Snow accumulation has potential climatological, hydrological and ecological impacts at a global scale. Satellite passive microwave radiometers have the potential to provide snow accumulation data with a historical record of over 30 years, however, current data products contain unknown uncertainty and error. Snowpack stratigraphy is the spatial variation in snowpack properties caused by the layered nature of the snowpack. Snowpack stratigraphy influences the accuracy and increases uncertainty in simulations of microwave emission from snow which in turn increases uncertainty in satellite derived estimates of snow water equivalent using microwave radiometers. Two methods were developed to help better quantify snowpack stratigraphy. An improved technique for characterising snowpack stratigraphy within a snow trench was developed. Secondly a new method was developed to quantify the density of ice layers that form in snowpacks with known error and uncertainty. Snowpack stratigraphy was characterised using the improved technique across the Trail Valley Creek watershed in the Canadian Northwest Territories. Two 50 m trenches and eleven 5 m trenches were dug across the range of landcover types found in the watershed. This dataset allowed layer boundary roughness to be characterised and the properties of snow layers to be mapped with an unprecedented level of accuracy. Ice lens density was measured 60 times at three locations in the Arctic and midlatitudes at locations with coincident ground based radiometer measurements. The impact that accurate parameterisation of density has on modelled estimates of brightness temperature was quantified. Simulations of microwave brightness temperatures were conducted using snow emission models at all locations. The output of these simulations, and comparison to ground based observations where available, allowed for the characterisation of variability in brightness temperature simulations caused by stratigraphic heterogeneity. The findings presented in this thesis will inform research aiming to better characterise the satellite error budget. Improvements in this area helps improve global snow mass and snow accumulation estimates.
|Publication status||Accepted/In press - Nov 2015|