Air-Sea Observations from ALPS

Jim Thomson, Sophia Merrifield, James Girton, Sebastiaan Swart, Chris Meinig, and Luc Lenain

Introduction

Air-sea interactions are essential processes in forecast and climate models, yet observations of these processes remain sparse. Despite significant progress over the last 50 years, the air-sea interaction community is still actively working on developing better understanding of the fundamental processes occurring in the coupling between the ocean and the atmosphere, such as the kinematics and dynamics of momentum, heat, moisture, and gas (carbon dioxide in particular) exchange (i.e., flux) between the atmosphere and ocean, as well as the structure of turbulence in the ocean boundary layer. Traditional methods observe these fluxes from research platforms (e.g., Grare et al., 2013), ships (e.g., Edson et al., 1998), and moored surface buoys (e.g., Edson et al., 2013). These approaches have driven considerable progress in air-sea flux estimation, including the TOGA-COARE routines for bulk estimates (Fairall et al., 2003). However, shipboard measurements often suffer from flow contamination and interference associated with the ship superstructure. Attempts have been made to account for those effects (Landwehr et al., 2015), but it remains a major source of error. Additionally, both ships and moorings can have significant operational costs and deployment restrictions. Alternative approaches using autonomous and Lagrangian platforms have emerged in recent years, with considerable progress made in the last decade. As the level of autonomy has improved, including capabilities such as AIS ship traffic avoidance, users and developers are pressing forward with more comprehensive suites of air-sea observations.

Autonomous surface platforms have their own challenges. While the small size of these platforms can be an advantage in making a minimal disturbance within the signal of interest (e.g., near-surface stratification, atmospheric turbulence), the platforms often experience significant motion contamination and limitations in sensor heights/depths. One example is in wind measurements, which are typically made above the wave-influenced layer (e.g., Hara and Sullivan, 2015) and corrected to a 10 m reference height. Small platforms often can only support short masts (1 m is common), and there may be significant wave sheltering effects in measuring winds at these heights. These effects are small for moderate wind speeds, and then become increasingly significant above 20 m s–1 (Donelan et al., 2012). Work is ongoing to improve interpretations of wind speed and wind stress (i.e., momentum flux) measured at low heights.

Another challenge for autonomous surface platforms is biofouling, because surface platforms are constantly in a productive zone (by definition). This is particularly relevant to heat flux estimates, because incoming short- and long-wave radiation often dominate the ocean’s surface heat budget. Downwelling radiometer measurements are thus essential, but these instruments perform best when routinely cleaned (which is difficult to achieve on autonomous platforms). These and other measurement challenges are being pursued by a broad community of developers and users. Many of these systems are well beyond demonstration phase and are in operational use for research and monitoring. The following is a brief survey of various recent developments in using ALPS for air- sea measurements. This list covers water platforms only, though there has been notable activity in making similar air-sea measurements from aerial platforms (e.g., Reineman et al., 2016).

 

Recent Developments and Examples of Air-Sea Fluxes from ALPS

Wind-Driven Autonomous Surface Vehicles

Wind-driven autonomous surface vehicles, such as the Saildrone (Saildrone Inc.), the Datamaran (Autonomous Marine Systems Inc.), and the Sailbuoy (Offshore Sensing AS), have demonstrated the ability to survey large areas of open ocean while collecting air-sea data. The Pacific Marine Environmental Laboratory at the National Oceanic and Atmospheric Administration (PMEL-NOAA) has been using Saildrones for multi-month research surveys in the Bering Sea (Meinig et al., 2015). Figure 1 shows the Saildrone and associated instrumentation.

Figure 1. A Saildrone deployed off the coast of Alaska, with a three-axis sonic anemometer at the top of the sail, along with temperature, humidity, and radiation on a forward probe. Image credit: Saildrone Inc.

 The Saildrone has a particular advantage of mast height for atmospheric measurements above the wave-affected layer. Figure 1 shows a three-axis sonic anemometer many meters above the surface, which is much higher than many of the other autonomous surface vehicles can support.

Wave-driven Autonomous Surface Vehicles

Wave-driven autonomous surface vehicles, such as the Liquid Robotics Wave Glider or the Autonaut, have become common platforms for air-sea observations. For example, Lenain and Melville (2014) used a Wave Glider to measure waves heights up to 10 m and winds up to 37 m s–1 in Tropical Cyclone Freda. Using the motion of the surface flotation for wave measurements, they measured and analyzed the evolution of the directional wave field as the storm passed near the wave glider. The Langmuir turbulence number, the Stokes depth scale, and the Stokes drift computed from measurements of these directional wave spectrum across the track of TC Freda showed remarkable agreement with hurricane marine boundary layer studies that include numerical wind-wave model predictions as input to the Large Eddy Simulation (LES) model of the marine boundary layer (Sullivan et al., 2012).

Figure 2. A Wave Glider before deployment off the Antarctic Peninsula, with a three-axis sonic anemometer at the bow, along with temperature, humidity, and pressure sensors on a mast. Image credit: Avery Snyder (APL-UW)

Following on this success, Mitarai and McWilliams (2016) used a Wave Glider to measure winds up to 32 m s–1 during Typhoon Danas. More recently, Schmidt et al. (2017) used a Wave Glider to measure winds and evaluate global satellite and reanalysis wind products. Very recently, Thomson and Girton (2017) used a Wave Glider to observe air-sea interactions across the fronts of the Antarctic Circumpolar Current (ACC) in a mission that lasted four months and spanned wave heights up to 6 m and winds up to 18 m s–1. As shown in Figure 2, their sensor payload included many of the same sensors that NOAA-PMEL has integrated on the Saildrone, such as a three-axis sonic anemometer.

Wave Gliders have also been used to estimate air-sea gas exchange, notably of CO2 (Monteiro et al., 2015), which has provided insight into the scale of variability of bio-physical exchange at the sea surface. The gas exchange application has progressed rapidly in recent years, with autonomy dramatically increasing the amount of data collected (e.g., Sutton et al., 2014).

Fuel/Electric Autonomous Surface Vehicles

In addition to wind- or wave-powered systems, there are many fuel/electric-powered autonomous surface crafts in use for data collection, such as the C-Enduro from ASV Global. Many of these systems are in use for air-sea measurements (e.g., Srinivasan et al., 2013). Codiga (2015) demonstrated coastal surveys with such a system. Hole et al. (2016) demonstrated directional wave estimation from such systems. These systems generally have less endurance than their wind- or wave-powered counterparts, but deployments exceeding a month and more have been successfully completed.

Lagrangian Surface Drifters

Figure 3. Lagrangian drifters: (a) ASIS, (b) SIO minibuoy, (c) Spoondrift Spotter, (d) SWIFT.

Although lacking the navigation capability of autonomous surface vehicles, Lagrangian surface drifters provide excellent air-sea observations. In many cases, the Lagrangian nature of the platform provides robust estimates of surface currents and waves (e.g., Herbers et al., 2012), as well as a reference frame with minimal contamination of turbulent signals (Thomson, 2012). Such platforms have included detailed measurements of the high-frequency tail of the wave spectrum (Graber et al., 2000) and evolution during high winds (Drennan et al., 2014). These platforms have also been used to measure the motions within breaking waves (Amador and Canals, 2016). As demonstrated by the Scripps minibuoys, deploying large numbers of drifting assets can supplement existing/conventional operational networks, such as the National Data Buoy Center (NDBC). Figure 3 shows a selection of drifters presently in use for research and operational data collection. Many other similar systems are available commercially, as well as produced by various academic research labs. In some cases, buoys that are traditionally moored, like the Woods Hole Oceanographic Institution’s air-sea flux buoys, can be allowed to drift as Lagrangian platforms.

 

Future Work with ALPS

ALPS will undoubtedly continue to expand the quantity and quality of air-sea observations collected for both research and operational uses. Specific advances in the near future may include:

  • Extended endurance of platforms, including engineering solutions to harness energy from waves, currents, or winds, as well as energy storage improvements.
  • Improved motion correction of sensor data (via integrated/synchronous IMUs)
  • Autonomous feature sampling (e.g., mapping fronts)
  • Antifouling/cleaning for radiometers and other optical sensors
  • Ocean profiles (via automated casting or towed chains)
  • Lower atmosphere profiles (via partner/coordinated unmanned aerial systems)
  • Development of novel biogeochemical and physical sensors
  • Automated coordination between unmanned platforms (aerial, surface, and underwater vehicles)

 

References

Amador, A., and M. Canals. 2016. Design and development of an instrumented drifter for Lagrangian measurements of inertial particle dynamics in breaking waves. IEEE Journal of Oceanic Engineering 41(1):82–93, https://doi.org/10.1109/JOE.2015.2389591.

Codiga, D.L. 2015. A marine autonomous surface craft for long-duration, spatially explicit, multidisciplinary water column sampling in coastal and estuarine systems. Journal of Atmospheric and Oceanic Technology 32(3):627–641, https://doi.org/10.1175/JTECH-D-14-00171.1.

Donelan, M.A., M. Curcic, S.S. Chen, and A.K. Magnusson. 2012. Modeling waves and wind stress. Journal of Geophysical Research 117, C00J23, https://doi.org/​10.1029/2011JC007787.

Drennan, W.M., H.C. Graber, C.O. Collins, A. Herrera, H. Potter, R.J. Ramos, and N.J. Williams. 2014. EASI: An air–sea interaction buoy for high winds. Journal of Atmospheric and Oceanic Technology 31(6):1,397–1,409, https://doi.org/10.1175/JTECH-D-13-00201.1.

Edson, J.B., A.A. Hinton, K.E. Prada, J.E. Hare, and C.W. Fairall. 1998. Direct covariance flux estimates from mobile platforms at sea. Journal of Atmospheric and Oceanic Technology 15(2):547–562, https://doi.org/​10.1175/1520-0426(1998)015<0547:DCFEFM>2.0.CO;2.

Edson, J.B., V. Jampana, R.A. Weller, S.P. Bigorre, A.J. Plueddemann, C.W. Fairall, S.D. Miller, L. Mahrt, D. Vickers, and H. Hersbach. 2013. On the exchange of momentum over the open ocean. Journal of Physical Oceanography 43(8):1,589–1,610, https://doi.org/10.1175/JPO-D-12-0173.1.

Fairall, C., E. Bradley, J. Hare, A. Grachev, and J. Edson. 2003. Bulk parameterization of air-sea fluxes: Updates and verification for the COARE algorithm. Journal of Climate, 16:571–591, https://doi.org/​10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2.

Graber, H., E. Terray, M. Donelan, W. Drennan, J.V. Leer, and D. Peters. 2000. ASIS–​A new air-sea interaction spar buoy: Design and performance at sea. Journal of Atmospheric and Oceanic Technology 17(5):708–720, https://doi.org/10.1175/​1520-0426(2000)017<0708:AANASI>2.0.CO;2.

Grare, L., L. Lenain, and W.K. Melville. 2013. Wave-coherent airflow and critical layers over ocean waves. Journal of Physical Oceanography 43(10):2,156–2,172, https://doi.org/10.1175/JPO-D-13-056.1.

Hara, T., and P.P. Sullivan. 2015. Wave boundary layer turbulence over surface waves in a strongly forced condition. Journal of Physical Oceanography 45(3):868–883, https://doi.org/10.1175/JPO-D-14-0116.1.

Herbers, T.H.C., P.F. Jessen, T.T. Janssen, D.B. Colbert, and J.H. MacMahan. 2012. Observing ocean surface waves with GPS tracked buoys. Journal of Atmospheric and Oceanic Technology 29(7):944–959, https://doi.org/10.1175/JTECH-D-11-00128.1.

Hole, L.R., I. Fer, and D. Peddie. 2016. Directional wave measurements using an autonomous vessel. Ocean Dynamics 66(9):1,087–1,098, https://doi.org/10.1007/s10236-016-0969-4.

Landwehr, S., N. O’Sullivan, and B. Ward. 2015. Direct flux measurements from mobile platforms at sea: Motion and airflow distortion corrections revisited. Journal of Atmospheric and Oceanic Technology 32(6):1,163–1,178, https://doi.org/​10.1175/JTECH-D-14-00137.1.

Lenain, L., and W.K. Melville. 2014. Autonomous surface vehicle measurements of the ocean’s response to Tropical Cyclone Freda. Journal of Atmospheric and Oceanic Technology 31(10):2,169–2,190, https://doi.org/10.1175/JTECH-D-14-00012.1.

Meinig, C., N. Lawrence-Slavas, R. Jenkins, and H.M. Tabisola. 2015. The use of saildrones to examine spring conditions in the bering sea: Vehicle specification and mission performance. In OCEANS 2015 – MTS/IEEE Washington, October 19–22, 2015, https://doi.org/10.23919/OCEANS.2015.7404348.

Mitarai, S., and J.C. McWilliams. 2016. Wave glider observations of surface winds and currents in the core of Typhoon Danas Geophysical Research Letters 43:11,312–11,319, https://doi.org/10.1002/2016GL071115.

Monteiro, P.M.S., L. Gregor, M. Lévy, S. Maenner, C.L. Sabine, and S. Swart. 2015. Intraseasonal variability linked to sampling alias in air-sea CO2 fluxes in the Southern Ocean. Geophysical Research Letters 42:8,507–8,514, https://doi.org/​10.1002/2015GL066009.

Reineman, B.D., L. Lenain, and W.K. Melville. 2016. The use of ship-launched fixed-wing uavs for measuring the marine atmospheric boundary layer and ocean surface processes. Journal of Atmospheric and Oceanic Technology 33(9):2,029–2,052, https://doi.org/10.1175/JTECH-D-15-0019.1.

Schmidt, K.M., S. Swart, C. Reason, and S. Nicholson. 2017. Evaluation of satellite and reanalysis wind products with in situ wave glider wind observations in the Southern Ocean. Journal of Atmospheric and Oceanic Technology, https://doi.org/​10.1175/JTECH-D-17-0079.1.

Srinivasan, R., V. Suseentharan, G.C. Vivek, C. Thangavel, V. Gowthaman, and T. Sudhakar. 2013. Development of an autonomous catamaran based observatory system for collecting meteorological and oceanographic parameters at Gulf of Khambhat-Gujarat. In 2013 Ocean Electronics (SYMPOL), Conference held October 23–25, 2013, Kochi, India, https://doi.org/10.1109/SYMPOL.2013.6701938.

Sullivan, P.P., L. Romero, J.C. McWilliams, and W.K. Melville. 2012. Transient evolution of Langmuir turbulence in ocean boundary layers driven by hurricane winds and waves. Journal of Physical Oceanography 42(11):1,959–1,980, https://doi.org/​10.1175/JPO-D-12-025.1.

Sutton, A.J., C.L. Sabine, S. Maenner-Jones, N. Lawrence-Slavas, C. Meinig, R.A. Feely, J.T. Mathis, S. Musielewicz, R. Bott, P.D. McLain, and others. 2014. A high-​frequency atmospheric and seawater pCO2 data set from 14 open-ocean sites using a moored autonomous system. Earth System Science Data 6(2):353–366, https://doi.org/10.5194/essd-6-353-2014.

Thomson, J. 2012. Wave breaking dissipation observed with SWIFT drifters. Journal of Atmospheric and Oceanic Technology 29(12):1,866–1,882, https://doi.org/​10.1175/JTECH-D-12-00018.1.

Thomson, J., and J. Girton. 2017. Sustained measurements of Southern Ocean air-sea coupling from a wave glider autonomous surface vehicle. Oceanography 30(2):104–109, https://doi.org/10.5670/oceanog.2017.228.

 

Authors

Jim Thomson, Applied Physics Laboratory, University of Washington, Seattle, WA, USA, jthomson@apl.washington.edu

Sophia Merrifield, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA, smerrifield@ucsd.edu

James Girton, Applied Physics Laboratory, University of Washington, Seattle, WA, USA, girton@apl.washington.edu

Sebastiaan Swart, University of Gothenburg, Gothenburg, Sweden, sebastiaan.swart@marine.gu.se

Chris Meinig, National Oceanic and Atmospheric Administration/Pacific Marine Environmental Laboratory, Seattle, WA, USA, christian.meinig@noaa.gov

Luc Lenain, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA, llenain@ucsd.edu