Project Title: NNA: Remote Sensing of Arctic Sea Ice Using the Super Dual Auroral Radar Network (Award# 1836426)
PI:Shepherd, Simon G (email@example.com) Phone:(603) 646.0096 Institute/Department:Dartmouth College, Thayer School of Engineering IPY Project? Funding Agency:US\Federal\NSF\GEO\OPP\ARC\AON Program Manager:Dr. Roberto Delgado (firstname.lastname@example.org) Discipline(s): |Data Management |Meteorology and Climate |
Science Summary: Changes in Arctic sea ice have wide-ranging societal and ecological impacts. The opening of northern shipping routes, reliability of ice roads for access to coastal communities, and extraction of undersea resources have economic implications for countries around the world. Indigenous peoples depend on local marine mammal populations as a source of cultural and nutritional value, however Arctic marine mammals are particularly vulnerable to reductions in sea ice cover as have been occurring for more than 30 years. The presence and extent of Arctic sea ice can also influence the transfer of heat between the ocean and atmosphere, affecting both local and remote seasonal weather patterns. Sustained observations of Arctic sea ice are therefore crucial for understanding current trends and predicting future changes in the global climate system. This project will contribute to the National Science Foundation's Arctic Observing Network by developing new techniques for the extraction of sea ice characteristics from a long-running data set collected by an existing network of space weather monitors.
The Super Dual Auroral Radar Network (SuperDARN) is an international network of ground-based space weather radars which have operated continuously in the Arctic and Antarctic regions for more than 30 years. These high-frequency (HF) radars use over-the-horizon (OTH) radio wave propagation to detect ionospheric plasma structures across ranges of several thousand kilometers. As a byproduct of this technique, the transmitted radar signals frequently reflect from the Earth’s surface and can be observed as ground backscatter echoes. This project will analyze historical and ongoing SuperDARN ground backscatter data for extraction of Arctic sea ice parameters, and comparisons will be made to sea ice measurements obtained from space-based microwave remote sensors. An operational sea ice data product derived from the SuperDARN HF radar observations will be delivered to the National Science Foundation's Arctic Data Center for long-term preservation and accessibility by the broader Arctic research community. Improvements in the detection and geolocation of SuperDARN ground backscatter echoes will not only benefit future studies of land/sea surface features in radar observations but also increase the quality of global space weather maps of ionospheric plasma convection. This project aligns with one of the National Science Foundation’s 10 Big Ideas for Navigating the New Arctic by leveraging a large data set from an existing observational technology and network.
This award is cofunded by the Arctic Sciences section, Office of Polar Programs, the Office of International Science & Engineering and the Geospace section, Division of Atmospheric and Geospace Sciences.
Logistics Summary: Long-running observations of sea ice extent and characteristics are crucial to understanding environmental changes in the Arctic. The Super Dual Auroral Radar Network (SuperDARN) is an international network of ground-based, high-frequency (HF) space weather radars which have operated
continuously in the Arctic and Antarctic regions for more than 30 years. The objectives of this project are to analyze historical and ongoing SuperDARN ground backscatter measurements for extraction of Arctic sea ice parameters. Comparisons will be made to sea ice measurements obtained from space-based microwave remote sensors. An operational sea ice data product derived from SuperDARN HF radar observations will be submitted to the Arctic Data Center for long-term preservation and accessibility by the broader Arctic research community.
No fieldwork is conducted.
Parameters used to generate this report:, Grant# = "1836426", IPY = "ALL"
Number of projects returned based on your query parameters = 1