Comment on amt-2022-2 Article Swipe
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· 2022
· Open Access
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· DOI: https://doi.org/10.5194/amt-2022-2-rc2
· OA: W4213181663
<strong class="journal-contentHeaderColor">Abstract.</strong> 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/m<sup>2</sup> and 100 g/m<sup>2</sup> 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/m<sup>2</sup>. 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/m<sup>2</sup>. 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.
