Jointly assimilating synthetic albedo and snow depth observations improves glacier mass balance simulations by up to 86% in twin experiments, with the particle batch smoother best for albedo and the ensemble smoother best for snow depth under low snowfall.
Cryosphere, 12(1), 247--270, ISSN 19940424 ( 10.5194/tc-12-247-2018 )
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Glacier data assimilation on an Arctic glacier: Learning from large ensemble twin experiments
Jointly assimilating synthetic albedo and snow depth observations improves glacier mass balance simulations by up to 86% in twin experiments, with the particle batch smoother best for albedo and the ensemble smoother best for snow depth under low snowfall.