudy conditions, cumulus humilis hour of day

28. Comparison with ground measurements and accuracy
general difficulties: point versus area and
time integrated versus area integrated
1200
ground
1000 satellite
800
W/m²
600
400
200
0
DNI time serie for 30.4.2000, Almería 0 6 12 Folie 28 18 24
overcast conditions, strato cumulus hour of day

29. Satellite data and nearest
neighbour stations
Satellite derived
data fit better to
a selected site
than ground
measurements
from a site
farther than
25 km away.
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30. Inter annual variability
Strong inter annual and regional 1999
variations deviation
to mean
2000
kWh/m²a
2001
Average of the direct normal 2002
irradiance from 1999-2003
2003
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31. Long-term variability of solar irradiance
7 to 10 years of measurement to get long-term mean within 5%
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32. Ground measurements vs.
satellite derived data
Ground measurements Satellite data
Advantages Advantages
+ high accuracy (depending on sensors) + spatial resolution
+ high time resolution + long-term data (more than 20 years)
+ effectively no failures
+ no soiling
+ no ground site necessary
+ low costs
Disadvantages
- high costs for installation and O&M Disadvantages
- soiling of the sensors - lower time resolution
- sometimes sensor failure - low accuracy at high time resolution
- no possibility to gain data of the past
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33. Combining Ground and
Satellite Assessments
Satellite data
Long term average
Year to year variability
Regional assessment
Ground data
Site specific
High temporal resolution possible
(up to 1 min to model transient effects)
Good distribution function
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34. Matching Ground and Satellite Data
Why do ground and satellite data not match?
Due to uncertainties in:
Atmospheric Parameters, most prominent Aerosols
Cloud transmission:
The cloud index is a combination of cloud fraction and
transparency. A semi transparent cloud can be distinguished well
from a fractional cloud cover.
Parameterization may depend on prevailing cloud types in the
region.
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35. Procedure for Matching Ground
and Satellite Data
Satellite Ground
assessment measurements
Comparison Recalculation
cloud correction
Separation of clear sky with alternative cloud
and cloud conditions transmission tables
clear sky correction
Recalculation Selection
with alternative of best cloud
atmospheric input transmission table
Transfer MSG to MFG
Selection
Recalculation of
of best atmospheric input
long term time series
Best fit
satellite data
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36. Benchmarking of Time Series Products
First order measures:
Bias, root mean square error, standard
deviation
Exact match of data pairs in time
Sometime this match is not necessary
(e.g. system layout with historical data)
Second order measures:
Based on Kolmogrov-Smirnov Test
Match of distribution functions
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37. Validation of the MFG data base
DNI Bias RMSE Correl.
Coeff
overall -3.78% 27.52% 0.86
PSA 0.65 30.25% 0.89
SedeBoqer -6.86% 29.01% 0.84
Tamanrasset -5.83% 25.22% 0.84
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38. Using Solar Resource Data for Policy Analysis
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39. Policy questions
How potential is there for a certain technology?
Are there prime areas to develop a technology?
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40. 1st Question: How much Potential?
Is the technology feasible, are there enough resources?
Is there sufficient area to depoly the technology?
To which share of the (national) demand can they contribute?
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41. Assessing Potentials - Outline
Theoretical Potential
The Amount of solar energy
on the whole area
Technical potential
Limited to suitable areas
Economic
Potential
Limited to
economic
sites
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42. Assessing Potentials – CSP Sample
DNI map of the region
Theoretical Potential
The Amount of solar energy
on the whole area
Technical potential DNI map minus
Limited to suitable areas excluded areas
Economic
Potential Technical potential only
Limited to for DNI e.g. above 2000
economic kWh/m²
sites
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43. Assessment of Potentials,
e.g. Large Solar
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44. Assessment of Potentials,
e.g. Large Solar
+ Industry,
settlements
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45. Assessment of Potentials,
e.g. Large Solar
+ Industry,
settlements
+ Hydrology
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46. Assessment of Potentials,
e.g. Large Solar
+ Industry,
settlements
+ Hydrology
+ Geomorphology
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47. Assessment of Potentials,
e.g. Large Solar
+ Industry,
settlements
+ Hydrology
+ Geomorphology
+ Protected Areas
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48. Assessment of Potentials,
e.g. Large Solar
+ Industry,
settlements
+ Hydrology
+ Geomorphology
+ Protected Areas
+ Wood
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49. Assessment of Potentials,
e.g. Large Solar
+ Industry,
settlements
+ Hydrology
+ Geomorphology
+ Protected Areas
+ Wood
+ Agriculture
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50. Assessment of Potentials,
e.g. Large Solar
+ Industry,
settlements
+ Hydrology
+ Geomorphology
+ Protected Areas
+ Wood
+ Agriculture
+ Slope
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51. Assessment of Potentials,
e.g. Large Solar
+ Industry,
settlements
+ Hydrology
+ Geomorphology
+ Protected Areas
+ Wood
+ Agriculture
+ Power lines
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52. Assessment of Potentials,
e.g. Large Solar
+ Industry,
settlements
+ Hydrology
+ Geomorphology
+ Protected Areas
+ Wood
+ Agriculture
+ Power lines
+ Gas pipelines
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53. Assessment of Potentials,
e.g. Large Solar
+ Industry,
settlements
+ Hydrology
+ Geomorphology
+ Protected Areas
+ Wood
+ Agriculture
+ Power lines
+ Gas pipelines
+ Oil pipelines
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54. Solar Radiation at Usable Areas
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55. CSP Potential
200000
175000
150000
Area [km²]
125000
Area with certain
100000
75000
level of solar radiation
50000
25000
0
1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 >
2800
Electricity Potential [TWh/y]
DNI [kWh/m²a] 25000
20000
15000
10000
Technical potential
5000
calc. with power plant model
0
00
00
00
00
00
00
00
00
00
00
00
00
28
18
19
20
21
22
23
24
25
26
27
28
>
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56. Costal Potential (<20m a.s.l.)
e.g. for sea water desalination
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57. Coastal Potential in Egypt
Coastal Potential - Egypt (20 m a. s. l.)
180
Electricity Potential [TWh/y]
160
140
120
100
2008 power demand
80
60
40
20
0
00
00
00
00
00
00
00
00
00
00
> 0
00
0
18
19
20
21
22
23
24
25
26
27
28
28
DNI [kWh/m²a]
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58. Rooftop Potential
Assuming:
• 5% usage of populated area
• 10% PV efficiency
PV Rooftop potential approx. 5 TWh
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59. Potential of Concentrating Solar Power
in Jordan DNI Classes
• Demand 2050: 53 TWh/y [kWh/m²/y]
• Potential CSP: 5884 TWh/y < 1900
1,900
2,000
2,100
2,200
2,300
2,400
2,500
2,600
2,700
Exclusion Criteria
Urban areas
Population density
Hydrography
Land cover
Protected areas
Topography
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60. CSP power generation potential in Jordan
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61. 2nd Question:
Which areas the most interesting?
Where are resources available?
Are they close enough to the demand centers and infrastructure?
Which resource are available close to demand centers and
intfrastructure?
Can I optimize my spatial planning according to the resource
distribution?
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62. Identification of technology specific hot spots
General approach
Resource Land resource
Site ranking Hot spots
assessment assessment
- DNI - Spatial data - Distance to - Available area
- GHI - Technology infrastructure - Max. installable capacity
- Wind speed specific land - Resource - Representative hourly
exclusion criteria availability resource profiles
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63. New Approach for Site Ranking
Prerequisite: GIS data for resources and infrastructure
Idea, giving Points to:
Level of available resource
Distance to the electricity grid
Distance to settlements
Distance to infrastructure
Ranking based on the sum of points.
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64. Determination of weights - DNI
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65. Determination of weights –
Population and Infrastructure
Population Streets
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66. Determination of weights –
Electricity Network
Substations Transmission lines
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Using Solar Resource Data for Policy Analysis > Carsten Hoyer-Klick > Solar Med Atlas Stakeholder Workshop, Cairo, Nov. 1st, 2011

67. Final Ranking
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Using Solar Resource Data for Policy Analysis > Carsten Hoyer-Klick > Solar Med Atlas Stakeholder Workshop, Cairo, Nov. 1st, 2011

68. Identification of CSP Hot Spots
in Jordan
Exclusion map Hot spots
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Using Solar Resource Data for Policy Analysis > Carsten Hoyer-Klick > Solar Med Atlas Stakeholder Workshop, Cairo, Nov. 1st, 2011

69. Thank you for your attention!
Questions & Answers
Either now or carsten.hoyer-klick@dlr.de
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