Articles | Volume 15, issue 5
https://doi.org/10.5194/amt-15-1537-2022
© Author(s) 2022. 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-15-1537-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Remote sensing of solar surface radiation – a reflection of concepts, applications and input data based on experience with the effective cloud albedo
DWD, Frankfurter Str. 135, 63067 Offenbach, Germany
Uwe Pfeifroth
DWD, Frankfurter Str. 135, 63067 Offenbach, Germany
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Cited articles
Alexandri, G., Georgoulias, A., and Balis, D.: Effect of Aerosols, Tropospheric NO2 and Clouds on Surface Solar Radiation over the Eastern Mediterranean (Greece), Remote Sens., 13, 2587, https://doi.org/10.3390/rs13132587, 2021. a
Amillo, A. M. G., Huld, T., and Müller, R.: A New Database of Global and Direct Solar Radiation Using the Eastern Meteosat Satellite, Remote Sens.,
6, 8165–8189, https://doi.org/10.3390/rs6098165, 2014. a
Amillo, A. M. G., Huld, T., Vourlioti, P., Müller, R., and Norton, M.: Application of Satellite-Based Spectrally-Resolved Solar Radiation Data to PV Performance Studies, Energies, 8, 3455–3488, https://doi.org/10.3390/en8053455, 2015. a, b, c
Amillo, A. M. G., Ntsangwane, L., Huld, T., and Trentmann, J.: Comparison of
satellite-retrieved high-resolution solar radiation datasets for South
Africa, J. Energy South. Afr., 29, 2018. a
Antonanzas, J., Osorio, N., Escobar, R., Urraca, R., Martinez-de Pison, F., and Antonanzas-Torres, F.: Review of photovoltaic power forecasting, Sol. Energy, 136, 78–111, https://doi.org/10.1016/j.solener.2016.06.069, 2016. a
Babst, F., Mueller, R., and Hollmann, R.: Verification of NCEP Reanalysis Shortwave Radiation with Mesoscale Remote Sensing Data, Geosciences and Remote Sensing Letters, 5, 34–38, https://doi.org/10.1109/LGRS.2007.907537, 2008. a
Barbieri, F., Rajakaruna, S., and Gosh, A.: Very short-term photovoltaic power forecasting with cloud modeling: A review,
Renew. Sust. Energ. Rev., 75, 242–263, https://doi.org/10.1016/j.rser.2016.10.068, 2017. a
Bechtold, P., Köhler, M., Jung, T., Doblas-Reyes, F., Leutbecher, M., Rodwell, M. J., Vitart, F., and Balsamo, G.: Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time-scales, Q. J. Roy. Meteor. Soc., 134, 1337–1351, https://doi.org/10.1002/qj.289, 2008. a
Behr, H., Hollmann, R., and Müller, R.: Surface radiation at sea: Validation
of satellite-derived data with shipboard measurements, Meteorol. Z., 18, 61–74, https://doi.org/10.1127/0941-2948/2009/356, 2009. a
Bellouin, N., Quaas, J., Morcrette, J.-J., and Boucher, O.: Estimates of aerosol radiative forcing from the MACC re-analysis, Atmos. Chem. Phys., 13, 2045–2062, https://doi.org/10.5194/acp-13-2045-2013, 2013. a
Benedetti, A., Morcrette, J.-J., Boucher, O., Dethof, A., Engelen, R., Fisher, M., Flentje, H., Huneeus, N., Jones, L., Kaiser, J., Kinne, S., Mangold, A., Razinger, M., Simmons, A., and Suttie, M.: Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: 2. Data assimilation, J. Geophys. Res., 114, D06206,
https://doi.org/10.1029/2008JD011235, 2009. a
Betcke, J., Kuhlemann, R., Hammer, A., Drews, A., Lorenz, E., Girodo, M.,
Heinemann, D., Wald, L., Cros, S., Schroedter-Homscheidt, M., Holzer-Popp,
T., Gesell, G., Erbertseder, T., Kosmale, M., Hildenbrand, B., Dagestad,
K.-F., Olseth, J., Ineichen, P., Reise, C., and Krebs, W.: Energy-Specific
Solar Radiation Data from Meteosat Second Generation (MSG): The Heliosat-3
Project, Final Report, Carl von Ossietzky University of Oldenburg, https://doi.org/10.13140/RG.2.1.2054.6406, 2006. a
Betts, A. K., Ball, J. H., Bosilovich, M., Viterbo, P., Zhang, Y., and Rossow, W. B.: Intercomparison of water and energy budgets for five Mississippi subbasins between ECMWF re-analysis (ERA-40) and NASA Data Assimilation Office fvGCM for 1990–1999, J. Geophys. Res., 108, 8618,
https://doi.org/10.1029/2002JD003127, 2003. a
Beyer, H., Costanzo, C., and Heinemann, D.: Modifications of the Heliosat
procedure for irradiance estimates from satellite images, Sol. Energy, 56,
207–212, https://doi.org/10.1016/0038-092X(95)00092-6, 1996. a, b, c, d
Bird, R. E. and Hulstrom, R. L.: A simplified clear sky model for direct and
diffuse insolation on horizontal surfaces, Tech. Rep. Contract No. EG-77-C-01-4042, Solar Energy Reasearch Institute, 1981. a
Bishop, J. and Rossow, W.: Spatial and temporal variability of global surface
solar irradiance, J. Geophys. Res., 96, 839–858,
https://doi.org/10.1029/91JC01754, 1991. a, b
Bugliaro, L., Piontek, D., Kox, S., Schmidl, M., Mayer, B., Müller, R., Vázquez-Navarro, M., Peters, D. M., Grainger, R. G., Gasteiger, J., and Kar, J.: Combining radiative transfer calculations and a neural network for the remote sensing of volcanic ash using MSG/SEVIRI, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2021-270, in review, 2021. a
Burrows, J. P. and Chance, K. V.: SCIAMACHY and GOME: The scientific
objectives, J. Atm. Chem., 1715, 502–511, https://doi.org/10.1117/12.140201, 1992. a
Carrer, D., Roujean, J.-L., and Meurey, C.: Comparing Operational MSG/SEVIRI
Land Surface Albedo Products From Land SAF With Ground Measurements and
MODIS, IEEE T. Geosci. Remote, 48, 1714–1728,
https://doi.org/10.1109/TGRS.2009.2034530, 2010. a, b, c
Carrer, D., Ceamanos, X., Moparthy, S., Vincent, C., C. Freitas, S., and Trigo, I. F.: Satellite Retrieval of Downwelling Shortwave Surface Flux and Diffuse Fraction under All Sky Conditions in the Framework of the LSA SAF Program (Part 1: Methodology), Remote Sens., 11, 2523, https://doi.org/10.3390/rs11212532, 2019. a
Carroll, B. W. and Ostlie, D. A.: An introduction to modern astrophysics, Cambridge University Press, ISBN 978-1-108-42216-1, 2017. a
Castelli, M., Stöckli, R., Zardi, D., Tetzlaff, A., Wagner, J., Belluardo, G., Zebisch, M., and Petitta, M.: The HelioMont method for assessing solar irradiance over complex terrain: Validation and improvements, Remote Sens. Environ., 152, 603–613, https://doi.org/10.1016/j.rse.2014.07.018, 2014. a, b, c, d, e
CM SAF: Products SIS*, SID*, Creator CM SAF, http://wui.cmsaf.eu, last access: 15 March 2022. a
CMSAFpubl:
http://www.cmsaf.eu/SiteGlobals/Forms/Suche/EN/JournalSearch_Form.html?nn=1885934,
last access: 25 September 2021. a
Cornejo-Bueno, L., Casanova-Mateo, J., Sanz-Justo, S., and Salcedo-Sanz, S.:
Machine learning regressors for solar radiation estimation from satellite
data, Sol. Energy, 183, 768–775, https://doi.org/10.1016/j.solener.2019.03.079, 2019. a
Cox, S., Gupta, S., Mikovitz, J., Chiacchio, M., Zhang, T., and Stackhouse, P.: The NASA/GEWEX Surface Radiation Budget data set: Results and Analysis, in: IRS 2004: Current Problems in Atmospheric Radiation, edited by: Fischer, H. and Sohn, B.-J., A. Deepa, Hampton, Va, 419–422, ISBN 9780937194485, 2004. a
Cox, S., Lope, A., Watson, A., and Jennifer, L. E.: Renewable Energy Data, Analysis, and Decisions: A Guide for Practitioners, Tech. Rep. NREL/TP-6A20-68913, NREL, prepared under Task No. WFED.10355.08.01.11, Contract Number DE-AC36-08GO28308, https://www.nrel.gov/docs/fy18osti/68913.pdf (last access: 14 March 2022), 2018. a
Cros, S., Albuisson, M., and Wald, L.: Simulating Meteosat-7 broadband
radiances using two visible channels of Meteosat-8, Sol. Energy, 80,
361–367, https://doi.org/10.1016/j.solener.2005.01.012, 2006. a, b
Daggash, H. A. and MacDowell, N.: Delivering low-carbon electricity systems in sub-Saharan Africa: insights from Nigeria, Energy Environ. Sci., 14,
4018–4037, https://doi.org/10.1039/D1EE00746G, 2021. a
Darnell, W., Staylor, W., Gupta, S., Ritchey, N., and Wilber, A.: Seasonal
variation of surface radiation budget derived from ISCCP-C1 data, J. Geophys. Res., 97, 15741–15760, https://doi.org/10.1029/92JD00675, 1992. a, b, c
Dee, D. P. and Uppala, S.: Variational bias correction of satellite radiance
data in the ERA-interim reanalyis, Q. J. Roy. Meteor. Soc., 135, 1830–1841, https://doi.org/10.1002/qj.493, 2009. a
Deneke, H. and Feijt, A.: Estimation surface solar irradiance from METEOSAT
SEVIRI-derived cloud properties, Remote Sens. Environ., 112,
3131–3141, https://doi.org/10.1016/j.rse.2008.03.012, 2008. a
Dewitte, S., Cornelis, J., Müller, R., and Munteanu, A.: Artificial Intelligence Revolutionises Weather Forecast, Climate Monitoring and Decadal
Prediction, Remote Sens., 13, 3209, https://doi.org/10.3390/rs13163209, 2021. a
Dobler, A., Müller, R., and Ahrens, B.: Development and evaluation of a
simple method to estimate evaporation from satellite data, Meteorol. Z., 20, 615–623, https://doi.org/10.1127/0941-2948/2011/0256, 2011. a
Drücke, J., Borsche, M., James, P., Kaspar, F., Pfeifroth, U., Ahrens, B., and Trentmann, J.: Climatological analysis of solar and wind energy in Germany using the Grosswetterlagen classification, Renew. Energ., 164, 1254–1266, https://doi.org/10.1016/j.renene.2020.10.102, 2021. a
Dürr, B., Zelenka, A., Müller, R., and Philipona, R.: Verification of CM-SAF
and MeteoSwiss satellite based retrievals of surface shortwave irradiance
over the Alpine region, Int. J. Remote Sens., 31, 4179–4198,
https://doi.org/10.1080/01431160903199163, 2010. a
ECMWF: MACC, ECMWF [data set], http://apps.ecmwf.int/datasets/data/macc-reanalysis/levtype=ml, last access: 15 March 2022. a
Emde, C., Buras-Schnell, R., Kylling, A., Mayer, B., Gasteiger, J., Hamann, U., Kylling, J., Richter, B., Pause, C., Dowling, T., and Bugliaro, L.: The libRadtran software package for radiative transfer calculations (version 2.0.1), Geosci. Model Dev., 9, 1647–1672, https://doi.org/10.5194/gmd-9-1647-2016, 2016. a
European Commission: EC1: Energy for the Future: Renewable Sources of Energy, Commission of the European Communities, Green Paper for a Community Strategy, COM(96) 576 Final, Brussels, 20 November 1996, https://europa.eu/documents/comm/white_papers/pdf/com97_599_en.pdf (last access: 16 March 2022), 1996. a
European Commission: EC2: A 2030 framework for climate and energy policies, European Commision: Greenpaper, Brussels, COM (2013) 169 Final, 27 March 2013, https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2013:0169:FIN:en:PDF (last access: 16 March 2022), 2013. a
Farahat, A., Kambezidis, H., Almazroui, M., and Ramadan, E.: Solar Potential in Saudi Arabia for Southward-Inclined Flat-Plate Surfaces, Appl. Sci., 11, 4101, https://doi.org/10.3390/app11094101, 2021. a
Fell, F., Bennartz, R., Cahill, B., Lattanzio, A., Muller, J.-P., Schulz, J.,
Shane, N., Trigo, I., and Watson, G. W.: Evaluation of the Meteosat Surface
Albedo Climate Data Record, Tech. Rep. Final Report, Commissioned by
EUMETSAT, https://www.eumetsat.int/media/37708 (last access: 14 March 2022), 2021. a, b, c
Fleig, A. J., Bhartia, P. K., Wellemeyer, G., and Silberstein, D. S.: Seven years of total ozone from the TOMS instrument – a report on data quality, Geophys. Res. Lett., 13, 1355–1358, https://doi.org/10.1029/GL013i012p01355, 1986. a
Fontoynont, M., Dumortier, D., Heinemann, D., Hammer, A., Olseth, J.,
Skartveit, A., Ineichen, P., Reise, C., Page, J., Roche, L., Beyer, H., and
Wald, L.: Satellight: An European Programme Dedicated to Serving Daylight
Data Computed from Meteosat Images, in: Proceeding of the Lux Europa
Conference, Amsterdam, the Netherlands, 11—14 May 1997,
http://www.satellight.com/indexgT.htm (last access: 14 March 2022), 1997. a
Fountoulakis, I., Kosmopoulos, P., Papachristopoulou, K., Raptis, I.-P.,
Mamouri, R.-E., Nisantzi, A., Gkikas, A., Witthuhn, J., Bley, S., Moustaka,
A., Buehl, J., Seifert, P., Hadjimitsis, D., Kontoes, C., and Kazadzis, S.:
Effects of Aerosols and Clouds on the Levels of Surface Solar Radiation and
Solar Energy in Cyprus, Remote Sens., 13, 2319, https://doi.org/10.3390/rs13122319, 2021. a
Gallucci, D., Romano, F., Cersosimo, A., Cimini, D., Di Paola, F., Gentile, S., Geraldi, E., Larosa, S., Nilo, S. T., Ricciardelli, E., and Viggiano, M.:
Nowcasting Surface Solar Irradiance with AMESIS via Motion Vector Fields of
MSG-SEVIRI Data, Remote Sens., 10, 845, https://doi.org/10.3390/rs10060845, 2018. a
Gardner, A., Maclean, I., Gaston, K., and Bütikofer, L.: Forecasting future
crop suitability with microclimate data, Agr. Syst., 190, 103084,
https://doi.org/10.1016/j.agsy.2021.103084, 2021a. a
Gardner, A. S., Gaston, K. J., and Maclean, I. M. D.: Accounting for
inter-annual variability alters long-term estimates of climate suitability,
J. Biogeogr., 48, 1960–1971, https://doi.org/10.1111/jbi.14125,
2021b. a
Geiger, B., Carrer, D., Franchisteguy, L., Roujean, J. L., and Meurey, C.: Land Surface Albedo Derived on a Daily Basis From Meteosat Second Generation
Observations, IEEE T. Geosci. Remote, 46, 3841–3856, https://doi.org/10.1109/TGRS.2008.2001798, 2008. a
Gilgen, H., Roesch, A., Wild, M., and Ohmura, A.: Decadal changes in shortwave irradiance at the surface in the period from 1960 to 2000 estimated from Global Energy Balance Archive Data, J. Geophys. Res., 114, D00D08,
https://doi.org/10.1029/2008JD011383, 2009. a
Girodo, M.: Untersuchung von 3-D Wolkeneffekten auf die satelliten-gestützte
Berechnung der solaren Einstrahlung, Master's thesis, School of Mathematics
and Natural Sciences, Universtiy of Oldenburg, 2003. a
Girodo, M., Mueller, R., and Heinemann, D.: Influence of three-dimensional
cloud effects on satellite derived solar irradiance estimation – First
approaches to improve the Heliosat method, Sol. Energy, 80, 1145–1159,
https://doi.org/10.1016/j.solener.2005.09.005, 2006. a
GOES: GOES-R Series Data Book, NOAA – NASA, cDRL PM-14, https://www.goes-r.gov/downloads/resources/documents/GOES-RSeriesDataBook.pdf (last access: 14 March 2022), 2019. a
Govaerts, Y., Clerici, M., and Clerbaux, N.: Operational calibration of the
meteosat radiometer VIS band, IEEE T. Geosci. Remote, 42,
1900–1914, https://doi.org/10.1109/TGRS.2004.831882, 2004. a, b, c, d
Gupta, S., Ritchey, N., Wilber, A., and Whitlock, C.: A climatology of Surface Radiation Budget Derived from Satellite Data, J. Climate, 12, 2691–2709, https://doi.org/10.1175/1520-0442(1999)012<2691:ACOSRB>2.0.CO;2, 1999. a, b
Hammer, A., Heinemann, D., Hoyer, C., Kuhlemann, R., Lorenz, E., Mueller, R.,
and Beyer, H.: Solar Energy Assessment Using Remote Sensing Technologies,
Remote Sens. Environ., 86, 423–432, https://doi.org/10.1016/S0034-4257(03)00083-X, 2003. a, b, c, d
Harries, J., Russel, J., Hanafin, J., Brindley, H., Futyan, J., Rufus, J., Kellock, S., Matthews, G., Wrigley, R., Last, A., Mueller, J., Mossavati, R., Ashmall, J., Sawyer, E., Parker, D., Caldwell, P., Allan, P., Smith, A., Bates, J., Coan, B., Stewart, B., Lepine, D., Cornwall, D., Corney, D., Rickets, M., Drummond, D., Smart, D., Cutler, R., Dewitte, S., Clerbaux, N., Gonzales, A., Ipe, A., Bertrand, C., Joukoff, A., CrommelYnck, D., Nelms, N., Llewellyn-Jones, D. T., Butcher, G., Smith, L., Szewczyk, Z. P., Mlynczak, P., Slingo, A., Allan, R., and Ringer, M.: The Geostationary Earth Radiation Budget Project, B. Am. Meteorol. Soc., 86, 945–960, https://doi.org/10.1175/BAMS-86-7-945, 2005. a, b
Harrison, E. F., Barkstrom, B. R., Ramanathan, V., Cess, R. D., and Gibson,
G. G.: Seasonal Variation of Cloud Radiative Forcing Derived From the Earth
Radiation Budget Experiment, J. Geophys. Res., 95,
18687–18703, https://doi.org/10.1029/JD095iD11p18687, 1990. a
Helfrich, S., Min, L., Kongoli, C., Nagdimunov, L., and Rodriguez, E.: Interactive Multisensor Snow and Ice Mapping System Version 3 (IMS V3), Algorithm theoretical basis document, version 2.5, NOAA NESDIS Center for Satellite Applications and Research, https://nsidc.org/sites/nsidc.org/files/technical-references/IMS_V3_ATBD_V2.5.pdf (last access: 16 March 2022), 2018. a, b
Helmert, J., Lange, M., Dong, J., de Rosnay, P., Gustafsson, D., Churulin, E.,
Kurzeneva, E., Müller, R., Trentmann, J., Souverijns, N., Koch, R.,
Böhm, U., Bartik, M., Osuch, M., Rozinkina, I., Bettems, J.-M.,
Samuelsson, P., Marcucci, F., and Milelli, M.: 1st Snow Data Assimilation
Workshop in the framework of COST HarmoSnow ESSEM 1404, Meteorol. Z., 27, 325–333, https://doi.org/10.1127/metz/2018/0906, 2018. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons,
A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati,
G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global
reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hess, M., Koepke, P., and Schult, I.: Optical properties of aerosols and
clouds: The software package OPAC, B. Am. Meteorol. Soc., 79, 831–844,
https://doi.org/10.1175/1520-0477(1998)079<0831:OPOAAC>2.0.CO;2, 1998. a
Hinkelmann, L. M., Stackhouse, P. W., Wielicki, B. A., Zhang, T., and Wilson,
S. R.: Surface insolation trends from satellite and ground measurements:
Comparison and challenges, J. Geophys. Res., 114, D00D20,
https://doi.org/10.1029/2008JD011004, 2009. a
Huld, T. and Amillo, A.: Estimating PV Module Performance over Large Geographical Regions: The Role of Irradiance, Air Temperature, Wind Speed and
Solar Spectrum, Energies, 8, 5159–5181, https://doi.org/10.3390/en8065159, 2015. a, b, c
Huld, T., Müller, R., and Gambardella, A.: A new solar radiation database for estimating PV performance in Europe and Africa, Sol. Energy, 86, 1803–1815, https://doi.org/10.1016/j.solener.2012.03.006, 2012. a, b
Ineichen, P.: A broadband simplified version of the Solis clear sky model, Sol. Energy, 82, 758–762, 2008. a
Ineichen, P. and Perez, R.: A new airmass independent formulation for the
Linke turbidity coefficient, Sol. Energy, 73, 151–157,
https://doi.org/10.1016/S0038-092X(02)00045-2, 2002. a, b
Ineichen, P., Barroso, C., Geiger, B., Hollmann, R., and Mueller, R.:
Satellite Application Facilities irradiance products: hourly time step
comparison and validation, Int. J. Remote Sens., 30,
5549–5571, https://doi.org/10.1080/01431160802680560, 2009. a, b, c
Inness, A., Baier, F., Benedetti, A., Bouarar, I., Chabrillat, S., Clark, H., Clerbaux, C., Coheur, P., Engelen, R. J., Errera, Q., Flemming, J., George, M., Granier, C., Hadji-Lazaro, J., Huijnen, V., Hurtmans, D., Jones, L., Kaiser, J. W., Kapsomenakis, J., Lefever, K., Leitão, J., Razinger, M., Richter, A., Schultz, M. G., Simmons, A. J., Suttie, M., Stein, O., Thépaut, J.-N., Thouret, V., Vrekoussis, M., Zerefos, C., and the MACC team: The MACC reanalysis: an 8 yr data set of atmospheric composition, Atmos. Chem. Phys., 13, 4073–4109, https://doi.org/10.5194/acp-13-4073-2013, 2013. a
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019. a
Jackson, J. D.: Classical Electrodynamics, Wiley, ISBN 978-0471309321, 1998. a
JMA: Himawari-8/9 – Himawari Standard Data User's Guide, Japan Meteorological Agency, 1-3-4 Otemachi, Chiyoda-ku, Tokyo, 100-8122 Japan, version 1.3, 3 July 2017, https://www.data.jma.go.jp/mscweb/en/himawari89/space_segment/hsd_sample/HS_D_users_guide_en_v13.pdf (last access: 16 March 2022), 2017. a
Journée, M., Müller, R., and Bertrand, C.: Solar resource assessment in the Bebelux by merging Meteosat-derived climat data and ground measurements, Sol. Energy, 86, 3561–3574, https://doi.org/10.1016/j.solener.2012.06.023, 2012. a
Kato, S., Ackerman, T., Mather, J., and Clothiaux, E.: The k-distribution method and correlated-k approximation for a short-wave radiative transfer, J. Quant. Spectrosc. Radiat. Transfer, 62, 109–121, https://doi.org/10.1016/S0022-4073(98)00075-2, 1999. a
Kato, S., Hinkelman, L. M., and Cheng, A.: Estimate of satellite-derived cloud optical thickness and effective radius errors and their effect on computed domain-averaged irradiances, J. Geophys. Res., 111, D17201,
https://doi.org/10.1029/2005JD006668, 2006. a
Kato, S., Loeb, N. G., Rose, F. G., Doelling, D. R., Rutan, D. A., Caldwell,
T. E., Yu, L., and Weller, R. A.: Surface Irradiances Consistent with
CERES-Derived Top-of-Atmosphere Shortwave and Longwave Irradiances, J. Climate, 26, 2719–2740, https://doi.org/10.1175/JCLI-D-12-00436.1, 2013. a
Kulesza, K.: Influence of air pressure patterns over Europe on solar radiation variability over Poland (1986–2015), Int. J. Climatol.,
41, E354–E367, https://doi.org/10.1002/joc.6689, 2021. a
Kulesza, K. and Bojanowski, J. S.: Homogenization of incoming solar radiation
measurements over Poland with satellite and climate reanalysis data, Sol.
Energy, 225, 184–199, https://doi.org/10.1016/j.solener.2021.07.031, 2021. a
Li, Z., Leighton, H., Masuda, K., and Takashima, T.: Estimation of SW Flux
Absorbed at the Surface from TOA Refelcted Flux, J. Climate, 6, 317–330,
https://doi.org/10.1175/1520-0442(1993)006<0317:EOSFAA>2.0.CO;2, 1993. a
Lipponen, A., Mielonen, T., Pitkänen, M. R. A., Levy, R. C., Sawyer, V. R., Romakkaniemi, S., Kolehmainen, V., and Arola, A.: Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land, Atmos. Meas. Tech., 11, 1529–1547, https://doi.org/10.5194/amt-11-1529-2018, 2018. a
Loeb, N. G., Kato, S., Loukachine, K., and Manalo-Smith, N.: Angular Distribution Models for Top-of-Atmosphere Radiative Flux Estimation from the
Clouds and the Earth's Radiant Energy System Instrument on the Terra
Satellite. Part I: Methodology, J. Atmos. Ocean. Tech., 22, 338–351, https://doi.org/10.1175/JTECH1712.1, 2005. a
Lorenz, E., Betcke, J., Drews, A., Heinemann, D., Toggweiler, P., Stettler, S., van Sark, W., Heilscher, G., Wiemken, E., Heydenreich, W., and Beyer, H. G.: PVSAT-2: Intelligent performance check of PV system operation based on
satellite data, in: 19th European Photovolatic Solar Energy Conference, 7–11 June 2004, Paris, France, ISBN 3936338140, 2004. a
Mayer, B. and Kylling, A.: Technical note: The libRadtran software package for radiative transfer calculations – description and examples of use, Atmos. Chem. Phys., 5, 1855–1877, https://doi.org/10.5194/acp-5-1855-2005, 2005. a, b, c
Morcrette, J. J., Boucher, O., Jones, L., Salmond, D., Bechtold, P., Beljaars, A., Benedetti, A., Bonet, A., Kaiser, J. W., Razinger, M., Schulz, M., Serrar, S., Simmons, A. J., Sofiev, M., Suttie, M., Tompkins, A. M., and
Untch, A.: Aerosol analysis and forecast in the European Centre for
Medium-Range Weather Forecasts Integrated Forecast System: Forward modeling, J. Geophys. Res., 114, D06206, https://doi.org/10.1029/2008JD011235, 2009. a
Möser, W.: Globalstrahlung aus Satellitenmessungen, Tech. rep.,
Mitteilungen aus dem Institut für Geophysik und Meteorologie der
Universität zu Köln, 1983. a
Möser, W. and Raschke, E.: Incident Solar Radiation over Europe
Estimated from METEOSAT Data, J. Clim. Appl. Meteorol., 23, 166–170,
https://doi.org/10.1175/1520-0450(1984)023<0166:ISROEE>2.0.CO;2, 1984. a, b, c, d
MTG-Weblink: Meteosat Third Generation,
https://www.eumetsat.int/meteosat-third-generation, last access: 15 March 2022. a
Mueller, R. and Träger-Chatterjee, C.: Brief Accuracy Assessment of Aerosol Climatologies for the Retrieval of Solar Surface Radiation, Atmosphere, 1, 9699–9729, https://doi.org/10.3390/atmos5040959, 2014. a
Mueller, R. and Trentmann, J.: Algorithm Theoretical Baseline Document –
Meteosat Solar Surface Radiation and effective Cloud Albedo Climate Data
Records – Heliosat SARAH, Tech. Rep. SAF/CM/DWD/ATBD/METEOSAT_HEL 1.3,
Eumetsats CM SAF, https://doi.org/10.5676/EUM_SAF_CM/SARAH/V001, 2015. a, b, c, d
Mueller, R., Dagestad, K., Ineichen, P., Schroedter-Homscheidt, M., Cros, S., Dumortier, D., Kuhlemann, R., Olseth, J., Piernavieja, G., Resie, C., Wald, L., and Heinemann, D.: Rethinking satellite based solar irradiance modelling, The SOLIS clear-sky module, Remote Sens. Environ., 91, 160–174, 2004. a, b, c, d, e, f, g, h
Mueller, R., Matsoukas, C., Gratzki, A., Hollmann, R., and Behr, H.: The
CM-SAF operational scheme for the satellite based retrieval of solar
surface irradiance – a LUT based eigenvector hybrid approach, Remote
Sens. Environ., 113, 1012–1024, https://doi.org/10.1016/j.rse.2009.01.012,
2009. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o
Müller, R. W.: Agrar Meteorology and Radiation, in: Encyclopedia of
Sustainability Science and Technology, Springer, accepted
for publication, ISBN 978-1-4419-0852-0, 2021. a
Müller-Schmied, H., Müller, R., Sanchez-Lorenzo, A., Ahrens, B., and
Wild, M.: Evaluation of radiation components in a global freshwater model
with station-based observations, Water, 8, 450, https://doi.org/10.3390/w8100450, 2016. a
Nakajima, T. and King, M.: Determination of the Optical Thickness and Effective Particle Radius of Clouds from Reflected Solar Radiation Measurements. Part I: Theory, J. Atmos. Sci., 47, 1878–1893, https://doi.org/10.1175/1520-0469(1990)047<1878:DOTOTA>2.0.CO;2, 1990. a, b
Nicodemus, F., Richmond, J., Hsia, J., Gimbsberg, I., and Limperis, T.: Geometrical Consideration and Nomenclature for Reflectance, Tech. rep., U.S. Department of Commerce, National Bureau of Standards, https://graphics.stanford.edu/courses/cs448-05-winter/papers/nicodemus-brdf-nist.pdf
(last access: 16 March 2022), 1977. a
NREL: NREL2: Standard Tables for References Solar Spectral Irradiance at Air Mass 1.5: Direct Normal and Hemispherical for a 37∘ Tilted Surface (Withdrawn 2005), aSTM International, West Conshohocken, PA, https://www.nrel.gov/grid/solar-resource/spectra-am1.5.html (last access: 15 March 2022), 1998. a
NREL: NREL1, report, https://www.nrel.gov/gis/solar.html, last access: 15 March 2022. a
Ohmura, A., Dutton, E. G., Forgan, B., Fröhlich, C., Gilgen, H., Hegner, H., Heimo, A., Konig-Langlo, G., McArthur, B., Müller, G., Philipona, R., Pinker, R., Whitlock, C. H., Dehne, K., and Wild, M.: Baseline Surface Radiation Network (BSRN/WCRP): New precision radiometry for climate research, B. Am. Meteorol. Soc., 79, 2115–2136, 1998. a
OpenDEM: https://www.opendem.info/link_dem.html, last access: 15 March 2022. a
Perez, R., Renne, D., Seals, R., and Zelenka, A.: The strength of
satellite-based solar resource assessment, in: Production of
Site/Time-specific Irradiances from Satellite and Ground Data, Report 98-3,
New York State Energy Research and Development Authority, Corporate Plaza
West, 286 Washington Evenue Extension, Albany, NY 12203-6399, 1998. a
Perez, R., Aguiar, R., Collares-Pereira, M., Dumortier, D., Estrada-Cajigal, V., Gueymard, C., Ineichen, P., Littlefair, P., Lund, H., Michalsky, J., Olseth, J., Renne, D., Rymes, M., Skartveit, A., Vignola, F., and Zelenka, A.: Solar resource assessment: A review, in: Solar Energy – The state of the
art, ISES Position Papers, 497–562, James & James Science Publishers, London, ISBN 1 902916239, 2001. a
Perez, R., Ineichen, P., Moore, K., Kmiecik, M., Chain, C., George, R., and
Vignola, F.: A new operational model for satellite-derived irradiances:
description and validation, Sol. Energy, 73, 307–317,
https://doi.org/10.1016/S0038-092X(02)00122-6, 2002. a
Peura, M. and Hohti, H.: Optical flow in radar images, in: Proceedings of the
Third European Conference on Radar Meteorology (ERAD), 6–10 September 2004, Visby, Island of Gotland, Sweden, ISBN 9783936586299, 2004. a
Pfeifroth, U., Sanchez-Lorenzo, A., Manara, V., Trentmann, J., and Hollmann,
R.: Trends and Variability of Surface Solar Radiation in Europe Based On
Surface-and Satellite-Based Data Records, J. Geophys. Res.-Atmos., 123, 1735–1754, https://doi.org/10.1002/2017JD027418, 2018. a, b, c
Pfeifroth, U., Trentmann, J., and Kothe, S.: Validation Report – Meteosat Solar Surface Radiation and Effective Cloud Albedo Climate Data Record SARAH-2.1 climate data records, Tech. Rep. SAF/CM/DWD/ATBD/METEOSAT/HEL 2.4, EUMETSATS CM SAF, https://doi.org/10.5676/EUM_SAF_CM/SARAH/V002_01, 2019a. a, b, c, d, e
Pfeifroth, U., Trentmann, J., and Kothe, S.: Product User Manual Meteosat Solar Surface Radiation and Effective Cloud Albedo Climate Data Record SARAH-2.1 climate data records, Tech. Rep. SAF/CM/DWD/VAL/METEOSAT/HEL 2.4, EUMETSATS CM SAF, https://doi.org/10.5676/EUM_SAF_CM/SARAH/V002_01, 2019b. a, b, c, d
Pfeifroth, U., Trentmann, J., and Kothe, S.: Algorithm Theoretical Baseline Document – Meteosat Solar Surface Radiation and Effective Cloud Albedo Climate Data Record SARAH-2.1 climate data records, Tech. Rep. SAF/CM/DWD/VAL/METEOSAT/HEL 2.3, EUMETSATS CM SAF, https://doi.org/10.5676/EUM_SAF_CM/SARAH/V002_01, 2019c. a
Pincus, R. and Evans, K. F.: Computational cost and accuracy in calculating
three-dimensional radiative transfer: Results for new implementations of
Monte Carlo and SHDOM, J. Atmos. Sci., 66, 3131–3146,
https://doi.org/10.1175/2009JAS3137.1, 2009. a
Pinker, R. and Laszlo, I.: Modelling Surface Solar Irradiance for Satellite
Applications on a Global Scale, J. Appl. Meteor., 31, 166–170,
https://doi.org/10.1175/1520-0450(1992)031<0194:MSSIFS>2.0.CO;2, 1992. a, b
Pinker, R. T., Zhang, B., and Dutton, E. G.: Do satellites detect trends in
surface solar radiation?, Science, 308, 850–854,
https://doi.org/10.1126/science.1103159, 2005. a
Posselt, R., Mueller, R., Stöckli, R., and Trentmann, J.: Spatial and
temporal homogeneity of solar surface irradiance across satellite
generations, Remote Sens., 3, 1029–1046, https://doi.org/10.3390/rs3051029,
2011a. a, b, c, d
Ramanthan, R. and Cess, R. A.: Cloud Radiative forcing and Climate: Results
from the Earth Radiation Budget Experiment, Science, 243, 57–63,
https://doi.org/10.1126/science.243.4887.57, 1989. a
Raza, M. Q., Nadarajah, M., and Ekanayake, C.: On recent advances in PV output power forecast, Sol. Energy, 136, 125–144,
https://doi.org/10.1016/j.solener.2016.06.073, 2016. a
Rigollier, M., Levefre, M., and Wald, L.: The method Heliosat-2 for deriving
shortwave solar radiation from satellite images, Sol. Energy, 77, 159–169,
https://doi.org/10.1016/j.solener.2004.04.017, 2004. a, b, c, d
Riihelä, A. and Kallio-Myers, V.: Validation Report Surface albedo CLARA
Edition 2.1, Tech. Rep. SAF/CM/FMI/VAL/CLARA/SAL 2.4, EUMETSATS CM SAF,
https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V002_01, 2020. a
Riihelä, A., Carlund, T., Trentmann, J., Müller, R., and Lindfors, A. V.:
Validation of CM SAF Surface Solar Radiation Datasets over Finland and
Sweden, Remote Sens., 7, 6663–6682, https://doi.org/10.3390/rs70606663, 2015. a, b
Roebeling, R., Feijt, J., and Stammes, P.: Cloud property retrievals for climate monitoring: Implications of differences between SEVIRI and Meteosat-8
and AVHRR on NOAA-17, J. Geophys. Res., 111, D20210, https://doi.org/10.1029/2005JD006990, 2005. a
Rossow, W. and Garder, L.: Cloud Detection Using Satellite Measurments of
Infrared and Visible Radiances for ISCCP, J. Climate, 6, 2341–2369, https://doi.org/10.1175/1520-0442(1993)006<2341:CDUSMO>2.0.CO;2, 1993. a
Rossow, W. B. and Zhang, Y.-Z.: Calculation of surface and top of atmosphere
radiative fluxes from physical quantities based on ISCCP data sets: 2.
Validation and first results., J. Geophys. Res., 100,
1167–1197, https://doi.org/10.1029/94JD02746, 1995. a
Schmetz, J., Pili, Tjemkes, P. S., Just, D., Kerkmann, J., Rota, S., and Ratier, A.: An introduction to Meteosat Second Generation (MSG), B. Am. Meteorol. Soc., 83, 977–992, https://doi.org/10.1175/1520-0477(2002)083<0977:AITMSG>2.3.CO;2, 2002. a, b
Schulz, J., Albert, P., Behr, H.-D., Caprion, D., Deneke, H., Dewitte, S., Dürr, B., Fuchs, P., Gratzki, A., Hechler, P., Hollmann, R., Johnston, S., Karlsson, K.-G., Manninen, T., Müller, R., Reuter, M., Riihelä, A., Roebeling, R., Selbach, N., Tetzlaff, A., Thomas, W., Werscheck, M., Wolters, E., and Zelenka, A.: Operational climate monitoring from space: the EUMETSAT Satellite Application Facility on Climate Monitoring (CM-SAF), Atmos. Chem. Phys., 9, 1687–1709, https://doi.org/10.5194/acp-9-1687-2009, 2009. a, b
Sengupta, M. and Peter, G.: Evaluation of Clear Sky Models for Satellite-Based Irradiance Estimates, Tech. Rep. NREL/TP-5D00-60735, National Renewable Energy Laboratory, https://www.nrel.gov/docs/fy14osti/60735.pdf (last access: 16 March 2022), 2003. a
Senkal, O. and Kuleli, T.: Estimation of solar radiation over Turkey using
artificial neural network and satellite data, Appl. Energ., 86, 1222–1228,
https://doi.org/10.1016/j.apenergy.2008.06.003, 2009. a
Shuai, Y., Tuerhanjiang, L., Shao, C., Gao, F., Zhou, Y., Xie, D., Liu, T.,
Liang, J., and Chu, N.: Re-understanding of land surface albedo and related
terms in satellite-based retrieval, Big Earth Data, 4, 45–67,
https://doi.org/10.1080/20964471.2020.1716561, 2020. a, b
Sirch, T., Bugliaro, L., Zinner, T., Möhrlein, M., and Vazquez-Navarro, M.: Cloud and DNI nowcasting with MSG/SEVIRI for the optimized operation of concentrating solar power plants, Atmos. Meas. Tech., 10, 409–429, https://doi.org/10.5194/amt-10-409-2017, 2017. a
Skartveit, A., Olseth, J., and Tuft, M.: An hourly diffuse fraction model with correction for variability and surface albedo., Sol. Energy, 63, 173–183, https://doi.org/10.1016/S0038-092X(98)00067-X, 1998. a
Sonka, M., Hlavac, V., and Roger, B.: Image Processing, Analysis, and Machine
Vision, International Edition, CENGAGE Learning, ISBN 978-1-133-59360-7,
2014. a
Stamnes, K., Tsay, S., Wiscombe, W., and Jayaweera, K.: Numerically stable
algorithm for discrete-ordinate-method radiative transfer in multiple
scattering and emitting layered media, Appl. Optics, 27, 2502–2509,
https://doi.org/10.1364/AO.27.002502, 1988. a
Szeliski, R.: Computer Vision Algorithms and Applications, Springer, ISBN 978-1848829343, 2011. a
Takenaka, H., Nakajima, T. Y., Higurashi, A., Higuchi, A., Takamura, T.,
Pinker, R. T., and Nakajima, T.: Estimation of solar radiation using a neural network based on radiative transfer, J. Geophys. Res.-Atmos., 116, D08215, https://doi.org/10.1029/2009JD013337, 2011. a
Tournadre, B., Gschwind, B., Saint-Drenan, Y.-M., and Blanc, P.: An improved cloud index for estimating downwelling surface solar irradiance from various satellite imagers in the framework of a Heliosat-V method, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2020-480, in review, 2021. a, b
Träger-Chatterjee, C., Mueller, R. W., Trentmann, J., and Bendix, J.:
Evaluation of ERA-40 and ERA-interim re-analysis incoming surface shortwave
radiation datasets with mesoscale remote sensing data, Meteorol. Z., 19, 631–640, https://doi.org/10.1127/0941-2948/2010/0466, 2010. a
Träger-Chatterjee, C., Müller, R. W., and Bendix, J.: Analysis of extreme summers and prior late winter/spring conditions in central Europe, Nat. Hazards Earth Syst. Sci., 13, 1243–1257, https://doi.org/10.5194/nhess-13-1243-2013, 2013. a
Träger-Chatterjee, C., Müller, R., and Bendix, J.: Analysis and
Discussion of Atmospheric Precursor of European Heat Summers, Adv. Meteorol., 2014, 427916, https://doi.org/10.1155/2014/427916, 2014. a
Trolliet, M., Walawender, J. P., Bourlès, B., Boilley, A., Trentmann, J., Blanc, P., Lefèvre, M., and Wald, L.: Downwelling surface solar irradiance in the tropical Atlantic Ocean: a comparison of re-analyses and satellite-derived data sets to PIRATA measurements, Ocean Sci., 14, 1021–1056, https://doi.org/10.5194/os-14-1021-2018, 2018. a
UCARteam: Calculating Planetary Energy Balance and Temperature, https://scied.ucar.edu/earth-system/planetary-energy-balance-temperature-calculate, last access: 15 March 2022. a
Urbich, I., Benidx, J., and Müller, R.: A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo, Remote Sens., 10, 955,
https://doi.org/10.3390/rs10060955, 2018. a, b
Urbich, I., Bendix, J., and Müller, R.: The Seamless Solar Radiation
(SESORA) Forecast for Solar Surface Irradiance – Method and Validation,
Remote Sens., 11, 2576, https://doi.org/10.3390/rs11212576, 2019. a, b, c, d
Urbich, I., Bendix, J., and Müller, R.: Development of a Seamless Forecast for Solar Radiation Using ANAKLIM++, Remote Sens., 12, 3672,
https://doi.org/10.3390/rs12213672, 2020. a, b
Urraca, R., Gracia-Amillo, A. M., Koubli, E., Huld, T., Trentmann, J.,
Riihelä, A., Lindfors, A. V., Palmer, D., Gottschalg, R., and
Antonanzas-Torres, F.: Extensive validation of CM SAF surface radiation
products over Europe, Remote Sens. Environ., 199, 117–186,
https://doi.org/10.1016/j.rse.2017.07.013, 2007. a
Urraca, R., Gracia-Amillo, A. M., Huld, T., Martinez-de Pison, F. J., Trentmann, J., Lindfors, A. V., Riihelä, A., and Sanz-Garcia, A.: Quality
control of global solar radiation data with satellite-based products, Sol.
Energy, 158, 49–62, https://doi.org/10.1016/j.solener.2017.09.032, 2017. a, b
Urraca, R., Gracia-Amillo, A. M., Martinez-de Pison, F. J., Kaspar, F., and
Sanz-Garcia, A.: Evaluation of global horizontal irradiance estimates from
ERA5 and COSMO-REA6 reanalyses using ground and satellite-based data, Sol.
Energy, 164, 339–354, https://doi.org/10.1016/j.solener.2018.02.059, 2018. a
Urraca, R., Sanz-Garcia, A., and Sanz-Garcia, I.: BQC: A free web service to
quality control solar irradiance measurements across Europe, Sol. Energy,
211, 1–10, https://doi.org/10.1016/j.solener.2020.09.055, 2020. a, b
Wang, D. D., Liang, S. L., He, T., and Yu, Y. Y.: Direct Estimation of Land
Surface Albedo from VIIRS Data: Algorithm Improvement and Preliminary
Validation., J. Geophys. Res.-Atmos., 118, 12577–12586, https://doi.org/10.1002/2013JD020417, 2013. a
Wang, L. and Qu, J.: NMDI: A normalized multi-band drought index for
monitoring soil and vegetation moisture with satellite remote sensing,
Geophys. Res. Lett., 34, L20405, https://doi.org/10.1029/2007GL031021, 2007. a
Wang, P., Stammes, P., and Mueller, R.: Surface solar irradiance from SCIAMACHY measurements: algorithm and validation, Atmos. Meas. Tech., 4, 875–891, https://doi.org/10.5194/amt-4-875-2011, 2011. a
Wang, P., Sneep, M., Veefkind, J., Stammes, P., and Levelt, P.: Evaluation of
broadband surface solar irradiance derived from the Ozone Monitoring
Instrument, Remote Sens. Environ., 149, 88–99,
https://doi.org/10.1016/j.rse.2014.03.036, 2014. a
Wang, Y., Trentmann, J., Yuan, W., and Wild, M.: Validation of CM SAF CLARA-A2 and SARAH-E surface solar radiation datasets over China, Remote Sens., 10, 1977, https://doi.org/10.3390/rs10121977, 2018. a
Whitlock, C., Charlock, T., Staylor, W., Pinker, R., Laszlo, I., Ohmury, A., Gilgen, H., Konzelmann, T., DiPasquale, R., Moats, C., LeCroy, S., and Ritchey, N.: First global WCRP shortwave surface radiation budget data set, B. Am. Meteorol. Soc., 76, 905–922, https://doi.org/10.1175/1520-0477(1995)076<0905:FGWSSR>2.0.CO;2, 1995. a
Wild, M.: Global dimming and brightening: A review, J. Geophys. Res., 114, D00D16, https://doi.org/10.1029/2008JD011470, 2009. a
Wild, M., Wacker, S., Yang, S., and Sanchez-Lorenzo, A.: Evidence for Clear-Sky Dimming and Brightening in Central Europe, Geophys. Res. Lett., 48, e2020GL092216, https://doi.org/10.1029/2020GL092216, 2021. a
Wirth, H.: Recent Facts About Photovoltaics in Germany, Tech. rep., ISE Fraunhofer, https://www.ise.fraunhofer.de/en/publications/studies/recent-facts-about-pv-in-germany.html
(last access: 16 March 2022), 2021.
a
WMO: Manual on the Global Observing System, WMO-No. 544, Volume I., Geneva, https://community.wmo.int/wmo-no-544-manual-global-observing-system
(last access: 16 March 2022), 2010. a
Woick, H., Dewitte, S., Feijt, A., Gratzki, A., Hechler, P., Hollmann, R., Karlsson, K.-G., Laine, V., Loewe, P., Nitsche, H., Werscheck, M., and Wollenweber, G.: The Satellite Application Facility on Climate Monitoring,
Adv. Space Res., 30, 2405–2410, https://doi.org/10.1016/S0273-1177(02)80290-3, 2002. a
Wolff, B., Kühnert, J., Lorenz, E., Kramer, O., and Heinemann, D.: Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction, and cloud motion data, Sol. Energy, 135, 197–208, https://doi.org/10.1016/j.solener.2016.05.051, 2016. a
Yang, D. and Gueymard, C. A.: Probabilistic post-processing of gridded
atmospheric variables and its application to site adaptation of shortwave
solar radiation, Sol. Energy, 225, 427–443,
https://doi.org/10.1016/j.solener.2021.05.050, 2021. a
Yeom, J., Park, S., Char, T., Kim, J., and Lee, C.: Spatial Assessment of Solar Radiation by Machine Learning and Deep Neural Network Models Using Data
Provided by the COMS MI Geostationary Satellite: A Case Study in South
Korea., Sensors, 19, 2082, https://doi.org/10.3390/s19092082, 2019. a
Zach, C., Pock, T., and Bischof, H.: A duality based approach for realtime TV-L1 optical flow, edited by: Hamprecht, F. A., Schnörr, C., and Jähne, B., Pattern Recognition, DAGM 2007, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, vol. 4713, 214–223, https://doi.org/10.1007/978-3-540-74936-3_22, 2007. a
Zhang, Y. C., Rossow, W. B., and Lacis, A. A.: Calculation of surface and top
of atmosphere radiative fluxes from physical quantities based on ISCCP data
sets: 1. Method and sensitivity to input data uncertainties, J. Geophys. Res., 100, 1149–1165, https://doi.org/10.1029/94JD02747, 1995. a
Ziemke, J. R., Chandra, S., Labow, G. J., Bhartia, P. K., Froidevaux, L., and Witte, J. C.: A global climatology of tropospheric and stratospheric ozone derived from Aura OMI and MLS measurements, Atmos. Chem. Phys., 11, 9237–9251, https://doi.org/10.5194/acp-11-9237-2011, 2011. a
Short summary
The great works of physics teach us that a central paradigm of science should be to make methods and theories as easy as possible and as complex as needed. This paper provides a brief review of remote sensing of solar surface irradiance based on this paradigm.
The great works of physics teach us that a central paradigm of science should be to make methods...