Abstract
Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer—Earth Orbiting System (EOS) (AMSR-E) onboard the National Aeronautics and Space Administration’s (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical AMSR-E data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current AMSR-E operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the AMSR-E algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size.
Highlights
Snow is a key element of the water and energy cycles and the knowledge of bulk snow properties at both local and global scales is crucial for hydrological and climatological applications
The retrieval of snow depth from passive microwave brightness temperatures can be improved by using retrieval approaches that dynamically combine ancillary snow depth information when compared to established algorithms based solely on regression or electromagnetic modeling without ancillary inputs [26]
We note that the new algorithm performs considerably better than the current algorithm especially over Siberia
Summary
Snow is a key element of the water and energy cycles and the knowledge of bulk snow properties at both local and global scales is crucial for hydrological and climatological applications. Extremely valuable and precious for regional and local scale applications, in-situ point-scale measurements are limited by their spatial coverage, with often only snow depth, and not SWE, being recorded at many locations. Spaceborne passive microwave data can be used to estimate snow depth and SWE at large spatial scales and high-temporal resolution, having been measuring the natural upwelling microwave radiation from the Earth for more than 35 years [9,10]. It has been demonstrated that the combination of space-borne passive microwave data with in-situ observations in an assimilation scheme improves the snow depth and SWE retrieval accuracy with respect to the case when only interpolation of synoptic surface observations is used [22]. The retrieval of snow depth from passive microwave brightness temperatures can be improved by using retrieval approaches that dynamically combine ancillary snow depth information (e.g., from snow physical models driven with surface meteorological data) when compared to established algorithms based solely on regression or electromagnetic modeling without ancillary inputs [26]
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