# Difference between revisions of "JSG T.32"

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− | <big>'''JSG 0.19: | + | <big>'''JSG 0.19: Time series analysis in geodesy'''</big> |

− | Chair: '' | + | Chair: ''Wieslaw Kosek (Poland)''<br> |

− | Affiliation:''Comm. | + | Affiliation:''Comm. 3 and GGOS'' |

__TOC__ | __TOC__ | ||

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===Terms of Reference=== | ===Terms of Reference=== | ||

− | + | Observations of the space geodesy techniques and on the Earth's surface deliver a global picture of the Earth dynamics represented in the form of time series which describe 1) changes of the Earth surface geometry, 2) the fluctuations in the Earth orientation, and 3) the variations of the Earth’s gravitational field. The Earth's surface geometry, rotation and gravity field are the three components of the Global Geodetic Observing System (GGOS) which integrates them into one unique physical and mathematical model. However, temporal variations of these three components represent the total, integral effect of all global mass exchange between all elements of the Earth’s system including the Earth's interior and fluid layers: atmosphere, ocean and land hydrology. | |

− | |||

− | + | Different time series analysis methods have been applied to analyze all these geodetic time series for better understanding of the relations between all elements of the Earth’s system as well as their geophysical causes. The interactions between different components of the Earth’s system are very complex so the nature of considered signals in the geodetic time series is mostly wideband, irregular and non-stationary. Thus, it is recommended to apply wavelet based spectra-temporal analysis methods to analyze these geodetic time series as well as to explain their relations to geophysical processes in different frequency bands using time-frequency semblance and coherence methods. These spectra-temporal analysis methods and time-frequency semblance and coherence may be further developed to display reliably the features of the temporal or spatial variability of signals existing in various geodetic data, as well as in other source data sources. | |

− | + | Geodetic time series include for example horizontal and vertical deformations of site positions determined from observations of space geodetic techniques. These site positions change due to e.g. plate tectonics, postglacial rebound, atmospheric, hydrology and ocean loading and earthquakes. However they are used to build the global international terrestrial reference frame (ITRF) which must be stable reference for all other geodetic observations including e.g. satellite orbit parameters and Earth's orientation parameters which consist of precession, nutation, polar motion and UT1-UTC that are necessary for transformation between the terrestrial and celestial reference frames. Geodetic time series include also temporal variations of Earth's gravity field where 1 arc-deg spherical harmonics correspond to the Earth’s centre of mass variations (long term mean of them determines the ITRF origin) and 2 degree spherical harmonics correspond to Earth rotation changes. Time series analysis methods can be also applied to analyze data on the Earth's surface including maps of the gravity field, sea level, ice covers, ionospheric total electron content and tropospheric delay as well as temporal variations of such surface data. The main problems to deal with include the estimation of deterministic (including trend and periodic variations) and stochastic (non-periodic variations and random changes) components of the geodetic time series as well as the application of digital filters for extracting specific components with a chosen frequency bandwidth. | |

− | + | The multiple methods of time series analysis may be encouraged to be applied to the preprocessing of raw data from various geodetic measurements in order to promote the quality level of enhancement of signals existing in these data. The topic on the improvement of the edge effects in time series analysis may also be considered, since they may affect the reliability of long-range tendency (trends) estimated from data series as well as the real-time data processing and prediction. | |

− | + | For coping with small geodetic samples one can apply simulation-based methods and if the data are sparse, Monte-Carlo simulation or bootstrap technique may be useful. Understanding the nature of geodetic time series is very important from the point of view of appropriate spectral analysis as well as application of filtering and prediction methods. | |

===Objectives=== | ===Objectives=== | ||

− | + | * Study of the nature of geodetic time series to choose optimum time series analysis methods for filtering, spectral analysis, time frequency analysis and prediction. | |

− | + | * Study of Earth's geometry, rotation and gravity field variations and their geophysical causes in different frequency bands. | |

− | * | + | * Evaluation of appropriate covariance matrices for the time series by applying the law of error propagation to the original measurements, including weighting schemes, regularization, etc. |

− | * | + | * Determination of the statistical significance levels of the results obtained by different time series analysis methods and algorithms applied to geodetic time series. |

− | * | + | * Development and comparison of different time series analysis methods in order to point out their advantages and disadvantages. |

− | + | * Recommendations of different time series analysis methods for solving problems concerning specific geodetic time series. | |

− | * | ||

===Program of activities=== | ===Program of activities=== | ||

− | * | + | * Launching of a website about time series analysis in geodesy providing list of papers from different disciplines as well as unification of terminology applied in time series analysis. |

− | + | * Working meetings at the international symposia and presentation of research results at the appropriate sessions. | |

− | * | ||

===Membership=== | ===Membership=== | ||

− | '' ''' | + | '' '''Wieslaw Kosek (Poland), chair''' <br /> Michael Schmidt (Germany) <br /> Jan Vondrák (Czech Republic) <br /> Waldemar Popinski (Poland) <br /> Tomasz Niedzielski (Poland) <br /> Johannes Boehm (Austria) <br /> Dawei Zheng (China) <br /> Yonghong Zhou (China) <br /> Mahmut O. Karslioglu (Turkey) <br /> Orhan Akyilmaz (Turkey) <br /> Laura Fernandez (Argentina) <br /> Richard Gross (USA) <br /> Olivier de Viron (France) <br /> Sergei Petrov (Russia) <br /> Michel Van Camp (Belgium) <br /> Hans Neuner (Germany) <br /> Xavier Collilieux (France) <br />'' |

## Revision as of 10:10, 29 April 2016

**JSG 0.19: Time series analysis in geodesy**

Chair: *Wieslaw Kosek (Poland)*

Affiliation:*Comm. 3 and GGOS*

### Terms of Reference

Observations of the space geodesy techniques and on the Earth's surface deliver a global picture of the Earth dynamics represented in the form of time series which describe 1) changes of the Earth surface geometry, 2) the fluctuations in the Earth orientation, and 3) the variations of the Earth’s gravitational field. The Earth's surface geometry, rotation and gravity field are the three components of the Global Geodetic Observing System (GGOS) which integrates them into one unique physical and mathematical model. However, temporal variations of these three components represent the total, integral effect of all global mass exchange between all elements of the Earth’s system including the Earth's interior and fluid layers: atmosphere, ocean and land hydrology.

Different time series analysis methods have been applied to analyze all these geodetic time series for better understanding of the relations between all elements of the Earth’s system as well as their geophysical causes. The interactions between different components of the Earth’s system are very complex so the nature of considered signals in the geodetic time series is mostly wideband, irregular and non-stationary. Thus, it is recommended to apply wavelet based spectra-temporal analysis methods to analyze these geodetic time series as well as to explain their relations to geophysical processes in different frequency bands using time-frequency semblance and coherence methods. These spectra-temporal analysis methods and time-frequency semblance and coherence may be further developed to display reliably the features of the temporal or spatial variability of signals existing in various geodetic data, as well as in other source data sources.

Geodetic time series include for example horizontal and vertical deformations of site positions determined from observations of space geodetic techniques. These site positions change due to e.g. plate tectonics, postglacial rebound, atmospheric, hydrology and ocean loading and earthquakes. However they are used to build the global international terrestrial reference frame (ITRF) which must be stable reference for all other geodetic observations including e.g. satellite orbit parameters and Earth's orientation parameters which consist of precession, nutation, polar motion and UT1-UTC that are necessary for transformation between the terrestrial and celestial reference frames. Geodetic time series include also temporal variations of Earth's gravity field where 1 arc-deg spherical harmonics correspond to the Earth’s centre of mass variations (long term mean of them determines the ITRF origin) and 2 degree spherical harmonics correspond to Earth rotation changes. Time series analysis methods can be also applied to analyze data on the Earth's surface including maps of the gravity field, sea level, ice covers, ionospheric total electron content and tropospheric delay as well as temporal variations of such surface data. The main problems to deal with include the estimation of deterministic (including trend and periodic variations) and stochastic (non-periodic variations and random changes) components of the geodetic time series as well as the application of digital filters for extracting specific components with a chosen frequency bandwidth.

The multiple methods of time series analysis may be encouraged to be applied to the preprocessing of raw data from various geodetic measurements in order to promote the quality level of enhancement of signals existing in these data. The topic on the improvement of the edge effects in time series analysis may also be considered, since they may affect the reliability of long-range tendency (trends) estimated from data series as well as the real-time data processing and prediction.

For coping with small geodetic samples one can apply simulation-based methods and if the data are sparse, Monte-Carlo simulation or bootstrap technique may be useful. Understanding the nature of geodetic time series is very important from the point of view of appropriate spectral analysis as well as application of filtering and prediction methods.

### Objectives

- Study of the nature of geodetic time series to choose optimum time series analysis methods for filtering, spectral analysis, time frequency analysis and prediction.
- Study of Earth's geometry, rotation and gravity field variations and their geophysical causes in different frequency bands.
- Evaluation of appropriate covariance matrices for the time series by applying the law of error propagation to the original measurements, including weighting schemes, regularization, etc.
- Determination of the statistical significance levels of the results obtained by different time series analysis methods and algorithms applied to geodetic time series.
- Development and comparison of different time series analysis methods in order to point out their advantages and disadvantages.
- Recommendations of different time series analysis methods for solving problems concerning specific geodetic time series.

### Program of activities

- Launching of a website about time series analysis in geodesy providing list of papers from different disciplines as well as unification of terminology applied in time series analysis.
- Working meetings at the international symposia and presentation of research results at the appropriate sessions.

### Membership

* Wieslaw Kosek (Poland), chair Michael Schmidt (Germany) Jan Vondrák (Czech Republic) Waldemar Popinski (Poland) Tomasz Niedzielski (Poland) Johannes Boehm (Austria) Dawei Zheng (China) Yonghong Zhou (China) Mahmut O. Karslioglu (Turkey) Orhan Akyilmaz (Turkey) Laura Fernandez (Argentina) Richard Gross (USA) Olivier de Viron (France) Sergei Petrov (Russia) Michel Van Camp (Belgium) Hans Neuner (Germany) Xavier Collilieux (France) *