GNSSseg, a Statistical Method for the Segmentation of Daily GNSS IWV Time Series
Homogenization is an important and crucial step to improve the usage of observational data for climate analysis. This work is motivated by the analysis of long series of GNSS Integrated Water Vapour (IWV) data, which have not yet been used in this context. This paper proposes a novel segmentation me...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/14/3379 |
| _version_ | 1797443954208145408 |
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| author | Annarosa Quarello Olivier Bock Emilie Lebarbier |
| author_facet | Annarosa Quarello Olivier Bock Emilie Lebarbier |
| author_sort | Annarosa Quarello |
| collection | DOAJ |
| description | Homogenization is an important and crucial step to improve the usage of observational data for climate analysis. This work is motivated by the analysis of long series of GNSS Integrated Water Vapour (IWV) data, which have not yet been used in this context. This paper proposes a novel segmentation method called segfunc that integrates a periodic bias and a heterogeneous, monthly varying, variance. The method consists in estimating first the variance using a robust estimator and then estimating the segmentation and periodic bias iteratively. This strategy allows for the use of the dynamic programming algorithm, which is the most efficient exact algorithm to estimate the change point positions. The performance of the method is assessed through numerical simulation experiments. It is implemented in the R package GNSSseg, which is available on the CRAN. This paper presents the application of the method to a real data set from a global network of 120 GNSS stations. A hit rate of 32% is achieved with respect to available metadata. The final segmentation is made in a semi-automatic way, where the change points detected by three different penalty criteria are manually selected. In this case, the hit rate reaches 60% with respect to the metadata. |
| first_indexed | 2024-03-09T13:05:32Z |
| format | Article |
| id | doaj.art-3b55860b983b44ba975edfb332ffec57 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T13:05:32Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3b55860b983b44ba975edfb332ffec572023-11-30T21:49:10ZengMDPI AGRemote Sensing2072-42922022-07-011414337910.3390/rs14143379GNSSseg, a Statistical Method for the Segmentation of Daily GNSS IWV Time SeriesAnnarosa Quarello0Olivier Bock1Emilie Lebarbier2Capgemini Engineering, 75016 Paris, FranceInstitut de Physique du Globe de Paris, Université Paris Cité, CNRS, IGN, 75005 Paris, FranceLaboratoire Modal’X, UPL, Université Paris Nanterre, 92000 Nanterre, FranceHomogenization is an important and crucial step to improve the usage of observational data for climate analysis. This work is motivated by the analysis of long series of GNSS Integrated Water Vapour (IWV) data, which have not yet been used in this context. This paper proposes a novel segmentation method called segfunc that integrates a periodic bias and a heterogeneous, monthly varying, variance. The method consists in estimating first the variance using a robust estimator and then estimating the segmentation and periodic bias iteratively. This strategy allows for the use of the dynamic programming algorithm, which is the most efficient exact algorithm to estimate the change point positions. The performance of the method is assessed through numerical simulation experiments. It is implemented in the R package GNSSseg, which is available on the CRAN. This paper presents the application of the method to a real data set from a global network of 120 GNSS stations. A hit rate of 32% is achieved with respect to available metadata. The final segmentation is made in a semi-automatic way, where the change points detected by three different penalty criteria are manually selected. In this case, the hit rate reaches 60% with respect to the metadata.https://www.mdpi.com/2072-4292/14/14/3379change point detectiondynamic programminghomogenization climate seriesGNSS IWV series |
| spellingShingle | Annarosa Quarello Olivier Bock Emilie Lebarbier GNSSseg, a Statistical Method for the Segmentation of Daily GNSS IWV Time Series Remote Sensing change point detection dynamic programming homogenization climate series GNSS IWV series |
| title | GNSSseg, a Statistical Method for the Segmentation of Daily GNSS IWV Time Series |
| title_full | GNSSseg, a Statistical Method for the Segmentation of Daily GNSS IWV Time Series |
| title_fullStr | GNSSseg, a Statistical Method for the Segmentation of Daily GNSS IWV Time Series |
| title_full_unstemmed | GNSSseg, a Statistical Method for the Segmentation of Daily GNSS IWV Time Series |
| title_short | GNSSseg, a Statistical Method for the Segmentation of Daily GNSS IWV Time Series |
| title_sort | gnssseg a statistical method for the segmentation of daily gnss iwv time series |
| topic | change point detection dynamic programming homogenization climate series GNSS IWV series |
| url | https://www.mdpi.com/2072-4292/14/14/3379 |
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