Laser & Optoelectronics Progress, Volume. 60, Issue 17, 1701002(2023)
Retrieving Atmospheric Motion Vectors from Geostationary Satellite Images Using Generative Adversarial Networks
Fig. 1. Weighting functions of water vapor channel and infrared channel of GOES satellite, quoted from
Fig. 2. U-net architecture used by generator of pix2pix. Dimensions of data are shown as (channels, width, height)
Fig. 3. Test error for experiments using 1 h satellite image intervals, pix2pix architecture, high resolution data, and without visible channels. (a) 40000 iterations; (b) 500000 iterations
Fig. 4. Comparison of wind speed retrieved by neural network and wind speed of NCEP/NCAR reanalysis. (a) 850 hPa; (b) 200 hPa
Fig. 5. Spatial distribution of MVD of wind retrieved by neural network. (a) 850 hPa; (b) 200 hPa
Fig. 6. Comparison of wind field retrieved by neural network and wind field of NCEP/NCAR reanalysis. (a) 850 hPa wind direction (streamline) and speed (shaded) retrieved by neural network at October 9, 2019 12:00 UTC; (b) same as (a), but with NCEP/NCAR reanalysis data; (c) 200 hPa wind direction (streamline) and speed (shaded) retrieved by neural network at October 9, 2019 12:00 UTC; (d) same as (c), but with NCEP/NCAR reanalysis data; (e)-(h) same as (a)-(d), but at January 26, 2018 18:00 UTC
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Xiaoyong Li, Keyi Chen. Retrieving Atmospheric Motion Vectors from Geostationary Satellite Images Using Generative Adversarial Networks[J]. Laser & Optoelectronics Progress, 2023, 60(17): 1701002
Category: Atmospheric Optics and Oceanic Optics
Received: Mar. 17, 2022
Accepted: Jun. 13, 2022
Published Online: Aug. 29, 2023
The Author Email: Keyi Chen (ckydlt@aliyun.com)