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遥感技术与应用  2020, Vol. 35 Issue (4): 808-819    DOI: 10.11873/j.issn.1004-0323.2020.4.0808
甘肃遥感学会专栏     
遥感资料在WRF-Chem沙尘模拟中的应用
韩天1,2(),潘小多1,3,王旭峰1(),黄广辉1,韦海宁1,2
1.中国科学院西北生态环境资源研究院 遥感与信息资源实验室,甘肃 兰州 730000
2.中国科学院大学,北京 100049
3.国家青藏高原科学数据中心,中国科学院青藏高原研究所,北京 100101
Application of Remote Sensing Data in WRF-Chem Model Simulating Sandstorm
Tian Han1,2(),Xiaoduo Pan1,3,Xufeng Wang1(),Guanghui Huang1,Haining Wei1,2
1.Laboratory of Remote Sensing and Geospatial Science, Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou 730000,China
2.University of Chinese Academy of Sciences,Beijing 100049,China
3.National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research,Chinese Academy of Sciences,Beijing 100101,China
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摘要:

沙尘暴的起沙过程是沙尘循环中的重要部分,起沙过程模拟的准确性对于输送和沉降过程的准确模拟十分重要。WRF-Chem (Weather Research and Forecasting with Chemistry)是目前应用最广泛的沙尘暴模拟模式之一,但目前WRF-Chem对于起沙量的模拟具有很大的不确定性,受下垫面和土壤湿度的影响较大。WRF-Chem模式中的下垫面数据比较老旧,且驱动WRF-Chem模式的资料中土壤湿度是偏高的。土地覆被和土壤水分等遥感产品的日趋成熟,为WRF-Chem模拟沙尘暴提供了新的选择和契机,因此,将AMSR2 (the Advanced Microwave Scanning Radiometer 2) 土壤湿度和MODIS (Moderate Resolution Imaging Spectroradiometer) 土地利用遥感产品及实地调查资料替换WRF-Chem下垫面,利用WRF-Chem模拟了2018年3月26日至28日发生在我国华北地区的一场沙尘过程,用于探究下垫面参数对WRF-Chem模式模拟沙尘暴的精度产生的影响。共开展了4组实验,包括1组控制实验和3组对照实验,3组对照实验分别是在控制试验基础上仅替换土壤水分初始场、仅替换土地覆被和土壤质地以及同时替换土地覆被、土壤质地及土壤水分初始场。在加入遥感数据之后,3组对照实验的模拟精度比控制实验均有所提高,其中同时替换土壤水分初始场、土地覆被和土壤质地对模式模拟结果改善最大。改善后PM10模拟的相关系数提高了0.30,平均偏差减少了31.18 μg/m3,均方根误差减少了21.70 μg/m3;AOD (Aerosol Optical Depth) 的相关系数提高了0.14,平均偏差减少了0.29,均方根误差减少了0.18,仅替换土壤水分初始场效果次之,仅替换土地覆被和土壤质地对于模拟结果改善不大。以上结果表明:加入遥感资料可以有效提高WRF-Chem对沙尘过程的模拟精度。

关键词: 遥感产品土壤湿度土地覆被沙尘暴WRF-Chem    
Abstract:

Sand emission process of sandstorm is a fundamental part of sand-dust cycle. Sand emission process simulating accuracy plays a crucial role in correctly simulating sand transporting and settling process. As one of the most widely used sandstorm models, WRF-Chem (Weather Research and Forecasting with Chemistry) is used to simulate the sandstorm happened during March 26 and March 28, 2018 in northern China in this study. It is reported that uncertainties in underlying surface and soil moisture initial status in WRF-Chem can lead to great bias in its simulating results. Remote sensing products like land cover and soil moisture products have been widely accepted for their higher accuracy, which provides an opportunity for WRF-Chem simulating sandstorms better. Therefore, to examine the effects of initial field uncertainties on sandstorm simulating, we simulated a sandstorm using WRF-Chem by replacing the underlying surface and soil moisture initial field with new version soil database, MODIS (Moderate Resolution Imaging Spectroradiometer) land cover products and AMSR2 (the Advanced Microwave Scanning Radiometer 2) soil moisture products. Four experiments were carried out, including a control experiment and three contrast experiments. The three contrast experiments are organized by only replacing the soil moisture initial field, only replacing land cover and soil texture, and replacing both. After replacing traditional initial field with remote sensing data, the simulation accuracy all has improved. Among the three contrast experiments, replacing all three parameters (land cover, soil texture and soil moisture) has the greatest improvement: the correlation coefficient of PM10 increases by 0.30, the average deviation reduces by 31.18 μg/m3, the root mean square error reduces by 21.7 μg/m3, the correlation coefficient of AOD (Aerosol Optical Depth) improves by 0.14, the average deviation reduces by 0.29, the root mean square error reduces by 0.18. The contrast experiment which only replacing soil moisture performs the second, followed by only replacing land cover and soil texture which does not improve the simulation results much. In conclusion, the simulation accuracy of sandstorm is improved by introducing the remote sensing products.

Key words: Remote sensing products    Soil moisture    Land cover    Sandstorm    WRF-Chem
收稿日期: 2019-09-18 出版日期: 2020-09-15
ZTFLH:  TP79  
基金资助: 国家自然科学基金项目(41471292);青海省重大科技专项(2017?SF?A6)
通讯作者: 王旭峰     E-mail: hantian@lzb.ac.cn;wangxufeng@lzb.ac.cn
作者简介: 韩天(1996-),女,山西长治人,硕士研究生,主要从事沙尘暴数值模拟以及沙尘暴预测研究。E?mail:hantian@lzb.ac.cn
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引用本文:

韩天,潘小多,王旭峰,黄广辉,韦海宁. 遥感资料在WRF-Chem沙尘模拟中的应用[J]. 遥感技术与应用, 2020, 35(4): 808-819.

Tian Han,Xiaoduo Pan,Xufeng Wang,Guanghui Huang,Haining Wei. Application of Remote Sensing Data in WRF-Chem Model Simulating Sandstorm. Remote Sensing Technology and Application, 2020, 35(4): 808-819.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.4.0808        http://www.rsta.ac.cn/CN/Y2020/V35/I4/808

图1  研究区及地面站点的位置和海拔
图2  模式下垫面数据和遥感产品对比
空气质量监测站点经度/oE纬度/oN海拔/m
酒泉98.5139.752 817
武威102.6237.931 688
银川106.2338.441 135
锡林郭勒盟116.1043.931 049
太原112.5637.90909
张家口114.8840.79966
承德117.9640.92505
天津117.7939.253
北京116.4339.8843
表1  空气质量监测站点信息表
参数化方案选项Namelist 变量模块
Land surface3sf_surface_physicsRUC
PBL model2bl_pbl_physicsMellor-Yamada-Janjic TKE
Surface similarity2sf_sfclay_physicsMonin-Obukhov
Microphysics2mp_physicsLin
Shortwave radiation2ra_sw_physics(old)Goddard
Longwave radiation1ra_lw_physicsRRTM
Cloud physics5cu_physicsKain-Freitas
表2  WRF-Chem模式气象模块参数化方案设置
Namelist 变量选项模块
bio_emiss_opt3Megan
chem_opt112MOZCART
dust_opt4UOC
dust_scheme3shao 11
biomass_burn_opt1
表3  WRF-Chem模式化学模块参数化方案设置
图3  实验方案设计
图4  站点PM10观测与WRF-Chem模拟结果对比图
PM10RBIAS(μg/m3)RMSE(μg/m3)
控制实验0.27-82.51396.59
对照实验一0.41-29.80407.97
对照实验二0.29-79.24403.57
对照实验三0.57-51.33374.89
表4  PM10模拟值与观测值比较
图5  站点PM2.5观测与WRF-Chem模拟结果对比图
PM2.5RBIAS(μg/m3)RMSE(μg/m3)
控制实验0.28-46.1066.18
对照实验一0.3-21.2786.70
对照实验二0.29-45.4865.98
对照实验三0.35-32.3664.83
表5  PM2.5模拟值与观测值比较
图6  H08 卫星AOD点反演值与WRF-Chem模拟结果的对比
图7  4组实验模拟AOD与H08 AOD产品的空间分布图
AODRBIASRMSE
控制实验0.53-0.440.60
对照实验一0.63-0.370.54
对照实验二0.56-0.470.62
对照实验三0.67-0.150.42
表6  AOD模拟值与H08卫星AOD反演值比较
图8  站点观测PM2.5与对照试验三(WRF-Chem模拟直径小于2.5 μm的沙尘气溶胶、非沙尘气溶胶浓度对比图)
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