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.
Highlights
Ice clouds play an important role in the global climate (Liou, 1986), and their distribution strongly affects precipitation and 25 water cycle (Eliasson et al, 2011; Field and Heymsfield, 2015)
This study presents a global Ice water path (IWP) retrieval based on deep neural networks using the measurements from Microwave Humidity Sounder (MWHS) onboard the FengYun-3B (FY-3B) satellite
75 In this paper, we present an analysis of IWP retrieval from the FY-3B/MWHS observations based on the deep neural network
Summary
Ice clouds play an important role in the global climate (Liou, 1986), and their distribution strongly affects precipitation and 25 water cycle (Eliasson et al, 2011; Field and Heymsfield, 2015). The long time series and global observation of ice clouds are essential for understanding the Earth's climate system. Satellite remote sensing can measure different cloud microphysics. Microwave measurement can penetrate deeper into cloud layers to measure thick and dense ice clouds, while infrared and visible instruments are mainly used for thin clouds measurement around the cloud-top (Liu and Curry, 1998; Weng and Grody, 2000; Stubenrauch et al, 2013). Discussion started: 28 January 2022 c Author(s) 2022.
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