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

Kühnlein, Meike

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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Главный автор:	Kühnlein, Meike
Другие авторы:	Nauss, Thomas (Prof.) (Научный руководитель)
Формат:	Dissertation
Язык:	английский
Опубликовано:	Philipps-Universität Marburg 2014
Предметы:	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
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A machine learning based 24-h-technique for an area-wide rainfall retrieval using MSG SEVIRI data over Central Europe

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