Evaluation of passive microwave dry snow detection algorithms and application to SWE retrieval during seasonal snow accumulation

Abstract

The microwave emission of snow has distinctive, frequency-dependent characteristics that enable the use of passive microwave remote sensing for the detection of (dry) snow alongside the study of further snow properties. Passive microwave approaches for the retrieval of snow water equivalent (SWE) often implement dry snow detection as one of the main processing steps. Reliable dry snow detection is thus crucial; however, common algorithms tend to underestimate snow cover extent. This study conducts a long-term assessment of six dry snow detection algorithms as part of ongoing GlobSnow SWE product development. We focus on terrestrial snow on the Northern Hemisphere, using exhaustive in situ snow depth measurements and spatially complete snow maps by the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS). In addition to using conventional daily snow masks, cumulative snow masks are investigated as means to counteract underestimation and the results emphasise their potential for this purpose. Best performances are achieved by the cumulative versions of the algorithm of the EUMETSAT H SAF H11 product and of the decision tree by Grody and Basist (1996), which agree with in situ data by approximately 81% and 84% whilst differing from IMS maps by only about 15% and 11%, respectively. Their validation for hemispheric SWE retrieval in the GlobSnow framework, using in situ snow course SWE observations, shows a noticeable improvement of the current statistics especially during shallow snow conditions in autumn—reducing the bias by up to 2.2 mm and the RMSE by up to 2.0 mm. This highlights the impact that the algorithm choice for dry snow detection has on the quality of SWE retrieval and therefore on long-term climate data records.