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...

Full description

Saved in:
Bibliographic Details
Main Author: Kühnlein, Meike
Contributors: Nauss, Thomas (Prof.) (Thesis advisor)
Format: Doctoral Thesis
Language:English
Published: Philipps-Universität Marburg 2014
Subjects:
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
Online Access:PDF Full Text
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items