Extraction of Snow Mass from Passive Microwave Data
Sandells, Mel; Davenport, Ian; Quaife, Tristan; van Leeuwen, Peter Jan
University of Reading, UNITED KINGDOM
More than one billion people rely on melt for their water supply. Snow is a vital component of the water cycle, yet we know little about its global distribution, nor whether it has changed with the climate in spite of decades of satellite data. Microwave observations contain information about the snow, as scattering and absorption of electromagnetic radiation is sensitive to the snow amount. The microwave behaviour of snow is also governed by other properties such as the size of the snow crystals, and these need to be taken into account in order to perform retrievals of snow mass.
Data assimilation offers the potential to improve snow mass estimates that currently assume a fixed snow grain size and density in their simple algorithms. Physically-based models have been shown to be able to reproduce observed grain sizes driven by meteorological inputs. Coupled with a microwave scattering model, it is possible to simulate satellite brightness temperatures. However, these physically-based models are highly non-linear, which makes them unsuitable for most of the standard data assimilation techniques. We propose a new retrieval system, which is being developed as part of a collection of ESA Data Assimilation projects. This system uses particle filter data assimilation techniques to combine the computationally efficient Snobal-SHAW snow model and Helsinki University of Technology microwave emission model with observations of brightness temperature to retrieve global snow mass. Simulations of snow properties and microwave brightness temperatures will be presented, and the data assimilation framework will be discussed. With the new retrieval system, it may be possible to reprocess the historical dataset of passive microwave observations to determine whether the snow mass has changed over time.