{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T01:13:09Z","timestamp":1780535589498,"version":"3.54.1"},"reference-count":56,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T00:00:00Z","timestamp":1668038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AI for Science program of the Center for Data and Computing at the University of Chicago","award":["SES-1463644"],"award-info":[{"award-number":["SES-1463644"]}]},{"name":"AI for Science program of the Center for Data and Computing at the University of Chicago","award":["DE-AC02-06CH11357"],"award-info":[{"award-number":["DE-AC02-06CH11357"]}]},{"DOI":"10.13039\/100000001","name":"Center for Robust Decision-Making on Climate and Energy Policy (RDCEP)","doi-asserted-by":"publisher","award":["SES-1463644"],"award-info":[{"award-number":["SES-1463644"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"Center for Robust Decision-Making on Climate and Energy Policy (RDCEP)","doi-asserted-by":"publisher","award":["DE-AC02-06CH11357"],"award-info":[{"award-number":["DE-AC02-06CH11357"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research","doi-asserted-by":"publisher","award":["SES-1463644"],"award-info":[{"award-number":["SES-1463644"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research","doi-asserted-by":"publisher","award":["DE-AC02-06CH11357"],"award-info":[{"award-number":["DE-AC02-06CH11357"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Clouds play an important role in the Earth\u2019s energy budget, and their behavior is one of the largest uncertainties in future climate projections. Satellite observations should help in understanding cloud responses, but decades and petabytes of multispectral cloud imagery have to date received only limited use. This study describes a new analysis approach that reduces the dimensionality of satellite cloud observations by grouping them via a novel automated, unsupervised cloud classification technique based on a convolutional autoencoder, an artificial intelligence (AI) method good at identifying patterns in spatial data. Our technique combines a rotation-invariant autoencoder and hierarchical agglomerative clustering to generate cloud clusters that capture meaningful distinctions among cloud textures, using only raw multispectral imagery as input. Cloud classes are therefore defined based on spectral properties and spatial textures without reliance on location, time\/season, derived physical properties, or pre-designated class definitions. We use this approach to generate a unique new cloud dataset, the AI-driven cloud classification atlas (AICCA), which clusters 22 years of ocean images from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA\u2019s Aqua and Terra instruments\u2014198 million patches, each roughly 100 km \u00d7 100 km (128 \u00d7 128 pixels)\u2014into 42 AI-generated cloud classes, a number determined via a newly-developed stability protocol that we use to maximize richness of information while ensuring stable groupings of patches. AICCA thereby translates 801 TB of satellite images into 54.2 GB of class labels and cloud top and optical properties, a reduction by a factor of 15,000. The 42 AICCA classes produce meaningful spatio-temporal and physical distinctions and capture a greater variety of cloud types than do the nine International Satellite Cloud Climatology Project (ISCCP) categories\u2014for example, multiple textures in the stratocumulus decks along the West coasts of North and South America. We conclude that our methodology has explanatory power, capturing regionally unique cloud classes and providing rich but tractable information for global analysis. AICCA delivers the information from multi-spectral images in a compact form, enables data-driven diagnosis of patterns of cloud organization, provides insight into cloud evolution on timescales of hours to decades, and helps democratize climate research by facilitating access to core data.<\/jats:p>","DOI":"10.3390\/rs14225690","type":"journal-article","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T21:33:02Z","timestamp":1668115982000},"page":"5690","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["AICCA: AI-Driven Cloud Classification Atlas"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5669-8565","authenticated-orcid":false,"given":"Takuya","family":"Kurihana","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Chicago, Chicago, IL 60637, USA"},{"name":"The Center for Robust Decision-Making on Climate and Energy Policy, Chicago, IL 60637, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1829-5196","authenticated-orcid":false,"given":"Elisabeth J.","family":"Moyer","sequence":"additional","affiliation":[{"name":"The Center for Robust Decision-Making on Climate and Energy Policy, Chicago, IL 60637, USA"},{"name":"Department of the Geophysical Sciences, University of Chicago, Chicago, IL 60637, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2129-5269","authenticated-orcid":false,"given":"Ian T.","family":"Foster","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Chicago, Chicago, IL 60637, USA"},{"name":"Data Science and Learning Division, Argonne National Laboratory, Lemont, IL 60439, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1175\/1520-0477(1991)072<0002:ICDP>2.0.CO;2","article-title":"ISCCP cloud data products","volume":"71","author":"Rossow","year":"1991","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2394","DOI":"10.1175\/1520-0442(1993)006<2394:COIAOC>2.0.CO;2","article-title":"Comparison of ISCCP and other cloud amounts","volume":"6","author":"Rossow","year":"1993","journal-title":"J. Clim."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2261","DOI":"10.1175\/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2","article-title":"Advances in understanding clouds from ISCCP","volume":"80","author":"Rossow","year":"1999","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_4","unstructured":"World Meteorological Organization (2022, November 01). International Cloud Atlas. Available online: https:\/\/cloudatlas.wmo.int\/."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2373","DOI":"10.1175\/MWR-D-11-00121.1","article-title":"Stratocumulus clouds","volume":"140","author":"Wood","year":"2012","journal-title":"Mon. Weather Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8665","DOI":"10.1029\/2018GL077787","article-title":"CloudNet: Ground-based cloud classification with deep convolutional neural network","volume":"45","author":"Zhang","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_7","unstructured":"Rasp, S., Schulz, H., Bony, S., and Stevens, B. (2019). Combining crowd-sourcing and deep learning to understand meso-scale organization of shallow convection. arXiv."},{"key":"ref_8","unstructured":"Zantedeschi, V., Falasca, F., Douglas, A., Strange, R., Kusner, M., and Watson-Parris, D. (2022, November 01). Cumulo: A dataset for learning cloud classes. NeurIPS Workshop on Tackling Climate Change with Machine Learning. Available online: https:\/\/www.climatechange.ai\/papers\/neurips2019\/11."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6989","DOI":"10.5194\/amt-13-6989-2020","article-title":"Applying deep learning to NASA MODIS data to create a community record of marine low-cloud mesoscale morphology","volume":"13","author":"Yuan","year":"2020","journal-title":"Atmos. Meas. Tech."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5459","DOI":"10.5194\/amt-13-5459-2020","article-title":"Leveraging spatial textures, through machine learning, to identify aerosols and distinct cloud types from multispectral observations","volume":"13","author":"Marais","year":"2020","journal-title":"Atmos. Meas. Tech."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1002\/qj.3662","article-title":"Sugar, gravel, fish and flowers: Mesoscale cloud patterns in the trade winds","volume":"146","author":"Stevens","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Visa, A., Iivarinen, J., Valkealahti, K., and Simula, O. (1998). Neural network based cloud classifier. Industrial Applications of Neural Networks, World Scientific.","DOI":"10.1142\/9789812816955_0035"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1109\/72.737500","article-title":"A study of cloud classification with neural networks using spectral and textural features","volume":"10","author":"Tian","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e2019GL085190","DOI":"10.1029\/2019GL085190","article-title":"Discovering the importance of mesoscale cloud organization through unsupervised classification","volume":"47","author":"Denby","year":"2020","journal-title":"Geophys. Res. Lett."},{"key":"ref_15","unstructured":"Kurihana, T., Foster, I.T., Willett, R., Jenkins, S., Koenig, K., Werman, R., Barros Lourenco, R., Neo, C., and Moyer, E.J. (2019, January 2\u20134). Cloud classification with unsupervised deep learning. Proceedings of the 9th International Workshop on Climate Informatics, Paris, France."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/BF02289588","article-title":"Hierarchical clustering schemes","volume":"32","author":"Johnson","year":"1967","journal-title":"Psychometrika"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hinton, G.E., Krizhevsky, A., and Wang, S. (2011). Transforming auto-encoders. International Conference on Artificial Neural Networks, Springer.","DOI":"10.1007\/978-3-642-21735-7_6"},{"key":"ref_18","first-page":"4103325","article-title":"Data-driven cloud clustering via a rotationally invariant autoencoder","volume":"60","author":"Kurihana","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","unstructured":"Adams, D. (1979). The Hitchhikers Guide to the Galaxy, Random House."},{"key":"ref_20","unstructured":"MODIS Characterization Support Team (2017). MODIS\/Aqua 1km Calibrated Radiances Product, Goddard Space Flight Center."},{"key":"ref_21","unstructured":"MODIS Characterization Support Team (2017). MODIS\/Terra 1km Calibrated Radiances Product, Goddard Space Flight Center."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.1109\/TGRS.2007.895841","article-title":"Stripe noise reduction in MODIS data by combining histogram matching with facet filter","volume":"45","author":"Rakwatin","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/38.56302","article-title":"NetCDF: An interface for scientific data access","volume":"10","author":"Rew","year":"1990","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_24","unstructured":"Loizides, F., and Schmidt, B. (2016). Jupyter Notebooks\u2014A publishing format for reproducible computational workflows. Positioning and Power in Academic Publishing: Players, Agents and Agendas, IOS Press."},{"key":"ref_25","unstructured":"Kurihana, T. (2022, November 01). Rotation-Invariant Cloud Clustering Code. Available online: https:\/\/github.com\/RDCEP\/clouds."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chard, R., Li, Z., Chard, K., Ward, L., Babuji, Y., Woodard, A., Tuecke, S., Blaiszik, B., Franklin, M.J., and Foster, I.T. (2019, January 20\u201324). DLHub: Model and data serving for science. Proceedings of the IEEE International Parallel and Distributed Processing Symposium, Rio de Janeiro, Brazil.","DOI":"10.1109\/IPDPS.2019.00038"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MCC.2014.52","article-title":"Efficient and secure transfer, synchronization, and sharing of big data","volume":"1","author":"Chard","year":"2014","journal-title":"IEEE Cloud Comput."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chard, R., Babuji, Y., Li, Z., Skluzacek, T., Woodard, A., Blaiszik, B., Foster, I.T., and Chard, K. (2020, January 23\u201326). FuncX: A federated function serving fabric for science. Proceedings of the 29th International Symposium on High-performance Parallel and Distributed Computing, Stockholm, Sweden.","DOI":"10.1145\/3369583.3392683"},{"key":"ref_29","unstructured":"Hinton, G.E., and Richard, S.Z. (1994). Autoencoders, minimum description length and Helmholtz free energy. Advances in Neural Information Processing Systems 6, Morgan-Kaufmann."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/44565","article-title":"Learning the parts of objects by non-negative matrix factorization","volume":"401","author":"Lee","year":"1999","journal-title":"Nature"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Matsuo, T., Fukuhara, H., and Shimada, N. (2017, January 24\u201328). Transform invariant auto-encoder. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8206047"},{"key":"ref_32","unstructured":"Nair, V., and Hinton, G.E. (2010). Rectified linear units improve restricted Boltzmann machines. Int. Conf. Mach. Learn., Available online: https:\/\/icml.cc\/Conferences\/2010\/papers\/432.pdf."},{"key":"ref_33","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume":"37","author":"Ioffe","year":"2015","journal-title":"Int. Conf. Mach. Learn."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1080\/01621459.1963.10500845","article-title":"Hierarchical grouping to optimize an objective function","volume":"58","author":"Ward","year":"1963","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_35","unstructured":"Jenkins, S., Moyer, E.J., Foster, I.T., Kurihana, T., Willett, R., Maire, M., Koenig, K., and Werman, R. (2019). Developing unsupervised learning models for cloud classification. AGU Fall Meet. Abstr., A51U-2673."},{"key":"ref_36","first-page":"1","article-title":"Big data reduction technique using parallel hierarchical agglomerative clustering","volume":"45","author":"Moertini","year":"2018","journal-title":"IAENG Int. J. Comput. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1145\/2786984.2786995","article-title":"Scikit-learn: Machine learning without learning the machinery","volume":"19","author":"Varoquaux","year":"2015","journal-title":"Getmobile Mob. Comput. Commun."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jin, C., Liu, R., Chen, Z., Hendrix, W., Agrawal, A., and Choudhary, A. (2015\u20132, January 30). A scalable hierarchical clustering algorithm using Spark. Proceedings of the IEEE First International Conference on Big Data Computing Service and Applications, Redwood City, CA, USA.","DOI":"10.1109\/BigDataService.2015.67"},{"key":"ref_39","unstructured":"Sumengen, B., Rajagopalan, A., Citovsky, G., Simcha, D., Bachem, O., Mitra, P., Blasiak, S., Liang, M., and Kumar, S. (2021). Scaling hierarchical agglomerative clustering to billion-sized datasets. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Monath, N., Dubey, K.A., Guruganesh, G., Zaheer, M., Ahmed, A., McCallum, A., Mergen, G., Najork, M., Terzihan, M., and Tjanaka, B. (2021, January 14\u201318). Scalable hierarchical agglomerative clustering. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Singapore.","DOI":"10.1145\/3447548.3467404"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Babuji, Y., Woodard, A., Li, Z., Katz, D.S., Clifford, B., Kumar, R., Lacinski, L., Chard, R., Wozniak, J.M., and Foster, I.T. (2019, January 22\u201329). Parsl: Pervasive parallel programming in Python. Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing, Phoenix, AZ, USA.","DOI":"10.1145\/3307681.3325400"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Santos, J.M., and Embrechts, M. (2009). On the use of the adjusted Rand index as a metric for evaluating supervised classification. International Conference on Artificial Neural Networks, Springer.","DOI":"10.1007\/978-3-642-04277-5_18"},{"key":"ref_43","unstructured":"Von Luxburg, U. (2010). Clustering Stability: An Overview, Now Publishers Inc."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"7734","DOI":"10.1175\/JCLI-D-13-00024.1","article-title":"Global weather states and their properties from passive and active satellite cloud retrievals","volume":"26","author":"Tselioudis","year":"2013","journal-title":"J. Clim."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1029\/2018JD028595","article-title":"A comparison of cloud classification methodologies: Differences between cloud and dynamical regimes","volume":"123","author":"McDonald","year":"2018","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4259","DOI":"10.1002\/2017JD028196","article-title":"Regional regime-based evaluation of present-day general circulation model cloud simulations using self-organizing maps","volume":"123","author":"Schuddeboom","year":"2018","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_47","first-page":"17","article-title":"EBK-means: A clustering technique based on elbow method and k-means in WSN","volume":"105","author":"Bholowalia","year":"2014","journal-title":"Int. J. Comput. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1111\/1467-9868.00293","article-title":"Estimating the number of clusters in a data set via the gap statistic","volume":"63","author":"Tibshirani","year":"2001","journal-title":"J. R. Stat. Soc. Ser. Stat. Methodol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s00382-016-3107-6","article-title":"Simplified ISCCP cloud regimes for evaluating cloudiness in CMIP5 models","volume":"48","author":"Jin","year":"2017","journal-title":"Clim. Dyn."},{"key":"ref_51","unstructured":"(2022, May 01). ISCCP Definition of Cloud Types, Available online: https:\/\/isccp.giss.nasa.gov\/cloudtypes.html."},{"key":"ref_52","unstructured":"Gumley, L., Descloitres, J., and Schmaltz, J. (2003). Creating Reprojected True Color MODIS Images: A Tutorial, University of Wisconsin\u2014Madison."},{"key":"ref_53","unstructured":"Riggs, G.A., Hall, D.K., and Rom\u00e1n, M.O. (2022, November 01). MODIS Snow Products Collection 6 User Guide, Available online: https:\/\/modis-snow-ice.gsfc.nasa.gov\/uploads\/C6_MODIS_Snow_User_Guide.pdf."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1038\/s41561-019-0310-1","article-title":"Possible climate transitions from breakup of stratocumulus decks under greenhouse warming","volume":"12","author":"Schneider","year":"2019","journal-title":"Nat. Geosci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1177\/10943420211027539","article-title":"Unprecedented cloud resolution in a GPU-enabled full-physics atmospheric climate simulation on OLCF\u2019s Summit supercomputer","volume":"36","author":"Norman","year":"2022","journal-title":"Int. J. High Perform. Comput. Appl."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF01908075","article-title":"Comparing partitions","volume":"2","author":"Hubert","year":"1985","journal-title":"J. Classif."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5690\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:14:09Z","timestamp":1760145249000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5690"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,10]]},"references-count":56,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14225690"],"URL":"https:\/\/doi.org\/10.3390\/rs14225690","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,10]]}}}