[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119423-en":3,"doc-seo-119423-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119423,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Cloudy with a chance of precision: satellite’s autoconversion rates forecasting powered by machine learning","Precipitation is one of the most relevant weather and climate processes, with formation rates strongly influenced by aerosol-cloud-precipitation interactions. These interactions are a major source of uncertainty in estimating radiative forcing from climate change. High-resolution simulations like ICON-LEM provide insight but are too costly for wide, long-term coverage. The paper introduces machine-learning models that forecast autoconversion rates from both climate-model data and satellite observations, comparing results visually and statistically to reference simulations and demonstrating strong feature identification and agreement.","Environmental Data Science (2024), 3: e23, 1–22  \ndoi:10.1017/eds.2024.24  \nAPPLICATION PAPER   \nCloudy with a chance of precision: satellite’s autoconversion rates forecasting powered by machine learning  \nMaria Carolina Novitasari 1 , Johannes Quaas2,3  and Miguel R. D. Rodrigues 1  \n1Department of Electronic and Electrical Engineering, University College London, London, UK  \n2Leipzig Institute for Meteorology, Universität Leipzig, Leipzig, Germany  \n3ScaDS.AI-Center for Scalable Data Analytics and AI, Leipzig, Germany Corresponding author: Maria Carolina Novitasari; [Email: maria.novitasari.20@ucl.ac.uk](Email: maria.novitasari.20@ucl.ac.uk)  \nReceived: 29 July 2023; Revised: 21 January 2024; Accepted: 08 August 2024  \nKeywords: autoconversion rates; aerosol-cloud interactions; machine learning; precipitation formation; remote sensing  \nAbstract  \nPrecipitation is one of the most relevant weather and climate processes. Its formation rate is sensitive to perturbations such as by the interactions between aerosols, clouds, and precipitation. These interactions constitute one of the biggest uncertainties in determining the radiative forcing of climate change. High-resolution simulations such as the ICOsahedral non-hydrostatic large-eddy model (ICON-LEM) offer valuable insights into these interactions. However, due to exceptionally high computation costs, it can only be employed for a limited period and area. We address this challenge by developing new models powered by emerging machine learning approaches capable of forecasting autoconversion rates—the rate at which small droplets collide and coalesce becoming larger droplets—from satellite observations providing long-term global spatial coverage for more than two decades. In particular, our approach involves two phases: (1)we develop machine learning models which are capable of predicting autoconversion rates by leveraging high-resolution climate model data,(2) we repurpose our best machine learning model to predict autoconversion rates directly from satellite observations. We compare the performance of our machine learning models against simulation data under several different conditions, showing from both visual and statistical inspections that our approaches are able to identify key features of the reference simulation data to a high degree. Additionally, the autoconversion rates obtained from the simulation output and satellite data (predicted) demonstrate statistical concordance. By efficiently predicting this, we advance our comprehension ofoneofthe key processes in precipitation formation, crucial for understanding cloud responses to anthropogenic aerosols and, ultimately, climate change.  \nImpact Statement  \nThe ability to predict autoconversion rates directly from satellite data holds significant potential in enhancing our understanding of how clouds respond to anthropogenic aerosols. We develop a new process, powered by machine learning techniques, leading up to models that are able to predict autoconversion rates directly from satellite data. We argue that our approach is far less computationally expensive than atmospheric simulation-based methods, it offers reasonably good outcomes, and it can therefore easily leverage the wide availability ofmassive satellite data collection with long-term data provision. This exemplifies how important machine learning is for comprehending how aerosols affect clouds and, ultimately, climate change.  \n This research article was awarded Open Data badge for transparent practices. See the Data Availability Statement for details.  \n©The Author(s), 2024. Published by Cambridge University Press. This is an Open Access article, distributed under the terms ofthe Creative Commons Attribution licence ([http://creativecommons.org/licenses/by/4.0](http://creativecommons.org/licenses/by/4.0)), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.  \n[https://doi.org/10.1017/eds.2024.24","cbCaieanT1B5SFOu","https://ap.wps.com/l/cbCaieanT1B5SFOu","pdf",9612919,1,22,"English","en",105,"# Abstract\n# Impact Statement\n# 1. Introduction\n## Climate change and aerosol-cloud interactions\n## Effects of aerosols on clouds and precipitation\n# 2. Data and Methods\n## Machine-learning strategy using model and satellite inputs\n# 3. Results\n## Performance under multiple conditions\n## Agreement between simulation and satellite predictions\n# 4. Conclusion","[{\"question\":\"What are autoconversion rates in this research?\",\"answer\":\"Autoconversion rates describe the process rate at which small droplets collide and coalesce into larger droplets, a key step in precipitation formation.\"},{\"question\":\"Why do high-resolution simulations like ICON-LEM limit precipitation studies?\",\"answer\":\"They provide detailed insight but have exceptionally high computational costs, restricting simulations to limited time and spatial coverage.\"},{\"question\":\"How does the proposed machine-learning approach use satellite data?\",\"answer\":\"It repurposes a best-performing machine-learning model to predict autoconversion rates directly from satellite observations, enabling long-term global spatial coverage.\"}]","Cloudy with a chance of precision: satellite’s autoconversion rates forecasting powered by machine learning | PDF",1785724221,55,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"cloudy-with-a-chance-of-precision-satellites-autoconversion-rates-forecasting-powered-by-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/cloudy-with-a-chance-of-precision-satellites-autoconversion-rates-forecasting-powered-by-machine-learning/119423/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What are autoconversion rates in this research?","Question",{"text":75,"@type":76},"Autoconversion rates describe the process rate at which small droplets collide and coalesce into larger droplets, a key step in precipitation formation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why do high-resolution simulations like ICON-LEM limit precipitation studies?",{"text":80,"@type":76},"They provide detailed insight but have exceptionally high computational costs, restricting simulations to limited time and spatial coverage.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed machine-learning approach use satellite data?",{"text":84,"@type":76},"It repurposes a best-performing machine-learning model to predict autoconversion rates directly from satellite observations, enabling long-term global spatial coverage.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]