Oelsmann J., Marcos M., Passaro M., Sanchez L., Dettmering D., Dangendorf S., Seitz F.
Data supplement to 'Vertical land motion reconstruction unveils non-linear effects on relative sea level changes from 1900-2150' (data)
Zenodo
2023

Schlembach F., Passaro M.
PySAMOSA: An Open source Software Framework for Retracking SAMOSA-based, Open Ocean and Coastal Waveforms of SAR Satellite Altimetry (software)
Zenodo
2023

Oelsmann J., Marcos M., Passaro M., Sanchez L., Dettmering D., Dangendorf S., Seitz F.
Data supplement to 'Vertical land motion reconstruction unveils non-linear effects on relative sea level changes from 1900-2150' (data)
2023

Treu S., Muis S., Dangendorf D., Wahl T., Oelsmann J., Heinicke S., Frieler K., Mengel M.
Hourly Coastal water levels with Counterfactual (HCC) (data)
The Hourly Coastal water levels with Counterfactual (HCC) dataset provides coastal water levels to be used as input data for ISIMIP3a (www.isimip.org, Frieler et al. 2023). The dataset describes relative and geocentric historical coastal water levels, utilizing data from several specialized sources. It incorporates reconstructed water level data from the Hybrid Reconstructions (HR) dataset by Dangendorf et al. (2019), which describes the long-term evolution of historical water levels. Vertical land motion information is provided by Oelsmann et al. (2023), adding crucial details about changes in land elevation. The dataset also integrates a reanalysis of hourly variations in water levels caused by wind, air pressure, and astronomical tides, as detailed in the Coastal Dataset for the Evaluation of Climate Impact (CoDEC) by Muis et al. (2020). A counterfactual scenario is derived by removing the quadratic trend observed in sea levels from 1900 to 2015. This results in a hypothetical condition where the sea level has been stationary since 1900. When referring to the HCC dataset, the paper Treu et al. (2023) https://doi.org/10.5194/essd-2023-112 should be cited.
2023

Schlembach F.
Development of a Novel Coastal Retracking Algorithm for SAR Satellite Altimetry
Dissertation
2023

Pavlis E.C., Pearlman M.R., Carabajal C.C., Ricklefs R., Schwatke C., Wilkinson M., Kirchner G., Luceri V., Otsubo T., Torre J.-M., Schreiber U., Kucharski D., Courde C., Blossfeld M., Ventura-Traveset J., Delva P., Merkowitz S.M., Sherwood R., Rodriguez J., Couhert A., Lemoine F.
International Laser Ranging Service (ILRS)
Reports 2019-2023 of the International Association of Geodesy (IAG), Travaux de l'AIG
2023
Volume 43
Jul

Glomsda M., Seitz M., Bloßfeld M., Angermann D.
Investigating the datum parameters of new solutions by IVS AC DGFI-TUM
In: Haas, Rüdiger; Schroth, Eva; Neidhardt, Alexander (Eds.); Proceedings of the 26th European VLBI Group for Geodesy and Astrometry Working Meeting
2023

Schwatke C., Dettmering D., Passaro M., Hart-Davis M.G., Scherer D., Müller F.L., Bosch W., Seitz F.
OpenADB: DGFI‐TUM's Open Altimeter Database
Geoscience Data Journal
2023
Dec

Costa S.M., Sánchez L., Piñón D., Tarrio-Mosquera J.A., Guimarães G., Gómez D., Drewes H., Mackern-Oberti M.V., Antokoletz E.D., de Matos A.C.O.C., Blitzkow D., da Silva A., Inzunza J., España D., Rodríguez O., Rozas-Bornes S., Guagni H., González G., Paucar-Llaja O., Pampillón J.M., Alvarez-Calderón A.
Status of the SIRGAS Reference Frame: Recent Developments and New Challenges
International Association of Geodesy Symposia
2023

Natras R., Soja B., Schmidt M.
Uncertainty Quantification for Machine Learning-Based Ionosphere and Space Weather Forecasting: Ensemble, Bayesian Neural Network, and Quantile Gradient Boosting
Space Weather
2023