2024 CSDMS meeting-071
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Browse abstracts
PaleoSTeHM - A modern, scalable Spatio-Temporal Hierarchical Modeling framework for paleo-environmental data
Yucheng Lin,
Rutgers University Piscataway , United States. yc.lin@rutgers.edu
Robert Kopp, Rutgers University Piscataway New Jersey, United States. robert.kopp@rutgers.edu
Alexander Reedy, Rutgers University Piscataway New Jersey, United States. ar2208@eps.rutgers.edu
For paleo environmental studies, a key challenge is to partitioning physical signals operated under multiple spatio-temporal scales. For example, paleo relative sea-level (RSL) data record a combined signal from global ice-ocean mass exchange induced global mean sea-level change and gravitational, rotational and deformational effects, along with regional and local RSL change caused by changing ocean density, groundwater storage and sediment redistribution. Here we present an open-sourced spatio-temporal hierarchical model framework (PaleoSTeHM) that is conceptually suitable for investigating this problem by separating the underlying phenomenon of interest and its variability from the noisy mechanisms by which this underlying process is observed. PaleoSTeHM is built upon a modern, scalable machine-learning framework and offers flexible modelling and analytical choices. In this presentation, we will show some of the modelling choices in PaleoSTeHM along with an example application for Holocene sea level change. Also, we will seek inputs from potential users for this framework in order to make this co-develop framework more sustainable and allows a wide range of paleo-sea level and -climate researchers to easily and robustly incorporate spatio-temporal statistical modeling into their work.
