A comparison of multiplatform wind products in the South China Sea during summer and autumn in 2019

Sea surface wind(SSW)observations from a newly developed“Black Pearl”wave glider,the Chinese-French Oceanography Satellite(CFOSAT),the HY-2A microwave scatterometer,and a recently released high-resolution atmospheric reanalysis(ERA5)are evaluated with respect to in-situ buoy observations(115.46°E,19.85°N)from the South China Sea.Buoy observations from June to November 2019 are used to evaluate the wind estimates from the different platforms.The comparisons show that the HY-2A and CFOSAT scatterometer wind speeds have mean root mean square errors(RMSEs)of approximately 1.6 and 1.6 m/s,respectively,and the corresponding mean wind direction RMSEs are approximately 19°and 17°,which indicates that these satellite retrievals meet the requirements of design engineering missions.The wind speed and wind direction RMSEs of ERA5 are approximately 1.9 m/s and 33°,respectively.The correlation coefficients between the HY-2A,CFOSAT,and ERA5 wind speeds and the buoy observations are 0.86,0.85,and 0.84,respectively,and the corresponding coefficients of the wind direction are 0.98,0.98,and 0.93,respectively,at a 95%confidence level.However,the wind sensor in the wave glider provides relatively poor-quality observations compared with the buoy measurements and has higher wind speed and wind direction RMSEs of 2.9 m/s and 50.1°,respectively.Taylor diagrams are utilized to illustrate comprehensive wind comparisons between the multiplatform observations and buoy observations.The results help identify the basic biases in SSWs among different products and enhance confidence in the future use of SSW data for studies of upper ocean dynamics and climate analysis.Suggestions are also off ered to help improve the design of next-generation wave gliders.

Seasonal variations in wave-induced stress over global ocean based on China France Oceanography Satellite

Due to the scarcity of simultaneous observations on global-scale wind and wave spectra,there has been limited research on the characteristics of global wave-induced stress and wind stress with wave effects using observed wave spectra,particularly their seasonal variations.The China France Oceanography Satellite(CFOSAT)for the first time can simultaneously observe global sea surface wind and wave spectra,providing a solid data basis for investigating this difficult issue.In this study,the seasonal characteristics of global sea surface wave-induced stress and wind stress were analyzed by combining one-year simultaneous wind and wave observations from CFOSAT with a wave boundary layer model.Waveinduced stress was divided into wind-wave-induced stress and swell-induced stress based on different wave forms.The results showed that the wave-induced stress presented a significant inverse correlation with swell index.A higher swell index corresponded to a larger proportion of swell-induced stress,resulting in a decrease in wind stress,and vice versa,wind-wave-induced stress was dominant,resulting in an increase in wind stress.From spring to winter in the Northern Hemisphere(NH),wind-wave-induced stress predominated in the westerly belt of the Southern Hemisphere(SH),while swell-induced stress predominated near the equator.Further analysis revealed that the seasonal variation in wind-waveinduced stress in the SH was not significant,however,wind-wave-induced stress during the boreal summer was significantly lower than that in other seasons.The absolute value of swell-induced stress in the SH showed a trend of decrease and then increase from spring to winter.The percentage of increase or decrease in wind stress after considering the waveinduced stress showed a roughly symmetrical pattern between the NH and SH during the spring and autumn seasons,while the summer and winter seasons showed an asymmetrical feature.Wave-induced stress significantly modulated wind stress,resulting in zonal mean variations by up to±30%.This finding further highlights the important modulation of surface waves on wind stress at the global scale.