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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)
[article]
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'eauRésumé : (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
in Earth and space science > vol 7 n° 5 (May 2020) . - n° e2020EA001121[article]Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors / Boris Kargoll in Journal of geodesy, vol 94 n° 5 (May 2020)
[article]
Titre : Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors Type de document : Article/Communication Auteurs : Boris Kargoll, Auteur ; Gaël Kermarrec, Auteur ; Hamza Alkhatib, Auteur ; Johannes Korte, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] algorithme espérance-maximisation
[Termes IGN] analyse vectorielle
[Termes IGN] auto-régression
[Termes IGN] bruit blanc
[Termes IGN] corrélation croisée normalisée
[Termes IGN] erreur aléatoire
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle stochastique
[Termes IGN] régression linéaire
[Termes IGN] série temporelle
[Termes IGN] station GPS
[Termes IGN] valeur aberranteRésumé : (auteur) The iteratively reweighted least-squares approach to self-tuning robust adjustment of parameters in linear regression models with autoregressive (AR) and t-distributed random errors, previously established in Kargoll et al. (in J Geod 92(3):271–297, 2018. https://doi.org/10.1007/s00190-017-1062-6), is extended to multivariate approaches. Multivariate models are used to describe the behavior of multiple observables measured contemporaneously. The proposed approaches allow for the modeling of both auto- and cross-correlations through a vector-autoregressive (VAR) process, where the components of the white-noise input vector are modeled at every time instance either as stochastically independent t-distributed (herein called “stochastic model A”) or as multivariate t-distributed random variables (herein called “stochastic model B”). Both stochastic models are complementary in the sense that the former allows for group-specific degrees of freedom (df) of the t-distributions (thus, sensor-component-specific tail or outlier characteristics) but not for correlations within each white-noise vector, whereas the latter allows for such correlations but not for different dfs. Within the observation equations, nonlinear (differentiable) regression models are generally allowed for. Two different generalized expectation maximization (GEM) algorithms are derived to estimate the regression model parameters jointly with the VAR coefficients, the variance components (in case of stochastic model A) or the cofactor matrix (for stochastic model B), and the df(s). To enable the validation of the fitted VAR model and the selection of the best model order, the multivariate portmanteau test and Akaike’s information criterion are applied. The performance of the algorithms and of the white noise test is evaluated by means of Monte Carlo simulations. Furthermore, the suitability of one of the proposed models and the corresponding GEM algorithm is investigated within a case study involving the multivariate modeling and adjustment of time-series data at four GPS stations in the EUREF Permanent Network (EPN). Numéro de notice : A2020-291 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01376-6 Date de publication en ligne : 10/05/2020 En ligne : https://doi.org/10.1007/s00190-020-01376-6 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95120
in Journal of geodesy > vol 94 n° 5 (May 2020)[article]Accounting for spatiotemporal correlations of GNSS coordinate time series to estimate station velocities / Clément Benoist in Journal of geodynamics, vol 135 (April 2020)
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Titre : Accounting for spatiotemporal correlations of GNSS coordinate time series to estimate station velocities Type de document : Article/Communication Auteurs : Clément Benoist , Auteur ; Xavier Collilieux , Auteur ; Paul Rebischung , Auteur ; Zuheir Altamimi , Auteur ; Olivier Jamet , Auteur ; Laurent Métivier , Auteur ; Kristel Chanard , Auteur ; Liliane Bel, Auteur Année de publication : 2020 Projets : GEODESIE / Coulot, David, Université de Paris / Clerici, Christine Article en page(s) : n° 101693 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes IGN] corrélation
[Termes IGN] covariance
[Termes IGN] données spatiotemporelles
[Termes IGN] repère de référence terrestre conventionnel
[Termes IGN] série temporelle
[Termes IGN] vitesseRésumé : (auteur) It is well known that GNSS permanent station coordinate time series exhibit time-correlated noise. Spatial correlations between coordinate time series of nearby stations are also long-established and generally handled by means of spatial filtering techniques. Accounting for both the temporal and spatial correlations of the noise via a spatiotemporal covariance model is however not yet a common practice. We demonstrate in this paper the interest of using such a spatiotemporal covariance model of the stochastic variations in GNSS time series in order to estimate long-term station coordinates and especially velocities.
We provide a methodology to rigorously assess the covariances between horizontal coordinate variations and use it to derive a simple exponential spatiotemporal covariance model for the stochastic variations in the IGS repro2 station coordinate time series. We then use this model to estimate station velocities for two selected datasets of 10 time series in Europe and 11 time series in the USA. We show that coordinate prediction as well as velocity determination from short time series are improved when using this spatiotemporal model, as compared with the case where spatiotemporal correlations are ignored.Numéro de notice : A2020-460 Affiliation des auteurs : Géodésie+Ext (mi2018-2019) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jog.2020.101693 Date de publication en ligne : 13/01/2020 En ligne : https://doi.org/10.1016/j.jog.2020.101693 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95385
in Journal of geodynamics > vol 135 (April 2020) . - n° 101693[article]Antenna phase center correction differences from robot and chamber calibrations: the case study LEIAR25 / Grzegorz Krzan in GPS solutions, vol 24 n° 2 (April 2020)
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Titre : Antenna phase center correction differences from robot and chamber calibrations: the case study LEIAR25 Type de document : Article/Communication Auteurs : Grzegorz Krzan, Auteur ; Karol Dawidowicz, Auteur ; Pawel Wielgosz, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] antenne GLONASS
[Termes IGN] antenne GNSS
[Termes IGN] antenne GPS
[Termes IGN] centre de phase
[Termes IGN] chambre anéchoïque
[Termes IGN] correction du signal
[Termes IGN] étalonnage d'instrument
[Termes IGN] instrumentation Leica
[Termes IGN] positionnement par GNSS
[Termes IGN] positionnement ponctuel précis
[Termes IGN] robot
[Termes IGN] série temporelle
[Termes IGN] signal GNSSRésumé : (auteur) In recent years, the Global Navigation Satellite Systems (GNSS) have been intensively modernized, resulting in the introduction of new carrier frequencies for GPS and GLONASS and the development of new satellite systems such as Galileo and BeiDou (BDS). For this reason, the absolute field antenna calibrations performed so far for the two legacy carrier frequencies, the GPS and GLONASS, seem to be insufficient. Hence, all antennas will require a re-calibration of their phase center variations for the new signals to ensure the highest measurement accuracy. Currently, two absolute calibration methods are used to calibrate GNSS antennas: field calibration using a robot and calibration in an anechoic chamber. Unfortunately, differences in these methodologies also result in a disparity in the obtained antenna phase center corrections (PCC). Therefore, we analyze the differences between individual PCC obtained with these two methods, specifically for the Leica AR-25 antenna model (LEIAR25). In addition, the influence of PCC differences on the GNSS-derived position time series for 19 EUREF Permanent GNSS Network (EPN) stations was also assessed. The results show that the calibration method has a noticeable impact on PCC models. PCC differences determined for the ionosphere-free combination may reach up over 20 mm and can be transferred to the position domain. Further tests concerning the positioning accuracy showed that for horizontal coordinates differences between solutions were mostly below 1 mm, exceeding 2 mm only at two stations for the GLONASS solution. However, the height component differences exceeded 5 mm for four, six and six stations out of 19 for the GPS, GLONASS and Galileo solutions, respectively. These differences are strongly dependent on large L2 calibration differences. Numéro de notice : A2020-081 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-0957-5 Date de publication en ligne : 11/02/2020 En ligne : https://doi.org/10.1007/s10291-020-0957-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94650
in GPS solutions > vol 24 n° 2 (April 2020)[article]Assessment of geocenter motion estimates from the IGS second reprocessing / Yifang Ma in GPS solutions, vol 24 n° 2 (April 2020)
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Titre : Assessment of geocenter motion estimates from the IGS second reprocessing Type de document : Article/Communication Auteurs : Yifang Ma , Auteur ; Paul Rebischung , Auteur ; Zuheir Altamimi , Auteur ; Weiping Jiang, Auteur Année de publication : 2020 Projets : 3-projet - voir note / Clerici, Christine Article en page(s) : n° 55 Note générale : bibliographie
This study is supported by the National Science Fund for Distinguished Young Scholars (No. 41525014) and the National Key R&D Program of China (No. 2018YFC15036).Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes IGN] analyse comparative
[Termes IGN] données TLS (télémétrie)
[Termes IGN] International Terrestrial Reference Frame
[Termes IGN] mouvement du géocentre
[Termes IGN] série temporelle
[Termes IGN] variation saisonnière
[Termes IGN] variation temporelleRésumé : (auteur) We investigate geocenter motion time series derived from the combined solutions and six individual analysis center (AC) solutions of the International GNSS Service (IGS) second reprocessing campaign using the network shift approach, in terms of noise content, long-term trends, periodic and aperiodic variations. We assess these GNSS geocenter motion estimates by comparison with independent estimates from satellite laser ranging (SLR). The GNSS geocenter time series exhibit correlated noise which is better represented by a white plus power–law noise model in the X and Y directions, and by a white plus first-order autoregressive (or generalized Gauss–Markov) noise model in the Z direction. The GNSS geocenter time series include expected seasonal variations, but also spurious draconitic signals, particularly in the Z direction. GNSS annual geocenter motion estimates are in reasonable agreement with SLR estimates in the X and Y directions. In the Z direction, however, the annual signals derived from the IGS solutions disagree with SLR estimates, except for three particular ACs. This suggests that the different orbit modeling strategies used by these ACs may constitute an improvement over the conventional strategy employed by the other ACs. The background noise in GNSS and SLR geocenter time series finally appears to be correlated, suggesting that it might partly reflect real, aperiodic geocenter motion. Numéro de notice : A2020-838 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-0968-2 Date de publication en ligne : 10/03/2020 En ligne : https://doi.org/10.1007/s10291-020-0968-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98264
in GPS solutions > vol 24 n° 2 (April 2020) . - n° 55[article]Comparative analysis of different atmospheric surface pressure models and their impacts on daily ITRF2014 GNSS residual time series / Zhao Li in Journal of geodesy, vol 94 n°4 (April 2020)PermalinkImproving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series / Maylis Lopes in Methods in ecology and evolution, vol 11 n° 4 (April 2020)PermalinkSpatiotemporal variation of NDVI in the vegetation growing season in the source region of the yellow river, China / Mingyue Wang in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)PermalinkTemporal Validation of Four LAI Products over Grasslands in the Northeastern Tibetan Plateau / Gaofei Yin in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 4 (April 2020)PermalinkWavelet and non-parametric statistical based approach for long term land cover trend analysis using time series EVI data / Niraj Priyadarshi in Geocarto international, vol 35 n° 5 ([01/04/2020])PermalinkEvaluation of the high-rate GNSS-PPP method for vertical structural motion / Mosbeh R. Kaloop in Survey review, vol 52 n° 371 (March 2020)PermalinkRecent sea level change in the black sea from satellite altimetry and tide gauge observations / Nevin Betül Avsar in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)PermalinkA sequential Monte Carlo framework for noise filtering in InSAR time series / Mehdi Khaki in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)PermalinkSmoothing and predicting celestial pole offsets using a Kalman filter and smoother / Jolanta Nastula in Journal of geodesy, Vol 94 n°3 (March 2020)PermalinkSpectral–spatial–temporal MAP-based sub-pixel mapping for land-cover change detection / Da He in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)Permalink