1) The document presents a method for short-term forecasting of surface solar irradiance at night using satellite imagery, allowing forecasts before sunrise. 2) It defines cloud classes based on brightness temperature differences in infrared satellite images at night, and derives a cloud index for each class by mapping infrared values to historical daytime cloud index values. 3) Validation using over 100 German weather stations over 6 months showed the nighttime cloud index can accurately forecast global horizontal irradiance in the hours before sunrise.

1. Short-Term Forecasting of Surface Solar
Irradiance
Based on Meteosat-SEVIRI Data
Using a Nighttime Cloud lndex
Annette Hammer
Energy Meteorology Group
Institute of Physics
Carl von Ossietzky University Oldenburg
6th PV Performance Modeling and Monitoring
Workshop, 24. October 2016, Freiburg

2. Overview
1. Motivation and aim
2. Define cloud classes in brightness temperature
difference images
3. Derivation of cloud index for each cloud class
4. Validation
5. Summary
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3. Motivation
Satellite images are operationally used to forecast surface
solar irradiance within the next hours:
1. Meteosat Second generation
HRVIS images
(300-700nm, 1km*1km)
1. Heliosat Method:
cloud index → solar irradiance
2. Cloud motion vectors
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4. Cloudindex n(VIS)
2014-11-02 0600 UTC
?
at night no HRVIS information
--> short term forecasting only possible after sunrise
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5. Aim: define a nighttime cloud index!

Meteosat infrared channels (here 10.8 and 3.9 µm)
Effective Brightness Temperatures T10.8, T3.9
and their Difference BTD=T3.9 − T10.8

well known quantities in nighttime cloud and fog
detection

up to now not used to calculate a cloud index
(Note: BTD is different for day and night, T3.9 consists
of reflected solar and emitted thermal radiation) 5

6. BTD image observations
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7. BTD image observations
limb cooling
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8. BTD image observations
Noise: very cold clouds
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9. BTD image observations
Noise: very cold clouds
Fog and Low Stratus
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10. Observations

Cloud free land and cloud free ocean surfaces
have a similar shade of grey

limb cooling

Fog and low stratus look dark

Other clouds look bright

Very cold thick clouds show noise (opaque ==
high cloud index)
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11. Satellite viewing angle correction (BTD→BTD*)
Shift of peaks:
limb cooling
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12. Cloud classes in BTD*-histogram
P
cloud
free
other cloudsFLS
- δ -
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13. Definition of cloud classes in BTD* image
P: Position of cloud free ocean or land peak in BTD* frequency distribution
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14. Map T10.8 or BTD* to cloud index n

For each cloud class a different transformation is used
cloud free: nnight = 0
FLS: nnight = f1(BTD*)
other: nnight = f2(T10.8)
very cold: nnight = f3(T10.8)

night values are related to cloud index values a few hours later,

not pixel-by-pixel but statistically regarding their cumulative
frequenqy distributions
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15. Illustration of transformation
Illustration: map T10.8 to cloud index n
F3 (T10.8) N3 (nday)
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16. Map T10.8 / BTD* to cloud index n
1. Cumulative frequency distributions
2. Transformation that maps each T10.8 / BTD* value to
the daytime cloud index with the same quantile
F1 (BTD*) ≡ N1 (nday)
F3 (T10.8) ≡ N3 (nday)
nnight = f1 (BTD*) = (N1) ¹⁻ F1 (BTD*)
nnight = f3 (T10.8) = (N3) ¹⁻ F3 (BTD*)
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17. Transformations f1(BTD*) and f3(T10.8)
FLS f1 very cold f3
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18. Training of transformations f1, f2 and f3
f1(BTD*) for fog and low stratus and
f3(T10.8) for very cold clouds
have been trained in months with a lot of such clouds
(f1: April 2013, 29 nights and f2: Feb 2013, 15 nights)
f2(T10.8) for other clouds
is taken from yesterday for today, to follow seasonal
temperature changes
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19. Cloudindex n(VIS)
2014-11-02 0600 UTC
Composite
Cloudindex n(BTD) Cloudindex n(VIS)
2014-11-02 0600 UTC
?
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20. Validation
Quality of daytime cloud index can be validated with
global horiziontal irradiance (compare result of
Heliosat method with measurements)
But: Nighttime cloud index can not be validated in this
way → Validate forecasted irradiance!
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21. Germany, 116 stations, Sep 2014 to Feb 2015,
hourly values of global horizontal irradiance
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22. Summary
With a nighttime cloud index it is possible to forecast
global horizontal irradiance for the next hours before
sunrise
Effective brightness temperature values and brightness
temperature differences are used to classify clouds
and are mapped to cloud index values with a
statistical transformation (QuantileQuantilePlot)
For three cloud classes such transformations have been
developed 22

23. Reference
Hammer, A.; Kühnert, J.; Weinreich, K.; Lorenz, E.:
Short-Term Forecasting of Surface Solar Irradiance Based on
Meteosat-SEVIRI Data Using a Nighttime Cloud Index.
Remote Sensing, 2015, 7, 9070-9090; doi:10.3390/rs70709070
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24. Acknowledgements
This work has been supported by the German Federal Environmental
Foundation DBU (Deutsche Bundesstiftung Umwelt) and the German
Federal Ministry of Economics and Technology BMWi (Bundesministerium
für Wirtschaft und Technologie). We thank the German Weather Service
(DWD) and meteogroup GmbH for providing global horizontal irradiance
measurement data.
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