Very-near-surface ocean currents are dominated by wind and wave
forcing and have large impacts on the transport of buoyant materials in the
ocean. Surface currents, however, are under-resolved in most operational
ocean models due to the difficultly of measuring ocean currents close to, or
directly at, the air–sea interface with many modern instrumentations. Here,
observations of ocean currents at two depths within the first meter of the
surface are made utilizing trajectory data from both drogued and undrogued
Consortium for Advanced Research on Transport of Hydrocarbon in the Environment
(CARTHE) drifters, which have draft depths of 60 and 5 cm, respectively.
Trajectory data of dense, colocated drogued and undrogued drifters were
collected during the Lagrangian Submesoscale Experiment (LASER) that took
place from January to March of 2016 in the northern Gulf of Mexico.
Examination of the drifter data reveals that the drifter velocities become
strongly wind- and wave-driven during periods of high wind, with the
pre-existing regional circulation having a smaller, but non-negligible,
influence on the total drifter velocities. During these high wind events, we
deconstruct the total drifter velocities of each drifter type into their
wind- and wave-driven components after subtracting an estimate for the
regional circulation, which pre-exists each wind event. In order to capture
the regional circulation in the absence of strong wind and wave forcing, a
Lagrangian variational method is used to create hourly velocity field
estimates for both drifter types separately, during the hours preceding each
high wind event. Synoptic wind and wave output data from the Unified Wave
INterface-Coupled Model (UWIN-CM), a fully coupled atmosphere, wave and
ocean circulation model, are used for analysis. The wind-driven component of
the drifter velocities exhibits a rotation to the right with depth between
the velocities measured by undrogued and drogued drifters. We find that the
average wind-driven velocity of undrogued drifters (drogued drifters) is
∼3.4 %–6.0 % (∼2.3 %–4.1 %) of the wind
speed and is deflected ∼5–55∘
(∼30–85∘) to the right of the wind, reaching higher
deflection angles at higher wind speeds. Results provide new insight on the
vertical shear present in wind-driven surface currents under high winds,
which have vital implications for any surface transport problem.
Introduction
Very-near-surface currents are especially sensitive to wind and wave
forcing, which dominates the dynamics in the upper few centimeters of the
ocean (Wu, 1983). Plastics at the surface have been observed to be
transported to our coasts by wind- and wave-induced currents and can be
transported differentially depending on their buoyancy, which dictates their
positioning in the upper water column (Isobe et al., 2014). Through
numerical modeling, Le Hénaff et al. (2012) found that wind- and
wave-induced currents had a strong impact on the fate of surface oil during
the Deepwater Horizon oil spill in 2010. Another modeling study showed that
in extreme events like hurricanes, Stokes drift, or the forward velocity
induced by the depth decaying orbital motion of waves (Stokes, 1847), plays
a major role in accurately predicting the movement of Lagrangian particles
at the surface (Curcic et al., 2016). Despite the drastic impact on the
dispersal of buoyant pollutants, wind- and wave-driven dynamics in the upper
few centimeters are poorly understood and not resolved in modern operational
ocean models, which have used a wide range of parameterizations of
wind-driven dynamics over relatively deep surface layers, forced with
climatological winds (Chassignet et al., 2003, 2007).
Observational data that capture the vertical shear within the first meter
of wind-driven surface currents are very limited in the real ocean as well.
This is mainly due to the limitations of the instrumentation used to gather
these physical data. The majority of Lagrangian drifter studies have used
classic drifter designs, namely CODE and Surface Velocity Program (SVP) drifters (Davis, 1985; Lumpkin and Pazos, 2007), that span depths ranging from 1 to 30 m (Lumpkin et al.,
2017) and are therefore unrepresentative of the wind forced current at the
very surface. Santala and Terray (1992) describe the difficulty in measuring
surface velocities, as well as near-surface shear, due to wave bias present
when utilizing any instrumentation whose motion is dependent on wave action.
Acoustic Doppler current profilers (ADCPs) are not able to accurately
measure velocities near an undulating boundary, making it difficult to
sample surface layers above depths of 0.5 m (Cole and Symonds, 2015;
Sentchev et al., 2017). High-frequency (HF) radars, excluding those with
multi-frequency capabilities, are known to measure vertically integrated
surface currents, with about 80 % of the radar signal originating in the
upper 1 m, depending on the electromagnetic frequency (∼16 MHz) of the
radar, such that vertical shear within this depth is not
detectible in the measurement (Stewart and Joy, 1974; Rohrs et al., 2015).
Some work, however, has shown promise in detecting
vertical shear by calculating Doppler shifts from multiple Bragg wave peaks
simultaneously (Ivonin et al., 2004).
Using a novelty suite of instruments in the Gulf of Mexico, Laxague et al. (2018) observed wind- and wave-induced surface currents within 1 cm of the
surface, under low winds (U10= 4 m s-1), to be twice as fast as the average current over
the first 1 m and 4 times as fast as the average current over the upper
10 m (the upper 10 m average being equal to ∼0.16 m s-1). Another study using drifters of
multiple draft depths and HF radar data led to a similar result, showing
that HF radar velocity measurements taken at ∼2–3 m depth,
were, on average, only 55.7 % of that measured with ultra-thin drifters
with 5 cm draft depths, whose absolute velocity was on the order of 0.3 m s-1 (Morey et al., 2018). Comparison of
surface velocities measured with ultra-thin drifters of 5 and 10 cm
draft depths also showed a rapid decay of velocity away from the surface,
with the 10 cm drifters traveling 10 % slower on average (Morey et al.,
2018). The sharp change in wind- and wave-induced velocities observed in
these two studies reveals the need for increased vertical resolution of these
surface measurements (Laxague et al., 2018; Morey et al., 2018).
Review of literature on wind-driven surface currents using real
ocean, observational data.
LiteratureInstrumentMeasurementWindAvg. velocity ofAvg. deflectionuseddepthmagnitudewind-drivenangle to thecurrentright of(% of wind)windArdhuin et al. (2009)HF radar0–1 m1–19 m s-10.4 %–0.8 %45–70∘Kim et al. (2010)HF radar0–1 m1–5 m s-12 %–5 %50–90∘Berta et al. (2018)HF radar0–0.75 m10–20 m s-12 %25–30∘Sentchev et al. (2017)ADCP0.5–0.75 m1–6 m s-12 %–4 %12–25∘Poulain et al. (2009)Undrogued SVP0–0.25 m0–15 m s-12 %17–20∘CODE drifter0–1 m1 %28–30∘Röhrs and ChristenseniSphere0–0.15 m0–20 m s-13 %–5 %64∘(2015)CODE drifter0.3–1 m2.4 %84∘Current workCARTHE undrogued0–0.05 m11–20 m s-13.6 %–6 %5–55∘CARTHE drogued0–0.60 m2.3 %–4.1 %30–85∘
Classical Ekman theory states that wind-driven currents at the very surface
travel at a deflection angle of 45∘ to the right of the wind in the
Northern Hemisphere, when one assumes a balance between Coriolis and
friction under stationary, homogeneous conditions (Ekman, 1905). Laboratory
studies have shown that total surface drift currents induced by wind and
waves combined travel at ∼3.1 % of the wind velocity,
with the wind-induced drift decreasing and the wave-induced drift
increasing, with increasing fetch (Wu, 1983). Various observational studies
on wind-driven currents over the upper ∼1 m of the surface,
which utilized a range of different instruments including HF radars, ADCPs
and drifters of various types (i.e. CODE, iSphere or undrogued SVP
drifters), have reported a wide range of deflection angles ranging from
15 to 90∘ to the right of the wind at varying wind speeds
(Ardhuin et al., 2009; Poulain et al., 2009; Kim et al., 2010; Röhrs and
Christensen, 2015; Sentchev et al., 2017; Berta et al., 2018). Individual
results and details of these studies are presented in Table 1. Combined
results of these studies produce a range of estimates for the magnitude of
wind-induced currents, with reported values ranging from 0.4 % to 5 %
of the wind velocity, over the upper ∼1 m, showing that
results may vary significantly based on environmental conditions and
methodology (Ardhuin et al., 2009; Poulain et al., 2009; Kim et al., 2010;
Röhrs and Christensen, 2015; Sentchev et al., 2017; Berta et al., 2018).
Changes in upper ocean stratification and mixed layer depth have been shown
to have large effects on the deflection angle and relative velocity of
surface currents with respect to the wind (Kudryavtsev and Soloviev, 1990;
Sutherland et al., 2016). Rascle and Ardhuin (2009) showed how the
complicated relationship between wave-induced mixing and varying
stratification can result in quasi-Eulerian wind-driven surface currents
ranging from ∼1 % to 3 % of the wind speed, having
deflection angles ranging from ∼35 to 90∘.
Here, we use drogued and undrogued Consortium for Advanced Research on Transport
of Hydrocarbon in the Environment (CARTHE) drifters, having draft depths of
60 and 5 cm, respectively (Novelli et al., 2017), in order to observe
very-near-surface currents in the northern Gulf of Mexico during high wind
conditions. The data used for analysis were collected during the Lagrangian
Submesoscale Experiment (LASER), a campaign in which over 1000 biodegradable
CARTHE drifters were deployed during the winter of 2016 (Haza et al., 2018).
Utilizing a subset of the CARTHE drifters from the experiment, we focus on
three synoptic-scale high wind events during which wind and waves seem to
dominate the forcing of the surface flows measured by both drifter types.
During these high wind events, we deconstruct the full drifter velocities of
each drifter type into their wind- and wave-driven components after
subtracting an estimate for the regional circulation which pre-exists each
wind event. We then report on the vertical shear of the wind-driven
component calculated from the undrogued and drogued drifter velocities. To
the authors' knowledge, these findings are the first reported estimates of
vertical shear in upper 1 m, involving a direct velocity deconstruction and
differentiation between wind- and wave-driven components under high wind
conditions (∼12–20 m s-1).
Upon inspection of the total velocity of the drifters during the high wind
events, we hypothesize that there are three dominant components that drive
the drifter velocities: a wind-driven component, a wave-driven component
and the regional circulation that pre-existed each high wind event. Assuming
a simple linear superposition of the velocity components, we define the
total velocity of each drifter type as the depth-averaged integral:
u¯T=1h∫0husz+uwz+urczdz,
where uw is the purely wind-driven velocity, us is the Stokes
drift velocity, urc is the regional circulation that exists before the
increase in synoptic winds, z is the depth being evaluated, and h is the
draft depth of the respective drifter. Given this definition of the total
drifter velocities, we neglect possible nonlinear interactions between
velocity components, which will be considered in future investigations.
The three components in Eq. (1) were estimated as follows: us was
calculated numerically with the Unified Wave INterface-Coupled Model
(UWIN-CM; Chen et al., 2013) during the LASER campaign and stored for later
analysis, urc is estimated from the dense population of drifters in the
region in the hours preceding substantial increases in synoptic winds using
Lagrangian variational analysis (LAVA; Taillandier et al., 2006) to
create Eulerian velocity fields of the regional circulation, and we solve
for uw by subtracting the estimated regional circulation and Stokes
drift velocity from the total velocity of the drifters. To evaluate the
performance of the LAVA-derived velocity fields, we use Aviso sea surface
temperature (SST) and absolute geostrophic velocity products, in addition to
ocean current velocity fields from the Navy Coordinate Ocean Model (NCOM)
for comparison.
In the northwestern Mediterranean Sea, the underlying geostrophic currents
were shown to retain their structure and influence the surface flow under
high wind forcing for time periods on the order of 2–3 d (Berta et al.,
2018). In the present study, the timescales over which the flow features of
the regional circulation, urc, retain their structure under strong
winds are difficult to determine given the current dataset. However, the
subtraction of the pre-existing regional circulation from the total velocity
of the drifters during the high wind events shows a posteriori that the
pre-existing regional circulation retains its structure, to a reasonable
extent, during the high wind analysis periods on which we focus. After
subtracting the pre-existing regional circulation and numerically calculated
Stokes drift velocity, us, from the full velocity of the drifters,
u¯T, during each period of high wind, we are left with an estimate
for the average, purely wind-driven component, uw, of each drifter type.
The paper is organized as follows: Sect. 2 describes the CARTHE drifters,
the configuration of UWIN-CM and NCOM, and the Aviso data products. Section 3
explains LAVA used to create the
estimated Eulerian velocity fields of the pre-existing regional circulation,
as well as the calculations involved in the deconstruction of the total
drifter velocities, u¯T, along each drifter trajectory during the
high wind events. Results are presented in Sect. 4, with a discussion of
the results following in Sect. 5. Concluding remarks are presented in
Sect. 6.
DataCARTHE drifter
The CARTHE drifter is a biodegradable surface drifter that consists of a
Spot Trace GPS unit by Global Star, a torus float which contains the GPS
housing, two interlocking panels that form the drogue and a flexible rubber
tube that connects the drogue and float (Novelli et al., 2017). During the
LASER experiment, over 1000 CARTHE drifters were deployed with drogues;
however, over the first 7 weeks of the experiment, approximately 40 % of
the drifters lost their drogues (Haza et al., 2018). Drogued and undrogued
drifters have draft depths of 60 and 5 cm, respectively, and have been
extensively analyzed with respect to their specific drift characteristics
during laboratory experiments performed by Novelli et al. (2017). For this
study, a subset of colocated drogued and undrogued drifters from the LASER
campaign is used for analysis, due to their opportunistic location during
the passage of large atmospheric fronts across the northern Gulf of Mexico.
Drogue loss during the experiment mostly coincided with large storm and wave
events and the precision of the determined time of drogue loss was 0.5 to
3 h for 85 % of the drifters (Haza et al., 2018). The method for drogue
loss detection is based on the differential velocities of the drifters, as
the undrogued drifters are preferentially accelerated by the higher
velocities of wind- and wave-driven currents present in the shallower
surface layer in which the undrogued drifters reside. In addition, undrogued
drifters display a decreased, more sporadic GPS transmission rate, due to
their tendency to be flipped by large or breaking waves, which points the
GPS antennae downwards and reduces the ability of the GPS to transmit until
the drifter is flipped upright again. Despite this, 80 % of the time
intervals between transmissions by undrogued drifters are still between 4.5
and 5.5 min, but with notably higher outliers than the drogued drifters
(Haza et al., 2018). The algorithm for drogue detection used by Haza et al. (2018)
was validated using a subset of 50 drifters with known drogue status
and was shown to distinguish drogued and undrogued drifters with an accuracy
of 94 %–100 %. This very successful drogue detection algorithm has provided
the opportunity to utilize both drogued and undrogued drifters to study the
variation of very-near-surface currents with depth (Haza et al., 2018).
GPS transmissions reported each drifter's location every 5 min during
the experiment with an accuracy of about 7 m. In addition to the extensive
categorization of drogued and undrogued drifters, the drifter trajectory
data were also quality controlled for missing transmissions and linearly
interpolated to regular 15 min intervals (Haza et al., 2018). Velocities
were then calculated, resulting in estimates for the average velocity of
each drifter over 15 min intervals.
When using drifters to study ocean currents, concerns about different
sources of velocity slip must be addressed. Extensive laboratory testing
performed by Novelli et al. (2017) using half-scale drogued and undrogued
drifters showed that in the absence of wind and waves drogued (undrogued)
drifters travel within 0.01 m s-1 (0.02 m s-1) of the mean Eulerian current
averaged over the draft depth of the given drifter. Under the effects of
waves, observed in the absence of wind, the undrogued drifter unsurprisingly
feels an acceleration due to Stokes drift, but the drogued drifter feels a
reduced wave-induced acceleration due to the flexible tether holding
together its float and drogue. This mechanical decoupling partially removes
the effect of the Stokes drift acting on the drogued drifter, mainly
dampening the effects of Stokes drift above the drogue (Novelli et al.,
2017).
To characterize the slip velocity associated with wind, waves and Eulerian
current, Novelli et al. (2017) defines the “absolute” slip velocity as the
difference between the velocity of a drifter and the depth-integrated
current over each drifters' draft. The absolute slip velocity of both
drogued and undrogued drifters during laboratory testing was found to
decrease with increasing wind speed, decreasing from 3 to 0.1 cm s-1
for the drogued drifter and from 14 to 1 cm s-1 for
the undrogued drifter, for wind speeds from 8.1 to 23 cm s-1.
This phenomenon is thought to be caused by wind separation from the ocean
surface due to the presence of surface gravity waves (Novelli et al., 2017).
Another laboratory experiment, focused on measuring the turbulent air flow
over wind generated waves in a similar wind-wave tank, found wind separation
to occur over 90 % of short wind waves at 10 m wind speeds of
∼16 m s-1 (Buckley and
Veron, 2017). The extent to which wind separation occurs and its
effectiveness of sheltering the drifters from wind slip in the real ocean is
difficult to quantify given the scale differences between the laboratory
wave tank and open ocean. However, another recent experiment using full-size
CARTHE drogued and undrogued drifters in the real ocean, alongside a suite
of instruments including an ADCP and polarimetric camera, showed that
velocities calculated using both drifter types fell within the range of
velocities measured by other instrumentation over the corresponding draft of
each drifter (Laxague et al., 2018). Because the extent to which velocity
slip affects the drifters in the real ocean is not well known during high
wind and wave conditions, we chose not to attempt a correction of the
drifter velocities to account for such measurement errors.
UWIN-CM
During the extent of the LASER campaign, daily 72 h real-time forecasts
were produced using UWIN-CM,
running a fully coupled atmosphere, surface gravity wave and ocean
circulation system (Chen et al., 2013; Chen and Curcic, 2016b). The UWIN-CM
model atmospheric component is comprised of the atmospheric non-hydrostatic
Weather Research and Forecasting (WRF) model featuring advanced research WRF
dynamical core with 4 km horizontal resolution over the Gulf of Mexico with
36 vertical layers (Skamarock et al., 2008; Haza et al., 2018).
The surface gravity wave model is the University of Miami Wave Model v2
(UMWM; Donelan et al., 2012), with the same 4 km resolution as the
atmospheric component. The three-dimensional Stokes drift velocity fields
are calculated as the full integral:
us=∫02π∫kminkmaxωk2cosh2kd+z2sinh2kdFk,θdkdθ,
where ω is the angular frequency, k is the wavenumber, d is the mean
water depth, z is the depth being evaluated, F is the wavenumber energy
spectrum, and θ is the direction of the waves (Stokes, 1847;
Phillips, 1977). Over the entirety of LASER, k ranged from 0.0039 to
16.0739 rad m-1, in 37 logarithmic increments, which corresponds
to a range of wavelengths from 1611 to 0.39 m, respectively. The Hybrid
Coordinate Ocean Model (HYCOM) v2.2 is used as the ocean circulation model;
however, no model output from this circulation model is used for the analysis
of wind-driven currents. The surface layer of the HYCOM model has a minimum
thickness of 3 m, which makes the model unrepresentative of the depths
sampled by the drifters and very difficult to validate with observational
data (Wallcraft et al., 2009). Comparison of drifter data and surface
velocities from the circulation model showed obvious discrepancies at the
spatial scales necessary for this study.
The coupling between the model components is as follows: WRF passes
radiative and heat fluxes, as well as precipitation rates to the ocean
model, as well as the air density and wind profiles to the wave model. The
wave model (UMWM) passes vectorial atmosphere stress and vectorial ocean
stress to the atmosphere model and ocean circulation model, respectively.
The ocean circulation model passes SST to the atmosphere model and surface
current fields and ocean density to the wave model. WRF and UMWM fields are
exchanged every 60 s, while HYCOM fields are exchanged every 120 s (Chen and
Curcic, 2016b).
The UWIN-CM is initialized daily using initial and boundary conditions from
Global Forecast System (GFS) and global HYCOM fields along with the previous
day's UMWM wave forecast. Coupling between models is executed using the
Earth System Modeling Framework (ESMF) in which all components are exchanged
between models every minute (Hill et al., 2004). Hourly 10 m wind (U10) and
Stokes drift velocity data from 24 to 48 h of each
daily 72 h forecast were stored and used for the analysis performed in
this study. Initially having a temporal resolution of 1 h, the UWIN-CM
model output was interpolated to 15 min intervals to match the time
resolution of the drifter data.
NCOM
The ocean circulation component of NCOM is chosen for comparison to the
LAVA-derived velocity fields, over the ocean component of the UWIN-CM, due
to its increased resolution of 1 km and data assimilation of available
satellite and in situ observations (Jacobs et al., 2014). NCOM forecasts
were produced in real time during the LASER campaign and were utilized to
guide scientists in the field. The NCOM velocity fields are produced on
3 h intervals, having a vertical resolution of 2 m from 0 to 10 m, with
larger vertical layers as depth increases. The surface wind stress for the
NCOM ocean circulation is calculated using atmospheric conditions from the
Coupled Ocean/Atmosphere Mesoscale Predictions System (COAMPS; Hodur, 1997).
Surface heat fluxes are determined from ocean model SST and 10 m air
temperature and humidity, through bulk flux formulations. Locally generated
tidal forcings are implemented using the Oregon State University global
Ocean Inverse Solution (OTIS) (Egbert and Erofeeva, 2002). During LASER, the
NCOM forecasts were able to accurately represent the mesoscale velocity
field in the Gulf of Mexico, to a certain extent, due to the assimilation of
altimetry-based sea surface height (SSH) (Haza et al., 2019). However, Haza et al. (2019) found
the wind stress parameterization implemented with COAMPS to considerably
underestimate the wind-driven component of the surface current when trying
to reconstruct drifter trajectories using NCOM surface velocities. For this
reason, the authors chose not to rely on the NCOM wind stress for
information on wind-driven drifter velocities.
Aviso data
Absolute geostrophic currents and SST products, produced by Collecte
Localisation Satellites (CLS) from Aviso satellite altimetry data, are used
in this study to assess the performance of the estimated velocity fields
created using the drifter data and LAVA in the hours preceding each high
wind event. The absolute geostrophic current is deduced from fields of
absolute dynamic topography using stencil width methodology, which provides
daily (24 h intervals) velocity fields with horizontal resolutions of
0.25∘ (∼28 km) (Arbic et al., 2012). The Aviso SST
products in the Gulf of Mexico are measured by four intercalibrated satellite
infrared radiometers and have a resolution of 0.02∘
(∼2 km) in latitude and longitude. Each 24 h period, over
which the SST measurements are processed and averaged, is centered at
00:00 UTC of each day.
Research area in the northern Gulf of Mexico, ESE of the
Mississippi River Delta. National Data Buoy Center (NDBC) buoy locations used for validation of UWIN-CM
wind and wave data are shown in yellow triangles. Domains corresponding to
each high wind event analyzed are shown in the red (24 February),
blue (22 January) and black (20 March) boxes. Black hash
marks show the ship track where data in Fig. 2 were collected. Grey contours
show the bathymetry at 100, 500 and 2500 m.
MethodsHigh wind events and region of interest
The domain for this study lies to the east and southeast of the Mississippi
River Delta spanning from 27.5 to 30.5∘N and
90 to 86.5∘W. The spatial extent of the data used for
each high wind event is outlined in Fig. 1, along with National Data Buoy Center (NDBC) buoy locations
used for verification of the UWIN-CM wind data. Initial deployment of these
drifters occurred during January and February of 2016 with the intent of
capturing submesoscale dynamics on spatial scales of tens of meters to tens
of kilometers (Haza et al., 2018). The large number of drifters deployed
during the LASER campaign, and their relatively long transmission period of
about 3 months, provided an opportunity to collect data over a range of scales
and environmental conditions. The region and time periods chosen exhibit
large numbers of colocated drogued and undrogued drifters during the
passage of synoptic atmospheric fronts, which drive a large-momentum input
into the oceanic boundary layer.
Potential density (a), salinity (b) and temperature (c) data along
the eastward-traveling transect shown in Fig. 1, measured by moving vessel
profiler (MVP) conductivity–temperature–depth (CTD) casts taken on 28 January from 16:48 to 21:00 UTC.
In order to characterize the vertical structure of flow features typical in
this region, a moving vessel profiler (MVP) collected conductivity–temperature–depth (CTD) measurements when
possible during the first month of the experiment, focusing on the fronts
and eddies that form at the intersection of cold, fresh Mississippi outflow
water and the warmer, saltier Gulf of Mexico waters. Figure 2 shows a
typical transect (outlined in Fig. 1) of potential density, salinity and
temperature across a frontal structure in the region. These transects
display less dense Mississippi outflow waters extending down to
∼30 m depth from the surface, flowing over the denser
interior gulf water. The interior gulf water, seen to extend to the surface
towards the eastern end of the transect, shows a well-mixed surface layer
down to ∼80 m depth. Other transects across frontal areas
measured with the MVP show very similar structures in this region, making
Fig. 2 representative of the typical stratification observed between these
water masses.
Wind velocity magnitudes (a, b, c) and directions (d, e, f) during the
hours preceding, and including, each high wind event. Dashed vertical black
lines show the beginning and end of the high wind analysis periods.
Dashed (solid) red lines indicate beginning of the hour over which the
pre-existing regional circulation was estimated with undrogued (drogued)
drifters. U10 wind data from the UWIN-CM are plotted at the nearest point to
every drifter location. Wind observations from NDBC buoys BURL1, 42040 and
42012 are plotted as well. Solid lines show sustained winds, while dashed
lines show wind gusts. All wind directions are plotted as the direction in
which the wind is traveling. Buoy 42040 was only operational during the
first wind event and BURL1 did not record any wind direction data during the
experiment.
For this study, we choose to focus on three high wind events that occurred
on 22 January, 24 February and 20 March 2016.
Available wind data from nearby NDBC buoys (BURL1, 42040 and 42012), along
with the UWIN-CM wind data associated with each drifter position during and
before the high wind event, are plotted in Fig. 3. The time periods over
which we perform the deconstruction of the total measured surface currents
during each high wind event are marked by vertical black lines, and the
solid and dashed vertical red lines show the start of the hour over which the
pre-existing regional circulation is estimated from drogued and undrogued
drifters, respectively (Fig. 3). Validation of the UWIN-CM U10 output with
available NDBC buoy data revealed that during the passage of atmospheric
fronts through the domain there exists a fair amount of variability in the
modeled wind magnitude resulting, at times, in a large difference between
observed and modeled data. For this reason, we exclude any data associated
with modeled wind magnitude ranges above 6 m s-1 at any given 15 min time step.
Significant wave height (a), mean wavelength (b), mean wave period
(c) and mean wave direction (d) from the UWIN-CM and NDBC buoy 42040 during
the January high wind event. Dashed vertical black lines show the beginning
and end of the high wind analysis periods. Dashed (solid) red lines indicate
beginning of the hour over which the pre-existing regional circulation was
estimated with undrogued (drogued) drifters. Wave data from the UWIN-CM,
plotted at the nearest point to every drifter location, are shown in blue.
The solid black lines show the UWIN-CM output data closest to the
coordinates of buoy 42040, while observations from buoy 42040 are shown with
black dots. Wavelength data were not collected by this NDBC station.
Wave data from the UWIN-CM are also compared to the available NDBC wave data
in Fig. 4. Only data from NDBC buoy 42040 during the January high wind event
are shown for wave model validation, as there were no wave data collected from
buoy BURL1, and buoy 42040 stopped functioning before the February event.
Comparison of model output and observational data shows that the significant
wave height is modeled accurately, especially under high winds, while the
wave period could be slightly underestimated by the wave model. Mean wave
direction is initially offset by ∼30∘ at the
beginning of the high wind analysis period, but comes into good agreement
about halfway through the analysis window. Wave data from buoy 42012 also
showed good agreement with the UWIN-CM data near the buoy coordinates during
the March event, but as drifters move further offshore during the high wind
event, the nearshore location of the buoy becomes less representative of the
conditions influencing the drifters.
For further validation of the Stokes drift velocity computed with the
UWIN-CM model, we compare higher-frequency wave data from buoy 42040 to those
of the model. Figure 5a, b show the 2-D wave energy spectrum from 1 h
during the high wind analysis period from the model output and calculated
from the NDBC buoy using the Longuet-Higgins (1963) Fourier expansion method
coefficients provided by NDBC. Figure 5c shows the 1-D wave density spectrum
of the modeled and observed waves over the same hour displayed in Fig. 5a, b.
For validation of the Stokes velocity used by UWIN-CM, Eq. (2) is used
to calculate the Stokes velocity using the 2-D wave spectrum from buoy
42040. Buoy 42040 is a 2.1 m diameter, Self-Contained Ocean Observations Payload (SCOOP)-type buoy that extends down
∼1.68 m from the water line and reports wave spectrum data
from 0.02 to 0.48 Hz (Bouchard et al., 2018). In order to make a fair
comparison, we calculate the Stokes velocity using the 2-D modeled wave
spectrum from 0.0313 to 0.5 Hz, which are the closest corresponding
frequency bins to that of the NDBC data. We then integrate the modeled
Stokes velocity from the surface down to 1.68 m in order to accurately
compare the buoy and wave model calculated Stokes velocities. Both the
Stokes drift magnitude and direction calculated from the modeled and buoy
wave spectrums are plotted in Fig. 5d.
2-D wave energy spectrum from (a) UWIN-CM and (b) NDBC buoy 42040
on 23 January at 00:00 UTC. Circles around the center of the plots
show wavelengths ranging from 100 m (smallest) to 20 m (largest) on 10 m
intervals. (c) 1-D wave density spectrum from UWIN-CM and NDBC from the same
hour. (d) Time series of Stokes drift velocity magnitude (solid lines) and
direction (asterisks) calculated from the 2-D wave spectrum from buoy 42040
and the UWIN-CM model using Eq. (2). Vertical black lines indicate the period of
velocity deconstruction. The UWIN-CM Stokes drift velocity plotted here is
integrated from the surface down to 1.68 m, which is roughly the depth of the
SCOOP-style NDBC buoy used at station 42040. All data from UWIN-CM are
extracted from the nearest point to the location of buoy 42040.
The modeled wave spectra, both 1-D and 2-D, compare reasonably well to that
of buoy 42040, with the obvious difference being the magnitude and slight
offset of the peak seen in the spectrum at ∼0.12 Hz (Fig. 5a, b, c).
The model seems to underestimate this peak, while overestimating
the contribution from waves in the frequency range of ∼0.15–0.3 Hz.
Figure 5a–c display typical examples of both the modeled and
observed wave spectra during the January high wind analysis period, which
results in the slight over estimation of the Stokes drift magnitude
calculated from the modeled wave spectrum seen in Fig. 5d. The NDBC buoy
could have some trouble accurately recording smaller-scale motions from
higher frequency waves under intense winds and coinciding strong surface
currents, causing some discrepancy. The model- and buoy-calculated Stokes
velocities do show good agreement with regards to direction.
Drogued (a, b, c) and undrogued (d, e, f) drifters used to create
Eulerian velocity fields of the pre-wind-event regional circulation
estimates using LAVA. Raw drifter
locations are plotted at the end of the hour over which the velocity fields
are created. Numbers in the bottom right corner of plots display the number of
drifters used for each velocity field construction. Panels correspond to
subdomains shown in Fig. 1.
LAVA and pre-existing regional
circulation
Initial inspection of the total drifter velocities during each high wind
event showed larger-than-expected spatial variation in drifter velocities,
including velocities which depict surface currents traveling to the left of
the wind, which challenges previous wind-driven surface current theory and
observations. Previous work that has studied instantaneous wind-driven
dynamics over a range of spatial scales has illustrated the need to account
for the circulation present before the observed increase in synoptic winds
in order to isolate the wind-driven component of the flow (Sentchev et al.,
2017; Berta et al., 2018). Based on these studies, we hypothesize that the
spatial variability observed in the total velocities is due to the regional
circulation that pre-existed the period of increasing winds, which retains
its structure on timescales long enough to influence the surface flow
during each high wind event.
Eulerian velocity fields of the regional circulation that preceded
each high wind event, created using LAVA for drogued drifters in the area.
Also plotted are 24 h averages of Aviso SST data during (a, b, c) and after (d, e, f) each high
wind event. Titles of each plot list the beginning of each
hour over which the velocity field was created from drifter trajectories and
the day corresponding to the Aviso SST data. The 24 h averages, produced by
Aviso, are centered on 00:00 UTC of the day listed. In each velocity
field, every third vector is plotted for visibility. Velocity fields on the
left and right side panels are identical.
In order to resolve surface currents in our region of interest at adequate
spatial and temporal resolution, we utilize LAVA (Taillandier et al., 2006) and the available drifter data in
the region to estimate the regional circulation. In a previous study,
geostrophic velocities derived from altimetry data and surface Ekman current
velocities parameterized using forecasted winds have been shown to have
inadequate spatial and temporal resolution, in order to accurately estimate
surface currents in the northern Gulf of Mexico on scales less than the
order of 100 km in space and 1 week in time (Berta et al., 2015). LAVA
allows us to use the undrogued and drogued drifter trajectory data in the
region to create Eulerian velocity fields that are statistically robust and
on the spatial and temporal scales on the order of 10 km and hours,
respectively (Taillandier et al., 2006; Berta et al., 2015). LAVA has been
used in previous studies to create velocity fields using purely Lagrangian
data, as well as blending drifter trajectory data with Eulerian velocity
fields derived from altimetry and HF radars. LAVA has proven especially
useful when providing near-real-time (NRT) information that can be useful to
first responders of oil spills, search-and-rescue efforts and other surface
transport problems (Taillandier et al., 2006; Chang et al., 2011; Berta et
al., 2014, 2015).
Eulerian velocity fields of the regional circulation that preceded
each high wind event, created using LAVA for undrogued drifters in the area,
plotted on top of the same 24 h averages of SST data shown in Fig. 7.
Titles and data are plotted in the same manner as in Fig. 7.
Given our hypothesis that, during the high wind events, the total velocity of
the drifters is partly composed of the regional circulation that pre-existed
the onset of high winds, an estimate for this circulation must be removed
from the total velocity of the drifters in order to isolate the wind- and
wave-driven components. We utilize LAVA and the available drogued and
undrogued drifter data in the region to create semi-instantaneous hourly
Eulerian velocity fields for both drifter types, separately. The drifters
used for the velocity field construction for each event are plotted in Fig. 6.
The event on 22 January occurred very close to the initial time of
deployment, while the drogued drifters were still tightly packed spatially
and little drogue loss has occurred, leaving a smaller, but adequate, number
of undrogued drifters to utilize for the estimate of the pre-existing
circulation. The events on 24 February and 20 March occur at
a later time after deployment, thus resulting in a more even amount of
drogued and undrogued drifters that are also more evenly spread throughout
the region; however, drogued drifters do show a tendency to converge upon one
another, which is evident in the spatial organization of the drifters seen in Fig. 6.
Aviso-based geostrophic velocities (a, b, c) and NCOM velocities
(d, e, f) associated with each high wind event, along with relevant SST data.
SST maps were chosen based on coinciding timing with each high wind event
and/or visibility of circulation patterns. The date of each daily
geostrophic velocity or 3 h NCOM velocity field are listed for each
panel. Dates of SST data are listed in the same manner as in Figs. 7 and 8.
NCOM velocities are plotted at 60 m depth for the January and February
events, while they are plotted at 15 m for the March event. Velocity arrows for geostrophic
velocities are scaled twice as large as those of NCOM fields.
LAVA utilizes drifter trajectories in order to create average velocity
fields over multiple time steps depending on the parameters R, Ta,
Δt and Δx, which need to be prescribed before implementation
of the analysis. R determines the spatial range over which the velocity of
each drifter is spread from the drifter location through finite iterations
of the diffusion equation and should be chosen as the typical length scale,
or Rossby radius, for the targeted oceanic dynamics to be resolved in the
given region. For this analysis, R is set to 10 km, which is same value used
for a previous study which utilized drifter data in the same region of the
northern Gulf of Mexico (Berta et al., 2015). Δt for this study is
15 min, which is the time resolution of the quality-controlled drifter
trajectories which have been linearly interpolated to regular 15 min
intervals (Haza et al., 2018). The analysis timescale, Ta, is the
larger time window over which consecutive velocity fields created using LAVA
are averaged and should be shorter than the Lagrangian timescale
(Taillandier et al., 2006). Lagrangian timescales within this region have
been calculated to be as small as ∼1–3 h, which
corresponded to spatial scales ranging from 0.4 to 3.5 km (Gonçalves et
al., 2019), and as large as ∼1–3 d, corresponding to
spatial scales ranging from ∼10 to 35 km (Ohlmann and Niiler,
2005). Here, Ta is set to 1 h, which is adequately short given our
assignment of 10 km as the typical horizontal length scale being resolved by
LAVA. Δx is the spatial resolution of the discretized velocity field
and needs to be assigned such that Δx<R, defined here as
Δx=1.5 km (Taillandier et al., 2006; Berta et al., 2015).
Drifter trajectories within 2Δx of one another are averaged to
become single-drifter trajectories positioned along their center of mass
before the production of Eulerian velocity fields (Berta et al., 2015).
Drogued (a, b, c) and undrogued (d, e, f) drifter trajectories during
the hours of analysis for each high wind event. Time periods over which the
total velocity deconstructions were calculated are shown in each title.
Portions of the trajectories shown in black (red) depict segments which
overlap (do not overlap) spatially with each respective LAVA-derived
Eulerian velocity field in Figs. 7 and 8. Trajectories missing head markers are a result of either drogue
loss or lost GPS transmission during the experiment. The total number of retained trajectory data points, defined as number of drifter days given 15 min time steps, and the percentage of coverage between trajectories and each respective Eulerian velocity field in Figs. 7 and 8 are listed within each plot.
After the Eulerian velocity fields are created, a kinetic energy mask is
also implemented to exclude small velocities that are an artifact of
assigning radially decreasing velocities away from drifter locations using
LAVA. To avoid these unrealistically small velocities, any values in the
Eulerian velocity fields that represent less than 10 % of the hourly
averaged kinetic energy in the velocity field are discarded. This relatively
low threshold of kinetic energy was chosen to maximize the coverage between
the Eulerian velocity field estimates of the pre-existing regional
circulation and drifter trajectory data during each high wind event.
The hourly velocity fields are created during low wind conditions, hours
before the passage of synoptic-scale atmospheric fronts, which produce very
high winds over the entire region. To avoid unnecessarily smoothing the
Eulerian velocity fields through averaging in time, we choose one hourly
velocity field per event, per drifter type, to use for analysis. Hourly
Eulerian velocity fields are chosen based on the criteria that there are
relatively low wind velocities present, the time gap between the velocity
field and onset of high wind is not needlessly long and how well the Eulerian
velocity field overlies the drifter trajectories during the high wind event.
The Eulerian velocity fields used for analysis are plotted in Figs. 7 and 8.
Each hour over which these velocity fields are created is also indicated in
Fig. 3. The Eulerian velocity fields are plotted on top of Aviso-derived
24 h averages of SST temperature, in order to validate the flow
structures that are seen in the reconstructed velocity fields using LAVA.
For each high wind event, the drogued and undrogued velocity fields are
directly compared to two SST fields: one corresponding as closely as
possible in time to the high wind event and one corresponding to the
following day, in order to visualize the change in the pre-existing regional
circulation during each high wind event (Figs. 7, 8).
Scatter plots of deflection angles of drogued (a, c, e) and
undrogued (b, d, f) drifter velocities before (a, b) and after
(c, d, e, f) subtracting estimates for the regional circulation during the high wind
periods analyzed. Panels (c) and (d) are created using the NCOM velocity fields
during the high wind events (Fig. 9), while panels (e) and (f) use the
LAVA-derived estimates observed before each high wind event (Figs. 7 and 8).
Data points that do not have both individual drifter velocity measurements
and coinciding data in the LAVA-derived Eulerian velocity fields of the
pre-existing circulation during the high wind event are not included in any
panel. Deflection angles are plotted against the U10 wind magnitude from the
UWIN-CM model at the nearest point to the drifter locations in the domain.
Stokes drift estimates from the UWIN-CM during the time periods shown in
Figs. 7 and 8, are on average an order of magnitude less than the total
drifter velocities for both drifter types, having average magnitudes of
0.02 m s-1 for the drogued case and 0.04 m s-1 for the undrogued case. Smoothing of
the constructed velocity fields that arises from the velocity spreading and
averaging performed by LAVA over the length scale R makes the uncertainties
from the influence of Stokes drift insignificant during these low wind
conditions. Inevitably, there also exists some influence from the wind during
these time periods, which is most likely greater than the magnitude of the
Stokes drift. However, the qualitative agreement of the velocity field
estimates and the SST fields, evident in Figs. 7 and 8, suggests that both
drifter types are capturing the dynamics of the underlying circulation
rather than the instantaneous wind-driven effects during these times.
Average of the Eulerian LAVA-derived velocity field estimates of
the pre-wind event regional circulation plotted as a percentage in time of the
average combined wind- and wave-driven flow for drogued and undrogued
drifters. Dashed vertical lines depict the analysis period for the total
velocity deconstruction.
To provide a comparison to the LAVA-derived velocity fields, geostrophic
velocity fields from altimetry data and NCOM velocities fields during each
high wind event are shown in Fig. 9, plotted on top of associated SST
temperature data. During the January and February wind events, we select the
NCOM ocean velocity at 60 m depth at the beginning of the high wind
analysis periods shown in Fig. 3. This depth is chosen based on the
stratification shown in Fig. 2, with 60 m being just above the sharp pycnocline
seen at ∼80 m, thus being able to capture the mixed layer
dynamics driving the regional flow at the surface, but deep enough as to not
be affected by any wind stress in the model. As for the March event, much of
the domain is bathymetrically limited due to its proximity to the coast,
forcing the authors to use a shallower depth of 15 m for comparison.
Velocity fields during, rather than before, each high wind event are used to
allow the model to account for any structural changes the flow may have
experienced during the time gap between the LAVA-derived velocity fields and
the analysis period during high winds. Such evolution of the regional
circulation will not be observed by the geostrophic velocities from
altimeter data as spatial and temporal resolution is much coarser.
Number of undrogued (a) and drogued (b) drifter positions
where the velocity deconstruction was performed, binned by wind velocity
magnitude on 0.5 m s-1 intervals. Point
velocity measurements calculated from drifter trajectories that do not
coincide spatially with available data in the pre-existing circulation
velocity fields are not included.
Deconstruction of total surface current
The total drifter velocities, u¯T, calculated between each
15 min drifter position during the high wind events, are stored at the
later drifter coordinate used for each calculation. The time gap between the
observation of the regional circulation, urc, and the beginning of the
high wind analysis period is chosen such that the increase in winds shows a
noticeable change in all the drifter trajectories across the given domain.
Investigation of wind-driven currents at high temporal resolution has shown
that the surface current response time to changing winds can be described as
quasi-instantaneous, with the surface currents lagging the wind by
∼40 min (Sentchev et al., 2017). Having a temporal gap
between these observations ensures the surface velocities, across the
region, have ample time to adjust to the increasing winds before any
calculations are made. In some cases, the time gap is extended to allow
drifters to enter the region where the corresponding observation of the
pre-existing regional circulation exists. In addition, we stop the analysis
for each high wind event at the apex of the increasing wind velocity
magnitude, beyond which the drifters exhibit large inertial motions due to
the decrease in momentum input from the wind. The drifter trajectories,
during the period over which the deconstruction of the total drifter
velocities is performed, are shown in Fig. 10.
Average deflection angles at drifter locations during periods of
high wind binned by UWIN-CM wind magnitudes on 0.5 m s-1 intervals. Horizontal dotted lines denote
0, 45 and 90∘ to the right of the wind. (a) UWIN-CM Stokes
drift velocity direction at the surface (0 m) and at 0.4 m
depth. (b) Velocity direction of drogued and undrogued drifters after the
subtraction of the pre-existing circulation estimate. (c) Velocity direction
of the purely wind-driven component of the drifter velocities. All error
bars show +/- 1 standard deviation within each bin. Data in wind bins
lower than 11.5 m s-1 were omitted due to
lack of data points and large standard deviations.
From the full drifter velocities, calculated on 15 min intervals during
each wind event, we subtract the nearest point velocity in the estimated
LAVA velocity field of the pre-existing regional circulation, urc. Each
velocity field has a spatial resolution of 1.5 km, which is adequate to
preserve the flow features given the typical length scale of 10 km in the
region (Berta et al., 2015). In the event that the nearest point to the
drifter location in the LAVA velocity field estimate does not have a value,
due to a lack data in the velocity field, the data are discarded. The
trajectories in Fig. 10 are color coded to show where the drifter
trajectories and Eulerian velocity fields overlap during the high wind event
and where data coverage is missing. Subtraction of the regional circulation
estimates from altimeter-based geostrophic velocities and NCOM velocity
fields (Fig. 9) is also performed in the same manner. For accurate
comparison, any data points along the drifter trajectories that were
excluded during the subtraction of the LAVA velocity field estimates were
also excluded from the calculation involving the Aviso and NCOM velocity
fields.
Average velocity magnitudes at drifter locations during periods
of high wind binned by UWIN-CM wind magnitude on 0.5 m s-1 intervals. (a) UWIN-CM Stokes drift velocity
magnitude at the surface (0 m) and at 0.4 m depth. (b) Velocity magnitude of
drogued and undrogued drifters after the subtraction of the pre-existing
circulation estimate. (c) Velocity magnitude of the purely wind-driven
component of the drifter velocities. All error bars show +/- 1 standard
deviation within each bin. Data bins omitted in Fig. 14 are also excluded.
The subtraction of multiple hourly adjacent LAVA velocity fields, earlier
and later in time, were examined a posteriori for each event and found to
produce similar end results. The hourly LAVA velocity fields chosen for this
analysis were those that had the most data points in common with the
respective drifter trajectories during the high wind event or those that
retained a similar amount of data points during the subtraction (within 3.5 %
of the maximum amount of retained trajectory data for each specific
case) but resulted in larger decreases in the standard deviation of the
deflection angle between drifters and wind velocity. This decision is
motivated by the assumption that the isolated effects of wind and waves will
result in a more directionally uniform velocity field than that of a
surface flow being influenced by the regional circulation estimates shown in
Figs. 7 and 8. Thus, by removing more variation in the deflection angles
between the wind and drifter velocities, we better represent the combined
wind- and wave-driven components, assuming the wind and wave directions are
well aligned. All velocity estimates and calculations are performed using
drogued and undrogued drifter data separately.
After subtracting the estimates for the pre-existing regional circulation
from the full drifter velocities during the high wind events, the remaining
velocity is an estimate for the combined wind- and wave-driven flow
(us+uw), referred to here after as the wind-wave-driven velocity. To
further deconstruct this velocity estimate, we subtract the UWIN-CM-modeled
Stokes drift velocity (us) fields, again at the nearest point to each
drifter location for each 15 min interval. Because the large wind-driven
events being analyzed are of synoptic scale, the UWIN-CM-modeled Stokes
velocity and U10 winds are relatively uniform in the region of study, making
the 4 km resolution of the data adequate to perform meaningful calculations
in this manner.
Because the drogued drifters display a filtering characteristic of the
surface Stokes drift, as illustrated in laboratory testing performed by
Novelli et al. (2017), the Stokes drift at 0.4 m depth is used for the
calculation with drogued drifter-derived velocities (0.4 m being the
vertical center of the drogue). From the undrogued-drifter-associated
velocities, we subtract the surface (z=0 m) Stokes drift
velocity. After subtracting the wave-driven component, us, from the
wind-wave-driven velocity, we are left with an estimate for the average
wind-driven drifter velocities, uw, over each respective drifter draft
depth. The average wind-wave-driven and wind-driven drifter velocities are
compared to the modeled U10 wind data, using the U10 wind velocity datum at
the nearest point to each drifter location at every time step.
Results
The main flow features seen in the estimated Eulerian velocity fields and
SST fields show evidence of flow features having spatial scales on the order
of tens of kilometers (Figs. 7 and 8). Observations of smaller flow features
in the velocity fields are limited by the 1.5 km resolution and typical
length scale of 10 km set by the chosen LAVA configuration. The regional
circulation observed, by either drifter type, prior to the wind events on
24 February and 20 March shows an abundance of meanders, eddies
and frontal features, whereas the regional circulation pre-existing the wind
event on 22 January suggests that a somewhat larger feature, closer
to the order of 100 km, is driving the majority of the flow to the northeast
with smaller-scale variability seen throughout the flow. The velocity field
vectors for all panels in Figs. 7 and 8 show good agreement with the
structures highlighted by the SST data, supporting the validity of the
LAVA-estimated fields. Features on these spatial scales (10–100 km) are known to
have characteristic timescales on the order of days to weeks
(Ozgokmen et al., 2016), which suggests
that the main flow features observed in the pre-existing regional
circulation are likely retain their overall structure, to a certain extent,
during the high wind analysis periods presented here. This is also supported
by the SST fields averaged over the 24 h period following each high wind
event shown in the right-hand panels of Figs. 7 and 8. Although the SST
field changes somewhat during the high wind event, the pre-existing
circulation estimates seem to be valid for an appreciable amount of time,
evident in the overlap between eddies, fronts and filaments observed in the
SST field and the velocity vectors created with LAVA 2–3 d previously.
Comparison between Figs. 7 and 8 with Fig. 9 displays robust discrepancies
between the velocity fields created using LAVA and drifter data, altimetry
data and NCOM. The coarse resolution of the geostrophic velocities does not
capture the circulation highlighted in the SST data. The geostrophic
velocities are also appreciably smaller than either LAVA or NCOM (vectors
for the geostrophic velocities in Fig. 9 are magnified by a factor of 2
compared to those of the NCOM circulation). Velocities showing the NCOM
circulation seem to better complement the structures observed in the SST
data compared to the geostrophic velocities (Fig. 9); however, compared to
the LAVA-derived velocity fields in Figs. 7 and 8, there still exists a fair
amount of disagreement between the NCOM and LAVA velocity fields, possibly
the most pronounced example being the velocity fields associated with the
January wind event.
The trajectories of both drogued and undrogued drifters during the hours of
increasing winds, over which the total velocity deconstructions were
performed, are shown in Fig. 10. Overall, the trajectories show that the
larger-scale synoptic winds and coinciding wave-induced motions are the
dominant driving forces in the drifter movement, evident in the similarities
in drifter tracts across each domain. Influence of the pre-existing regional
circulation can also be observed in the variability among drifter
trajectories. The undrogued trajectories seem to show less variability
across the domain than for the drogued case, suggesting the wind- and
wave-driven components are even more dominant in the surface layer measured
by the undrogued drifters.
Scatter plots of the difference between the wind direction and the direction
of the total velocity of the drogued and undrogued drifters, i.e. deflection
angles, along the trajectory segments shown in black in Fig. 10, are shown
in Fig. 11a and b, respectively. Figure 11c, d show the deflection angles
between the wind and drifter velocities, after the NCOM velocity fields of
the underlying regional circulation during the high wind event (Fig. 9) have
been subtracted from the full velocity of the drifters, while Fig. 11e, f
show the same deflection angles, calculated using the LAVA-derived estimates
of the regional circulation (Figs. 7, 8). Any data points lost during the
subtraction of the LAVA-derived regional circulation velocity field, shown
as red segments in Fig. 10, were also excluded from the scatter plots
showing the full drifter velocities (Fig. 11a, b) and the scatter plots
created using the NCOM velocity fields (Fig. 11c, d) for accurate
comparison. Deflection angles are plotted against the point value wind
magnitude from the UWIN-CM model, closest to the drifter location at the
time of the velocity measurement.
It is evident from Fig. 11 that the resulting scatter plots, after
subtracting the LAVA-estimated velocities, have a more organized and
compressed scatter than that of the total drifter velocity deflection
angles. This is especially apparent for the drogued drifter case. The
scatter plots created after the NCOM velocity fields have been subtracted
from the full drifter velocity and have a wider, more disorganized spread than
that of the full drifter velocities, with a large portion of the deflection
angles being directed to the left of the wind direction for both drogued and
undrogued drifters. Resulting deflection angles using NCOM velocity fields
of varying depths between 2 and 200 m, and varying in time from the timing
of each LAVA velocity field observation to the end of the high wind analysis
window, did not show more compressed or organized scatter. Deflection
angles calculated after removing the Aviso geostrophic velocities (not
shown) from the full drifter velocities revealed little to no change
compared to those of the total drifter velocities, due to the relatively
small magnitude of the geostrophic velocities in the region. Because of the
negligible change in deflection angle caused by the subtraction of the
geostrophic velocities and the unorganized and misaligned deflection angles
resulting from the subtraction of the NCOM velocity fields, the remainder of
the results only focuses on those calculated using the LAVA-derived estimates
for the regional circulation.
The average velocity magnitudes of the calculated wind-wave-driven drifter
velocities are compared to the average magnitude of the LAVA-derived
pre-existing regional circulation for each wind event and drifter type in
Fig. 12. The magnitude of the regional circulation is plotted as a
percentage of the combined wind- and wave-driven velocity magnitude. This
percentage decreases as the wind- and wave-driven effects become
increasingly large during each high wind event. During the analysis periods
over which we deconstruct the total surface current (shown by vertical lines
in Fig. 12), the pre-existing circulation is 30 %–45 % as large as the
combined wind-wave-driven velocity calculated using drogued drifters and
25 %–30 % as large as that measured by undrogued drifters.
The wind-wave-driven, wind-driven and wave-driven (Stokes drift) velocities
are binned by wind magnitude on 0.5 m s-1
intervals. The total number of drifter positions, per wind bin, where the
drifter velocity deconstruction is performed, are plotted in Fig. 13, which
shows that the most robust results exist over wind bins from 14.5 to 18 m s-1 for both drogued and undrogued
drifters. It should be noted that although the sample size varies
considerably between drifter types and over the assigned wind bins, both
drifter types have substantial sample sizes over most wind bins presented,
given the relatively short time periods of analysis. The sample size of
drifter measurements is also listed for each high wind event in Fig. 10.
Averages and standard deviations of the velocity components were computed
within each bin. The average deflection angles from the wind direction of the
Stokes velocity, wave-wind-driven velocity and wind-driven velocity for each
respective drifter type are shown in Fig. 14. The magnitudes of the same
velocity components are plotted as a percentage of the wind magnitude in
Fig. 15. Figures 14c and 15c are obtained by subtracting the given Stokes
drift velocity (Figs. 14a, 15a) from the estimated wind-wave-driven
velocities (Figs. 14b, 15b). Bins lower than 11.5 m s-1 have been omitted in Figs. 14 and 15 due to lack
of data points and large variation of deflection angle in the given bins.
Error bars indicate 1 standard deviation about the mean for each bin
(Figs. 14, 15).
The average wind-wave-driven component of the undrogued drifter velocity
varies with increasing wind speed, traveling between ∼4 and 40∘,
to the right of the wind, as winds increase
from 11.5 to 20 m s-1 (Fig. 14b). On average,
the magnitude of this velocity component varies from 4.5 % to 7.1 % of the
wind speed (Fig. 15b). For the drogued drifter case, the deflection angle of
the wind-wave-driven component varies from ∼26 to 60∘, as winds increase over the same range, traveling
at 2.8 %–4.6 % of the wind speed, on average (Figs. 14b and 15b). The total
wind-wave-driven velocities exhibit larger deflection angles as wind speeds
increase, with the drogued-drifter-derived velocity traveling at a slower
speed and being deflected ∼5–28∘ further
to the right than that of the undrogued drifters.
Our estimates for purely wind-driven velocity components (Figs. 14c and 15c)
show the undrogued drifter wind-driven velocity traveling ∼5∘ to
the right of the wind, at 6.0 % of the wind speed during
12 m s-1 winds. The deflection angle
gradually increases to ∼55∘ to the right of the
wind as wind speeds increase to 20 m s-1.
The average velocity magnitude varies between 3.4 % and 6.0 % of the wind speed
over this wind interval. The deflection angle of the drogued drifter
wind-driven velocity ranges from ∼30 to 85∘,
again with higher deflection angles occurring at higher wind speeds. The
velocity magnitude of this component varies from 2.3 % to 4.1 % of the wind
speed over the given wind speed interval. The difference in deflection angle
between undrogued and drogued wind-driven velocities varies between
∼8 and 30∘, with the drogued drifter
component traveling further to the right at a slower velocity. In both
cases, there seems to exist an increase in deflection angle with increasing
wind speed.
Discussion
Comparison of the geostrophic velocities calculated from altimetry data in
Fig. 9 with SST data and the LAVA-derived velocity fields (Figs. 7, 8)
reveals that the regional circulation in our region of interest cannot be
adequately described by geostrophic motions alone. Since the Aviso
geostrophic velocities are much smaller in magnitude than the LAVA and NCOM
velocity fields, it is apparent that geostrophic balance is not the dominant
forcing in the region. The structures observed in the SST data and
LAVA-derived velocity fields (Figs. 7, 8), which have horizontal length
scales of only a few tens of kilometers, are created through the interaction
between the dense interior waters of the Gulf of Mexico and the less dense
Mississippi River outflow waters. These features are smaller than the
typical mesoscale structures resolved by the altimetry-based SSH
measurements, which suggests little information about the regional
circulation is gained from the assimilation of the altimetry data due to the
region's proximity away from the dominate mesoscale structures observed in
the Gulf of Mexico. This complements the findings of Berta et al. (2015),
which found altimeter-based geostrophic velocities to vary significantly
from the velocity of CODE-style drifters in the northern Gulf of Mexico.
This would also suggest that there are limited constraints on the modeled
ocean circulation resulting from missing initial conditions away from the
altimetrically resolved mesoscale structures. Although the assimilation of
altimetry data may improve NCOM performance overall, it does not provide
enough information to accurately describe the dominant dynamics of the
regional circulation for the given domain. Haza et al. (2019) provides
supporting results to this hypothesis, showing that during LASER the
forecasted NCOM ocean velocities agreed reasonably well with observations in
the interior Gulf of Mexico where mesoscale structures dominate the flow
but were very different from observation in the northern gulf where smaller-scale
structures dominate. The increased scatter of the deflection angles,
shown in Fig. 11c–d, also implies that the regional circulation is not
accurately represented by the NCOM ocean velocities. The increased agreement
of the structures seen in SST data and the LAVA-derived velocity fields, as
well the comparison between scatter plots shown in Fig. 11, reveals that the
velocity fields created using the observational drifter data are more
equipped to described the regional circulation specific to this area than
altimetry-based or modeled velocities.
Comparison of scatter plots in Fig. 11a, b and e, f indicates that the
pre-existing regional circulation still influences the velocities measured
by both drifter types during these large wind-driven events. The scatter of
deflection angles between different drifter types also suggests that the
relatively deeper layer measured by the drogued drifter exhibits larger
variation in velocity due to the regional circulation component of the flow.
The velocities observed with undrogued drifters seem to be more dominantly
driven by the wind and wave components, as removing the influence of the
regional circulation results in a less drastic compression of the scatter.
In both the drogued and undrogued cases, the removal of the LAVA-estimated
regional circulation results in a decrease in the standard deviation of the
deflection angle for all wind bins from 11.5 to 20 m s-1.
Comparison of the relative magnitudes of the combined wind-wave-driven
velocities and the pre-existing circulation estimates confirms that the
drifter velocities are dominantly wind- and wave-driven during the high wind
periods (Fig. 12). Analysis of the plots in Fig. 12 led the authors
a posteriori to determine the optimal windows for the velocity
deconstruction results during the high wind events. With respect to the
subtraction of the pre-existing regional circulation and the improvement of
the scatter in deflection angles (seen in Fig. 11e, f), the most robust
results, for both drifter types, occur when the relative strength of the
wind-wave-driven flow to the pre-existing regional circulation reaches a
plateau (Fig. 12). This is also when the wind, and therefore the
wind-wave-driven component, is the strongest, which suggests that any error
introduced during the velocity deconstruction makes up a smaller percentage
of the calculated velocities. Extending the velocity deconstruction analysis
windows, by 2 h before and 2 h after, does not significantly change
the averages and trends shown in Figs. 14 and 15 but does, however, slightly
disorganize the scatter of the deflection angles seen in Fig. 11e, f. This
investigation of sensitivity also aided in determining the optimal time
periods for analysis.
Figure 14b shows that the estimated deflection angle of the combined wind-
and wave-driven velocities of drogued and undrogued drifters varies
significantly with increasing wind velocity. Since the deflection angle of
the Stokes drift velocity (Fig. 14a) is almost constant with wind speed, the
increase in deflection angles seen in Fig. 14b is most likely due to a
change in the wind-driven momentum input. The increased deflection angle
observed between the two drifter types seems to be predominately wind driven
based on the relative magnitudes of Stokes drift and estimated wind-driven
velocities (Fig. 15). Classical Ekman theory is based on the balance between
Coriolis and vertical viscosity in the water column, which, given the
parameterization for wind stress and viscosity assigned by Ekman (1905),
results in a wind-driven surface current deflected 45∘ to the
right of the wind in the Northern Hemisphere, which spirals to the right and
decreases in magnitude with depth. In contrast to Ekman, the “slab”
solution, based on enhanced surface mixing due to breaking waves and
shear-induced turbulence, prescribes a linear decrease of wind stress with
depth, resulting in a surface current which travels 90∘ to the
right of the wind uniformly with depth (Pollard and Millard, 1970).
The findings presented here seem to support aspects of both the Ekman and
slab solutions, as there does appear to be a rotation of the wind-driven
drifter velocities with depth, but overall, these velocities display a
larger deflection than predicted by Ekman at larger wind speeds. This can
possibly be explained by enhanced vertical mixing under high wind and wave
conditions, which acts to diminish the effects of stratification and
distribute wind-driven momentum into the water column. This theory motivates
the assignment of a linearly decreasing wind stress with depth used in the
slab model solution by Pollard (1970), which results in deflection angles of
90∘ over all depths of the mixed layer. The observed increase in
deflection angle with wind speed may suggest a gradual change in wind-driven
flow regimes, from surface Ekman dynamics to more slab-like dynamics, as
increasing wind velocity and subsequent turbulence from vertical shear and
breaking waves mix momentum vertically in the wind-driven layer. However,
there still exists a rotation of the wind-driven current with depth which
cannot be explained using slab-like dynamics alone.
Comparison to more recent observational studies is nontrivial due to
differences in instrumentation, measurement depth and time resolution of
measurements. Many previous studies have performed spectral analysis on
years of HF radar or drifter data with time resolutions varying from 0.5 to 6 h
and calculated correlations between wind and surface currents in
order to isolate the wind-driven component of the flow. Results from these
studies find a range of deflection angles from 17 to 90∘ to
the right of the wind, with current magnitudes varying from 0.4 % to 5 % of
the wind speed (Ardhuin et al., 2009; Poulain et al., 2009; Kim et al.,
2010; Röhrs and Christensen, 2015). Sentchev et al. (2017) used
instantaneous vertical ADCP measurements to calculate the wind-driven effect
on surface currents (0.5–75 m depth) during sea breeze conditions (wind
speeds of ∼3–6 m s-1),
finding wind-driven deflection angles of 12–25∘ and
current magnitudes of 2 %–4 % of wind speed. The study also found a
rotation to the right was present between wind-driven velocities of
increasing measurement depth. Berta et al. (2018) used hourly HF radar
measurements to observe the influence of large wind events lasting 1–3 d.
After subtracting the geostrophic component of the flow to isolate the
wind-driven current, they found surface velocity deflection angles and
magnitudes of 25–30∘ and ∼2 % of the
wind speed, respectively. More details of these previous studies on
wind-driven dynamics can be found in Table 1.
The findings portrayed in this paper seem to be within the range of
previously reported results for wind-driven surface flows with discrepancies
likely resulting from the differences in the depth of the measurement, range
of wind magnitudes, upper ocean stratification or methodology in isolating
the wind-driven component. Several of these previous studies also utilized
datasets with a portion of surface current measurements occurring close to
the coast, where topography could have added noise to the measurements (Poulain
et al., 2009; Kim et al., 2010; Röhrs and Christensen, 2015; Berta et
al., 2018). With the exception of Ardhuin et al. (2009), these past studies
have not accounted for Stokes drift in their measurements, sometimes due to
negligible wave influence under low wind conditions, possibly resulting in
smaller deflection angles due to the near alignment of winds and wave
direction (Poulain et al., 2009; Kim et al., 2010; Röhrs and Christensen
2015; Sentchev et al., 2017; Berta et al., 2018). The UWIN-CM-modeled Stokes
velocity used for this study seems to be in good agreement with the full
Stokes velocity magnitude and deflection angle, with respect to the wind,
presented by the previous literature (Ardhuin et al., 2009).
We explore the possible influence of wind-driven effects during the
observation of the pre-existing regional circulation by conducting a
sensitivity test where an estimated wind-driven velocity (based on the
UWIN-CM wind velocities) is removed from the LAVA-derived velocity fields
before the total velocity deconstruction is performed. Relying on previous
literature, we estimate that during low wind conditions, the undrogued
drifters travel at 3.5 % of the wind, being deflected 5∘ to the
right, while the drogued drifters travel at 1.5 % of the wind, being
deflected 25∘ to the right. These values were chosen, based on the
results of all the literature shown in Table 1, to represent moderate
wind-driven effects on the surface drifters. Laboratory testing and field
experiment results using the CARTHE drifters also aided in assigning the
differential velocities between drifter type (Laxague et al., 2018; Novelli
et al., 2017). Performing the total velocity deconstruction with this
estimated wind-driven effect removed from the pre-existing circulation, on
average, results in a decrease of wind-driven velocities magnitudes by 0.58 %
and 0.34 % of the wind velocity for undrogued and drogued drifters,
respectively. The average deflection angles of the wind-driven component of
both drifter types become more aligned with the wind by about 5∘
for drogued drifters and 7∘ for undrogued. While there is some
change in the final magnitudes and deflection angles calculated, the overall
trends seen in Figs. 14 and 15 do not change as a result of this sensitivity
analysis. Additionally, the scatter plots of deflection angles (Fig. 11e, f)
become slightly more scattered (standard deviation slightly increased) with
this estimated wind influence removed and become even less organized when
the estimated wind-driven velocities during the low wind periods are
increased further. Because the true values of the wind-driven velocity
components during these low wind-periods are not well known, and the
standard deviations of deflection angle are slightly increased by accounting
for such, we do not account for any wind-driven influence during the low
wind periods for the final results. The assignment of decreasing velocities
away from the drifter locations up to a length R, prescribed by the LAVA
algorithm, in addition to averaging, acts to smooth out the absolute
velocities of the individual drifters, which adds complexity to accounting
for wind-driven effects during these low wind periods.
Another source of error in the wind-driven measurements reported here is the
possible evolution of the regional circulation during the time gap between
the observation of the pre-existing regional circulation and the velocity
deconstruction during the high wind period. Although difficult to quantify
given the available data, the regional circulation is evolving, to a certain
extent, during the time gap between the observation of the regional
circulation and the analysis periods of high winds. In addition, the
regional circulation could begin to be modified through interactions with
the large wind- and wave-induced currents. The extent to which this is
occurring can be seen qualitatively in Figs. 7 and 8 by comparing the change
of the SST fields in time. One example of this is evident in the
southeastward progression of the front seen in the SST fields associated
with the January high wind event. The movement of this front seems to
generally mimic the movement of the drifters during this high wind event
shown in the top panels of Fig. 10. The overall timescales on which these
flow features evolve or are altered by each high wind event are beyond the
scope of this paper, given the available dataset. However, the relatively
short timescales of these periods of high winds and the overall agreement
between the pre-existing circulation estimates and the SST fields (shown in
Figs. 7 and 8) suggest that the method of velocity deconstruction used here
is adequate. In addition, the decreased variability seen in the deflection
angles after the subtraction of the pre-existing regional circulation (Fig. 11)
suggests, a posteriori, that the regional circulation does maintain its
structure to a reasonable extent during the periods of increasing winds
being analyzed.
Another possible source of error comes from the velocity slip
characteristics of the drifters mentioned above, mostly relevant in the
undrogued case. The magnitude of the wind-wave-driven and purely wind-driven
velocities of the undrogued drifters are among the higher estimates
previously reported (Ardhuin et al., 2009; Poulain et al., 2009; Kim et al.,
2010; Sentchev et al., 2017; Berta et al., 2018). The effect on the velocity
slip of these drifters due to windage and wave steepening has been tested in
the lab by Novelli et al. (2017). Although there exists a large difference
in scales between the lab and the real ocean, the differences between the
combined wind-wave-driven velocity magnitudes of drogued and undrogued
drifters (∼2 % of the wind speed on average) in the
current study are in good agreement to that of laboratory studies using
half-scale drifters and a past field experiment using full-size drifters
(Laxague et al., 2018; Novelli et al., 2017). As mentioned above, laboratory
testing of undrogued drifters showed that the total velocity slip could be
as high as 14 cm s-1 but was shown to
decrease to only 1 cm s-1 with increasing
wind speeds (over a range of 15–23 m s-1
in the lab) due to the sheltering of drifters from the wind by increasing
wave heights (Novelli et al., 2017). This could partly explain the gradual
decrease in wind-driven velocities for both drifter types from wind bins
11.5–18 m s-1, especially given the
relatively high wind-driven velocities seen at lower wind bins. Exactly how
much wind slip is occurring during specific wind velocities and wave heights
in the real ocean is difficult to determine, but the average magnitude of
the observed vertical shear seems to be in relatively good agreement with
past experiments and laboratory testing performed with these drifters
(Laxague et al., 2018; Novelli et al., 2017).
The momentum input from large breaking waves into the surface currents at
the very high wind speeds studied here could also cause an increase in
velocities observed in the wind-driven surface currents estimated from
either drifter type. In addition, the surfing behavior of the undrogued
drifters could amplify this increase in observed velocities further.
Velocity slip due to wind and breaking waves could also account for some of
difference seen in deflection angles between the drifter types, as the
influence of wind and waves would keep the undrogued drifters more in line
with the wind. Ardhuin et al. (2009) attributes larger deflection angles of
wind-driven currents to enhanced mixing due to wave breaking, which could be
a congruous theory to our observation of increasing deflection angles at
higher wind speeds. Enhanced mixing caused by breaking waves acts to mix
the vertical momentum of surface currents, likely resulting in larger
deflection angles at shallower depths (Rascle et al., 2006).
The results presented here, to the authors' knowledge, are among the few
studies able to reported estimates of the vertical shear of wind- and
wave-driven velocity components in the upper 1 m of the ocean under this
regime of high winds (11.5–20 m s-1). The
combined wind-wave-driven velocity of the undrogued drifters calculated here
is, on average, ∼1.6 times greater than that measured by
drogued drifters for the wind magnitudes presented. These results support
the finding of Laxague et al. (2018), which showed that, in the presence of
negligible stratification in the upper layer, total surface currents in the
upper 1 cm are twice as fast as the average current over the first 1 m
(measured to be 0.57 and 0.3 m s-1, respectively) due to wind- and
wave-driven vertical velocity shear. It should also be noted, however, that
vertical shear in the presence of strong mixing (i.e. wave breaking) acts to
diminish vertical velocity gradients, which could suggest some of the shear
observed is due to the velocity slip of the undrogued drifters (Sutherland
et al., 2016). The true novelty of the results presented here lies in the
instantaneous estimates for the vertical shear of the purely wind-driven
velocities calculated from each drifter type made possible through the
method of velocity deconstruction used here. The deconstruction of the total
velocity measured from drogued and undrogued drifters gives us an estimate,
directly related to the wind velocity, of the wind-driven vertical shear at
the very surface of the ocean, which has proved difficult to measure by
previous studies and has significant implications for surface transport
problems in the real ocean.
The high wind events focused on here only occurred for a small number of
days during the LASER campaign, which is typical for the northern Gulf of
Mexico in the winter. Since the velocity profile within the upper meter has
been shown to be very dynamic, being affected by the general oceanic
circulation as well as local wind-wave-driven mechanisms, it is important to
observe near-surface currents under different environmental conditions and
at greater vertical resolution. Attention also needs to be given to the
transport of Lagrangian particles by breaking waves which induce vertical
motion and mixing which can alter wind-driven currents.
Conclusions
We use a combination of stored output data from the UWIN-CM fully coupled
atmosphere–wave–ocean model and observational trajectory data from both
drogued and undrogued CARTHE drifters to calculate an estimate for purely
wind-driven drifter velocities during periods of strong, increasing winds.
The use of colocated drogued and undrogued drifters provides measurements
for the vertical shear between the upper 5 cm and upper 60 cm surface
layers. Using LAVA, we are able to create
velocity fields in the hours leading up to the high wind events studied
that serve as an estimate for the pre-existing regional circulation which is
found to still affect the drifter velocities during periods of high winds.
After subtracting the regional circulation from our measured drifter
velocities, we analyze the relationship between wind velocity, Stokes drift
and wind-driven velocity of each drifter type. On average, we find the
wind-driven velocity component between drifter types to decrease in
magnitude and rotate to the right of the wind with depth, with the undrogued
(drogued) component traveling ∼5–55∘
(∼30–85∘) at 3.4 %–6.0 % (2.3 %–4.1 %)
of the wind speed over the range of 12–20 m s-1. Both wind-driven velocities display an increase
in deflection angle with increasing wind speed, sustaining an average
difference of 8–30∘ between the layers sampled.
This study is among the few (Sentchev et al., 2017; Berta et al., 2018) that
have investigated the real-time response of very-near-surface currents to
increasing wind. We are able to observe characteristics of the vertical
shear present between the upper 60 cm (drogued) and 5 cm (undrogued) of the
wind-driven component of the drifter velocities. Observations of vertical
shear this close to the surface in the real ocean, especially during high
wind events of this nature, are very scarce due to the limitations of
present-day instrumentation. The vertical velocity profile within the upper
meter has been shown to exhibit a large amount of shear, with velocities at
the surface (upper few centimeters) being largely dominated by wind and
waves (Laxague et al., 2018; Morey et al., 2018). This highlights the
importance of very-near-surface observations, such as those presented in
this paper, as this vertical shear can have large impacts on the transport
of pollutants of varying size and buoyancy, like plastics and oil.
Incorporating vertical shear due to wind and waves within the upper meter
could have important implications for fate prediction of pollutants
transported at the ocean surface.
Data availability
Raw, processed drifter trajectory data and drogue
classification are publicly available through the Gulf of Mexico Research
Initiative Information and Data Cooperative (GRIIDC) under
https://data.gulfresearchinitiative.org/data/R4.x265.000:0027 (last access: 25 September 2017; D'Asaro et al., 2016), https://data.gulfresearchinitiative.org/data/R4.x265.237:0001 (last access: 25 September 2017; D'Asaro et al., 2017), and
https://data.gulfresearchinitiative.org/data/R4.x265.000:0044 (last access: 25 September 2017; Haza et al., 2017),
respectively. The UWIN-CM data can also be
obtained from GRIIDC under https://data.gulfresearchinitiative.org/data/R4.x265.000:0045 (last access: 25 September 2017; Chen et al., 2016a), where the
moving vessel profiler CTD data can also be found under
https://data.gulfresearchinitiative.org/data/R4.x265.000:0028 (last access: 28 June 2017; Klymak et al., 2016). Aviso SST and absolute geostrophic
velocity products, produced by Collecte Localisation Satellites (CLS) with
support from CNES, are available upon request.
Author contributions
The formal analysis was carried out by JL as a PhD
candidate under the supervision of TO. All authors contributed to the
conceptualization of the work. MB trained JL to implement the necessary
algorithm (LAVA) used to performed the formal analysis.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This research was made possible by a grant from the Gulf of
Mexico Research Initiative. The authors would like to thank all those
involved in planning and executing LASER, as well as the scientists involved
with the data processing and drogued detection that made this analysis
possible. The authors would like to express their gratitude to Milan Curcic for sharing his expertise of the UWIN-CM model.
Financial support
This research has been supported by the Gulf of Mexico
Research Initiative (grant no. GR009731).
Review statement
This paper was edited by Ilker Fer and reviewed by Fabrice Ardhuin and two anonymous referees.
ReferencesArbic, B. K., Scott, R. B., Chelton, D. B., Richman, J. G., and Shriver, J. F.:
Effects on stencil width on surface ocean geostrophic velocity and vorticity
estimation from gridded satellite altimeter data, J. Geophys. Res., 117,
C03029, 10.1029/2011JC007367, 2012.Ardhuin, F., Marié, L., Rascle, N., Forget, P., and Roland, A.:
Observation and estimation of Lagrangian, Stokes, and Eulerian currents induced by wind and waves at the sea surface, J. Phys.
Oceanogr., 39, 2820–2838, 10.1175/2009JPO4169.1, 2009.Berta, M., Bellomo, L., Magaldi, M.G., Griffa, A., Molcard, A., Marmain, J.,
Borghini, M. and Taillandier, V.: Estimating Lagrangian transport blending
drifters with HF radar data and models: results from the TOSCA experiment in
the Ligurian Current (North Western Mediterranean Sea), Prog. Oceanogr.,
128, 15–29, 10.1016/j.pocean.2014.08.004, 2014.Berta, M., Griffa, A., Magaldi, M. G., Özgökmen, T. M., Poje, A. C.,
Haza, A. C., and Olascoaga, M. J.: Improved surface velocity and trajectory
estimates in the Gulf of Mexico from blended satellite altimetry and drifter
data, J. Atmos. Ocean. Tech., 32, 1880–1901,
10.1175/JTECH-D-14-00226.1, 2015.Berta, M., Bellomo, L., Griffa, A., Magaldi, M. G., Molcard, A., Mantovani, C., Gasparini, G. P., Marmain, J., Vetrano, A., Béguery, L., Borghini, M., Barbin, Y., Gaggelli, J., and Quentin, C.: Wind-induced variability in the Northern Current (northwestern Mediterranean Sea) as depicted by a multi-platform observing system, Ocean Sci., 14, 689–710, 10.5194/os-14-689-2018, 2018.Buckley, M. P. and Veron, F.: The turbulent airflow over wind generated
surface waves, Eur. J. Mech. B. Fluids, 73, 132–143,
10.1016/j.euromechflu.2018.04.003, 2019.
Bouchard, R. H., Jensen, R. E., Riley, R. LeBlanc, L. A., and Fiorentino, L.
A.: Calibration and Field Evaluation of the National Data Buoy Center's New
Wave Measurement System, AMS 98th Annual Meeting, Austin, TX, 9 January
2018, Session 7, 2018.Chang, Y., Hammond, D., Haza, A. C., Hogan, P., Huntley, H. S., Kirwan Jr,
A. D., Lipphardt Jr, B. L., Taillandier, V., Griffa, A., and Özgökmen,
T. M.: Enhanced estimation of sonobuoy trajectories by velocity
reconstruction with near-surface drifters, Ocean Modell., 36, 179–197,
10.1016/j.ocemod.2010.11.002, 2011.
Chassignet, E. P., Smith, L. T., Halliwell, G. R., and Bleck, R.: North Atlantic
simulations with the Hybrid Coordinate Ocean Model (HYCOM): Impact of the
vertical coordinate choice, reference pressure, and thermobaricity, J. Phys.
Oceanogr., 33, 2504–2526, 2003.Chassignet, E. P., Hurlburt, H. E., Smedstad, O. M., Halliwell, G. R., Hogan,
P. J., Wallcraft, A. J., Baraille, R., and Bleck, R.: The HYCOM (hybrid
coordinate ocean model) data assimilative system, J. Mar. Syst., 65, 60–83,
10.1016/j.jmarsys.2005.09.016, 2007.Chen, S. S., Zhao, W., Donelan, M. A., and Tolman, H. L.: Directional wind–wave
coupling in fully coupled atmosphere–wave–ocean models: Results from
CBLAST-Hurricane, J. Atmos. Sci., 70, 3198–3215,
10.1175/JAS-D-12-0157.1, 2013.Chen, S. S. and Curcic, M.: Unified Wave INterface Coupled Model (UWIN-CM) output from LASER, Distributed by: Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC), Harte Research Institute, Texas A&M University-Corpus Christi, available at: https://data.gulfresearchinitiative.org/data/R4.x265.000:0045, 10.7266/N7KW5DH7, last access: 26 January 2017, 2016a.Chen, S. S. and Curcic, M.: Ocean surface waves in Hurricane Ike (2008) and
Superstorm Sandy (2012): Coupled model predictions and observations, Ocean
Modell., 103, 161–176, 10.1016/j.ocemod.2015.08.005, 2016b.
Cole, R. and Symonds, D.: A 25 year collaboration using ADCPs, in: 2015
IEEE/OES 11th Current, Waves and Turbulence Measurement Workshop, St.
Petersburg, FL, USA, March 2015, 1–10, 2015.Curcic, M., Chen, S. S., and Özgökmen, T. M.: Hurricane-induced ocean
waves and stokes drift and their impacts on surface transport and dispersion
in the Gulf of Mexico, Geophys. Res. Lett., 43, 2773–2781,
10.1002/2015GL067619, 2016.
Davis, R. E.: Drifter observations of coastal surface currents during CODE:
The method and descriptive view, J. Geophys. Res.-Oceans, 90, 4741–4755,
1985.D'Asaro, E., Guigand, C., Haza, A., Huntley, H., Novelli, G., Özgökmen, T., and Ryan, E.: Lagrangian Submesoscale Experiment (LASER) surface drifters, raw, Gulf of Mexico, January–May 2016, Distributed by: Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC), Harte Research Institute, Texas A&M University-Corpus Christi, available at: https://data.gulfresearchinitiative.org/data/R4.x265.000:0027, 10.7266/N7MS3R6V, last access: 25 September 2017, 2016.D'Asaro, E., Guigand, C., Haza, A., Huntley, H., Novelli, G., Özgökmen, T., and Ryan, E: Lagrangian Submesoscale Experiment (LASER) surface drifters, interpolated to 15-minute intervals, Distributed by: Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC), Harte Research Institute, Texas A&M University-Corpus Christi, available at: https://data.gulfresearchinitiative.org/data/R4.x265.237:0001, 10.7266/N7W0940J, last access: 25 September 2017.Donelan, M. A., Curcic, M., Chen, S. S., and Magnusson, A. K.: Modeling waves
and wind stress, J. Geophys. Res.-Oceans, 117, C11, 10.1029/2011JC007787, 2012.Egbert, G. D. and Erofeeva, S. Y.: Efficient inverse modeling of barotropic
ocean tides, J. of Atmos. Ocean. Tech., 19, 183–204, 10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2, 2002.
Ekman, V. W.: On the influence of the Earth's rotation on ocean-currents,
Arkiv for Matematik, Astronomi, Och Fysik, 2, 1–52, 1905.Gonçalves, R. C., Iskandarani, M., Özgökmen, T., and Thacker,
W. C.: Reconstruction of submesoscale velocity field from surface drifters,
J. Phys. Oceanogr., 49, 941–958, 10.1175/JPO-D-18-0025.1, 2019.Haza, A. C., D'Asaro, E., Chang, H., Chen, S., Curcic, M., Guigand, C., Huntley, H. S., Jacobs, G., Novelli, G., Ozgokmen, T. M., Poje, A. C., Ryan, E., and Shcherbina, A.: Drogue status of the Lagrangian Submesoscale Experiment (LASER) surface drifters, Distributed by: Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC), Harte Research Institute, Texas A&M University-Corpus Christi, available at: https://data.gulfresearchinitiative.org/data/R4.x265.000:0044, 10.7266/N7QN656H, last access: 25 September 2017.Haza, A. C., D'Asaro, E., Chang, H., Chen, S., Curcic, M., Guigand, C.,
Huntley, H. S., Jacobs, G., Novelli, G., Özgökmen, T. M., and Poje,
A. C.: Drogue-loss detection for surface drifters during the Lagrangian
submesoscale experiment (LASER), J. Atmos. Ocean. Tech., 35, 705–725,
10.1175/JTECH-D-17-0143.1, 2018.Haza, A. C., Paldor, N., Ozgokmen, T. M., Curcic, M., Chen, S. S., and
Jacobs, G. A.: Wind-based estimations of ocean surface currents from massive
clusters of drifters in the Gulf of Mexico, J. Geophys. Res.-Oceans, 124, 5844–5869, 10.1029/2018JC014813, 2019.Hodur, R. M.: The Naval Research Laboratory's Coupled Ocean/Atmosphere
Mesoscale Prediction System (COAMPS), Mon. Weather Rev., 125, 1414–1430,
10.1175/1520-0493(1997)125<1414:TNRLSC>2.0.CO;2, 1997.Hill, C., DeLuca, C., Suarez, M., and Da Silva, A.: The architecture of the
earth system modeling framework, Comput. Sci. Eng., 6, 18–28,
10.1109/MCISE.2004.1255817, 2004.Isobe, A., Kubo, K., Tamura, Y., Nakashima, E., and Fujii, N.: Selective
transport of microplastics and mesoplastics by drifting in coastal waters,
Mar. Pollut. Bull., 89, 324–330,
10.1016/j.marpolbul.2014.09.041, 2014.Ivonin, D. V., Broche, P., Devenon, J. L., and Shrira, V. I.: Validation of HF
radar probing of the vertical shear of surface currents by acoustic Doppler
current profiler measurements. J. Geophys. Res.-Oceans, 109, C4,
10.1029/2003JC002025, 2004.Jacobs, G. A., Bartels, B. P., Bogucki, D. J., Beron-Vera, F. J., Chen, S. S.,
Coelho, E. F., Curcic, M., Griffa, A., Gough, M., Haus, B. K., and Haza, A. C.:
Data assimilation considerations for improved ocean predictability during
the Gulf of Mexico Grand Lagrangian Deployment (GLAD), Ocean Modell., 83,
98–117, 10.1016/j.ocemod.2014.09.003, 2014.Kim, S. Y., Cornuelle, B. D., and Terrill, E. J.: Decomposing observations of
high-frequency radar-derived surface currents by their forcing mechanisms:
Locally wind-driven surface currents, J. Geophys. Res.-Oceans, 115,
C12046 10.1029/2010JC006223, 2010.Klymak, J., D'Asaro, E., and Shcherbina, A.: Lagrangian Submesoscale Experiment (LASER) Moving Vessel Profiler CTD, northern Gulf of Mexico, January–February 2016, Distributed by: Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC), Harte Research Institute, Texas A&M University-Corpus Christi, available at: https://data.gulfresearchinitiative.org/data/R4.x265.000:0028, 10.7266/N7H130FC, last access: 28 June 2017, 2016.Kudryavtsev, V. N. and Soloviev, A. V.: Slippery near-surface layer of the
ocean arising due to daytime solar heating, J. Phys. Oceanogr., 20,
617–628, 10.1175/1520-0485(1990)020<0617:SNSLOT>2.0.CO;2, 1990.Laxague, N. J., Özgökmen, T. M., Haus, B. K., Novelli, G., Shcherbina,
A., Sutherland, P., Guigand, C. M., Lund, B., Mehta, S., Alday, M., and
Molemaker, J.: Observations of Near-Surface Current Shear Help Describe
Oceanic Oil and Plastic Transport, Geophys. Res. Lett., 45, 245–249,
10.1002/2017GL075891, 2018.Le Hénaff, M., Kourafalou, V. H., Paris, C. B., Helgers, J., Aman, Z. M.,
Hogan, P. J., and Srinivasan, A.: Surface evolution of the Deepwater Horizon
oil spill patch: combined effects of circulation and wind-induced drift,
Environ. Sci. Technol., 46, 7267–7273, 10.1021/es301570w,
2012.
Longuet-Higgins, M. S., Cartwright, D. E., and Smith, N. D.: Observations of the
directional specyrum of sea waves using the motion of a floating buoy, Ocean
Wave Spectra, Prentice-Hall, Englewood Cliffs, NJ, 1963.
Lumpkin, R. and Pazos, M.: Measuring surface currents with Surface Velocity
Program drifters: the instrument, its data, and some recent results, in:
Lagrangian Analysis and Prediction of Coastal and Ocean Dynamics, edited by:
Griffa, A, Kirwan, A. D., Mariano, A., Özgökmen, T. M., and Rossby,
T., Cambridge University Press, Cambridge, 39–67, 2007.Lumpkin, R., Özgökmen, T., and Centurioni, L.: Advances in the
application of surface drifters, Annu. Rev. Mar. Sci., 9, 59–81,
10.1146/annurev-marine-010816-060641, 2017.Morey, S., Wienders, N., Dukhovskoy, D., and Bourassa, M.: Measurement
characteristics of near-surface currents from ultra-thin drifters, drogued
drifters, and HF radar, Remote Sens., 10, 1633,
10.3390/rs10101633, 2018.Novelli, G., Guigand, C. M., Cousin, C., Ryan, E. H., Laxague, N. J., Dai, H.,
Haus, B. K., and Özgökmen, T. M.: A biodegradable surface drifter for
ocean sampling on a massive scale, J. Atmos. Ocean. Tech., 34, 2509–2532,
10.1175/JTECH-D-17-0055.1, 2017.Ohlmann, J. C. and Niiler, P. P.: Circulation over the continental shelf in
the northern Gulf of Mexico, Prog. Oceanogr., 64, 45–81,
10.1016/j.pocean.2005.02.001, 2005.Özgökmen, T. M., Chassignet, E. P., Dawson, C. N., Dukhovskoy, D.,
Jacobs, G., Ledwell, J., Garcia-Pineda, O., MacDonald, I. R., Morey, S. L.,
Olascoaga, M. J., and Poje, A. C.: Over what area did the oil and gas spread
during the 2010 Deepwater Horizon oil spill?, Oceanography, 29, 96–107,
10.5670/oceanog.2016.74, 2016.
Phillips, O. M.: Dynamics of the Upper Ocean, Cambridge University Press,
Cambridge, 336 pp., 1977.Pollard, R. T.: On the generation by winds of inertial waves in the ocean,
Deep-Sea Res., 17, 795–812,
10.1016/0011-7471(70)90042-2, 1970.Pollard, R. T. and Millard Jr, R. C.: Comparison between observed and
simulated wind-generated inertial oscillations, Deep-Sea Res., 17, 813–821,
10.1016/0011-7471(70)90043-4 ,1970.Poulain, P. M., Gerin, R., Mauri, E., and Pennel, R.: Wind effects on drogued
and undrogued drifters in the Eastern Mediterranean, J. Atmos. Ocean. Techn.,
26, 1144–1156, 10.1175/2008JTECHO618.1, 2009.Rascle, N., Ardhuin, F., and Terray, E. A.: Drift and mixing under the ocean
surface: A coherent one-dimensional description with application to
unstratified conditions, J. Geophys. Res.-Oceans, 111, C03016,
10.1029/2005JC003004, 2006.Rascle, N. and Ardhuin, F.: Drift and mixing under the ocean surface
revisited: Stratified conditions and model-data comparisons, J. Geophys.
Res., 114, C02016, 10.1029/2007JC004466, 2009.Röhrs, J., Sperrevik, A. K., Christensen, K. H., Broström, G., and
Breivik, Ø.: Comparison of HF radar measurements with Eulerian and
Lagrangian surface currents, Ocean Dynam., 65, 679–690,
10.1007/s10236-015-0828-8, 2015.Röhrs, J. and Christensen, K. H.: Drift in the uppermost part of the
ocean, Geophys. Res. Lett., 42, 10349–10356,
10.1002/2015GL066733, 2015.Santala, M. J. and Terray, E. A.: A technique for making unbiased
estimates of current shear from a wave-follower, Deep-Sea Res., 39, 607–622, 10.1016/0198-0149(92)90091-7, 1992.Sentchev, A., Forget, P., and Fraunié, P.: Surface current dynamics under
sea breeze conditions observed by simultaneous HF radar, ADCP and drifter
measurements, Ocean Dynam., 67, 499–512,
10.1007/s10236-017-1035-6, 2017.Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda,
M. G., Huang, X. Y., Wang, W., and Powers, J. G.: A description of the advanced
research WRF Version 3, NCAR Technical Note NCAR/TN-475 + STR, National
Center for Atmospheric Research, 10.5065/D68S4MVH, 2008.Stewart, R. H. and Joy, J. W.: HF radio measurements of surface currents, Deep-Sea Res., 21, 1039–1049,
10.1016/0011-7471(74)90066-7, 1974.
Stokes, G. G.: On the theory of oscillatory waves, Transactions of the
Cambridge Philosophical Society, 8, 441–473, 1847.Sutherland, G., Marié, L., Reverdin, G., Christensen, K. H.,
Broström, G., and Ward, B.: Enhanced turbulence associated with the
diurnal jet in the ocean surface boundary layer, J. Phys. Oceanogr., 46,
3051–3067, 10.1175/JPO-D-15-0172.1, 2016.Taillandier, V., Griffa, A., and Molcard, A.: A variational approach for the
reconstruction of regional scale Eulerian velocity fields from Lagrangian
data, Ocean Modell., 13, 1–24, 10.1016/j.ocemod.2005.09.002,
2006.
Wallcraft, A. J., Metzger, E. J., and Carroll, S. N.: Software design
description for the Hybrid Coordinate Ocean Model (HYCOM) version 2.2, Naval
Research Laboratory Tech. Rep. NRL/MR/7320-09-9166, NRL, 2009.Wu, J.: Sea-surface drift currents induced by wind and waves, J. Phys.
Oceanogr., 13, 1441–145, 10.1175/1520-0485(1983)013<1441:SSDCIB>2.0.CO;2, 1983.