Short-Term Forecasting of Surface Solar Irradiance Based on Meteosat-SEVIRI Data Using a Nighttime Cloud lndex
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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 2
3 Motivation Satellite images are operationally used to forecast surface solar irradiance within the next hours: 1. Meteosat Second generation HRVIS images ( nm, 1km*1km) 1. Heliosat Method: cloud index solar irradiance 2. Cloud motion vectors 3
4 ? Cloudindex n(vis) UTC at night no HRVIS information --> short term forecasting only possible after sunrise 4
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 6
7 BTD image observations limb cooling 7
8 BTD image observations Noise: very cold clouds 8
9 BTD image observations Noise: very cold clouds Fog and Low Stratus 9
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) 10
11 Satellite viewing angle correction (BTD BTD*) Shift of peaks: limb cooling 11
12 Cloud classes in BTD*-histogram cloud free -δ- FLS other clouds P 12
13 Definition of cloud classes in BTD* image P: Position of cloud free ocean or land peak in BTD* frequency distribution 13
14 Map T10.8 or BTD* to cloud index n For each cloud class a different transformation is used cloud free: FLS: nnight = 0 nnight = f1(btd*) other: very cold: nnight = f2(t10.8) 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 14
15 Illustration: map T10.8 to cloud index n F3 (T10.8) N 3 (nday) Illustration of transformation 15
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*) 16
17 Transformations f1(btd*) and f3(t10.8) FLS f1 very cold f3 17
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 18
19 ? Cloudindex n(vis) UTC Composite Cloudindex n(btd) Cloudindex n(vis) UTC 19
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! 20
21 Germany, 116 stations, Sep 2014 to Feb 2015, hourly values of global horizontal irradiance 21
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, ; doi: /rs
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. 24
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