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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2019-249
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-249
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 29 Jul 2019

Submitted as: research article | 29 Jul 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Automatic Quality Control of the Meteosat First Generation Measurements

Freek Liefhebber1, Sarah Lammens1, Paul Brussee1, André Bos1, Viju O. John2, Frank Rüthrich2, Jacobus Onderwaater2, Michael G. Grant2, and Jörg Schulz2 Freek Liefhebber et al.
  • 1Science and Technology B.V., Delft, the Netherlands
  • 2EUMETSAT, Darmstadt, Germany

Abstract. Now that the Earth has been monitored by satellites for more than 40 years, Earth Observation images can be used to study how the Earth system behaves over extended periods. Such long-term studies require the combination of data from multiple instruments, with the earliest data sets being of particular importance in establishing a baseline for trend analysis. As the quality of these earlier datasets is often lower, careful quality control is essential, but the sheer size of these image sets makes an inspection by hand impracticable. Therefore, one needs to resort to automatic methods to inspect these Earth Observation images for anomalies. In this paper, we describe the design of a system that performs an automatic anomaly analysis on Earth Observation images, in particular the Meteosat first generation measurements. The design of this system is based on a preliminary analysis of the typical anomalies that can be found in the data set. This preliminary analysis was conducted by hand on a representative subset and resulted in a finite list of anomalies that needed to be detected in the whole data set. The automated anomaly detection system employs a dedicated detection algorithm for each of these anomalies. The result is a system with a high probability of detection and low false alarm rate. Furthermore, most of these algorithms are able to pinpoint the anomalies to the specific pixels affected in the image, allowing the maximum use of the data available.

Freek Liefhebber et al.
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Status: final response (author comments only)
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Freek Liefhebber et al.
Freek Liefhebber et al.
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Latest update: 21 Oct 2019
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Short summary
The paper addresses the need for automatic quality control of a whole series of Earth Observation (EO) time series extending a period of over 40 years. Such a data set is a valuable may provide important information about trends related to geo-physical processes. Furthermore, as the data set is that large, there is a need to completely automate the processes as otherwise the effort would become impracticable. The result is a system with a high probability of detection and low false alarm rate.
The paper addresses the need for automatic quality control of a whole series of Earth...
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