Linear correction method for improved atmospheric vertical profile retrieval based on ground-based microwave radiometer
Linear correction method for improved atmospheric vertical profile retrieval based on ground-based microwave radiometer
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基于地基微波辐射计的改进大气垂直廓线反演线性校正方法
DOI：
10.1016/j.atmosres.2019.104678
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发表时间：
2020-02
影响因子：
5.5
通讯作者：
Zhou Di
中科院分区：
文献类型：
--
作者：
Zhao Yuxin;Yan Hualong;Wu Peng;Zhou Di
反向传播神经网络（BPNN）是微波辐射计最常用的反演算法。很少有研究人员试图专门提高训练集的质量，这显着影响检索结果，并可以最大限度地减少错误和不确定性，在模拟的亮度温度（BTs）的BPNN。利用哈尔滨地区2012年2月至2017年8月的探空资料、探空资料计算的BT和单色辐射传输模式，建立了哈尔滨地区BPNN局地反演和订正方法。修正后，模拟和观测的BT之间的相关性得到改善。采用三组校正前后的比较分析结果：（i）总均方根误差和总平均绝对误差;（ii）三个层的均方根误差和平均绝对误差;（iii）晴天和阴天的均方根误差和平均绝对误差。研究结果有助于微波遥感大气温湿度的理论发展。
The back-propagation neural network (BPNN) is the most commonly used retrieval algorithm for microwave radiometers. Few researchers have attempted specifically to enhance training set quality, which markedly affects retrieval results and can minimize error and uncertainty in simulated brightness temperatures (BTs) in the BPNN. A local BPNN retrieval and correction method were established in this study using radiosonde data, BTs calculated from the radiosonde data, and a monochromatic radiative transfer model (February 2012 to August 2017) in Harbin. The correlation between simulated and observed BTs was improved after correction. The results were analyzed using three sets of comparisons before and after correction: (i) total root mean square errors and total mean absolute errors; (ii) root mean square errors and mean absolute errors in three layers; and (iii) root mean square errors and mean absolute errors under clear days and cloudy days. The results of this study contribute to the theoretical development of microwave remote sensing of atmospheric temperature and humidity.
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影响因子：
1.9
作者：
S. Rambabu;J. Pillai;A. Agarwal;G. Pandithurai
通讯作者：
S. Rambabu;J. Pillai;A. Agarwal;G. Pandithurai
微波辐射计反演与无线电探空仪探测的大气剖面比较
DOI：
10.1002/2015jd023438
发表时间：
2015
期刊：
Journal of Geophysical Research-Atmosphere
影响因子：
--
作者：
Guirong Xu;Baike Xi;Wengang Zhang;Chunguang Cui
通讯作者：
Chunguang Cui
影响因子：
64.8
作者：
RUMELHART, DE;HINTON, GE;WILLIAMS, RJ
通讯作者：
WILLIAMS, RJ
影响因子：
--
作者：
S. Clough;M. Iacono;J. Moncet
通讯作者：
S. Clough;M. Iacono;J. Moncet
DOI：
10.1002/2014jd022838
发表时间：
2015-05
期刊：
Journal of Geophysical Research: Atmospheres
影响因子：
--
作者：
R. Renju;C. Suresh Raju;N. Mathew;Tinu Antony;K. Krishna Moorthy
通讯作者：
R. Renju;C. Suresh Raju;N. Mathew;Tinu Antony;K. Krishna Moorthy