• Ballard, S. , H. Graber , M. Caruso , and R. Romeiser , 2018: Comparison of X-band SAR and COAMPS model wind fields and other air-sea interaction parameters in Monterey Bay. 2018 IEEE Int. Geoscience and Remote Sensing Symp., Valencia, Spain, IEEE, https://doi.org/10.1109/IGARSS.2018.8518950.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Compaleo, J. , C. Yardim , and L. Xu , 2021: Refractivity-from-clutter capable, software-defined, coherent-on-receive marine radar. Radio Sci. , 56, e2020RS007173, https://doi.org/10.1029/2020RS007173.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doyle, J. , Q. Jiang , R. Smith , and V. Grubii , 2011: Three dimensional characteristics of stratospheric mountain waves during T-REX. Mon. Wea. Rev. , 139, 323, https://doi.org/10.1175/2010MWR3466.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Edson, J. B. , and Coauthors , 2013: On the exchange of momentum over the open ocean. J. Phys. Oceanogr. , 43, 15891610, https://doi.org/10.1175/JPO-D-12-0173.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grachev, A. A. , L. S. Leo , H. J. Fernando , C. W. Fairall , E. Creegan , B. W. Blomquist , A. J. Christman , and C. M. Hocut , 2018: Air–sea/land interaction in the coastal zone. Bound.-Layer Meteor. , 167, 181210, https://doi.org/10.1007/s10546-017-0326-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hodur, R. , 1997: The Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). Mon. Wea. Rev. , 125, 14141430, https://doi.org/10.1175/1520-0493(1997)125<1414:TNRLSC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Högström, U. , E. Sahlée , A.-S. Smedman , A. Rutgersson , E. Nilsson , K. K. Kahma , and W. M. Drennan , 2018: The transition from downward to upward air–sea momentum flux in swell-dominated light wind conditions. J. Atmos. Sci. , 75, 25792588, https://doi.org/10.1175/JAS-D-17-0334.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lund, B. , H. C. Graber , and R. Romeiser , 2012: Wind retrieval from shipborne nautical X-band radar data. IEEE Trans. Geosci. Remote Sens. , 50, 38003811, https://doi.org/10.1109/TGRS.2012.2186457.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lund, B. , H. C. Graber , J. Xue , and R. Romeiser , 2013: Analysis of internal wave signatures in marine radar data. IEEE Trans. Geosci. Remote Sens. , 51, 48404852, https://doi.org/10.1109/TGRS.2012.2230635.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lund, B. , and Coauthors , 2018: Near‐surface current mapping by shipboard marine X‐band radar: A validation. J. Atmos. Oceanic Technol. , 35, 10771090, https://doi.org/10.1175/JTECH-D-17-0154.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • MacMahan , J. , 2017 : Increased aerodynamic roughness owing to surfzone foam . J. Phys. Oceanogr. , 47 , 21152122 , https://doi.org/10.1175/JPO-D-17-0054.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • MacMahan, J. , E. Thornton , J. Koscinski , and Q. Wang , 2018: Field observations and modeling of surf zone sensible heat flux. J. Appl. Meteor. Climatol. , 57, 13711383, https://doi.org/10.1175/JAMC-D-17-0228.1.

    • Search Google Scholar
    • Export Citation
  • National Geophysical Data Center, 2003, U.S. Coastal Relief Model vol. 7—­Central Pacific. NOAA National Centers for Environmental Information, accessed 1 January 2016, https://doi.org/10.7289/V50Z7152.

    • Crossref
    • Export Citation
  • National Research Council, 1992: Coastal Meteorology: A Review of the State of the Science. National Academies Press, 112 pp., https://doi.org/10.17226/1991.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ortiz-Suslow, D. G. , B. K. Haus , N. J. Williams , N. M. Laxague , A. J. H. M. Reniers , and H. C. Graber , 2015: The spatial-temporal variability of air-sea momentum fluxes observed at a tidal inlet. J. Geophys. Res. Oceans , 120, 660676, https://doi.org/10.1002/2014JC010412.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ortiz-Suslow, D. G. , B. K. Haus , H. C. Graber , N. Williams , and J. H. MacMahan , 2018: Observations of air-sea momentum flux variability across the inner shelf. J. Geophys. Res. Oceans , 123, 89708993, https://doi.org/10.1029/2018JC014348.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ortiz-Suslow, D. G. , and Coauthors , 2019: Interactions between nonlinear internal ocean waves and the atmosphere. Geophys. Res. Lett. , 46, 92919299, https://doi.org/10.1029/2019GL083374.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Potter, H. , 2015: Swell and the drag coefficient. Ocean Dyn. , 65, 375384, https://doi.org/10.1007/s10236-015-0811-4.

  • Ramos, R. J. , B. Lund , and H. C. Graber , 2009: Determination of internal wave properties from X-band radar observations. Ocean Eng. , 36, 10391047, https://doi.org/10.1016/j.oceaneng.2009.07.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tamura, H. , W. M. Drennan , C. O. Collins , and H. C. Graber , 2018: Turbulent airflow and wave-induced stress over the ocean. Bound.-Layer Meteor. , 169, 4766, https://doi.org/10.1007/s10546-018-0359-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Q. , D. P. Alappattu , S. Billingsley , B. Blomquist , R. J. Burkholder , A. J. ­Christman , and C. Yardim , 2018: CASPER: Coupled Air–Sea Processes and Electromagnetic Ducting Research. Bull. Amer. Meteor. Soc. , 99, 14491471, https://doi.org/10.1175/BAMS-D-16-0046.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, L. , C. Yardim , S. Mukherjee , R. J. Burkholder , Q. Wang , and H. J. S. Fernando , 2021: Frequency diversity in electromagnetic remote sensing of lower atmospheric refractivity. IEEE Trans. Antennas Propag. , 70, 547558, https://doi.org/10.1109/TAP.2021.3090828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, Z. , and Coauthors , 2018: Numerical study on the effect of air-sea-land interaction on the atmospheric boundary layer in coastal area. Atmosphere , 9, 51, https://doi.org/10.3390/atmos9020051.

    • Search Google Scholar
    • Export Citation
  • Yang, Z. , and Coauthors , 2019: Measurement-based numerical study of effects of realistic land topography and stratification on coastal marine atmospheric surface layer. Bound.-Layer Meteor. , 171, 289314, https://doi.org/10.1007/s10546-018-00423-2.

    • Search Google Scholar
    • Export Citation
  • Zhang, F. W. , W. M. Drennan , B. K. Haus , and H. C. Graber , 2009: On wind-wave-current interaction during the Shoaling Waves Experiment. J. Geophys. Res., 114, C01018, https://doi.org/10.1029/2008JC004998.

    • Search Google Scholar
    • Export Citation
  • View in gallery
    Fig. 1.

    (i) Location of CLASI Monterey Bay field studies. (ii) Synthetic aperture radar (SAR) image from the 2016 CLASI pilot study showing either atmospheric lee waves or internal waves (Ortiz-Suslow et al. 2019) south of the Santa Cruz mountains. (iii) A schematic of the “coast-aware” parameterization that is the ultimate goal of the CLASI data collection. (iv) A perspective of one of the study areas showing (v) the high-resolution atmospheric modeling that will bridge the gap between the field measurements and microscale weather prediction modeling. Data sources and credits: Elevation data: National Geophysical Data Center (2003). SAR: COSMO-Skymed (Italian Space Agency, CSTARS for downlink and processing). Image credit: J. MacMahan (iv) and D. Ortiz-Suslow (v).

  • View in gallery
    Fig. 2.

    (a) Fixed measurement locations of MB deployments shown as colored circles, including ASIS buoys, I-SPAR, and shore tower observations [central bay electromagnetic ducting line June–October 2021 (red), south central bay June–August 2021 (green), Asilomar Peninsula August–October 2021 (yellow), northern bay to be conducted June–August 2022 (blue)]. (b) Depth contours of 10, 20, 30, and 50 m shown increasing moving offshore Sentinel-1A SAR derived surface wind at 2106 UTC 11 Jun 2016. (c) I-SPAR station schematic showing bottom-mounted Nortek Signature 1000 Doppler profiler denoted as ADCP, RBR CTD samples and temperature profiles as well as I-SPAR mooring. (d) I-SPAR mooring photo with tether and Spotter. (e) ASIS photo.

  • View in gallery
    Fig. 3.

    (a) OSU LATROP drone. (b) NPS small boat with Heliokite carrying radiosonde. (c) LATROP radar. (d) EM transmission loss measurement tower and sketch of approach showing propagation measurements conducted to each EM-ASIS (inset i denoted by red location markers) from tower. (e) TO airplane based atmospheric surface layer observations (inset i) with CTV payload (inset ii).

  • View in gallery
    Fig. 4.

    (a) Observed winds (courtesy of Dr. David Ortiz-Suslow and Mr. Richard Lind, Naval Postgraduate School; left column) and COAMPS-predicted (right column) 10 m above surface wind speed (m s−1) frequency by wind direction (°) bins for the period 2014–18. Two example points area analyzed: a coastal site in the area of experiment 1 (top row) and a coastal site in the area of experiment 3 (bottom row). Modeled data consist of 3–12-h forecasts at a 4-km horizontal resolution using the nearest land-based model grid point to the observation. The top row includes data from the June–August period at 2000 LT only. The bottom row includes data from September–November at 0800 LT only. Data points where the wind speed was less than 2 m s−1 were removed. Figure credit: Jacob Yung (Science Applications International Corporation). (b) Schematic of LES nested within COAMPS. (c) LES of cross-shore and alongshore wind velocity fields at Elkhorn Slough, MB. (d) LES of wind-wave interaction.

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CLASI: Coordinating Innovative Observations and Modeling to Improve Coastal Environmental Prediction Systems

Brian K. HausRosenstiel School of Marine and Atmospheric Sciences, University of Miami, Miami, Florida;

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David G. Ortiz-SuslowNaval Postgraduate School, Monterey, California;

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James D. DoyleU.S. Naval Research Laboratory, Monterey, California;

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David D. FlaggU.S. Naval Research Laboratory, Monterey, California;

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Hans C. GraberRosenstiel School of Marine and Atmospheric Sciences, University of Miami, Miami, Florida;

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Jamie MacMahanNaval Postgraduate School, Monterey, California;

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Lian ShenUniversity of Minnesota, Minneapolis, Minnesota;

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Qing WangNaval Postgraduate School, Monterey, California;

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Neil J. WillamsRosenstiel School of Marine and Atmospheric Sciences, University of Miami, Miami, Florida;

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Caglar YardimOhio State University, Columbus, Ohio

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Abstract

The Coastal Land–Air–Sea Interaction (CLASI) project aims to develop new “coast-aware” atmospheric boundary and surface layer parameterizations that represent the complex land–sea transition region through innovative observational and numerical modeling studies. The CLASI field effort involves an extensive array of more than 40 land- and ocean-based moorings and towers deployed within varying coastal domains, including sandy, rocky, urban, and mountainous shorelines. Eight Air–Sea Interaction Spar (ASIS) buoys are positioned within the coastal and nearshore zone, the largest and most concentrated deployment of this unique, established measurement platform. Additionally, an array of novel nearshore buoys and a network of land-based surface flux towers are complemented by spatial sampling from aircraft, shore-based radars, drones, and satellites. CLASI also incorporates unique electromagnetic wave (EM) propagation measurements using a coherent array, drone receiver, and a marine radar to understand evaporation duct variability in the coastal zone. The goal of CLASI is to provide a rich dataset for validation of coupled, data assimilating large-eddy simulations (LES) and the Navy’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). CLASI observes four distinct coastal regimes within Monterey Bay, California (MB). By coordinating observations with COAMPS and LES simulations, the CLASI efforts will result in enhanced understanding of coastal physical processes and their representation in numerical weather prediction (NWP) models tailored to the coastal transition region. CLASI will also render a rich dataset for model evaluation and testing in support of future improvements to operational forecast models.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Brian K. Haus, bhaus@miami.edu

Abstract

The Coastal Land–Air–Sea Interaction (CLASI) project aims to develop new “coast-aware” atmospheric boundary and surface layer parameterizations that represent the complex land–sea transition region through innovative observational and numerical modeling studies. The CLASI field effort involves an extensive array of more than 40 land- and ocean-based moorings and towers deployed within varying coastal domains, including sandy, rocky, urban, and mountainous shorelines. Eight Air–Sea Interaction Spar (ASIS) buoys are positioned within the coastal and nearshore zone, the largest and most concentrated deployment of this unique, established measurement platform. Additionally, an array of novel nearshore buoys and a network of land-based surface flux towers are complemented by spatial sampling from aircraft, shore-based radars, drones, and satellites. CLASI also incorporates unique electromagnetic wave (EM) propagation measurements using a coherent array, drone receiver, and a marine radar to understand evaporation duct variability in the coastal zone. The goal of CLASI is to provide a rich dataset for validation of coupled, data assimilating large-eddy simulations (LES) and the Navy’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). CLASI observes four distinct coastal regimes within Monterey Bay, California (MB). By coordinating observations with COAMPS and LES simulations, the CLASI efforts will result in enhanced understanding of coastal physical processes and their representation in numerical weather prediction (NWP) models tailored to the coastal transition region. CLASI will also render a rich dataset for model evaluation and testing in support of future improvements to operational forecast models.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Brian K. Haus, bhaus@miami.edu

For some time (National Research Council 1992), it has been recognized that operational wind forecasts are deficient at the coastal boundary. These wind field errors propagate downstream into coastal wave, current, and storm surge forecast systems degrading the forecast accuracy, which directly impacts public safety and national security operations and decision-making within the coastal domain. The current state-of-the-art operational numerical weather forecast systems cannot explicitly represent sharp transitions at the coastline and instead smear the inshore and offshore winds across a grid-size-dependent region of up to 6 km in each direction to represent the wind at the coast. Additionally, strong gradients in topography, surface roughness, heating, and evaporation present similar challenges in coastal margins. These can fundamentally change the unresolved processes that impact environmental predictions.

Hence, a focused effort is necessary to understand the main contributing factors to model errors within the complex coastal region. In particular, the surface layer ­parameterizations used within most forecast models do not account for the dynamic feedback between wind stress, short surface waves, and upper ocean currents (Grachev et al. 2018; Ortiz-Suslow et al. 2015, 2018), or the role that shoaling wind and swell waves, coastal temperature fronts and internal waves, and subsurface and subaerial topography have on winds as well as temperature, moisture, and clouds.

The CLASI experimental program

To address the Navy’s outstanding needs for operational numerical weather prediction in these challenging littoral environments, the Office of Naval Research (ONR) funded a pilot study in 2016 to quantify errors in the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) model near the Monterey Bay (MB) coastline as summarized in Fig. 1. The Coastal Land–Air–Sea Interaction (CLASI) pilot focused on advancing the capabilities of the Navy’s COAMPS (Hodur 1997; Doyle et al. 2011) to simulate the atmosphere and ocean ­environment close to the land–sea boundaries at ultrahigh resolutions (grid spacing < 1 km). The CLASI Pilot study was a highly successful field campaign which highlighted significant spatial and temporal variability of winds, waves, and air–sea fluxes associated with terrain/coastal conditions that are challenges for environmental models.

Based on these encouraging results the ONR CLASI Departmental Research Initiative was designed to provide comprehensive longer-term fluxes from Air–Sea Interaction Spar (ASIS) and shallow water I-SPAR buoy moorings (Fig. 2) supplemented with satellite-, aircraft-, and drone-based remote observations, land-based towers, boat observations, and high-resolution large-eddy simulation (LES) (Fig. 1). It is anticipated that the unique CLASI dataset will provide a framework for developing “coast-aware” parameterizations of air–sea coupling that can be used when open ocean formulations such as COARE-3 (Edson et al. 2013) are not applicable. These may include drag coefficients that consider topography, waves, and/or currents. The investigation will evaluate surface momentum and scalar turbulent fluxes over land along the coast, where existing parameterizations do not sufficiently capture the complex three-dimensional impacts of this transition zone. The CLASI datasets will also serve as a benchmark for future atmospheric surface layer parameterization development.

Fig. 1.
Fig. 1.

(i) Location of CLASI Monterey Bay field studies. (ii) Synthetic aperture radar (SAR) image from the 2016 CLASI pilot study showing either atmospheric lee waves or internal waves (Ortiz-Suslow et al. 2019) south of the Santa Cruz mountains. (iii) A schematic of the “coast-aware” parameterization that is the ultimate goal of the CLASI data collection. (iv) A perspective of one of the study areas showing (v) the high-resolution atmospheric modeling that will bridge the gap between the field measurements and microscale weather prediction modeling. Data sources and credits: Elevation data: National Geophysical Data Center (2003). SAR: COSMO-Skymed (Italian Space Agency, CSTARS for downlink and processing). Image credit: J. MacMahan (iv) and D. Ortiz-Suslow (v).

Citation: Bulletin of the American Meteorological Society 103, 3; 10.1175/BAMS-D-20-0304.1

Fig. 2.
Fig. 2.

(a) Fixed measurement locations of MB deployments shown as colored circles, including ASIS buoys, I-SPAR, and shore tower observations [central bay electromagnetic ducting line June–October 2021 (red), south central bay June–August 2021 (green), Asilomar Peninsula August–October 2021 (yellow), northern bay to be conducted June–August 2022 (blue)]. (b) Depth contours of 10, 20, 30, and 50 m shown increasing moving offshore Sentinel-1A SAR derived surface wind at 2106 UTC 11 Jun 2016. (c) I-SPAR station schematic showing bottom-mounted Nortek Signature 1000 Doppler profiler denoted as ADCP, RBR CTD samples and temperature profiles as well as I-SPAR mooring. (d) I-SPAR mooring photo with tether and Spotter. (e) ASIS photo.

Citation: Bulletin of the American Meteorological Society 103, 3; 10.1175/BAMS-D-20-0304.1

The CLASI science team have expanded the scope of the project to include the study of electromagnetic wave (EM) propagation in the atmospheric surface layer known as the evaporation duct (ED) (Wang et al. 2018). The CLASI EM component includes continuous measurements of the near-surface atmospheric refractive environment from specially outfitted ASIS buoys (qT-ASIS), small-boat tethered balloon profiling, and low-level sampling by an instrumented Twin Otter (TO) research aircraft and its controlled towed vehicle (CTV). Concurrent radar propagation loss measurements (PLM) for S, C, and X band are being conducted between the three qT-ASIS buoys, shore-based and TO beacons and a coherent receiver array on the 10-m tower, a two-element drone-based receiver, and a coherent-on-receive marine radar at the Moss Landing Marine Laboratory (Compaleo et al. 2021; Xu et al. 2021).

Beginning in June 2021 and lasting through August 2022, the CLASI field program in MB includes three distinct experiments, each focused on a different land–air–sea interaction regime (Fig. 2). The major goal of the first deployment is to sample the cross-shore variability of wind, stress, and ED conditions in the vicinity of a planar sandy beach and relatively weak changes in inland topography. The second MB-based effort shifts focus to the Monterey ­Peninsula region at the southern end of the bay. It is typified by a complex, rocky shoreline and provides an opportunity to sample land–air–sea interactions across a headland with strong changes in both subsurface and subaerial topography that create a range of coastal wind and wave conditions. CLASI’s third experiment focuses on the northern coastline of MB, where the Santa Cruz Mountains topographically constrain the winds and create strong alongshore and cross-shore variability of primarily alongshore flows. An ED array extending from 30 km offshore to 8 km inland of Moss Landing, California, including qT-ASIS buoys and flux towers, complements each of the three MB experiments. This array provides a long-term time series of the cross-shore evolution of fluxes, wind, temperature, and humidity.

Air–sea interaction observations.

The ASIS buoy (Fig. 2) is ideal for making direct measurements of the air–sea fluxes due to its design as a stable floating platform which allows measurements of turbulence in the atmospheric and oceanic boundary layers with minimal platform motion or flow distortion. For CLASI, ASIS is deployed with three-dimensional sonic anemometers to directly measure the air–sea fluxes of momentum (i.e., wind stress) and heat in the near-surface, undisturbed flow. These sensors directly resolve the turbulent motions that primarily contribute to the fluxes. The buoys have both GPS navigation and inertial motion systems to remove platform motion from the measured fast-sampling ultrasonic anemometry.

There is growing experimental evidence (much of it derived from observations made aboard ASIS) that ocean surface waves influence the momentum flux and calculation of the wind stress vector in both magnitude and direction (Zhang et al. 2009; Ortiz-Suslow et al. 2018; Tamura et al. 2018). Observations also suggest that the direction of the surface wind stress is not coaligned with the direction of the wind when swell and/or large wind waves are present (Potter 2015; Högström et al. 2018). For CLASI, ASIS buoys resolve the local directional wave spectrum using an array of robust wave wires. This information will help to elucidate the link between surface wind stress, magnitude, and direction, and the local sea state composed of swell and wind sea.

In addition to five standard ASIS buoys, three specifically designed ASIS buoys for CLASI were deployed to measure the near-surface gradients of wind, temperature, and humidity within 0.5–1 m of the (instantaneous) surface and extending to 3–4 m above the mean water level. These qT-ASIS buoys are equipped with radiometric sea surface temperature (SST), up/downwelling long/shortwave radiation, and a subsurface temperature profile along the ASIS frame and mooring line extending from within 1–1.5 m of the surface to 10–30-m depths (depending on the overall mooring depth). The ASIS deployment orchestrated for CLASI is the largest ever executed and the most extensive use of ASIS to simultaneously sample within a coastal region—eight total buoys, including three qT-ASIS.

The I-SPAR buoy measures wind stress and sensible heat flux at 5 m above mean sea level in water depths of 8–25 m from a low-profile, fixed mooring capable of making long-term measurements of air–sea fluxes at energetic, shallow water sites (MacMahan 2017, MacMahan et al. 2018). The I-SPAR consists of a three-dimensional ultrasonic anemometer, temperature, and humidity sensor and an IMU sensor. A bottom-mounted, upward-facing Nortek Signature 1000 is collocated with the I-SPAR. It measures directional wave information, current profiles, and vertical backscatter to detect near-surface bubbles via whitecapping, and turbulence. A temperature/CTD string provides estimates of stratification and to identify fronts and internal waves common to the inner shelf. An example of a collocated deployed I-SPAR, ADCP, and T/CTD string is provided in Fig. 2. This is the first full-scale deployment of this innovative system which offers an unprecedented opportunity to fill the capability gap between land-based towers and the deeper ASIS.

We have deployed 12 additional directional wave buoys in conjunction with the ASIS and I-SPAR moorings. We have chosen the highly capable yet simple to deploy Spoondrift Spotter buoy which uses GPS and an inertial motion unit (IMU) to measure wave directional spectra, The buoy samples at a rate up to 2.5 Hz and provides directional wave spectra over a full 360° with a frequency range of 0.03–1.0 Hz and a vertical displacement accuracy of ∼±2 cm. They are solar powered and have Iridium communications to provide real-time wave statistics and position data.

Land-based measurements.

A network of land towers complements the in-water platforms. Coordinated with the qT-ASIS array in the central portion of the bay, an approximately east–west line of surface flux towers runs from the shore extending inland to ∼8 km. This array includes a 10-m shore tower equipped with three level of momentum, temperature, and moisture fluxes and mean wind the temperature/humidity profiles (Fig. 3), as well as radiometric SST of the upwind surf zone, up/downwelling long/shortwave radiation, a wind lidar, and continuous ocean surface visualization via high-definition camera. The other surface flux towers along this line are 6-m-tall single mast tripods topped with IRGASON systems and mean temperature, humidity, and wind measurements. Land-based flux stations are installed as needed at other coastal sites for different phases of the experiment. Additional, shore-based flux masts are installed for the second and third experiments within the surf zone across the subaerial beach to the top of the dune. An IRGASON system is installed on one of these shore towers, to provide latent heat and CO2 flux. These nearshore towers will span an array approximately ±0.5 km of the coastline.

Fig. 3.
Fig. 3.

(a) OSU LATROP drone. (b) NPS small boat with Heliokite carrying radiosonde. (c) LATROP radar. (d) EM transmission loss measurement tower and sketch of approach showing propagation measurements conducted to each EM-ASIS (inset i denoted by red location markers) from tower. (e) TO airplane based atmospheric surface layer observations (inset i) with CTV payload (inset ii).

Citation: Bulletin of the American Meteorological Society 103, 3; 10.1175/BAMS-D-20-0304.1

Fig. 4.
Fig. 4.

(a) Observed winds (courtesy of Dr. David Ortiz-Suslow and Mr. Richard Lind, Naval Postgraduate School; left column) and COAMPS-predicted (right column) 10 m above surface wind speed (m s−1) frequency by wind direction (°) bins for the period 2014–18. Two example points area analyzed: a coastal site in the area of experiment 1 (top row) and a coastal site in the area of experiment 3 (bottom row). Modeled data consist of 3–12-h forecasts at a 4-km horizontal resolution using the nearest land-based model grid point to the observation. The top row includes data from the June–August period at 2000 LT only. The bottom row includes data from September–November at 0800 LT only. Data points where the wind speed was less than 2 m s−1 were removed. Figure credit: Jacob Yung (Science Applications International Corporation). (b) Schematic of LES nested within COAMPS. (c) LES of cross-shore and alongshore wind velocity fields at Elkhorn Slough, MB. (d) LES of wind-wave interaction.

Citation: Bulletin of the American Meteorological Society 103, 3; 10.1175/BAMS-D-20-0304.1

Remote observations.

A Helmholtz Zentrum Geesthacht (HZG) Marine X-band radar (MR) system is installed on a shore tower located along the buoy transect line for each experiment. The system is used to retrieve the surface roughness with spatial resolution of 7.5 m out to a typical range of 3 km. From the surface roughness images the wind speed and direction (Lund et al. 2012), internal wave properties (Ramos et al. 2009; Lund et al. 2013), directional wavenumber spectrum, and surface current (Lund et al. 2018) are derived with spatial resolution of 100–500 m.

Both historical and new satellite synthetic aperture radar (SAR) and optical imagery are being collected over MB to understand the spatially and temporally varying wind and wind stress vectors in the nearshore waters (Ballard et al. 2018). We are focusing on SAR imagery because of its all-weather and around-the-clock capability. This is particularly important in MB, which often has cloudy conditions and a dense morning fog layer. New collects will be coordinated with boat and airborne observations to guide them to locations of interest and validate the satellite SAR data products. Interpretation of the SAR backscatter images will provide maps of waves, wind, drag coefficient, and wind stress vectors from the nearshore region out to the open ocean. With the large number of high-quality in situ measurements in the complex coastal zone CLASI offers a tremendous opportunity to improve SAR data analysis techniques.

Characterizing propagation in the coastal environment

The EM propagation component of CLASI exploits buoys, aircraft, PLM, small boats, radars, and drones (Fig. 3). The qT-ASIS buoys are specifically outfitted to capture ED conditions. Transmission of EM signals through the link between the qT-ASIS and shore-based tower provides continuous PLM throughout the experiments. The long time series of PLM is ideal for understanding the ED properties and their impact on EM signals. The TO aircraft is outfitted with turbulence sampling, fast-response temperature and water vapor sensors, a GPS IMU, and a downward-looking infrared pyrometer to measure SST. The TO is also instrumented to measure cloud microphysics, solar and thermal radiation, and aerosol physical properties. The aircraft–shore link provides range and altitude-dependent PL measurements with high spatial resolution. A recent addition to the TO is the CTV, which is a target drone instrumented to make similar measurements as on the TO. The CTV can maintain a radar altitude height of 10 m above the ocean waves while being towed from the TO 300 m above.

The University of Miami (U-Miami) and Ohio State University (OSU) groups both use UAS platforms to obtain relevant atmospheric variables in the nearshore environment. Since UAS are not perfect platforms owing to their payload limits, flight duration, and propeller wash potentially biasing measurements, new measurements and sampling schemes have been developed to improve their utility for mean and turbulence-scale perturbation measurements.

Experiments 1 and 2 each include an intense observing period, coinciding with TO operations, executed for coordinated sampling of the MB near-surface and regional boundary layer structure. This includes sampling from small, agile research vessels, surface layer profiling using a tethered balloon system, and 5–8 radiosonde launches per day from a shore site. The research vessels, the R/V John H. Martin (JHM) and a rigid hull inflatable boat (RHIB), operated by the Moss Landing Marine Laboratory (MLML) Marine Operation, are equipped with flux systems with ancillary bulk temperature, SST, and platform motion measurement.

Modeling

This project advances the capabilities of COAMPS by analyzing and validating model representation of key physical processes in the littoral region through concurrent simulations of the environment with COAMPS, idealized representations with COAMPS, and idealized simulation through LES (Fig. 4). We use LES as a research tool to study coastal land–air–sea interaction. The simulations utilize a powerful LES computational package called Wave-Ocean-Wind (WOW) developed in-house at the University of Minnesota, which can accurately handle wind, waves, wave breaking, currents, and sea spray in the coastal environment with complex topography as demonstrated in MB by Yang et al. (2018).

Each of the experimental domains will be directly captured in the LES with high spatial resolution (Fig. 4). The initial and boundary conditions in the LES will be based on the field measurements, with synergistic interactions between the LES-based study and other research components in CLASI. Moreover, LES results will be compared with the spatially distributed data from the X-band radar and satellite remote sensing, the TO-CTV and UASs. The LES results will provide detailed descriptions of the 3D flow field evolution in time and provide an understanding of the variability that is subgrid scale to COAMPS and critical to NWP physics parameterizations. To discover the physical mechanisms for the situations when Monin–Obukhov similarity theory fails in the coastal region, we will analyze the spatially inhomogeneous turbulent flow field to understand the role of land–sea inhomogeneity, internal boundary layer development, coastline angle, horizontal terrain gradients, and the effects of stratification on surface layer flux-profile relationships critical to near-surface wind prediction.

The propagation of EM signal in air is governed by the refractive index, which is a function of humidity and temperature. While many models of ED have been proposed in the past, they are based on homogeneous surface conditions and not applicable in the coastal region where strong heterogeneity associated with the land–sea contrast is present. Models using LES can be used as a powerful research tool to provide detailed descriptions of water vapor, temperature, and refractive index fields that affect the EM propagation features. One recent study (Yang et al. 2019) indicated that the ED height over land and water can be significantly different due to the effects of the complex land topography and the sea-land transition.

In summary, CLASI will utilize the suite of observations collected with LES to develop a coast-aware parameterization for surface turbulent fluxes within COAMPS, which presently insufficiently captures observed flux sensitivity to wind direction at the coast. Investigators will evaluate the impact of ground/soil moisture, terrain, and surface roughness on this observed behavior to diagnose a versatile, scale-aware parameterization for COAMPS.

Acknowledgments.

This work was sponsored by the U.S. Office of Naval Research (ONR) through an ONR Departmental Research Initiative (DRI), Coastal Land–Air–Sea Interaction (CLASI). We thank Dr. Reginald Beach and Dr. Daniel P. Eleuterio of ONR for supporting this project during very challenging times for research. Thanks to the captain and crew members of R/V Sally Ride and the R/V John Martin. Thanks to John Kemp, Jim Ryder, and Nico Llanos of the Woods Hole Oceanographic Mooring group for their invaluable contributions to the mooring design and deployment. Thanks to the Long Marine Lab Moss Landing Marine Laboratory, the city of Marina Water and Sewer Department, and the city of Pacific Grove Water and Sewer Department for providing locations and support for shore based stations. Thanks to the other researchers, students, and staff, without which this work would not be possible. From the University of Miami: Dr. William Drennan, Dr. Milan Curcic, Dr. Sanchit Mehta, Dr. Bjoern Lund, Jacob Brooks, Ryland Lewis, Rolando Gonzalez, Cedric Guigand, Samantha Medina, Samantha Ballard, Samantha Furtney, Jennifer Stone, and Frances May. From the Naval Postgraduate School: Charlotte Benbow and Paul Jessen, Christopher Miller, Jesus Ruiz-Plancarte, and Ryan Yamaguchi. From the Naval Research Laboratory: Dr. Xiaodong Hong, Dr. James Hlywiak, and Chad Hutchins. From Science Applications International Corporation: Jacob Yung and Daniel Geiszler. From Coastal Carolina University: Mathew Stanek. From the University of Wisconsin–Milwaukee: Andrew Westgate. From the University of Minnesota: Xuanting Hao and Jagmohan Singh.

Data availability statement.

Descriptions of measurements including instruments, quantities observed, locations, timing, etc. will be posted on the CLASI site (at https//clasidri.weebly.com). Local archives of raw data will be maintained at individual institutions. Quality controlled data will be made available on a dedicated archive set up through the University of Minnesota as it becomes available (at https://safl-nextcloud-tst-01.oit.umn.edu). A permanent site is being established at https://clasi.safl.umn.edu. For particularly large model runs, radar data files and imaging data will be available by request.

References

  • Ballard, S. , H. Graber , M. Caruso , and R. Romeiser , 2018: Comparison of X-band SAR and COAMPS model wind fields and other air-sea interaction parameters in Monterey Bay. 2018 IEEE Int. Geoscience and Remote Sensing Symp., Valencia, Spain, IEEE, https://doi.org/10.1109/IGARSS.2018.8518950.

    • Search Google Scholar
    • Export Citation
  • Compaleo, J. , C. Yardim , and L. Xu , 2021: Refractivity-from-clutter capable, software-defined, coherent-on-receive marine radar. Radio Sci. , 56, e2020RS007173, https://doi.org/10.1029/2020RS007173.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doyle, J. , Q. Jiang , R. Smith , and V. Grubii , 2011: Three dimensional characteristics of stratospheric mountain waves during T-REX. Mon. Wea. Rev. , 139, 323, https://doi.org/10.1175/2010MWR3466.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Edson, J. B. , and Coauthors , 2013: On the exchange of momentum over the open ocean. J. Phys. Oceanogr. , 43, 15891610, https://doi.org/10.1175/JPO-D-12-0173.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grachev, A. A. , L. S. Leo , H. J. Fernando , C. W. Fairall , E. Creegan , B. W. Blomquist , A. J. Christman , and C. M. Hocut , 2018: Air–sea/land interaction in the coastal zone. Bound.-Layer Meteor. , 167, 181210, https://doi.org/10.1007/s10546-017-0326-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hodur, R. , 1997: The Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). Mon. Wea. Rev. , 125, 14141430, https://doi.org/10.1175/1520-0493(1997)125<1414:TNRLSC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Högström, U. , E. Sahlée , A.-S. Smedman , A. Rutgersson , E. Nilsson , K. K. Kahma , and W. M. Drennan , 2018: The transition from downward to upward air–sea momentum flux in swell-dominated light wind conditions. J. Atmos. Sci. , 75, 25792588, https://doi.org/10.1175/JAS-D-17-0334.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lund, B. , H. C. Graber , and R. Romeiser , 2012: Wind retrieval from shipborne nautical X-band radar data. IEEE Trans. Geosci. Remote Sens. , 50, 38003811, https://doi.org/10.1109/TGRS.2012.2186457.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lund, B. , H. C. Graber , J. Xue , and R. Romeiser , 2013: Analysis of internal wave signatures in marine radar data. IEEE Trans. Geosci. Remote Sens. , 51, 48404852, https://doi.org/10.1109/TGRS.2012.2230635.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lund, B. , and Coauthors , 2018: Near‐surface current mapping by shipboard marine X‐band radar: A validation. J. Atmos. Oceanic Technol. , 35, 10771090, https://doi.org/10.1175/JTECH-D-17-0154.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • MacMahan , J. , 2017 : Increased aerodynamic roughness owing to surfzone foam . J. Phys. Oceanogr. , 47 , 21152122 , https://doi.org/10.1175/JPO-D-17-0054.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • MacMahan, J. , E. Thornton , J. Koscinski , and Q. Wang , 2018: Field observations and modeling of surf zone sensible heat flux. J. Appl. Meteor. Climatol. , 57, 13711383, https://doi.org/10.1175/JAMC-D-17-0228.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • National Geophysical Data Center, 2003, U.S. Coastal Relief Model vol. 7—­Central Pacific. NOAA National Centers for Environmental Information, accessed 1 January 2016, https://doi.org/10.7289/V50Z7152.

  • National Research Council, 1992: Coastal Meteorology: A Review of the State of the Science. National Academies Press, 112 pp., https://doi.org/10.17226/1991.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ortiz-Suslow, D. G. , B. K. Haus , N. J. Williams , N. M. Laxague , A. J. H. M. Reniers , and H. C. Graber , 2015: The spatial-temporal variability of air-sea momentum fluxes observed at a tidal inlet. J. Geophys. Res. Oceans , 120, 660676, https://doi.org/10.1002/2014JC010412.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ortiz-Suslow, D. G. , B. K. Haus , H. C. Graber , N. Williams , and J. H. MacMahan , 2018: Observations of air-sea momentum flux variability across the inner shelf. J. Geophys. Res. Oceans , 123, 89708993, https://doi.org/10.1029/2018JC014348.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ortiz-Suslow, D. G. , and Coauthors , 2019: Interactions between nonlinear internal ocean waves and the atmosphere. Geophys. Res. Lett. , 46, 92919299, https://doi.org/10.1029/2019GL083374.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Potter, H. , 2015: Swell and the drag coefficient. Ocean Dyn. , 65, 375384, https://doi.org/10.1007/s10236-015-0811-4.

  • Ramos, R. J. , B. Lund , and H. C. Graber , 2009: Determination of internal wave properties from X-band radar observations. Ocean Eng. , 36, 10391047, https://doi.org/10.1016/j.oceaneng.2009.07.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tamura, H. , W. M. Drennan , C. O. Collins , and H. C. Graber , 2018: Turbulent airflow and wave-induced stress over the ocean. Bound.-Layer Meteor. , 169, 4766, https://doi.org/10.1007/s10546-018-0359-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Q. , D. P. Alappattu , S. Billingsley , B. Blomquist , R. J. Burkholder , A. J. ­Christman , and C. Yardim , 2018: CASPER: Coupled Air–Sea Processes and Electromagnetic Ducting Research. Bull. Amer. Meteor. Soc. , 99, 14491471, https://doi.org/10.1175/BAMS-D-16-0046.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, L. , C. Yardim , S. Mukherjee , R. J. Burkholder , Q. Wang , and H. J. S. Fernando , 2021: Frequency diversity in electromagnetic remote sensing of lower atmospheric refractivity. IEEE Trans. Antennas Propag. , 70, 547558, https://doi.org/10.1109/TAP.2021.3090828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, Z. , and Coauthors , 2018: Numerical study on the effect of air-sea-land interaction on the atmospheric boundary layer in coastal area. Atmosphere , 9, 51, https://doi.org/10.3390/atmos9020051.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, Z. , and Coauthors , 2019: Measurement-based numerical study of effects of realistic land topography and stratification on coastal marine atmospheric surface layer. Bound.-Layer Meteor. , 171, 289314, https://doi.org/10.1007/s10546-018-00423-2.

    • Search Google Scholar
    • Export Citation
  • Zhang, F. W. , W. M. Drennan , B. K. Haus , and H. C. Graber , 2009: On wind-wave-current interaction during the Shoaling Waves Experiment. J. Geophys. Res., 114, C01018, https://doi.org/10.1029/2008JC004998.

    • Search Google Scholar
    • Export Citation
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