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

Abstract. Ice velocity is a crucial observation as it controls glacier mass redistribution and future geometry. While glacier annual velocities are now available in open-source worldwide, sub-annual velocity time series are still highly uncertain and available at heterogeneous temporal resolutions. This hinders our ability to understand flow processes, such as basal sliding or surges, and integration of these observations in numerical models. We introduce an open source and operational Python package called TICOI (Temporal Inversion using Combination of Observations and Interpolation). TICOI fuses multi-temporal and multi-sensor image-pair velocities produced by different processing chains, using the temporal closure principle. In this article, we provide extensive examples of TICOI application on the ITS_LIVE dataset and in-house velocity products. The results are validated using GNSS data collected on three glaciers with different dynamics in Yukon and western Greenland, including a surging glacier. Comparison with GNSS observations demonstrates a reduction in error by up to 50 % in comparison with the raw image-pair velocities and other post-processing methods. This increase in performance comes from the development of methodological strategies to enhance TICOI's robustness to temporal decorrelation and abrupt non-linear changes. TICOI also proves to be able to retrieve monthly velocity when only annual image-pair velocities are available. This package opens the door to the regularization of various datasets, enabling the creation of standardized sub-annual velocity products.