Articles | Volume 19, issue 10
https://doi.org/10.5194/tc-19-4555-2025
https://doi.org/10.5194/tc-19-4555-2025
Research article
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15 Oct 2025
Research article | Highlight paper |  | 15 Oct 2025

TICOI: an operational Python package to generate regular glacier velocity time series

Laurane Charrier, Amaury Dehecq, Lei Guo, Fanny Brun, Romain Millan, Nathan Lioret, Luke Copland, Nathan Maier, Christine Dow, and Paul Halas

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Cited articles

Altena, B., Scambos, T., Fahnestock, M., and Kääb, A.: Extracting recent short-term glacier velocity evolution over southern Alaska and the Yukon from a large collection of Landsat data, The Cryosphere, 13, 795–814, https://doi.org/10.5194/tc-13-795-2019, 2019. a, b
Altena, B., Kääb, A., and Wouters, B.: Correlation dispersion as a measure to better estimate uncertainty in remotely sensed glacier displacements, The Cryosphere, 16, 2285–2300, https://doi.org/10.5194/tc-16-2285-2022, 2022. a
Anderton, P. W.: Structural glaciology of a glacier confluence, Kaskawulsh glacier, Yukon Territory, Canada, Tech. rep., Research Foundation and the Institute of Polar Studies, The Ohio State, 1973. a
Arendt, A. A., Echelmeyer, K. A., Harrison, W. D., Lingle, C. S., and Valentine, V. B.: Rapid wastage of Alaska glaciers and their contribution to rising sea level, Science, 297, 382–386, 2002. a
Bartholomew, I., Nienow, P., Mair, D., Hubbard, A., King, M. A., and Sole, A.: Seasonal evolution of subglacial drainage and acceleration in a Greenland outlet glacier, Nat. Geosci., 3, 408–411, 2010. a
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Editorial statement
This study has the potential to facilitate information flow from Earth observations to geophysical models with particular focus on seasonally relevant glacier evolution processes. This has great value for downstream users and will be a substantial improvement to our treatment of ice velocity timeseries.
Short summary
While global annual glacier velocities are openly accessible, sub-annual velocity time series are still lacking. This hinders our ability to understand flow processes and the integration of these observations in numerical models. We introduce an open source Python package called TICOI (Temporal Inversion using linear Combinations of Observations, and Interpolation) to fuse multi-temporal and multi-sensor image-pair velocities produced by different processing chains to produce standardized sub-annual velocity products.
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