A machine learning based 24-h-technique for an area-wide rainfall retrieval using MSG SEVIRI data over Central Europe

The aim of the present study was to develop a 24-h-technique for the process-related and quantitative estimation of precipitation in connection with extra-tropical cyclones in the mid-latitudes based on MSG SEVIRI data using the machine learning algorithm random forest. The algorithms and approach...

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Detalhes bibliográficos

Autor principal:	Kühnlein, Meike
Outros Autores:	Nauss, Thomas (Prof.) (Orientador)
Formato:	Dissertation
Idioma:	inglês
Publicado em:	Philipps-Universität Marburg 2014
Assuntos:	Satellite remote sensing Geowissenschaften Random forests Wolkeneigenschaften Wolken Meteosat-8 SEVIRI Cloud properties Maschine learning Maschinelles Lernverfahren Angewandte Geographie Satellitenfernerkundung Clouds Precipitation Niederschlag Earth sciences
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