This thesis discusses the topic quality controls in the meteorological field and in particular optimize them by adjustment and construction of an automated pipeline for the quality checks. Three different kinds of pipelines are developed through this thesis: The most general one has the focus on high error detection with a low false positive rate. But a categorized pipeline is also designed, which classify the data in “good”, “bad” and “doubtful”. Furthermore a fast fault detection pipeline is derived from the general pipeline to make it possible to react nearline to hardware fails. In this thesis general fundamentals about meteorological coherence, statistical analysis and quality controls for meteorology are described. After that the approach of this thesis are lead by the development of the automated pipeline. Meteorological measurements and their corresponding quality controls got explored to optimize them. Beside an optimization of existing quality controls, new automated tests are developed within this thesis. The evaluation of the designed pipeline shows that the quality of the pipeline depends on the input parameters. The more information we have for the input the better is the pipeline working. But the specialty of the pipeline is that it works with any kind of input, so it is not limited to strict input parameters.

@mastersthesis{QCOMTWTAOD16,
	author	 = {Jennifer Truong},
	title	 = {{Quality Control of Meteorological Time-Series with the Aid of Data Mining}},
	advisors	 = {Julian Kunkel},
	year	 = {2016},
	month	 = {10},
	school	 = {Universität Hamburg},
	howpublished	 = {{Online \url{https://wr.informatik.uni-hamburg.de/_media/research:theses:jennifer_truong_quality_control_of_meteorological_time_series_with_the_aid_of_data_mining.pdf}}},
	type	 = {Master's Thesis},
	abstract	 = {This thesis discusses the topic quality controls in the meteorological field and in particular optimize them by adjustment and construction of an automated pipeline for the quality checks. Three different kinds of pipelines are developed through this thesis: The most general one has the focus on high error detection with a low false positive rate. But a categorized pipeline is also designed, which classify the data in “good”, “bad” and “doubtful”. Furthermore a fast fault detection pipeline is derived from the general pipeline to make it possible to react nearline to hardware fails. In this thesis general fundamentals about meteorological coherence, statistical analysis and quality controls for meteorology are described. After that the approach of this thesis are lead by the development of the automated pipeline. Meteorological measurements and their corresponding quality controls got explored to optimize them. Beside an optimization of existing quality controls, new automated tests are developed within this thesis. The evaluation of the designed pipeline shows that the quality of the pipeline depends on the input parameters. The more information we have for the input the better is the pipeline working. But the specialty of the pipeline is that it works with any kind of input, so it is not limited to strict input parameters.},
}