Factors affecting remotely sensed snow water equivalent uncertainty

State-of-the-art passive microwave remote sensing-based snow water equivalent (SWE) algorithms correct for factors believed to most significantly affect retrieved SWE bias and uncertainty. For example, a recently developed semi-empirical SWE retrieval algorithm accounts for systematic and random error caused by forest cover and snow morphology (crystal size — a function of location and time of year). However, we have found that climate and land surface complexities lead to significant systematic and random error uncertainties in remotely sensed SWE retrievals that are not included in current SWE estimation algorithms. Joint analysis of independent meteorological records, ground SWE measurements, remotely sensed SWE estimates, and land surface characteristics have provided a unique look at the error structure of these recently developed satellite SWE products. We considered satellite-derived SWE errors associated with the snow pack mass itself, the distance to significant open water bodies, liquid water in the snow pack and/or morphology change due to melt and refreeze, forest cover, snow class, and topographic factors such as large scale root mean square roughness and dominant aspect. Analysis of the nine-year Scanning Multichannel Microwave Radiometer (SMMR) SWE data set was undertaken for Canada where many in-situ measurements are available. It was found that for SMMR pixels with 5 or more ground stations available, the remote sensing product was generally unbiased with a seasonal maximum 20 mm average root mean square error for SWE values less than 100 mm. For snow packs above 100 mm, the SWE estimate bias was linearly related to the snow pack mass and the root mean square error increased to around 150 mm. Both the distance to open water and average monthly mean air temperature were found to significantly influence the retrieved SWE product uncertainty. Apart from maritime snow class, which had the greatest snow class affect on root mean square error and bias, all other factors showed little relation to observed uncertainties. Eliminating the drop-in-the-bucket averaged gridded remote sensing SWE data within 200 km of open water bodies, for monthly mean temperatures greater than � 2 -C, and for snow packs greater than 100 mm, has resulted in a remotely sensed SWE product that is useful for practical applications.