Improving snow depth on sea ice retrievals from multiple data sources

Abstract:

Knowledge of snow depth on sea ice is important for studying long-term change in the Arctic system. It is also important for the retrieval of sea ice thickness from radar and laser altimetry data since snow on sea ice uncertainties constitute one of the largest sources of error in the retrieval of sea ice thickness. Since 2009, NASA's Operation IceBridge mission has utilized a novel ultra-wideband snow radar system to take measurements of spring time snow depth on sea ice over a large portion of the Arctic basin. In this work we describe the physics of the often-times complex radar returns from snow-covered sea ice which are observed in the data sets and show how varying surface roughness and instrument characteristics impact the retrieval of geophysical properties. An improved technique for the retrieval of snow depth using a best model fit is shown which accounts for the varying surface types encountered as well as changing instrument characteristics. The newly processed IceBridge snow depth data sets are then combined with snow depth estimates from other sources using an optimal interpolation technique, allowing for basin-wide snow depth results to be used for snow depth studies and sea ice thickness determination from altimetry data.