Collaborative Research: EarthCube Capabilities: Open Polar Radar (OPoRa) Software and Service

Lead PI: Kirsty J Tinto , Dr. Robin E. Bell

Unit Affiliation: Marine and Polar Geophysics, Lamont-Doherty Earth Observatory (LDEO)

September 2021 - August 2024
Active
Global
Project Type: Research Outreach

DESCRIPTION: Earth’s polar ice sheets play a critical role in shaping sea level over geological time, yet ice-sheet response to contemporary climate change remains highly uncertain. Forty years of spaceborne observations reveal the recent acceleration in mass loss of the ice sheets through measured change in elevation, gravity, and ice-flow variation. It remains difficult, however, to use these satellite observations to predict future behavior. Airborne radar sounder measurements offer the potential to constrain ice-sheet surface dynamics because they can map out subsurface parameters on an ice-sheet-wide scale. However, five decades of radar data collection by multiple organizations deploying a range of systems with data distributed under inconsistent data policies and processing methods leads to siloed research resulting in lower efficiency and increased time to science. The Open Polar Radar team accounts for 83% of Antarctica radar sounder data and nearly complete Greenland and polar sea-ice coverage. These datasets will be placed in common formats and made available through a common interface with a common set of tools for scientists via an end-user driven process. Open Polar Radar has the potential to vastly improve ice-sheet models at multiple scales. This could radically improve the understanding of the ice-sheet collapse processes, revise the understanding of past ice-sheet dynamics, and rapidly improve sea-level rise estimates. Improved sea-level projections will lead to better mitigation strategies that could reduce the dangers and costs of coastal flooding. The radar software and services will be common to other sounding radar problems, including planetary applications. The shared tools will contribute to and leverage the EarthCube ecosystem leading to solutions to other challenges in data sharing and image analytics research. Open Polar Radar will contribute to the multidisciplinary training of undergraduate and graduate students and postdocs. The principal investigators will engage scientists with annual training workshops and hackathons and activities at discipline-specific meetings to gather feedback and to advance the radar capabilities of the cryospheric sciences community. The project will also include under-represented communities in summer research experiences.

The Open Polar Radar project will establish a software ecosystem to consolidate polar radar software and services and make the associated datasets standardized and searchable – all in a community driven process. The target audiences are radioglaciology domain scientists, AI data scientists, and the software and data engineers behind the various satellite, airborne, and ground-based ice penetrating radar data collected over ice sheets, mountain glaciers, sea ice, and planetary icy bodies. Polar radar sounder data are a largely untapped resource, because the challenges associated with decentralized processing and manual interpretation prevent advanced applications of radar data on an ice-sheet-wide scale. This work starts a growing database of AI training data for geophysical imagery, enabling computer vision techniques common for traditional photography to a field that has, until very recently, relied on humans to extract meaning from images of Earth’s subsurface. To enable sharing data for scientific innovation, this project proposes to: (1) Merge data processing chains from each institution into one open-source software suite, (2) Upgrade the current Open Polar Server to meet Findable Accessible Interoperable and Reusable (FAIR) principles and multi-institution needs as well as incorporate a number of new data layers, (3) Enhance the quality of all data products by employing the shared tools for noise removal and image enhancement driven by science applications and AI model needs, (4) Demonstrate several use cases including the application of combined AI and physical models for tracking englacial features to validate results in two regions that are intersections of multiple data providers’ coverage.