Articles | Volume 15, issue 12
https://doi.org/10.5194/amt-15-3683-2022
© 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-15-3683-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An alternative cloud index for estimating downwelling surface solar irradiance from various satellite imagers in the framework of a Heliosat-V method
Benoît Tournadre
MINES Paris, PSL Research University, O.I.E. – Centre Observation, Impacts, Energy, 06904 Sophia Antipolis, France
Benoît Gschwind
MINES Paris, PSL Research University, O.I.E. – Centre Observation, Impacts, Energy, 06904 Sophia Antipolis, France
Yves-Marie Saint-Drenan
MINES Paris, PSL Research University, O.I.E. – Centre Observation, Impacts, Energy, 06904 Sophia Antipolis, France
Xuemei Chen
MINES Paris, PSL Research University, O.I.E. – Centre Observation, Impacts, Energy, 06904 Sophia Antipolis, France
Rodrigo Amaro E Silva
MINES Paris, PSL Research University, O.I.E. – Centre Observation, Impacts, Energy, 06904 Sophia Antipolis, France
Philippe Blanc
CORRESPONDING AUTHOR
MINES Paris, PSL Research University, O.I.E. – Centre Observation, Impacts, Energy, 06904 Sophia Antipolis, France
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Cited articles
Amarasinghe, N., Platnick, S., and Meyer, K.: Overview of the MODIS Collection
6 Cloud Optical Property (MOD06) Retrieval Look-up Tables, NASA GSFC Cloud
Retrieval Product Team, https://atmosphere-imager.gsfc.nasa.gov/sites/default/files/ModAtmo/C6_LUT_document_final.pdf (last access: 6 February 2020),
2017. a
Anderson, G., Clough, S., Kneizys, F., Chetwynd, J., and Shettle, E.: AFGL
Atmospheric Constituent Profiles (0.120 km), Air Force Geophysics Laboratory, Hanscom Air Force Base, Bedford, Mass., Technical Report AFGL-TR-86-0110, 1986. a
Beyer, H. G., 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
Blanc, P. and Wald, L.: The SG2 algorithm for a fast and accurate computation
of the position of the Sun for multi-decadal time period, Sol. Energy, 86,
3072–3083, https://doi.org/10.1016/j.solener.2012.07.018, 2012. a
Buras, R., Dowling, T., and Emde, C.: New secondary-scattering correction in
DISORT with increased efficiency for forward scattering, J. Quant. Spectrosc. Ra., 112, 2028–2034,
https://doi.org/10.1016/j.jqsrt.2011.03.019, 2011. a
Cano, D.: Etude de l'ennuagement par analyse de séquences d'images de
satellite: application à l'évaluation du rayonnement solaire global
au sol, PhD thesis, Ecole Nationale Supérieure des Mines de
Paris, 1982. a
Cano, D., Monget, J., Albuisson, M., Guillard, H., Regas, N., and Wald, L.: A
method for the determination of the global solar radiation from
meteorological satellite data, Sol. Energy, 37, 31–39,
https://doi.org/10.1016/0038-092X(86)90104-0, 1986. a, b
Cox, S. J., Stackhouse, P. W., Gupta, S. K., Mikovitz, J. C., and Zhang, T.:
NASA/GEWEX shortwave surface radiation budget: Integrated data product with
reprocessed radiance, cloud, and meteorology inputs, and new surface albedo
treatment, in: AIP Conference Proceedings, Auckland, New Zealand, 16–22 April 2016, 1810, 090001,
https://doi.org/10.1063/1.4975541, 2017. a
Darnell, W. L., Staylor, W. F., Gupta, S. K., and Denn, F. M.: Estimation of
Surface Insolation Using Sun-Synchronous Satellite Data, J. Climate,
1, 820–835, https://doi.org/10.1175/1520-0442(1988)001<0820:EOSIUS>2.0.CO;2, 1988. a, b
Dave, J. V.: Effect of Aerosols on the Estimation of Total Ozone in an
Atmospheric Column from the Measurements of Its Ultraviolet Radiance, J. Atmos. Sci., 35, 899–911,
https://doi.org/10.1175/1520-0469(1978)035<0899:EOAOTE>2.0.CO;2, 1978. a
Driemel, A., Augustine, J., Behrens, K., Colle, S., Cox, C., Cuevas-Agulló, E., Denn, F. M., Duprat, T., Fukuda, M., Grobe, H., Haeffelin, M., Hodges, G., Hyett, N., Ijima, O., Kallis, A., Knap, W., Kustov, V., Long, C. N., Longenecker, D., Lupi, A., Maturilli, M., Mimouni, M., Ntsangwane, L., Ogihara, H., Olano, X., Olefs, M., Omori, M., Passamani, L., Pereira, E. B., Schmithüsen, H., Schumacher, S., Sieger, R., Tamlyn, J., Vogt, R., Vuilleumier, L., Xia, X., Ohmura, A., and König-Langlo, G.: Baseline Surface Radiation Network (BSRN): structure and data description (1992–2017), Earth Syst. Sci. Data, 10, 1491–1501, https://doi.org/10.5194/essd-10-1491-2018, 2018. a, b
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
EUMETSAT: Typical Radiometric Noise, Calibration Bias and Stability for
Meteosat-8, -9, -10 and -11 SEVIRI, EUMETSAT, Tech. Rep. EUM/OPS/TEN/07/0314, https://www.eumetsat.int/media/43503 (last access: 28 December 2021),
2019. a
Gasteiger, J., Emde, C., Mayer, B., Buras, R., Buehler, S., and Lemke, O.:
Representative wavelengths absorption parameterization applied to satellite
channels and spectral bands, J. Quant. Spectrosc.
Ra., 148, 99–115, https://doi.org/10.1016/j.jqsrt.2014.06.024, 2014. a, b
GCOS: The Global Observing System for Climate: Implementation Needs, Global Climate Observing System, Tech. Rep.
GCOS-200 (GOOS-2014),
https://doi.org/10.13140/RG.2.2.23178.26566, 2016. a
Gelaro, R., McCarty, W., Suarez, M. J., Todling, R., Molod, A., Takacs, L.,
Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K.,
Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A.,
da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert,
S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective
Analysis for Research and Applications, Version 2 (MERRA-2),
J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Greuell, W., Meirink, J. F., and Wang, P.: Retrieval and validation of global,
direct, and diffuse irradiance derived from SEVIRI satellite observations,
J. Geophys. Res.-Atmos., 118, 2340–2361,
https://doi.org/10.1002/jgrd.50194, 2013. a
Gschwind, B., Wald, L., Blanc, P., Lefevre, M., Schroedter-Homscheidt, M., and
Arola, A.: Improving the McClear model estimating the downwelling solar
radiation at ground level in cloud-free conditions – McClear-v3,
Meteorol. Z., 28, 147–163, https://doi.org/10.1127/metz/2019/0946,
2019. a, b
Gueymard, C. A.: Revised composite extraterrestrial spectrum based on recent
solar irradiance observations, Sol. Energy, 169, 434–440,
https://doi.org/10.1016/j.solener.2018.04.067, 2018. a
Gupta, S. K., Kratz, D. P., Stackhouse Jr., P. W., and Wilber, A. C.: The
Langley Parameterized Shortwave Algorithm (LPSA) for Surface
Radiation Budget Studies 1.0, NASA Langley Research Center, Hampton, Virginia, NASA/TP-2001-211272, https://ntrs.nasa.gov/api/citations/20020022720/downloads/20020022720.pdf (last access: 17 May 2019), 2001. a, b, c
Hao, D., Asrar, G. R., Zeng, Y., Zhu, Q., Wen, J., Xiao, Q., and Chen, M.:
Estimating hourly land surface downward shortwave and photosynthetically
active radiation from DSCOVR/EPIC observations, Remote Sens.
Environ., 232, 111320, https://doi.org/10.1016/j.rse.2019.111320, 2019. a
Hao, D., Asrar, G. R., Zeng, Y., Zhu, Q., Wen, J., Xiao, Q., and Chen, M.: DSCOVR/EPIC-derived global hourly and daily downward shortwave and photosynthetically active radiation data at resolution, Earth Syst. Sci. Data, 12, 2209–2221, https://doi.org/10.5194/essd-12-2209-2020, 2020. a
Herman, J., Huang, L., McPeters, R., Ziemke, J., Cede, A., and Blank, K.: Synoptic ozone, cloud reflectivity, and erythemal irradiance from sunrise to sunset for the whole earth as viewed by the DSCOVR spacecraft from the earth–sun Lagrange 1 orbit, Atmos. Meas. Tech., 11, 177–194, https://doi.org/10.5194/amt-11-177-2018, 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.: The ERA5 global
reanalysis, Q. J. Roy. Meteor. Soc., 146, 1–51,
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
Hewison, T. J., Doelling, D. R., Lukashin, C., Tobin, D., O. John, V., Joro,
S., and Bojkov, B.: Extending the Global Space-Based Inter-Calibration System
(GSICS) to Tie Satellite Radiances to an Absolute Scale, Remote Sens., 12, 1782,
https://doi.org/10.3390/rs12111782, 2020. a
Horvath, A. and Davies, R.: Anisotropy of water cloud reflectance: A comparison
of measurements and 1D theory, Geophy. Res. Lett., 31, 1,
https://doi.org/10.1029/2003GL018386, 2004. a
Huang, G., Li, Z., Li, X., Liang, S., Yang, K., Wang, D., and Zhang, Y.:
Estimating surface solar irradiance from satellites: Past, present, and
future perspectives, Remote Sens. Environ., 233, 111371,
https://doi.org/10.1016/j.rse.2019.111371, 2019. a
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, b
Jin, Z., Wielicki, B. A., Loukachine, C., Charlock, T. P., Young, D., and
Noël, S.: Spectral kernel approach to study radiative response of climate
variables and interannual variability of reflected solar spectrum, J.
Geophys. Res.-Atmos., 116, D10, https://doi.org/10.1029/2010JD015228, 2011. a
Kurucz, R. L.: Synthetic infrared spectra, in: Proceedings of the 154th
Symposium of the International Astronomical Union (IAU),
Tucson, Arizona, 2–6 March 1992, Kluwer, Acad., Norwell, MA, 154, 523–531, https://doi.org/10.1017/S0074180900124805, 1992. a, b
Lefèvre, M., Wald, L., and Diabaté, L.: Using reduced data sets ISCCP-B2
from the Meteosat satellites to assess surface solar irradiance, Sol. Energy, 81, 240–253, https://doi.org/10.1016/j.solener.2006.03.008, 2007. a, b
Lefèvre, M., Oumbe, A., Blanc, P., Espinar, B., Gschwind, B., Qu, Z., Wald, L., Schroedter-Homscheidt, M., Hoyer-Klick, C., Arola, A., Benedetti, A., Kaiser, J. W., and Morcrette, J.-J.: McClear: a new model estimating downwelling solar radiation at ground level in clear-sky conditions, Atmos. Meas. Tech., 6, 2403–2418, https://doi.org/10.5194/amt-6-2403-2013, 2013. a, b, c, d, e
Long, C. N. and Turner, D. D.: A method for continuous estimation of clear-sky
downwelling longwave radiative flux developed using ARM surface measurements,
J. Geophys. Res.-Atmos., 113, D18,
https://doi.org/10.1029/2008JD009936, 2008. a
Lorente, A., Boersma, K. F., Stammes, P., Tilstra, L. G., Richter, A., Yu, H., Kharbouche, S., and Muller, J.-P.: The importance of surface reflectance anisotropy for cloud and NO2 retrievals from GOME-2 and OMI, Atmos. Meas. Tech., 11, 4509–4529, https://doi.org/10.5194/amt-11-4509-2018, 2018. a, b
Lucht, W., Schaaf, C. B., and Strahler, A. H.: An algorithm for the retrieval
of albedo from space using semiempirical BRDF models, IEEE T.
Geosci. Remote, 38, 977–998, https://doi.org/10.1109/36.841980, 2000. a
Lyapustin, A., Wang, Y., Korkin, S., and Huang, D.: MODIS Collection 6 MAIAC algorithm, Atmos. Meas. Tech., 11, 5741–5765, https://doi.org/10.5194/amt-11-5741-2018, 2018. a
Marshak, A., Herman, J., Szabo, A., Blank, K., Carn, S., Cede, A., Geogdzhayev,
I., Huang, D., Huang, L.-K., Knyazikhin, Y., Kowalewski, M., Krotkov, N.,
Lyapustin, A., McPeters, R., Meyer, K. G., Torres, O., and Yang, Y.: Earth
Observations from DSCOVR EPIC Instrument, B. Am.
Meteorol. Soc., 99, 1829–1850, https://doi.org/10.1175/BAMS-D-17-0223.1, 2018. a
Mayer, B., Kylling, A., Emde, C., Buras, R., Hamann, U., Gasteiger, J., and
Richter, B.: libRadtran user's guide, Edition for libRadtran version 2.0.2, http://www.libradtran.org/doc/libRadtran.pdf (last access: 7 May 2018), 2017. a
Möser, W. and Raschke, E.: Mapping of global radiation and cloudiness
from Meteosat image data – Theory and ground truth comparisons,
Meteorol. Rundsch., 36, 33–41, 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
Mueller, R. and Träger-Chatterjee, C.: Brief Accuracy Assessment of
Aerosol Climatologies for the Retrieval of Solar Surface
Radiation, Atmosphere, 5, 959–972, 2014. a
Mueller, R., Matsoukas, C., Gratzki, A., Behr, H., and Hollmann, R.: 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
Mueller, R., Pfeifroth, U., and Traeger-Chatterjee, C.: Towards Optimal Aerosol
Information for the Retrieval of Solar Surface Radiation Using Heliosat,
Atmosphere, 6, 863–878, https://doi.org/10.3390/atmos6070863, 2015. a
Müller, R., Pfeifroth, U., Träger-Chatterjee, C., Trentmann, J., and Cremer,
R.: Digging the METEOSAT Treasure—3 Decades of Solar Surface Radiation,
Remote Sens., 7, 8067–8101, https://doi.org/10.3390/rs70608067, 2015. a, b
Ohmura, A., Dutton, E. G., Forgan, B., Fröhlich, C., Gilgen, H., Hegner, H.,
Heimo, A., König-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,
https://doi.org/10.1175/1520-0477(1998)079<2115:BSRNBW>2.0.CO;2, 1998. a
Pinker, R. and Laszlo, I.: Modeling surface solar irradiance for satellite
applications on a global scale, J. Appl. Meteorol. Clim., 31, 194–211,
https://doi.org/10.1175/1520-0450(1992)031<0194:MSSIFS>2.0.CO;2, 1992. a
Qu, Z., Gschwind, B., Lefevre, M., and Wald, L.: Improving HelioClim-3 estimates of surface solar irradiance using the McClear clear-sky model and recent advances in atmosphere composition, Atmos. Meas. Tech., 7, 3927–3933, https://doi.org/10.5194/amt-7-3927-2014, 2014. a
Qu, Z., Oumbe, A., Blanc, P., Espinar, B., Gesell, G., Gschwind, B., Klüser,
L., Lefèvre, M., Saboret, L., Schroedter-Homscheidt, M., and Wald, L.: Fast
radiative transfer parameterisation for assessing the surface solar
irradiance: The Heliosat-4 method, Meteorol. Z., 26, 33–57,
https://doi.org/10.1127/metz/2016/0781, 2017. a
Rigollier, C., Lefèvre, 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
Roujean, J.-L., Leroy, M., and Deschamps, P.-Y.: A bidirectional reflectance
model of the Earth's surface for the correction of remote sensing data,
J. Geophys. Res.-Atmos., 97, 20455–20468, 1992. a
Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X., Tsang, T.,
Strugnell, N. C., Zhang, X., Jin, Y., Muller, J.-P., Lewis, P., Barnsley, M.,
Hobson, P., Disney, M., Roberts, G., Dunderdale, M., Doll, C., d'Entremont,
R. P., Hu, B., Liang, S., Privette, J. L., and Roy, D.: First operational
BRDF, albedo nadir reflectance products from MODIS, Remote Sens.
Environ., 83, 135–148,
https://doi.org/10.1016/S0034-4257(02)00091-3, 2002. a
Sengupta, M., Habte, A., Wilbert, S., Gueymard, C., and Remund, J.: Best
Practices Handbook for the Collection and Use of Solar Resource Data for
Solar Energy Applications: Third Edition, National Renewable Energy Laboratory, Tech. Rep. NREL/TP-5D00-77635, https://www.nrel.gov/docs/fy21osti/77635.pdf (last access: 19 October 2021),
2021. a
Shettle, E.: Models of aerosols, clouds, and precipitation for atmospheric
propagation studies, in: AGARD Conference Proceedings, Copenhagen, Denmark, 9–13 October 1989, 454, www.researchgate.net/profile/Eric-Shettle/publication/234312286_Models_of_aerosols_clouds
(last access: 10 February 2020), 1990. a
Stammes, P., Sneep, M., de Haan, J. F., Veefkind, J. P., Wang, P., and Levelt,
P. F.: Effective cloud fractions from the Ozone Monitoring Instrument:
Theoretical framework and validation, J. Geophys. Res.-Atmos., 113, D16,
https://doi.org/10.1029/2007JD008820, 2008. a
Stengel, M., Stapelberg, S., Sus, O., Finkensieper, S., Würzler, B., Philipp, D., Hollmann, R., Poulsen, C., Christensen, M., and McGarragh, G.: Cloud_cci Advanced Very High Resolution Radiometer post meridiem (AVHRR-PM) dataset version 3: 35-year climatology of global cloud and radiation properties, Earth Syst. Sci. Data, 12, 41–60, https://doi.org/10.5194/essd-12-41-2020, 2020. a
Stephens, G. L., Gabriel, P. M., and Partain, P. T.: Parameterization of
atmospheric radiative transfer. Part I: Validity of simple models,
J. Atmos. Sci., 58, 3391–3409,
https://doi.org/10.1175/1520-0469(2001)058<3391:POARTP>2.0.CO;2, 2001. a
Stöckli, R.: The HelioMont Surface Solar Radiation Processing, MeteoSwiss, Tech. Rep. 93, https://www.meteoswiss.admin.ch/content/dam/meteoswiss/de/service-und-publikationen/Publikationen/doc/sr93stoeckli.pdf (last access: 10 June 2020),
2014. a
Tarpley, J.: Estimating incident solar radiation at the surface from
geostationary satellite data, J. Appl. Meteorol., 18, 1172–1181,
https://doi.org/10.1175/1520-0450(1979)018<1172:EISRAT>2.0.CO;2, 1979. a
Taylor, V. R. and Stowe, L.: Reflectance Characteristics of Uniform Earth and
Cloud Surfaces Derived From NIMBUS-7 ERB, J. Geophys. Res.-Atmos.,
89, 4987–4996, https://doi.org/10.1029/JD089iD04p04987, 1984. a
Tournadre, B. and Gschwind, B.: Index of /heliosat-v, Webservice-Energy [code and data set],
https://doi.org/10.23646/tg31-1452, 2022.
Trishchenko, A. P., Li, Z., Chang, F.-L., and Barker, H.: Cloud optical depths
and TOA fluxes: Comparison between satellite and surface retrievals from
multiple platforms, Geophys. Res. Lett., 28, 979–982,
https://doi.org/10.1029/2000GL012067, 2001. a
Veefkind, J. P., de Haan, J. F., Sneep, M., and Levelt, P. F.: Improvements to the OMI O2–O2 operational cloud algorithm and comparisons with ground-based radar–lidar observations, Atmos. Meas. Tech., 9, 6035–6049, https://doi.org/10.5194/amt-9-6035-2016, 2016. a
Wanner, W., Strahler, A., Hu, B., Lewis, P., Muller, J.-P., Li, X., Schaaf, C.,
and Barnsley, M.: Global retrieval of bidirectional reflectance and albedo
over land from EOS MODIS and MISR data: Theory and algorithm, J. Geophys. Res.-Atmos., 102, 17143–17161,
https://doi.org/10.1029/96JD03295, 1997. a, b
WMO: World Meteorological Organization's Guide to Instruments and Methods of Observation, Volume I – Measurement of Meteorological Variables, Chapter 7: Measurement of radiation, https://library.wmo.int/doc_num.php?explnum_id=10616 (last access: 2 May 2022), 2018. a
Xie, Y., Sengupta, M., and Dudhia, J.: A Fast All-sky Radiation Model
for Solar applications (FARMS): Algorithm and performance evaluation,
Sol. Energy, 135, 435–445, https://doi.org/10.1016/j.solener.2016.06.003, 2016.
a
Yang, P., Bi, L., Baum, B. A., Liou, K.-N., Kattawar, G. W., Mishchenko, M. I.,
and Cole, B.: Spectrally Consistent Scattering, Absorption, and
Polarization Properties of Atmospheric Ice Crystals at
Wavelengths from 0.2 to 100 µm, J. Atmos.
Sci., 70, 330–347, https://doi.org/10.1175/JAS-D-12-039.1, 2013. a
Zarzalejo, L. F., Polo, J., Martin, L., Ramirez, L., and Espinar, B.: A new
statistical approach for deriving global solar radiation from satellite
images, Sol. Energy, 83, 480–484, https://doi.org/10.1016/j.solener.2008.09.006,
2009. a, b
Zhang, H., Huang, C., Yu, S., Li, L., Xin, X., and Liu, Q.: A
Lookup-Table-Based Approach to Estimating Surface Solar
Irradiance from Geostationary and Polar-Orbiting Satellite Data,
Remote Sens., 10, 411, https://doi.org/10.3390/rs10030411, 2018. a
Zhang, Y.: Calculation of radiative fluxes from the surface to top of
atmosphere based on ISCCP and other global data sets: Refinements of the
radiative transfer model and the input data, J. Geophys. Res.,
109, D19, https://doi.org/10.1029/2003JD004457, 2004. a
Short summary
Solar radiation received by the Earth's surface is valuable information for various fields like the photovoltaic industry or climate research. Pictures taken from satellites can be used to estimate the solar radiation from cloud reflectivity. Two issues for a good estimation are different instrumentations and orbits. We modify a widely used method that is today only used on geostationary satellites, so it can be applied on instruments on different orbits and with different sensitivities.
Solar radiation received by the Earth's surface is valuable information for various fields like...