高原气象

高原气象 >

2017 , Vol. 36 >Issue 3: 776 - 787

DOI: https://doi.org/10.7522/j.issn.1000-0534.2016.00126

论文

基于陆面数据同化系统改进中国区域土壤湿度的模拟研究

  • 赖欣 ,
  • 文军 ,
  • 范广洲 ,
  • 宋海清 ,
  • 张永莉 ,
  • 朱丽华 ,
  • 王炳赟
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  • 成都信息工程大学大气科学学院 高原大气与环境四川省重点实验室/气候与环境变化联合实验室, 成都 610225;南京信息工程 大学气象灾害预报预警与评估协同创新中心, 南京 210044;内蒙古自治区生态与农业气象中心, 呼和浩特 010051

收稿日期: 2016-07-19

  网络出版日期: 2017-06-28

基金资助

国家自然科学基金项目（41505078，41275079，91537214，41305077，41405069）；成都信息工程大学校引进人才启动基金项目（KYTZ201639）；公益性行业（气象）科研专项（GYHY201506001，GYHY201006023）；四川省教育厅重点项目（16ZA0203）；成都信息工程大学中青年学术带头人科研基金（J201516，J201518）

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Improvement of Soil Moisture Simulation over Chinese Main Land by LDAS-IAP/CAS-1.0

  • LAI Xin ,
  • WEN Jun ,
  • FAN Guangzhou ,
  • SONG Haiqing ,
  • ZHANG Yongli ,
  • ZHU Lihua ,
  • WANG Bingyun
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  • College of Atmospheric Sciences, Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, Joint Laboratory of Climate and Environment Change, Chengdu University of Information Technology, Chengdu 610225, China;Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China;Ecological and Agricultural Meteorology Center of Inner Mongolia Autonomous Region, Hohhot 010051, China

Received date: 2016-07-19

  Online published: 2017-06-28

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摘要

利用中国区域地面气象要素驱动数据集（China Meteorological Forcing Dataset，CMFD），驱动中国科学院大气物理研究所陆面数据同化系统（LDAS-IAP/CAS-1.0），得到了2003-2010年中国区域土壤湿度数据集，同时不考虑同化卫星遥感亮温数据，直接驱动CLM3.0模拟了2003-2010年中国区域土壤湿度时空变化。将二者土壤湿度模拟结果、地面土壤湿度观测值、美国国家环境预报中心（NCEP）气候再分析数据（CFSR）、基于主动和被动微波传感器的全球土壤湿度数据（SM-MW）进行对比分析发现，考虑同化卫星遥感亮温后与不考虑同化模拟的土壤湿度空间分布有明显差异。将模拟、同化土壤湿度值与观测值对比发现，同化后的青海、甘肃、宁夏和陕西地区土壤湿度较模拟结果有一定的改善。相对于CFSR再分析数据和SM-MW遥感反演数据，模拟和同化土壤湿度值在35°N以南对土壤湿度空间分布的细节刻画更为细致。同化卫星遥感亮温数据后，从2003-2010年土壤湿度四季和年平均空间分布看出，土壤湿度空间分布从西北向东南增加。东北、江淮地区及青藏高原为土壤湿度高值区，新疆和内蒙古为土壤湿度低值区。从变化趋势来看，内蒙古、青藏高原和新疆南部年平均土壤湿度呈增加趋势，其他地区以减小趋势为主。

关键词： 土壤湿度; 模拟; 同化; 时空分布

本文引用格式

赖欣 , 文军 , 范广洲 , 宋海清 , 张永莉 , 朱丽华 , 王炳赟 . 基于陆面数据同化系统改进中国区域土壤湿度的模拟研究[J]. 高原气象, 2017 , 36(3) : 776 -787 . DOI: 10.7522/j.issn.1000-0534.2016.00126

Abstract

In order to validate the ability of The Land Data Assimilation System, and provide references for development and application of the system. The Land Data Assimilation System of Institute of Atmospheric Physics, Chinese Academy of Sciences(LDAS-IAP/CAS-1. 0) driven by the china meteorological forcing dataset(CMFD)was used to obtain soil moisture (SM) data from 2003 to 2010 over China. Without considering the assimilation of bright temperature of satellite remote sensing, the community land model version 3. 0 (CLM3. 0) driven by the same forcing data was used to simulate SM from 2003 to 2010 over China. The two simulated SM, observations, climate forecast system reanalysis (CFSR) of national centers for environmental prediction (NCEP) and global soil moisture data record based on active and passive micro wave sensors(SM-MW) were compared with each other. The results showed that the spatial distribution of simulated SM without considering the assimilation of bright temperature of satellite remote sensing and simulated SM considering the assimilation demonstrated obvious difference. Compared with observations, assimilated SM showed some improvement to simulated SM in Qinghai, Gansu, Ningxia and Shaanxi. The simulated and assimilated SM have more detail depiction in southern regions of 35°N compared to the other two products. After assimilation of bright temperature of satellite remote sensing, on the basis of the four seasons and annual SM from 2003 to 2010, the spatial distributions were characterized by a gradually increasing pattern from the northwest to southeast. From the spatial distributions, the most humid regions were located in the Northeast China Plain, the Jianghuai region, and the Qinghai-Tibetan Plateau, whereas dry regions were located in Xinjiang and Inner Mongolia. From the variation trend, the annual SM mainly increased in Inner Mongolia, the Tibetan Plateau and southern Xinjiang, and decreased mainly in other regions.

Key words： Soil moisture; Simulate; Assimilate; Temporal spatial dis

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