An approach for estimating time-variable rates from geodetic time series
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- 作者：Olga Didova ; Brian Gunter ; Riccardo Riva ; Roland Klees…
- 关键词：Time ; variable trend ; Kalman filter ; Non ; convex optimization problem ; Colored noise
- 刊名：Journal of Geodesy
- 出版年：2016
- 出版时间：November 2016
- 年：2016
- 卷：90
- 期：11
- 页码：1207-1221
- 全文大小：2,251 KB
- 刊物类别：Earth and Environmental Science
- 刊物主题：Earth sciences
Geophysics and Geodesy
Mathematical Applications in Geosciences - 出版者：Springer Berlin / Heidelberg
- ISSN：1432-1394
- 卷排序：90
文摘
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.