Abstract
Sea ice concentration (SIC) is an important metric used to characterize polar sea ice behavior. Understanding this behavior and accurately representing it is of critical importance for climate science research and also has important uses in the context of maritime navigation. An end-to- end workflow for generating learned concentration estimation models from synthetic aperture radar (SAR) data, trained on existing passive microwave (PMW) data, is presented here. A novel objective function was introduced to account for uncertainty in the PMW measurements, which can be extended to account for arbitrary sources of error in the training data, and a recent set of in situ observations was used to evaluate the reliability of the chosen PMW concentration estimation model. Google Colaboratory was used as the development platform, and all notebooks, training data, and trained models are available on GitHub. This chapter is an overview of the most interesting aspects of this investigation, and a detailed report is also available on GitHub.
Original language | English |
---|---|
Title of host publication | New Methodologies for Understanding Radar Data |
Publisher | Institution of Engineering and Technology |
Chapter | 10 |
Pages | 319-337 |
Number of pages | 19 |
ISBN (Electronic) | 9781839531897 |
ISBN (Print) | 9781839531880 |
DOIs | |
Publication status | Published - Oct 2021 |
Externally published | Yes |