Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2408.12106

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

Aishwarya Chakravarthy·Dhiman Mondal·John Barrett·Chet Ruszczyk·Pedro Elosegui
View original source ↗Read source document ↗Export metadata ↓

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.

Explore related subjects

physics.ao-phastro-ph.EPastro-ph.IM

Keep this discovery

BibTeXRIS

Aishwarya Chakravarthy, Dhiman Mondal, John Barrett, Chet Ruszczyk, Pedro Elosegui. 2024-08-22. RIS-Vis: A Novel Visualization Platform for Seismic, Geodetic, and Weather Data Relevant to Antarctic Cryosphere Science. https://arxiv.org/abs/2408.12106

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph↗
arXiv2026

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph↗