Detection of dry snow using spaceborne microwave radiometer data

Snow monitoring on global scale is an important task considering the essential role of snow cover in the Earth’s climate and the scarcity of ground-based snow observations. Snow has distinctive, frequency-dependent characteristics in terms of microwave emission.This enables the use of brightness temperatures, as measured by spaceborne passive microwave sensors, not only for the estimation of snow cover extent (SCE) through (dry) snow detection, but also for the retrieval of snow depth and snow water equivalent (SWE).Approaches for SWE retrieval, such as the methodology of the GlobSnow v3.0 SWE product, frequently implement dry snow detection as one of the main processing steps. Reliable dry snow detection is thus crucial, however, common algorithms are known to generally underestimate the presence of snow due to their sensitivity to vegetation and liquid water content of the snowpack, amongst other. Although several suggestions for improvement have been proposed, an extensive, long-term comparison has not been conducted. This thesis hence investigates six current dry snow detection algorithms and their intraseasonal performance in order to identify the most appropriate one for implementation in the GlobSnow SWE product. The aim is to improve the product which is primarily affected by underestimation during the snow accumulation period from September to February. The investigated algorithms are based on the brightness temperature difference involving primarily, but not exclusively, the 18/19-GHz and 37-GHz channels which are available for the SMMR, SSM/I and SSMIS instruments covering more than 40 years of observations. In addition to conventional daily snow masks, cumulative snow masks are investigated as a means to counteract underestimation. The assessment focuses on seasonal snow above 40° North, and is conducted for the snow seasons from 1979/1980 to 2017/2018 with reference to exhaustive, in situ snow depth data from multiple sources. In addition, spatially-complete SCE maps by the Interactive Multisensor Snow and Ice Mapping System serve as reference from 2007/2008 to 2016/2017, in order to evaluate the detected snow cover extent as a whole. The results emphasise the potential of cumulative masks to counteract underestimation and increase detection accuracy, and highlight the benefit of discriminating between different scattering sources, that could otherwise be mistaken for snow. Two methods are found to be overall best-performing: the empirically-derived algorithm of the EUMETSAT H SAF H11 product (applicable to SMMR, SSM/I and SSMIS), and the decision tree published by Grody and Basist in 1996 (applicable to SSM/I and SSMIS). Promising accuracies with respect to in situ data are achieved using cumulative masks, reaching approximately 0.83 and 0.80 for the approaches of Grody and Basist and of the H SAF product, respectively. Implementing the H SAF algorithm into the GlobSnow SWE product is expected to lead to immediate improvements of the latter and is thus planned, though falls outside the scope of this thesis. Further investigation is required to adapt the approach of Grody and Basist to the whole long-term passive microwave data record including SMMR data