International Snow Science Workshop Proceedings - Montana State University Library

Item: To the origin of the temperature bias in the AROME numerical weather forecast model : investigations at a high-altitude site

• • Title: To the origin of the temperature bias in the AROME numerical weather forecast model : investigations at a high-altitude site

    Proceedings: International Snow Science Workshop Proceedings 2018, Innsbruck, Austria

    Authors:

    • Isabelle Gouttevin  [ Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Études de la Neige, Grenoble, France ]
    • Vincent Vionnet  [ Centre for Hydrology, University of Saskatchewan, Saskatoon, Canada ]
    • Fatima Karbou  [ Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Études de la Neige, Grenoble, France ]
    • Hugo Merzisen  [ Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Études de la Neige, Grenoble, France ]
    • Yannick Deliot  [ Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Études de la Neige, Grenoble, France ]

    Date: 2018-10-07

    Abstract: The AROME-France meso-scale atmospheric model currently provides operational weather forecasts at about 1.3-km spatial resolution over a domain covering France and parts of the surrounding countries. In particular it covers the French Alps and Pyrénées. Recent studies dedicated to the evaluation of the potential of the high-resolution analysis and forecast field of AROME for snowrelated applications have highlighted significant biases in the surface temperature and relative humidity fields, with a marked diurnal cycle and increasing amplitude with altitude (Vionnet et al., 2016; Quéno et al., 2016; Dombrowski-Etchevers et al., 2017). Here, we make use the thorough set of meteorological and surface observations routinely collected at the high-altitude observatory Col du Lac Blanc (French Alps, 2720 m a.s.l., Apes d’Huez ski resort) to diagnose the origin and implications of this temperature biais in AROME. Among others, the possible contribution to this bias of an erroneous radiation budget, and of uncertainties in the snowpack thermal modelling, are considered by means of comparisons to observations and appropriate numerical experiments. This diagnostic will ultimately help improve weather forecast in mountain regions, of benefice for a variety of applications including avalanche forecasting and mountain hydrology.

    Object ID: ISSW2018_P08.9.pdf

    Language of Article: English

    Presenter(s):

    Keywords: Numerical weather prediction models - Mountain meteorology - Model biases.

    Page Number(s): 756-758

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