[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125488-en":3,"doc-seo-125488-105":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},125488,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Obtaining 3D convective characteristics from a machine learning-based integration of multi-sensor satellite observations - Dissertation","Convective clouds are key components of Earth’s climate system, shaping incoming solar and outgoing terrestrial radiation and modulating the hydrological cycle through feedback mechanisms. Their rapid development from cumulus to severe thunderstorms makes predicting convective evolution essential for effective risk assessment and mitigation, especially under a warming climate where extremes are expected to intensify. Large satellite datasets can reveal cloud behavior, yet extracting meaningful structure is difficult due to limited vertical resolution and temporal coverage. This thesis presents a machine learning framework that fuses multi-sensor observations to predict 3D cloud structures and evaluates accurate reconstruction of vertical structure and hydrometeor distributions.","Obtaining 3D convective characteristics from a machine learning-based integration of multi-sensor satellite observations  \nDissertation  \nfor the award of the academic degree of“Doctor rerum naturalium”(Dr. rer. nat.) in Atmospheric Science  \nat the faculties:  \n08-Physics, Mathematics and Computer Science,  \n09-Chemistry, Pharmacy, Geography and Geosciences,  \n10-Biology,  \nand University Medicine  \nof the Max Planck Graduate Center  \nJohannes Gutenberg University Mainz  \nsubmitted by  \nSarah Br¨uning  \nborn 30.11.1993 in Greven  \nMainz, 10 July 2025  \n1. Examiner: Prof. Dr. Holger Tost  \n2. Examiner: Prof. Dr. Michael Wand  \nDay of the oral examination: 08 September 2025  \nSarah Br¨uning: Obtaining 3D convective characteristics from a machine learning-based integration of multi-sensor satellite observations, published under CC-BY-SA-4.0  \nDeclaration  \nI hereby declare that I wrote the dissertation submitted without any unauthorized external assistance and used only sources acknowledged in the work. All textual passages which are appropriated verbatim or paraphrased from published and unpublished texts as well as all information obtained from oral sources are duly indicated and listed in accordance with bibliographical rules. In carrying out this research, I complied with the rules of standard scientific practice as formulated in the statutes of Johannes Gutenberg University Mainz to insure standard scientific practice.  \nSarah Br¨uning  \nMainz, 10 July 2025  \nAcknowledgments  \nRemoved for data protection reasons.  \nAbstract  \nConvective clouds are key players in the climate system of the Earth. They influence both incoming solar and outgoing terrestrial radiation, and they regulate the hydrological cycle through complex feedback mechanisms. Despite their importance, clouds remain one of the largest sources of uncertainty in climate models — posing persistent challenges to scientists around the globe. Among all cloud types, convective systems stand out due to their ability to evolve rapidly from harmless cumulus clouds into intense thunderstorms. Accurately predicting this evolution is vital for effective risk assessment and mitigation—especially as extreme weather events are expected to occur more frequently in a warming world.  \nSatellite observations offer profound insights into the behavior of convective clouds. Although the volume of satellite data has grown tremendously in recent decades, extracting meaningful patterns from these large and complex datasets remains a daunting task. However, recent advances in machine learning have introduced new tools that can help address this challenge. While satellite data often lack fine vertical resolution or have limited temporal coverage, machine learning techniques allow to bridge these gaps, revealing previously hidden patterns associated to the dynamic evolution of cloud systems.  \nThis thesis addresses current challenges by developing a machine learning framework that combines multiple satellite datasets to improve our understanding of convective clouds. Specifically, it uses 2D imagery from the geostationary MSG SEVIRI satellite to predict 3D cloud structures as observed by the CloudSat cloud profiling radar, which provides 2D vertical cross-sections of the radar reflectivity. This approach helps overcome current limitations in vertical cloud profiling. The model evaluation demonstrates that it can accurately reconstruct both the vertical structure and the distribution of hydrometeors. By leveraging the high spatial and temporal resolution of MSG SEVIRI alongside the vertical detail from CloudSat, this method considerably enhances the availability of 3D cloud structures across broad regions on Earth.  \nBuilding on this foundation, the thesis applies the predicted 3D cloud fields to investigate tropical convective cloud behavior, with a focus on the role of convective cores and large-scale spatial clustering in shaping cloud structure. An adapted, object-based detection algor","cbCaikjZbe8TtAg8","https://ap.wps.com/l/cbCaikjZbe8TtAg8","pdf",22237444,1,149,"English","en",105,"# Abstract\n## Background and challenge\n## Machine learning framework and data fusion\n## 3D structure prediction and evaluation\n## Applications to tropical convective behavior\n## Broader applicability and future value","[{\"question\":\"Why are convective clouds important for climate research and risk mitigation?\",\"answer\":\"They strongly affect radiation and the hydrological cycle, and their rapid transition from cumulus to thunderstorms makes their evolution crucial to predict for handling extreme-weather risks.\"},{\"question\":\"What is the core data fusion strategy used in the thesis?\",\"answer\":\"It combines 2D imagery from MSG SEVIRI with vertical cross-sections of radar reflectivity from CloudSat to infer 3D cloud structures.\"},{\"question\":\"How are the predicted 3D cloud fields used after reconstruction?\",\"answer\":\"The thesis applies them to study tropical convective behavior, using an adapted object-based algorithm to detect cloud objects and cores, then track their evolution and relate it to clustering and atmospheric dynamics.\"}]","Obtaining 3D convective characteristics from a machine learning-based integration of multi-sensor satellite observations - 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