Articles | Volume 14, issue 7
https://doi.org/10.5194/amt-14-5199-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-5199-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Cloud height measurement by a network of all-sky imagers
Institut für Solarforschung, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Paseo de Almería, 73, 2, 04001 Almeria, Spain
Institut für Vernetzte Energiesysteme, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Bijan Nouri
Institut für Solarforschung, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Paseo de Almería, 73, 2, 04001 Almeria, Spain
Stefan Wilbert
Institut für Solarforschung, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Paseo de Almería, 73, 2, 04001 Almeria, Spain
Thomas Schmidt
Institut für Vernetzte Energiesysteme, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Ontje Lünsdorf
Institut für Vernetzte Energiesysteme, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Jonas Stührenberg
Institut für Vernetzte Energiesysteme, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Detlev Heinemann
Institut für Vernetzte Energiesysteme, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Andreas Kazantzidis
Laboratory of Atmospheric Physics, Department of Physics, University of Patras, 26500 Patras, Greece
Robert Pitz-Paal
Institut für Solarforschung, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Linder Höhe, 51147 Cologne, Germany
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Cited articles
Aides, A., Levis, A., Holodovsky, V., Schechner, Y. Y., Althausen, D., and
Vainiger, A.: Distributed Sky Imaging Radiometry and Tomography, in: IEEE
Xplore/ 2020 IEEE International Conference on Computational Photography
(ICCP), Saint Louis, MO, USA, 24–26 April 2020, pp. 1–12, 2020. a
Allmen, M. C. and Kegelmeyer Jr., W. P.: The Computation of Cloud-Base Height
from Paired Whole-Sky Imaging Cameras, J. Atmos. Ocean.
Tech., 13, 97–113,
https://doi.org/10.1175/1520-0426(1996)013<0097:TCOCBH>2.0.CO;2, 1996. a, b
Beekmans, C., Schneider, J., Läbe, T., Lennefer, M., Stachniss, C., and Simmer, C.: Cloud photogrammetry with dense stereo for fisheye cameras, Atmos. Chem. Phys., 16, 14231–14248, https://doi.org/10.5194/acp-16-14231-2016, 2016. a, b
Bieliński, T.: A parallax shift effect correction based on cloud height for
geostationary satellites and radar observations, Remote Sens., 12, 365,
https://doi.org/10.3390/rs12030365, 2020. a
Blanc, P., Massip, P., Kazantzidis, A., Tzoumanikas, P., Kuhn, P., Wilbert, S.,
Schüler, D., and Prahl, C.: Short-term forecasting of high resolution
local DNI maps with multiple fish-eye cameras in stereoscopic mode, AIP
Conf. Proc., 1850, 140004, https://doi.org/10.1063/1.4984512, 2017. a, b, c
Blum, N., Schmidt, T., Nouri, B., Wilbert, S., Heinemann, D., Schmidt, T.,
Kuhn, P., Zarzalejo, L. F., and Pitz-Paal, R.: Optimierte Gruppierung
verschiedener Wolkenkameras im Oldenburger Nowcasting Netzwerk, in:
Tagungsunterlagen/ 34. PV-Symposium Bad Staffelstein, Bad
Staffelstein, Germany, 19–21 March 2019, pp. 552–562, 2019a. a
Blum, N., Schmidt, T., Nouri, B., Wilbert, S., Peerlings, E., Heinemann, D.,
Schmidt, T., Kuhn, P., Kazantzidis, A., Zarzalejo, L. F., and Pitz-Paal, R.:
Nowcasting of Irradiance Using a Network of All-Sky-Imagers, in: EU PVSEC
2019 Proceedings/ 36th European Photovoltaic Solar Energy Conference and
Exhibition, Marseille, France, 9–13 September 2019, pp. 1403–1409,
https://doi.org/10.4229/EUPVSEC20192019-5DO.2.1, 2019b. a
Chan, K. L., Wiegner, M., Flentje, H., Mattis, I., Wagner, F., Gasteiger, J., and Geiß, A.: Evaluation of ECMWF-IFS (version 41R1) operational model forecasts of aerosol transport by using ceilometer network measurements, Geosci. Model Dev., 11, 3807–3831, https://doi.org/10.5194/gmd-11-3807-2018, 2018. a
Cirés, E., Marcos, J., de la Parra, I., García, M., and Marroyo, L.:
The potential of forecasting in reducing the LCOE in PV plants under
ramp-rate restrictions, Energy, 188, 116053,
https://doi.org/10.1016/j.energy.2019.116053, 2019. a
Costa-Surós, M., Calbó, J., González, J., and Martin-Vide, J.:
Behavior of cloud base height from ceilometer measurements, Atmos.
Res., 127, 64–76, https://doi.org/10.1016/j.atmosres.2013.02.005, 2013. a
de Haij, M., Apituley, A., Koetse, W., and Bloemink, H.: Transition towards a
new ceilometer network in the Netherlands: challenges and experiences, in:
Instruments and Observing Methods Report No. 125/ WMO Technical Conference on
Meteorological and Environmental Instruments and Methods of Observation (CIMO
TECO 2016), Madrid, Spain, 27–30 September 2016,
available at: https://library.wmo.int/index.php?lvl=notice_display&id=19676#.XirnGzJKiUk (last access: 28 May 2021),
2016. a, b, c, d, e
Fabel, Y., Nouri, B., Wilbert, S., Blum, N., Triebel, R., Hasenbalg, M., Kuhn, P., Zarzalejo, L. F., and Pitz-Paal, R.: Applying self-supervised learning for semantic cloud segmentation of all-sky images, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2021-1, in review, 2021. a
Ghosh, S., Rahman, S., and Pipattanasomporn, M.: Distribution voltage
regulation through active power curtailment with PV inverters and solar
generation forecasts, IEEE T. Sustain. Energ., 8, 13–22,
https://doi.org/10.1109/TSTE.2016.2577559, 2016. a
Görsdorf, U., Mattis, I., Pittke, G., Bravo-Aranda, J. A., Brettle, M.,
Cermak, J., Drouin, M.-A., Geiß, A., Haefele, A., and Hervo, M.: The
ceilometer inter-comparison campaign CeiLinEx2015 — Cloud detection and
cloud base height, in: Instruments and Observing Methods Report No. 125/ WMO
Technical Conference on Meteorological and Environmental Instruments and
Methods of Observation (CIMO TECO 2016), Madrid, Spain, 27–30 September 2016, pp. 27–30, available at:
https://library.wmo.int/index.php?lvl=notice_display&id=19676#.XirnGzJKiUk (last access: 28 May 2021),
2016. a, b, c
Hamann, U., Walther, A., Baum, B., Bennartz, R., Bugliaro, L., Derrien, M., Francis, P. N., Heidinger, A., Joro, S., Kniffka, A., Le Gléau, H., Lockhoff, M., Lutz, H.-J., Meirink, J. F., Minnis, P., Palikonda, R., Roebeling, R., Thoss, A., Platnick, S., Watts, P., and Wind, G.: Remote sensing of cloud top pressure/height from SEVIRI: analysis of ten current retrieval algorithms, Atmos. Meas. Tech., 7, 2839–2867, https://doi.org/10.5194/amt-7-2839-2014, 2014. a
Heese, B., Flentje, H., Althausen, D., Ansmann, A., and Frey, S.: Ceilometer lidar comparison: backscatter coefficient retrieval and signal-to-noise ratio determination, Atmos. Meas. Tech., 3, 1763–1770, https://doi.org/10.5194/amt-3-1763-2010, 2010. a
Hogan, R. J., O'Connor, E. J., and Illingworth, A. J.: Verification of
cloud-fraction forecasts, Q. J. Roy. Meteor.
Soc., 135, 1494–1511, https://doi.org/10.1002/qj.481, 2009. a
Howie, R. M., Paxman, J., Bland, P. A., Towner, M. C., Cupak, M., Sansom,
E. K., and Devillepoix, H. A.: How to build a continental scale fireball
camera network, Exp. Astron., 43, 237–266,
https://doi.org/10.1007/s10686-017-9532-7, 2017. a
Isaac, G. A., Bailey, M., Boudala, F. S., Burrows, W. R., Cober, S. G.,
Crawford, R. W., Donaldson, N., Gultepe, I., Hansen, B., Heckman, I., Huang,
L. X., Ling, A., Mailhot, J., Milbrandt, J. A., Reid, J., and Fournier, M.:
The Canadian Airport Nowcasting System (CAN-Now), Meteorol.
Appl., 21, 30–49, https://doi.org/10.1002/met.1342, 2014. a
Kaur, A., Nonnenmacher, L., Pedro, H. T., and Coimbra, C. F.: Benefits of solar
forecasting for energy imbalance markets, Renew. Energ., 86, 819–830,
https://doi.org/10.1016/j.renene.2015.09.011, 2016. a
Khlopenkov, K., Spangenberg, D., and Smith Jr., W. L.: Fusion of Surface
Ceilometer Data and Satellite Cloud Retrievals in 2D Mesh Interpolating Model
with Clustering, in: Proc. SPIE 11152, Remote Sensing of Clouds and the
Atmosphere XXIV/ SPIE Remote Sensing 2019, Strasbourg, France, 9 October 2019, p. 111521F, https://doi.org/10.1117/12.2533370, 2019. a
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World map of the
Köppen-Geiger climate classification updated, Meteorol.
Z., 15, 259–263, https://doi.org/10.1127/0941-2948/2006/0130, 2006. a, b
Kuhn, P., Nouri, B., Wilbert, S., Prahl, C., Kozonek, N., Schmidt, T., Yasser,
Z., Ramirez, L., Zarzalejo, L., and Meyer, A.: Validation of an all‐sky
imager–based nowcasting system for industrial PV plants, Prog.
Photovoltaics, 26, 608–621,
https://doi.org/10.1002/pip.2968, 2018a. a
Kuhn, P., Wirtz, M., Killius, N., Wilbert, S., Bosch, J. L., Hanrieder, N.,
Nouri, B., Kleissl, J., Ramirez, L., Schroedter-Homscheidt, M., Heinemann,
D., Kazantzidis, A., Blanc, P., and Pitz-Paal, R.: Benchmarking three
low-cost, low-maintenance cloud height measurement systems and ECMWF cloud
heights against a ceilometer, Sol. Energy, 168, 140–152,
https://doi.org/10.1016/j.solener.2018.02.050, 2018b. a, b, c, d, e, f
Kuhn, P., Nouri, B., Wilbert, S., Hanrieder, N., Prahl, C., Ramirez, L.,
Zarzalejo, L., Schmidt, T., Yasser, Z., Heinemann, D., Tzoumanikas, P.,
Kazantzidis, A., Kleissl, J., Blanc, P., and Pitz-Paal, R.: Determination of
the optimal camera distance for cloud height measurements with two all-sky
imagers, Sol. Energy, 179, 74–88, https://doi.org/10.1016/j.solener.2018.12.038,
2019. a, b, c, d, e, f, g, h, i, j, k
Law, E. W., Prasad, A. A., Kay, M., and Taylor, R. A.: Direct normal irradiance
forecasting and its application to concentrated solar thermal output
forecasting–A review, Sol. Energy, 108, 287–307,
https://doi.org/10.1016/j.solener.2014.07.008, 2014. a
Luhmann, T.: Nahbereichsphotogrammetrie: Grundlagen, Methoden und Anwendungen,
Wichmann Verlag, Heidelberg, Germany, 2000. a
Macke, A., Seifert, P., Baars, H., Barthlott, C., Beekmans, C., Behrendt, A., Bohn, B., Brueck, M., Bühl, J., Crewell, S., Damian, T., Deneke, H., Düsing, S., Foth, A., Di Girolamo, P., Hammann, E., Heinze, R., Hirsikko, A., Kalisch, J., Kalthoff, N., Kinne, S., Kohler, M., Löhnert, U., Madhavan, B. L., Maurer, V., Muppa, S. K., Schween, J., Serikov, I., Siebert, H., Simmer, C., Späth, F., Steinke, S., Träumner, K., Trömel, S., Wehner, B., Wieser, A., Wulfmeyer, V., and Xie, X.: The HD(CP)2 Observational Prototype Experiment (HOPE) – an overview, Atmos. Chem. Phys., 17, 4887–4914, https://doi.org/10.5194/acp-17-4887-2017, 2017. a
Martucci, G., Milroy, C., and O'Dowd, C. D.: Detection of cloud-base height
using Jenoptik CHM15K and Vaisala CL31 ceilometers, J. Atmos.
Ocean. Tech., 27, 305–318, https://doi.org/10.1175/2009JTECHA1326.1, 2010. a
Mejia, F. A., Kurtz, B., Levis, A., de la Parra, Í., and Kleissl, J.: Cloud
tomography applied to sky images: A virtual testbed, Sol. Energy, 176,
287–300, https://doi.org/10.1016/j.solener.2018.10.023, 2018. a, b
Mobotix: Technical Specifications MOBOTIX Q25 Hemispheric, Data sheet, Mobotix
AG, Langmeil, Germany, available at:
https://www.mobotix.com/sites/default/files/2017-10/Mx_TS_Q25_en_20170515.pdf (last access: 28 May 2021),
2017. a
Nguyen, D. and Kleissl, J.: Stereographic methods for cloud base height
determination using two sky imagers, Sol. Energy, 107, 495–509,
https://doi.org/10.1016/j.solener.2014.05.005, 2014. a, b, c
Noh, Y.-J., Forsythe, J. M., Miller, S. D., Seaman, C. J., Li, Y., Heidinger,
A. K., Lindsey, D. T., Rogers, M. A., and Partain, P. T.: Cloud-Base Height
Estimation from VIIRS. Part II: A Statistical Algorithm Based on A-Train
Satellite Data, J. Atmos. Ocean. Tech., 34, 585–598,
https://doi.org/10.1175/JTECH-D-16-0110.1, 2017. a
Nouri, B., Kuhn, P., Wilbert, S., Prahl, C., Pitz-Paal, R., Blanc, P., Schmidt,
T., Yasser, Z., Santigosa, L. R., and Heineman, D.: Nowcasting of DNI maps
for the solar field based on voxel carving and individual 3D cloud objects
from all sky images, AIP Conf. Proc., 2033, 190011,
https://doi.org/10.1063/1.5067196, 2018. a
Nouri, B., Kuhn, P., Wilbert, S., Hanrieder, N., Prahl, C., Zarzalejo, L.,
Kazantzidis, A., Blanc, P., and Pitz-Paal, R.: Cloud height and tracking
accuracy of three all sky imager systems for individual clouds, Sol. Energy,
177, 213–228, https://doi.org/10.1016/j.solener.2018.10.079, 2019a. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q
Nouri, B., Wilbert, S., Kuhn, P., Hanrieder, N., Schroedter-Homscheidt, M.,
Kazantzidis, A., Zarzalejo, L., Blanc, P., Kumar, S., and Goswami, N.:
Real-Time Uncertainty Specification of All Sky Imager Derived Irradiance
Nowcasts, Remote Sens., 11, 1059, https://doi.org/10.3390/rs11091059,
2019b. a, b
Nouri, B., Wilbert, S., Segura, L., Kuhn, P., Hanrieder, N., Kazantzidis, A.,
Schmidt, T., Zarzalejo, L., Blanc, P., and Pitz-Paal, R.: Determination of
cloud transmittance for all sky imager based solar nowcasting, Sol. Energy,
181, 251–263, https://doi.org/10.1016/j.solener.2019.02.004, 2019c. a, b
Nouri, B., Noureldin, K., Schlichting, T., Wilbert, S., Hirsch, T.,
Schroedter-Homscheidt, M., Kuhn, P., Kazantzidis, A., Zarzalejo, L. F.,
Blanc, P., Yasser, Z., Fernández, J., and Pitz-Paal, R.: Optimization of
parabolic trough power plant operations in variable irradiance conditions
using all sky imagers, Sol. Energy, 198, 434–453,
https://doi.org/10.1016/j.solener.2020.01.045, 2020a. a, b
Nouri, B., Wilbert, S., Blum, N., Kuhn, P., Schmidt, T., Yasser, Z., Schmidt,
T., Zarzalejo, L. F., Lopes, F. M., Silva, H. G., Schroedter-Homscheidt, M.,
Kazantzidis, A., Raeder, C., Blanc, P., and Pitz-Paal, R.: Evaluation of an
All Sky Imager Based Nowcasting System for Distinct Conditions and Five
sites, AIP Conf. Proc., 2303, 180006, https://doi.org/10.1063/5.0028670,
2020b. a, b
Peng, Z., Yu, D., Huang, D., Heiser, J., Yoo, S., and Kalb, P.: 3D cloud
detection and tracking system for solar forecast using multiple sky imagers,
Sol. Energy, 118, 496–519, https://doi.org/10.1016/j.solener.2015.05.037, 2015. a, b
Reynolds, D. W., Clark, D. A., Wilson, F. W., and Cook, L.: Forecast-Based
Decision Support for San Francisco International Airport: A NextGen Prototype
System That Improves Operations during Summer Stratus Season, B.
Am. Meteorol. Soc., 93, 1503–1518,
https://doi.org/10.1175/BAMS-D-11-00038.1, 2012. a
Scaramuzza, D., Martinelli, A., and Siegwart, R.: A Toolbox for Easily
Calibrating Omnidirectional Cameras, in: 2006 IEEE/ RSJ International
Conference on Intelligent Robots and Systems, Beijing, China,
9–15 October 2006, pp. 5695–5701, https://doi.org/10.1109/IROS.2006.282372, 2006. a
Schmidt, T., Kalisch, J., Lorenz, E., and Heinemann, D.: Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts, Atmos. Chem. Phys., 16, 3399–3412, https://doi.org/10.5194/acp-16-3399-2016, 2016. a
Schmidt, T., Heinemann, D., Vogt, T., Blum, N., Nouri, B., Wilbert, S., and
Kuhn, P.: Energiemeteorologisches Wolkenkameranetzwerk für die
hochaufgelöste Kurzfristprognose der solaren Einstrahlung, in:
DACH-Tagung, Garmisch-Partenkirchen, Deutschland, 18–22 March 2019, 2019. a
Sky cameras: Homepage, https://www.solar-repository.sg/sky-cameras, last
access: 8 July 2020. a
Wang, G., Kurtz, B., and Kleissl, J.: Cloud base height from sky imager and
cloud speed sensor, Sol. Energy, 131, 208–221,
https://doi.org/10.1016/j.solener.2016.02.027, 2016. a
Wessel, B., Huber, M., Wohlfart, C., Marschalk, U., Kosmann, D., and Roth, A.:
Accuracy assessment of the global TanDEM-X Digital Elevation Model with GPS
data, ISPRS J. Photogramm., 139, 171–182,
https://doi.org/10.1016/j.isprsjprs.2018.02.017, 2018. a
World Meteorological Organization: Guide to meteorological instruments and
methods of observation, vol. I – Measurement of Meteorological Variables of
WMO – No. 8, WMO, Geneva, Switzerland, 29 edn., 2018. a
World Meteorological Organisation: Manual on Codes – International Codes, Volume I.1, Annex II to the WMO Technical Regulations: part A – Alphanumeric Code, 2019 edn., WMO, Geneva, Switzerland, 2019. a
Short summary
Cloud base height (CBH) is important, e.g., to forecast solar irradiance and, with it, photovoltaic production. All-sky imagers (ASIs), cameras monitoring the sky above their point of installation, can provide such forecasts and also measure CBH. We present a network of ASIs to measure CBH. The network provides numerous readings of CBH simultaneously. We combine these with a statistical procedure. Validation attests to significantly higher accuracy of the combination compared to two ASIs alone.
Cloud base height (CBH) is important, e.g., to forecast solar irradiance and, with it,...