Retrieval of ice water path from the Microwave Humidity Sounder (MWHS) aboard FengYun-3B (FY-3B) satellite polarimetric measurements based on a deep neural network
Retrieval of ice water path from the Microwave Humidity Sounder (MWHS) aboard FengYun-3B (FY-3B) satellite polarimetric measurements based on a deep neural network
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基于深度神经网络的风云三号乙（FY-3B）卫星极化测量微波湿度探测器（MWHS）冰水路径反演
DOI：
10.5194/amt-15-6489-2022
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发表时间：
2022-11
影响因子：
3.8
通讯作者：
Lanjie Zhang
中科院分区：
文献类型：
--
作者：
Wenyu Wang;Zhenzhan Wang;Qiurui He;Lanjie Zhang
冰水路径（IWP）是大气辐射中的一个重要云参数，其反演仍存在很大困难。近年sensing.in人工神经网络已成为大气遥感的一种流行方法.本文利用风云三号B卫星微波湿度探测仪（MWHS）的实测数据，提出了一种基于深度神经网络的全球IWP反演方法。由于FY-3B/MWHS在150 GHz处具有准极化通道，因此还研究了极化辐射差（PD）的影响。利用.MWHS和CloudSat 2C-ICE（CloudSat和CALIPSO Ice.Cloud Property Product）之间的搭配建立检索数据库。然后，训练了两种网络，分别用于云场景过滤和IWP检索。the.cloud滤波网络，微波信道的虚警率（FAR）为0.31，检测概率（POD）为0.61。对于IWP反演网络，比较了不同的辅助输入和通道输入组合。结果表明，五个MWHS通道结合扫描角、纬度和海洋/陆地掩模作为辅助变量的输入效果最好。应用云滤波网络和IWP反演网络，最终的均方根误差（RMSE）为916.76gm ~ 2，平均绝对百分误差（MAPE）为92%，相关系数（CC）为0.65。最后，选取一个热带地区的cyclone.case为例，通过MWHS和Cloud-.Sat同时测量，对网络的性能进行了测试，结果表明，与2C-ICE有很好的相关性（0.73）。最后，MWHS的全球年平均IWP与2C-ICE的非常接近，并且与仅使用183 GHz信道相比，150 GHz信道在中纬度地区给出了显著的改善。close.to
The ice water path (IWP) is an important cloud.parameter in atmospheric radiation, and there are still great.difficulties in its retrieval. Artificial neural networks have.become a popular method in atmospheric remote sensing.in recent years. This study presents a global IWP retrieval.based on deep neural networks using the measurements.from the Microwave Humidity Sounder (MWHS) aboard.the FengYun-3B (FY-3B) satellite. Since FY-3B/MWHS has.quasi-polarization channels at 150 GHz, the effect of the polarimetric.radiance difference (PD) was also studied. A retrieval.database was established using collocations between.MWHS and CloudSat 2C-ICE (CloudSat and CALIPSO Ice.Cloud Property Product). Then, two types of networks were.trained for cloud scene filtering and IWP retrieval. For the.cloud filtering network, the microwave channels show a capacity.with a false alarm ratio (FAR) of 0.31 and a probability.of detection (POD) of 0.61. For the IWP retrieval network,.different combination inputs of auxiliaries and channels.were compared. The results show that the five MWHS.channels combined with scan angle, latitude, and the ocean/-.land mask of inputs of auxiliary variables perform best. Applying.the cloud filtering network and IWP retrieval network,.the final root mean squared error (RMSE) is 916.76 gm2,.the mean absolute percentage error (MAPE) is 92 %, and.the correlation coefficient (CC) is 0.65. Then, a tropical cyclone.case measured simultaneously by MWHS and Cloud-.Sat was chosen to test the performance of the networks, and.the result shows a good correlation (0.73) with 2C-ICE. Finally,.the global annual mean IWP of MWHS is very close.to that of 2C-ICE, and the 150 GHz channels give a significant.improvement in the midlatitudes compared to using only.183 GHz channels.
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影响因子：
--
作者：
R. Austin;A. Heymsfield;G. Stephens
通讯作者：
R. Austin;A. Heymsfield;G. Stephens
影响因子：
6.3
作者：
J. Gong;Xiping Zeng;Dong L. Wu;S. Munchak;Xiaowen Li;S. Kneifel;D. Ori;L. Liao;D. Barahona
通讯作者：
J. Gong;Xiping Zeng;Dong L. Wu;S. Munchak;Xiaowen Li;S. Kneifel;D. Ori;L. Liao;D. Barahona
DOI：
10.1109/tpami.2018.2858826
发表时间：
2020-02-01
影响因子：
23.6
作者：
Lin, Tsung-Yi;Goyal, Priya;Dollar, Piotr
通讯作者：
Dollar, Piotr
DOI：
10.1029/2012jd018381
发表时间：
2013-03
期刊：
Journal of Geophysical Research: Atmospheres
影响因子：
--
作者：
S. Eliasson;Gerrit Holl;Stefan Buehler;Thomas Kuhn;M. Stengel;Flavio Iturbide-Sanchez;M. Johnston
通讯作者：
S. Eliasson;Gerrit Holl;Stefan Buehler;Thomas Kuhn;M. Stengel;Flavio Iturbide-Sanchez;M. Johnston
影响因子：
5.2
作者：
J. Gong;Xiping Zeng;Dong L. Wu;Xiaowen Li
通讯作者：
J. Gong;Xiping Zeng;Dong L. Wu;Xiaowen Li