Centre de documentation
scientifique

An approach for estimating time-variable rates from geodetic time series / Olga Didova in Journal of geodesy, vol 90 n° 11 (November 2016)  

Public [article]  inJournal of geodesy > vol 90 n° 11 (November 2016) . - pp 1207 - 1221 Titre : An approach for estimating time-variable rates from geodetic time series Type de document : Article/Communication Auteurs : Olga Didova, Auteur ; Brian Gunter, Auteur ; Riccardo Riva, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 1207 - 1221 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] Antarctique [Termes IGN] calotte glaciaire [Termes IGN] compensation par moindres carrés [Termes IGN] données GRACE [Termes IGN] filtre de Kalman [Termes IGN] Global Ocean Observing System [Termes IGN] montée du niveau de la mer [Termes IGN] niveau moyen des mers [Termes IGN] optimisation (mathématiques) [Termes IGN] positionnement par GPS [Termes IGN] série temporelle Résumé : (Auteur) There has been considerable research in the literature focused on computing and forecasting sea-level changes in terms of constant trends or rates. The Antarctic ice sheet is one of the main contributors to sea-level change with highly uncertain rates of glacial thinning and accumulation. Geodetic observing systems such as the Gravity Recovery and Climate Experiment (GRACE) and the Global Positioning System (GPS) are routinely used to estimate these trends. In an effort to improve the accuracy and reliability of these trends, this study investigates a technique that allows the estimated rates, along with co-estimated seasonal components, to vary in time. For this, state space models are defined and then solved by a Kalman filter (KF). The reliable estimation of noise parameters is one of the main problems encountered when using a KF approach, which is solved by numerically optimizing likelihood. Since the optimization problem is non-convex, it is challenging to find an optimal solution. To address this issue, we limited the parameter search space using classical least-squares adjustment (LSA). In this context, we also tested the usage of inequality constraints by directly verifying whether they are supported by the data. The suggested technique for time-series analysis is expanded to classify and handle time-correlated observational noise within the state space framework. The performance of the method is demonstrated using GRACE and GPS data at the CAS1 station located in East Antarctica and compared to commonly used LSA. The results suggest that the outlined technique allows for more reliable trend estimates, as well as for more physically valuable interpretations, while validating independent observing systems. Numéro de notice : A2016-798 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-016-0918-5 En ligne : http://dx.doi.org/ 10.1007/s00190-016-0918-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82575 [article]