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TOSCA - Towards an Optimal estimation based Snowfall Characterization Algorithm

Within this DFG-funded project, a unique combination of remote sensing instruments has been deployed at the Environmental Research Station Schneefernerhaus for deriving microphysical properties of falling snow. Numerous well-established algorithms for deriving the liquid phase of precipitation – either from satellite or from the ground – have been developed and are being routinely applied. Algorithms for deriving snowfall, in contrast, are much less sophisticated and suffer from large uncertainties. This poses a major problem in climate research, because a large part of the global precipitation falls as snow and liquid precipitation often occurs via the ice phase. The global distribution of snowfall is thus very important for climate studies, especially concerning the rapid changes of the hydrological cycle in the Polar Regions.

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The main problem in the derivation of snowfall parameters is the high spatial and temporal variability of the snow crystals, whose interactions with atmospheric radiation is very difficult to describe. The TOSCA project addresses this point in combining the unique information contained in a combination of passive radiometers, active radar technology and in-situ measurement methods within a unified retrieval scheme. The integrated retrieval algorithm to be developed will aim at deriving the vertical distribution of the specific mass of snow. This includes not only carrying out and carefully evaluating the measurements, but also the modelling of the interaction (i.e. scattering processes) of atmospheric radiation with the snow crystals.

 
start.txt · Zuletzt geändert: 2011/04/12 16:34 von stefan