RIS-Vis: A Novel Visualization Platform for Seismic, Geodetic, and Weather Data Relevant to Antarctic Cryosphere Science

TL;DR

RIS-Vis is an innovative visualization platform that integrates seismic, geodetic, and weather data to support Antarctic cryosphere research, enhancing monitoring of ice shelf stability and potentially scaling to other regions.

Contribution

The paper introduces RIS-Vis, a scalable, Python-based visualization platform that consolidates multiple data types for Antarctic ice shelf monitoring, streamlining analysis for scientists.

Findings

RIS-Vis effectively visualizes seismic, geodetic, and meteorological data.

The platform simplifies data analysis, saving time for researchers.

RIS-Vis can be adapted for other ice shelves globally.

Abstract

Antarctic ice shelves play a vital role in preserving the physical conditions of the Antarctic cryosphere and the Southern Ocean, and beyond. By serving as a buttressing force, ice shelves prevent sea-level rise by restraining the flow of continental ice and glaciers to the sea. Sea-level rise impacts the global environment in multiple ways, including flooding habitats, eroding coastlines, and contaminating soil and groundwater. It is therefore essential to monitor the stability of Antarctic ice shelves, for which a variety of complementary data sources is required. We have developed RIS-Vis, a novel data visualization platform to monitor Antarctic ice shelves. Although focused on the Ross Ice Shelf (RIS), RIS-Vis could be readily scaled to monitor other ice shelves around Antarctica, and elsewhere. Currently, RIS-Vis is capable of analyzing and visualizing seismic, geodetic, and weather data to provide meaningful information for Antarctic cryosphere research. RIS-Vis was built using Python libraries including Obspy, APScheduler, and the Plotly Dash framework, and uses SQLite as the backing database. Visualizations developed on RIS-Vis include filtered seismic waveforms, spectrograms, and power spectral densities, geodetic-based ice-shelf flow, and meteorological variables such as atmospheric temperature and pressure. The dashboard visualization platform abstracts away the time-intensive analysis process of raw data and allows scientists to better concentrate on RIS science.