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首页> 外文期刊>Journal of hydrometeorology >To the Origin of a Wintertime Screen-Level Temperature Bias at High Altitude in a Kilometric NWP Model
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To the Origin of a Wintertime Screen-Level Temperature Bias at High Altitude in a Kilometric NWP Model

机译：To the Origin of a Wintertime Screen-Level Temperature Bias at High Altitude in a Kilometric NWP Model

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摘要

High-resolution numerical weather prediction (NWP) systems present a strong potential to provide meteorological information in alpine terrain for diverse applications. However, they still suffer from biases highly detrimental for practical purposes. In this study, we investigate the origin of a significant wintertime screen-level temperature bias in forecasts of the AROME-France NWP system in high-altitude, snow-covered alpine terrain. For this purpose, a thorough set of meteorological and snow observations from two high-altitude instrumental sites is used. Targeted numerical simulations are carried out to disentangle the contributions to this bias coming from atmospheric fields, from the snow scheme, and from the coupling between the snowpack and the atmosphere. At both sites, the wind speed and incoming longwave radiation appear significantly negatively biased in AROME in the winter season. Using targeted offline simulations, we show that the simulation errors in these screen-level fields contribute to an average of 67 of the screen-level temperature bias of AROME, while the contribution of errors in the incoming shortwave radiation is negligible. Additionally, the screen-level temperature of AROME is not majorly impacted by changes in the complexity and especially the vertical layering of the snow model. However, it appears particularly sensitive to the parameterization of turbulent fluxes in stable conditions. Evidence suggest that these findings could at least partially be generalized to the whole AROME-France alpine domain. Hence, reducing the high-altitude, winter screen-level temperature bias in AROME may in great part proceed from improving the simulation of atmospheric fields and eliminating some bias compensations in the model.

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