Asia-Pacific J. Atmos. Sci., 46(4), 513-523, 2010 DOI:10.1007/s13143-010-0030-3
NOTE
On Drag Coefficient Parameterization with Post Processed Direct Fluxes Measurements over the Ocean Hyun-Mi Oh1, Kyung-Ja Ha1, Ki-Young Heo1, Kyung-Eak Kim2, Sang-Jong Park1, Jae-Seol Shim3 and Larry Mahrt4 1
Division of Earth Environmental System, Pusan National University, Busan, Korea Department of Astronomy and Atmospheric Sciences, Kyungpook National University, Daegu, Korea 3 Korean Ocean Research and Development Institute, Ansan, Korea 4 COAS, Oregon State University, Corvallis, OR, USA 2
(Manuscript received 16 October 2009; revised 27 July 2010; accepted 25 August 2010) © The Korean Meteorological Society and Springer 2010
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. Key words: Post processes, turbulent fluxes, quality control, tilt correction, drag coefficient
1. Introduction In last decades there have been significant efforts to increase knowledge of the turbulent exchange of momentum, heat, and moisture between ocean and atmosphere. To understand physical processes related to atmosphere-ocean interaction, the flux measurements over the ocean are necessary. The parameterizations of turbulent fluxes are based on the results of field experiments conducted over the oceans. Therefore reliable field experiments data are required to develop new parameterization method. In order to observe oceanic environment of East Asia, the Ieodo Ocean Research Station (IORS) was constructed by Korean government in 2003. Since then, various atmospheric, oceanic
Corresponding Author: Kyung-Ja Ha, Division of Earth Environmental System, Pusan National University, Busan, Korea. E-mail:
[email protected]
and environmental data have been collected: direct flux data from sonic anemometer, visibility, precipitation, wave height, etc. These data are very useful to examine the process of the air-sea interaction and to observe monsoon flow and air quality. However, the data observed over the ocean require post processes prior to analysis of the data, because of the data contaminations due to noise by sea-salt, sea spray, and flow distortion. Direct observation of turbulent fluxes is conducted with fastresponse instruments. The fast-response data require post processes in order to ensure high quality data. Major components of post processes are quality control (QC) and tilt correction. The former is used to remove erroneous data and the latter is used to rotate to the streamline coordinate. The quality check of flux data is one of the important processes because rainfall or foggy conditions lead to unfavorable humidity signals, as the glass window of the hygrometer is contaminated by water drop. In other cases, unfavorable wind direction can be increased by the effects of booms or other structures. The source of the large momentum flux errors is the cross contamination of velocities that occurs in a tilted sensor (Wilczak et al., 2001). Therefore the tilt correction of the turbulence sensors into a streamwise direction is an imperative task to eliminate flow distortion caused by booms or other structures. Flux observation includes a significant amount of data from fast response measurements. It is not practical to remove manually unphysical data in the fast response data. Many researchers have developed automatic procedures which enables QC in more efficient way since Højstrup (1993) first developed a data screening procedure for spikes. Foken and Wichura (1996) developed algorithms to classify the quality of the flux measurements that depend on the meteorological situation and on the homogeneity of underlying surface. The algorithms are composed of instationarity test, integral turbulent characteristics and spikes. Vickers and Mahrt (1997) developed an automatic quality control procedure to correct instrumentation problems such as electronic spike and plausible physical behavior in the flux data of tower and aircraft. The tilt correction is applied to remove the influences of flow distortion and vertical motion due to tilt of sensor and sloping topography. Tanner and Thurtell (1969) first developed tilt correction method in which the condition of
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the mean of v-wind component v = 0 and the mean of w-wind component w = 0 must be satisfied for every turbulent averaging period. Since then, various tilt correction methods have been developed (McMillen, 1988; Kaimal and Finnigan, 1994; Paw U et al., 2000; Wilczak et al., 2001; Finnigan et al., 2003). Post-processing of the flux data is an important issue especially for the data obtained over the ocean. Because of the hardship of management as well as inadvertent environment, oceanic flux data usually contain more portions of erratic data. This is one of reasons that flux parameterization over the ocean less successful than over the land. The sea surface wind stress is important because it drives the ocean circulation. The drag coefficient CD has been determined in many laboratory and field experiments. Despite many years of experimental studies to reduce uncertainty, observational data of CD is still very limited, over the ocean. The objective of the present study is to show how atmospheric and oceanic environment affect the quality of turbulent flux data observed over the ocean and to achieve an accurate formula of drag coefficient parameterization through considering seasons and stabilities. The removal ratio of the fast response data is analyzed, with regard to the variation of wind speed, relative humidity, visibility, atmospheric stability, and wave height. Measurement details and observation data are described in section 2. The methodology of post process is detailed in section 3. The results of QC and tilt correction methods are presented in section 4. Post processed momentum flux and drag coefficient parameterization are evaluated in section 5. Our conclusions are summarized in section 6.
2. Data The Ieodo Ocean Research Station (IORS; Korea Ocean Research & Development Institute, 2001) is constructed on Ieodo, a large underwater rock found in the Yellow Sea, (125o18'E, 32o12'N). It is located 149 km southwest from Marado, the southernmost island of Korea. IORS is an open sea station with no orographic influence over the southwestern part of the Korean Peninsula. This location is more appropriate to observe turbulent flux on the ocean than other ocean platforms which are located near coastal region (Johansson et al., 2003; Pospelove et al., 2009). Further its location is a good landmark for representative
Fig. 1. Equipments of Ieodo ocean research station (IORS). The height of floor (1,320 m2) is 33m from mean sea level. Meteorological tower is located on 10m from the floor. Sonic anemometer is installed on the boom of pillar under the deck at the height of 16 m and 12 m from mean sea level. The sonic is looking to northwestern (NW) direction during wintertime and southeastern (SE) during summertime, respectively.
flow in East Asian monsoon circulation (Oh and Ha, 2005). The depth of the water around IORS is about 40 m. As shown in the Fig. 1, the meteorological tower is deployed at the height of about 10 m from the floor, the most upper level of the platform. The height of the floor is 34 m above mean sea level. IORS has operated since May 2003. Oceanic and meteorological instruments deployed at IORS are shown in Table 1. Atmospheric, oceanic, and environmental data have been compiled regularly since 2004. The meteorological and oceanic elements that have been measured include wind, temperature, humidity, pressure, insolation, visibility, rainfall, significant wave height, and sea water temperature (−10 m), etc. These variables are produced in 10-minute interval. The turbulent fluxes have been observed by the an open path eddy covariance (OPEC) system composed of sonic anemometer (CSAT3, Campbell Scientific Inc., USA) and an open path infrared gas analyzer (IGRA: LI-7500, LiCor Inc., USA). The sonic anemometer is installed on the boom of the pillar under the deck at the heights of 16 m and 12 m from mean sea level.
Table 1. Description for meteorological variables observed at Ieodo ocean research station (IORS). Equipment
Variable
Manufacturer/Model
Wind monitor
wind direction / speed, gust wind direction / speed
RM Young/05106
Temp. & humidity sensor
temperature, humidity
VAISALA/HMP45A
Digital barometric sensor
Pressure
VAISALA/PTB210B
Secondary standard pyranometer
solar irradiance
EKO/MS-802
Sunshine duration meter
daily sunshine duration
EKO/MS-093
Rain gauge
Precipitation
VAISALA/RG13
Wave radar
significant wave height, peak period
MIROS/SM-001
C-T sensor
water temperature, salinity
Excellence In Instrumentation / Digital OEM C-T Sensor
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Fig. 2. Schematic diagram of the post processing for the fast response data.
The installed direction of sonic anemometer was alternated between the northwestern (NW) and the southeastern (SE) according to season to measure prevailing wind. The recording speed of the fast-response data is 10 Hz. The characteristics of the sonic anemometer and the IRGA were well documented in previous experiments (Foken et al., 1997; Foken, 1999).
3. Post processes The QC of fast-response data ensures accurate analysis in post processes. Figure 2 is a schematic diagram of the post process for fast-response data. The process consists of a weather check, the QC algorithm of Vickers and Mahrt (1997, VM checks), a flag check, a direction check, and tilt correction. The errors are distinguished as soft and hard flags in the VM checks. Hard flags identify unphysical data and soft flags identify unusual behavior that appears to be physical. The flagging criteria are applied as suggested by Vickers and Mahrt (1997).
The entire process of QC is performed in 30 minutes. Figure 3 is schematic diagram of QC process of turbulent flux data. As the first step, the weather check is made to eliminate data errors in rain or foggy conditions. The criteria of the weather check are as follows: rainfall is greater than 0 mm h−1 or the visibility is less than 2 km or the relative humidity is over 85%. These criteria were introduced to exclude the errors induced by water drops. Data obtained under the condition is replaced by a missing value (-999). As the second step, we apply VM checks to identify abnormal data. The VM checks include range, spike, amplitude resolution, skewness and kurtosis, the Haar mean and variance, and dropout checks. The range and spike checks are sequentially performed (hereafter VM sequential checks). The amplitude resolution, skewness and kurtosis, Haar mean and variance and dropout are performed in a parallel fashion (hereafter VM parallel checks). Errors are removed and corrected during the VM sequential checks, but the VM parallel checks only determine hard or soft
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Fig. 3. Schematic diagram of the quality control process for the turbulent flux data. The process is composed of direction check, rain day check and foggy day check.
Table 2. Criteria of range check for observed variables. Variable
Criteria
u-wind (u)
± 30 m s−1
v-wind (v)
± 30 m s−1
w-wind (w)
± 5 m s−1
temperature
−10~40oC
water vapor
0-50 g m−3
flags. The range check is a procedure used to detect unrealistic values beyond the absolute limit. The criteria of each variable are explained in Table 2. The values beyond the absolute limit are replaced by the missing value. And then spike detection and removal are performed. The spike is defined as a large shortlived departures from the mean (Schmid et al., 2000). The spike occurs due to electronic problems caused by water droplets (droplet-spike) and colder-than-ambient air (cold spike). When water droplets impact the surface of the transducer, the heat transfer rate increases and voltage spike occurs (Thomson and Hassman, 2001). Any point that is more than 3.5 standard deviations from the mean is detected as a spike in a moving window of 5-minute length according to the threshold reported by the Vickers and Mahrt (1997) and Højstrup (1993). The spike is replaced by a linearly interpolated value. When the numbers of spikes detected are greater than 1% of the total data points, the
record is hard-flagged. The amplitude resolution check estimates if the amplitude resolution is sufficient to capture turbulent fluctuation. The skewness and kurtosis of the linearly detrended data are used to detect instrumental or recording problems with physically unusual behavior. Haar mean and variance estimate data discontinuity (Mahrt, 1991). Haar mean and variance also distinguish soft and hard flags. Dropouts are defined as some part of the time series having a constant value. Water vapor data is flagged most frequently in the QC procedure. As the 4th step, when the total number of hard flags is larger than two in a 30-minute records, the record is treated as the missing value. The flag check criterion helps to distinguish serious errors. The removal ratios are 1.6% for u-wind, 2% for v-wind, 7.2% for w-wind, 2.4% for air temperature, and 15.2% for water vapor in this step. The direction check is performed to remove flow-distorted data from the platform. Yim et al. (2006) suggested that a wind direction can be utilized by applying the computational fluid dynamics technique without flow distortion. The mean horizontal wind direction, which has an average time of 30 minutes, is checked according to the criteria. The data observed in the northwestern boom is contaminated by the structure when the horizontal wind direction is between 90o and 180o. The data observed in the southeastern boom is contaminated by the structure when the horizontal wind direction is between 270o and 360o. The removal ratio is 49.7% of all data in Table 2 during the direction check.
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Tilt correction is the rotation process method wherein observational data from the sonic anemometer are rotated to the streamwise coordinate. Tilt correction is normally applied to remove the influences of flow distortion, apparent vertical motion due to sensor tilt or sloping topography (Vickers and Mahrt, 2006). Double rotation (hereafter, DR) is the most commonly and early applied method (Tanner and Thurtell, 1969). This method aligns the x-axis with the mean wind flow. McMillen (1988) suggested triple rotation (hereafter TR), that is, rotating y- and z-axes around the x-axis until the cross-stream stress becomes zero. The DR and TR can be applied in real time. Recently, Wilczak (2001) proposed the planar fit method (hereafter, PF), that is, rotating the ensemble-averaged flow to streamline coordinate. The PF requires sufficiently long data periods (weeks or longer) during which the anemometer position does not change. In the present study, the three different methods of tilt correction -DR, TR, and PF- are applied to identify applicable tilt correction method sonic anemometer in the IORS site.
4. Influencing factors on data quality We evaluated the rate of data removal in the flag check step. Figure 4 represents the total removal ratios of wind components, air temperature, and water vapor with regard to horizontal wind speed, relative humidity, significant wave height, visibility, and stability parameter (z/L). When wind speed is greater than 15 m s−1, the removal ratio of all variables increases (Fig. 4a). In the weak wind range (< 3 m s−1), the removal ratios increase up to 50%. In light wind conditions, anomalous stress values are often produced due to interaction between the wind and wave field over the sea (Grachev et al., 2003). Also, Monin-Obukhov theory fails to describe surface layer when winds are calm (Stull,
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1989; Ha et al., 2007). In this study, observation errors also often occur when wind speed is weak. Therefore, the QC process is very important to develop bulk formula from observation data in light wind conditions. In strong wind conditions, most errors are related to rain events. The increasing errors due to high humidity before rain events are filtered by flag check. Table 3 shows that the removal ratio of water vapor increases with the increase in relative humidity and significant wave heights greater than 3 m. As visibility decreases, the removal ratios of water vapor data and air temperature data increase. The removal ratio of water vapor increases in near-neutral and stable conditions. This result shows that latent heat flux over the ocean, which is measured using the eddy covariance method with vertical wind speed and water vapor density collected with a fast-response sonic anemometer, could include observation errors because the errors are not perfectly filtered by QC in the calm, humid, and stable atmospheric conditions. To illustrate difference in flow distortion among three tilt correction methods, we compare the flow distortion using each of the methods. Figure 4 shows comparisons of friction velocity by three tilt correction methods. The correlation coefficient between the two stresses by TR and PF is also 0.96. However, the comparison of the along-wind stress by TR and PF is not made because the along-wind stress by TR is set to zero by the third rotation. The correlation coefficient between the friction velocities by DR and PF is 0.90 and the coefficient between the TR and PF is 0.88 (Figs. 4a and b). The crosswind stress by DR shows good agreement with that by PF (not shown). The correlation coefficient between the two stresses by DR and PF is 0.96. The lateral stresses by DR are larger than those by PF. The correlation coefficient between the two stresses by DR and PF is 0.64. The crosswind stress by TR is also well correlated with that by PF (not shown).
Fig. 4. (a) Double rotation method (DR) versus planar fit method (PF) for the friction velocity and (b) triple rotation method (TR) versus PF the friction velocity. The solid line represents 1:1.
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Table 3. Total removal ratios (%) of the fast-response data as a function of horizontal wind speed, relative humidity, significant wave height, visibility, and z/L. WC, T and WV represent wind components, air temperature and water vapor, respectively. Category I Wind speed
Category II
Category III
Category IV
WC
T
WV
WC
T
WV
WC
T
WV
WC
T
WV
4.74
17.21
31.87
0.24
0.49
20.99
0.36
0.67
11.56
0.45
0.68
8.94
Relative humidity
0.0
0.0
51.28
0
7.51
26.09
0.12
0.89
13.35
1.19
1.95
8.6
Significant wave height
0.15
4.10
23.59
0.0
0.37
3.38
0.0
0.0
2.05
0.0
0.0
44.19
Visibility
0
16.67
50.0
0.63
5.62
82.52
0.0
6.06
28.99
0.31
0.69
13.83
z/L
0.05
1.81
5.31
0.0
0.23
5.85
0.0
0.0
33.33
0.0
0.0
25.0
Wind speed categorizing I : 0 ~ 3 m s−1
II : 3 ~ 6 m s−1
III : 6 ~ 9 m s−1
IV : above 9 m s−1
II : 70 ~ 80%
III : 60 ~ 70%
IV : below 60%
II : 1 ~ 2 m
III : 2 ~ 3 m
IV : above 3 m
II : 4 ~ 8 km
III : 8 ~ 9 km
IV : above 12 km
II : -0.2 ~ 0
III : 0 ~ 0.2
IV : above 0.2
Relative humidity categorizing I : 80 ~ 100% Significant wave height categorizing I:0~1m Visibility categorizing I : 0 ~ 4 km z/L categorizing I : below -0.2
Table 4. Frequency (percentile, %) of each stability by overwater stability categories classification for each season (three types). DJF
MAM
JJA
SON
Stable (z/L ≥ 0.4)
48 (2.7%)
168 (10.0%)
46 (4.8%)
12 (0.3%)
1251 (70.2%)
1081 (64.6%)
701 (72.4%)
2904 (73.3%)
484 (27.1%)
425 (25.4%)
221 (22.8%)
1044 (26.4%)
1783 (21.3%)
1674 (20.0%)
968 (11.5%)
3960 (47.2%)
Near Neutral (−0.4 < z/L < 0.4) Unstable (z/L ≤ −0.4)
5. Evaluation of post processed momentum flux and drag coefficient parameterization The previous studies presented the seasonal variation of turbulent fluxes. Bhat (2003) analyzes that latent heat flux shows seasonal dependence with increasing distance from the equator in extratropical region from Indian Ocean Experiment (INDOEX; Mitra, 1999) and Bay of Bengal Monsoon Experiment (BOBMEX; Bhat et al., 2001). Na et al. (1999) used weather maps of 18 years to find climatological annual and seasonal variation of surface heat flux in East Asian Marginal Sea. Subrahamanyam et al. (2008) studied climatological features of turbulent fluxes and the impact of Tsushima warm current from buoy data around Korean peninsula. But these studies used indirectly measured turbulent fluxes. Over the ocean, directly measured flux data during
several years is very limited. In the present study, we used direct flux measurement data of IORS from October 2004 to February 2008. This period is sufficient to examine seasonal variations of the turbulent fluxes and atmospheric stability. We used data observed at the heights of the 16 m and 12 m above mean sea level. Based on an offshore and coastal dispersion model (Hanna et al., 1985), Hsu (1992) suggested an overwater stability criterion as stable (z/L ≥ 0.4), near neutral (−0.4 < z/L < 0.4), and unstable (z/L ≤ −0.4). Table 4 shows the occurrence frequencies of each stability class according to season. Stable condition constitutes about 5%, near neutral condition about 70% and unstable condition about 25%. In the spring (MAM), stable condition is slightly increased than in other seasons. Using the post-processed momentum flux, the relation bet-
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Fig. 5. Friction velocity u* as a function of the wind speed at 10 m in terms of different seasons. (a) DJF, (b) MAM, (c) JJA and (d) SON. Open circle: stable, solid gray circle: near neutral and +: unstable state, respectively.
ween turbulent momentum flux and wind speed is examined to see the seasonal variation. Figure 5 shows friction velocity as a function of wind speed. The solid line indicates the relationship between the friction velocity calculated using Charnock relation and 10-m wind speed in neutral condition. κ u* = U10 -----------------ln ( z ⁄ z0 )
(1)
and αu 2 0.11v z0 = ----------*- + ------------g u*
(2)
where u*, U10, κ, z0, α, v and g are friction velocity, 10-m wind,
von-Karman constant, surface roughness length, Charnock parameter (α = 0.0185), viscosity of air, and gravitational acceleration, respectively. Except for summer (JJA), friction velocity is slightly underestimated by the relationship between the friction velocity and 10-m wind speed in near neutral state. The seasonal variation of the relation between turbulent fluxes and wind speed is again separated by stability. For the same wind speed, the friction velocities in stable and unstable condition are less than the friction velocity in near neutral state (Fig. 5). The drag coefficient CD is an important parameter to calculate wind speed in the numerical model. To improve the CD parameterization, CD is estimated from measured variables as
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Fig. 6. Drag coefficient of momentum, CD as a function of the wind speed at 10 m using data sets before quality control for different seasons (a) DJF, (b) MAM, (c) JJA and (d) SON. Open circle: stable, solid gray circle: near neutral and +: unstable state, respectively. 2
u* -, CD = -------2 u10
(3)
u ------ , u10 = ⎛⎝ -----*⎞⎠ ln 10 κ z0
(4)
and g 10 2 CD = ⎛⎝ κ ⁄ ln ----------2-⎞⎠ α u*
(5)
where CD, u*, u10, κ, z0, α and g are drag coefficient, friction velocity, 10-m wind, von-Karman constant, surface roughness
length, Charnock parameter, and gravitational acceleration, respectively. To identify the improvement of accuracy of CD estimation, Figs. 6 and 7 show CD as a function of wind speed before QC and after QC, respectively. The broad and erratic distributions of CD are shown in Fig. 6 before the quality control eliminates the outliers. The variation of CD shows different feature depending on seasons and stabilities (Fig. 7). Table 5 presents linear regression relations between CD and U10 in terms of different seasons and stabilities. In near neutral condition, slope coefficients of MAM and JJA are greater than those of other seasons and
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Fig. 7. Same as Fig. 6 except for using data sets after quality control.
Table 5. Linear regression relations between the drag coefficient CD and wind speed U10 in terms of different seasons and stabilities. z/L ≥ 0.4
0.4 < z/L < 0.4
z/L ≤ −0.4
3
2
10 CD = 0.84 + 0.058U10 (R2 = 0.71)
3
2
10 CD = 1.02 + 0.052U10 (R = 0.24)
DJF MAM
10 CD = 0.69 + 0.039U10 (R = 0.26)
10 CD = 0.77 + 0.066U10 (R = 0.61)
103 CD = 0.76 + 0.057U10 (R2 = 0.35)
JJA
103 CD = 0.65 + 0.054U10 (R2 = 0.46)
103 CD = 0.78 + 0.063U10 (R2 = 0.66)
103 CD = 0.83 + 0.032U10 (R2 = 0.25)
103 CD = 0.98 + 0.041U10 (R2 = 0.38)
103 CD = 0.83 + 0.042U10 (R2 = 0.53)
SON
3
2
3
stabilities, which imply that the application of CD parameterization should be categorized in terms of seasons and stabilities.
6. Summary and Conclusions In this study, we investigate how atmospheric and oceanic
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environment affect the errors in fast-response data to measure turbulent fluxes by applying post processes to the data observed over the ocean using long-term data sets from October 2004 to February 2008. The post process method included QC and tilt correction. The QC process included weather check, the QC algorithms of Vickers and Mahrt (1997, VM checks), flag check, direction check. The weather check was designed to eliminate errors in rainfall or foggy condition. In the present study, 18.9% of data were removed by weather check. The observation errors were flagged as hard and soft in the VM checks. When number of hard flag was larger than two, flagged records were treated as missing value. This criterion was set for automatic correction. The present result shows that the errors of flux measurements were closely related to horizontal wind speed, significant wave height, relative humidity, visibility, and stability parameter (z/L). Generally, rain had been recognized as a main cause of flux measurement problems. It should be noticed that errors also occurred in the atmospheric conditions of dense fog, high wave, and strong wind as well as light wind and stable condition. Especially, the removal ratio of water vapor data is significantly dependent on all aforementioned factors. The removal ratio of water vapor data increases with the value of stability parameter even though there are some oscillations in the removal ratio. Our results show an interesting aspect that the total removal ratio of water vapor data increases with decrease of wind speed and wave height when wind speed is less than 3 m s−1 and wave height is less than 1 m. In addition, erroneous flux data as a removal ratio (%) increases under the conditions of wind speed less than 5 m s−1 and z/L up to 0.2. The removal ratios of water vapor data and temperature inversely correlated with the visibility. Overall the most errors are come from light wind and stable conditions. To illustrate difference in flow distortion among three tilt correction methods, the effects of three tilt correction methods (DR, TR, and PF) on the momentum flux were compared. In crosswind stress, both DR and TR agree well with PF. However, agreement of along-wind stresses between DR and PF was poorer. Turbulent fluxes are analyzed according to stabilities and seasons. The results show that the turbulent fluxes have to be categorized in terms of stabilities and seasons. Stable (z/L ≥ 0.4) cases are about 5%, near neutral (−0.4 < z/L < 0.4) cases are about 70% and unstable (z/L ≤ −0.4) ratio are about 25%. The seasonal variations of heat fluxes are almost caused by seasonal variations in temperature and humidity. Friction velocities in near neutral state are greater than friction velocity in other states. The results show the distinctly different variation features of CD depending on seasons and stabilities. In order to achieve an accurate formula of drag coefficient, error should be eliminated prior to using observational data. In addition, seasonal modulations and stability considerations are needed for formula of CD according to the seasonal characteristics of atmosphere/ocean and stabilities.
Acknowledgments. We thank Korean ocean research and development institute for providing the data observed in the Ieodo ocean research station. This subject is supported by ministry of environment as ‘the eco-technopia 21 project’ and Korean Ocean Research Development Institute as ‘Construction of ocean research stations and their application studies’.
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