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    Rooftop laboratory tank results: (a) time series of clear and black flux-plate signals during clear-sky, partly cloudy, and overcast conditions, respectively. In the daytime, the black flux-plate signal is depressed more than the clear flux plate because of its greater solar absorption, (b) a correlation plot of MSF vs an Eppley black-and-white pyranometer, and (c) a time series for clear-sky conditions.

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    Lake Mendota environment and typical drift leg. Lake shoreline is indicated by the heavy solid line. Date and time of the drift leg are indicated as are MSFs used and mean values of variables measured onboard the research vessel. Auxiliary radiation and meteorological measurements are available from the ISIS and ASOS stations, respectively.

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    Lake Mendota drift leg time series at night. (top) MSF 1-Hz flux data as well as latent, sensible, and net longwave fluxes determined using reference instrumentation with the COARE version 2.6a bulk aerodynamic algorithm. (bottom) The 1-Hz temperature data from the MSF and bulk water PRT as well as the estimated water skin temperature from the COARE version 2.6a algorithm.

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    Lake Mendota drift leg time series at night. (a) MSF 1-Hz flux data. Flux plates are definitely submerged by wave action at 11.662 and 11.681 UTC; recovery is complete. The 11.651 submergence event is less clear. (b) MSF 1-Hz air, water, and flux-plate temperatures. The 11.662 submergence event is accompanied by water washing completely over the MSF affecting air temperature measurement. Wash-over events are used to diagnose flux-plate submergence events for removal from the data.

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    Lake Mendota drift leg time series at night. (top) MSF 1-Hz flux data and 1-min means of the reference net surface heat flux. Flux plates are submerged by wave action at 1.900, 1.903, 1.916, 1.922, 1.930, and 1.938 h; recovery is incomplete. Foam covers the flux plates from 1.903 to 1.916, reducing sensor float–measured fluxes. (bottom) Submergence sensor 1-Hz data indicating foam and submergence events can be detected. A value of ≈1.0 indicates NOT submerged.

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    Lake Mendota drift leg for a 2-h period near sunset. (a) Time series showing good correlation between MSF and turbulent fluxes (COARE version 2.6a). (b) Correlation plot of net solar flux. (c) MSF net flux as compared to that derived using several bulk algorithms.

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    Comparison of MSF measurements with net heat flux estimated using bulk aerodynamic parameterizations, net longwave flux, and net solar irradiance. Positive values indicate energy loss from the lake; negative values indicate energy gain. For clarity, MSF error bars are only plotted on COARE symbols.

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    The ASIT, meteorological (Met.) mast, and undersea node are elements of the MVCO. Moored IMET buoys at locations E and F include surface heat flux measurement instrumentation. Dots represent drifting MSF and guard buoy tracks for several deployments. Dots also show an M/V Nobska ship track, which coincides with an MSF deployment.

  • View in gallery

    A composite time series of (a) net surface heat flux and (b) net solar flux during CBLAST-Low 2003 from several platforms. Experiment times are normalized to solar daylight hours. Separate short-term deployments occurred in morning (m), early and late afternoon, as well as at sunset (s) to capture most of a daylight cycle.

  • View in gallery

    Correlation plots of net surface heat flux comparing (a) MSF vs M/V Nobska (using direct covariance for turbulent fluxes), (b) MSF vs ASIT (also using direct covariance), (c) ASIT vs M/V Nobska, and (d) MSF vs results derived using IMET buoy data and the COARE version 2.6a turbulent flux algorithm.

  • View in gallery

    Net solar flux intercomparison matrix. MSF vs (a) ASIT, (b) IMET buoy, and (c) M/V Nobska–based radiometers. Intercomparisons between estimated net solar flux from (d) ASIT, (e) IMET buoy, and (f) M/V Nobska radiometers.

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    Near-surface water temperature comparison between the MSF bulk water sensor (depth ∼1 cm) and the standard WOCE-type temperature probe on the Lagrangian drifter RDB01 (depth ∼ 10 cm).

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    Surface (skin) temperature intercomparsion matrix. MSF clear-plate temperature vs (a) M/V Nobska, (b) ASIT, and (c) IMET buoy. Intercomparisons between (d) ASIT, (e) IMET, and (f) M/V Nobska platforms.

  • View in gallery

    Skin–bulk temperature difference vs net surface heat flux for a sunset deployment. The MSF flux plate–bulk water temperature difference shows elevated flux-plate temperature during daylight becoming negative at night. Model results use IMET buoy data. COARE (solid circles) and CFC results are for a cool skin only, whereas COARE (open circles) includes both cool-skin and warm-layer effects.

  • View in gallery

    (top) Time series of net surface heat flux; (bottom) flux-plate submergence fraction. Mismatches in velocity between MSF, wind-driven seas, and ocean currents at a 15-m depth cause the flux plates to submerge for up to 60% of each 5-min sample period.

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Field Results from a Second-Generation Ocean/Lake Surface Contact Heat Flux, Solar Irradiance, and Temperature Measurement Instrument—The Multisensor Float

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  • 1 Department of Physics and Meteorology, Western Connecticut State University, Danbury, Connecticut
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Abstract

This paper describes results from two field programs that support development of a wave-following surface contact multisensor float (MSF) designed to simultaneously measure net surface heat flux, net solar irradiance, and water temperature. The results reported herein compare measurements from a second-generation design (circa 1998) against directly measured radiative fluxes as well as turbulent fluxes derived using both eddy correlation and bulk aerodynamic methods. The reference flux data are collected using instrumented towers, buoys, and research vessels. Comparisons show that MSF net surface fluxes and net solar irradiance are in generally good agreement with values that are measured or derived using standard instruments and methods, having root-mean-square differences less than approximately 15%. MSF near-surface bulk water temperature measurement shows good agreement with similar measurements from a drifting buoy. MSF measurement of water surface temperature is not definitively determined, although results suggest it may be a good measure of skin temperature at night.

MSF flux measurement occurs from within the aqueous conductive sublayer and so does not use turbulence models or parameterizations. At this time, results are most reliable in low wind conditions (2 m s−1U10 ≤ 7 m s−1) and relatively calm seas. In higher winds and more active seas, the imperfect surface drifting and wave-following characteristics of the second-generation system limit its performance. More fundamentally, perturbation to the aqueous conductive sublayer and modification of near-surface turbulence structure by the MSF may also limit flux measurement accuracy under certain conditions.

Corresponding author address: J. P. Boyle, Department of Physics and Meteorology, Western Connecticut State University, 181 White Street, Danbury, CT 06810. Email: boylej@wcsu.edu

Abstract

This paper describes results from two field programs that support development of a wave-following surface contact multisensor float (MSF) designed to simultaneously measure net surface heat flux, net solar irradiance, and water temperature. The results reported herein compare measurements from a second-generation design (circa 1998) against directly measured radiative fluxes as well as turbulent fluxes derived using both eddy correlation and bulk aerodynamic methods. The reference flux data are collected using instrumented towers, buoys, and research vessels. Comparisons show that MSF net surface fluxes and net solar irradiance are in generally good agreement with values that are measured or derived using standard instruments and methods, having root-mean-square differences less than approximately 15%. MSF near-surface bulk water temperature measurement shows good agreement with similar measurements from a drifting buoy. MSF measurement of water surface temperature is not definitively determined, although results suggest it may be a good measure of skin temperature at night.

MSF flux measurement occurs from within the aqueous conductive sublayer and so does not use turbulence models or parameterizations. At this time, results are most reliable in low wind conditions (2 m s−1U10 ≤ 7 m s−1) and relatively calm seas. In higher winds and more active seas, the imperfect surface drifting and wave-following characteristics of the second-generation system limit its performance. More fundamentally, perturbation to the aqueous conductive sublayer and modification of near-surface turbulence structure by the MSF may also limit flux measurement accuracy under certain conditions.

Corresponding author address: J. P. Boyle, Department of Physics and Meteorology, Western Connecticut State University, 181 White Street, Danbury, CT 06810. Email: boylej@wcsu.edu

1. Introduction

a. Ocean surface heat flux measurement

The atmosphere and oceans are a coupled system for a wide range of spatial and temporal scales. They interact at the air–sea interface through the exchange of momentum, heat, moisture, and other gaseous species.

To monitor climate variability, the need for accurate and continuous ocean surface heat flux measurement has long been recognized by interdisciplinary climate research programs such as Tropical Ocean and Global Atmosphere (TOGA), Climate Variability and Prediction (CLIVAR), and Surface Ocean–Lower Atmosphere Study (SOLAS). Large numbers of such measurements are required over broad regions of the ocean, so they must be made inexpensively. Satellite-borne remote sensing techniques have the potential to provide more accurate assessment of surface fluxes on a global basis (Liu 1990), and many validation projects are now underway (Thadathil et al. 1995; Jourdan and Gautier 1995; Liu 2004). However, remote sensing techniques will need to be supplemented by long-term, continuous in situ surface heat flux measurements. This need has been partially filled by the development of surface data acquisition networks such as the TOGA Tropical Atmosphere Ocean (TAO) buoy array, the many National Data Buoy Center (NDBC) platforms, and other coastal buoy systems. In general, these networks collect meteorological, solar radiation, and water temperature data. These variables are used to infer surface fluxes; they do not measure fluxes directly. They are also relatively expensive and require significant operational support.

Development and use of other in situ instruments for flux measurement are also underway, including shipboard systems (Fairall et al. 1997; Edson et al. 1998), and surface (Graber et al. 2000) and subsurface (D’Asaro 2004) drifting buoys. These techniques measure turbulence parameters in the atmospheric and oceanic surface layers, respectively.

b. Aqueous thermal conductive sublayer

Flow near an air–water interface exhibits two boundary regions: one in the atmosphere and another in the water. Within several millimeters of the interface the dynamics and thermodynamics are governed by molecular processes. Thermal radiative exchange between ocean and atmosphere occurs in the upper ≈20 μm of the water (McAlister and McLeish 1969). Below this extremely thin skin is a region where heat is transported predominately by thermal conduction. This conductive (molecular) sublayer varies in thickness from fractions of a millimeter to several millimeters (Wu 1971; Katsaros et al. 1977; Katsaros 1980). Below the conductive sublayer heat transport occurs via turbulent convective motion.

A conductive sublayer exists because of the generally persistent nature of evaporative and longwave radiative energy losses at the air–water interface combined with the damping of turbulent eddies near this interface. The presence of the conductive sublayer in the ocean is well established (Khundzhua and Andreyev 1974; Mammen and von Bosse 1990; Ward et al. 2004). The sublayer is spatially and temporally dynamic. It is stretched and distorted by passing capillary gravity waves and local wind stress (Longuet-Higgins 1969; Witting 1971; Wick and Jessup 1998). It is disrupted by breaking waves (whitecaps) and on the smallest scales by turbulent eddies partially or completely penetrating the layer from below, that is, surface-renewal theory (Liu and Businger 1975). Open-ocean measurements (Jessup et al. 1997a) indicate that the sublayer recovery time for whitecaps depends on the strength of the disruption as balanced by the restoring heat flux at the interface. Laboratory and field studies have characterized surface-renewal events (Katsaros et al. 1977; Wick et al. 1996).

Evidence using simultaneous video and infrared imaging cameras (Jessup et al. 1997b; Zappa et al. 2001, 2004) suggests the sublayer is also frequently disrupted by the “breaking” of short surface gravity waves without air entrainment (microscale wave breaking). In open-ocean measurements of sublayer recovery, when the surface is disrupted by thin cables (no whitecaps), Zappa et al. (1998) report recovery times of O(0.1 s) for wind speeds from 5 to 10 m s−1. These authors refer to field observations of skin–bulk temperature differences of 0.2 K at wind speeds up to 15 m s−1, implying the existence of a spatially and temporally measurable conductive sublayer, which can be disrupted, but recovers at time scales that depend on heat flux and background turbulence.

In general, the surface (skin) temperature can be from 0.2° to ∼1°C lower than the bulk water temperature, depending on net surface heat flux and subsurface turbulence characteristics. This variability decreases precision for remote versus bulk in situ temperature comparisons and results in uncertainty in calculation of latent, sensible, and upwelling longwave fluxes.

The molecular sublayer plays an important, if not dominant, role in heat and gas transport across the air–sea interface when it is basically intact, for example, for wind speeds up to 15 m s−1 and perhaps greater. The sublayer has long been considered to be sufficiently well behaved to justify its potential use to measure an average net surface heat flux under these conditions (McAlister and McLeish 1970; McKeown et al. 1995).

c. Instrument development history

Between approximately 1992 and 1997, the Space Science and Engineering Center (SSEC) at the University of Wisconsin—Madison was developing an electromechanical device called the Skin-Layer Ocean Heat Flux instrument (SOHFI). This device was designed to measure net heat transfer between the atmosphere and ocean (Suomi et al. 1996) by placement of a thin sensor within the aqueous thermal conductive sublayer. The advantages of this technique are its direct sensing at the ocean surface, its low cost, and its simple operation. Development included laboratory characterization (Sromovsky et al. 1999a), as well as a combination of freshwater and ocean field tests (Sromovsky et al. 1999b). During the freshwater field tests, reference data for the intercomparison were collected at sites located 5–7 km from the SOHFI sensors. Sromovsky et al. (1999b) indicate the considerable scatter between SOHFI-measured fluxes, and those calculated using standard techniques were likely due to problems with test conditions. SOHFI-measured fluxes during ocean deployments were compared with global circulation model predictions as well as flux estimates based on data collected from moored buoys, fixed towers, and ships generally 50–60 km from the SOHFI sensors. Based on these deployments, Sromovsky et al. (1999b) conclude that the “results so far are not definitive with respect to either the accuracy of the measurement, or the operating limits of the system. . . . comparisons [between SOHFI measurements and standard techniques] seem to be best when the input observations are made in close proximity to the SOHFI, yet we have a very small data set for which such comparisons are available” (bracketed text inserted by author). Further, it was found that SOHFI-measured fluxes were unexpectedly depressed during high wind conditions. It was unknown whether this anomalous condition was due to a fundamental limit in the measurement concept or poor performance of the instrument’s submergence detection system. Development continued on a smaller scale throughout 1998 and 1999 (Boyle 1999, 2000).

This paper discusses experiments performed in 1998 and 2003 to assess the accuracy of and evaluate operating limits for a second-generation multisensor float design, which basically follows instrumentation discussed in Sromovsky et al. (1999a, b). In particular, during the experiments reported herein, care was taken to ensure that reference measurements were made in close proximity to the multisensor float.

Section 2 briefly reviews the design of the sensor float and the measurement model for intercomparison with standard heat flux measurement techniques. Section 3 describes rooftop tests characterizing multisensor float response to solar radiation, which support interpretation of field experiment results. Section 4 describes short-term field experiments performed on Lake Mendota, Wisconsin, and in the ocean south of Martha’s Vineyard, Massachusetts, during the 2003 Coupled Boundary Layer Air–Sea Transfer (CBLAST) field program. Section 5 provides conclusions.

2. Sensor element mechanical design

a. Sensor float and flux plates

A detailed description of the sensor float and flux-plate design is available in Sromovsky et al. (1999a). A brief review of the second-generation “Greenland Sea” design used in all measurements reported herein is provided below.

The critical element of the SSEC SOHFI is the light, wave-following surface sensor float containing two thin flux plates. Two sheets of fiberglass mesh stretched across a toroid-shaped float support the flux plates. Surface tension acting on the mesh and buoyancy of the float balance, thereby keeping the flux plates within the conductive sublayer. The fiberglass mesh also acts as a diffuser for solar radiation. Two horizontal 24-gauge bare wires are located 3.175 mm above and below the mesh plane. These are used to detect and filter out data collected when the flux plates are submerged, that is, no longer in the conductive sublayer. To allow for flipping in high wind conditions, the sensor float and flux plates are top–bottom symmetric. A mercury switch is provided to determine orientation and adjust signal polarity accordingly.

Each flux plate consists of a thermopile and a chromel-constantan thermocouple bonded into a flexible polyester film sandwich. The thermopile consists of forty 5.1-μm-thick edge-bonded chromel-constantan junctions in series; sequential junctions alternate on the top and bottom of the thermal resistor (see Fig. 4 in Sromovsky et al. 1999a). For each sensor float, one flux plate has clear Mylar outer layers; the other uses black-dyed hostaphan (also a polyester film) outer layers. The darkened hostaphan transmits only 5% of visible light. This difference in solar absorption allows the sensor float to behave as a “black and white” net pyranometer, enabling solar irradiance to be distinguished from sensible, latent (evaporative), and net longwave heat fluxes. To prevent salt buildup caused by evaporation, two 30-gauge wires are glued onto each side of the flux plate. These move the fiberglass mesh away from the flux plate slightly, promoting diffusion of salt away from the thermopile.

Two thermocouples measure bulk water temperature at a depth of ∼1 cm. These are located on top and bottom of the buoyancy toroid; only one of these is submerged at a time. A thermistor sealed inside a balance float attached to the toroid provides a reference temperature for flux-plate and bulk water thermocouples.

Heat flux measured by each of the flux plates is given by Fourier’s postulate
i1520-0426-24-5-856-e1
where thermal conductivity (k) and thickness (d) are properties of the thermal resistor and ΔT is measured by thermocouple junctions above and below the thermal resistor. The calibration equation for each flux plate is
i1520-0426-24-5-856-e2
where the analog input signal is a voltage difference (ΔV) and the static calibration coefficient (s) incorporates the Seebeck coefficient, thermal resistor conductivity, and thickness. The Seebeck coefficient has a slight temperature dependence; therefore, flux-plate measurements require a temperature correction [ f (T)]. For typical meteorological/oceangraphic temperatures, this correction is less than 3%. Sromovsky et al. (1999a) report a nominal value of the calibration coefficient for this flux-plate design as 0.187 μV W−1 m2 at 21.1°C, with a tolerance of ±0.007 μV W−1 m2. The flux-plate time constant can be estimated using its thermal resistance properties; calculated values for bare flux plates are less than 3 ms (Ortolano and Hines 1983).

b. Previously known system limitations

There are two limitations that affect the accuracy of Greenland Sea design sensor float flux measurements.

1) Datalogger offset

During an evaporative calibration procedure, both flux plates consistently report a negative heat flux of approximately 5 W m−2 in zero flux conditions. Laboratory tests indicate this offset does not depend on the flux plates. Investigation of the Campbell Scientific datalogger (P. Wisniewski, SSEC, 1999, personal communication) indicates that an amplifier offset of 0.5–2 μV can exist for a range of environmental conditions. This offset voltage does not appear to be constant, as reported in Sromovsky et al. (1999a). The flux-plate calibration equation becomes
i1520-0426-24-5-856-e3

2) Sensor float orientation asymmetry

In principle, the sensor float is “top up”/”top down” symmetric; this allows for operation in active seas where the float may flip over. However, during sensor float evaporative heat flux calibration in quiescent conditions the response in the top-up and top-down configurations was found to differ by approximately 5%–10%. Initial field measurements of flux and flux-plate temperature in the top-up orientation were found to be unreliable. This situation cannot be fully attributed to amplifier offsets. It is likely associated with the variable impedance of the mercury orientation switch in dynamic conditions. However, it could also result from poor positional tolerance of the flux plates within the fiberglass mesh. In any case, because of this asymmetry, only data collected in the top-down configuration are reported in this paper.

c. The measurement model

To assess the accuracy of net surface heat fluxes measured by the wave-following buoy, now called a multisensor float (MSF), flux-plate signals are decomposed as
i1520-0426-24-5-856-e4
i1520-0426-24-5-856-e5
where Fclr, Fblk represent each MSF flux-plate measurement, FLS represents the combined evaporative and sensible heat flux acting across the conductive sublayer, FSWR,net is net solar flux (incorporating sea surface albedo and underwater backscattered solar radiation), and FLWR,net is the net longwave radiative flux. Positive fluxes are upward, out of the water, and negative fluxes are downward. The net solar flux is always downward. Here, αc, αb are flux-plate solar response coefficients. They are a measure of the solar radiation absorption characteristics for each flux plate. In this model, nighttime fluxes measured by each flux plate should be the same. During the day, the difference in flux-plate signals is proportional to sea surface irradiance.
To obtain the net solar flux from the MSF, Eqs. (4) and (5) are combined into Eq. (6), where Δα = αbαc:
i1520-0426-24-5-856-e6
Assuming a true net surface heat flux given by Fnet = FLS + FLWR,netFSWR,net, then net surface heat flux from each flux plate is found by combining Eqs. (4), (5), and (6),
i1520-0426-24-5-856-e7
i1520-0426-24-5-856-e8

Using only a single MSF, Eqs. (6), (7), and (8) simultaneously determine net solar and net surface heat flux (for both day and night), respectively.

3. Rooftop laboratory water tank solar irradiance tests

As seen above, to function during daylight hours the MSF must be able to accurately measure net solar irradiance at the sea surface. Several laboratory tests were performed using a rooftop water tank to evaluate MSF net solar flux measurement accuracy in comparison with fixed land-based pyranometers. The objective of these tests is to characterize the MSF solar response coefficients and support data analysis for the short-duration field deployments.

a. Test configuration

MSFs are placed in a 1.85-m-diameter, 0.4-m-deep rooftop water tank. The inside of the tank is coated with black paint of nominal emissivity (0.92). Radiometers for reference solar irradiance measurement include an Eppley precision spectral pyranometer (PSP) and an Eppley black-and-white pyranometer (model 8–48). In addition, a Weed Corporation 1000 Ω platinum resistance thermometer (PRT) measures bulk water temperature.

b. Solar irradiance: Analysis procedure and results

The MSF-calculated solar irradiance is given by Eq. (6), where the empirical solar scale factor (1/Δα) is determined using a best-fit analysis of MSF solar irradiance against Eppley pyranometers, which, in turn, are corrected for the effects of sea surface albedo and subsurface backscattered flux. For the reference flux an empirical algorithm is used to estimate water surface albedo using a single global broadband pyranometer (Payne 1972).

Figure 1a shows a time series of 5-min flux-plate means for 3 days. Daytime separation of clear and black flux-plate signals is evident, and the flux plates generally track together at night. MSF data indicate heat transfer into and out of the tank during a diurnal cycle. The linear best fit of (FclrFblk) versus FSWR,net based on the model 8–48 pyranometer yields 1/Δα = 2.83 ± 0.12, with a root-mean-square (rms) difference of 48 W m−2.

While the MSF response in partly cloudy and overcast conditions is relatively good (Fig. 1b), the clear-sky response is somewhat poor (Fig. 1c). The anomaly from Julian day (JD) 182.50–182.75 is observed to be due to variable direct beam shadowing of flux-plate thermocouple junctions by the upper submergence detection wire. This phenomemon is not reported in Sromovsky et al. (1999b) for the first-generation design, because in that case the submergence wire is on the buoyancy toroid, far from the flux plates. Relocation of this wire over the flux plates was intended to improve detection of flux-plate submergence. It is also found that the MSF cosine response is not ideal because of variable shadowing of flux-plate thermocouple junctions by fiberglass mesh strands (see Fig. 1c, from JD 182.25 to 182.4). This effect appears to be most pronounced in clear-sky conditions at low solar elevation angles. Again, this is not reported in Sromovsky et al. (1999b), possibly because each older flux-plate design had twice as many thermocouple junctions so that any shading caused by the mesh would not have been as influential.

In rooftop laboratory tank experiments there are too many unknowns (FLS, αc, αb) to completely solve Eqs. (4) and (5) and thereby determine the individual solar response coefficients. Instead, a cloud-modulated solar flux technique described in Sromovsky et al. (1999b) is used. During partly cloudy (JD 183.55–183.77) and overcast (JD 184.685–184.785) conditions, a response ratio (αc/αb) of 0.472 ± 0.025 is found. Knowledge of 1/Δα and the response ratio allows for the calculation of individual flux-plate solar response coefficients, here αb = 0.669 ± 0.045 and αc = 0.316 ± 0.021. The values reported in Sromovsky et al. (1999b) using this technique (αb = 0.522 ± 0.019 and αc = 0.232 ± 0.010) are for an older flux-plate design measured in the field on Lake Mendota, Wisconsin. Furthermore, Sromovsky et al. (1999b) use a LI-COR photodiode pyranometer for reference solar irradiance and do not consider water surface albedo effects. Therefore, significantly different values for solar response coefficients are not unexpected.

c. MSF bulk air and water temperature measurement

The shaded MSF water temperature sensor (depth ∼1 cm) compares favorably with the Weed PRT-measured bulk water temperature in clear-sky, partly cloudy, and overcast situations. Because it is unshielded, the MSF air thermocouple is not an accurate measure of air temperature. However, air temperature fluctuations correlate well with variations in solar irradiance, so this sensor is used as a diagnostic tool to identify cloud modulation of net fluxes thereby supporting interpretation of field experiment data.

4. Short-term field intercomparison deployments

Results from two short-duration field experiments performed on Lake Mendota in fall 1998 and in the ocean south of Martha’s Vineyard, Massachusetts, during the 2003 CBLAST field program are reported here. During these field campaigns all of the components of net surface heat flux and surface and near-surface temperature are independently measured or derived for a range of meteorological conditions using standard instrumentation and methods. Results are compared with MSF measurements.

These field experiments provide an opportunity to determine the accuracy of MSF-measured surface heat flux, solar irradiance, and temperature as well as to simultaneously assess the MSF dynamical response in sea, swell, and ocean currents to better characterize operational limits. In particular, the Lake Mendota experiments focus on characterizing the MSF, whereas the CBLAST Low Wind Component (CBLAST-Low) experiments are used to evaluate an ocean-going version of a complete MSF system.

a. Lake Mendota field program

1) Objectives and locations

The purpose of this field program is to investigate the depressed fluxes observed during higher winds in open-ocean deployments reported in Sromovsky et al. (1999b) and to assess the overall accuracy and performance of the MSF in typical field conditions.

Lake Mendota is a 10 km × 5 km freshwater lake adjacent to the University of Wisconsin—Madison with a mean depth of 12.4 m (Brock 1985). Most of the field tests took place in the western bay where the water depth is 16–20 m.

Figure 2 shows the lake shoreline and the location of auxiliary environmental measurements from the Integrated Surface Irradiance Study (ISIS) and Automated Surface Observing System (ASOS) stations. ISIS is a surface-based solar-monitoring program with 14 sites across the country (Hicks et al. 1996). The Wisconsin site is located on the roof of a 15-story building and monitors incoming solar radiation (visible and ultraviolet). ASOS is the primary national surface weather observation network. The ASOS station is at the local airport. ASOS measurements include wind speed and direction, ambient temperature and humidity, pressure, and visibility. Data from ISIS and ASOS supplement and support measurements taken as a part of this research.

2) Measurement technique

The original design of the SOHFI system consists of a sensor float tethered to a World Ocean Circulation Experiment (WOCE) buoy (Sybrandy and Niiler 1991) by a 7.6-m floating cable (see Fig. 3a in Sromovsky et al. 1999a). This untended, freely drifting system has been deployed in lake and ocean environments (Sromovsky et al. 1999b).

(i) Research platform and instrumentation

For design and performance assessment on Lake Mendota, the WOCE guard/support buoy is replaced by an inexpensive manned and instrumented research vessel (a catamaran). Use of this low-profile vessel allows for synchronized local measurement of meteorological variables and radiation, deployment of multiple MSFs for correlation studies, and direct observation of MSF performance. A single test occurs as follows: the vessel motors to a location approximately 1 km from the windward shore; the vessel is depowered and allowed to drift. MSFs are deployed and data are collected while drifting toward the leeward shore. The vessel drift rate is controlled to match MSF drift rate by using several different-sized drogues deployed 15–20 m behind the vessel at various depths. The onboard instrument suite includes meteorological variables (wind speed, air temperature and moisture, bulk water temperature), downwelling radiation (solar and longwave), and one or two MSFs. Figure 2 also shows a typical drift track including date and duration of leg, as well as mean meteorological and radiative transfer conditions.

Meteorological instrumentation is located on a 4.0-m mast. This mast is a 5-cm-diameter aluminum tube. Orthogonal bubble levels attached near the base measure vertical alignment. A running backstay is used to adjust mast angle. Wire stays and shrouds supporting the mast are attached 0.5 m below the instruments to minimize airflow perturbation.

A vane anemometer (R. M. Young Company) measuring wind speed and relative direction is mounted at the masthead. It has a threshold wind speed of 1.0 m s−1 and a distance constant of 2.7 m. A Vaisala probe measuring air temperature and relative humidity is located under a radiation shield near the masthead. Bulk water temperature is measured using a 1000 Ω PRT manufactured by the Weed Corporation. It is deployed 2 m below the surface.

A LI-COR photodiode pyranometer (LI-200SA) measures downwelling solar flux and an Eppley pyrgeometer (PIR) measures atmospheric longwave radiation. They are mounted approximately 0.25 m above the deck, outboard of the running backstay and shrouds.

Two MSFs are frequently used—one is tethered to the port side, the other to the starboard side. The MSFs drift approximately 5 m upwind of the research vessel and are typically 1–2 m apart.

Analog signals from all seven instruments are fed into two Campbell Scientific dataloggers located in weather-resistant enclosures on the deck. The sample rate for all sensors is 1.0 Hz; no online averaging is performed. A Garmin GPS is used to determine the drift track and synchronize all clocks to within 1 s.

During November and early December 1998 a number of separate drift experiments were performed. This period was chosen because cold air and high winds promote large heat fluxes, improving the MSF signal-to-noise ratio. The majority of drift legs occurred at night. Analysis of nighttime data is simplified because there is essentially no need for empirically determined solar response coefficients, that is, black and clear flux-plate signals should be identical.

(ii) Surface flux components
Turbulent latent and sensible fluxes (FLS = FL + FS) are calculated using several bulk aerodynamic parameterizations. The general form is
i1520-0426-24-5-856-e9
i1520-0426-24-5-856-e10
where ρair is air density and cp is the specific heat of air; U10 is the near-surface wind speed with inclusion of convective gustiness (Godfrey and Beljaars 1991); Lv is latent heat of vaporization and qs is saturation specific humidity, both of which are evaluated at water surface (skin) temperature (Tsfc).

The bulk transfer method is one of the simplest, most widely used techniques to determine surface heat flux (Smith et al. 1996). It only requires mean velocity, temperature, and humidity measurements at the surface and one level in the atmospheric surface layer. Because the surface layer is a region of essentially constant flux, a range of measurement heights yield acceptable turbulent flux estimates. Requirements for instrument leveling and orientation, synchronization, calibration, and test duration are less severe than for other methods. Three bulk transfer algorithms [Coupled Ocean–Atmosphere Response Experiment (COARE) version 2.6a (Fairall et al. 1996a; Bradley et al. 2000), Clayson–Fairall–Curry (CFC) Clayson et al. 1996), and University of Arizona (UA) (Zeng et al. 1998)] are used to calculate the phenomenological transfer coefficients, wind speed at a height of 10 m, conductive sublayer thickness, and water skin temperature. These methods are based on the classical Monin–Obukhov similarity theory for the atmospheric surface layer (Arya 2001), but include different empirical relations for aerodynamic surface roughness, solar radiation penetration, and aqueous thermal conductive sublayer dynamics.

The net longwave flux [Eq. (11)] is composed of the global broadband downwelling component (FLWR,dwn) measured by an upward-looking pyrgeometer and an upwelling component calculated based on water surface temperature,
i1520-0426-24-5-856-e11
where ɛf is the broadband flux emissivity for water. A value of 0.97 is used, consistent with Fairall et al. (1996a, 1998). Water surface temperature (Tsfc) is estimated using measured water temperature at a depth of 2 m, corrected for the warm convective layer and cool skin layer effects via a bulk turbulent flux algorithm (Fairall et al. 1996b).

The net solar flux (FSWR,net) includes the downwelling incident solar radiation minus that reflected at the air–water interface and backscattered from below the surface. As in the rooftop tank tests, a standard empirical algorithm is used to estimate water surface albedo using a single global broadband pyranometer (Payne 1972).

The time scale for data collection (1 Hz) is governed by processes in the aqueous conductive sublayer and wave boundary layer. A 1-min averaging period is chosen for MSF data to smooth fluctuations associated with surface-renewal events and surface gravity waves. For comparison purposes meteorological and radiometeric data are also averaged over 1 min. The duration of a drift experiment varied from 1 to 8 h. In some cases the duration of each test was limited because the drifting catamaran approached the lakeshore and horizontal homogeneity in the atmospheric surface layer could not be ensured. In other cases a steady-state surface heat flux was observed and additional data collection was not necessary to characterize MSF performance.

3) Lake Mendota results

Time series from several drift legs are discussed to illustrate the flux and temperature measurement behavior of the MSF on a natural water body. Tables 1, 2 and 3 (see also Fig. 7) summarize the Lake Mendota field results.

Figure 3 shows sample rate flux and temperature data for a 12-min period during a clear November night with light winds (U4 ≈ 2 m s−1). Latent, sensible, and net infrared heat fluxes all contribute to the removal of energy from the water; they are positive. The mean MSF flux is 176 W m−2. Combined latent and sensible fluxes are about 53 W m−2; the net infrared flux is about 124 W m−2. Nighttime flux-plate temperatures are lower than MSF bulk water measurements at a depth of approximately 1 cm. Although not identical to skin temperatures estimated using the COARE version 2.6a algorithm, MSF flux-plate temperatures are generally consistent with these calculations.

An important finding from this field program is that the algorithm used to detect flux-plate submergence, as reported in Sromovsky et al. (1999a), is essentially ineffective. Figure 4 is a plot of unprocessed MSF flux and temperature data for a 3-min period during low-to-moderate wind conditions at night. Figure 4a shows that MSF-measured fluxes frequently exhibit large negative transients, for example, at 11.662 and 11.681 UTC. These are due to the flux plate being submerged by wave action. While the detection system registers dramatic submergence events, for example, 11.662 UTC, it neither accounts for the time necessary for flux signal recovery, nor detects less dramatic submergence events, for example, 11.681 UTC. The temperature plot (Fig.4b) indicates that a wave washes over the entire float (seen by the MSF air thermocouple response) at 11.662 UTC. Figure 4b also shows that clear and black flux-plate temperatures are ∼0.7 K cooler than the bulk water temperature. This is generally consistent with Fairall et al. (1996a), indicating that the flux plates can be maintained in the conductive sublayer for reasonable time periods. It is believed the relatively long flux-plate recovery time is due to reestablishment of the conductive sublayer around the flux plates/fiberglass mesh after its disruption.

During several legs with wind speeds greater than approximately 4 m s−1, foam generated by breaking waves and concentrated in windrows by Langmuir circulation frequently covered the flux plates and submergence sensor. Figure 5 shows typical effects of foam on MSF fluxes and the submergence sensor during a 3-min period when U10 ≈ 7 m s−1.

As discussed above and reported in Boyle (1999, 2000), surface gravity waves surging onto the flux plates or washing over the MSF, as well as spray and foam, cause submergence events. It is likely that these were not entirely excluded from the flux-plate data during early sensor float deployments, thereby contributing to an underestimate of net surface heat flux and large intercomparision data scatter (Sromovsky et al. 1999a, b).

Overall, reasonable results are obtained after the effects of submergence and foam are filtered offline (algorithm discussed below). For example, Fig. 6a is a time series for a 2-h period before sunset during gusty conditions (U4 ≈ 5.5 m s−1). Good correlation exists between clear and black flux-plate signals and the net turbulent flux. There is also separation of clear and black flux-plate signals, with separation decreasing as the sun sets. Figures 6b and 6c are scatterplots comparing net solar flux and net surface heat flux determined using Eq. (6) and the average of Eqs. (7) and (8), respectively, against standard measurements. Numerical results for this drift experiment are available in Table 2.

4) Measurement accuracy and performance limitations

(i) Accuracy and submergence filtering
To estimate the fundamental accuracy of the existing MSF design flux measurements, consider the first derivative of Eq. (3) and represent the uncertainty in terms of the various measurement uncertainties,
i1520-0426-24-5-856-e12
Given the nominal flux-plate sensitivity (s = 0.187 μV W−1 m2) and a net surface flux of 215 W m−2, typical of the Lake Mendota dataset, then ΔV is 40 μV. The Campbell Scientific datalogger is accurate to 0.2% of full scale and the ±2.5 mV range is used; therefore, δV) = 5 μV. The worst-case temperature correction is assumed to be 2% ( f = 1.02) and δf = 0.01; δs is taken as the nominal tolerance in the static sensitivity reported in Sromovsky et al. (1999a), and δVoffset = 2 μV, as discussed earlier. We obtain
i1520-0426-24-5-856-e13

Assuming these errors are uncorrelated, the total rms error is 30 W m−2 or 14%. Clearly, the relatively low static calibration coefficient contributes most to the inherent flux-plate measurement uncertainty. A second significant contributor is the preamplifier offset. It should be noted that standard net flux measurement techniques yield a total rms error of approximately 20 W m−2 (here about 10% of the net surface heat flux), that is, approximately 10 W m−2 each for net solar (MacWhorter and Weller 1991) and longwave (Fairall et al. 1998) radiation components and approximately 5–15 W m−2 each for the interfacial turbulent fluxes using the bulk aerodynamic method (Fairall et al. 2003; see Table 2).

For wind speeds less than approximately 7 m s−1 the existing MSF design appears to be reasonably accurate when outliers from submergence events are removed from the data. To assess the accuracy of data in these conditions, a simple offline submergence-filtering algorithm was generated to compensate for the lack of a completely effective online submergence filtering. The following four criteria are used to remove data: 1) foam is detected by the submergence sensor as seen in Fig. 5; 2) a wash-over event is detected by the air temperature sensor, as seen in Fig. 4; 3) a surge of water onto the flux plate is detected by the submergence sensor; and, finally, 4) because not all surge events trigger the submergence sensor, a change in flux of more than 25% between sample periods (1 s) is judged to be unrealistic and defined as a submergence event. The algorithm assumes that flux-plate recovery requires approximately 5–10 s, as seen in Fig. 4.

Table 2 and Fig. 7 summarize fluxes from the Lake Mendota dataset after removing known flux-plate submergence events. The MSF-measured net surface heat flux is compared with net solar, net longwave, and turbulent fluxes derived using standard instruments and methods described in section 4a(2). Data from 12 and 21 November are collected at sunset, from 24 and 27 November at local noon, and from all other legs at night.

Table 3 presents the calculation of sublayer thickness and the skin–bulk temperature difference using subparameterizations within the bulk aerodynamic models. MSF ΔT values are flux plate minus MSF bulk water (∼1 cm). The clear and black flux plates show results at night consistent with a cool skin layer as in Fig. 3. These results appear to suggest that the flux-plate temperatures may prove to be a good measure of skin temperature at night. However, from Table 3 the MSF appears to measure a slightly greater temperature difference between the bulk water and skin than was predicted by empirical models. This may occur because the flux plates and fiberglass mesh would tend to suppress surface renewal, causing increased ΔT. It does not appear this has a significant effect on net surface and solar flux measurement by the MSF during the fall Lake Mendota experiments.

(ii) Operational limits

Several physical processes fundamentally limit the operation of the MSF. The measurement concept is only valid if the conductive sublayer plays a dominant role in heat transfer across the air–sea interface. In a vigorous wind-driven sea, breaking waves with spray and air entrainment greatly affect air–sea heat transfer. Evaporation of spray alters surface heat flux by modification of humidity and temperature profiles in the lowest several meters of the surface layer (Andreas 1992). In this situation, wind speeds ≥15 m s−1 would be expected, and the sublayer may not be sufficiently intact for use in measurement.

When the sublayer is basically intact, but seas are still vigorous, the imperfect wave-following characteristics, the inability to identify and automatically remove submergence events, and the top-up/top-down orientation asymmetry limit operation of the second-generation design MSF.

b. CBLAST-Low field experiment

1) Objectives, location, and platforms

An objective of the CBLAST field experiment is to observe and better understand the temporal and spatial variability of the air–sea interface by investigating processes responsible for the exchange heat, mass, and momentum in low wind conditions. Pilot experiments and the main experiment for CBLAST-Low took place at the relatively new Martha’s Vineyard Coastal Observatory (MVCO) during August 2001, 2002, and 2003. MVCO consists of three platforms: an atmospheric component (the meteorological mast), underwater components (the undersea node), and an Air–Sea Interaction Tower (ASIT), which spans the air–sea interface. During the August 2003 intensive operating period, researchers deployed instrumentation on vessels, aircraft, and moored and drifting buoys to measure relevant air–sea interaction variables.

Figure 8 shows the location of the MVCO platforms, two Improved Meteorological (IMET) moored buoys, and a track from the M/V Nobska, all of which are instrumented to measure the components of net surface heat flux. The MVCO, IMET, and M/V Nobska measurements yield reference fluxes and water temperatures that are used to assess the accuracy and performance of various MSF designs deployed during CBLAST-Low.

2) Instruments and methods

(i) Reference fluxes

Downwelling solar and longwave radiant fluxes are measured directly using pyranometers and pyrgeometers on the MVCO meteorological mast, ASIT, moored IMET buoys, and M/V Nobska. Net solar and longwave fluxes are calculated as in the Lake Mendota tests.

Instruments mounted on the ASIT and M/V Nobska estimate turbulent latent and sensible heat fluxes using two techniques: the direct covariance method and bulk aerodynamic parameterizations. The covariance technique (or eddy correlation method) is a direct measurement. As with the bulk aerodynamic method described earlier, data are collected from within the marine atmospheric surface layer. Conditions must be horizontally uniform with covariance stationarity, and fluxes are assumed to be essentially constant with height in the surface layer (Kraus and Businger 1994). Latent and sensible fluxes are
i1520-0426-24-5-856-e14
i1520-0426-24-5-856-e15
where is the mean covariance of turbulent fluctuations in vertical wind speed (w′) and specific humidity (q′); is the mean covariance of turbulent fluctuations in vertical wind speed and temperature (T ′); w, q, and T are mean vertical wind, specific humidity, and temperature, respectively. Other variables are described earlier. When the conditions of homogeneity and stationarity are met, the terms wq and wT are negligible for sensible heat and vapor fluxes. Two sets of direct covariance instrumentation on the ASIT are located 6 and 8 m above mean sea level (nominally). For the ASIT, measurements of vertical wind, moisture, and temperature occur at approximately 20 Hz with mean covariances calculated over a 20-min period to ensure relevant scales of turbulent eddies that are included in the average. Direct covariance flux measurement from the M/V Nobska uses the equipment and techniques similar to that reported in Fairall et al. (1997) and Edson et al. (1998). The direct covariance method does not use models or empirical parameterizations; however, it requires relatively expensive equipment, has constraints on platform motion and instrument leveling, and only measures turbulent fluxes. It is generally not amenable to long-term, untended operation at sea, but is considered to be the most accurate way to determine the turbulent flux components.

IMET moorings use bulk aerodynamic algorithms to derive turbulent fluxes. Meteorological and radiometric sensors on board IMET buoys are sampled at about 1-min intervals and typically are averaged over an hour. To maximize use of the data and investigate the accuracy of the high temporal resolution MSF measurements, fluxes are calculated using 5-min means of IMET buoy data.

(ii) Multisensor float system measurements

The MSF sample rate is 1 or 2 Hz. After removing known submergence events, a 5-min averaging period is used to smooth statistical fluctuations associated with surface renewal events, surface gravity waves, and noise. During CBLAST-Low, three sensor floats were used—one tethered to a recoverable drifting buoy (RDB01) and two tethered to an instrumented deployment–recovery vessel. MSFs tethered to the vessel had experimental design flux plates and are not discussed here.

The MSF–RDB01 system is similar to that reported in Sromovsky et al. (1999a), but designed for multiple short-term deployments. RDB01 is a WOCE-type Lagrangian drifting buoy designed to drift with currents at a depth of 15 m (Sybrandy and Niiler 1991). This support/guard buoy contains a datalogger, a GPS unit, and a spread spectrum transceiver for communication with the tending vessel. RDB01 also contains more sensitive MSF submergence sensing electronics and a thermilinear thermistor for sea surface temperature measurement that is consistent with the National Oceanic and Atmospheric Administration (NOAA) Global Drifter Buoy program. Deployment of RDB01 with an MSF consisted of a series of short-term, partially tended observations in conjunction with the CBLAST-Low intensive operating period in August 2003. The short duration is due to the necessity of maintaining the drifter near the ASIT and moored IMET buoys as well as the time limitations associated with chartering commercial fishing vessels to tend the drifter. Eight deployments were made; four of these coincided with the availability of the moored IMET buoy, ASIT, and M/V Nobska data for intercomparision and constituted most of the daylight portion of a diurnal cycle.

3) CBLAST-Low results

Figures 9 shows a composite time series for net heat and solar fluxes from all platforms during the MSF deployments. Figure 10 has correlation plots comparing MSF net surface heat flux with derived values using ASIT, M/V Nobska, and IMET platform measurements. Overall agreement is generally good, except for 21 August where clouds modulate the net fluxes differently for different platforms and on 15 August when the spatial variability in sea surface temperature apparently affects fluxes differently for each platform. Table 4 shows net surface heat flux rms differences for each of the deployments corresponding to panels in Fig. 10.

Figure 11 compares the net solar flux derived using several platforms, that is, MSF (drifting buoy), IMET (moored buoy), ASIT (fixed tower), and M/V Nobska (vessel). Considering the variability associated with clouds at the different sites and issues related to at-sea pyranometer accuracy for 5-min mean values (Katsaros and DeVault 1986; MacWhorter and Weller 1991), the MSF performance appears to be consistent with the standard technique applied at each platform. Table 5 reports net solar flux rms differences for each deployment corresponding to the panels in Fig. 11.

Figure 12 shows that MSF bulk water temperature measured at a depth of ∼1 cm appears to be a reasonably accurate measure of near-surface temperature based on comparison with the adjacent WOCE-type drifter (RDB01). The slight nonlinearity (slope 1.036) may be related to potential solar heating of the MSF bulk water temperature probe or because of the influence of unresolved, extremely shallow (≤10 cm) diurnal mixed layers.

Figure 13 is a scatterplot matrix of skin temperature. Figure 13a and 13b compare MSF clear flux-plate temperature with radiometric measurements from narrowband infrared thermometers on the ASIT and M/V Nobska. Conceptually, MSF flux-plate values should be a reasonably good measure of skin temperature (particularly at night) because they are designed to operate within the conductive sublayer. During daytime, absorption of solar radiation likely heats the flux plates more than the adjoining water as seen in Figs. 13a and 13b. Figures 13d and 13e compare IMET buoy-derived skin temperature, using the method of Fairall et al. (1996b), with radiometric measurements. The IMET buoy, like the MSF, seems to overpredict skin temperature. However, Fig. 13f appears to indicate that there is significant spatial variability in radiometric skin temperature, so the true skin temperature at the IMET buoy and MSF locations is not known exactly. The mean bias skin temperature for each deployment is shown in Table 6, where values correspond to panels in Fig. 13.

Figure 14 shows skin–bulk temperature difference versus net heat flux for a sunset deployment. Clear and black flux-plate temperatures are greater than MSF bulk water when the net flux is negative, consistent with the influence of solar heating. After sunset MSF cool skin and net heat flux measurements are compatible with cool skin flux temperature parameterizations (Fairall et al. 1996a; Clayson et al. 1996; Wick et al. 1996). Here, the daytime MSF skin–bulk temperature difference may reflect the influence of a shallow warm layer, if present, as seen by the comparison with COARE version 2.6a warm-layer results. However, MSF results would indicate a more rapid erosion of this warm layer than the COARE model.

A critically important finding from the CBLAST-Low experiment is that excessive submergence of the MSF can occur because of dissimilar drift characteristics between the WOCE-type guard buoy and wave-following MSF. This is particularly evident when surface waves and currents are moving in opposition to currents at the 15-m depth. Figure 15 is a time series for a 7.5-h period after sunrise. From the upper plot it is apparent that MSF net flux measurements are consistent with 5-min IMET means and 20-min ASIT bulk and covariance values until approximately 1645 UTC on 21 August (JD 233.70). The cause of the MSF flux discrepancy after this is seen in the lower plot; submergence of the flux plates occurs 40%–60% of the time. This frequency of submergence events is too great for accurate flux measurement. It is the worst-case result in CBLAST. Submergence here is due to a simultaneously increasing wind coupled with a 30° change in surface current direction while subsurface currents are steady. In this situation, it is likely the MSF is being dragged through the water by the WOCE buoy.

5. Conclusions

Based on the Lake Mendota and CBLAST-Low field experiments (supplemented by rooftop laboratory water tank tests) the following conclusions are made.

a. Surface flux and temperature measurement

The second-generation MSF system demonstrates reasonably accurate daytime and nighttime net surface heat flux measurements for wind speeds less than approximately 7 m s−1 in a variety of environmental conditions. However, this capacity depends on avoidance or the proper removal of sea-state-induced submergence events. The accuracy of the existing MSF design is also limited by the relatively low flux-plate static sensitivity.

MSF measurement of net solar flux compares favorably with land-based, shipboard, and buoy-mounted pyranometers in partly cloudy and overcast conditions. In clear-sky conditions MSF measurements are not optimum primarily because the upper submergence detection wire casts a shadow on the flux plates. In other situations fiberglass mesh strands shade some of the flux-plate thermocouples, degrading cosine response at low solar elevation angles.

The MSF bulk water temperature measured at a depth of ∼1 cm appears to be an accurate measure of near-surface bulk water temperature compared with standard WOCE-type temperature probe measurements at ∼10 cm depth. Clear and black flux-plate temperature sensors are directly exposed to solar radiation; therefore, they may not be a good measure of water skin temperature during the day. The ability of clear and black flux-plate sensors to measure water skin temperature at night is not definitively determined, although results reported herein suggest they may prove to be a good measure of skin temperature.

b. Operations/deployment

At the present time the light, wave-following MSFs cannot be deployed autonomously, but require a support/guard buoy for power and telecommunication requirements. To minimize wave- and current-induced submergence events, the support buoy must drift with the same characteristics as those of the MSF. Because of this, the existing WOCE-type Lagrangian drifter design may not be suitable. To accurately obtain measured fluxes during deployments of days and weeks in various sea states an online submergence-filtering algorithm must also be developed in conjunction with a new MSF/guard buoy system.

The following two operational functions are currently envisioned: 1) deployment of multiple instruments attached to small research vehicles or drifting buoys to support scientific characterization of air–sea interaction phenomenon such as net heat, gas and solar radiation fluxes, near-surface temperature structure, and wave dynamics, as well as 2) an independent untended operation, providing longer-term, continuous measurement of net fluxes and temperature for climatological studies and as “ground truth” for satellite remote sensing and global circulation modeling.

Acknowledgments

This research was supported by ONR Grant N000140-3-10343, NOAA/Sea Grant Technology Transfer Grant T-11-CT 02, and by the University of Wisconsin—Madison Space Science and Engineering Center (SSEC). I thank Fred Best, Larry Sromovosky, Paul Schnettler, Paul Wisniewski, and Henry Revercomb at SSEC. I especially thank R. A. Weller as well as J. Edson, Richard Payne, Jeff Lord, Lara Hutto, J. T. Farrar, and Jason Smith from the Woods Hole Oceanographic Institute for their support since 2001. I thank Matt Stommel and the crew of the M/V Nobska, Michael and Patricia Ryan, owners of the F/V Patricia Lynn, and Captain Don Smith, owner of the F/V Sea Fox, for their hard work and commitment during the CBLAST-Low deployments. Finally, I would like to thank W. Paul Menzel (NOAA/ORA) for his support during the difficult times at UW-Madison.

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Fig. 1.
Fig. 1.

Rooftop laboratory tank results: (a) time series of clear and black flux-plate signals during clear-sky, partly cloudy, and overcast conditions, respectively. In the daytime, the black flux-plate signal is depressed more than the clear flux plate because of its greater solar absorption, (b) a correlation plot of MSF vs an Eppley black-and-white pyranometer, and (c) a time series for clear-sky conditions.

Citation: Journal of Atmospheric and Oceanic Technology 24, 5; 10.1175/JTECH1898.1

Fig. 2.
Fig. 2.

Lake Mendota environment and typical drift leg. Lake shoreline is indicated by the heavy solid line. Date and time of the drift leg are indicated as are MSFs used and mean values of variables measured onboard the research vessel. Auxiliary radiation and meteorological measurements are available from the ISIS and ASOS stations, respectively.

Citation: Journal of Atmospheric and Oceanic Technology 24, 5; 10.1175/JTECH1898.1

Fig. 3.
Fig. 3.

Lake Mendota drift leg time series at night. (top) MSF 1-Hz flux data as well as latent, sensible, and net longwave fluxes determined using reference instrumentation with the COARE version 2.6a bulk aerodynamic algorithm. (bottom) The 1-Hz temperature data from the MSF and bulk water PRT as well as the estimated water skin temperature from the COARE version 2.6a algorithm.

Citation: Journal of Atmospheric and Oceanic Technology 24, 5; 10.1175/JTECH1898.1

Fig. 4.
Fig. 4.

Lake Mendota drift leg time series at night. (a) MSF 1-Hz flux data. Flux plates are definitely submerged by wave action at 11.662 and 11.681 UTC; recovery is complete. The 11.651 submergence event is less clear. (b) MSF 1-Hz air, water, and flux-plate temperatures. The 11.662 submergence event is accompanied by water washing completely over the MSF affecting air temperature measurement. Wash-over events are used to diagnose flux-plate submergence events for removal from the data.

Citation: Journal of Atmospheric and Oceanic Technology 24, 5; 10.1175/JTECH1898.1

Fig. 5.
Fig. 5.

Lake Mendota drift leg time series at night. (top) MSF 1-Hz flux data and 1-min means of the reference net surface heat flux. Flux plates are submerged by wave action at 1.900, 1.903, 1.916, 1.922, 1.930, and 1.938 h; recovery is incomplete. Foam covers the flux plates from 1.903 to 1.916, reducing sensor float–measured fluxes. (bottom) Submergence sensor 1-Hz data indicating foam and submergence events can be detected. A value of ≈1.0 indicates NOT submerged.

Citation: Journal of Atmospheric and Oceanic Technology 24, 5; 10.1175/JTECH1898.1

Fig. 6.
Fig. 6.

Lake Mendota drift leg for a 2-h period near sunset. (a) Time series showing good correlation between MSF and turbulent fluxes (COARE version 2.6a). (b) Correlation plot of net solar flux. (c) MSF net flux as compared to that derived using several bulk algorithms.

Citation: Journal of Atmospheric and Oceanic Technology 24, 5; 10.1175/JTECH1898.1

Fig. 7.
Fig. 7.

Comparison of MSF measurements with net heat flux estimated using bulk aerodynamic parameterizations, net longwave flux, and net solar irradiance. Positive values indicate energy loss from the lake; negative values indicate energy gain. For clarity, MSF error bars are only plotted on COARE symbols.

Citation: Journal of Atmospheric and Oceanic Technology 24, 5; 10.1175/JTECH1898.1

Fig. 8.
Fig. 8.

The ASIT, meteorological (Met.) mast, and undersea node are elements of the MVCO. Moored IMET buoys at locations E and F include surface heat flux measurement instrumentation. Dots represent drifting MSF and guard buoy tracks for several deployments. Dots also show an M/V Nobska ship track, which coincides with an MSF deployment.

Citation: Journal of Atmospheric and Oceanic Technology 24, 5; 10.1175/JTECH1898.1

Fig. 9.
Fig. 9.

A composite time series of (a) net surface heat flux and (b) net solar flux during CBLAST-Low 2003 from several platforms. Experiment times are normalized to solar daylight hours. Separate short-term deployments occurred in morning (m), early and late afternoon, as well as at sunset (s) to capture most of a daylight cycle.

Citation: Journal of Atmospheric and Oceanic Technology 24, 5; 10.1175/JTECH1898.1

Fig. 10.
Fig. 10.

Correlation plots of net surface heat flux comparing (a) MSF vs M/V Nobska (using direct covariance for turbulent fluxes), (b) MSF vs ASIT (also using direct covariance), (c) ASIT vs M/V Nobska, and (d) MSF vs results derived using IMET buoy data and the COARE version 2.6a turbulent flux algorithm.

Citation: Journal of Atmospheric and Oceanic Technology 24, 5; 10.1175/JTECH1898.1

Fig. 11.
Fig. 11.

Net solar flux intercomparison matrix. MSF vs (a) ASIT, (b) IMET buoy, and (c) M/V Nobska–based radiometers. Intercomparisons between estimated net solar flux from (d) ASIT, (e) IMET buoy, and (f) M/V Nobska radiometers.

Citation: Journal of Atmospheric and Oceanic Technology 24, 5; 10.1175/JTECH1898.1

Fig. 12.
Fig. 12.

Near-surface water temperature comparison between the MSF bulk water sensor (depth ∼1 cm) and the standard WOCE-type temperature probe on the Lagrangian drifter RDB01 (depth ∼ 10 cm).

Citation: Journal of Atmospheric and Oceanic Technology 24, 5; 10.1175/JTECH1898.1

Fig. 13.
Fig. 13.

Surface (skin) temperature intercomparsion matrix. MSF clear-plate temperature vs (a) M/V Nobska, (b) ASIT, and (c) IMET buoy. Intercomparisons between (d) ASIT, (e) IMET, and (f) M/V Nobska platforms.

Citation: Journal of Atmospheric and Oceanic Technology 24, 5; 10.1175/JTECH1898.1

Fig. 14.
Fig. 14.

Skin–bulk temperature difference vs net surface heat flux for a sunset deployment. The MSF flux plate–bulk water temperature difference shows elevated flux-plate temperature during daylight becoming negative at night. Model results use IMET buoy data. COARE (solid circles) and CFC results are for a cool skin only, whereas COARE (open circles) includes both cool-skin and warm-layer effects.

Citation: Journal of Atmospheric and Oceanic Technology 24, 5; 10.1175/JTECH1898.1

Fig. 15.
Fig. 15.

(top) Time series of net surface heat flux; (bottom) flux-plate submergence fraction. Mismatches in velocity between MSF, wind-driven seas, and ocean currents at a 15-m depth cause the flux plates to submerge for up to 60% of each 5-min sample period.

Citation: Journal of Atmospheric and Oceanic Technology 24, 5; 10.1175/JTECH1898.1

Table 1.

Range of measured variables for the Lake Mendota campaign.

Table 1.
Table 2.

Lake Mendota campaign fluxes (W m−2).

Table 2.
Table 3.

Lake Mendota campaign sublayer physics.

Table 3.
Table 4.

Net heat flux rms differences (W m−2) from the CBLAST-Low campaign: (a) MSF vs M/V Nobska, (b) MSF vs ASIT, (c) ASIT vs M/V Nobska, and (d) MSF vs IMET with COARE version 2.6a. Morning (m) and sunset (s).

Table 4.
Table 5.

Net solar flux rms differences (W m−2) from the CBLAST-Low campaign: (a) MSF vs ASIT, (b) MSF vs IMET, (c) MSF vs M/V Nobska, (d) M/V Nobska vs ASIT, (e) M/V Nobska vs IMET, and (f) IMET vs ASIT. Morning (m) and sunset (s).

Table 5.
Table 6.

Skin temperature mean bias (°C) from the CBLAST-Low campaign: (a) MSF-M/V Nobska, (b) MSF-ASIT, (c) MSF-IMET/COARE, (d) IMET/COARE-M/V Nobska, (e) IMET/COARE-ASIT and (f) ASIT-M/V Nobska. Morning (m) and sunset (s).

Table 6.
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