1. Introduction
Air–sea fluxes of momentum and heat, atmospheric turbulence in the surface atmospheric boundary layer, and wind–wave interactions are key characteristics that are needed to validate and improve dynamical models of the atmosphere, the upper ocean, and waves.
There remains a need to better parameterize the flux–drag relation as a function of sea state (e.g., Donelan et al. 1993, 1997), the determination of the so-called wind input function (e.g., Donelan et al. 2005), and to improve the Kolmogorov (1941) and Monin and Obukhov (1954) theories in stable boundary layers (e.g., Weill et al. 2003) or in the presence of swell (Smedman et al. 2009).
There are three main methods for estimating air–sea turbulent fluxes, namely, the eddy covariance (EC) method (e.g., Katsaros et al. 1993; Anctil et al. 1994; Edson et al. 1998), the spectral method or inertial dissipation (ID) method (Edson et al. 1991), and the similarity or bulk method (e.g., Liu et al. 1979; Fairall et al. 2003).
An experimental determination of air–sea turbulent exchanges has to fulfill four different requirements: 1) using reliable and accurate instruments and recording a complete set of variables; 2) using a measurement platform that has the least possible impact on the quality of measurement data; 3) using instruments as close as possible to the sea surface, that is, not too far from the mean water level; and 4) having a correct operating strategy. Although these rules are purely technical and operational, they can affect substantially the overall quality of collected data. Therefore, it is necessary to optimize not only the measurement instruments but also the measurement strategy and the platforms themselves.
The second, third, and fourth requirements are strongly conditioned by the platform used, which until now has been either a fixed platform, a towed platform (e.g., Edson et al. 1998), a research ship (e.g., Katsaros et al. 1993; Christensen et al. 2013), a floating platform, or a buoy (Anctil et al. 1994; Weller et al. 2012; Graber et al. 2000). Hereinafter, we attempt to briefly review the advantages and limitations of these platforms.
Fixed platforms such as the Woods Hole Oceanographic Institution (WHOI) Martha’s Vineyard platform (Edson et al. 2007) and the Black Sea platform (e.g., Soloviev and Kudryavtsev 2010) produce continuous sampling at the same location for several days to several years, which is of invaluable interest for the study of air–sea interactions. However, possible limitations are aerodynamic distortion, which largely depends on wind direction, and the fact that most of them are not in open sea conditions, in which case the results found with fixed platforms might be specific to the conditions associated with their geographic location.
Towed platforms, the use of which was pioneered by Katsaros (1980), are helpful for sampling small-scale variations of the sea surface temperature (SST) and of the sea surface salinity (SSS), for example. However, they have limitations for wave, downwelling radiation, and atmospheric turbulence measurements, because the apparatus naturally places itself in the disturbed atmospheric wake or in the bow wave of the ship. The proximity of the ship might affect radiation data because of its color and its thermal radiation.
Quality air–sea flux data can be collected in the open ocean with a research vessel (R/V) or a floating platform, such as the Floating Instrument Platform (FLIP; e.g., Miller et al. 2008). Unfortunately, such platforms are difficult to operate and ship cruises are expensive and thus infrequent. In spite of this, the use of R/Vs is by far the most convenient way to document flux variations in a large area, because R/Vs can cruise for up to several months. The main limitation of R/Vs is that they are affected by aerodynamic distortion of bulk quantities, but probably also at turbulent scales (Bourras et al. 2009). Note that data may also be affected by thermal effects (Berry et al. 2004). The aerodynamic distortion effect may have a large impact on the accuracy of flux estimates, because wind data are affected as a function of the shape of the ship, the location of the instruments on the ship, and the angle between the ship and the relative wind (Yelland et al. 2002). In addition, due to their large weight and dimension, most R/Vs have a large spectral motion peak in the same frequency range as waves and turbulence production (0.01–1 Hz), which is problematic for applying the EC method, for example. Other issues include the height of measurement, which may be considered as too far from the air–sea interface (+7 to +17 and −3 m for SST), or the position of the instruments that are located at the bow of the R/V, that is, far from the center of gravity of the R/V. As a result, the measurement height varies and the vertical wind component is disturbed. Associated with measurements on R/Vs is the issue of the displacement height, which may be difficult to assess (e.g., Brut et al. 2005; Popinet et al. 2004).
Buoys appear to be a good compromise for producing quality air–sea data, although an obvious limitation of buoys is that a ship is required for deployment, recovery, and maintenance, which is again a major operation. In addition, for buoys that are held in place for a long time (more than one month), instruments are more vulnerable to water, salt (corrosion or deposit on sensors), and fouling. To date, the Rosenstiel School of Marine and Atmospheric Science (RSMAS) Air–Sea Interaction Spar (ASIS) buoy is possibly the best attempt at buoy development dedicated to the study of air–sea interactions (Graber et al. 2000). It was recently proven to be successful even in severe weather conditions (Sahlée et al. 2012). Note that in very strong winds, buoys can tilt relative to the vertical (Howden et al. 2008). Measuring fluxes must be very difficult in such conditions with any platform. The ASIS buoy presumably has small aerodynamic distortion and can be equipped with various instruments, which include not only turbulence but also radiation, gas fluxes, and underwater instruments, thanks to its large payload capacity. Although there are few limitations associated with this design, it can be argued that it is rather heavy and large, which may limit the number of deployments. In addition, by its design, ASIS turbulence measurements are made at 3.5–4.5 m above the surface, whereas the study of turbulence even closer to the surface may be of interest (Grare et al. 2013). Instruments are not at a constant height above the surface. The pertinence of this argument may be discussed. However, for the study of wave–wave wind variation, constant height wind data may be of interest.
To overcome the above-described limitations in designing a new platform, the five following desirable criteria were taken into consideration:
Height above the sea surface should be constant, for analyzing wind–wave interaction.
Flow distortion, thermal effects, and effect of the platform on waves would have to be small.
Wind, temperature, and humidity measurements would be taken at heights smaller than existing designs (<3 m), and possibly at several heights, for investigating surface boundary layer profiles.
Data would be sampled far from the host ship, to avoid the effects of host ship wake and radiation.
Deployment and recovery would be as easy as possible, in order to ensure instrument check, cleaning, maintenance, and for maximizing data return.
2. Description of the platform
a. The platform
The OCARINA platform is a 2-m-long trimaran boat that weighs ~35 kg (Figs. 1, 2). It has a streamlined shape and a low profile. As such, OCARINA has small aerodynamic distortion whatever the direction of the incoming wind is (see section 4a). OCARINA is motorized with a propeller coupled to an autopilot and a remote control. It can be controlled to follow a particular route, drift as a wave rider, or maintain its position, within the limit of its battery life, which is currently 3 h at low speed (1.5 kt; 1 kt = 0.51 m s−1). Two independent 5000-mAh lithium polymer batteries of five 4.2-V cells each provide power for navigation, communication, science instruments, and data storage, for up to 12 h.
Deployment of OCARINA from the rear deck of CDLM during the FROMVAR 2011 experiment (Picture by H. Barrois, DT of INSU).
Citation: Journal of Atmospheric and Oceanic Technology 31, 5; 10.1175/JTECH-D-13-00055.1
Conceptual view of the OCARINA platform.
Citation: Journal of Atmospheric and Oceanic Technology 31, 5; 10.1175/JTECH-D-13-00055.1
The trimaran configuration was chosen because it reduces self-roll as opposed to monohull designs. Another advantage of the configuration is that the whole payload can be installed on the central float. A catamaran is easier to construct, but the payload has to be split between the two hulls, or it has to be installed in a bulky compartment between the two side hulls.
Weight was kept as low as possible to facilitate transport and deployment. But above all, weight is a key point that conditions structure motion together with the position of the instrument that measures turbulent wind and temperature fluctuations, presently located at the top of a central mast (so-called turbulent mast) on OCARINA, as can be seen in Figs. 1, 2. On OCARINA, the low weight coupled with a turbulent mast at the center of the ship induces minimal vertical motion relative to the surface (the platform does not dive or bounce on the surface). Note, however, that OCARINA presents the characteristic motion of trimarans, which consists of a small precession-like motion, that is, it rotates in circles between its two lateral hulls.
One particular feature that results from its low weight and its large projected horizontal surface is that the trimaran naturally follows the waves. Thus, to some extent (wavelengths larger than 4 m), it is possible to deduce wave features just from the recorded motion/inertial package.
The small size of OCARINA was well appreciated while carrying it on almost every type of host boat. It was even held on the rear deck of a 7-m-long inflatable boat. Thus, it was easily accepted on science experiments, which helps for missions planned at short notice, for example.
Overheating of electronic and mechanical components inside the main float was avoided by choosing an electric engine located in an underwater bulb.
b. Science payload
The science instruments are installed at five different locations on OCARINA, as illustrated in Figs. 1, 2. There are three masts for atmosphere and radiation measurements, one Sea-Bird Electronics SBE-37 on the side of the main hull for SST and SSS measurements, and one inertial platform at the horizontal center of OCARINA and at the level of the waterline. The inertial platform is an Xsens MTI-G device, which features three magnetometers, three accelerometers, three gyroscopes, a GPS, and a barometer. The MTI-G is light, weighs 70 g, and is small. Motion data are recorded at 50 Hz. The turbulent mast holds a Gill Instruments Limited R3-50 sonic anemometer, at 1.5 m above the surface. It samples the three wind components as well as sound speed (which is related to the virtual temperature of the air), at the rate of 50 Hz. The port mast holds a Vaisala WXT-520 meteorological station that measures air temperature and humidity, static pressure, rain, and wind, at 1 Hz. The station is located 1 m above the surface. This characteristic will be further discussed in section 7. The starboard mast holds a Kipp & Zonen CNR 4 radiation instrument, which measures upward and downward radiation fluxes in the visible and infrared spectra. Overall, OCARINA samples and records over 50 variables simultaneously. The instruments and sampled variables are summarized in Table 1.
Summary of the instruments installed on OCARINA, and a list of the instrument locations and sampled data. Only the main recorded variables are reported. Various ancillary data are also recorded for control and verification, such as temperature control or accuracy of GPS position data.
c. Data acquisition, automatic pilot, and transmission
A specific field-programmable gate array (FPGA)-based electronic board was designed for data acquisition. It provides a lightweight, low power consumption, and, above all, reliable method for sampling, sorting, and adding time and date independently for all data. After experiencing data synchronization problems from various serial ports in past experiments, we decided to proceed with on board asynchronous data processing. Every datum has its own time tag. Next, data are put in a heap memory and then stored and duplicated into two memory cards [secure digital (SD) cards] for robustness of the system, as represented in Fig. 3. Time is inferred from an internal clock that is periodically synchronized to a GPS clock signal. This system was tested and verified: the time deviation was never larger than 0.01 m s, which is small compared to the sampling period of the instruments, that is, 20 m s.
Flowchart of the data acquisition system. The data frames of each instrument (top row) are decoded. Next, a time tag based on a GPS clock is added, before the frames are stored in a buffer (middle row). When the buffer is full, its content is transferred to two SD cards, in parallel (bottom row).
Citation: Journal of Atmospheric and Oceanic Technology 31, 5; 10.1175/JTECH-D-13-00055.1
OCARINA currently has four operational modes. The first mode is a manual mode, which means that OCARINA is remotely controlled by an operator. This mode is helpful for deploying and recovering OCARINA from the host ship in minutes. The second mode is called “waypoint navigation”: OCARINA follows a list of programmed waypoints and cruises from one waypoint to the next. This mode can be used for surveys, or to test the impact of wind or wave orientation with respect to the platform. The third mode is “drift then return.” OCARINA first records its original position. Next, it drifts for 30 min and cruises back to its original position, repeatedly. The fourth and last mode is called “record position and route to follow.” The boat cruises for 30 min following the recorded route, and cruises back to its original position. Note that wave data cannot be inferred from recorded motion when OCARINA has its engine on.
OCARINA is controlled via a remote control with a 400-m range, or via satellite (Iridium system). Currently, satellite communication is only used to get the current position of OCARINA and to proceed to its recovery rather than for guidance, given its small operating range, which is ~10 km.
3. The FROMVAR 2011 experiment
The Front de Marée, Variabilité (FROMVAR), project was devoted to the study of the Ushant tidal front in Mer d’Iroîse, off the west coast of Brittany, France, which results from the tidal actions and air–sea interactions (http://wwz.ifremer.fr/epigram/Acces-aux-donnees/Campagnes/FROMVAR; Le Boyer et al. 2009). FROMVAR aimed at understanding the hydrological structure, the currents, and the dynamical processes associated with this front. The project consisted of several 1–2-week experiments every year since 2007. OCARINA was part of the last experiment, from 10 to 15 September 2011. In addition to OCARINA, air–sea flux data were acquired during every experiment, on board the French Institut National des Sciences des l’Univers (INSU) R/V Côtes de la Manche (CDLM), which is 26 m long. Unfortunately, not enough data were sampled in order to get a reliable comparison between the two platforms. Therefore, the CDLM flux estimates will not be discussed in the present paper.
During FROMVAR, OCARINA was deployed eight times in five days, from 30 min to more than 2 h each time. The locations at which OCARINA was deployed during the cruise of the CDLM are represented in Fig. 4. Bathymetry ranged from ~20 to ~120 m. Several deployments were conducted in the Bay of Douarnenez, thus not in open sea conditions. The 14 different weather conditions (referred to as 14 cases in the following) encountered during the experiment were from low to moderate wind speeds, from ~1 to ~8 m s−1, as reported in Table 2 (column 6). The surface boundary layer was either unstable or stable, with sometimes strong thermal stratification (Table 2). In the most stable case, the maximum difference between air temperature and sea surface temperature was equal to +1.1°C, with the formation of gravity waves in the boundary layer, as independently inferred from lidar measurements. Significant wave heights 
Locations from which OCARINA was operated during the FROMVAR 2011 experiment. The bathymetry is indicated with contour lines.
Citation: Journal of Atmospheric and Oceanic Technology 31, 5; 10.1175/JTECH-D-13-00055.1
Stability, wind, and wave conditions during the FROMVAR 2011 experiment, where Ur is the relative wind speed with respect to OCARINA and TA is air temperature measured at 1 m. The wind–wave angle is the difference between the angle of the true wind (wind with respect to ground) vector and the direction where the waves go. Both angles are counted counterclockwise.
(a) Comparison between 
Citation: Journal of Atmospheric and Oceanic Technology 31, 5; 10.1175/JTECH-D-13-00055.1
4. Distortion and motion characteristics
a. Flow distortion
Simulations were performed following the method described in Bourras et al. (2009). The fluid simulation software used is an industry standard named “Fluent,” from ANSYS, Inc. The air volume that encompasses the OCARINA surface is 20 m long × 20 m wide × 10 m tall. This air volume is gridded with an unstructured mesh, based on tetrahedral elements, the size of which is smaller, close to OCARINA. Several flow solvers, including laminar and the Reynolds stress model, were tested. They gave qualitatively similar results. The results presented in Fig. 6 are based on laminar simulations. Even in the worst-case scenario, when the wind comes across OCARINA, vertical wind distortion should be ~3° at 1.5 m above the platform, as shown in Fig. 6, which is less than half what is found for a 50–80-m-long R/V, that is, 7°–10° (Bourras et al. 2009). In addition, distortion is smaller than 3° if the wind comes from other directions, which is an advantage over large R/Vs for which 6° is a minimum value that increases with the wind angle (Bourras et al. 2009). At the location of the portside meteorological mast (at 1-m height), the simulations indicate that distortion is on the same order of magnitude as for the vertical mast at 1.5 m. Note that in the simulations, OCARINA is assumed to be horizontal, which is the case for averages over time periods larger than some minutes. For shorter time periods, steady simulations would not be meaningful without taking into account both waves and OCARINA motion, which was not attempted.
(a),(b) Numerical simulations of the vertical wind angle (°), with the hypothesis that OCARINA is horizontal. In (a), wind is aligned with the longitudinal axis of the boat, whereas the wind comes from the portside of the boat in (b). Gaps in (a),(b) correspond to regions where the absolute value of the angle is larger than 3° and 10°, respectively.
Citation: Journal of Atmospheric and Oceanic Technology 31, 5; 10.1175/JTECH-D-13-00055.1
According to FROMVAR observations, the mean vertical wind angle is 1.27° at the height of 1.5 m, as can be calculated from the angle values reported in Table 3 for the 14 cases. Thus, the actual distortion angles are ~2° smaller than in the simulations presented above, which is encouraging. Similarly, the mean vertical velocity is 0.12 m s−1, which is small compared to the velocity commonly found for research vessels, that is, ~1 m s−1 (e.g., Bourras et al. 2009). It was determined that rotation of the wind vector of an angle that would cancel the mean value of the vertical wind component had a very limited impact on the estimates of turbulent fluxes. It was concluded that it was not necessary to apply any distortion correction to the OCARINA data.
Mean vertical distortion angle (°) and vertical wind component (m s−1) for each 20-min bin.
b. Motion and its impact on wind measurements
Understanding how OCARINA moves on waves as a function of data sampling frequency is important because its shape and characteristics are different from other platforms. A typical wind and motion power spectrum is presented in Fig. 7, for a 20-min interval that started at 0722 UTC 14 September 2011, when the swell was largest. The relative wind curve (in black) reveals two broad peaks that correspond to swell (centered on 0.1 Hz) and to the resonating frequency of OCARINA (at 1 Hz). The swell peak corresponds well to the peak in the power spectrum of η (green curve). To assess whether the relationship between swell and wind is physical or is due to variations of ship attitude and motion, we performed a true wind calculation, as fully described in Pedreros et al. (2003). After application of the correction, the power spectrum of the along-wind component (blue curve on Fig. 7) still presents a large peak at the frequency of the swell. As a result, the peak in wind is actually related to a physical relationship between wind and swell. This relation will be further analyzed in section 6.
Noncorrected horizontal wind component (black), sea elevation (green), linear speed due to roll angle (red), and corrected horizontal wind component (blue). The oblique black line shows the −5/3 slope within the log/log axis.
Citation: Journal of Atmospheric and Oceanic Technology 31, 5; 10.1175/JTECH-D-13-00055.1
The peak at 1 Hz in Fig. 7 fits the linear velocity of OCARINA (deduced from roll angular velocity). After application of the correction, the peak is almost totally removed, which confirms that it was almost totally related to OCARINA motion.
Note the presence of a sharp peak at 10 Hz in Fig. 7 that presumably corresponds to a flexion mode of the turbulent mast, which is a vertical carbon tube, in interaction with the structure of the platform.
The frequency range that can be used for estimating fluxes with the spectral method, or ID method, in the following (Dupuis et al. 1997) is restricted to 3–9 Hz without correction because in this range, the slope of the linear fit to data is the closest to the value of −5/3, which is nominal for applying the ID method. Note that after correction, the frequency range could be extended to frequencies in the 1–3-Hz range. However, only the 3–9-Hz band was used because it is sufficient for applying the ID method.
In contrast, for the EC method the maximum frequency of the wind or temperature data was restricted to 2 Hz with a first-order low-pass filter. This cutoff frequency corresponds to the end of the turbulence production range and to the beginning of the “clean” inertial range, as illustrated in Fig. 7. The filter was applied so as to avoid parasite signals, such as the peak at 10 Hz in Fig. 7. The impact of this filter was tested: if no filter was applied, then the EC method slightly overestimated the other fluxes. In addition, the application of the filter improved the comparisons to the bulk and ID methods.
5. Air–sea fluxes
Comparison between u* values calculated with three different methods: (a) ID (spectral) vs bulk, (b) EC (covariance) vs bulk, and (c) EC vs spectral. The red lines indicate the 95% confidence interval for the linear fit. The cutoff time period of the high-pass filter was set to 1000 s for the EC method.
Citation: Journal of Atmospheric and Oceanic Technology 31, 5; 10.1175/JTECH-D-13-00055.1
To analyze why there is an overestimation for EC u* values, the cospectra of −u′w′ were calculated as a function of a normalized frequency (fz/U) and the experimental values found were compared to the empirical universal model of Kaimal et al. (1972), as shown in Fig. 9. Cases with a similar behavior were grouped together, namely, moderate wind (Figs. 9a,b; Table 2, rows 1–6 and 11–13), light wind and unstable conditions (Fig. 9c; Table 2, line 10), light wind and stable conditions (Fig. 9d; Table 2, rows 7–9), and no wind and stable conditions (Fig. 9e; Table 2, row 14). In Figs. 9a,b, there is an overall good agreement between the model of Kaimal et al. (1972) and OCARINA data. However, at time periods larger than 20 s, the data have higher spectrum power than the model. In contrast, in light wind conditions (Figs. 9c,d), the model and data do not agree well. Specifically, in stable cases (Figs. 9d,e) there is a peak in the OCARINA cospectra at fz/U = 0.5–0.9, which is not present in the model. As a test, the EC time series of u′ and w′ were high passed with decreasing cutoff time periods T from 1000 to 20 s. For each test, the rms and systematic deviations between EC and bulk u* estimates were checked. The best agreement was found with T = 35 s, which is shown in Figs. 10b,c. The rms deviation between the different u* estimates is 0.01 ms−1, and the slope of linear fit is 0.96–0.99, which is rather good. This indicates that for this platform there is a strong sensitivity to the cutoff period retained for estimating the covariance fluxes.
Cospectra of u′w′ for the 14 cases. Cases that presented a similar behavior were grouped. The red line represents the empirical model of Kaimal et al. (1972).
Citation: Journal of Atmospheric and Oceanic Technology 31, 5; 10.1175/JTECH-D-13-00055.1
Comparison between u*(top) and Hsv (bottom) estimates from three different methods (EC, ID, and bulk). The red lines indicate the 95% confidence interval for the linear fit. The cutoff time period of the high-pass filter was set to 35 s for application of the EC method.
Citation: Journal of Atmospheric and Oceanic Technology 31, 5; 10.1175/JTECH-D-13-00055.1
No humidity fluctuations were available with the set of instruments installed on OCARINA during FROMVAR. As a result, the latent heat flux could not be estimated with the EC method or the ID method. As the sensible heat flux also depends on humidity, a virtual sensible heat flux Hsv, better known as the buoyancy flux, was calculated instead.
The comparisons between Hsv estimated with the EC method, with the ID method, and with the bulk method are shown in Figs. 10d–f, respectively. Although there are not enough points of comparison to draw firm conclusions, the comparisons between EC and bulk Hsv values are encouraging (Figs. 10d,e). The rms deviation between EC (ID) and bulk Hsv values is 2.5 (3.3) W m−2, which is reasonable. However, the results presented in Fig. 10e indicate that in stable cases the large negative ID Hsv values are overestimated in comparison to the bulk values. The comparison between EC and ID Hsv values further reveals that the range of ID Hsv values is globally too large with respect to the range of EC Hsv values, as shown in Fig. 10f. The sensitivity of the bulk estimates to several input parameters inside the Coupled Ocean–Atmosphere Response Experiment (COARE) algorithm (Fairall et al. 2003), namely, jwave, jwarm, and jcool, which relate to the parameterization of the aerodynamic roughness length and to the skin minus bulk sea temperature calculation, was further tested. This had little impact on the results. We also attempted to account for the surface current from PREVIMER analyses in the calculation of the bulk estimates but this also had little impact on the results. Thus, for this platform, we recommend for Hsv to use EC or bulk estimates, but not the ID estimates of Hsv.
Comparison of u* values estimated with the profile method and with the bulk method. The red marks correspond to situations when the wind came from the starboard side of OCARINA, for which there may be an aerodynamic masking effect because of the starboard mast and the central mast. The empty diamonds correspond to calculations that account for stability, whereas the dots correspond to a neutral surface boundary layer hypothesis.
Citation: Journal of Atmospheric and Oceanic Technology 31, 5; 10.1175/JTECH-D-13-00055.1
Along-wind and crosswind components of the EC stress define the so-called stress angle 
Angle between the turbulent stress and the along-wind mean direction, as a function of the true wind speed.
Citation: Journal of Atmospheric and Oceanic Technology 31, 5; 10.1175/JTECH-D-13-00055.1
6. Wind–wave interaction
In this section, we investigate the relation between the instantaneous horizontal wind modulus U calculated with respect to the ground (as opposed to the wind relative to OCARINA) and η.
Eight cases are available for analysis, namely, four cases with wind and swell traveling in the same direction—that is, cases 7–10 in Table 2—and four cases when swells are traveling in the opposite direction to the winds—that is, cases 11–14 in Table 2. Each case corresponds to a time interval of 20 min, which corresponds to 60 000 samples.
Wave characteristics in open ocean conditions, during the FROMVAR experiment. On the x axis, one graduation corresponds to one 20-min bin.
Citation: Journal of Atmospheric and Oceanic Technology 31, 5; 10.1175/JTECH-D-13-00055.1
Spectra of (left) coherence and (right) phase between wind and sea surface elevation (eta). The gray-shaded areas highlight the spectral regions of the swell, where the coherence is the largest. Phases are expressed in degrees. Each spectrum is calculated on a 20-min interval that starts at the time indicated in the panel title.
Citation: Journal of Atmospheric and Oceanic Technology 31, 5; 10.1175/JTECH-D-13-00055.1
Detection of (a) coherence between U and η for the spectral region of the swell and (b) phase between U and η. In (a), the coherence between U and η is detected because the values are larger than 2 for all cases.
Citation: Journal of Atmospheric and Oceanic Technology 31, 5; 10.1175/JTECH-D-13-00055.1
Cases 7–10 present a phase shift of 3.0° between wind and waves. For cases 11–14, the average phase shift is even smaller—that is, 1.9°—as can be checked in Fig. 15b. The phase shift φ between η and the wind relative to the waves, which is defined as 
Phase shift between wave and phase-averaged wind speed over cases 7–14.
Citation: Journal of Atmospheric and Oceanic Technology 31, 5; 10.1175/JTECH-D-13-00055.1
Since real waves are not pure monochromatic sinusoidal functions, it is difficult to illustrate the mean wind profile along the phase of the wave. Instead, we analyzed the statistical relation between U and η. The time series of U and η were band filtered in the spectral region of maximum coherence, that is, between 0.06 and 0.18 Hz for cases 7–10, and between 0.08 and 0.15 Hz for cases 11–14. Next, the time series of U and η were normalized as 
Statistical elements of the relation between normalized (a),(b) Un and ηn and (c) wind amplitude.
Citation: Journal of Atmospheric and Oceanic Technology 31, 5; 10.1175/JTECH-D-13-00055.1
7. Discussion
A new design of platform is proposed for determining air–sea turbulent fluxes, surface heat budget, and some aspects of wave characteristics. OCARINA design and the results found in the present paper with OCARINA data are discussed hereafter, despite only a limited test set of 14 cases was available for validating the use of the platform and for investigating wave characteristics, turbulent fluxes, and wind–wave interactions.
a. Design
It was observed that the platform motion characteristics were fully compatible with the determination of fluxes (section 4b). OCARINA orients itself perpendicular to the relative wind, which is not the best configuration for airflow distortion. However, distortion is still small, ~3°, as was shown in section 4a.
The proposed design is obviously not the only possible response to the design criteria mentioned in section 1. Furthermore, the design is probably not better in terms of aerodynamic performance than what can be obtained by the ASIS buoy (Graber et al. 2000), at least for measurements performed at heights larger than 1.5 m.
With the chosen configuration, the three main following criticisms can be made. First, a host ship is still required to deploy and recover the platform. Second, it would be impossible to get time series longer than a day with no gaps because batteries would have to be charged after 12 h. Last, a light design means a very limited payload and electric power; thus, the range of instruments that can be installed on OCARINA is limited. Electric power would be even more limited in cold regions, where batteries would be less efficient. On the contrary, OCARINA was recently tested with success in tropical regions, with no overheating issues.
Wave heights for wavelengths larger than the size of the platform could be inferred from the motion of the platform with reasonable accuracy, on the order of 0.1 m in rms. However, as opposed to systems like ASIS, OCARINA cannot currently sample short waves. In addition, directional spectra of waves (hodographs) are presently not available. Note that ancillary gauges could be installed for obtaining these data, in the future.
Another limitation of OCARINA is that it drifts rather quickly under the effects of wind and surface current (<0.5 m s−1 drift in a 10-kt wind). Drift is an issue that must be taken into account while planning the mission of the host ship, or if obstacles are present, as in coastal areas. Adding a drogue (parachute) to limit the drift of OCARINA was not attempted, as it could affect the motion of OCARINA and thus the quality of the wave height estimates.
While designing OCARINA, a major concern was water intrusion on the instruments so close to the waves. Water could affect the quality of sonic anemometer and radiation data. In practice, this did not happen for winds up to 17 kt (recorded in the Bay of Douarnenez at the beginning of the FROMVAR experiment) and for waves up to 3.3 m (on 14 September 2011, during FROMVAR), possibly due to its light weight, its stability, its small size, and the subsequent constant height of the instruments above water. However, this could be problematic for steep waves and large spray generation. No simulation was made to further assess the maximum acceptable wave steepness or height that OCARINA could withstand.
b. Air–sea fluxes
Four flux estimation methods were applied, namely, the EC method, the ID method, the bulk method, and the profile method (for u* only). Only the friction velocity and the buoyancy flux were discussed in the present paper, as no instrument was available for measuring humidity fluctuations during the experiment. Cospectra of u′w′ exhibited similar behavior as the empirical model of Kaimal et al. (1972) for cases of unstable surface boundary layer. However, in the lower-frequency range, OCARINA cospectra were higher than those of the model. In addition, it was noticed that the comparisons between EC u* estimates and bulk and ID u* estimates were improved by a factor up to 2 if the lower parts of the spectra were filtered out in the EC method, with a cutoff time period of 35 s. Under stable conditions or in low wind conditions, the OCARINA cospectra of u′w′ were significantly different from the Kaimal et al. (1972) model, although the corresponding u* estimates were in good agreement with the other methods.
As OCARINA is a wave follower, the EC u* values measured at constant height above the surface could slightly differ from the EC u* values that would have been be measured at constant altitude [see the relationship in Grare et al. (2013), appendix A]. The order of magnitude of the differences will be checked in a future work.
We found that the bulk buoyancy flux compared well to EC estimates of the flux. In contrast, the buoyancy flux estimates from the ID method did not have a good fit to EC fluxes or bulk fluxes, especially in stable conditions. Unfortunately, too few points of comparison were available to draw firm conclusions. In a future work, this issue will be further investigated. The exponents of the inertial ranges of wind and temperature spectra, the critical Richardson number, and z-less parameterization will be tested and compared to recent results of Grachev et al. (2012).
c. Wind and waves
An effort to add a humidity fluctuation measurement instrument, a log, and a set of wave gauges to OCARINA is in progress. In its present configuration, OCARINA was recently deployed during the Salinity Processes in the Upper Ocean Regional Study (SPURS) Subtropical Atlantic Surface Salinity Experiment (STRASSE) (e.g., Reverdin et al. 2013), providing 120 h for data collection. Data processing and analysis are ongoing at Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), and will be the object of a future paper. The masts of OCARINA will be adapted to a wave glider in the near future (http://liquidr.com).
Acknowledgments
The authors are grateful to the captains and the crew of the INSU R/V Côtes de la Manche, DT of INSU at la Seyne sur Mer, B. Zucchini at IRPHE, and F. Rémy at Ecole Centrale de Marseille for their help. The development of OCARINA was funded by several sources that included IPSL, UPMC-EMERGENCE, and LATMOS.
REFERENCES
Anctil, F., Donelan M. A. , Drennan W. M. , and Graber H. C. , 1994: Eddy-correlation measurements of air–sea fluxes from a discus buoy. J. Atmos. Oceanic Technol., 11, 1144–1150, doi:10.1175/1520-0426(1994)011<1144:ECMOAS>2.0.CO;2.
Belcher, S. E., and Hunt J. C. R. , 1993: Turbulent shear flow over slowly moving waves. J. Fluid Mech., 251, 109–148, doi:10.1017/S0022112093003350.
Berry, D. I., Kent E. C. , and Taylor P. K. , 2004: An analytical model of heating errors in marine air temperatures from ships. J. Atmos. Oceanic Technol., 21, 1198–1215, doi:10.1175/1520-0426(2004)021<1198:AAMOHE>2.0.CO;2.
Bourras, D., and Coauthors, 2009: Turbulent air-sea fluxes in the Gulf of Guinea during the AMMA experiment. J. Geophys. Res., 114, C04014, doi:10.1029/2008JC004951.
Brut, A., Butet A. , Durand P. , Caniaux G. , and Planton S. , 2005: Air-sea exchanges in the equatorial area from the EQUALANT99 dataset: Bulk parameterizations of turbulent fluxes corrected for airflow distortion. Quart. J. Roy. Meteor. Soc., 131, 2497–2538, doi:10.1256/qj.03.185.
Christensen, K. H., Röhrs J. , Ward B. , Fer I. , Broström G. , Saetra O. , and Breivik O. , 2013: Surface wave measurements using a ship-mounted ultrasonic altimeter. Methods Oceanogr.,6, 1–15, doi:10.1016/j.mio.2013.07.002.
Cohen, J. E., and Belcher S. E. , 1999: Turbulent shear flow over fast-moving waves. J. Fluid Mech.,386,345–371, doi:10.1017/S0022112099004383.
Donelan, M. A., Dobson F. W. , Smith S. D. , and Anderson R. J. , 1993: On the dependence of sea surface roughness on wave development. J. Phys. Oceanogr., 23, 2143–2149, doi:10.1175/1520-0485(1993)023<2143:OTDOSS>2.0.CO;2.
Donelan, M. A., Drennan W. M. , and Katsaros K. B. , 1997: The air–sea momentum flux in conditions of wind sea and swell. J. Phys. Oceanogr., 27, 2087–2099, doi:10.1175/1520-0485(1997)027<2087:TASMFI>2.0.CO;2.
Donelan, M. A., Babanin A. V. , Young I. R. , Banner M. L. , and McCormick C. , 2005: Wave-follower field measurements of the wind-input spectral function. Part I: Measurements and calibrations. J. Atmos. Oceanic Technol., 22, 799–813, doi:10.1175/JTECH1725.1.
Dupuis, H., Taylor P. K. , Weill A. , and Katsaros K. , 1997: Inertial dissipation method applied to derive turbulent fluxes over the ocean during the Surface of the Ocean, Fluxes and Interactions with the Atmosphere/Atlantic Stratocumulus Transition Experiment (SOFIA/ASTEX) and Structure des Echanges Mer-Atmosphere, Proprietes des Heterogeneites Oceaniques: Recherche Experimentale (SEMAPHORE) experiments with low to moderate wind speeds. J. Geophys. Res., 102, 21 115–21 129, doi:10.1029/97JC00446.
Edson, J. B., Fairall C. W. , Mestayer P. G. , and Larsen S. E. , 1991: A study of the inertial-dissipation method for computing air-sea fluxes. J. Geophys. Res.,96, 10 689–10 711, doi:10.1029/91JC00886.
Edson, J. B., Hinton A. A. , Prada K. E. , Hare J. E. , and Fairall C. W. , 1998: Direct covariance flux estimates from mobile platforms at sea. J. Atmos. Oceanic Technol., 15, 547–562, doi:10.1175/1520-0426(1998)015<0547:DCFEFM>2.0.CO;2.
Edson, J. B., and Coauthors, 2007: The Coupled Boundary Layers and Air–Sea Transfer experiment in low winds. Bull. Amer. Meteor. Soc., 88, 341–356, doi:10.1175/BAMS-88-3-341.
Fairall, C. W., Bradley E. F. , Hare J. E. , Grachev A. A. , and Edson J. B. , 2003: Bulk parameterization of air–sea fluxes: Updates and verification for the COARE algorithm. J. Climate, 16, 571–591, doi:10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2.
Graber, H. C., Terray E. A. , Donelan M. A. , Drennan W. M. , Van Leer J. C. , and Peters D. B. , 2000: ASIS—A new Air–Sea Interaction Spar buoy: Design and performance at sea. J. Atmos. Oceanic Technol., 17, 708–720, doi:10.1175/1520-0426(2000)017<0708:AANASI>2.0.CO;2.
Grachev, A. A., Fairall C. W. , Hare J. E. , and Edson J. B. , 2001: Wind stress over sea waves. Preprints, 11th Conf. on Interaction of the Sea and Atmosphere, San Diego, CA, Amer. Meteor. Soc., 1.4. [Available online at https://ams.confex.com/ams/Polar-AirSe/webprogram/Paper20171.html.]
Grachev, A. A., Andreas E. L. , Fairall C. W. , Guest P. S. , and Persson P. O. G. , 2012: The critical Richardson number and limit of applicability of Monin-Obukhov similarity theory in the stable atmospheric boundary layer. Preprints, 20th Symp. on Boundary Layers and Turbulence, Boston, MA, Amer. Meteor. Soc., 3B.5. [Available online at https://ams.confex.com/ams/20BLT18AirSea/webprogram/Paper209287.html.]
Grare, L., Peirson W. L. , Branger H. , Walker J. W. , Giovanangeli J.-P. , and Makin V. , 2013: Growth and dissipation of wind-forced deep-water waves. J. Fluid Mech., 722, 5–50, doi:10.1017/jfm.2013.88.
Howden, S., Gilhousen D. , Guinasso N. , Walpert J. , Sturgeon M. , Bender L. , 2008: Hurricane Katrina winds measured by a buoy‐mounted sonic anemometer. J. Atmos. Oceanic Technol.,25, 607–616, doi:10.1175/2007JTECHO518.1.
Kaimal, J. C., Wyngaard J. C. , Izumi Y. , and Coté O. R. , 1972: Spectral characteristics of surface-layer turbulence. Quart. J. Roy. Meteor. Soc., 98, 563–589, doi:10.1002/qj.49709841707.
Katsaros, K. B., 1980: The aqueous thermal boundary layer. Bound.-Layer Meteor., 18, 107–127, doi:10.1007/BF00117914.
Katsaros, K. B., Donelan M. A. , and Drennan W. M. , 1993: Flux measurements from a SWATH ship in SWADE. J. Mar. Syst., 4, 117–132, doi:10.1016/0924-7963(93)90003-5.
Kolmogorov, A. N., 1941: The local structure of turbulence in incompressible viscous fluid for very large Reynolds numbers. Izv. Acad. Sci. USSR,30, 299–303.
Kudryavtsev, V. N., Makin V. K. , and Meirink J. F. , 2001: Simplified model of the air flow above waves. Bound.-Layer Meteor., 100, 63–90, doi:10.1023/A:1018914113697.
Le Boyer, C., Cambon G. , Daniault N. , Herbette S. , Le Cann B. , Marié L. , Morin P. , 2009: Observations of the Ushant tidal front in September 2007. Cont. Shelf Res.,29, 1026–1037, doi:10.1016/j.csr.2008.12.020.
Lecornu, F., and De Roeck Y.-H. , 2009: PREVIMER—Coastal observations and forecasts. Houille Blanche,1, 60–63, doi:10.1051/lhb:2009006.
Liu, W. T., Katsaros K. B. , and Businger J. A. , 1979: Bulk parameterization of air–sea exchanges of heat and water vapor including the molecular constraints at the interface. J. Atmos. Sci., 36, 1722–1735, doi:10.1175/1520-0469(1979)036<1722:BPOASE>2.0.CO;2.
Miller, S. D., Hristov T. S. , Edson J. B. , and Friehe C. A. , 2008: Platform motion effects on measurements of turbulence and air–sea exchange over the open ocean. J. Atmos. Oceanic Technol., 25, 1683–1694, doi:10.1175/2008JTECHO547.1.
Monin, A. S., and Obukhov A. M. , 1954: Basic laws of turbulent mixing in the surface layer of the atmosphere. Tr. Geofiz. Inst., Akad. Nauk SSSR, 24, 163–187.
Morisset, S., and Reverdin G. , 2011: Prototype drifter study: “Tag proto” (SMRULOCEAN). LOCEAN-UMPC Internal Rep., 38 pp.
Pedreros, R., Dardier G. , Dupuis H. , Graber H. C. , Drennan W. M. , Weill A. , Guérin C. , and Nacass P. , 2003: Momentum and heat fluxes via the eddy correlation method on the R/V L’Atalante and an ASIS buoy. J. Geophys. Res., 108, 3339, doi:10.1029/2002JC001449.
Popinet, S., Smith M. , and Stevens C. , 2004: Experimental and numerical study of the turbulence characteristics of airflow around a research vessel. J. Atmos. Oceanic Technol., 21, 1575–1589, doi:10.1175/1520-0426(2004)021<1575:EANSOT>2.0.CO;2.
Reverdin, G., and Coauthors, 2013: Surpact: A SMOS surface drifter for air-sea interaction. Oceanography, 26, 48–56, doi:10.5670/oceanog.2013.04.
Sahlée, E., Drennan W. M. , Potter H. , and Rebozo M. A. , 2012: Waves and air-sea fluxes from a drifting ASIS buoy during the Southern Ocean Gas Exchange experiment. J. Geophys. Res., 117, C08003, doi:10.1029/2012JC008032.
Semedo, A., Saetra Ø. , Rutgersson A. , Kahma K. K. , and Pettersson H. , 2009: Wave-induced wind in the marine boundary layer. J. Atmos. Sci., 66, 2256–2271, doi:10.1175/2009JAS3018.1.
Smedman, A., Högström U. , Sahleé E. , Drennan W. M. , Kahma K. K. , Pettersson H. , and Zhang F. , 2009: Observational study of marine atmospheric boundary layer characteristics during swell. J. Atmos. Sci., 66, 2747–2763, doi:10.1175/2009JAS2952.1.
Smith, S. D., 1988: Coefficients for sea surface wind stress, heat flux, and wind profiles as a function of wind speed and temperature. J. Geophys. Res., 93, 15 467–15 472, doi:10.1029/JC093iC12p15467.
Soloviev, Y. P., and Kudryavtsev V. N. , 2010: Wind-speed undulations over swell: Field experiment and interpretation. Bound.-Layer Meteor., 136, 341–363, doi:10.1007/s10546-010-9506-z.
Sullivan, P. P., Edson J. B. , Hristov T. , and McWilliams J. C. , 2008: Large-eddy simulations and observations of atmospheric marine boundary layers above nonequilibrium surface waves. J. Atmos. Sci., 65, 1225–1245, doi:10.1175/2007JAS2427.1.
Weill, A., and Coauthors, 2003: Toward a better determination of turbulent air–sea fluxes from several experiments. J. Climate, 16, 600–618, doi:10.1175/1520-0442(2003)016<0600:TABDOT>2.0.CO;2.
Weller, R. A., Bigorre S. P. , Jonathan J. L. , Ware D. , and Edson J. B. , 2012: A surface mooring for air–sea interaction research in the Gulf Stream. Part I: Mooring design and instrumentation. J. Atmos. Oceanic Technol., 29, 1363–1376, doi:10.1175/JTECH-D-12-00060.1.
Yelland, M. J., Moat B. I. , Pascal R. W. , and Berry D. I. , 2002: CFD model estimates of the airflow distortion over research ships and the impact on momentum flux measurements. J. Atmos. Oceanic Technol., 19, 1477–1499, doi:10.1175/1520-0426(2002)019<1477:CMEOTA>2.0.CO;2.