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Development of new homogenisation methods for GNSS atmospheric data. Application to the analysis of climate trends and variability.

Abstract : The long series of integrated water vapor content (IWV) measured by GNSS are affected by inhomogeneities linked to changes in the instrumentation, the environment and the data processing procedure. The homogenization of these series is a crucial step for applications in climatology, e.g. estimating the trend and decennial variability. Due to the high natural variability of IWV, segmentation must be applied to IWVdifferences between GNSS observations and a reference which in our application is currently the ERA-Interim reanalysis. We have developed a segmentation method dedicated to the detection of abrupt changes in the mean which takes into account a periodic bias and a heterogeneous variance in these data. The algorithm first calculates the monthly variance with a robust estimator. Then, it estimates the number of breaks fixed iteratively (i) the variable bias and (ii) the positions of the break points and the means of thesignal, this for all the numbers of breaks tested. In a maximum likelihood approach thise stimation is carried out and it is based on the dynamic programming algorithm which is the only one to provide the exact solution in a reasonable time. Finally, the optimal number of change-points is chosen using a penalized model selection method. Four penalty criteria are proposed. The method was tested and optimized using numerical simulations and applied to IWV GNSS data for 120 stations in the global IGS network for the period from January 1995 to December 2010. The results on this data were validated using IGS metadata. The method is implemented and distributed in the form of an R package GNSSseg available on th CRAN.
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Submitted on : Friday, January 22, 2021 - 1:16:30 PM
Last modification on : Friday, October 21, 2022 - 3:34:06 PM
Long-term archiving on: : Friday, April 23, 2021 - 7:02:45 PM


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  • HAL Id : tel-03771164, version 1


Annarosa Quarello. Development of new homogenisation methods for GNSS atmospheric data. Application to the analysis of climate trends and variability.. Statistics [stat]. Sorbonne Universite; IGN (Institut National de l’Information Géographique et Forestière), 2020. English. ⟨NNT : ⟩. ⟨tel-03771164v1⟩



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