Abstract
A new method for sequential combination of troposphere time series is presented. Unlike the EUREF [ The IAG (International Association of Geodesy) Reference Frame Sub-Commission for Europe.] time series analysis strategy which works in the post-processed mode, the method considered here is capable of combining troposphere solutions sequentially as a new batch of data becomes available. It provides combined troposphere estimates and their standard deviations. In addition to time series biases, the method determines weights to maintain the consistency of combined solutions using variance component estimation. The time series biases and the weights are determined sequentially using estimates obtained at the previous step of time series combination as the a priori information. The mathematical description of the method is presented. The results of experimental combination of troposphere solutions obtained by different EUREF and COST [The French acronym for European co-operation in the field of scientific and technical research.] -716 Analysis Centers confirm the ability of the method to combine troposphere time series grouped in daily batches (post-processing mode) and in hourly batches (near real-time mode).
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Notes
See list of acronyms of the ACs at the end of the paper.
References
COST (2005) COST-716 Action homepage. Web site: http://www.oso.chalmers.se/∼kge/cost716.html/, accessed Oct 2005
Douša J (2001) Towards an operational near real-time precipitable water vapor estimation. Phys Chem Earth (A) 26(6–8):189–194
EUREF (2005) Combined EUREF troposphere solutions. Ftp-site: ftp://igs.ifag.de/EUREF/products, accessed Oct 2005
Gelb A (ed) (1974) Applied optimal estimation. The M.I.T. Press, Massachusetts Institute of Technology, Cambridge, Massachusetts
Gendt G (1997) IGS combination of tropospheric estimates (Status Report). In: Proceedings IGS analysis centre workshop, Pasadena
Gendt G, Reigber C, Dick G (2001) Near real-time water vapor estimation in a German GPS network-first results from the ground program of the HCF GASP project. Phys Chem Earth (A) 26(6–8):413–416
Gustaffson N (2002) Assimilation of ground-based GPS data in HIRLAM 3D-Var. HIRLAM workshop on variational data assimilation and remote sensing, Helsinki, pp 89–96
Koch K (1999) Parameter estimation and hypothesis testing in linear models. Springer, Berlin Heidelberg New York
Stoew B, Jarlemark P, Johansson J, Elgered G (2001) Real-time processing of GPS data delivered by SWEPOS. Phys Chem Earth (A) 26(6–8):493–496
Teunissen P (1988) Towards a least-squares framework for adjusting and testing of both functional and stochastic models. Internal research memo. Geodetic Computing Center, Delft. A preprint of the original report of 1988 is also available in 2004, vol 26, Mathematical Geodesy and Positioning Series. Delft University of Technology, the Netherlands
Tiberius C (1998) Recursive data processing for kinematic GPS surveying. PhD Thesis, Delft University of Technology, the Netherlands
Ware R, Fulker D, Stein S, Anderson D, Avery S, Clark R, Droegemeier K, Kuettner J, Minster J, Sorooshian S (2000) SuomiNet: a real-time national GPS network for atmospheric research and education. Bull Am Meteorol Soc 81:677–694
Acknowledgements
This work was supported by the European Commission, through the 5th Framework Program, contract no. EVG1-CT-2002-00080 in support of the TOUGH project.
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An erratum to this article can be found at http://dx.doi.org/10.1007/s10291-006-0039-3
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Keshin, M. Sequential combination of troposphere time series. GPS Solut 11, 37–47 (2007). https://doi.org/10.1007/s10291-006-0028-6
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DOI: https://doi.org/10.1007/s10291-006-0028-6