On drag coefficient parameterization with post processed direct fluxes measurements over the ocean

Abstract

This study presents an evaluation of the atmospheric factors influencing the post-processing for fast-response data of horizontal momentum, vertical wind component, temperature, and water vapor to measure turbulent fluxes. They are observed at the Ieodo ocean research station over the Yellow Sea during the period of October 2004 to February 2008. The post process methods employed here are composed of quality control and tilt correction for turbulent flux measurement. The present result of quality control on the fast-response data shows that total removal ratio of the data generally depends on the factors such as a wind speed, relative humidity, significant wave height, visibility, and stability parameter (z/L). Especially, the removal ratio of water vapor data is significantly increased on light wind and strong stability conditions. The results show that the total removal ratio of water vapor data increases when wind speed is less than 3 m s −1 and wave height is less than 1 m. The total removal ratio of water vapor data also increases with the value of the stability parameter. Three different algorithms of tilt correction methods (double rotation, triple rotation, and planar fit) are applied to correct the tilt of the sonic anemometer used in the observation. Friction velocities in near neutral state are greater than friction velocity in other states. Drag coefficients are categorized in terms of stabilities and seasons.

Observed drag coefficients in high winds in the near offshore of the South China Sea

“…Sonic anemometer measurements are crucial to both the eddy covariance method and the inertial dissipation method, as indicated in equations and , respectively. It is necessary to apply several corrections to raw sonic anemometer data to produce fluctuations and means [ Foken et al ., ; Lee et al ., ; Oh et al ., ]. Eddy covariance data processing has been widely investigated in previous studies [ Lee et al ., ] and consists of several steps: spike detection, coordinate rotation, and detrending to separate the turbulent signals from the mean flow [ Lee et al ., ].…”

Physical Processes in Sea Fog Formation and Characteristics of Turbulent Air-Sea Fluxes at Socheongcho Ocean Research Station in the Yellow Sea

“…Those two systems sampled three-dimensional wind components, air pressure, air temperature, and absolute carbon dioxide and water vapor densities at 20 Hz, and then processed to turbulent fluxes, such as latent heat flux, sensible heat flux, momentum flux, friction velocity, and carbon dioxide flux at 30 min intervals using a data logger (CR3000, Campbell Scientific, United States). To cleanse the erratic signals from the original turbulent fluxes, we post-processed them by checking absolute limits and de-spiking in a similar fashion to the method of Oh et al (2010Oh et al ( , 2011. During the analysis period from April to June 2016, the coverage of turbulent fluxes was 59.6% (61.3%) for latent heat flux, 63.9% (62.1%) for sensible heat flux, and 43.2% (51.6%) for friction velocity at the northeastern (southwestern) direction.…”

“…To better understand the underlying processes driving air-sea interactions, direct and accurate quantification of the turbulent fluxes over the ocean surface are needed (Garratt, 1992;Paw et al, 2000;Ha et al, 2007;Oh et al, 2010Oh et al, , 2011Yun et al, 2015;Katz and Zhu, 2017). Despite the importance of extensive observational data, most experiments on air-sea interactions are usually limited to short durations and require buoys that are located too close to land, deeming them unsuitable to accurately and explicitly explain oceanic characteristics (Yelland et al, 2009).…”

Observations Utilizing Korea Ocean Research Stations and their Applications for Process Studies

“…The fast-response data should be corrected through the quality control to reduce abnormal information. A postprocessing algorithm including quality control and tilt correction designed by Oh et al (2010Oh et al ( , 2011 has contributed to improve the quality of I-ORS data.…”