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Cloud Influence on Atmospheric Humidity Profile Retrieval by Ground-based Microwave Radiometer
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摘要: 利用中国气象局大气探测试验基地的L波段探空数据和微波辐射计观测数据，采用MonoRTM辐射传输模型作为正演亮温模型，BP (back propagation) 神经网络作为反演工具，在由亮温反演大气湿度廓线的过程中，添加与样本匹配的云底高度和云厚度信息，建立新的反演模型，使新反演模型得到的反演湿度廓线和未添加云信息的反演湿度廓线分别与探空数据进行对比，获取两种反演方法各高度层的均方根误差，分析云信息对反演大气湿度廓线的影响。对比结果表明：未添加云信息时，测试样本的反演湿度廓线与探空廓线的相关系数平均值为0.685，而添加云信息后，相关系数平均值为0.805。相比未添加云信息的反演廓线，添加云信息之后多数高度层的均方根误差均有不同程度减小，而在有云以上高度层表现尤为明显。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.
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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
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
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
(a)2013年5月2日07：00, (b)2013年5月27日19：00, (c)2014年6月19日19：00, (d)2014年6月20日19：00
Fig. 4 Comparison of inversion profiles with cloud information, inversion profiles without cloud information and sounding profiles in the case of low cloud
(a)0700 BT 2 May 2013, (b)1900 BT 27 May 2013, (c)1900 BT 19 Jun 2014, (d)1900 BT 20 Jun 2014
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
(a)2013年3月20日07：00, (b)2013年5月25日19：00, (c)2014年6月10日07：00, (d)2014年7月16日19：00
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
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
(a)2013年4月20日07：00, (b)2013年7月23日07：00, (c)2014年5月28日19：00, (d)2014年6月4日19：00
Fig. 6 Comparison of inversion profiles with cloud information, inversion profiles without cloud information and sounding profiles in the case of high cloud
(a)0700 BT 20 Apr 2013, (b)0700 BT 23 Jul 2013, (c)1900 BT 28 May 2014, (d)1900 BT 4 Jun 2014
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
图 8 以探空数据为标准，添加云信息反演廓线与未添加云信息反演廓线的相关系数对比
Fig. 8 Related coefficient contrast between inversion profiles with cloud information and inversion profiles without cloud information using sounding profile as the standard
表 1 正演亮温与观测亮温数据对比
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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