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A novel machine learning-based satellite retrieval of volcanic ash for Meteosat covering the petrological variability

Piontek, Dennis (2022) A novel machine learning-based satellite retrieval of volcanic ash for Meteosat covering the petrological variability. Dissertation, Ludwig-Maximilians-Universität München. doi: 10.5282/edoc.29799.

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Official URL: https://edoc.ub.uni-muenchen.de/29799/


Item URL in elib:https://elib.dlr.de/186372/
Document Type:Thesis (Dissertation)
Title:A novel machine learning-based satellite retrieval of volcanic ash for Meteosat covering the petrological variability
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Piontek, DennisDLR, IPAUNSPECIFIED
Date:2022
Journal or Publication Title:Universitätsbibliothek der Ludwig-Maximilians-Universität München
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.5282/edoc.29799
Number of Pages:176
Status:Published
Keywords:Vulkanasche, Satellitenretrieval, Eyjafjallajökull, Puyehue-Cordón Caulle
Institution:Ludwig-Maximilians-Universität München
Department:Fakultät für Physik
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Atmospheric and climate research
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics > Cloud Physics
Deposited By: Piontek, Dennis
Deposited On:13 May 2022 09:00
Last Modified:13 May 2022 09:00

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