Difference between revisions of "IC SG1"
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| − | <big>''' | + | <big>'''JSG 0.1: Application of time-series analysis in geodesy'''</big> |
| − | Chair: '' | + | Chair: ''W. Kosek (Poland)''<br> |
| − | Affiliation:'' | + | Affiliation:''GGOS, all commissions'' |
__TOC__ | __TOC__ | ||
===Introduction=== | ===Introduction=== | ||
| − | The | + | Observations provided by modern space geodetic tech-niques (geometric and gravimetric) deliver a global picture of dynamics of the Earth. Such observations are usually represented as time series which describe (1) changes of surface geometry of the Earth due to horizontal and verti-cal deformations of the land, ocean and cryosphere, (2) fluctuations in the orientation of the Earth divided into pre-cession, nutation, polar motion and spin rate, and (3) variations of the Earth’s gravitational field and the centre of mass of the Earth. The vision and goal of GGOS is to understand the dynamic Earth’s system by quantifying our planet’s changes in space and time and integrate all obser-vations and elements of the Earth’s system into one unique physical and mathematical model. To meet the GGOS requirements, all temporal variations of the Earth’s dynamics – which represent the total and hence integral effect of mass exchange between all elements of Earth’s system including atmosphere, ocean and hydrology – should be properly described by time series methods. |
| + | Various time series methods have been applied to analyze such geodetic and related geophysical time series in order to better understand the relation between all elements of the Earth’s system. The interactions between different components of the Earth’s system are very complex, thus the nature of the considered signals in the geodetic time series is mostly wideband, irregular and non-stationary. Therefore, the application of time frequency analysis methods based on wavelet coefficients – e.g. time-fre-quency cross-spectra, coherence and semblance – is neces-sary to reliably detect the features of the temporal or spatial variability of signals included in various geodetic data, and other associated geophysical data. | ||
| + | Geodetic time series may include, for instance, temporal variations of site positions, tropospheric delay, ionospheric total electron content, masses in specific water storage compartments or estimated orbit parameters as well as sur-face data including gravity field, sea level and ionosphere maps. The main problems to be scrutinized concern the estimation of deterministic (including trend and periodic variations) and stochastic (non-periodic variations and random fluctuations) components of the time series along with the application of the appropriate digital filters for extracting specific components with a chosen frequency bandwidth. The application of semblance filtering enables to compute the common signals, understood in frame of the time-frequency approach, which are embedded in vari-ous geodetic/geophysical time series. | ||
| + | Numerous methods of time series analysis may be em-ployed for processing raw data from various geodetic measurements in order to promote the quality level of signal enhancement. The issue of improvement of the edge effects in time series analysis may also be considered. In-deed, they may either affect the reliability of long-range tendency (trends) estimated from data or the real-time pro-cessing and prediction. | ||
| + | The development of combination strategies for time- and space-dependent data processing, including multi-mission sensor data, is also very important. Numerous observation techniques, providing data with different spatial and temporal resolutions and scales, can be combined to com-pute the most reliable geodetic products. It is now known that incorporating space variables in the process of geo-detic time series modelling and prediction can lead to a significant improvement of the prediction performance. Usually multi-sensor data comprises a large number of individual effects, e.g. oceanic, atmospheric and hydro-logical contributions. In Earth system analysis one key point at present and in the future will be the development of separation techniques. In this context principal compo-nent analysis and related techniques can be applied. | ||
| + | |||
| + | |||
| + | |||
===Objectives=== | ===Objectives=== | ||
| − | * | + | * To study geodetic time series and their geophysical causes in different frequency bands using time series analysis methods, mainly for better understanding of their causes and prediction improvement. |
| − | * | + | * The evaluation of appropriate covariance matrices corre-sponding to the time series by applying the law of error propagation, including weighting schemes, regulariza-tion, etc. |
| − | * | + | * Determining statistical significance levels of the results obtained by different time series analysis methods and algorithms applied to geodetic time series. |
| − | * | + | * The comparison of different time series analysis methods and their recommendation, with a particular emphasis put on solving problems concerning specific geodetic data. |
| − | * | + | * Developing and implementing the algorithms – aiming to seek and utilize spatio-temporal correlations – for geo-detic time series modelling and prediction. |
| − | * | + | * Better understanding of how large-scale environmental processes, such as for instance oceanic and atmospheric oscillations and climate change, impact modelling strate-gies employed for numerous geodetic data. |
| + | * Developing combination strategies for time- and space-dependent data obtained from different geodetic observa-tions. | ||
| + | * Developing separation techniques for integral measure-ments in individual contributions. | ||
===Program of activities=== | ===Program of activities=== | ||
| − | + | Updating the webpage, so that the information on time series analysis and its application in geodesy (including relevant multidisciplinary publications and the unification of terminology applied in time series analysis) will be available. | |
| − | + | Participating in working meetings at the international sym-posia and presenting scientific results at the appropriate sessions. | |
| − | + | Collaboration with other working groups dealing with geo-detic time-series e.g. Cost ES0701 Improved constraints on models of GIA or the Climate Change Working Group. | |
| − | |||
| + | ===Members=== | ||
| − | + | '' '''Athanasios Dermanis, (Greece, Chair)'''<br /> Zuheir Altamimi (France) <br /> Hermann Drewes (Germany) <br /> Fernando Sansò (Italy) <br /> Claude Boucher (France) <br /> Gerard Petit (France) <br /> Xavier Collilieux (France) <br /> Axel Nothnagel (Germany) <br /> Erricos Pavlis (USA) <br /> Jim Ray (USA) <br /> Frank Lemoine (USA) <br /> Geoff Blewitt (USA) <br /> Ludovico Biagi (Italy) <br /> Thomas Herring (USA) <br /> Pascal Willis (France) <br />'' | |
| − | '' ''' | + | '' '''W. Kosek (Poland), chair'''<br /> R. Abarca del Rio (Chile)<br /> O. Akyilmaz (Turkey)<br /> J. Böhm (Austria)<br /> L. Fernandez (Argentina)<br /> R. Gross (USA)<br /> M. Kalarus (Poland)<br /> M. O. Karslioglu (Turkey)<br /> H. Neuner (Germany)<br /> T. Niedzielski (Poland)<br /> S. Petrov (Russia)<br /> W. Popinski (Poland)<br /> M. Schmidt (Germany)<br /> M. van Camp (Belgium)<br /> O. de Viron (France)<br /> J. Vondrák (Czech Republic)<br /> D. Zheng (China)<br /> Y. Zhou (China)<br />'' |
Revision as of 09:51, 29 June 2012
JSG 0.1: Application of time-series analysis in geodesy
Chair: W. Kosek (Poland)
Affiliation:GGOS, all commissions
Introduction
Observations provided by modern space geodetic tech-niques (geometric and gravimetric) deliver a global picture of dynamics of the Earth. Such observations are usually represented as time series which describe (1) changes of surface geometry of the Earth due to horizontal and verti-cal deformations of the land, ocean and cryosphere, (2) fluctuations in the orientation of the Earth divided into pre-cession, nutation, polar motion and spin rate, and (3) variations of the Earth’s gravitational field and the centre of mass of the Earth. The vision and goal of GGOS is to understand the dynamic Earth’s system by quantifying our planet’s changes in space and time and integrate all obser-vations and elements of the Earth’s system into one unique physical and mathematical model. To meet the GGOS requirements, all temporal variations of the Earth’s dynamics – which represent the total and hence integral effect of mass exchange between all elements of Earth’s system including atmosphere, ocean and hydrology – should be properly described by time series methods. Various time series methods have been applied to analyze such geodetic and related geophysical time series in order to better understand the relation between all elements of the Earth’s system. The interactions between different components of the Earth’s system are very complex, thus the nature of the considered signals in the geodetic time series is mostly wideband, irregular and non-stationary. Therefore, the application of time frequency analysis methods based on wavelet coefficients – e.g. time-fre-quency cross-spectra, coherence and semblance – is neces-sary to reliably detect the features of the temporal or spatial variability of signals included in various geodetic data, and other associated geophysical data. Geodetic time series may include, for instance, temporal variations of site positions, tropospheric delay, ionospheric total electron content, masses in specific water storage compartments or estimated orbit parameters as well as sur-face data including gravity field, sea level and ionosphere maps. The main problems to be scrutinized concern the estimation of deterministic (including trend and periodic variations) and stochastic (non-periodic variations and random fluctuations) components of the time series along with the application of the appropriate digital filters for extracting specific components with a chosen frequency bandwidth. The application of semblance filtering enables to compute the common signals, understood in frame of the time-frequency approach, which are embedded in vari-ous geodetic/geophysical time series. Numerous methods of time series analysis may be em-ployed for processing raw data from various geodetic measurements in order to promote the quality level of signal enhancement. The issue of improvement of the edge effects in time series analysis may also be considered. In-deed, they may either affect the reliability of long-range tendency (trends) estimated from data or the real-time pro-cessing and prediction. The development of combination strategies for time- and space-dependent data processing, including multi-mission sensor data, is also very important. Numerous observation techniques, providing data with different spatial and temporal resolutions and scales, can be combined to com-pute the most reliable geodetic products. It is now known that incorporating space variables in the process of geo-detic time series modelling and prediction can lead to a significant improvement of the prediction performance. Usually multi-sensor data comprises a large number of individual effects, e.g. oceanic, atmospheric and hydro-logical contributions. In Earth system analysis one key point at present and in the future will be the development of separation techniques. In this context principal compo-nent analysis and related techniques can be applied.
Objectives
- To study geodetic time series and their geophysical causes in different frequency bands using time series analysis methods, mainly for better understanding of their causes and prediction improvement.
- The evaluation of appropriate covariance matrices corre-sponding to the time series by applying the law of error propagation, including weighting schemes, regulariza-tion, etc.
- Determining statistical significance levels of the results obtained by different time series analysis methods and algorithms applied to geodetic time series.
- The comparison of different time series analysis methods and their recommendation, with a particular emphasis put on solving problems concerning specific geodetic data.
- Developing and implementing the algorithms – aiming to seek and utilize spatio-temporal correlations – for geo-detic time series modelling and prediction.
- Better understanding of how large-scale environmental processes, such as for instance oceanic and atmospheric oscillations and climate change, impact modelling strate-gies employed for numerous geodetic data.
- Developing combination strategies for time- and space-dependent data obtained from different geodetic observa-tions.
- Developing separation techniques for integral measure-ments in individual contributions.
Program of activities
Updating the webpage, so that the information on time series analysis and its application in geodesy (including relevant multidisciplinary publications and the unification of terminology applied in time series analysis) will be available. Participating in working meetings at the international sym-posia and presenting scientific results at the appropriate sessions. Collaboration with other working groups dealing with geo-detic time-series e.g. Cost ES0701 Improved constraints on models of GIA or the Climate Change Working Group.
Members
Athanasios Dermanis, (Greece, Chair)
Zuheir Altamimi (France)
Hermann Drewes (Germany)
Fernando Sansò (Italy)
Claude Boucher (France)
Gerard Petit (France)
Xavier Collilieux (France)
Axel Nothnagel (Germany)
Erricos Pavlis (USA)
Jim Ray (USA)
Frank Lemoine (USA)
Geoff Blewitt (USA)
Ludovico Biagi (Italy)
Thomas Herring (USA)
Pascal Willis (France)
W. Kosek (Poland), chair
R. Abarca del Rio (Chile)
O. Akyilmaz (Turkey)
J. Böhm (Austria)
L. Fernandez (Argentina)
R. Gross (USA)
M. Kalarus (Poland)
M. O. Karslioglu (Turkey)
H. Neuner (Germany)
T. Niedzielski (Poland)
S. Petrov (Russia)
W. Popinski (Poland)
M. Schmidt (Germany)
M. van Camp (Belgium)
O. de Viron (France)
J. Vondrák (Czech Republic)
D. Zheng (China)
Y. Zhou (China)
