Three Dimensional Computer Simulations of Maxwell Equations and Snow Microstructures for Active and Passive Microwave Observations of Snow on Sea Ice in a Changing Climate
Snow cover plays a key role in Arctic sea ice processes, and it has thinned significantly in recent decades. In winter, it insulates sea ice from cold air temperatures, slowing sea ice growth. In spring-summer, snow albedo exerts a control on solar heat absorbed by the sea ice and underlying ocean, impacting ice melt processes. Thus, snow thickness, distribution, and density, can moderate the recovery of the sea ice pack or exacerbate its loss. Airborne and satellite remote sensing are used to monitor snow depth over sea ice. Interpretation of the remotely sensed microwave signals requires accurate models of how the microwaves interact with the snow. This project will develop electromagnetic models that relate the microwave signatures quantitatively to physical parameters of snow. It will contribute to STEM workforce development by providing support for the training of a graduate student. The computer code for the model will be made publicly available and provide a resource for the Arctic science community. The project will improve the extraction of snow parameters from airborne and spaceborne observations through the modeling of the interaction between snow particles and electromagnetic waves in the microwave region. In particular, the project will generate 3D simulations of the solution to Maxwell's equations in the presence of 3D snow microstructure. Results will be applied to the analysis of active and passive microwave remote sensing data of snow over sea ice. Three-dimensional computer simulations of microstructures of snow that resemble that of real snow will be performed on High Performance Computing (HPC) systems to compute scattering and emission of snow covering sea-ice. The use of large scale high performance computations will permit the simulations to be run over relatively large spatial domains.