PhD Research Fellow in global snow depths from remote sensing and modeling (ref 212917)

SNOWDEPTH – “Global snow depths from spaceborne remote sensing for permafrost, high-elevation precipitation, and climate reanalyses” is a Young Researcher project funded by the Research Council of Norway (RCN). SNOWDEPTH will develop novel methods to measure snow depth profiles from satellite data and generate global snow depth maps using complementary data and models with data assimilation methods. The project has a special focus on applications of spaceborne snow depth data. Together with partners in Switzerland and Norway, we will use the snow depth data to improve permafrost modelling, increase understanding of high-altitude precipitation, and assess the potential to improve climate reanalyses in data-sparse regions. 

The project has a data processing and an application part. During part I, the successful candidate will work with remote sensing data from the laser satellite ICESat-2, data from digital elevation models (DEMs) as well as optical and microwave satellite data to retrieve global snow depth measurements. To generate time series of snow depth maps, the candidate will use the snow depth data together with a snow model and climate data within an ensemble-based data assimilation scheme.

Part two of the project focuses on applications. The candidate is expected to analyse the novel snow depth data at local and/or global scale with a particular focus on mountainous and/or vegetated areas. The primary application areas of the project are permafrost modelling, high-altitude precipitation, and climate reanalyses, where snow depth measurements have a strong potential to advance science. One particular project goal the PhD candidate may work with is to integrate the global snow depth data into the CryoGrid permafrost model (github.com/CryoGrid/CryoGrid).

For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/212917/phd-research-fellow-in-global-snow-depths-from-remote-sensing-and-modeling