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In this paper, we learn the semantics of sky/cloud images, which allows an automatic annotation of pixels with different class labels. We model the various labels/classes with a continuous-valued multi-variate distribution. Using a set of training images, the distributions for different labels are learnt, and subsequently used for labeling test images. We also present a method to determine the number of clusters. Our proposed approach is the first for multi-class sky-cloud image annotation and achieves very good results.", "proceedingsTitle": "", "conferenceName": "IEEE International Conference on Image Processing (ICIP)", "publisher": "IEEE", "place": "", "date": "2015", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "636-640", "series": "", "seriesNumber": "", "DOI": "", "ISBN": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "Zotero", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "5V74X4A4" ], "relations": {}, "dateAdded": "2019-05-09T09:58:25Z", "dateModified": "2025-08-15T20:40:58Z" } }, { "key": "JCFGZ9Z5", "version": 3001, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/JCFGZ9Z5", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/JCFGZ9Z5", "type": "text/html" } }, "meta": { "createdByUser": { "id": 3386529, "username": "Peter Drake", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/peter_drake", "type": "text/html" } } }, "creatorSummary": "Abu-Mostafa et al.", "parsedDate": "2012", "numChildren": 0 }, "data": { "key": "JCFGZ9Z5", "version": 3001, "itemType": "book", "title": "Learning from data: a short course", "creators": [ { "creatorType": "author", "firstName": "Yaser S.", "lastName": "Abu-Mostafa" }, { "creatorType": "author", "firstName": "Malik", "lastName": "Magdon-Ismail" }, { "creatorType": "author", "firstName": "Hsuan-Tien", "lastName": "Lin" } ], "abstractNote": "", "series": "", "seriesNumber": "", "volume": "", "numberOfVolumes": "", "edition": "", "date": "2012", "publisher": "AMLbook.com", "place": "S.l.", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "numPages": "201", "ISBN": "978-1-60049-006-4", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "Learning from data", "language": "eng", "libraryCatalog": "K10plus ISBN", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "5V74X4A4" ], "relations": {}, "dateAdded": "2025-08-15T17:36:50Z", "dateModified": "2025-08-15T17:36:50Z" } }, { "key": "HW5BGNM7", "version": 3000, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/HW5BGNM7", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/HW5BGNM7", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/267330/items/3YPDP9PV", "type": "application/json" } }, "meta": { "createdByUser": { "id": 3386529, "username": "Peter Drake", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/peter_drake", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "HW5BGNM7", "version": 3000, "parentItem": "3YPDP9PV", "itemType": "note", "note": "Previous editions: 2019, 2017", "tags": [], "relations": {}, "dateAdded": "2025-08-15T17:36:02Z", "dateModified": "2025-08-15T17:36:02Z" } }, { "key": "IAKKLH27", "version": 3000, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/IAKKLH27", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/IAKKLH27", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/267330/items/3YPDP9PV", "type": "application/json" } }, "meta": { "createdByUser": { "id": 3386529, "username": "Peter Drake", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/peter_drake", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "IAKKLH27", "version": 3000, "parentItem": "3YPDP9PV", "itemType": "note", "note": "The fundamentals of machine learning. The machine learning landscape ; End-to-end machine learning project ; Classification ; Training models ; Support vector machines ; Decision trees ; Ensemble learning and random forests ; Dimensionality reduction ; Unsupervised learning techniques -- Neural networks and deep learning. Introduction to artificial neural networks with Keras ; Training deep neural networks ; Custom models and training with TensorFlow ; Loading and preprocessing data with TensorFlow ; Deep computer vision using convolutional neural networks ; Processing sequences using RNNs and CNNs ; Natural language processing with RNNs and attention ; Autoencoders, GANs, and diffusion models ; Reinforcement learning ; Training and deploying TensorFlow models at scale", "tags": [], "relations": {}, "dateAdded": "2025-08-15T17:36:02Z", "dateModified": "2025-08-15T17:36:02Z" } }, { "key": "3YPDP9PV", "version": 3000, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/3YPDP9PV", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/3YPDP9PV", "type": "text/html" } }, "meta": { "createdByUser": { "id": 3386529, "username": "Peter Drake", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/peter_drake", "type": "text/html" } } }, "creatorSummary": "Géron", "parsedDate": "2023", "numChildren": 2 }, "data": { "key": "3YPDP9PV", "version": 3000, "itemType": "book", "title": "Hands-on machine learning with Scikit-Learn, Keras and TensorFlow: concepts, tools, and techniques to build intelligent systems", "creators": [ { "creatorType": "author", "firstName": "Aurélien", "lastName": "Géron" } ], "abstractNote": "\"Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This best-selling book uses concrete examples, minimal theory, and production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. With this updated third edition, author Aurelien Geron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started\"--", "series": "", "seriesNumber": "", "volume": "", "numberOfVolumes": "", "edition": "Third edition", "date": "2023", "publisher": "O'Reilly Media, Inc", "place": "Beijing", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "numPages": "834", "ISBN": "978-1-0981-2597-4", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "Hands-on machine learning with Scikit-Learn, Keras and TensorFlow", "language": "", "libraryCatalog": "Library of Congress ISBN", "callNumber": "QA76.73.P98 G45 2023", "rights": "", "extra": "", "tags": [ { "tag": "Apprentissage automatique", "type": 1 }, { "tag": "Artificial intelligence", "type": 1 }, { "tag": "Intelligence artificielle", "type": 1 }, { "tag": "Machine learning", "type": 1 }, { "tag": "Python (Computer program language)", "type": 1 }, { "tag": "Python (Langage de programmation)", "type": 1 }, { "tag": "TensorFlow", "type": 1 }, { "tag": "artificial intelligence", "type": 1 } ], "collections": [ "5V74X4A4" ], "relations": {}, "dateAdded": "2025-08-15T17:36:02Z", "dateModified": "2025-08-15T17:36:02Z" } }, { "key": "A7XYFY48", "version": 2999, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/A7XYFY48", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/A7XYFY48", "type": "text/html" } }, "meta": { "createdByUser": { "id": 3386529, "username": "Peter Drake", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/peter_drake", "type": "text/html" } } }, "creatorSummary": "Russell and Norvig", "parsedDate": "2021", "numChildren": 0 }, "data": { "key": "A7XYFY48", "version": 2999, "itemType": "book", "title": "Artificial intelligence: a modern approach", "creators": [ { "creatorType": "author", "firstName": "Stuart J.", "lastName": "Russell" }, { "creatorType": "author", "firstName": "Peter", "lastName": "Norvig" } ], "abstractNote": "\"Updated edition of popular textbook on Artificial Intelligence. This edition specific looks at ways of keeping artificial intelligence under control\"--", "series": "Pearson series in artificial intelligence", "seriesNumber": "", "volume": "", "numberOfVolumes": "", "edition": "Fourth edition", "date": "2021", "publisher": "Pearson", "place": "Hoboken", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "numPages": "", "ISBN": "978-0-13-461099-3", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "Artificial intelligence", "language": "", "libraryCatalog": "Library of Congress ISBN", "callNumber": "Q335 .R86 2021", "rights": "", "extra": "", "tags": [ { "tag": "Artificial intelligence", "type": 1 } ], "collections": [ "5V74X4A4" ], "relations": {}, "dateAdded": "2025-08-15T17:35:08Z", "dateModified": "2025-08-15T17:35:08Z" } }, { "key": "QMLT5VYZ", "version": 2991, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/QMLT5VYZ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/QMLT5VYZ", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/267330/items/5AIWQF7H", "type": "application/json" } }, "meta": { "createdByUser": { "id": 861130, "username": "jkleiss", "name": "Jessica Kleiss", "links": { "alternate": { "href": "https://www.zotero.org/jkleiss", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "QMLT5VYZ", "version": 2991, "parentItem": "5AIWQF7H", "itemType": "attachment", "linkMode": "imported_file", "title": "Lin et al. - 2023 - Recent advances in intra-hour solar forecasting A.pdf", "accessDate": "", "url": "", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Lin et al. - 2023 - Recent advances in intra-hour solar forecasting A.pdf", "md5": "f111241501122446f97771f4c1155054", "mtime": 1717105308298, "tags": [], "relations": {}, "dateAdded": "2024-07-23T00:09:47Z", "dateModified": "2024-07-23T00:09:47Z" } }, { "key": "5AIWQF7H", "version": 2990, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/5AIWQF7H", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/5AIWQF7H", "type": "text/html" } }, "meta": { "createdByUser": { "id": 861130, "username": "jkleiss", "name": "Jessica Kleiss", "links": { "alternate": { "href": "https://www.zotero.org/jkleiss", "type": "text/html" } } }, "creatorSummary": "Lin et al.", "parsedDate": "2023-01", "numChildren": 1 }, "data": { "key": "5AIWQF7H", "version": 2990, "itemType": "journalArticle", "title": "Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods", "creators": [ { "creatorType": "author", "firstName": "Fan", "lastName": "Lin" }, { "creatorType": "author", "firstName": "Yao", "lastName": "Zhang" }, { "creatorType": "author", "firstName": "Jianxue", "lastName": "Wang" } ], "abstractNote": "As the penetration of solar energy generation into power systems keeps rising, intra-hour solar forecasting (IHSF) is becoming increasingly important for the secure and economical operation of a power system. One major difficulty in providing very accurate IHSF emanates from rapid cloud changes in the sky. The ground-based sky image (GSI) provides the intuitive information of intra-hour cloud changes and has thus been widely utilized in studies on IHSF. This paper presents a systematic review of the state-of-the-art of ground-based sky image-based intra-hour solar forecasting (GSI-IHSF). To our knowledge, we first propose a generic framework of GSI-IHSF consisting of four modules, i.e., sky image acquisition, sky image preprocessing, cloud forecasting, and solar forecasting. Then, as for each module, this paper introduces its core function, shows the major challenges, briefly reviews several extensively used techniques, summarizing research trends. Finally, this paper offers a prospect of GSI-IHSF research, discusses recent advances that demonstrate the potential for a great improvement in forecast accuracy, pointing out some new requirements and challenges that should be further investigated in the future.", "publicationTitle": "International Journal of Forecasting", "publisher": "", "place": "", "date": "2023-01", "volume": "39", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "244–265", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1016/j.ijforecast.2021.11.002", "citationKey": "", "url": "https://www.sciencedirect.com/science/article/pii/S016920702100176X", "accessDate": "2024-05-30", "PMID": "", "PMCID": "", "ISSN": "0169-2070", "archive": "", "archiveLocation": "", "shortTitle": "Recent advances in intra-hour solar forecasting", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Cloud forecasting" }, { "tag": "Computer vision" }, { "tag": "Ground-based sky image" }, { "tag": "Intra-hour solar forecasting" }, { "tag": "Machine learning" }, { "tag": "Ramp-down forecasting" } ], "collections": [], "relations": {}, "dateAdded": "2024-07-23T00:09:47Z", "dateModified": "2024-07-23T00:09:47Z" } }, { "key": "NK85IT2I", "version": 2988, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/NK85IT2I", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/NK85IT2I", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/267330/items/RM5K5STG", "type": "application/json" } }, "meta": { "createdByUser": { "id": 861130, "username": "jkleiss", "name": "Jessica Kleiss", "links": { "alternate": { "href": "https://www.zotero.org/jkleiss", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "NK85IT2I", "version": 2988, "parentItem": "RM5K5STG", "itemType": "attachment", "linkMode": "imported_url", "title": "Werkmeister et al. - 2015 - Comparing satellite- to ground-based automated and.pdf", "accessDate": "2024-07-22T22:56:04Z", "url": "https://amt.copernicus.org/articles/8/2001/2015/amt-8-2001-2015.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Werkmeister et al. - 2015 - Comparing satellite- to ground-based automated and.pdf", "md5": "cc96dd26eb637782795ed1835a907ba4", "mtime": 1721688964045, "tags": [], "relations": {}, "dateAdded": "2024-07-22T22:56:04Z", "dateModified": "2024-07-22T22:56:35Z" } }, { "key": "RM5K5STG", "version": 3011, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/RM5K5STG", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/RM5K5STG", "type": "text/html" } }, "meta": { "createdByUser": { "id": 861130, "username": "jkleiss", "name": "Jessica Kleiss", "links": { "alternate": { "href": "https://www.zotero.org/jkleiss", "type": "text/html" } } }, "creatorSummary": "Werkmeister et al.", "parsedDate": "2015-05-06", "numChildren": 1 }, "data": { "key": "RM5K5STG", "version": 3011, "itemType": "journalArticle", "title": "Comparing satellite- to ground-based automated and manual cloud coverage observations – a case study", "creators": [ { "creatorType": "author", "firstName": "A.", "lastName": "Werkmeister" }, { "creatorType": "author", "firstName": "M.", "lastName": "Lockhoff" }, { "creatorType": "author", "firstName": "M.", "lastName": "Schrempf" }, { "creatorType": "author", "firstName": "K.", "lastName": "Tohsing" }, { "creatorType": "author", "firstName": "B.", "lastName": "Liley" }, { "creatorType": "author", "firstName": "G.", "lastName": "Seckmeyer" } ], "abstractNote": "In this case study we compare cloud fractional cover measured by radiometers on polar satellites (AVHRR) and on one geostationary satellite (SEVIRI) to ground-based manual (SYNOP) and automated observations by a cloud camera (Hemispherical Sky Imager, HSI). These observations took place in Hannover, Germany, and in Lauder, New Zealand, over time frames of 3 and 2 months, respectively. \n\n Daily mean comparisons between satellite derivations and the ground-based HSI found the deviation to be 6 ± 14% for AVHRR and 8 ± 16% for SEVIRI, which can be considered satisfactory. AVHRR's instantaneous differences are smaller (2 ± 22%) than instantaneous SEVIRI cloud fraction estimates (8 ± 29%) when compared to HSI due to resolution and scenery effect issues. All spaceborne observations show a very good skill in detecting completely overcast skies (cloud cover ≥ 6 oktas) with probabilities between 92 and 94% and false alarm rates between 21 and 29% for AVHRR and SEVIRI in Hannover, Germany. In the case of a clear sky (cloud cover lower than 3 oktas) we find good skill with detection probabilities between 72 and 76%. We find poor skill, however, whenever broken clouds occur (probability of detection is 32% for AVHRR and 12% for SEVIRI in Hannover, Germany). \n\n In order to better understand these discrepancies we analyze the influence of algorithm features on the satellite-based data. We find that the differences between SEVIRI and HSI cloud fractional cover (CFC) decrease (from a bias of 8 to almost 0%) with decreasing number of spatially averaged pixels and decreasing index which determines the cloud coverage in each \"cloud-contaminated\" pixel of the binary map. We conclude that window size and index need to be adjusted in order to improve instantaneous SEVIRI and AVHRR estimates. Due to its automated operation and its spatial, temporal and spectral resolution, we recommend as well that more automated ground-based instruments in the form of cloud cameras should be installed as they cover larger areas of the sky than other automated ground-based instruments. These cameras could be an essential supplement to SYNOP observation as they cover the same spectral wavelengths as the human eye.", "publicationTitle": "Atmospheric Measurement Techniques", "publisher": "Copernicus GmbH", "place": "", "date": "2015-05-06", "volume": "8", "issue": "5", "section": "", "partNumber": "", "partTitle": "", "pages": "2001-2015", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.5194/amt-8-2001-2015", "citationKey": "", "url": "https://amt.copernicus.org/articles/8/2001/2015/", "accessDate": "2024-07-22T22:55:56Z", "PMID": "", "PMCID": "", "ISSN": "1867-1381", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "English", "libraryCatalog": "Copernicus Online Journals", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2024-07-22T22:55:56Z", "dateModified": "2024-07-22T22:55:56Z" } }, { "key": "AAI4XRTU", "version": 2983, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/AAI4XRTU", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/AAI4XRTU", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/267330/items/ZZEWT22V", "type": "application/json" } }, "meta": { "createdByUser": { "id": 861130, "username": "jkleiss", "name": "Jessica Kleiss", "links": { "alternate": { "href": "https://www.zotero.org/jkleiss", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "AAI4XRTU", "version": 2983, "parentItem": "ZZEWT22V", "itemType": "attachment", "linkMode": "imported_url", "title": "Kim et al. - 2023 - Estimation of 24 h continuous cloud cover using a .pdf", "accessDate": "2024-07-22T22:42:22Z", "url": "https://amt.copernicus.org/articles/16/5403/2023/amt-16-5403-2023.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Kim et al. - 2023 - Estimation of 24 h continuous cloud cover using a .pdf", "md5": "a307d8a9265887e28b178d55636fa2ee", "mtime": 1721688142665, "tags": [], "relations": {}, "dateAdded": "2024-07-22T22:42:22Z", "dateModified": "2024-07-22T22:45:48Z" } }, { "key": "ZZEWT22V", "version": 3011, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/ZZEWT22V", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/ZZEWT22V", "type": "text/html" } }, "meta": { "createdByUser": { "id": 861130, "username": "jkleiss", "name": "Jessica Kleiss", "links": { "alternate": { "href": "https://www.zotero.org/jkleiss", "type": "text/html" } } }, "creatorSummary": "Kim et al.", "parsedDate": "2023-11-13", "numChildren": 1 }, "data": { "key": "ZZEWT22V", "version": 3011, "itemType": "journalArticle", "title": "Estimation of 24 h continuous cloud cover using a ground-based imager with a convolutional neural network", "creators": [ { "creatorType": "author", "firstName": "Bu-Yo", "lastName": "Kim" }, { "creatorType": "author", "firstName": "Joo Wan", "lastName": "Cha" }, { "creatorType": "author", "firstName": "Yong Hee", "lastName": "Lee" } ], "abstractNote": "In this study, we aimed to estimate cloud cover with high accuracy using images from a camera-based imager and a convolutional neural network (CNN) as a potential alternative to human-eye observation on the ground. Image data collected at 1 h intervals from 2019 to 2020 at a staffed weather station, where human-eye observations were performed, were used as input data. The 2019 dataset was used for training and validating the CNN model, whereas the 2020 dataset was used for testing the estimated cloud cover. Additionally, we compared satellite (SAT) and ceilometer (CEI) cloud cover to determine the method most suitable for cloud cover estimation at the ground level. The CNN model was optimized using a deep layer and detailed hyperparameter settings. Consequently, the model achieved an accuracy, bias, root mean square error (RMSE), and correlation coefficient (R) of 0.92, −0.13, 1.40 tenths, and 0.95, respectively, on the test dataset, and exhibited approximately 93 % high agreement at a difference within ±2 tenths of the observed cloud cover. This result demonstrates an improvement over previous studies that used threshold, machine learning, and deep learning methods. In addition, compared with the SAT (with an accuracy, bias, RMSE, R, and agreement of 0.89, 0.33 tenths, 2.31 tenths, 0.87, and 83 %, respectively) and CEI (with an accuracy, bias, RMSE, R, agreement of 0.86, −1.58 tenths, 3.34 tenths, 0.76, and 74 %, respectively), the camera-based imager with the CNN was found to be the most suitable method to replace ground cloud cover observation by humans.", "publicationTitle": "Atmospheric Measurement Techniques", "publisher": "Copernicus GmbH", "place": "", "date": "2023-11-13", "volume": "16", "issue": "21", "section": "", "partNumber": "", "partTitle": "", "pages": "5403-5413", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.5194/amt-16-5403-2023", "citationKey": "", "url": "https://amt.copernicus.org/articles/16/5403/2023/", "accessDate": "2024-07-22T22:42:13Z", "PMID": "", "PMCID": "", "ISSN": "1867-1381", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "English", "libraryCatalog": "Copernicus Online Journals", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2024-07-22T22:42:13Z", "dateModified": "2024-07-22T22:42:40Z" } }, { "key": "4PZ7MLWG", "version": 2977, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/4PZ7MLWG", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/4PZ7MLWG", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/267330/items/GBTMC6QC", "type": "application/json" } }, "meta": { "createdByUser": { "id": 861130, "username": "jkleiss", "name": "Jessica Kleiss", "links": { "alternate": { "href": "https://www.zotero.org/jkleiss", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "4PZ7MLWG", "version": 2977, "parentItem": "GBTMC6QC", "itemType": "attachment", "linkMode": "imported_url", "title": "Tian et al. - 2021 - Summertime Continental Shallow Cumulus Cloud Detec.pdf", "accessDate": "2024-05-23T19:28:08Z", "url": "https://www.mdpi.com/2072-4292/13/12/2309/pdf?version=1623493407", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Tian et al. - 2021 - Summertime Continental Shallow Cumulus Cloud Detec.pdf", "md5": "a4f1470194f05e54f4e68eeebd91e28d", "mtime": 1716492488816, "tags": [], "relations": {}, "dateAdded": "2024-05-23T19:28:08Z", "dateModified": "2024-07-22T22:25:05Z" } }, { "key": "VD4VR7IC", "version": 2976, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/VD4VR7IC", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/VD4VR7IC", "type": "text/html" } }, "meta": { "createdByUser": { "id": 861130, "username": "jkleiss", "name": "Jessica Kleiss", "links": { "alternate": { "href": "https://www.zotero.org/jkleiss", "type": "text/html" } } }, "creatorSummary": "Forster et al.", "parsedDate": "2021", "numChildren": 0 }, "data": { "key": "VD4VR7IC", "version": 2976, "itemType": "bookSection", "title": "The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity", "creators": [ { "creatorType": "author", "firstName": "P", "lastName": "Forster" }, { "creatorType": "author", "firstName": "K", "lastName": "Storelvmo" }, { "creatorType": "author", "firstName": "K", "lastName": "Armour" }, { "creatorType": "author", "firstName": "al.", "lastName": "et" } ], "abstractNote": "", "bookTitle": "Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change", "series": "", "seriesNumber": "", "volume": "", "numberOfVolumes": "", "edition": "", "date": "2021", "publisher": "Cambridge University Press", "place": "Cambridge, United Kingdom and New York, NY", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "pages": "923-1054", "ISBN": "", "DOI": "", "citationKey": "", "url": "https://doi.org/10.1017/9781009157896.009", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2024-07-22T22:00:49Z", "dateModified": "2024-07-22T22:12:59Z" } }, { "key": "6SHSTFVS", "version": 2962, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/6SHSTFVS", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/6SHSTFVS", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/267330/items/AFAS4MPY", "type": "application/json" } }, "meta": { "createdByUser": { "id": 861130, "username": "jkleiss", "name": "Jessica Kleiss", "links": { "alternate": { "href": "https://www.zotero.org/jkleiss", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "6SHSTFVS", "version": 2962, "parentItem": "AFAS4MPY", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2024-07-22T21:34:29Z", "url": "https://acp.copernicus.org/articles/24/1587/2024/acp-24-1587-2024.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Bock and Lauer - 2024 - Cloud properties and their projected changes in CM.pdf", "md5": "6bb4dc3b3bcbc2d93d71ef0801f10d1e", "mtime": 1721684069186, "tags": [], "relations": {}, "dateAdded": "2024-07-22T21:34:29Z", "dateModified": "2024-07-22T21:34:29Z" } }, { "key": "AFAS4MPY", "version": 3011, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/AFAS4MPY", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/AFAS4MPY", "type": "text/html" } }, "meta": { "createdByUser": { "id": 861130, "username": "jkleiss", "name": "Jessica Kleiss", "links": { "alternate": { "href": "https://www.zotero.org/jkleiss", "type": "text/html" } } }, "creatorSummary": "Bock and Lauer", "parsedDate": "2024-02-05", "numChildren": 1 }, "data": { "key": "AFAS4MPY", "version": 3011, "itemType": "journalArticle", "title": "Cloud properties and their projected changes in CMIP models with low to high climate sensitivity", "creators": [ { "creatorType": "author", "firstName": "Lisa", "lastName": "Bock" }, { "creatorType": "author", "firstName": "Axel", "lastName": "Lauer" } ], "abstractNote": "Since the release of the first Coupled Model Intercomparison Project version 6 (CMIP6) simulations, one of the most discussed topics is the higher effective climate sensitivity (ECS) of some of the models, resulting in an increased range of ECS values in CMIP6 compared to previous CMIP phases. An important contribution to ECS is the cloud climate feedback. Although climate models have continuously been developed and improved over the last few decades, a realistic representation of clouds remains challenging. Clouds contribute to the large uncertainties in modeled ECS, as projected changes in cloud properties and cloud feedbacks also depend on the simulated present-day fields.\n\n In this study, we investigate the representation of both cloud physical and radiative properties from a total of 51 CMIP5 and CMIP6 models. ECS is used as a simple metric to group the models, as the sensitivity of the physical cloud properties to warming is closely related to cloud feedbacks, which in turn are known to have a large contribution to ECS. Projected changes in the cloud properties in future scenario simulations are analyzed by the ECS group. In order to help with interpreting the projected changes, model results from historical simulations are also analyzed.\n\n The results show that differences in the net cloud radiative effect as a reaction to warming among the three model groups are driven by changes in a range of cloud regimes rather than individual regions. In polar regions, high-ECS models show a weaker increase in the net cooling effect of clouds, due to warming, than the low-ECS models. At the same time, high-ECS models show a decrease in the net cooling effect of clouds over the tropical ocean and the subtropical stratocumulus regions, whereas low-ECS models show either little change or even an increase in the cooling effect. Over the Southern Ocean, the low-ECS models show a higher sensitivity of the net cloud radiative effect to warming than the high-ECS models.", "publicationTitle": "Atmospheric Chemistry and Physics", "publisher": "Copernicus GmbH", "place": "", "date": "2024-02-05", "volume": "24", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "1587-1605", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.5194/acp-24-1587-2024", "citationKey": "", "url": "https://acp.copernicus.org/articles/24/1587/2024/", "accessDate": "2024-07-22T21:34:18Z", "PMID": "", "PMCID": "", "ISSN": "1680-7316", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "English", "libraryCatalog": "Copernicus Online Journals", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2024-07-22T21:34:18Z", "dateModified": "2024-07-22T21:34:18Z" } }, { "key": "DCPRFCAV", "version": 2958, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/DCPRFCAV", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/DCPRFCAV", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/267330/items/JEW2I3NH", "type": "application/json" } }, "meta": { "createdByUser": { "id": 861130, "username": "jkleiss", "name": "Jessica Kleiss", "links": { "alternate": { "href": "https://www.zotero.org/jkleiss", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "DCPRFCAV", "version": 2958, "parentItem": "JEW2I3NH", "itemType": "attachment", "linkMode": "imported_file", "title": "May et al. - 2024 - Automated Segmentation of Tropical Cyclone Clouds .pdf", "accessDate": "", "url": "", "note": "", "contentType": "application/pdf", "charset": "", "filename": "May et al. - 2024 - Automated Segmentation of Tropical Cyclone Clouds .pdf", "md5": "e384aea4ea4ad5040b3574cfc9f68f83", "mtime": 1720650864667, "tags": [], "relations": {}, "dateAdded": "2024-07-10T22:37:35Z", "dateModified": "2024-07-10T22:37:35Z" } }, { "key": "X77PRNYS", "version": 2956, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/X77PRNYS", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/X77PRNYS", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/267330/items/JEW2I3NH", "type": "application/json" } }, "meta": { "createdByUser": { "id": 861130, "username": "jkleiss", "name": "Jessica Kleiss", "links": { "alternate": { "href": "https://www.zotero.org/jkleiss", "type": "text/html" } } } }, "data": { "key": "X77PRNYS", "version": 2956, "parentItem": "JEW2I3NH", "itemType": "attachment", "linkMode": "imported_url", "title": "IEEE Xplore Abstract Record", "accessDate": "2024-07-10T22:37:20Z", "url": "https://ieeexplore.ieee.org/document/10414079", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "10414079.html", "md5": "eba899f06912abf3edeba1bac3ede8e9", "mtime": 1720651040696, "tags": [], "relations": {}, "dateAdded": "2024-07-10T22:37:20Z", "dateModified": "2024-07-10T22:37:20Z" } }, { "key": "JEW2I3NH", "version": 2954, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/JEW2I3NH", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/JEW2I3NH", "type": "text/html" } }, "meta": { "createdByUser": { "id": 861130, "username": "jkleiss", "name": "Jessica Kleiss", "links": { "alternate": { "href": "https://www.zotero.org/jkleiss", "type": "text/html" } } }, "creatorSummary": "May et al.", "parsedDate": "2024", "numChildren": 2 }, "data": { "key": "JEW2I3NH", "version": 2954, "itemType": "journalArticle", "title": "Automated Segmentation of Tropical Cyclone Clouds in Geostationary Infrared Images", "creators": [ { "creatorType": "author", "firstName": "Joshua", "lastName": "May" }, { "creatorType": "author", "firstName": "Liang", "lastName": "Hu" }, { "creatorType": "author", "firstName": "Elizabeth A.", "lastName": "Ritchie" }, { "creatorType": "author", "firstName": "Mehrtash", "lastName": "Harandi" }, { "creatorType": "author", "firstName": "J. Scott", "lastName": "Tyo" } ], "abstractNote": "We demonstrate that a convolutional neural network (CNN) based on the U-Net architecture can be used to create a cloud mask dataset that accurately identifies the clouds associated with tropical cyclones (TCs). The CNN can be trained using a single year of cloud masks produced by an earlier first-principles algorithm, and the results are insensitive to the specific year of training data used. These masks were originally created to compute the upwelling radiation due to TC clouds, and we show that the predicted masks result in both pixel areas and radiation calculations that are nearly identical to those computed using the earlier masks.", "publicationTitle": "IEEE Geoscience and Remote Sensing Letters", "publisher": "", "place": "", "date": "2024", "volume": "21", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "1-5", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1109/LGRS.2024.3358733", "citationKey": "", "url": "https://ieeexplore.ieee.org/document/10414079", "accessDate": "2024-07-10T22:37:06Z", "PMID": "", "PMCID": "", "ISSN": "1558-0571", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "Conference Name: IEEE Geoscience and Remote Sensing Letters", "tags": [], "collections": [], "relations": {}, "dateAdded": "2024-07-10T22:37:06Z", "dateModified": "2024-07-10T22:37:06Z" } }, { "key": "L82ED4QA", "version": 2953, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/L82ED4QA", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/L82ED4QA", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/267330/items/RTPR4DYG", "type": "application/json" } }, "meta": { "createdByUser": { "id": 861130, "username": "jkleiss", "name": "Jessica Kleiss", "links": { "alternate": { "href": "https://www.zotero.org/jkleiss", "type": "text/html" } } } }, "data": { "key": "L82ED4QA", "version": 2953, "parentItem": "RTPR4DYG", "itemType": "annotation", "annotationType": "highlight", "annotationText": "Domain transfer in CNNs is usually achieved by using the output of the fully connected (FC) layer of the network as features, which captures the overall spatial layout information. For cloud image representation, however, as the shape of the cloud is changeable, the deep convolutional features might be a better choice.", "annotationComment": "Does this relate to our work?", "annotationColor": "#ffd400", "annotationPageLabel": "816", "annotationSortIndex": "00001|004142|00452", "annotationPosition": "{\"pageIndex\":1,\"rects\":[[505.091,330.055,563.185,339.181],[312,318.055,563.115,327.181],[312,306.174,563.099,315.3],[312,294.175,563.209,303.3],[312,282.175,563.175,291.3],[312,270.294,563.17,279.42],[312,258.294,340.667,267.42]]}", "tags": [], "relations": {}, "dateAdded": "2024-07-03T18:31:58Z", "dateModified": "2024-07-03T18:32:08Z" } }, { "key": "RTPR4DYG", "version": 2950, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/RTPR4DYG", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/RTPR4DYG", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/267330/items/WIIXVCXB", "type": "application/json" } }, "meta": { "createdByUser": { "id": 861130, "username": "jkleiss", "name": "Jessica Kleiss", "links": { "alternate": { "href": "https://www.zotero.org/jkleiss", "type": "text/html" } } }, "numChildren": 1 }, "data": { "key": "RTPR4DYG", "version": 2950, "parentItem": "WIIXVCXB", "itemType": "attachment", "linkMode": "imported_file", "title": "Shi et al. - 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2020 - Shallow cumuli cover and its uncertainties from gr.pdf", "accessDate": "", "url": "", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Riley et al. - 2020 - Shallow cumuli cover and its uncertainties from gr.pdf", "md5": "7d52b558673d836c2dee4568a31bf5c6", "mtime": 1719870912134, "tags": [], "relations": {}, "dateAdded": "2024-07-01T21:55:19Z", "dateModified": "2024-07-01T21:55:19Z" } }, { "key": "SK6SAEXN", "version": 3011, "library": { "type": "group", "id": 267330, "name": "TSI library", "links": { "alternate": { "href": "https://www.zotero.org/groups/tsi_library", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/267330/items/SK6SAEXN", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tsi_library/items/SK6SAEXN", "type": "text/html" } }, "meta": { "createdByUser": { "id": 861130, "username": "jkleiss", "name": "Jessica Kleiss", "links": { "alternate": { "href": "https://www.zotero.org/jkleiss", "type": "text/html" } } }, "creatorSummary": "Riley et al.", "parsedDate": "2020-04-23", "numChildren": 2 }, "data": { "key": "SK6SAEXN", "version": 3011, "itemType": "journalArticle", "title": "Shallow cumuli cover and its uncertainties from ground-based lidar–radar data and sky images", "creators": [ { "creatorType": "author", "firstName": "Erin A.", "lastName": "Riley" }, { "creatorType": "author", "firstName": "Jessica M.", "lastName": "Kleiss" }, { "creatorType": "author", "firstName": "Laura D.", "lastName": "Riihimaki" }, { "creatorType": "author", "firstName": "Charles N.", "lastName": "Long" }, { "creatorType": "author", "firstName": "Larry K.", "lastName": "Berg" }, { "creatorType": "author", "firstName": "Evgueni", "lastName": "Kassianov" } ], "abstractNote": "Cloud cover estimates of single-layer shallow cumuli obtained from narrow field-of-view (FOV) lidar–radar and wide-FOV total sky imager (TSI) data are compared over an extended period (2000–2017 summers) at the established United States Atmospheric Radiation Measurement mid-continental Southern Great Plains site. We quantify the impacts of two factors on hourly and sub-hourly cloud cover estimates: (1) instrument-dependent cloud detection and data merging criteria and (2) FOV configuration. Enhanced observations at this site combine the advantages of the ceilometer, micropulse lidar (MPL) and cloud radar in merged data products. Data collected by these three instruments are used to calculate narrow-FOV cloud fraction (CF) as a temporal fraction of cloudy returns within a given period. Sky images provided by TSI are used to calculate the wide-FOV fractional sky cover (FSC) as a fraction of cloudy pixels within a given image. To assess the impact of the first factor on CF obtained from the merged data products, we consider two additional subperiods (2000–2010 and 2011–2017 summers) that mark significant instrumentation and algorithmic advances in the cloud detection and data merging. We demonstrate that CF obtained from ceilometer data alone and FSC obtained from sky images provide the most similar and consistent cloud cover estimates; hourly bias and root-mean-square difference (RMSD) are within 0.04 and 0.12, respectively. However, CF from merged MPL–ceilometer data provides the largest estimates of the multiyear mean cloud cover, about 0.12 (35 %) and 0.08 (24 %) greater than FSC for the first and second subperiods, respectively. CF from merged ceilometer–MPL–radar data has the strongest subperiod dependence with a bias of 0.08 (24 %) compared to FSC for the first subperiod and shows no bias for the second subperiod. The strong period dependence of CF obtained from the combined ceilometer–MPL–radar data is likely results from a change in what sensors are relied on to detect clouds below 3 km. After 2011, the MPL stopped being used for cloud top height detection below 3 km, leaving the radar as the only sensor used in cloud top height retrievals. To quantify the FOV impact, a narrow-FOV FSC is derived from the TSI images. We demonstrate that FOV configuration does not modify the bias but impacts the RMSD (0.1 hourly, 0.15 sub-hourly). In particular, the FOV impact is significant for sub-hourly observations, where 41 % of narrow- and wide-FOV FSC differ by more than 0.1. A new “quick-look” tool is introduced to visualize impacts of these two factors through integration of CF and FSC data with novel TSI-based images of the spatial variability in cloud cover. The influence of cloud field organization, such cloud streets parallel to the wind direction, on narrow- and wide-FOV cloud cover estimates can be visually assessed.", "publicationTitle": "Atmospheric Measurement Techniques", "publisher": "Copernicus GmbH", "place": "", "date": "2020-04-23", "volume": "13", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "2099-2117", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.5194/amt-13-2099-2020", "citationKey": "", "url": "https://amt.copernicus.org/articles/13/2099/2020/", "accessDate": "2024-07-01T21:52:31Z", "PMID": "", "PMCID": "", "ISSN": "1867-1381", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "English", "libraryCatalog": "Copernicus Online Journals", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2024-07-01T21:52:31Z", "dateModified": "2024-07-01T21:52:31Z" } } ]