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| Titel |
Limited constraint, robust Kalman filtering for GNSS troposphere tomography |
| VerfasserIn |
W. Rohm, K. Zhang, J. Bosy |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 7, no. 5 ; Nr. 7, no. 5 (2014-05-27), S.1475-1486 |
| Datensatznummer |
250115768
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| Publikation (Nr.) |
 copernicus.org/amt-7-1475-2014.pdf |
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| Zusammenfassung |
| The mesoscale variability of water vapour (WV) in the troposphere is a
highly complex phenomenon and modelling and monitoring the WV distribution is
a very important but challenging task. Any observation technique that can
reliably provide WV distribution is essential for both monitoring and
predicting weather. The global navigation satellite system (GNSS) tomography technique is a powerful tool that builds
upon the critical ground-based GNSS infrastructure (e.g. Continuous
Operating Reference Station – CORS – networks) that can be used to sense the
amount of WV. Previous research shows that the 3-D WV field from GNSS tomography
has an uncertainty of 1 hPa. However, all the models used in GNSS tomography
heavily rely on a priori information and constraints from non-GNSS measurements. In
this study, 3-D GNSS tomography models are investigated based on a limited
constrained approach – i.e. horizontal and vertical correlations between
voxels were not introduced, instead various a priori information were added into the
system. A case study is designed and the results show that proposed
solutions are feasible by using a robust Kalman filtering technique and
effective removal of linearly dependent observations and parameters.
Discrepancies between reference wet refractivity data derived from the
Australian Numerical Weather Prediction (NWP) model (ACCESS) and the GNSS
tomography model using both simulated and real data are 4.2 ppm (mm km−1) and
6.2 ppm (mm km−1), respectively, which are essentially in the same order of accuracy. |
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