高原气象

高原气象 >

2014 , Vol. 33 >Issue 2: 474 - 482

DOI: https://doi.org/10.7522/j.issn.1000-0534.2012.00191

论文

中国高分辨率地表粗糙度分布研究

  • 李沁怡 ,
  • 蔡旭晖 ,
  • 宋宇
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  • 北京大学 环境科学与工程学院/环境模拟与污染控制国家重点联合实验室, 北京 100871

收稿日期: 2012-05-13

  网络出版日期: 2014-04-28

基金资助

国家重点基础研究发展规划项目（2010CB428501）；国家高技术研究发展计划项目（2008AA06A415，2009AA06A41802）；财政部/科技部公益性行业（气象）科研专项（GYHY201006014）

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Research of the Distribution of National Scale Surface Roughness Length with High Resolution in China

  • LI Qinyi ,
  • CAI Xuhui ,
  • SONG Yu
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  • College of Environmental Sciences and Engineering, Peking University, State Joint Key Lab of Environmental Simulation and Pollution Control, Beijing 100871, China

Received date: 2012-05-13

  Online published: 2014-04-28

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摘要

利用2010年欧洲空间局（ESA）最新发布的高分辨率全球土地覆盖卫星观测数据，研究中国地区的地表粗糙度分布。参考两个常用气象模式CALMET和WRF中有关土地覆盖类型与地表粗糙度的分配方案，分别建立ESA土地覆盖类型数据与地表粗糙度的映射关系，编制了水平分辨率约300 m的中国地表粗糙度分布，并与实际观测数据进行比较分析。结果表明：（1）使用ESA土地覆盖类型数据有利于反映地表粗糙度更细致的空间分布，但无法反映城市区域内部的粗糙度变化；（2）基于CALMET模式的映射关系给出的地表粗糙度数值系统偏高；（3）基于WRF模式的映射关系能够给出更合理的地表粗糙度，与实际观测更相符，并可部分反映地表粗糙度的季节变化。

关键词： 地表粗糙度; 土地覆盖; 高分辨率; 卫星资料

本文引用格式

李沁怡 , 蔡旭晖 , 宋宇 . 中国高分辨率地表粗糙度分布研究[J]. 高原气象, 2014 , 33(2) : 474 -482 . DOI: 10.7522/j.issn.1000-0534.2012.00191

Abstract

The normal methods of calculating the surface roughness length were summarized, both with and without observation. In addition, some widely used ways of deriving surface roughness length with remote sensing data were presented as well. The high resolution global land cover data based on satellite observation published lately by European Space Agency (ESA) was utilized to study the distribution of surface roughness length in China. The relationship between ESA land cover categories and surface roughness length was established, which referred to the schemes assigning surface roughness length according to land cover categories used in two common meteorology models—CALMET and WRF, and was utilized to produce the distribution of surface roughness length about 300 m resolution at national scale of China, and comparised with the observation data published by other researchers. The results show that: (1) ESA land cover data could reflect the fine space distribution of surface roughness length but failed in the roughness changes in urban area. (2) The surface roughness length values based on the mapping relationship used in CALMET model were systematic high. (3) The surface roughness length values based on the assigning relationship used in WRF tended to be more reasonable and more consistent with the practical observation, including the seasonal variation of surface roughness length.

Key words： Surface roughness le; Land cover; High resolution; Satellite remote sen

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