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Applying Horizontal Diffusion on Pressure Surface to Mesoscale Models on Terrain-Following Coordinates

机译:将压力面上的水平扩散应用于地形跟随坐标的中尺度模型

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The National Centers for Environmental Prediction regional spectral model and mesoscale spectral model (NCEP RSM/MSM) use a spectral computation on perturbation. The perturbation is defined as a deviation between RSM/MSM forecast value and their outer model or analysis value on model sigma-coordinate surfaces. The horizontal diffusion used in the models applies perturbation diffusion in spectral space on model sigma-coordinate surfaces. However, because of the large difference between RSM/MSM and their outer model or analysis terrains, the perturbation on sigma surfaces could be large over steep mountain areas as horizontal resolution increases. This large perturbation could introduce systematical error due to artificial vertical mixing from horizontal diffusion on sigma surface for variables with strong vertical stratification, such as temperature and humidity. This nonnegligible error would eventually ruin the forecast and simulation results over mountain areas in high-resolution modeling. To avoid the erroneous vertical mixing on the systematic perturbation, a coordinate transformation is applied in deriving a horizontal diffusion on pressure surface from the variables provided on terrain-following sigma coordinates. Three cases are selected to illustrate the impact of the horizontal diffusion on pressure surfaces, which reduces or eliminates numerical errors of mesoscale modeling over mountain areas. These cases address concerns from all aspects, including unstable and stable synoptic conditions, moist and dry atmospheric settings, weather and climate integrations, hydrostatic and nonhydrostatic modeling, and island and continental orography. After implementing the horizontal diffusion on pressure surfaces for temperature and humidity, the results show better rainfall and flow pattern simulations when compared to observations. Horizontal diffusion corrects the warming, moistening, excessive rainfall, and convergent flow patterns around high mountains under unstable and moist synoptic conditions and corrects the cooling, drying, and divergent flow patterns under stable and dry synoptic settings.
机译:国家环境预测中心区域光谱模型和中尺度光谱模型(NCEP RSM / MSM)使用摄动上的谱计算。扰动定义为RSM / MSM预测值与模型sigma坐标表面上的外部模型或分析值之间的偏差。模型中使用的水平扩散在模型sigma坐标曲面上的光谱空间中应用扰动扩散。但是,由于RSM / MSM与它们的外部模型或分析地形之间的巨大差异,随着水平分辨率的提高,在陡峭的山区上sigma表面的扰动可能很大。对于具有强垂直分层的变量(例如温度和湿度),由于在sigma表面水平扩散造成的人工垂直混合,这种较大的扰动可能会引入系统误差。这种不可忽略的误差最终会破坏高分辨率建模中对山区的预测和模拟结果。为了避免在系统扰动上错误地垂直混合,在从跟随地形的sigma坐标提供的变量中推导了压力面的水平扩散时,应用了坐标变换。选择了三种情况来说明水平扩散对压力表面的影响,这减少或消除了山区中尺度模型的数值误差。这些案例解决了各个方面的问题,包括不稳定和稳定的天气条件,潮湿和干燥的大气环境,天气和气候整合,流体静力学和非流体静力学模拟以及岛屿和大陆地形。在温度和湿度的压力表面上进行水平扩散后,与观测值相比,结果显示了更好的降雨和流型模拟。水平扩散校正了不稳定和潮湿天气条件下高山周围变暖,潮湿,过多的降雨和会聚流型,并校正了稳定和干燥天气条件下的冷却,干燥和发散流型。

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