Cloud Influence on Atmospheric Humidity Profile Retrieval by Ground-based Microwave Radiometer

Che Yunfei; Ma Shuqing; Yang Ling; Xing Fenghua; Li Siteng

doi:10.11898/1001-7313.20150207

Vol.26, NO.2, 2015

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Article Navigation > Journal of Applied Meteorological Science > 2015 > 26(2): 193-202

Che Yunfei, Ma Shuqing, Yang Ling, et al. Cloud influence on atmospheric humidity profile retrieval by ground-based microwave radiometer. J Appl Meteor Sci, 2015, 26(2): 193-202. DOI: 10.11898/1001-7313.20150207.

Citation:

Che Yunfei, Ma Shuqing, Yang Ling, et al. Cloud influence on atmospheric humidity profile retrieval by ground-based microwave radiometer. J Appl Meteor Sci, 2015, 26(2): 193-202. DOI: 10.11898/1001-7313.20150207.

Citation:

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Cloud Influence on Atmospheric Humidity Profile Retrieval by Ground-based Microwave Radiometer

DOI: 10.11898/1001-7313.20150207

1.
Chengdu University of Information Technology, Chengdu 610225
2.
Meteorological Observation Center of CMA, Beijing 100081

Received Date: 2014-10-21
Rev Recd Date: 2015-01-08
Publish Date: 2015-03-31

Abstract

Abstract

There are a lot of limitations on measurement accuracy, cost and continuity of time in the meteorological sounding operations, which are two or four times a day. In order to obtain continuous atmospheric profile data, many methods are developed, among which the way of measuring atmospheric temperature and humidity profiles by the microwave radiometer is relatively mature. However, the ability of the microwave radiometer with infrared sensors is very limited in measuring the cloud, it can only get the height of cloud, and sometimes it brings large deviations. The deviation result in great uncertainty in distributed cloud microwave absorption, causing errors during the inversion of temperature and humidity profiles, so how to improve the accuracy of inversion on cloud is an urgent problem to solve. A method is implemented using atmospheric profiles from L-band sounding radar and brightness temperature observed with microwave radiometer, and MonoRTM is taken as a forward atmospheric radiative transfer model and the tool of retrieval is BP neural network. The matching cloud information is added and a new model of retrieval is created when retrieving atmospheric humidity profiles. Root mean square error (RMSE) values on each height layer with two kinds of inversion method are obtained and the impact of cloud information on atmospheric humidity profile retrieval is analyzed through comparison.Results show that the average of correlation between inversion humidity profiles is improved from 0.6850 to 0.8050 after adding cloud information. Compared with inversion profiles without cloud information, RMSE values on the vast majority of height layers after adding cloud information are reduced to various degrees, which is particularly obvious at layers with cloud.The study shows that the method of adding cloud information on the process of inversion is feasible. In order to improve the ability to observe the atmospheric profile lines in cloudy days, combined information of cloud distribution and brightness temperature of microwave radiation can be used to retrieve the temperature and humidity, in condition the joint observation of cloud radar and microwave radiation is available.
- microwave radiometer;
- cloud information;
- retrieval method;
- atmospheric humidity profile

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References(27)

Figures and Tables

图 1 探空数据判定的云信息与毫米波云雷达数据对比

(a) 云底高度，(b) 云厚度

Fig. 1 The comparison of cloud information decided by sounding and data of cloud radar

(a) cloud-base height, (b) cloud thickness

DownLoad: Full-Size Img PowerPoint

图 2 正演亮温与观测亮温变化

(a)22.24 GHz通道，(b)58.00 GHz通道

Fig. 2 The simulated and themeasured brightness temperatures

(a) channel of 22.24 GHz, (b) channel of 58.00 GHz

DownLoad: Full-Size Img PowerPoint

Fig. 3 The structure diagram of BPNN without cloud information

DownLoad: Full-Size Img PowerPoint

图 4 低云情况下，添加云信息反演廓线、未添加云信息反演廓线与探空廓线对比

Fig. 4 Comparison of inversion profiles with cloud information, inversion profiles without cloud information and sounding profiles in the case of low cloud

DownLoad: Full-Size Img PowerPoint

图 5 中云情况下，添加云信息反演廓线、未添加云信息反演廓线与探空廓线对比

Fig. 5 Comparison of inversion profiles with cloud information, inversion profiles without cloud information and sounding profiles in the case of middle cloud

(a)0700 BT 20 Mar 2013, (b)1900 BT 25 May 2013, (c)0700 BT 10 Jun 2014, (d)1900 BT 16 Jul 2014

DownLoad: Full-Size Img PowerPoint

图 6 高云情况下，添加云信息反演廓线、未添加云信息反演廓线与探空廓线对比

Fig. 6 Comparison of inversion profiles with cloud information, inversion profiles without cloud information and sounding profiles in the case of high cloud

DownLoad: Full-Size Img PowerPoint

图 7 添加云信息反演廓线和未添加云信息反演廓线与探空廓线对比的均方根误差

(a) 低云情况，(b) 中云情况，(c) 高云情况

Fig. 7 Root mean square error contrast between inversion profiles with cloud information and inversion profiles without cloud information using sounding profiles as the standard

(a) low cloud, (b) middle cloud, (c) high cloud

DownLoad: Full-Size Img PowerPoint

Fig. 8 Related coefficient contrast between inversion profiles with cloud information and inversion profiles without cloud information using sounding profile as the standard

DownLoad: Full-Size Img PowerPoint

Table 1 Comparison between the simulated and the measured brightness temperatures

通道频率/GHz	标准差/K	相关系数
22.24	2.196	0.986
23.04	1.966	0.987
23.84	1.603	0.987
25.44	1.354	0.983
26.24	1.069	0.984
27.84	0.975	0.981
31.4	1.080	0.974
51.26	1.493	0.959
52.28	1.226	0.958
53.86	0.431	0.943
54.94	0.271	0.968
56.66	0.294	0.972
57.3	0.338	0.969
58	0.418	0.961

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References

[1]	周秀骥.中尺度气象学研究与中国气象科学研究院.应用气象学报, 2006, 17(6):665-671. doi: 10.11898/1001-7313.20060604
[2]	陈明轩, 俞小鼎, 谭晓光.对流天气临近预报技术的发展与研究进展.应用气象学报, 2004, 15(6):754-766. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20040693&flag=1
[3]	赵从龙, 蔡化庆, 宋玉东.对流层水汽和液态水的地基微波遥感探测.应用气象学报, 1991, 2(2):200-207. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=19910226&flag=1
[4]	段英, 吴志会.利用地基遥感方法检测大气中汽态、液态水含量分布特征的分析.应用气象学报, 1991, 10(1):34-40. http://www.cnki.com.cn/Article/CJFDTOTAL-YYQX901.004.htm
[5]	胡树贞, 马舒庆, 陶法.地基双波段测云系统及其对比实验.应用气象学报, 2012, 23(4):441-450. doi: 10.11898/1001-7313.20120407
[6]	Chan P W.Performance and application of a multi-wavelength, gr-ound-based microwave radiometer in intense convective weather.Meteorologische Zeitschrift, 2009, 18(3):253-265. doi: 10.1127/0941-2948/2009/0375
[7]	Knupp K R, Ware R, Cimini D, et al.Ground-based passive microwave profiling during dynamic weather conditions.J Atmos Ocean Technol, 2009, 26(6):1057-1073. doi: 10.1175/2008JTECHA1150.1
[8]	刘红燕.三年地基微波辐射计观测温度廓线的精度分析.气象学报, 2011, 69(4):719-728. doi: 10.11676/qxxb2011.063
[9]	Frate F D, Giovanni S.A combined natural orthogonal function/neural netwok technique for the radiometric estimation of atmospheric profiles.Radio Science, 1998, 33(2):405-410. doi: 10.1029/97RS02219
[10]	Lohnert U, Crewell S, Simmer C.Profiling cloud liquid water by combining active and passive microwave measurements with cloud model statistics.J Atmos Ocean Technol, 2001, 18(8):1354-1366. doi: 10.1175/1520-0426(2001)018<1354:PCLWBC>2.0.CO;2
[11]	James C L, Eugene E C.A new retrieval for cloud liquid water path using a ground-based microwave radiometer and measurements of cloud temperature.J Geophys Res, 2001, D13: 14485-14500. https://utah.pure.elsevier.com/en/publications/a-new-retrieval-for-cloud-liquid-water-path-using-a-ground-based-
[12]	Poore K D.Cloud Base, Top and Thickness Climatology from RAOB and Surface Data. Cloud Impacts on DOD Operations and Systems 1991 Conference.1991.
[13]	Poore K D, Wang J H, Rossow W B.Cloud layer thicknesses from a combination of surface and upper-air observations.J Climate, 1995, 8(3):550-568. doi: 10.1175/1520-0442(1995)008<0550:CLTFAC>2.0.CO;2
[14]	周玉驰. 地基多通道微波辐射计反演大气温湿廓线的研究. 北京: 中国科学院研究生院, 2010.
[15]	David D T, Shepard A C, James C L.Retrieving liquid water path and precipitable water vapor from the atmospheric radiation measurement (ARM) microwave radiometers.IEEE Transactions on Geoscience and Remote Sensing, 2007, 11:3680-3690. https://www.researchgate.net/profile/David_Turner16/publication/220051964_Retrieving_Liquid_Wat0er_Path_and_Precipitable_Water_Vapor_From_the_Atmospheric_Radiation_Measurement_ARM_Microwave_Radiometers/links/0c960517aef7bed41e000000/Retrieving-Liquid-Wat0er-Path-and-Precipitable-Water-Vapor-From-the-Atmospheric-Radiation-Measurement-ARM-Microwave-Radiometers.pdf
[16]	Clough S A, Shephard M W, Mlawer E J.Atmospheric radiative transfer modeling:A summary of the AER codes.Journal of Quantitative Spectroscopy & Radiative Transfer, 2005, 91:233-244. https://www.aer.com/news-events/resource-library/atmospheric-radiative-transfer-modeling-summary-aer-codes
[17]	黄兴友, 张曦, 冷亮, 等.基于MonoRTM模型的微波辐射计反演方法研究.气象科学, 2013, 33(2):138-145. doi: 10.3969/2012jms.0127
[18]	周开利, 康耀红.神经网络模型及其Matlab仿真程序设计.北京:清华大学出版社, 2005.
[19]	刘亚亚, 毛节泰, 刘钧, 等.地基微波辐射计遥感大气廓线的BP神经网络反演方法研究.高原气象, 2010, 29(6):1514-1523. http://cpfd.cnki.com.cn/Article/CPFDTOTAL-ZGQX201111009017.htm
[20]	姚志刚, 陈洪滨.利用神经网络从118.75 GHz附近通道亮温反演大气温度.气象科学, 2006, 26(3):252-259. http://www.cnki.com.cn/Article/CJFDTOTAL-QXKX200603002.htm
[21]	刘旸, 官莉.人工神经网络法反演晴空大气湿度廓线的研究.气象, 2011, 37(3):318-324. doi: 10.7519/j.issn.1000-0526.2011.03.009
[22]	Simon Haykin. 叶世伟, 史忠植, 译. 神经网络原理. 北京: 机械工业出版社, 2004: 33-175.
[23]	高大启.有教师的线性基本函数前向神经网络结构研究.计算机学报, 1998, 21(1):80-86. http://www.cnki.com.cn/Article/CJFDTOTAL-DLYX703.005.htm
[24]	王振会, 李青, 楚艳丽, 等.地基微波辐射计工作环境对K波段亮温观测影响.应用气象学报, 2014, 25(6):711-721. doi: 10.11898/1001-7313.20140607
[25]	张培昌, 王振会.大气微波遥感基础.北京:气象出版社, 1995:346-384.
[26]	邢业新, 娄国伟, 李兴国, 等.云、雨天气对3 mm波段天空亮温的影响.现代防御技术, 2010, 38(5):82-85. http://www.cnki.com.cn/Article/CJFDTOTAL-XDFJ201005023.htm
[27]	中国气象局.地面气象观测规范.北京:气象出版社, 2013:11-16.