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DOI10.5194/hess-22-5711-2018
Assimilation of passive microwave AMSR-2 satellite observations in a snowpack evolution model over northeastern Canada
Larue F.; Royer A.; De Sève D.; Roy A.; Cosme E.
发表日期2018
ISSN1027-5606
起始页码5711
结束页码5734
卷号22期号:11
英文摘要Over northeastern Canada, the amount of water stored in a snowpack, estimated by its snow water equivalent (SWE) amount, is a key variable for hydrological applications. The limited number of weather stations driving snowpack models over large and remote northern areas generates great uncertainty in SWE evolution. A data assimilation (DA) scheme was developed to improve SWE estimates by updating meteorological forcing data and snowpack states with passive microwave (PMW) satellite observations and without using any surface-based data. In this DA experiment, a particle filter with a Sequential Importance Resampling algorithm (SIR) was applied and an inflation technique of the observation error matrix was developed to avoid ensemble degeneracy. Advanced Microwave Scanning Radiometer 2 (AMSR-2) brightness temperature (TB) observations were assimilated into a chain of models composed of the Crocus multilayer snowpack model and radiative transfer models. The microwave snow emission model (Dense Media Radiative Transfer-Multi-Layer model, DMRT-ML), the vegetation transmissivity model (ω-τopt), and atmospheric and soil radiative transfer models were calibrated to simulate the contributions from the snowpack, the vegetation, and the soil, respectively, at the top of the atmosphere. DA experiments were performed for 12 stations where daily continuous SWE measurements were acquired over 4 winters (2012-2016). Best SWE estimates are obtained with the assimilation of the TBs at 11, 19, and 37 GHz in vertical polarizations. The overall SWE bias is reduced by 68% compared to the original SWE simulations, from 23.7 kgm..2 without assimilation to 7.5 kgm..2 with the assimilation of the three frequencies. The overall SWE relative percentage of error (RPE) is 14.1% (19% without assimilation) for sites with a fraction of forest cover below 75 %, which is in the range of accuracy needed for hydrological applications. This research opens the way for global applications to improve SWE estimates over large and remote areas, even when vegetation contributions are up to 50% of the PMW signal. © 2018 Author(s).
语种英语
scopus关键词Microwaves; Snow; Vegetation; Advanced microwave scanning radiometer; Brightness temperatures; Importance re samplings; Meteorological forcing; Radiative transfer model; Satellite observations; Snow water equivalent; Top of the atmospheres; Radiative transfer; algorithm; AMSR-E; brightness temperature; data assimilation; microwave radiation; model; radiative transfer; satellite data; simulation; snow water equivalent; snowpack; transmissivity; Canada; Crocus
来源期刊Hydrology and Earth System Sciences
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/159864
作者单位Larue, F., CARTEL, Université de Sherbrooke, Sherbrooke, QC, Canada, Centre d'Études NordiquesQC, Canada, IREQ, Hydro-QuébecQC, Canada; Royer, A., CARTEL, Université de Sherbrooke, Sherbrooke, QC, Canada, Centre d'Études NordiquesQC, Canada; De Sève, D., IREQ, Hydro-QuébecQC, Canada; Roy, A., CARTEL, Université de Sherbrooke, Sherbrooke, QC, Canada, Centre d'Études NordiquesQC, Canada, Département de Geógraphie, Université de Montreál, Montreál, QC, Canada, Université du Québec À Trois-RivièreQC, Canada; Cosme, E., Institut des Geósciences de l'Environnement, IGE, UGA-CNRS, Grenoble, France
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GB/T 7714
Larue F.,Royer A.,De Sève D.,et al. Assimilation of passive microwave AMSR-2 satellite observations in a snowpack evolution model over northeastern Canada[J],2018,22(11).
APA Larue F.,Royer A.,De Sève D.,Roy A.,&Cosme E..(2018).Assimilation of passive microwave AMSR-2 satellite observations in a snowpack evolution model over northeastern Canada.Hydrology and Earth System Sciences,22(11).
MLA Larue F.,et al."Assimilation of passive microwave AMSR-2 satellite observations in a snowpack evolution model over northeastern Canada".Hydrology and Earth System Sciences 22.11(2018).
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