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  • 标题:Impact of satellite-based lake surface observations on the initial state of HIRLAM. Part II: Analysis of lake surface temperature and ice cover
  • 本地全文:下载
  • 作者:Homa Kheyrollah Pour ; Laura Rontu ; Claude Duguay
  • 期刊名称:Tellus A: Dynamic Meteorology and Oceanography
  • 电子版ISSN:1600-0870
  • 出版年度:2016
  • 卷号:68
  • 期号:s1
  • DOI:10.3402/tellusa.v66.21395
  • 摘要:Abstract This paper presents results from a study on the impact of remote-sensing Lake Surface Water Temperature (LSWT) observations in the analysis of lake surface state of a numerical weather prediction (NWP) model. Data assimilation experiments were performed with the High Resolution Limited Area Model (HIRLAM), a three-dimensional operational NWP model. Selected thermal remote-sensing LSWT observations provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Along-Track Scanning Radiometer (AATSR) sensors onboard the Terra/Aqua and ENVISAT satellites, respectively, were included into the assimilation. The domain of our experiments, which focussed on two winters (2010–2011 and 2011–2012), covered northern Europe. Validation of the resulting objective analyses against independent observations demonstrated that the description of the lake surface state can be improved by the introduction of space-borne LSWT observations, compared to the result of pure prognostic parameterisations or assimilation of the available limited number of in-situ lake temperature observations. Further development of the data assimilation methods and solving of several practical issues are necessary in order to fully benefit from the space-borne observations of lake surface state for the improvement of the operational weather forecast. This paper is the second part of a series of two papers aimed at improving the objective analysis of lake temperature and ice conditions in HIRLAM.
  • 关键词:Optimal Interpolation ; data assimilation ; Lake Surface Water Temperature ; satellite observations ; lake ice cover ; numerical weather prediction ; MODIS ; AATSR
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