An approach for estimating time-variable rates from geodetic time series

Olga Didova, Brian Gunter, Riccardo Riva, Roland Klees, Lutz Rose-Koerner

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)
43 Downloads (Pure)

Abstract

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
Original languageEnglish
Pages (from-to)1207-1221
Number of pages15
JournalJournal of Geodesy
Volume90
Issue number11
DOIs
Publication statusE-pub ahead of print - 3 Jun 2016

Keywords

  • Time-variable trend
  • Kalman filter
  • Non--convex optimization problem
  • Colored noise

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