Articles | Volume 15, issue 11
https://doi.org/10.5194/essd-15-4901-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-4901-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
02 Nov 2023
Data description paper |
| 02 Nov 2023
| 02 Nov 2023
CLARA-A3: The third edition of the AVHRR-based CM SAF climate data record on clouds, radiation and surface albedo covering the period 1979 to 2023
Karl-Göran Karlsson
CORRESPONDING AUTHOR
Meteorological Research Unit, Research and Development Department, The Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, 602 10 Norrköping, Sweden
Martin Stengel
Satellite-Based Climate Monitoring, Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach am Main, Germany
Jan Fokke Meirink
R&D Satellite Observations, Royal Netherlands Meteorological Institute (KNMI), Utrechtseweg 297, 3731 GA De Bilt, the Netherlands
Aku Riihelä
Meteorological Research, Finnish Meteorological Institute, Helsinki, 00101, Finland
Jörg Trentmann
Satellite-Based Climate Monitoring, Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach am Main, Germany
Tom Akkermans
Remote Sensing from Space, Royal Meteorological Institute of Belgium, 1180 Brussels, Belgium
Diana Stein
Satellite-Based Climate Monitoring, Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach am Main, Germany
Abhay Devasthale
Meteorological Research Unit, Research and Development Department, The Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, 602 10 Norrköping, Sweden
Salomon Eliasson
Meteorological Research Unit, Research and Development Department, The Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, 602 10 Norrköping, Sweden
Erik Johansson
Meteorological Research Unit, Research and Development Department, The Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, 602 10 Norrköping, Sweden
Nina Håkansson
Meteorological Research Unit, Research and Development Department, The Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, 602 10 Norrköping, Sweden
Irina Solodovnik
Satellite-Based Climate Monitoring, Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach am Main, Germany
Nikos Benas
R&D Satellite Observations, Royal Netherlands Meteorological Institute (KNMI), Utrechtseweg 297, 3731 GA De Bilt, the Netherlands
Nicolas Clerbaux
Remote Sensing from Space, Royal Meteorological Institute of Belgium, 1180 Brussels, Belgium
Nathalie Selbach
Satellite-Based Climate Monitoring, Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach am Main, Germany
Marc Schröder
Satellite-Based Climate Monitoring, Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach am Main, Germany
Rainer Hollmann
Satellite-Based Climate Monitoring, Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach am Main, Germany
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Cheng You, Michael Tjernström, and Abhay Devasthale
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In winter when solar radiation is absent in the Arctic, the poleward transport of heat and moisture into the high Arctic becomes the main contribution of Arctic warming. Over completely frozen ocean sectors, total surface energy budget is dominated by net long-wave heat, while over the Barents Sea, with an open ocean to the south, total net surface energy budget is dominated by the surface turbulent heat.
Kerttu Kouki, Petri Räisänen, Kari Luojus, Anna Luomaranta, and Aku Riihelä
The Cryosphere, 16, 1007–1030, https://doi.org/10.5194/tc-16-1007-2022, https://doi.org/10.5194/tc-16-1007-2022, 2022
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We analyze state-of-the-art climate models’ ability to describe snow mass and whether biases in modeled temperature or precipitation can explain the discrepancies in snow mass. In winter, biases in precipitation are the main factor affecting snow mass, while in spring, biases in temperature becomes more important, which is an expected result. However, temperature or precipitation cannot explain all snow mass discrepancies. Other factors, such as models’ structural errors, are also significant.
Terhikki Manninen, Emmihenna Jääskeläinen, Niilo Siljamo, Aku Riihelä, and Karl-Göran Karlsson
Atmos. Meas. Tech., 15, 879–893, https://doi.org/10.5194/amt-15-879-2022, https://doi.org/10.5194/amt-15-879-2022, 2022
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A new method for cloud-correcting observations of surface albedo is presented for AVHRR data. Instead of a binary cloud mask, it applies cloud probability values smaller than 20% of the A3 edition of the CLARA (CM SAF cLoud, Albedo and surface Radiation dataset from AVHRR data) record provided by the Satellite Application Facility on Climate Monitoring (CM SAF) project of EUMETSAT. According to simulations, the 90% quantile was 1.1% for the absolute albedo error and 2.2% for the relative error.
Wim C. de Rooy, Pier Siebesma, Peter Baas, Geert Lenderink, Stephan R. de Roode, Hylke de Vries, Erik van Meijgaard, Jan Fokke Meirink, Sander Tijm, and Bram van 't Veen
Geosci. Model Dev., 15, 1513–1543, https://doi.org/10.5194/gmd-15-1513-2022, https://doi.org/10.5194/gmd-15-1513-2022, 2022
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This paper describes a comprehensive model update to the boundary layer schemes. Because the involved parameterisations are all built on widely applied frameworks, the here-described modifications are applicable to many NWP and climate models. The model update contains substantial modifications to the cloud, turbulence, and convection schemes and leads to a substantial improvement of several aspects of the model, especially low cloud forecasts.
Manu Anna Thomas, Abhay Devasthale, and Michael Kahnert
Atmos. Chem. Phys., 22, 119–137, https://doi.org/10.5194/acp-22-119-2022, https://doi.org/10.5194/acp-22-119-2022, 2022
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The Southern Ocean (SO) covers a large area of our planet and its boundary layer is dominated by sea salt aerosols during winter. These aerosols have large implications for the regional climate through their direct and indirect effects. Using satellite and reanalysis data, we document if and how the aerosol properties over the SO are dependent on different local meteorological parameters. Such an observational assessment is necessary to improve the understanding of atmospheric aerosol processes.
Sandra Vázquez-Martín, Thomas Kuhn, and Salomon Eliasson
Atmos. Chem. Phys., 21, 18669–18688, https://doi.org/10.5194/acp-21-18669-2021, https://doi.org/10.5194/acp-21-18669-2021, 2021
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High-resolution top- and side-view images of snow ice particles taken by the D-ICI instrument are used to determine the shape; size; cross-sectional area; fall speed; and, based upon these properties, the mass of the individual snow particles. The study analyses the relationships between these fundamental properties as a function of particle shape and highlights that the choice of size parameter, maximum dimension or another characteristic length, is crucial when relating fall speed to mass.
Manu Anna Thomas, Abhay Devasthale, and Tiina Nygård
Atmos. Chem. Phys., 21, 16593–16608, https://doi.org/10.5194/acp-21-16593-2021, https://doi.org/10.5194/acp-21-16593-2021, 2021
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The impact of transported pollutants and their spatial distribution in the Arctic are governed by the local atmospheric circulation or weather states. Therefore, we investigated eight different atmospheric circulation types observed during the spring season in the Arctic. Using satellite and reanalysis datasets, this study provides a comprehensive assessment of the typical circulation patterns that can lead to enhanced or reduced pollution concentrations in the different sectors of the Arctic.
Hartwig Deneke, Carola Barrientos-Velasco, Sebastian Bley, Anja Hünerbein, Stephan Lenk, Andreas Macke, Jan Fokke Meirink, Marion Schroedter-Homscheidt, Fabian Senf, Ping Wang, Frank Werner, and Jonas Witthuhn
Atmos. Meas. Tech., 14, 5107–5126, https://doi.org/10.5194/amt-14-5107-2021, https://doi.org/10.5194/amt-14-5107-2021, 2021
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The SEVIRI instrument flown on the European geostationary Meteosat satellites acquires multi-spectral images at a relatively coarse pixel resolution of 3 × 3 km2, but it also has a broadband high-resolution visible channel with 1 × 1 km2 spatial resolution. In this study, the modification of an existing cloud property and solar irradiance retrieval to use this channel to improve the spatial resolution of its output products as well as the resulting benefits for applications are described.
Susanne Crewell, Kerstin Ebell, Patrick Konjari, Mario Mech, Tatiana Nomokonova, Ana Radovan, David Strack, Arantxa M. Triana-Gómez, Stefan Noël, Raul Scarlat, Gunnar Spreen, Marion Maturilli, Annette Rinke, Irina Gorodetskaya, Carolina Viceto, Thomas August, and Marc Schröder
Atmos. Meas. Tech., 14, 4829–4856, https://doi.org/10.5194/amt-14-4829-2021, https://doi.org/10.5194/amt-14-4829-2021, 2021
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Water vapor (WV) is an important variable in the climate system. Satellite measurements are thus crucial to characterize the spatial and temporal variability in WV and how it changed over time. In particular with respect to the observed strong Arctic warming, the role of WV still needs to be better understood. However, as shown in this paper, a detailed understanding is still hampered by large uncertainties in the various satellite WV products, showing the need for improved methods to derive WV.
Erik Johansson, Abhay Devasthale, Michael Tjernström, Annica M. L. Ekman, Klaus Wyser, and Tristan L'Ecuyer
Geosci. Model Dev., 14, 4087–4101, https://doi.org/10.5194/gmd-14-4087-2021, https://doi.org/10.5194/gmd-14-4087-2021, 2021
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Understanding the coupling of clouds to large-scale circulation is a grand challenge for the climate community. Cloud radiative heating (CRH) is a key parameter in this coupling and is therefore essential to model realistically. We, therefore, evaluate a climate model against satellite observations. Our findings indicate good agreement in the seasonal pattern of CRH even if the magnitude differs. We also find that increasing the horizontal resolution in the model has little effect on the CRH.
Sandra Vázquez-Martín, Thomas Kuhn, and Salomon Eliasson
Atmos. Chem. Phys., 21, 7545–7565, https://doi.org/10.5194/acp-21-7545-2021, https://doi.org/10.5194/acp-21-7545-2021, 2021
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In this work, we present new fall speed measurements of natural snow particles and ice crystals. We study the particle fall speed relationships and how they depend on particle shape. We analyze these relationships as a function of particle size, cross-sectional area, and area ratio for different particle shape groups. We also investigate the dependence of the particle fall speed on the orientation, as it has a large impact on the cross-sectional area.
Alejandro Baró Pérez, Abhay Devasthale, Frida A.-M. Bender, and Annica M. L. Ekman
Atmos. Chem. Phys., 21, 6053–6077, https://doi.org/10.5194/acp-21-6053-2021, https://doi.org/10.5194/acp-21-6053-2021, 2021
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We study the impacts of above-cloud biomass burning plumes on radiation and clouds over the southeast Atlantic using data derived from satellite observations and data-constrained model simulations. A substantial amount of the aerosol within the plumes is not classified as smoke by the satellite. The atmosphere warms more with increasing smoke aerosol loading. No clear influence of aerosol type, loading, or moisture within the overlying aerosol plumes is detected on the cloud top cooling rates.
Terhikki Manninen, Kati Anttila, Emmihenna Jääskeläinen, Aku Riihelä, Jouni Peltoniemi, Petri Räisänen, Panu Lahtinen, Niilo Siljamo, Laura Thölix, Outi Meinander, Anna Kontu, Hanne Suokanerva, Roberta Pirazzini, Juha Suomalainen, Teemu Hakala, Sanna Kaasalainen, Harri Kaartinen, Antero Kukko, Olivier Hautecoeur, and Jean-Louis Roujean
The Cryosphere, 15, 793–820, https://doi.org/10.5194/tc-15-793-2021, https://doi.org/10.5194/tc-15-793-2021, 2021
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The primary goal of this paper is to present a model of snow surface albedo (brightness) accounting for small-scale surface roughness effects. It can be combined with any volume scattering model. The results indicate that surface roughness may decrease the albedo by about 1–3 % in midwinter and even more than 10 % during the late melting season. The effect is largest for low solar zenith angle values and lower bulk snow albedo values.
Marloes Gutenstein, Karsten Fennig, Marc Schröder, Tim Trent, Stephan Bakan, J. Brent Roberts, and Franklin R. Robertson
Hydrol. Earth Syst. Sci., 25, 121–146, https://doi.org/10.5194/hess-25-121-2021, https://doi.org/10.5194/hess-25-121-2021, 2021
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The net exchange of water between the surface and atmosphere is mainly determined by the freshwater flux: the difference between evaporation (E) and precipitation (P), or E−P. Although there is consensus among modelers that with a warming climate E−P will increase, evidence from satellite data is still not conclusive, mainly due to sensor calibration issues. We here investigate the degree of correspondence among six recent
satellite-based climate data records and ERA5 reanalysis E−P data.
Ina Neher, Susanne Crewell, Stefanie Meilinger, Uwe Pfeifroth, and Jörg Trentmann
Atmos. Chem. Phys., 20, 12871–12888, https://doi.org/10.5194/acp-20-12871-2020, https://doi.org/10.5194/acp-20-12871-2020, 2020
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Photovoltaic power is one current option to meet the rising energy demand with low environmental impact. Global horizontal irradiance (GHI) is the fuel for photovoltaic power installations and needs to be evaluated to plan and dimension power plants. In this study, 35 years of satellite-based GHI data are analyzed over West Africa to determine their impact on photovoltaic power generation. The major challenges for the development of a solar-based power system in West Africa are then outlined.
Caroline A. Poulsen, Gregory R. McGarragh, Gareth E. Thomas, Martin Stengel, Matthew W. Christensen, Adam C. Povey, Simon R. Proud, Elisa Carboni, Rainer Hollmann, and Roy G. Grainger
Earth Syst. Sci. Data, 12, 2121–2135, https://doi.org/10.5194/essd-12-2121-2020, https://doi.org/10.5194/essd-12-2121-2020, 2020
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We have created a satellite cloud and radiation climatology from the ATSR-2 and AATSR on board ERS-2 and Envisat, respectively, which spans the period 1995–2012. The data set was created using a combination of optimal estimation and neural net techniques. The data set was created as part of the ESA Climate Change Initiative program. The data set has been compared with active CALIOP lidar measurements and compared with MAC-LWP AND CERES-EBAF measurements and is shown to have good performance.
Cited articles
Akkermans, T. and Clerbaux, N.: Narrowband-to-broadband conversions for top-of-atmosphere reflectance from the Advanced Very High Resolution Radiometer (AVHRR), Remote Sens., 12, 305, https://doi.org/10.3390/rs12020305 , 2020.
Akkermans, T. and Clerbaux, N.: Retrieval of Daily Mean Top-of-Atmosphere Reflected Solar Flux Using the Advanced Very High Resolution Radiometer (AVHRR) Instruments, Remote Sens., 13, 3695, https://doi.org/10.3390/rs13183695, 2021.
Baum, B. A., Yang, P., Heymsfield, A. J., Schmitt, C. G., Xie, Y., Bansemer, A., Hu, Y.-X., and Zhang, Z.: Improvements in shortwave bulk scattering and absorption models for the remote sensing of ice clouds, J. Appl. Meteorol. Clim., 50, 1037–1056, https://doi.org/10.1175/2010JAMC2608.1, 2011.
Bodas-Salcedo, A., Webb, M. J., Bony, S., Chepfer, H., Dufresne, J.-L., Klein, S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John, V. O.: COSP: satellite simulation software for model assessment, B. Am. Meteorol. Soc., 92, 1023–1043, https://doi.org/10.1175/2011BAMS2856.1, 2011.
Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B., Mazzariello, J., Czerwinski, W., Pasquarella, V. J., Haertel, R., Ilyushchenko, S., Schwehr, K., Weisse, M., Stolle, F., Hanson, C., Guinan, O., Moore, R. and Tait, A. M.: Dynamic World, Near real-time global 10 m land use land cover mapping, Sci. Data, 9, 1–17, 2022.
CALIPSO Science Team: CALIPSO/CALIOP Level 2, Lidar 5 km Cloud Layer Product, version 4.20, Hampton, VA, USA: NASA Atmospheric Science Data Center (ASDC), https://doi.org/10.5067/CALIOP/CALIPSO/LID_L2_05KMCLAY-STANDARD-V4-20 (last access: 27 December 2022), 2021.
Canty, T., Mascioli, N. R., Smarte, M. D., and Salawitch, R. J.: An empirical model of global climate – Part 1: A critical evaluation of volcanic cooling, Atmos. Chem. Phys., 13, 3997–4031, https://doi.org/10.5194/acp-13-3997-2013, 2013.
Cao, Y., Liang, S., Chen, X., and He, T.: Assessment of sea ice albedo radiative forcing and feedback over the Northern Hemisphere from 1982 to 2009 using satellite and reanalysis data, J. Climate, 28, 1248–1259, 2015.
Clerbaux, N., Russell, J. E., Dewitte, S., Bertrand, C., Caprion, D., De Paepe, B., Sotelino, L. G., Ipe, A., Bantges, R., and Brindley, H. E.: Comparison of GERB instantaneous radiance and flux products with CERES Edition-2 data, Remote Sens. Environ., 113, 102–114, 2009.
Clerbaux, N., Akkermans, T., Baudrez, E., Velazquez Blazquez, A., Moutier, W., Moreels, J., and Aebi, C.: The climate monitoring SAF outgoing longwave radiation from AVHRR, Remote Sens., 12, 929, https://doi.org/10.3390/rs12060929 , 2020.
Cracknell, A. P.: The Advanced Very High Resolution Radiometer (AVHRR), Taylor & Francis Ltd, 556 pp., ISBN 9780748402090, 1997.
De Haan, J. F., Bosma, P., and Hovenier, J. W.: The adding method for multiple scattering calculations of polarized light, Astron. Astrophys., 183, 371–391, 1987.
Devasthale, A. and Karlsson, K.-G.: Decadal Stability and Trends in the Global Cloud Amount and Cloud Top Temperature in the Satellite-Based Climate Data Records, Remote Sens., 15, 3819, https://doi.org/10.3390/rs15153819, 2023.
Diekmann, F. J., Happ, S., Rieland, M., Benesch, W., Czeplack, G., and Kasten, F.: An operational estimate of global solar irradiance at ground level from METEOSAT data: results from 1985 to 1987, Meteorol. Rdsch., 41, 65–79, 1988.
Doelling, D. R., Loeb, N. G., Keyes, D. F., Nordeen, M. L., Morstad, D., Nguyen, C., Wielicki, B., Young, D. F., and Sun, M.: Geostationary enhanced temporal interpolation for CERES flux products, J. Atmos. Ocean. Tech., 30, 1072–1090, 2013.
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.
Dybbroe, A., Karlsson, K.-G., and Thoss, A.: NWCSAF AVHRR Cloud Detection and Analysis Using Dynamic Thresholds and Radiative Transfer Modelling. Part I: Algorithm Description, J. Appl. Meteor., 44, 39–54, https://doi.org/10.1175/JAM-2188.1, 2005.
Eliasson, S., Karlsson, K.-G., and Willén, U.: A simulator for the CLARA-A2 cloud climate data record and its application to assess EC-Earth polar cloudiness, Geosci. Model Dev., 13, 297–314, https://doi.org/10.5194/gmd-13-297-2020, 2020.
Ellingson, R. G.: Surface Longwave Fluxes from satellite observations: A critical review, Remore Sens. Environ., 51, 89–97, 1995.
EUMETSAT: AVHRR Fundamental Data Record – Release 1 – Multimission, European Organisation for the Exploitation of Meteorological Satellites [data set], https://doi.org/10.15770/EUM_SEC_CLM_0060, 2023a.
EUMETSAT: PyGAC AVHRR FDR Release 1 Validation Report, EUM/OPS/DOC/22/1282616, version v1F, 2023b.
Fausto, R. S., van As, D., Mankoff, K. D., Vandecrux, B., Citterio, M., Ahlstrøm, A. P., Andersen, S. B., Colgan, W., Karlsson, N. B., Kjeldsen, K. K., Korsgaard, N. J., Larsen, S. H., Nielsen, S., Pedersen, A. Ø., Shields, C. L., Solgaard, A. M., and Box, J. E.: Programme for Monitoring of the Greenland Ice Sheet (PROMICE) automatic weather station data, Earth Syst. Sci. Data, 13, 3819–3845, https://doi.org/10.5194/essd-13-3819-2021, 2021.
Fiedler, S., Kinne, S., Huang, W. T. K., Räisänen, P., O'Donnell, D., Bellouin, N., Stier, P., Merikanto, J., van Noije, T., Makkonen, R., and Lohmann, U.: Anthropogenic aerosol forcing – insights from multiple estimates from aerosol-climate models with reduced complexity, Atmos. Chem. Phys., 19, 6821–6841, https://doi.org/10.5194/acp-19-6821-2019, 2019a.
Fiedler, S., Stevens, B., Gidden, M., Smith, S. J., Riahi, K., and van Vuuren, D.: First forcing estimates from the future CMIP6 scenarios of anthropogenic aerosol optical properties and an associated Twomey effect, Geosci. Model Dev., 12, 989–1007, https://doi.org/10.5194/gmd-12-989-2019, 2019b.
Foster, M. J., Phillips, C., Heidinger, A. K., Borbas, E. E., Li, Y., Menzel, W. P., Walther, A., and Weisz, E.: PATMOS-x Version 6.0: 40 Years of Merged AVHRR and HIRS Global Cloud Data, J. Climate, 36, 1143–1160, https://doi.org/10.1175/JCLI-D-22-0147.1, 2023.
Håkansson, N., Adok, C., Thoss, A., Scheirer, R., and Hörnquist, S.: Neural network cloud top pressure and height for MODIS, Atmos. Meas. Tech., 11, 3177–3196, https://doi.org/10.5194/amt-11-3177-2018, 2018.
Heidinger, A. K.: Climate Algorithm Theoretical Basis Document (C-ATBD): Fundamental Climate Data Record (CDR) of Reflectance from AVHRR Bands 1, 2 and 3a, CDR Program Document Number CDRP-ATBD-0184, Revision 2, 2018.
Heidinger, A. K., Straka, W. C., Molling, C. C., Sullivan, J. T., and Wu, X. Q.: Deriving an inter-sensor consistent calibration for the AVHRR solar reflectance data record, Int. J. Rem. Sens., 31, 6493–6517, 2010.
Heidinger, A. K., Foster, J. M., Walther, A., and Zhao, X.: The Pathfinder Atmospheres – Extended AVHRR Climate Dataset, B. Am. Meteorol. Soc., 95, 909–922, https://doi.org/10.1175/BAMS-D-12-00246.1, 2014.
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., Holm, E., Janiskova, 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, 2020.
Hobbs, P. V. and Rangno, A. L.: Ice Particle Concentrations in Clouds, J. Atmos. Sci., 42, 2523–2549, 1985.
Hofmann, M. and Seckmeyer, G.: A new model for estimating the diffuse fraction of solar irradiance for photovoltaic system simulations, Energies, 10, 248, https://doi.org/10.3390/en10020248 , 2017.
Jääskeläinen, E., Manninen, T., Tamminen, J., and Laine, M.: The Aerosol Index and Land Cover Class Based Atmospheric Correction Aerosol Optical Depth Time Series 1982–2014 for the SMAC Algorithm, Remote Sens., 9, 1095, https://doi.org/10.3390/rs9111095 , 2017.
Karlsson, J. and Svensson, G.: Consequences of poor representation of Arctic sea-ice albedo and cloud-radiation interactions in the CMIP5 model ensemble, Geophys. Res. Lett., 40, 4374–4379, 2013.
Karlsson, K.-G. and Devasthale, A.: Inter-Comparison and Evaluation of the Four Longest Satellite-Derived Cloud Climate Data Records: CLARA-A2, ESA Cloud CCI V3, ISCCP-HGM, and PATMOS-x, Remote Sens., 10, 1567–1593, https://doi.org/10.3390/rs10101567, 2018.
Karlsson, K.-G. and Håkansson, N.: Characterization of AVHRR global cloud detection sensitivity based on CALIPSO-CALIOP cloud optical thickness information: demonstration of results based on the CM SAF CLARA-A2 climate data record, Atmos. Meas. Tech., 11, 633–649, https://doi.org/10.5194/amt-11-633-2018, 2018.
Karlsson, K.-G., Riihelä, A., Müller, R., Meirink, J. F., Sedlar, J., Stengel, M., Lockhoff, M., Trentmann, J., Kaspar, F., Hollmann, R., and Wolters, E.: CLARA-A1: a cloud, albedo, and radiation dataset from 28 yr of global AVHRR data, Atmos. Chem. Phys., 13, 5351–5367, https://doi.org/10.5194/acp-13-5351-2013, 2013.
Karlsson, K.-G., Anttila, K., Trentmann, J., Stengel, M., Fokke Meirink, J., Devasthale, A., Hanschmann, T., Kothe, S., Jääskeläinen, E., Sedlar, J., Benas, N., van Zadelhoff, G.-J., Schlundt, C., Stein, D., Finkensieper, S., Håkansson, N., and Hollmann, R.: CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data, Atmos. Chem. Phys., 17, 5809–5828, https://doi.org/10.5194/acp-17-5809-2017, 2017a.
Karlsson, K.-G., Håkansson, N., Mittaz, J. P. D., Hanschmann, T., and Devasthale, A.: Impact of AVHRR Channel 3b Noise on Climate Data Records: Filtering Method Applied to the CM SAF CLARA-A2 Data Record, Remote Sens., 9, 568–581, https://doi.org/10.3390/rs9060568, 2017b.
Karlsson, K.-G., Anttila, K., Trentmann, J., Stengel, M., Solodovnik, I., Meirink, J. F., Devasthale, A., Kothe, S., Jääskeläinen, E., Sedlar, J., Benas, N., van Zadelhoff, G.-J., Stein, D., Finkensieper, S., Håkansson, N., Hollmann, R., Kaiser, J., and Werscheck, M.: CLARA-A2.1: CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data – Edition 2.1, Satellite Application Facility on Climate Monitoring [data set], https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V002 _01, 2020a.
Karlsson, K.-G., Johansson, E., Håkansson, N., Sedlar, J. and Eliasson, S.: Probabilistic Cloud Masking for the Generation of CM SAF Cloud Climate Data Records from AVHRR and SEVIRI Sensors, Remote Sens., 12, 713, https://doi.org/10.3390/rs12040713, 2020b.
Karlsson, K.-G., Devasthale, A., and Eliasson, S.: Global Cloudiness and Cloud Top Information from AVHRR in the 42-Year CLARA-A3 Climate Data Record Covering the Period 1979–2020, Remote Sens., 15, 3044, https://doi.org/10.3390/rs15123044, 2023a.
Karlsson, K.-G., Stengel, M., Meirink, J. F., Riihelä, A., Trentmann, J., Akkermans, T., Stein, D., Devasthale, A., Eliasson, S., Johansson, E., Håkansson, N., Solodovnik, I., Benas, N., Clerbaux, N., Selbach, N., Schröder, M., and Hollmann, R.: CLARA-A3: CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data – Edition 3, Satellite Application Facility on Climate Monitoring [data set], https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003, 2023b.
Kidwell, K. B.: NOAA Polar Orbiter Data Users Guide, edited by: Kidwell, K. B. and National Climatic Data Center (U.S.), National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, National Climatic Data Center, Satellite Data Services Division, 1995.
Kleipool, Q., Rozemeijer, N., van Hoek, M., Leloux, J., Loots, E., Ludewig, A., van der Plas, E., Adrichem, D., Harel, R., Spronk, S., ter Linden, M., Jaross, G., Haffner, D., Veefkind, P., and Levelt, P. F.: Ozone Monitoring Instrument (OMI) collection 4: establishing a 17-year-long series of detrended level-1b data, Atmos. Meas. Tech., 15, 3527–3553, https://doi.org/10.5194/amt-15-3527-2022, 2022.
Lee, H. T., Gruber, A., Ellingson, R. G., and Laszlo, I.: Development of the HIRS outgoing longwave radiation climate dataset, J. Atmos. Ocean. Tech., 24, 2029–2047, 2007.
Lee, H. T., Schreck, C. J., and Knapp, K. R.: Generation of the daily OLR climate data record, in: 2014 EUMETSAT meteorological satellite conference, 22–26 September 2014, Geneva, Switzerland, 22–26, 2014.
Loeb, N. G., Manalo-Smith, N., Kato, S., Miller, W. F., Gupta, S. K., Minnis, P., and Wielicki, B. A.: Angular distribution models for top-of-atmosphere radiative flux estimation from the Clouds and the Earth's Radiant Energy System instrument on the Tropical Rainfall Measuring Mission satellite. Part I: Methodology, J. Appl. Meteorol., 42, 240–265, 2003.
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, 2005.
Loeb, N. G., Doelling, D. R., Wang, H., Su, W., Nguyen, C., Corbett, J. G., Liang, L., Mitrescu, C., Rose, F. G., and Kato, S.: Clouds and the Earth's Radiant Energy System (CERES) energy balanced and filled (EBAF) top-of-atmosphere (TOA) Edition 4.0 data product, J. Climate, 31, 895–918, https://doi.org/10.1175/JCLI-D-17-0208.1, 2018.
Manninen, T., Andersson, K., and Riihelä, A.: Topography correction of the CM-SAF surface albedo product SAL, in: EUMETSAT Meteorological Satellite Conference Proceedings, 5–9 September 2011, Oslo, Norway, 2011.
Manninen, T., Jääskeläinen, E., and Riihelä, A.: Black and white-sky albedo values of snow: In situ relationships for AVHRR-based estimation using CLARA-A2 SAL, Can. J. Remote Sens., 45, 350–367, 2019.
Manninen, T., Jääskeläinen, E., Siljamo, N., Riihelä, A., and Karlsson, K.-G.: Cloud-probability-based estimation of black-sky surface albedo from AVHRR data, Atmos. Meas. Tech., 15, 879–893, https://doi.org/10.5194/amt-15-879-2022, 2022.
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.
Mueller, R. W., Dagestad, K.-F., Ineichen, P., Schroedter-Homsscheidt, M., Cros, S, Dumortier, D., Kuhlemann, R., Olseth, J. A., Izquierdo, G. P., Reise, C., Wald, L., and Heinemann, D.: Rethinking satellite-based solar irradiance modelling – The SOLIS clear-sky module, Remote Sens. Environ., 91, 160–174, https://doi.org/10.1016/j.rse.2004.02.009, 2004.
Nakajima, T. and King, M. D.: Determination of the optical thick-ness and effective particle radius of clouds from reflected solar radiation measurements, part 1: Theory, J. Atmos. Sci., 47, 1878–1893, https://doi.org/10.1175/1520-0469(1990)047<1878:DOTOTA>2.0.CO;2, 1990.
Nakajima, T. Y., Ishida, H., Nagao, T. M., Hori, M., Letu, H., Higuchi, R., Tamaru, N., Imoto, N., and Yamazaki, A.: Theoretical basis of the algorithms and early phase results of the GCOM-C (Shikisai) SGLI cloud products, Prog. Earth Planet. Sci., 6, 1–25, 2019.
NWC SAF: Algorithm Theoretical Basis Document for Cloud Micro Physics of the NWC/PPS, EUMETSAT Satellite Application Facility on Nowcasting and Very Short Range Forecasting, NWC/CDOP3/PPS/SMHI/SCI/ATBD/CMIC, Issue 3.0, 12 October 2021.
Ohmura, A.: Physical Basis for the Temperature-Based Melt-Index Method, J. Appl. Meteor., 40, 753–761, 2001.
Oleson, K. W., Bonan, G. B., Schaaf, C., Gao, F., Jin, Y., and Strahler, A.: Assessment of global climate model land surface albedo using MODIS data, Geophys. Res. Lett., 30, https://doi.org/10.1029/2002GL016749, 2003.
OSI SAF: Global Sea Ice Concentration Climate Data Record v2.0 – Multimission, EUMETSAT SAF on Ocean and Sea Ice [data set], https://doi.org/10.15770/EUM_SAF_OSI_0008, 2017a.
OSI SAF: Global Sea Ice Concentration (netCDF) – DMSP, EUMETSAT SAF on Ocean and Sea Ice, https://doi.org/10.15770/EUM_SAF_OSI_NRT_2004, 2017b.
OSI SAF: Global Sea Ice Concentration Interim Climate Data Record Release 2 – DMSP, EUMETSAT SAF on Ocean and Sea Ice [data set], https://doi.org/10.15770/EUM_SAF_OSI_NRT_2008, 2020.
Pavolonis, M. J., Heidinger, A. K., and Uttal, T.: Daytime Global Cloud Typing from AVHRR and VIIRS: Algorithm Description, Validation, and Comparisons, J. Appl. Meteor., 44, 804–826, https://doi.org/10.1175/JAM2236.1, 2005.
Perovich, D. K., Grenfell, T. C., Light, B., and Hobbs, P. V.: Seasonal evolution of the albedo of multiyear Arctic sea ice, J. Geophys. Res., 107, 8044, https://doi.org/10.1029/2000JC000438, 2002.
Pfreundschuh, S., Eriksson, P., Duncan, D., Rydberg, B., Håkansson, N., and Thoss, A.: A neural network approach to estimating a posteriori distributions of Bayesian retrieval problems, Atmos. Meas. Tech., 11, 4627–4643, https://doi.org/10.5194/amt-11-4627-2018, 2018.
Pinty, B., Lattanzio, A., Martonchik, J. V., Verstraete, M. M., Gobron, N., Taberner, M., Widlowski, J.-L., Dickinson, R. E., and Goverts, Y.: Coupling diffuse sky radiation and surface albedo, J. Atmos. Sci., 62, 2580–2591, 2005.
Platnick, S., Hubanks, P., Meyer, K., and King, M. D.: MODIS Atmosphere L3 Monthly Product (08_L3). NASA MODIS Adaptive Processing System, Goddard Space Flight Center, https://doi.org/10.5067/MODIS/MYD08_M3.006, 2015a.
Platnick, S., King, M. D., Meyer, K. G., Wind, G., Amarasinghe, N., Marchant, B., Arnold, G. T., Zhang, Z., Hubanks, P. A., Ridgway, B., and Riédi, J.: MODIS Cloud Optical Properties: User Guide for the Collection 6 Level-2 MOD06/MYD06 Product and Associated Level-3 Datasets, Version 1.0, https://modis-images.gsfc.nasa.gov/_docs/C6MOD06OPUserGuide.pdf (last access: 7 July 2023), 2015b.
Platnick, S., Meyer, K. G., King. M. D., Wind, G., Amarasinghe, N., Marchant, B., Arnold, G. T., Zhang, Z., Hubanks, P. A., Holz, R. E., Yang, P., Ridgway, W. L., and Riedi, J.: The MODIS Cloud Optical and Microphysical Products: Collection 6 Updates and Examples From Terra and Aqua, IEEE T. Geosci. Remote, 55, 502–525, https://doi.org/10.1109/TGRS.2016.2610522, 2017.
Platnick, S., Meyer, K., Wind, G., Holz, R. E., Amarasinghe, N., Hubanks, P. A., Marchant, B., Dutcher, S., and Veglio, P.: The NASA MODIS-VIIRS Continuity Cloud Optical Properties Products, Remote Sens., 13, 2, https://doi.org/10.3390/rs13010002, 2021.
Rahman, H. and G. Dedieu, G.: SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum, Int. J. Remote Sens., 15, 123–143, https://doi.org/10.1080/01431169408954055, 1994.
Ri, X., Tana, G., Shi, C., Nakajima, T. Y., Shi, J., Zhao, J., Xu, J., and Letu, H.: Cloud, Atmospheric Radiation and Renewal Energy Application (CARE) Version 1.0 Cloud Top Property Product From Himawari-8/AHI: Algorithm Development and Preliminary Validation, IEEE T. Geosci. Remote, 60, 1–11, 2022.
Riihelä, A., Manninen, T., Laine, V., Andersson, K., and Kaspar, F.: CLARA-SAL: a global 28 yr timeseries of Earth's black-sky surface albedo, Atmos. Chem. Phys., 13, 3743–3762, https://doi.org/10.5194/acp-13-3743-2013, 2013.
Roman, M. O., Schaaf, C. B., Lewis P., Gao, F., Anderson G. P., Privette, J. L., Strahler, A. H., Woodcock, C. E., and Barnsley, M.: Assessing the coupling between surface albedo derived from MODIS and the fraction of diffuse skylight over spatially-characterized landscapes Remote Sens. Environ., 114, 738–60, https://doi.org/10.1016/j.rse.2009.11.014, 2010.
Rossow, W. B. and Schiffer, R. A.: ISCCP Cloud Data Products, B. Am. Meteorol. Soc., 71, 2–20, 1991.
Rutan, D. A., Kato, S., Doelling, D. R., Rose, F. G., Nguyen, L. T., Caldwell, T. E., and Loeb, N. G.: CERES Synoptic Product: Methodology and Validation of Surface Radiant Flux, J. Atmos. Ocean. Tech., 32, 1121–1143, 2015.
Schiffer, R. A. and Rossow, W. B.: The International Satellite Cloud Climatology Project (ISCCP): The First Project of the World Climate Research Programme, B. Am. Meteorol. Soc., 64, 779–784, 1983.
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.
Song, Z., Liang, S., Wang, D., Zhou, Y., and Jia, A.: Long-term record of top-of-atmosphere albedo over land generated from AVHRR data, Remote Sens. Environ., 211, 71–88, 2018.
Stammes, P.: Spectral radiance modelling in the UV-Visible range, in: IRS 2000: Current problems in Atmospheric Radiation, edited by: Smith, W. L. and Timofeyev, Y. M. A., Deepak, Hampton, VA, 385–388, 2001.
Stengel, M., Stapelberg, S., Sus, O., Schlundt, C., Poulsen, C., Thomas, G., Christensen, M., Carbajal Henken, C., Preusker, R., Fischer, J., Devasthale, A., Willén, U., Karlsson, K.-G., McGarragh, G. R., Proud, S., Povey, A. C., Grainger, R. G., Meirink, J. F., Feofilov, A., Bennartz, R., Bojanowski, J. S., and Hollmann, R.: Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project, Earth Syst. Sci. Data, 9, 881–904, https://doi.org/10.5194/essd-9-881-2017, 2017.
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.
Stephens, G. L.: Radiation profiles in extended water clouds: II. Parameterization schemes, J. Atmos. Sci., 35, 2123–2132, https://doi.org/10.1175/1520-0469(1978)035<2123:RPIEWC>2.0.CO;2, 1978.
Stubenrauch, C. J., Rossow, W. B., Kinne, S., Ackerman, S., Cesana, G., Chepfer, H., Di Girolamo, L., Getzewich, B., Guignard, A., Heidinger, A., Maddux, B., Menzel, W. P., Minnis, P., Pearl, C., Platnick, S., Poulsen, C., Riedi, J., Sun-Mack, S., Walther, A., Winker, D., Zeng, S., and Zhao, G.: Assessment of global cloud datasets from satellites: Project and Database initiated by the GEWEX Radiation Panel, B. Am. Meteorol. Soc., 23, 1031–1049, https://doi.org/10.1175/BAMS-D-12-00117.1, 2013.
Tabazadeh, A., Djikaev, Y. S., and Reiss, H.: Surface crystallization of supercooled water in clouds, P. Natl. Acad. Sci. USA, 99, 15873–15878, 2003.
Thackeray, C. W. and Hall, A.: An emergent constraint on future Arctic sea-ice albedo feedback, Nat. Clim. Change, 9, 972–978, 2019.
Vihma, T., Jaagus, J., Jakobson, E., and Palo, T.: Meteorological conditions in the Arctic Ocean in spring and summer 2007 as recorded on the drifting ice station Tara, Geophys. Res. Lett., 35, https://doi.org/10.1029/2008GL034681, 2008.
Walton, C. C., Sullivan, J. T., Rao, C. R. N., and Weinreb, M. P.: Corrections for detector nonlinearities and calibration inconsistencies of the infrared channels of the Advanced Very High Resolution Radiometer, J. Geoph. Res.-Oceans, 103, 3323–3337, https://doi.org/10.1029/97JC02018, 1998.
Wang, D. and Liang, S.: Estimating high-resolution top of atmosphere albedo from Moderate Resolution Imaging Spectroradiometer data, Remote Sens. Environ., 178, 93–103, 2016.
Wielicki, B. A., Barkstrom, B. R., Harrison, E. F., Lee III, R. B., Smith, G. L., and Cooper, J. E.: Clouds and the Earth's Radiant Energy System (CERES): An earth observing system experiment, B. Am. Meteorol. Soc., 77, 853–868, 1996.
Wielicki, B. A., Barkstrom, B. R., Baum, B. A., Charlock, T. P., Green, R. N., Kratz, D. P., Lee, R. B., Minnis, P., Smith, G. L., Wong, T., Young, D. F., Cess, R. D., Coakley, J. A., Crommelynck, D. A. H., Donner, L., Kandel, R., King, M. D., Miller, A. J., Ramanathan, V., Randall, D. A., Stowe, L., and Welch, R. M.: Clouds and the Earth's Radiant Energy System (CERES): algorithm overview, IEEE T. Geosci. Remote, 36, 1127–1141, 1998.
Winker, D.: CALIPSO LID L2 5km Standard HDF File – Version 4.10, NASA Langley Research Center Atmospheric Science Data Center DAAC [data set], https://doi.org/10.5067/CALIOP/CALIPSO/LID_L2_05kmCLay-Standard-V4-20, 2016.
Xiong, X., Stamnes, K., and Lubin, D.: Surface Albedo over the Arctic Ocean Derived from AVHRR and Its Validation with SHEBA Data, J. Appl. Meteorol., 41, 413–425, 2002.
Yang, F., Mitchell, K., Hou, Y.-T., Dai, Y., Zeng, X., Wang, Z., and Liang, X.-Z.: Dependence of Land Surface Albedo on Solar Zenith Angle: Observations and Model Parametrization, J. Appl. Meteorol. Clim., 47, 2963–2982, 2008.
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.
Young, A. H., Knapp, K. R., Inamdar, A., Hankins, W., and Rossow, W. B.: The International Satellite Cloud Climatology Project H-Series climate data record product, Earth Syst. Sci. Data, 10, 583–593, https://doi.org/10.5194/essd-10-583-2018, 2018.
Young, D. F., Minnis, P., Doelling, D. R., Gibson, G. G., and Wong, T.: Temporal interpolation methods for the Clouds and the Earth's Radiant Energy System (CERES) experiment, J. Appl. Meteorol., 37, 572–590, 1998.
Short summary
This paper presents a global climate data record on cloud parameters, radiation at the surface and at the top of atmosphere, and surface albedo. The temporal coverage is 1979–2020 (42 years) and the data record is also continuously updated until present time. Thus, more than four decades of climate parameters are provided. Based on CLARA-A3, studies on distribution of clouds and radiation parameters can be made and, especially, investigations of climate trends and evaluation of climate models.
This paper presents a global climate data record on cloud parameters, radiation at the surface...
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