Preprints
https://doi.org/10.5194/amt-2022-2
https://doi.org/10.5194/amt-2022-2
 
28 Jan 2022
28 Jan 2022
Status: a revised version of this preprint is currently under review for the journal AMT.

Retrieval of ice water path from the FY-3B MWHS polarimetric measurements based on deep neural network

Wenyu Wang1, Zhenzhan Wang1, Qiurui He1,2, and Lanjie Zhang3 Wenyu Wang et al.
  • 1Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
  • 2School of Information Technology, Luoyang Normal University, Luoyang 471934, China
  • 3School of Information& Communication Engineering Beijing Information Science And Technology University, Beijing 100101, China

Abstract. Ice water path (IWP) is an important cloud parameter in atmospheric radiation, and there are still great difficulties in retrieval. The artificial neural network is 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 Microwave Humidity Sounder (MWHS) onboard the FengYun-3B (FY-3B) satellite. Since FY-3B/MWHS has quasi-polarization channels at 150 GHz, the effect of polarimetric radiance difference (PD) is also investigated. A retrieval database is established using collocations between MWHS and CloudSat 2C-ICE. Then two types of networks are trained for cloud scene filtering and IWP retrieval, respectively. For the cloud filtering network, using IWP of 10 g/m2 and 100 g/m2 as the threshold show the filtering accuracy of 86.48 % and 94.22 % respectively. For the IWP retrieval network, different training input combinations of auxiliary information and channels are compared. The results show that the MWHS IWP retrieval performs well at IWP > 100 g/m2. The mean and median relative errors are 72.02 % and 46.29 % compared to the 2C-ICE IWP. PD shows an important impact when IWP is larger than 1000 g/m2. At last, two tropical cyclone cases are chosen to test the performance of the networks, the results show a good agreement with the characteristics of the brightness temperature observed by the satellite. The monthly MWHS IWP shows a good consistency compared to the ERA5 and 2C-ICE while it is lower than MODIS IWP.

Wenyu Wang et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-2', Anonymous Referee #1, 09 Feb 2022
    • AC1: 'Reply on RC1', Wenyu Wang, 30 Mar 2022
  • RC2: 'Comment on amt-2022-2', Anonymous Referee #2, 18 Feb 2022
    • AC2: 'Reply on RC2', Wenyu Wang, 30 Mar 2022

Wenyu Wang et al.

Wenyu Wang et al.

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Short summary
This paper uses a neural network approach to retrieval the ice water path measured by FY-3B/MWHS, focusing on its unique 150 GHz quasi-polarized channels. The level-2 product of CloudSat is used as the reference value for the neural network. The results show that the polarization information is significantly helpful for the retrieval in scenes with thicker cloud ice.