对流尺度集合预报在复杂地形中的应用研究概述

Application Research of Convective Scale Ensemble Prediction in Complex Terrain

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作者:

  • 刘侃 国防科技大学气象海洋学院 长沙 410073
  • 陈超辉 国防科技大学气象海洋学院 长沙 410073
  • 何宏让 国防科技大学气象海洋学院 长沙 410073

中文摘要:

复杂地形是导致数值天气预报不确定性产生的重要来源之一,特别是陡峭且不连续的山峰和峡谷地形,其形态和表面性质的差异对大气运动会产生极其复杂的影响,而基于对流尺度的集合预报是减少地形引起的模式预报不确定性的有效手段。因此,细致全面地梳理对流尺度集合预报在复杂地形背景下的应用研究至关重要。本文重点从模式地形的扰动设计出发,围绕地形效应对数值模式的影响,关注模式地形转换过程的不确定性,全面梳理高分辨率数值模拟中处理复杂地形的方法,涵盖构造不同垂直坐标系、使用平滑插值方法、搭建地形因子与降水之间的模型、考虑相同的初始场和侧边界条件并插值到不同地形高度等,克服梯度、高度、起伏度等地形因子的背景误差缺陷,从而改善预报质量。

中文关键词:

集合预报,复杂地形,对流尺度,模式地形构造,地形因子,地形扰动设计,降水预报

KeyWords:

ensemble prediction, complex terrain, convective scale, model-based terrain construction, topographical factor, terrain disturbance design, precipitation forecast

Abstract:

Complex terrain is one of the important sources of uncertainty in numerical weather prediction, especially the steep and discontinuous mountain and canyon terrain. The differences in their shapes and surface properties have extremely complex effects on atmospheric motion, and the convection scale ensemble forecast is an effective means to solve the uncertainty of model forecast caused by terrain. Therefore, it is very important to thoroughly and comprehensively sort out the application research of convective scale ensemble forecast in complex terrain background. This article focuses on the disturbance design of the model terrain, considers the influence of terrain effects on the numerical models, pays attention to the uncertainty of the model terrain conversion process, and comprehensively reviews the methods of dealing with complex terrain in high-resolution numerical simulation. It covers constructing different vertical coordinate systems, using smooth interpolation methods, building a model linking terrain factors and precipitation, considering the same initial field and lateral boundary conditions and interpolating to different terrain heights and overcoming background error defects of terrain factors such as terrain gradient, height, and degree fluctuations , thereby improving the forecast quality.

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