Optimal mathematical and statistical models to estimate vertical crustal movements using satellite altimetry and tide gauge data. Iz, H. B., Yang, T. Y., Shum, C. K., & Kuo, C. Y. 9(1):144–153.
Optimal mathematical and statistical models to estimate vertical crustal movements using satellite altimetry and tide gauge data [link]Paper  doi  abstract   bibtex   
Abstract Knowledge of vertical crustal movement is fundamental to quantify absolute sea level changes at tide gauge locations as well as for satellite altimetry calibration validations. While GPS measurements at collocated tide gauge stations fulfill this need, currently only few hundred tide gauge stations are equipped with GPS, and their measurements do not span a long period of time. In the past, several studies addressed this problem by calculating relative and geocentric trends from the tide gauge and satellite altimetry measurements respectively, and then difference the two trends to calculate the rate of changes at the tide gauge stations. However, this approach is suboptimal. This study offers an optimal statistical protocol based on the method of condition equations with unknown parameters . An example solution demonstrates the proposed mathematical and statistical models’ optimality in estimating vertical crustal movement and its standard error by comparing them with the results of current methods. The proposed model accounts for the effect of autocorrelations in observed tide gauge and satellite altimetry sea level time series, adjusts observed corrections such as inverted barometer effects, and constraints tide gauge and satellite altimeter measurement to close. The new model can accommodate estimating other systematic effects such as pole tides that are not eliminated by differencing.
@article{iz_optimal_2019,
	title = {Optimal mathematical and statistical models to estimate vertical crustal movements using satellite altimetry and tide gauge data},
	volume = {9},
	issn = {2081-9943},
	url = {http://www.degruyter.com/view/j/jogs.2019.9.issue-1/jogs-2019-0014/jogs-2019-0014.xml},
	doi = {10.1515/jogs-2019-0014},
	abstract = {Abstract
            
              Knowledge of vertical crustal movement is fundamental to quantify absolute sea level changes at tide gauge locations as well as for satellite altimetry calibration validations. While {GPS} measurements at collocated tide gauge stations fulfill this need, currently only few hundred tide gauge stations are equipped with {GPS}, and their measurements do not span a long period of time. In the past, several studies addressed this problem by calculating relative and geocentric trends from the tide gauge and satellite altimetry measurements respectively, and then difference the two trends to calculate the rate of changes at the tide gauge stations. However, this approach is suboptimal. This study offers an optimal statistical protocol based on the
              method of condition equations with unknown parameters
              . An example solution demonstrates the proposed mathematical and statistical models’ optimality in estimating vertical crustal movement and its standard error by comparing them with the results of current methods. The proposed model accounts for the effect of autocorrelations in observed tide gauge and satellite altimetry sea level time series, adjusts observed corrections such as inverted barometer effects, and constraints tide gauge and satellite altimeter measurement to close. The new model can accommodate estimating other systematic effects such as pole tides that are not eliminated by differencing.},
	pages = {144--153},
	number = {1},
	journaltitle = {Journal of Geodetic Science},
	author = {Iz, H. Bâki and Yang, T. Y. and Shum, C. K. and Kuo, C. Y.},
	urldate = {2020-10-19},
	date = {2019-01-01},
	keywords = {Satellite altimetry, Sea level rise, Tide gauges, Condition equations with unknown parameters, Optimal statistical solution, Vertical crustal movements},
	file = {Texte intégral:C\:\\Users\\ilico\\Zotero\\storage\\HIXR3LLA\\Iz et al. - 2019 - Optimal mathematical and statistical models to est.pdf:application/pdf}
}
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