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

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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Detalles Bibliográficos
Autor principal: Kühnlein, Meike
Otros Autores: Nauss, Thomas (Prof.) (Orientador)
Formato: Dissertation
Lenguaje:inglés
Publicado: Philipps-Universität Marburg 2014
Materias:
Wolkeneigenschaften
Geowissenschaften
Satellite remote sensing
Wolken
Random forests
Cloud properties
Precipitation
Angewandte Geographie
Meteosat-8 SEVIRI
Satellitenfernerkundung
Maschinelles Lernverfahren
Maschine learning
Clouds
Niederschlag
Earth sciences
Acceso en línea:Texto Completo PDF
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