Abstract
A neural network nonlinear regression algorithm is developed for retrieving ocean surface wind speed from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar measurements. The neural network is trained with CALIPSO ocean surface and atmospheric backscatter measurements together with collocated Advanced Microwave Scanning Radiometer for EOS (AMSR-E) ocean surface wind speed. Ocean surface wind speeds are derived by applying the neural network algorithm to CALIPSO measurements between 2008 and 2020. CALIPSO wind speed measurements of 2015 are also compared with Advanced Microwave Scanning Radiometer 2 (AMSR-2) measurements on the Global Change Observation Mission–Water “Shizuku” (GCOM-W) satellite. Aerosol optical depths are then derived from CALIPSO’s ocean surface backscatter signal and theoretical ocean surface reflectance calculated from CALIPSO wind speed and Cox-Munk wind–surface slope variance relation. This CALIPSO wind speed retrieval technique is an improvement from our previous studies, as it can be applied to most clear skies with optical depths up to 1.5 without making assumptions about aerosol lidar ratio.
Highlights
Ocean surface capillary-gravity wave slopes follow Gaussian distribution
The neural network is trained with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) ocean surface and atmospheric backscatter measurements together with collocated Advanced Microwave Scanning Radiometer for EOS (AMSR-E) ocean surface wind speed
An innovative neural network retrieval algorithm is developed for retrieving ocean surface wind speed from CALIPSO lidar measurements
Summary
Ocean surface capillary-gravity wave slopes follow Gaussian distribution. The variance of the Gaussian distribution increases linearly with the atmospheric wind speed 10 m above the ocean surface (Cox and Munk, 1954). The nonlinear neural network algorithm automatically adjusts aerosol lidar ratios that minimize wind speed retrieval errors using the ocean surface and atmospheric signals. CALIPSO lidar backscatter measurements of January 2008 and collocated ocean surface wind speed measurements from AMSR-E instruments on Aqua satellite (75 s ahead of CALIPSO) are used for training the neural network CALIPSO ocean surface wind speed algorithms. The neural network algorithm is applied to CALIPSO measurements between 2008 and 2020, when the lidar is tilted 3° off-nadir to derive ocean surface wind speeds at 1 km resolution. As the CALIPSO algorithm is applied to thinner aerosols (ocean surface attenuated backscatter greater than 0.002 sr−1), CALIPSO’s measurements of dust optical depths in the tropical Atlantic region are much lower than MODIS. CALIPSO ocean surface backscatter and CALIPSO wind speed from the neural network retrieval
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