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Homogenizing GPS integrated water vapor time series: Benchmarking break detection methods on synthetic data sets / Roeland Van Malderen in Earth and space science, vol 7 n° 5 (May 2020)  

Public [article]  inEarth and space science > vol 7 n° 5 (May 2020) . - n° e2020EA001121 Titre : Homogenizing GPS integrated water vapor time series: Benchmarking break detection methods on synthetic data sets Type de document : Article/Communication Auteurs : Roeland Van Malderen, Auteur ; Eric Pottiaux, Auteur ; Anna Klos, Auteur ; P. Domonkos, Auteur ; Michal Elias, Auteur ; Tong Ning, Auteur ; Olivier Bock , Auteur ; J. Guijarro, Auteur ; F. Alshawaf, Auteur ; M. Hoseini, Auteur ; Annarosa Quarello , Auteur ; et al., Auteur Année de publication : 2020 Projets : GNSS4SWEC / Article en page(s) : n° e2020EA001121 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] coordonnées GNSS [Termes IGN] données hétérogènes [Termes IGN] homogénéisation [Termes IGN] jeu de données [Termes IGN] prévision météorologique [Termes IGN] série temporelle [Termes IGN] station permanente [Termes IGN] teneur intégrée en vapeur d'eau Résumé : (auteur) We assess the performance of different break detection methods on three sets of benchmark data sets, each consisting of 120 daily time series of integrated water vapor differences. These differences are generated from the Global Positioning System (GPS) measurements at 120 sites worldwide, and the numerical weather prediction reanalysis (ERA‐Interim) integrated water vapor output, which serves as the reference series here. The benchmark includes homogeneous and inhomogeneous sections with added nonclimatic shifts (breaks) in the latter. Three different variants of the benchmark time series are produced, with increasing complexity, by adding autoregressive noise of the first order to the white noise model and the periodic behavior and consecutively by adding gaps and allowing nonclimatic trends. The purpose of this “complex experiment” is to examine the performance of break detection methods in a more realistic case when the reference series are not homogeneous. We evaluate the performance of break detection methods with skill scores, centered root mean square errors (CRMSE), and trend differences relative to the trends of the homogeneous series. We found that most methods underestimate the number of breaks and have a significant number of false detections. Despite this, the degree of CRMSE reduction is significant (roughly between 40% and 80%) in the easy to moderate experiments, with the ratio of trend bias reduction is even exceeding the 90% of the raw data error. For the complex experiment, the improvement ranges between 15% and 35% with respect to the raw data, both in terms of RMSE and trend estimations. Numéro de notice : A2020-335 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1029/2020EA001121 Date de publication en ligne : 20/04/2020 En ligne : https://doi.org/10.1029/2020EA001121 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96837 [article]