Estimation of Ocean Surface Wind Speed and Direction From Polarimetric Radiometry Data

2008 ◽  
Author(s):  
David R. Lyzenga
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Ocean Surface ◽  
Surface Wind Speed ◽  
Ocean Surface Wind

Ocean surface wind speed retrieval from C-band quad-polarized SAR measurements at optimal spatial resolution

2020 ◽  
Vol 12 (2) ◽  
pp. 155-164
Author(s):  
He Fang ◽  
William Perrie ◽  
Gaofeng Fan ◽  
Tao Xie ◽  
Jingsong Yang
Keyword(s):  
Wind Speed ◽  
Spatial Resolution ◽  
Surface Wind ◽  
Ocean Surface ◽  
Surface Wind Speed ◽  
Ocean Surface Wind

scholarly journals Evaluation of the Ocean Surface Wind Speed Change following the Super Typhoon from Space-Borne GNSS-Reflectometry

2020 ◽  
Vol 12 (12) ◽  
pp. 2034 ◽  
Author(s):  
Hongsu Liu ◽  
Shuanggen Jin ◽  
Qingyun Yan
Keyword(s):  
Wind Speed ◽  
Correlation Coefficient ◽  
Surface Wind ◽  
Ocean Surface ◽  
Surface Wind Speed ◽  
Wind Speeds ◽  
Mean Error ◽  
Super Typhoon ◽  
The Mean ◽  
Ocean Surface Wind

Ocean surface wind speed is an essential parameter for typhoon monitoring and forecasting. However, traditional satellite and buoy observations are difficult to monitor the typhoon due to high cost and low temporal-spatial resolution. With the development of spaceborne GNSS-R technology, the cyclone global navigation satellite system (CYGNSS) with eight satellites in low-earth orbit provides an opportunity to measure the ocean surface wind speed of typhoons. Though observations are made at the extremely efficient spatial and temporal resolution, its accuracy and reliability are unclear in an actual super typhoon case. In this study, the wind speed variations over the life cycle of the 2018 Typhoon Mangkhut from CYGNSS observations were evaluated and compared with European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-5 (ERA-5). The results show that the overall root-mean-square error (RMSE) of CYGNSS versus ECMWF was 4.12 m/s, the mean error was 1.36 m/s, and the correlation coefficient was 0.96. For wind speeds lower and greater than 15 m/s, the RMSE of CYGNSS versus ECMWF were 1.02 and 4.36 m/s, the mean errors were 0.05 and 1.61 m/s, the correlation coefficients were 0.91 and 0.90, and the average relative errors were 9.8% and 11.6%, respectively. When the typhoon reached a strong typhoon or super typhoon, the RMSE of CYGNSS with respect to ERA-5 from ECMWF was 5.07 m/s; the mean error was 3.57 m/s; the correlation coefficient was 0.52 and the average relative error was 11.0%. The CYGNSS estimation had higher precision for wind speeds below 15 m/s, but degraded when the wind speed was above 15 m/s.

scholarly journals Retrieving Aerosol Optical Depth and High Spatial Resolution Ocean Surface Wind Speed From CALIPSO: A Neural Network Approach

2021 ◽  
Vol 1 ◽  
Author(s):  
Anna Murphy ◽  
Yongxiang Hu
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Surface Wind ◽  
Ocean Surface ◽  
Satellite Observation ◽  
Surface Wind Speed ◽  
Neural Network Approach ◽  
Backscatter Signal ◽  
The Neural Network ◽  
Ocean Surface Wind

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.

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