NOAA Technical Memorandum ERL ETL-XXX

BULK METEOROLOGICAL AND TURBULENT FLUX MEASUREMENTS FROM R/P FLIP DURING THE COASTAL OCEAN PROBING EXPERIMENT

Jeffrey E. Hare

Cooperative Institute for Research in Environmental Sciences

NOAA/The University of Colorado

Christopher W. Fairall

NOAA Environmental Technology Laboratory

Environmental Technology Laboratory

Boulder, Colorado

March 1998

NOTICE

Mention of a commercial company or product does not constitute an endorsement by NOAA/ETL. Use for publicity or advertising purposes of information from this publication or concerning proprietary products or the tests of such products is not authorized.

CONTENTS

ABSTRACT	1

1. INTRODUCTION	1

2. METHODS OF MEASURING TURBULENT FLUXES APPLIED IN COPE	4

2.1 Eddy Correlation or Covariance Method	4

2.2 Inertial-Dissipation Method	5

2.3 Bulk Method	9

3. FLUX SYSTEM IMPLEMENTATION	11

3.1 Instrumentation	11

3.2 R/P FLIP Motion Correction	12

3.3 Summary of the R/P FLIP Deployment	15

4. RESULTS	17

4.1 Processing	17

4.2 Filtering	18

4.3 Final Products	19

5. CONCLUSIONS	21

6. ACKNOWLEDGEMENTS	21

7. REFERENCES	21

FIGURES	27

APPENDIX A	85

Bulk Meteorological and Turbulent Flux Measurements from R/P FLIP During the Coastal Ocean Probing Experiment

Jeffrey E. Hare1 and Christopher W. Fairall

ABSTRACT. This report describes an investigation of the behavior of the coastal micrometeorology and air-sea transfer properties during the Coastal Ocean Probing Experiment (COPE) conducted off the Oregon coast in September and October 1995. Measurements were obtained with the Environmental Technology Laboratory's air-sea turbulent flux system installed aboard the R/P FLIP. A companion system was deployed on an additional platform, the R/V Snowgoose. The R/P FLIP flux system operated for nine days from September 20 to September 28 and included four different heights of instrumentation. However, no data were acquired from the lowest level of instrumentation. Ultrasonic anemometer and turbulence pressure data were obtained from nominal heights 6.6, 12.6, and 16.6 meters above the mean sea surface. Turbulent humidity information was logged from a device mounted at 12 meters. Mean sea surface temperature, air temperature and humidity, and solar and infrared radiative fluxes were also obtained. The data from this deployment have been processed into ten- minute data segments containing 49 mean and turbulence variables and are available in a variety of formats. The variables include bulk meteorology, turbulent fluxes and variances, and structure functions. The results show that over the entire deployment the average 10-m winds were approximately 6 ms-1 from the south, and the surface layer was approximately neutrally stable for the same period. The mean wind stress over the entire experiment was about 0.07 Nm-2, while the mean sensible and latent heat fluxes were approximately 12 and 39 Wm-2, respectively. Time series of the turbulent fluxes and mean meteorology are shown.

1. INTRODUCTION

In September of 1995 the NOAA Environmental Technology Laboratory (ETL) participated in the Coastal Ocean Probing Experiment (COPE). COPE is the second field experiment associated with the ETL Advanced Sensor Applications Program (ASAP), which is a joint Department of Defense (DoD)/NOAA investigation of remote sensing of the ocean surface and air-sea interaction in the coastal environment. An overview of COPE is provided in Kropfli and Clifford (1996), while a description of the initial ASAP experiment (San Clemente Oecan Probing Experiment or SCOPE) is found in Kropfli and Clifford (1994).

The main emphasis of COPE is the study of oceanic and atmospheric properties which affect the ocean surface as seen by remote sensors. Two research ships (the R/P FLIP and the R/VSnowgoose), several aircraft, and shore-based remote sensing systems were deployed in the vicinity of the coast of Tillamook, Oregon. The Research Platform FLIP (R/P FLIP) was moored in 150m deep water about 20km off the coast at 45o 45.2' N latitude and 124o 16.9' W longitude. This site was chosen due to the presence of tidally-forced internal waves established near the Columbia River outflow. These internal waves provide surface strain features which may be sampled by the remote sensors deployed during the experiment and by satellite radar. Investigations are underway to determine the modulation of the atmospheric surface layer turbulence by the presence of the internal waves (Otto et al. 1998). Details on the ETL remote sensing results from COPE can be found in Kropfli et al. (1998), Levinson and Banta (1998), Otto et al. (1998), Shaw (1996), Shaw and Churnside (1997a), Shaw and Churnside (1997b), Stanton and Ostrovsky (1998), Trevorrow (1998), Trokhimovski et al. (1995), Trokhimovski et al. (1998), Westwater et al. (1998a), and Westwater et al. (1998b).

R/P FLIP had three booms deployed for the wide assemblage of atmosphere and ocean instrumentation. Table 1 provides a brief summary of the participants and measurements on R/P FLIP and at other sites during COPE.

The ETL air-sea interaction group provided instrumentation on both of the research platforms for the measurement of the turbulent fluxes of heat, moisture and momentum over the coastal water. These flux measurements are integral for the interpretation of the remote sensor (for example, radar) measurements and for support to modeling efforts in the COPE program. In addition, measurements of radiative fluxes (upwelling and downwelling longwave and solar irradiance), mean air temperature and humidity, wind speed and direction, and bulk sea surface temperature (SST) were included in the deployment.

Wind stress (or momentum flux) over the ocean occurs as a result of the interaction of the wind with the surface, which is nearly always characterized by capillary waves, gravity waves, and surface currents. The wind stress induces the surface waves and some of the currents. In turn, the stress is modulated by the surface features, which are often not locally driven. This complex interaction between the lower atmosphere and upper ocean is not particularly well described by parameterizations such as the drag coefficient. Furthermore, centimeter wavelength radars receive return signals as a result of Bragg scattering from capillary waves and velocity shears on the surface, and the relationship between the radar power return and wind stress is poorly understood. In addition, the combination of radiative and heat fluxes drives the local ocean mixing which affects the surface features. Understanding the relationship between the atmospheric and oceanic processes is essential for interpretation of the remotely sensed observations such as those obtained during COPE and those obtained from observational satellites.

COPE is a comprehensive campaign designed to address the ambiguity in our current understanding of the above-mentioned processes and their effect on the surface through the use of remote sensors. The turbulent flux measurements described in this document are fundamental to the analysis of the remote sensor data. A companion document (Fairall and Hare, 1997)

TABLE 1. List of participants and sensors on R/P FLIP and other sites during COPE

R/P FLIP Sensors	Institution
Turbulent flux package	NOAA/ETL
Video spectral sensor	NOAA/ETL
Laser glint sensor	NOAA/ETL
Scanning 60 GHz radiometer	NOAA/ETL
Mailbox microwave radiometer	NOAA/ETL
CTD/ADCP	Naval Postgraduate School (NPS)
Microstructure profiler/current meters	NPS
Ku-band scatterometer	U. of Wash./Applied Physics Lab (UW/APL)
X-band short range radar	Johns Hopkins Univ./APL (JHU/APL)
Scanning laser slope gauge/wave wires	Woods Hole Oceanographic Inst. (WHOI)
Surface infrared imager	UW/APL
Buoy currents/temperature profiler	UW/APL
Laser wave gauge	UW/APL
Infrared radiometers	UW/APL
Wave measurement	UW/APL
5-band surface radiometer	UW/APL
-k sonar	Institute of Ocean Sciences (IOS), Canada
R/V Snowgoose Sensors	Institution
Turbulent flux package	NOAA/ETL
CTD/ADCP	University of Washington (UW)
Other Site Sensors	Institution
Ka-band Doppler radar (Onion Peak)	NOAA/ETL
X-band Doppler radar (Onion Peak)	NOAA/ETL
CODAR (Onion Peak)	NOAA/ETL
Rawinsondes (Arch Cape)	NOAA/ETL
Ceilometer (Arch Cape)	NOAA/ETL
Doppler lidar (Arch Cape)	NOAA/ETL
Buoy meteorological data (Cape Falcon)	National Data Buoy Center (NDBC)
Moored thermistor chains (Cape Falcon)	IOS
Wind profiler	NOAA/ETL (Astoria, Crescent City, Medford)
Wind profiler w/ RASS	NOAA/ETL (Ballston,Nestucca Bay,Newport)
CO2 Doppler lidar (NASA DC-8)	NOAA/ETL
Dual-channel wave radiometer(Blimp)	NOAA/ETL
37 GHz polarimeter (Blimp)	NOAA/ETL
2-10 m infrared radiometers (hor/vert)	NOAA/ETL
Microwave scatterometer (Blimp)	UW/APL
Radar altimeter	UW/APL
Imaging infrared radiometer	UW/APL

describes the deployment and processing of the turbulence data obtained on the R/VSnowgoose. In this report we describe the instrumentation, processing techniques, and results from the R/P FLIP measurements.

In Section 2, we provide some details about the methods used to compute the turbulent fluxes. Section 3 is a description of the ETL instrumentation and motion correction algorithm used on the COPE data set.

2. METHODS OF MEASURING TURBULENT FLUXES APPLIED IN COPE

2.1 Eddy Correlation or Covariance Method

The well-established direct covariance or eddy-correlation flux measurement technique is the standard for surface layer flux measurements as it is a direct computation of the covariance at the height of measurement. In contrast, the inertial-dissipation technique provides an estimate of the flux at the surface (see Section 2.2). By definition, the kinematic vertical flux of momentum is the covariance , the kinematic vertical temperature flux is , and the kinematic vertical moisture flux is the covariance . In these definitions, and are the turbulent perturbations of the streamwise and vertical components of the wind, respectively; and are the turbulent perturbations of the temperature and humidity, respectively; and the overline denotes an average over a theoretically infinite ensemble of turbulent eddies. We can make estimates of the true ensemble average to within some statistical sampling uncertainty with the appropriate selection of an averaging time interval (Mahrt et al. 1996; Lenschow et al. 1994).

In analogous fashion, we define the turbulent fluxes of momentum, sensible, and latent heat, respectively,

where is the air density, is the specific heat of air, and is the latent heat of vaporization of water. These expressions demonstrate the relative ease with which eddy correlation estimates of the turbulent fluxes can be made. It appears that all that is necessary to obtain the turbulent

fluxes is the computation of the covariances. However, on a seagoing research vessel the measurement of the velocity components necessary to compute the covariances becomes difficult

due to the motion of the platform and flow distortion. In the case of a ship, the anemometer is fixed with respect to the frame of the ship and moves with it, requiring significant correction to the measured velocity components. Although the R/P FLIP is a floating platform, it is not subject to the relatively large motions of the typical research vessel at sea. Furthermore, flow distortion around R/P FLIP is much less than that imposed by the typical research vessel (Ching, 1976). The profiles of R/P FLIP's hull and the boom assembly used during the COPE deployment allow us to make turbulence measurements in a relatively low distortion environment. Note that the velocities associated with the wave-driven motion of R/P FLIP were measured during COPE and have been removed from the wind component measurements. Finally, the latent heat flux defined in (3) must be corrected for the effect of the relative displacement of the sonic anemometer and the infrared hygrometer according to the theory of Kristensen et al. (1997).

2.2 Inertial-Dissipation Method

Measurements of the earth-referenced velocity components necessary to compute the covariances in (1)-(3) from a research ship require correction for platform motion (Fujitani, 1985; Edson et al., 1998). This necessity dictates the added expense and complication of measurement of the accelerations and rotations of the platform. The platform-motion corrections are obtained in part from integration of the measured accelerations, which makes the algorithm more complex and increases the uncertainty of the flux estimate.

The inertial-dissipation (ID) method has been used extensively on research vessels at sea (Anderson, 1993; Fairall et al., 1990; Edson et al., 1991; Edson et al., 1997; Yelland et al., 1994) as it represents a reasonable compromise between the relatively complicated direct covariance method and the bulk parameterizations (see Section 2.3). This technique is based on Monin- Obukhov Similarity (MOS) principles and therefore relies on empirically-derived dimensionless functions. These functions are typically determined using covariance methods over land surfaces, and are either assumed or verified to be applicable over the sea surface (Fairall and Larsen, 1986; Edson and Fairall, 1997). The ID technique is attractive because it utilizes the high-frequency portion of the velocity and scalar spectra, where platform motions are negligible compared to the turbulence level. The ID technique has also been shown to be less sensitive to flow distortion than the eddy correlation method (Edson et al., 1991). Because it is based on autovariance statistics, this technique has the advantage that time averaging tends to converge more rapidly than the eddy covariance statistics. The ID method relies on approximations to the variance budgets expressed as empirical functions of stability which are only accurate in the mean. However, this technique produces a true turbulence statistic obtained from the measured frequency spectrum, providing a more direct estimate of the flux than the bulk method. For a more detailed discussion, the reader is directed to the papers by Kaimal et al. (1968), Fairall et al. (1990), Edson et al. (1991), and Fairall and Hare (1997).

Direct dissipation measurements are rarely made over the ocean because the sensors required to make such very high frequency measurements typically cannot withstand the marine environment. The velocity sensors (hot wires) and temperature sensors (cold wires) required to make direct dissipation measurements are expensive, difficult to calibrate, fragile, and temperamental. They have not historically performed well in the marine environment. The ID method makes use of rugged instruments with stable calibrations, such as the sonic anemometer and the infrared hygrometer. Therefore, all that is necessary to make reasonably accurate measurement of fluxes at sea are the above mentioned instruments and a logging/processing computer (Fairall et al., 1990).

The ID method works remarkably well to obtain the momentum flux (Fairall et al., 1990). Recent research indicates that the method works best above the wave-boundary layer (Edson and Fairall, 1997) because MOS is violated in the lowest layer above the waves. Although the current technology allows for salt-free use of the sonic temperature signal, the noise levels are relatively high (Larsen et al., 1993). Therefore, the restricted sensitivity of the sonic anemometer/ thermometer limits our ability to make the rather subtle sensible heat flux measurements within the marine surface layer (Schotanus et al., 1983; Larsen et al., 1993). Infrared hygrometric measurement has improved dramatically within the last decade (Fairall and Young, 1991). However, sensitivity is limited if the hygrometer windows become obscured by sea salt or rain. Regular cleaning of the unit can be accomplished by hand or with a standard garden hose and copper tubing properly positioned to spray onto the window surfaces.

The values of the vertical surface fluxes of momentum, sensible heat, and latent heat can be related to the MOS surface layer scaling parameters for velocity (), temperature (), and humidity () as

where the subscript 'o' denotes that the fluxes are at the surface. These scaling parameters are also used to define the Obukhov stability length

where is the von Kármán constant (nominally 0.4), is the mean temperature, and is the gravitational acceleration. The Obukhov length can be viewed as the scale which is proportional to the height above the surface where buoyancy production and mechanical production of turbulence are equal. By convention, this scale is negative for unstable surface conditions and positive for a locally stable situation.

Within the inertial subrange of isotropic turbulence, the one-dimensional variance wavenumber spectra for velocity, temperature, and humidity can be expressed as functions of wavenumber magnitude, , as (Fairall and Larsen, 1986)

where is the structure function parameter for velocity (), temperature (), or humidity (). Using Taylor's hypothesis, we can obtain the frequency spectra as in

where is the mean wind speed. The structure function parameters are defined in terms of the difference in the meteorological variable at locations and , where is a spatial separation within the inertial subrange by

Since structure function parameters obey MOS (Wyngaard and Clifford, 1978; Fairall et al., 1990), we can express the scaling parameters for velocity, temperature, and humidity in terms of the structure function parameters:

where is the height above the surface, are the empirically derived dimensionless structure functions, and is the dimensionless height. Using the frequency spectra to find the scaling parameters described in (9), we can compute the scaling parameters in (11) and therefore arrive at the value of the surface fluxes in (4)-(6).

In practice, corrections must be made to the spectra to counteract the effects of spatial and temporal averaging of the velocity and temperature signals from the sonic anemometers. Time averaging error is due to the nonsimultaneous emission of the sonic pulses used to compute the sonic temperature. An additional spectral correction is necessary if the sampling interval is less than the integral time scale (Kaimal et al., 1968). A further correction to the temperature signal from the sonic anemometer is required because of velocity cross-talk. After the measured spectra have been corrected as described above, we obtain the structure function parameters using

where the summation is over the specified limits which are within the inertial subrange, is the total number of frequency bins averaged, and is the Wyngaard and Clifford (1977) correction for anisotropy and inaccuracies in using Taylor's hypothesis (Edson et al., 1991; Hill, 1995). For example, the correction for the streamwise velocity spectrum is:

where are the variances of the wind components . The correction expressed in (13) represents on average less than 1% reduction in the spectral estimates for the entire COPE analysis.

For the COPE data set, each spectrum was examined for the -5/3 slope inertial subrange criteria prior to frequency range selection in the averaging described in (12). This test provides confidence that the estimates for the structure function parameters have been obtained from portions of the streamwise velocity, temperature, and humidity spectra which lie within the inertial subrange.

To describe the dimensionless structure function parameters used in (11), we implement the recent developments from Edson and Fairall (1997)

and

where the constant (4.02) is a result of the Corrsin relation (Fairall et al., 1990), is the Kolmogorov constant, which we assume is 0.52 (Fairall et al., 1990), = is the Obukhov- Corrsin constant which we assume equals 0.8, and is the von Kármán constant for which we assume the value 0.4.

2.3 Bulk Method

The bulk method of determining turbulent fluxes has been used extensively on historical data sets from ships and buoys at sea (Large and Pond, 1981). Furthermore, bulk algorithms are used to parameterize the lower boundary conditions for a wide variety of atmospheric models (Garratt, 1977). Therefore, although the bulk method is not a direct measurement of the turbulence, it is an important component of any comprehensive turbulent flux measurement campaign. Future improvements in the bulk parameterizations of the turbulent fluxes over the sea are necessary to provide realistic estimates of the wind stress and heat fluxes for use in modelling atmospheric flow in those regimes.

An extensive set of literature has been developed around the bulk method (Smith, 1980; Blanc, 1987; Smith, 1988; Geernaert, 1990). We will not provide details on the method here, but refer the reader to the descriptions in the literature. For the COPE data set, we employed the TOGA COARE Bulk Flux Algorithm (Fairall et al., 1996) which follows MOS and includes separate models for the ocean's cool skin and diurnal warm layer. The basic structure of the algorithm is based on the Liu-Katsaros-Businger (Liu et al., 1979) approach with modifications to the specification of the roughness/stress relationship, the inclusion of a gustiness velocity to account for boundary layer scale variability, improved functions to describe the convective limit, computation of the contributions to the heat flux as a result of local precipitation, and correction for the Webb effect (Webb et al., 1980). It is noted that the TOGA COARE Bulk Flux Algorithm is particularly well-suited for subtropical marine environments. However, the routine is also sufficiently general and is therefore favorably applied to the COPE data set.

The standard bulk expressions for the wind stress components and scalar heat fluxes are

where , , are the bulk transfer coefficients for stress, sensible heat, and latent heat, respectively; is the potential temperature, is the water vapor mixing ratio, is one of the horizontal wind components relative to the fixed earth. Each of the preceding quantities are measured at some atmospheric reference height, . In addition, is the average wind speed; is the density of air; is the sea surface interface (skin) temperature; is one of the surface current components; and is the interfacial value of the water vapor mixing ratio that is computed from the saturation mixing ratio for pure water at the SST as in

where the factor 0.98 takes into account the reduction in vapor pressure caused by a typical salinity of 34 parts per thousand. Note that

where is the air temperature and is the relative humidity, both obtained at .

The measurement of SST presents a special challenge for bulk flux measurement. Strictly speaking, the sea surface temperature required in (19) is the interfacial (skin) temperature of the water which is in direct contact with the air. The sensible, latent, and longwave radiative fluxes are realized within a fraction of a millimeter of the surface. This heat flux results in a "cool skin" (Woodcock, 1941; Fairall et al., 1996) which can be 0.2-0.5 K cooler than the bulk water immediately below. Typical bulk thermometers placed in the water are usually unable to resolve this thin layer due to their physical size and therefore can only characterize the bulk temperature. In addition, about half of the incoming solar radiation is absorbed in the upper meter of the ocean, so there are substantial diurnal variations in the temperature profile of the upper few meters. The details of this profile are determined by the solar absorption characteristics of the water, the solar time of day, cloudiness, and the convective and wind driven turbulent mixing in the ocean mixed layer (Fairall et al., 1996). A properly calibrated and corrected radiative thermometer can measure the actual interface temperature unambiguously (Emery et al., 1994). Unlike bulk sensors, radiative SST sensors do not require corrections for the cool skin and warm layer effects. The cool skin and warm layer algorithms require additional data input: solar and infrared radiative fluxes, and the sensor depth.

Details on the computation of the bulk transfer coefficients in (18)-(20) and on the TOGA COARE Bulk Flux Algorithm can be found in Fairall et al. (1996).

3. FLUX SYSTEM IMPLEMENTATION

3.1 Instrumentation

As with the R/V Snowgoose flux package (Fairall and Hare, 1997), the heart of the turbulent flux measurement system is the sonic anemometer/thermometer and an infrared fast response hygrometer (Cerni et al., 1987; Fairall and Young, 1991). Both instruments have been specifically selected for their ability to withstand the rigors of the marine environment (Fairall et al., 1979). In addition, a platform-motion measuring system is required for covariance flux estimation (see Section 3.2; Hare et al., 1992; Edson et al., 1998). This motion system (motion- pak) consists of a set of triaxial mounted accelerometers and a set of triaxial mounted angular rate sensors in a single housing. R/P FLIP's gyrocompass was used to obtain the heading information. For a ship deployment, the anemometer, hygrometer, and motion system are typically mounted as close together as possible on a mast near the ship's bow. For the R/P FLIP deployment during COPE, a 20m mast was deployed from a port side boom (see Figure 1). This mast, which is 20 meters away from R/P FLIP, is in a nearly zero flow distortion region. Furthermore, the length of the mast enabled us to mount four sonic anemometers at heights of 4m, 6.6m, 12.6m, and 16.6m above the mean sea surface.

A fast-response infrared hygrometer (Ophir Corporation IR-2000) was mounted on the mast at a height of 12m. For mean air temperature and humidity a Väisälä temperature and relative humidity sensor (HMP-35C) was mounted on the mast at 12m. Uplooking and downlooking pyranometers (Epply PSP shortwave) and pyrgeometers (Eppley PIR infrared) were deployed on the R/P FLIP face boom during COPE. At the beginning and end of the experiment, all four of these units were positioned upward for intercomparison and calibration. Mean bulk SST was sampled with a YSI 46040 thermistor mounted in a brass housing sealed into a length of tygon tubing (named the ETL "sea snake"). This measurement was intercompared and calibrated with the bulk near-surface measurements of the group from the Naval Postgraduate School. Finally, a Global Positioning System (GPS) receiver was deployed for recording a standard time stamp. Table 2 provides a list of the instrumentation deployed on R/P FLIP by the ETL Flux Group during COPE. A set of six wave height measuring wires was installed at the base of the mast, and four turbulent pressure sensors were mounted at the heights of the sonic anemometers. The description of these instruments and their data analysis will be provided in a later document.

Mean meteorological variables (SST, infrared and solar irradiance, air temperature and humidity) were sampled by a Campbell 21X Datalogger and transfered via serial data line to a personal computer for storage. The analog signals of the motion-pak, turbulent pressure, and

TABLE 2. List of in situ and near-surface instruments deployed by ETL aboard the R/P FLIP during COPE.

Instrument	Type	Measurement	Information
Thermometer	Väisälä HMP-35C	Air temperature,	Mounted at 11m
Hygrometer	Väisälä HMP-35C	Mean humidity,	Mounted at 11m
Thermistor	YSI-46040	SST,	Sea snake
Pyranometer	Eppley PSP	Solar irradiance,	Up/Down
Pyrgeometer	Eppley PIR	Infrared irradiance,	Up/Down
Sonic anemometer /thermometer	Gill Solent	Turbulent wind, Temperature,	4 levels

= 20.83 Hz
Infrared hygrometer	Ophir IR-2000	Turbulent moisture,	Mounted at 12m

= 20 Hz
GPS	Lorance receiver	Time stamp	Ship speed/course
Gyrocompass	R/P FLIP	Heading,
Motion sensor	Systron-Donner Motion-pak	Accelerations Rotation rates,	Mounted at 9m

= 20 Hz
Microbarosensor	ETL (N-B probe)	Turbulent pressure,	4 levels

wave-wires were digitized at 20 Hz by a Campbell 21X Datalogger, and the resulting serial data were made available via RS232. Fast-response signals (all sonic wind components and

temperature, humidity, platform accelerations and rates of rotation, pressure, wave wires, GPS) were logged using a Hewlett-Packard workstation (HP9000 Model 745i/50) with the HP-Unix operating system and a SCSI serial data interface. This computer is capable of logging the incoming data at the required rate and provides the convenience of the multi-tasking environment.

3.2 R/P FLIP Motion Correction

R/P FLIP has been used extensively for atmospheric and oceanic boundary layer study (for example, Pond et al., 1971; Large and Pond., 1981) A variety of approaches have been used to correct wind sensors for platform motion. True inertial navigation systems (Axford, 1968) are standard for research aircraft. These systems are expensive and subject to the so-called Schuler oscillation, so simpler techniques have been sought for ships where the platform mean vertical velocity is unambiguously zero. The basic approach we are using follows that of Fujitani (1985) and is similar to the approach of Anctil et al. (1994). Details on the methods used on the COPE data set can be found in Edson et al. (1998). The true wind vector (i.e., uncontaminated by motion) can be written as

where is the desired wind velocity vector in the fixed (earth) reference coordinate system,

is the measured (sonic anemometer) wind velocity vector in the platform frame of reference, is the coordinate transformation matrix for rotation of the platform coordinate system to the reference coordinates, is the angular velocity vector of the platform coordinate system, is the position vector of the wind sensor with respect to motion-pak, and is the translational velocity vector at the location of the motion measurement. Note that this last term includes not only the translational velocities of the ship (heave, surge, and sway), but also the apparent translational velocities sensed by the measurement package as a result of angular rotations of the ship about the center of motion.

In order to find the coordinate transformation matrix in (24), we require three angular variables (roll, ; pitch, ; and yaw, ) to describe the platform's orientation with respect to the earth frame of reference and the angular velocity vector in the platform coordinate frame. We can define

to describe the time rate of change of the platform orientation where the sign of the yaw is defined for the left-handed convention of the gyrocompass, and the other angular rates' signs are defined for right-handed rotation. The overdots indicate the time derivative of the variables in (25). We note here that the cross-product term in (24) is typically small for a ship if the sonic anemometer and motion-pak system are mounted near one another (i.e. the vector components of are small). Because R/P FLIP's angular rotations are small, this cross-product is neglible for the COPE data set. The rotational coordinate transformation matrix is defined as

where the trigonometric manipulations are determined from the combination of three separate small-angle rotations of the platform coordinate system about the fixed earth coordinates.

The motion-pak system deployed on R/P FLIP provides us with the rotation rates on the right side of (25) as well as the accelerations in the platform frame of reference

where the first term on the right side is the acceleration components in the platform coordinate system and the second term is the gravitational contribution due to tilting of the platform along the roll and pitch axes. Here, we define the , , and axes as along the bow-stern, along the port-starboard, and upward directions, respectively. These tilt-induced accelerations will eventually need to be removed before we intergrate the accelerations to compute the platform velocities.

Examination of (24)-(27) reveals the difficulty of using the motion-pak signals to correct for platform motion: we need to compute the roll, pitch, and yaw angles of the platform with respect to the fixed earth frame of reference. To summarize Edson et al. (1998), we can illustrate the technique employed using Laplace transform notation and complimentary filtering as in the following expressions:

where represents the differentiation operator, is a time constant, is the heading information from the R/P FLIP compass, and we have made small-angle approximations. The first terms on the right side of the expressions in (28)-(29) can be interpreted as the low frequency tilts deduced from the accelerometers in the motion measurement system. The second term represents an integration of the angular rates obtained from the measurements. In practice, the multiplier outside of the brackets on the right side of the expressions in (28)-(30) can be represented with a first-order digital Butterworth filter with a cutoff frequency, , which is a function of the time step between samples and the time constant as

The choice of the time constant is dependent on the platform response to the wave field. For R/P FLIP's motion, we use a time constant

where 1/= 100 seconds.

The translational velocities are found by integrating the accelerations in (27) after the tilt induced gravitational signal has been removed. In effect, the translational velocities are separated into high-pass and low-pass components:

The highpass components are computed by rotating the accelerations into the fixed frame using the transformation matrix, subtracting out the gravitational vector, integrating the remainder, and highpass filtering the resulting velocities. The lowpass components are found from GPS measurements of the speed over ground, or in the case of R/P FLIP, from measurement of the surface current.

Finally, because the sonic anemometer/thermometer sampling rate is 20.83 Hz, this signal was digitally re-sampled at 20 Hz to be compatible with the motion-pak system and infrared hygrometer sampling rates.

3.3 Summary of the R/P FLIP Deployment

Table 3 shows a time line for operation of the various aspects of the ETL R/P FLIP deployment during COPE. The following is an abbreviated description of the operations on the R/P FLIP over the course of the COPE deployment:

The first week on board was devoted mostly to installing sensors, running cables, and connecting to data acquisition systems. Work was slowed the first few days by heavy fog and drizzle with moderately strong winds. Some measurements began on Sept. 13; about 90% of the instruments were on line by September 18. There were several overflights by aircraft from the

TABLE 3. Time line of operation of ETL various systems on R/P FLIP during COPE. A small "x" indicates that the system was in partial operation and/or was only functional during a portion of the day indicated. A large "X" denotes full system operations throughout the day.

Julian Day (Sept 15 - 28)
258	259	260	261	262	263	264	265	266	267	268	269	270	271
Slow Means	x	x	x	X	X	X	X	x	x	x	X	X	X	x
Turbulence	x	x	x	X	X	X	X	x	x
Pressure	X	X	X	x	x	x	X	X	x
Wave wires	x	X	x	X	X	X

University of Wyoming and JPL, plus one by the blimp with NOAA/ETL and University of Washington APL scientists on board. R/V Snowgoose made current and CTD measurements closer to shore and in transects from shore out to R/P FLIP. Conditions were variable with winds from 0 to 11 m/s; surface signatures of internal wave packets were observed almost daily.

The week of September 18 - 25 was devoted to intensive measurements. There were several overflights by the NASA/JPL DC-8 with the NOAA/ETL Doppler lidar on board plus daily flights by the blimp. R/V Snowgoose made two visits to R/P FLIP to exchange personnel and make CTD transects. The lowest level sonic anemometer failed to operate before data acquisition began.

A near-surface buoy was used by UW APL scientists to obtain thermal and salinity structure in the upper 2m of the ocean. UW and ETL scientists deployed a total of eight narrowband thermal radiometers, four broadband flux radiometers, and a thermal imaging system to investigate the oceanic cool skin and its dynamics. These systems operated effectively all week, and we expect them to yield the most comprehensive cool-skin data ever obtained.

There were recurring problems with unfavorable environmental conditions. For example, two days of strong easterly winds led to a plague of insects and strong wakes from R/P FLIP on the turbulence system.

Despite these inconveniences, an unprecedented data set on internal waves, turbulence, and ocean surface properties was obtained. Internal wave packet signatures have been clearly observed with the oceanographic measurements, radar, and visually (see Otto et al., 1998). The data acquisition system ran nearly continuously from 1600 hours on September 20 to 1600 hours on September 28.

An unusually strong storm with 17m/s winds struck R/P FLIP on September 26. This storm coincided with two ERS-1 overpasses. The storm destroyed the wave-wire system and dragged R/P FLIP's seaward anchor, which caused the platform to rotate into an unfavorable and potentially dangerous orientation. In addition, the storm forced the failure of the sonic anemometer at 6.6 meters. With three more storms of unknown intensity anticipated from the west, it was decided that prudence demanded a cessation of most of the scientific operations by September 28.

4. RESULTS

4.1 Processing

In theory, accurate measurement of turbulent fluxes requires an average of an infinite ensemble of samples. In order to estimate the turbulent fluxes from a point measurement such as that of the COPE system, it is necessary to average over time sufficient to provide a reasonable representation of the ensemble. The averaging time required to estimate the fluxes depends on the accuracy required, the intensity level of the turbulence, and the integral time scale (Wyngaard, 1973; Mahrt et al., 1996). Therefore, the averaging time must be of sufficient duration to accurately sample the largest scales, but short enough to limit the non-stationary effects of the diurnal cycle and mesoscale variability.

According to Fairall et al. (1990), the majority of the uncertainty associated with the eddy covariance hourly statistics under ideal conditions is due to atmospheric variability and is on the order of 20% to 50%. This uncertainty dictates that the fluxes be computed over shorter segments of the hour. In effect, these shorter segments represent a trend removal in the flux estimates which can be averaged over the hour to provide the hourly estimate. As it is necessary to sufficiently sample the largest eddies in the field, the shortest segments from which to estimate fluxes is on the order of ten minutes (Panofsky and Dutton, 1984). This is the approach taken for the COPE data set for the covariance, ID, and bulk flux estimates.

For the ID fluxes, a 8192-point FFT was applied to the ten-minute, zero-mean streamwise wind component, the fast humidity information, and the sonic temperature signal. After subtracting the noise level, the resulting spectrum was then smoothed in frequency space to provide a nominally 33-point spectrum from which to estimate the spectral density. The inertial subrange was tested for each spectrum based on the -5/3 power law prior to computation of the structure function parameters in (9). The resulting width of the inertial subrange frequencies is 0.7-3.6 Hz for the hygrometric data, 0.35-3 Hz for the sonic temperature, and 0.6-3.5 Hz for the streamwise sonic wind signal. Each of these spectra was visually inspected to ensure data quality in the inertial subrange.

Finally, based on comparison with other COPE investigator's air temperature instruments, the ETL mean air temperature measurements were reduced by 0.5 degrees before inclusion in the flux algorithms.

4.2 Filtering

In order to reduce uncertainty and to provide for the cleanest possible products, it is necessary to conditionally filter the raw data set for periods wherein the measured data quality does not meet certain criteria. For example, wind directions passing through R/P FLIP's structure or the boom/mast are rejected. By examination of the ratio of the standard deviation of the vertical velocity (or sonic temperature) over the mean wind speed vs. the mean wind direction, it is easily seen where significant flow distortion can occur. For the COPE data set, the eliminated wind directions are those from true 300o-120o. This eliminates approximately 25% of the 1116 total ten-minute estimates. In addition, the standard deviation and mean of the clear channel count output of the Ophir hygrometer is a decent indicator of rain, sea spray, or window obscurification. When the standard deviation of this figure is above a chosen threshold (about 25) or when the mean drops below a certain number of counts (about 2500), any estimates of latent heat flux are eliminated and other fields are suspect. Of course, these diagnostics indicate signal loss in the hygrometric data, but they are also useful for interpreting spurious data points in other fields. For example, a large standard deviation of the clear channel counts may indicate strong rainfall, which may indicate potentially inaccurate stress estimates due to rain contamination in the sonic. Table 4 provides a composite of the number of computed fluxes from the various methods after filtering.

TABLE 4. Total number of filtered estimates for each of the measured fluxes at each indicated height and method. The total pre-filtered number of estimates at a given height is 1116.

Covariance	Momentum Flux	Sensible Heat Flux	Latent Heat Flux
Level 3 (16.6m)	757	740	601
Level 2 (12.6m)	768	748	589
Level 1 (6.6m)	495	493	444
Inertial-dissipation	Momentum Flux	Sensible Heat Flux	Latent Heat Flux
Level 3	792	761	602
Level 2	789	760	591
Level 1	504	500	443
Bulk	Momentum Flux	Sensible Heat Flux	Latent Heat Flux
Level 2	840	840	840

4.3 Final Products

The complete time series of data products is available on the ETL System Demonstration

and Integration Division 4 (ET4) Data Management System (DMS) as an ASCII, Quatro-Pro, or NetCDF file. A summary printout and description of a subset of this information is Appendix A.

The ten-minute time series of bulk meteorological data and flux variables is given in Figures 2-6 for each day of the R/P FLIP deployment during COPE. The covariance estimates of momentum flux are systematically lower than those of either the ID or bulk methods. This can be seen very clearly in Figure 2f, which is the plot for day 268. The limitations and differences between the three flux estimation techniques has been outlined in the discussion in Section 2. Figure 3 shows that during high wind events, the ID sensible heat flux estimates diverge from the bulk and covariance values. This can be seen most clearly in the latter hours (UTC) of Julian day 269 into the early morning of day 270 (Figures 3g-h). Also, differences between the bulk estimates of latent heat flux and the other methods are seen throughout the time series in Figure 4. The mean meteorological variables: air and sea surface temperatures, wind speed and direction, and relative humidity are shown in Figure 5. Finally, the downwelling infrared and solar radiative fluxes are shown in Figure 6.

Figure 7 shows the one-hour averaged covariance wind stress and heat flux estimates at each of the three levels from COPE. Similarly, in Figure 8 we have plotted the one-hour averaged time series of the ID estimates of the fluxes for all levels. Comparison of the estimates of the sensible heat flux in Figure 7b with the ID flux in Figure 8b shows clearly the relatively large scatter of the covariance estimates. In addition, we see that the ID estimates of all of the fluxes (Figure 8a-c) show a relatively strong dependence on height above the surface compared to the covariance estimates (Figure 7a-c). Although it is not clear what causes this height dependence in the estimates, it is possible that the presence of the underlying waves affects the spectral estimates (Antonia and Chambers, 1980; Edson et al., 1997).

In order to compare the results obtained from the different methods, we present Figure 9 which shows the one-hour averaged time series of each of the turbulent fluxes. In these plots, the covariance and ID fluxes have been averaged with height to result in a single estimate of the flux for each hour. Figure 9a shows that the covariance stress estimates appear to be biased near zero for light wind conditions. One hypothesis for this observation is that momentum is being imparted to the atmosphere from the waves, which offsets the downward flux (Edson et al., 1997; Edson and Fairall, 1997).

The mean estimates of the meteorological variables for the entire R/P FLIP data set are found in Table 5. Note that the overall air-sea temperature difference was very small on average for the COPE deployment, implying near-neutral stability. Significant discrepancies between the mean bulk heat flux estimates and those obtained from other methods is shown very clearly in this table. Also, the lowest level covariance and ID sensible heat flux estimates differ from those computed at the other levels. As noted above, the height dependence of the ID momentum flux,

TABLE 5. Mean values of the meteorological parameters over the course of the entire COPE deployment.

Meteorological Parameter	Mean	Units
Wind speed (sonic, level 3)	6.6	ms-1
Wind speed (sonic, level 2)	5.8	ms-1
Wind speed (sonic, level 1)	5.4	ms-1
Mean wind direction (all levels)	189.2	deg true
Sea surface temperature (sea snake)	17.0	oC
Air temperature (12m)	16.9	oC
Sea surface humidity	11.7	g/kg
Air humidity (12m)	9.5	g/kg
Relative humidity (12m)	76.8	%
Sensible heat flux (covariance, level 3)	14.8	Wm-2
Sensible heat flux (covariance, level 2)	10.4	Wm-2
Sensible heat flux (covariance, level 1)	9.4	Wm-2
Sensible heat flux (ID,level 3)	13.9	Wm-2
Sensible heat flux (ID,level 2)	13.8	Wm-2
Sensible heat flux (ID,level 1)	16.3	Wm-2
Sensible heat flux (bulk, level 2)	5.7	Wm-2
Latent heat flux (covariance, level 3)	39.0	Wm-2
Latent heat flux (covariance, level 2)	34.3	Wm-2
Latent heat flux (covariance, level 1)	39.2	Wm-2
Latent heat flux (ID, level 3)	36.6	Wm-2
Latent heat flux (ID, level 2)	45.0	Wm-2
Latent heat flux (ID, level 1)	54.4	Wm-2
Latent heat flux (bulk, level 2)	27.4	Wm-2
Momentum flux (covariance, level 3)	0.056	Nm-2
Momentum flux (covariance, level 2)	0.069	Nm-2
Momentum flux (covariance, level 1)	0.063	Nm-2
Momentum flux (ID, level 3)	0.067	Nm-2
Momentum flux (ID, level 2)	0.086	Nm-2
Momentum flux (ID, level 1)	0.095	Nm-2
Momentum flux (bulk, level 2)	0.058	Nm-2
Downward solar flux	133.8	Wm-2
Downward infrared flux	352.7	Wm-2
Upward solar flux	17.9	Wm-2
Upward infrared flux	396.6	Wm-2

sensible heat flux, and latent heat flux estimates is demonstrated very clearly over the course of the experiment.

5. CONCLUSIONS

The Coastal Ocean Probing Experiment (COPE) was implemented in September of 1995 and was designed to further elucidate our understanding of the air-sea processes responsible for the signatures observed with a variety of remote sensing systems. As part of this interpretive effort, the ETL turbulent flux system was operated for a period of nine days on the R/P FLIP during COPE. A similar system which is described in Fairall and Hare (1997) was deployed on the R/V Snowgoose. Four levels of flux instrumentation were installed on the R/P FLIP mast, but only the upper three sonic anemometer/thermometers were functional during the experiment. All measured velocity components were corrected for platform motion. Three different methods were applied during postprocessing of the data set to obtain ten-minute averaged estimates of the turbulent fluxes of momentum, heat, and moisture: the direct eddy-covariance method, the inertial-dissipation technique, and the TOGA COARE bulk flux algorithm. These products were filtered for relative wind direction and significant rain and/or sea spray and are available via the World Wide Web on the ETL Data Management System. Some inconsistencies in the data set are hypothesized to be the result of the influence of the underlying wave field, but this is a focus for future research efforts.

6. ACKNOWLEDGEMENTS

This document has been generated as part of the joint NOAA/DoD Advanced Sensor Applications Program. The authors wish to acknowledge the significant contributions of David Gregg (formerly of ETL), James Wilczak of ETL, James Edson of the Woods Hole Oceanographic Institution, Rick Chapman of the Johns Hopkins University Applied Physics Laboratory, Carl Friehe of the University of California at Irvine, Gary Wick (formerly of the University of Colorado), Timothy Stanton of the Naval Postgraduate School, and the captain and crew of R/P FLIP. In addition, the authors thank the careful reviews of this document by Robert Banta and Robert Kropfli, both of ETL.

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FIGURES