LIMNOLOGY
and
OCEANOGRAPHY: METHODS
Limnol. Oceanogr.: Methods 10, 2012, 934–951
© 2012, by the American Society of Limnology and Oceanography, Inc.
Autonomous detection and sampling of water types and fronts
in a coastal upwelling system by an autonomous underwater
vehicle
Yanwu Zhang*, John P. Ryan, James G. Bellingham, Julio B. J. Harvey, and Robert S. McEwen
Monterey Bay Aquarium Research Institute
Abstract
Coastal upwelling occurs under the combined effect of wind stress and Earth’s rotation. The nutrients carried
up by upwelling have great impact on primary production and fisheries. For using autonomous underwater vehicles (AUVs) to investigate complex coastal upwelling ecosystems, we have developed algorithms for an AUV to
autonomously distinguish between upwelling and stratified water columns based on the vertical temperature difference between shallow and deep depths, and to accurately detect an upwelling front based on the horizontal gradient of the vertical temperature difference in the water column. During a June 2011 experiment in Monterey Bay,
California, the Dorado AUV flew on a transect from an upwelling shadow region (stratified water column),
through an upwelling front, and into an upwelling water column. Running our algorithms, the AUV successfully
classified the three distinct water types, accurately located the narrow front, and acquired targeted water samples
from the three water types. Molecular analysis of the AUV-acquired water samples shows that mussels, calanoid
copepods, and podoplean copepods were most abundant in the upwelling shadow region and nonexistent in the
upwelling water column. Calanoid copepods were moderately abundant in the water samples collected from the
upwelling front. These results are largely consistent with previous findings from zooplankton population surveys
conducted with the Dorado AUV in Monterey Bay in 2009. The novel detecting and targeted sampling capabilities permit an AUV to autonomously conduct “surgical sampling” of a complex marine ecosystem.
stratification and makes water more homogeneous over
depth. Consider an alongshore equator-ward wind on the west
coast as illustrated in Fig. 1. The wind stress combined with
the effect of Earth’s rotation will lead to a net transport of a
surface layer of water offshore, called Ekman transport (Kundu
1990). To conserve mass, deep water wells up to replace the
displaced surface water – thus the name “upwelling.” The
upwelling process brings cooler, saltier, and usually nutrientrich deep water upward, replacing warmer, fresher, and nutrient-depleted surface water (Kudela et al. 2008). The nutrients
carried up by upwelling have great impact on primary production and fisheries.
When a wind persistently blows from northwest to southeast (typically in spring and summer) along the California
coastline, intense upwelling takes place at Point Año Nuevo
(to the northwest of Monterey Bay labeled in Fig. 2) (Ramp et
al. 2005). For instance, during the June 2011 CANON (Controlled, Agile, and Novel Observing Network) Experiment,
upwelling of cold deep water to the surface was shown by low
sea surface temperature (SST) at Point Año Nuevo and in the
upwelling filaments spreading southward from Point Año
Nuevo, as displayed in Fig. 2. However, a mountain range
along the northern coast reduces wind-driven turbulent verti-
When surface water is warmed by sunlight, temperature at
the surface is higher than that at depth, forming a stratified
water column (density increasing with depth) that is gravitationally stable (Wright 1995). Coastal upwelling (Huyer 1983)
(Kudela et al. 2008) is a wind-driven ocean process that breaks
*Corresponding author: E-mail: [email protected];
7700 Sandholdt Road, Moss Landing, CA 95039, U.S.A.;
Phone: (831)775-2028; Fax: (831)775-1646
Acknowledgment
This work was supported by the David and Lucile Packard
Foundation. The authors would like to thank the MBARI Dorado AUV
team Hans Thomas, Duane Thompson, and Douglas Conlin, as well as
the R/V Zephyr crew. The authors are thankful to Drs. Francisco Chavez,
Christopher Scholin, and Robert Vrijenhoek for their leadership and support during the CANON experiments, as well as their helpful suggestions on improving the algorithms. Thanks also go to Raphael Kudela
and Cindy Ruhsam (University of California at Santa Cruz) for processing
and providing the satellite AVHRR SST data, and to Mike McCann and
Thom Maughan for supporting MBARI’s Shore-Side Data System (SSDS)
and Oceanographic Decision Support System (ODSS), respectively. The
authors would also like to thank the anonymous reviewers for their comments and suggestions for improving the paper.
DOI 10.4319/lom.2012.10.934
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plankton and zooplankton populations, as well as physical
processes that can locally enhance plankton aggregation and
nutrient supply (Ryan et al. 2010a; Ryan et al. 2010b; Harvey
et al. 2012), thus playing an important role in ocean ecosystems.
Autonomous underwater vehicles (AUVs) are becoming
increasingly useful platforms in oceanographic studies due to
their mobility, efficiency, and growing autonomy. An early use
of AUVs for studying ocean fronts was in the 1996 Haro Strait
Frontal Dynamics Experiment (Bellingham 1997). In a coordinated effort to map the tidal fronts (Farmer et al. 1995), two
Odyssey-class AUVs were deployed along with ships and
drifters. The AUVs conducted high-resolution surveys of temperature, salinity, and current velocity in the frontal zone
(Bellingham et al. 1997; Zhang and Willcox 1997). An acoustic
source was also installed on one vehicle for frontal mapping
by acoustic tomography (Schmidt et al. 1996). The Harvard
Ocean Prediction System (HOPS) ocean model (Robinson et al.
1996) was run to predict the front’s location and guide the
AUVs’ deployment. However, the AUVs were not capable of
autonomously detecting the front.
A prior effort aimed at detecting and tracking fronts in
Monterey Bay involved an approach called “mixed initiative”
(Rajan et al. 2009). This approach begins with an AUV conducting a reconnaissance survey and sending a compressed
data set back to a scientist onshore via satellite. The scientist
examines the data for the presence of a temperature front and
derives parameters that will permit the AUV to locate the center of the front: (1) the depth at which to transit during the
search, and (2) the water temperature at the center of the front
cal mixing in the northern Monterey Bay. The northern bay is
also sheltered from the prevailing southward flow of
upwelling filaments (from Point Año Nuevo) by its coastal
recess. Due to such atmospheric and oceanic sheltering, the
northern bay is referred to as the Monterey Bay “upwelling
shadow” (Breaker and Broenkow 1994; Graham and Largier
1997), featuring stratified water columns with relatively high
SST (as shown in Fig. 2) due to enhanced residence time and
local heating. The boundary between the stratified, biologically enriched water of the upwelling shadow, and the unstratified, biologically impoverished water transported southward
from the Point Año Nuevo upwelling center, is called the
“upwelling front.” Upwelling fronts support enriched phyto-
Fig. 1. A wind blowing from north to south drives upwelling along a
north-south coastline in the northern hemisphere.
Fig. 2. SST in Monterey Bay during the June 2011 CANON Experiment. The line along 36.9°N (from 121.9°W to 122.25°W) marks the Dorado AUV’s
31-km transect in Mission 2011.164.05 (see Assessment). Time is in Pacific Daylight Time (PDT). Advanced Very High Resolution Radiometer (AVHRR)
SST data courtesy of Kudela Lab (University of California at Santa Cruz) and National Oceanographic and Atmospheric Administration (NOAA) CoastWatch.
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samplers (called “gulpers”) (Bird et al. 2007) for taking water
samples back to shore for laboratory analysis, as shown in the
lower panel in Fig. 3. Once triggered, each gulper acquires a
1.8-L water sample in less than 2 s.
In a series of field programs in Monterey Bay from October
2009 to April 2011, the Dorado AUV ran our autonomous
peak-capture algorithm (Zhang et al. 2010b) to trigger the
gulpers for acquiring water samples with peak chlorophyll fluorescence signals from phytoplankton patches, or water samples with peak optical backscatter signals from intermediate
nepheloid layers (Zhang et al. 2010a). Such targeted water
sampling allowed biologists to successfully monitor fluctuations in harmful microalgae (Psuedo-nitzschia spp.), the toxin
they produce (domoic acid) and co-occurring zooplankton
(invertebrate larvae and copepods) over space and time. To
advance ecological studies of a coastal upwelling system, we
at the search depth. This data-informed human initiative is
relayed back to the AUV and thereafter integrated with
machine initiative for the AUV to track the front using a
repeated sequence of localization and mapping. An additional
parameter sent by the scientist to the AUV—the vertical scale
over which to map the front—permits optimization of horizontal resolution through the frontal zone. These capabilities
successfully demonstrated adaptive control of an AUV using
onboard planning and execution. However, the requirement
for a human in the loop imposes limitations and introduces
potential failure points.
The Dorado AUV (Bellingham et al. 2000; Sibenac et al.
2002) developed at the Monterey Bay Aquarium Research
Institute (MBARI) is shown in Fig. 3. It is propeller driven,
with a typical speed of about 1.5 m/s. In addition to a suite of
sensors, the AUV is equipped with ten 1.8-L syringe-like water
Fig. 3. Upper panel: the Dorado AUV. Lower panel: the ten 1.8-L water samplers (“gulpers”) installed in the mid-section of the vehicle (courtesy of Larry
Bird and Alana Sherman of MBARI).
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The different vertical structures of temperature in stratified
and upwelling water columns, and in the narrow front
between them, are illustrated in the upper panel in Fig. 4. In
upwelling water, the vertical temperature difference between
shallow and deep depths is small due to the upwelling process;
but in stratified water, the vertical temperature difference is
large (warm at the surface and cold at depth). In the narrow
upwelling front, the vertical temperature difference has a
sharp horizontal gradient. Phytoplankton patches, featuring
high chlorophyll fluorescence, can form in the stratified water
column, as illustrated in the lower panel in Fig. 4. Some phytoplankton patches appear in the form of thin layers, with
thickness ranging from < 1 m to a few meters, and can horizontally extend for kilometers (Cowles et al. 1998; Ryan et al.
2008, 2010b).
AUV algorithm for distinguishing between upwelling and
stratified water columns
The key to classification is finding the feature that maximizes the separability between classes. In an upwelling water
column, temperature, salinity, and other properties are much
more homogeneous over depth than in stratified water. Drawing on this difference, we formulated a simple yet effective
classifier for differentiating upwelling and stratified water
columns—the vertical temperature difference in the water column (Zhang et al. 2011a):
set a more challenging goal: to enable the AUV to
autonomously recognize and acquire targeted water samples
from the three distinct water types—the stratified water column, the upwelling water column, and their frontal boundary
(i.e., the upwelling front).
The work presented in this paper represents the first fully
autonomous AUV mission that achieved the above goal, by
synergetically running the following three algorithms:
An algorithm for an AUV to autonomously distinguish
between upwelling water and stratified water based on the vertical temperature difference in the water column (Zhang et al.
2011a, 2012a). We will summarize the algorithm in Materials
and procedures as the basis for developing the front-detection
algorithm (see below).
An algorithm for an AUV to autonomously and accurately
detect an upwelling front based on the horizontal gradient of
the vertical temperature difference in the water column. The
algorithm was succinctly reported in (Zhang et al. 2011b). We
will fully present the algorithm in Materials and procedures.
An improved autonomous peak-capture algorithm for phytoplankton patch sampling in the stratified water column. An
introduction of the original algorithm and highlights of the
improvements will be given in Materials and procedures.
The first time the set of three algorithms were integratively
applied on an AUV was in the CANON Experiment in June
2011. In a front-crossing mission, the Dorado AUV flew on a
31 km transect from an upwelling shadow region (i.e., a stratified water column), through an upwelling front, and into an
upwelling water column (the transect is marked by the eastwest line in Fig. 2). Running the set of three algorithms along
with our gulper management program, the AUV
autonomously classified stratified water and upwelling water,
accurately detected the upwelling front, and acquired targeted
water samples from the three distinct water types (stratified
water, upwelling water, and the narrow front between them).
This unprecedented AUV mission will be presented in detail in
Assessment.
DTempvert = |Tempshallow – Tempdeep|
(1)
where Tempshallow and Tempdeep are temperatures at shallow and
deep depths, respectively. DTempvert is significantly smaller in
upwelling water than in stratified water.
If DTempvert falls below a threshold threshDTemp, the water column is classified as an upwelling water column:
DTempvert ≤ threshDTemp
(2)
Suppose an AUV flies from stratified water to upwelling
water on a sawtooth (i.e., yo-yo) trajectory (in the vertical
dimension). The algorithm for the AUV to determine that it
has departed from the stratified water column and entered the
upwelling water column is illustrated in Fig. 5. Previous temperature measurements in Monterey Bay indicated that DTempvert between 5 m and 20 m depths provided a strong contrast
between upwelling and stratified water columns. Hence we set
the shallow depth to 5 m and the deep depth to 20 m in Eq.
1. For classification, we set the threshold threshDTemp = 1°C
(again based on previous AUV temperature measurements in
Monterey Bay) in Eq. 2.
On each yo-yo profile (descent or ascent), the AUV records
Temp5m and Temp20m to calculate DTempvert = |Temp5m – Temp20m|.
When DTempvert falls below threshDTemp for 5 consecutive yo-yo
profiles, the AUV determines that it has entered an upwelling
water column. The requirement of DTempvert on 5 consecutive
yo-yo profiles falling below threshDTemp is for robust detection of
Materials and procedures
The Dorado AUV, as shown in Fig. 3, has a length of 4.2 m
and a diameter of 0.53 m at the midsection. The vehicle’s sensor suite (Thompson 2007) includes Sea-Bird SBE3 temperature
and SBE4 conductivity sensors, a Paroscientific 8CB4000-I pressure sensor, a HOBI Labs HydroScat-2 sensor for measuring
chlorophyll fluorescence at 700 nm wavelength and optical
backscatter at 420 nm and 700 nm wavelengths, a Sea-Bird
SBE43 oxygen sensor, and a Satlantic ISUS (In Situ Ultraviolet
Spectrophotometry) sensor for measuring the concentration of
nitrate anions. In addition, the AUV has ten gulpers, as shown
in the lower panel of Fig. 3. Each gulper has a one-way valve
opening, which extends through the vehicle’s hull. When triggered, an electromagnetic pin-puller releases a dual spring
array under tension, which causes a plunger to suck in the 1.8L water sample in less than 2 s (Bird et al. 2007).
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Fig. 4. Different vertical structures of temperature (upper panel) and chlorophyll fluorescence (lower panel) in stratified and upwelling water columns,
and in the narrow front between them. Temperature from high to low is represented by color ranging from orange to blue (i.e., the temperature
decreases with depth) in the upper panel. A phytoplankton layer in the stratified water column is marked red (indicating high chlorophyll fluorescence)
in the lower panel.
Fig. 5. Illustration of the algorithm for an AUV to determine that it has departed from a stratified water column and entered an upwelling water column. The first four yo-yo profiles that satisfy DTempvert ≤ threshDTemp are marked blue. The fifth such yo-yo profile is marked green.
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an upwelling water column (i.e., small patches of water with
low DTempvert will be ignored). Conversely, suppose an AUV
flies from upwelling water to stratified water. When DTempvert
rises above threshDTemp for 5 consecutive yo-yo profiles, the AUV
determines that it has entered a stratified water column. The
horizontal distance covered by one yo-yo profile depends on
the yo-yo depth range as well as the AUV’s flight attitude. In
Dorado AUV Mission 2011.164.05 (to be presented in Assessment), the horizontal distance separated by 5 yo-yo profiles
was about 650 m.
AUV algorithm for detecting an upwelling front
The vertical temperature difference DTempvert is large in
stratified water but small in upwelling water. As an AUV flies
on a yo-yo trajectory (in the vertical dimension) on a transect
from stratified water to upwelling water, DTempvert drops
sharply (along the horizontal direction) in the narrow
upwelling front (conversely, DTempvert rises sharply in the narrow upwelling front on an AUV transect from upwelling water
to stratified water). Hence we have developed an AUV algorithm that uses the horizontal gradient of DTempvert to detect
an upwelling front. The algorithm was succinctly reported in
(Zhang et al. 2011b) and is fully presented herein.
The algorithm is illustrated in Fig. 6 for an AUV flying from
stratified water to upwelling water. When the vehicle flies
through the narrow upwelling front, DTempvert drops sharply.
The horizontal gradient of DTempvert is calculated as follows
GradDTemp_vert(n) = DTempvert(n) – avg{DTempvert(n – 5 : n – 9)}
where n is the index of the current yo-yo profile; avg{·} stands
for the average. The AUV calculates the horizontal gradient of
DTempvert by finding the difference between DTempvert of the
current yo-yo profile (index n) and DTempvert of several yo-yo
profiles ago—the reference level. The reference level is represented by the average DTempvert from profile n-5 to n-9—the
reference profiles. Thus the interval between the current profile and the center of the reference profiles is 7. The rationale
for leaving a 7-profile interval is as follows. The frontal zone
has fine-scale variabilities. Placing the reference profiles in the
preceding water type (e.g., the stratified water as in Fig. 6) at
some distance away from the current profile, and finding the
difference between the reference level (i.e., the average DTempvert of the reference profiles) and DTempvert of the current profile, provides a robust contrast that reflects the horizontal
change of water types.
Yo-yo profile n qualifies as an “upwelling-front yo-yo profile” if both of the following conditions are met (Eq. 4 and
Eq. 5):
The key signature of an upwelling front is high horizontal
gradient of DTempvert:
|GradDTemp_vert(n)| ≥ threshTempGrad
(4)
where threshTempGrad is a horizontal gradient threshold. In
Dorado AUV Mission 2011.164.05, we set threshTempGrad = 0.2°C
over a horizontal distance of about 900 m separated by 7 yoyo profiles.
(3)
Fig. 6. Illustration of the AUV algorithm for detecting an upwelling front when flying from stratified water to upwelling water. The first two yo-yo profiles that satisfy |GradDTemp_vert| ≥ threshTempGrad and DTempvert ≤ threshDTemp_front are marked purple. The third such yo-yo profile is marked red.
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Development of the peak-capture algorithm was motivated
by the goal of optimally using AUVs for studying phytoplankton thin layers (Cowles et al. 1998; Sullivan et al. 2010;
McManus et al. 2008; Ryan et al. 2008, 2010b). Subsurface layers of phytoplankton across a wide range of thickness are common in coastal waters, and the algorithm is effective for capturing the peak signal regardless of the layer’s thickness. In
this section, we first introduce the original algorithm in the
context of a subsurface phytoplankton layer. Then we highlight the subsequent improvements we have made to the algorithm. In Assessment, we demonstrate how the Dorado AUV
ran this algorithm to effectively sample plankton populations
in subsurface layers, some of which qualified as “thin layers.”
Within a phytoplankton layer, a high biomass concentration leads to high levels of chlorophyll fluorescence and optical backscatter signals (Ryan et al. 2010b; Sullivan et al. 2010).
When an AUV traverses the layer, in theory it could detect the
fluorescence peak and trigger the gulper to acquire a water
sample. However, a detection delay is unavoidable for any
real-time peak-detection algorithm—the peak is detected only
when it has just passed. Such a delay is particularly a problem
for a thin layer: even a small delay will result in water-sample
acquisition occurring past the fluorescence peak target. To
overcome the peak-detection delay problem, our algorithm
takes advantage of the AUV’s yo-yo trajectory (in the vertical
dimension), as illustrated in Fig. 7. In one yo-yo cycle, e.g., an
ascent profile followed by a descent profile, the vehicle crosses
the layer twice, measuring a fluorescence peak at each cross-
Due to the upwelling front’s proximity to the upwelling water,
the vertical temperature difference in the upwelling front is low:
DTempvert(n) ≤ threshDTemp_front
(5)
where threshDTemp_front is a vertical temperature difference threshold. We set threshDTemp_front = 1.5°C, which is 0.5°C higher than
the upwelling water threshold threshDTemp = 1°C, due to the consideration that in the upwelling front temperature is vertically
more homogeneous than in the stratified water, but not yet as
vertically homogeneous as in the upwelling water column.
On detection of 3 consecutive upwelling-front yo-yo profiles, the AUV determines that it has robustly detected the
upwelling front.
AUV algorithm for capturing chlorophyll fluorescence peaks
We have previously developed a peak-capture algorithm for
an AUV to autonomously detect chlorophyll fluorescence
peaks for triggering water sample acquisition in phytoplankton
patches (Zhang et al. 2010b). The algorithm cross-checks for
concurrent high values of the optical backscatter signal to
ensure that the sampled fluorescence peaks are true biomass
maxima. Using sliding temporal windows, the algorithm keeps
track of the background levels of chlorophyll fluorescence and
optical backscatter signals, as well as the fluorescence signal’s
average peak level on successive yo-yo profiles, thereby adapting sensitivity to ambient conditions. With signal processing
in the time domain, the algorithm can localize phytoplankton
patches at any depth in the water column.
Fig. 7. On an AUV’s yo-yo trajectory traversing a phytoplankton layer, on the first crossing, the vehicle detects the chlorophyll fluorescence peak with
a delay but saves the peak signal value in a sliding window. On the second crossing, the AUV triggers a gulper when the fluorescence measurement
reaches the saved peak signal value, thus capturing the peak with no delay. If no triggering occurs on the second crossing, the AUV can trigger a gulper
at the third crossing if the signal level condition is met, labeled as the “backup triggering.”
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detected peaks’ baseline is calculated as the average within a
sliding window (of a pre-set duration), rather than a running
average from the mission start.
2. When low-pass filtering the raw fluorescence or backscatter measurements, the largest raw measurement value inside
the low-pass filter window is excluded to prevent any singular
spurious measurement from affecting the low-pass filtered
output.
3. For peak detection on the AUV’s first crossing of the
layer, within the sliding window (see Fig. 7), we first use a sliding median filter (shorter than the sliding window) to exclude
spurious fluorescence peaks. The maximum output from the
sliding median filter is saved as the peak fluorescence signal
value. On the AUV’s second crossing of the layer, for triggering a gulper, it is required that two consecutive fluorescence
measurements reach (or exceed) the saved peak signal value to
prevent triggering by a single anomalous fluorescence measurement.
4. When a layer lies near the shallow limit of the AUV’s yoyo trajectory, due to the delay in detection of the vehicle’s attitude change, the AUV may not trigger a gulper on the second
crossing of the layer. Even for a deep layer, signal variation in
the layer may also render no triggering on the AUV’s second
crossing. To make up for this possibly missed opportunity, we
have added an option of allowing the AUV to trigger a gulper
at the third crossing (i.e., the “backup triggering” labeled in
Fig. 7) as long as the signal level condition is met.
ing. We assume that at two consecutive crossings that are no
more than several hundred meters apart, the two peaks will
have similar signal levels. On the vehicle’s first crossing (on
the ascent profile), the peak detection (by tracking the fluorescence signal’s slope) comes with a delay, but the vehicle
uses a sliding window (which saves a number of past samples)
to save the signal value of the true peak (which has just
passed). On the second crossing (on the descent profile), as
soon as the fluorescence measurement reaches the saved peak
value, a sampling is triggered. This way, the sampling is effectively targeted on the peak.
To reduce noise, the raw fluorescence measurements are
low-pass filtered by taking the moving-average of a few data
samples, generating Fllp. The low-pass filtered backscatter signal bblp is generated in the same way. On the AUV’s first crossing of the phytoplankton layer, a peak is detected when the
slope of Fllp changes from positive to negative. The background fluorescence signal level Flbkgd is calculated as the running average from the mission start. The background backscatter signal level bbbkgd is calculated in the same way. On
detection of fluorescence peaks, the algorithm cross-checks for
concurrent high values of optical backscatter to ensure that
water-sample acquisition targets true particle peaks and not
simply physiologically controlled fluorescence peaks (Ryan et
al. 2010b). Out of the detected fluorescence peaks, only those
that have both high fluorescence and high backscatter signals
are considered “real peaks” in a phytoplankton layer,
the criteria being (1)
Fllp
Flbkgd
≥ a Fl AND (2)
bblp
bbbkgd
Assessment
≥ abb where aFl
The Monterey Bay primary upwelling source, the Point Año
Nuevo upwelling center (see Fig. 2), and the upwelling shadow
in the northern recess of the bay together form a system that
hosts recurring creation and dynamic evolution of upwelling
fronts. The objective of the June 2011 CANON Experiment
was to advance the interdisciplinary study of the dynamic
frontal zone. A key goal was to employ the Dorado AUV to
acquire water samples precisely in the three different types of
water columns across the upwelling front (i.e., in the
upwelling shadow (stratified water), in the upwelling water,
and in the narrow front between them) for investigating contrasting plankton communities in the distinct water masses.
The Dorado AUV was deployed on a 31-km transect along
36.9°N (from 121.9°W to 122.25°W), as marked in Fig. 2.
Water depth along this transect ranges from 24 m at the east
end to 208 m at the west end. This transect was selected based
on multi-year satellite SST and chlorophyll fluorescence line
height (FLH) data for the month of June that showed high
horizontal gradients between the upwelling filament and the
upwelling shadow toward the northern end of the bay, thus
providing a relatively high probability of encountering
strongly contrasting water types across an upwelling front.
Evolutions of upwelling fronts in Monterey Bay are highly
dynamic, making it impossible to accurately predict the
front’s location for preprogramming the AUV’s water sam-
and abb are factors larger than one. The peaks’ baseline
Fl_PEAKbaseline is calculated as the running average of the “real
peaks” from the mission start. Only those peaks that are
higher than Fl_PEAKbaseline can lead to the triggering of a gulper
on the AUV’s second crossing of the phytoplankton layer.
Note that on the second crossing of the layer, the AUV triggers
a gulper when the raw fluorescence measurement (rather than
Fllp) reaches the saved peak value. Hence no delay due to lowpass filtering is introduced.
The algorithm has been used in many AUV missions for
acquiring water samples from phytoplankton patches of various thicknesses and at various depths. For Dorado AUV Mission 2011.164.05 to be presented in Assessment, we will give
the parameter settings as well as the detected layer’s thickness
and peak-to-background signal ratio.
Based on the original algorithm, we have made the following improvements. The Dorado AUV ran the improved peakcapture algorithm in Mission 2011.164.05.
1. The background level of fluorescence (or backscatter) is
calculated as the average on the last N yo-yo profiles (each yoyo profile typically contains hundreds of data samples. N is a
pre-set number), rather than a running average from the mission start. This way, the background level calculation is not
affected by signal levels in the distant past. Likewise, the
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the Earth-referenced velocity of the AUV when the seabed was
in range) and the measured heading and attitude. The AUV
periodically (about every 47 minutes) ascended to the surface
for global positioning system (GPS) fixes to correct for the
vehicle’s underwater navigation errors.
The flow diagram of the AUV’s classification of different
water columns and targeted sampling in the respective water
columns is given in Fig. 8. Our gulper management program
[originally devised by the fifth and second coauthors (Zhang
et al. 2010b) and then extended by the first and second coauthors] allocates gulpers to respective water types, and applies
triggering constraints (lockout time interval, max-wait time,
and lockout depth, defined below) to optimize sampling performance.
In the stratified water column, when the AUV traverses a
phytoplankton patch (which can be close to the surface or the
bottom, or in the interior of the water column), it detects the
chlorophyll fluorescence peak and saves the peak fluorescence
signal value on the first crossing. (On detection of fluorescence peaks, the algorithm cross-checks for concurrent high
values of optical backscatter). On the second (or third) crossing, when the fluorescence measurement reaches the saved
peak signal level, the AUV triggers a gulper on condition that
the elapsed time since the last triggering exceeds the lockout
time interval and the depth exceeds the lockout depth. The
lockout time intervals between triggerings are set to prevent
“dense triggerings” that would use up gulpers over a short distance. The lockout depth is set to prevent gulping air bubbles.
pling locations. The AUV must make decisions by itself. During the June 2011 experiment, by running the presented algorithms, the Dorado AUV autonomously distinguished the
water types, accurately located the front, and triggered water
samplings in the three distinct water columns, as demonstrated in Mission 2011.164.05.
Overview of the Dorado AUV’s autonomous detection and
targeted sampling in Mission 2011.164.05
The Dorado AUV started Mission 2011.164.05 at 13:35
(PDT) on 13 June 2011 from the east end of the 31-km transect line, flying westward, on a yo-yo trajectory between the
surface and 25-m depth (except for a small portion near the
east end of the transect where the water depth is smaller than
33 m). Note that the AUV maintained a minimum altitude of
about 8 m for safety. When the vehicle reached the west end
of the transect line, it turned around to fly eastward. The
AUV’s average horizontal speed was about 1 m/s. Its average
vertical speed was about 0.15 m/s on descent profiles and 0.29
m/s on ascent profiles, respectively. Thus the flight-path angle
of the yo-yo trajectory (the angle between the trajectory and
⎛ 0.15 m / s ⎞
the horizontal) was β flight _ path = atan ⎜
⎟ = 9° on descent
⎝ 1m/s ⎠
⎛ 0.29 m / s ⎞
profiles and β flight _ path = atan ⎜
⎟ = 16° on ascent profiles.
⎝ 1m/s ⎠
The AUV’s underwater navigation was by dead reckoning
based on the vehicle’s estimated speed (aided by a Teledyne
RD Instruments Doppler velocity log (DVL) which provided
Fig. 8. Flow diagram of the AUV’s classification of different water columns and targeted sampling in the respective water columns.
942
Zhang et al.
AUV sampling across an upwelling front
No. 3, 4, 5, 6 were triggered in the narrow upwelling front;
gulpers No. 7, 8, 9 were triggered in the upwelling water column. The AUV reached the west end of the transect line in 9.6
h, and then turned around to fly eastward. We only show the
westbound transect in Fig. 9 and the ensuing figures, because
all water samplings had been completed on the westbound
flight.
Peak-capture sampling of the phytoplankton layer in the
stratified water column
In the upwelling shadow region (near the east end of the
transect), there existed a phytoplankton layer with an intense
chlorophyll fluorescence signal, as shown in the upper panel
of Fig. 9. The Dorado AUV ran our peak-capture algorithm to
trigger three gulpers (No. 0, 1, 2) at chlorophyll fluorescence
peaks in the layer, as marked by the black triangles in the
upper panel in Fig. 10.
The fluorescence signal and the AUV’s depth associated
with the three triggerings are shown in the three columns in
the middle and lower panels in Fig. 10. In the middle panel,
the raw fluorescence measurement is shown by the black solid
line. The 8-sample (at 4 Hz sampling rate) low-pass filtered fluorescence signal Fllp is shown by the magenta line (the lowpass filtered backscatter signal bblp was calculated in the same
way but not shown due to limit of space). Detection of the
peaks (marked by circles) was by tracking the slope of Fllp. The
background fluorescence signal level Flbkgd, calculated as the
average on the last 10 yo-yo profiles (or the last n profiles if
the profile index n < 10), is shown by the black dotted line
(the background backscatter signal level bbbkgd was calculated
in the same way but not shown). Out of the detected fluorescence peaks, only those that had both high fluorescence and
high backscatter signals were considered “real peaks” in a phytoplankton layer, the criteria being (1) Fllp ≥ 1.5 Flbkgd; (2) bblp ≥
1.2 bbbkgd. The peaks’ baseline Fl_PEAKbaseline was calculated as a
20-minute moving-average of the “real peaks,” as shown by
the blue dashed line. Only those peaks that were higher than
Fl_PEAKbaseline were eligible for leading to the triggering of a
gulper.
For triggering gulper No. 0, the AUV detected the fluorescence peak on ascent (i.e., the first crossing of the phytoplankton layer) and used the 8-sample sliding window to save
the peak signal value as marked by the solid triangle (a 5-sample median filter was used to exclude spurious peaks). On the
AUV’s descent (i.e., the second crossing of the layer), when
two consecutive fluorescence measurements reached (or
exceeded) the saved peak signal level, the vehicle triggered
gulper No. 0, as marked by the hollow triangle. Gulpers No. 1
and 2 were triggered on the AUV’s third crossings of the phytoplankton layer. The lockout time interval was set to 2700 s,
and the lockout depth was set to 1.5 m. The fluorescence peakto-background ratios at the triggerings of gulpers No. 0, 1, and
2, were 4.3, 5.4, and 7.2, respectively. The vertical full-widthat-half-maximum (FWHM) of the layer captured by gulpers
No. 0, 1, and 2 was 6.8 m, 1.9 m, and 1.5 m, respectively. All
On the completion of a yo-yo profile, the AUV checks
whether GradDTemp_vert and DTempvert meet the conditions for an
upwelling front (see Fig. 6). If so, a “front flag” is set. The
depth of the maximum low-pass filtered fluorescence on the
preceding yo-yo profile has been recorded. On the ensuing yoyo profile, when the AUV reaches that depth, a gulper is triggered (as long as the lockout time interval and lockout depth
conditions are also satisfied). The purpose is to acquire water
samples with high fluorescence signals in the front. After a
triggering, the front flag is reset.
If the conditions for an upwelling front are not met, the
AUV further checks whether DTempvert meets the condition for
an upwelling water column (see Fig. 5). If the upwelling condition is met and the lockout time interval condition is also
satisfied, the AUV triggers a gulper at either a pre-set depth or
at the lower turning point of the yo-yo profile (for acquiring a
water sample at a relatively deep spot in the upwelling water
column).
A “max-wait time” is set for each gulper. In each type of
water column, if the max-wait time has elapsed but the detection conditions have not been met, the AUV will trigger a
gulper under the only condition of lockout depth, so that the
AUV will return to shore with all gulpers filled even if some
water samples are not target samples.
In Dorado AUV Mission 2011.164.05, the ten gulpers were
pre-allocated to the three types of water columns as follows:
three (No. 0, 1, 2) in the stratified water, four (No. 3, 4, 5, 6)
in the upwelling front, and the remaining three (No. 7, 8, 9)
in the upwelling water. We allocated more gulpers for the
upwelling front because of high interest in studying plankton
populations in the front, and also because it was very hard to
acquire water samples from the narrow front using traditional
methods. No gulper was triggered on max-wait time, i.e., all
gulpers detected the respective water columns and acquired
targeted water samples.
An overview of the triggerings of the ten gulpers is shown
in Fig. 9, with longitude and elapsed time (since the start of
the mission) both marked on the x-axis. The elapsed time was
proportional to the AUV’s traveled distance, but their relationship was not exactly linear because the AUV’s surfacing
frequency and duration (for GPS fixes) varied over the mission. Chlorophyll fluorescence and temperature measured by
the AUV are displayed in the upper and lower panels, respectively. The measured temperature shows that the AUV started
from stratified water, flew through a narrow front, and into
upwelling water. The measured chlorophyll fluorescence
shows that in the stratified water column (in the upwelling
shadow region), there existed a phytoplankton layer with an
intense chlorophyll fluorescence signal. The AUV
autonomously distinguished among the three water types and
triggered the gulpers in the respective water columns, as
marked by the corresponding symbols in Fig. 9: gulpers No. 0,
1, 2 were triggered at chlorophyll fluorescence peaks in the
phytoplankton layer in the stratified water column; gulpers
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Zhang et al.
AUV sampling across an upwelling front
Fig. 9. In Mission 2011.164.05, the Dorado AUV flew westward on a 31-km transect along 36.9°N, on a yo-yo trajectory from the surface to 25 m
depth (except for a shallow-water portion near the east end of the transect). Chlorophyll fluorescence and temperature measured by the AUV are shown
in the upper and lower panels, respectively. Triggerings of the AUV’s ten gulpers in the three distinct water columns are marked by the corresponding
symbols.
three triggerings, particularly gulpers No. 1 and 2, captured
high peaks of chlorophyll fluorescence. By the criteria for phytoplankton thin layers (in terms of fluorescence peak-to-background ratio and layer thickness) as given in (Ryan et al. 2010b),
the layer from which gulpers No. 1 and 2 acquired peak-fluorescence water samples was a phytoplankton thin layer.
Front detection and sampling in the narrow upwelling
front
At about 6 h after the start of the AUV mission, the vehicle
encountered the upwelling front, signified by a sharp horizontal gradient of DTempvert as shown in the middle panel in
Fig. 11: to the left of the front DTempvert was large, but to the
right of the front DTempvert was small. Running our front detec-
tion algorithm, the Dorado AUV achieved the primary goal of
accurately detecting the front and consequently triggering the
four gulpers (No. 3, 4, 5, 6) within the narrow front, as marked
by the red diamonds in the lower panel of Fig. 11. The lockout time interval was set to 300 s, and the lockout depth was
set to 1.5 m. Due to a coding error, the secondary goal of triggering at the depth corresponding to the maximum fluorescence on the preceding yo-yo profile was not achieved. The
error has since been corrected.
The AUV detected the front by calculating DTempvert(n) (by
Eq. 1 with the shallow and deep depths set to 5 m and 20 m
respectively) and GradDTemp_vert(n) (by Eq. 3) in real time. These
two variables were not logged, although the Sea-Bird CTD
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Zhang et al.
AUV sampling across an upwelling front
Fig. 10. Triggerings of gulpers No. 0, 1, and 2 at chlorophyll fluorescence peaks in a phytoplankton layer (upper panel). Details of the three gulpers’
triggerings are shown in the middle and lower panels and explained in the text.
marked red. The upward arrow on the threshTempGrad = 0.2°C red
dashed line (in middle panel) indicates the “ ≥ ” condition in
Eq. 4. The downward arrow on the threshDTemp_front = 1.5°C red
dashed line (in upper panel) indicates the “ ≤ ” condition in
Eq. 5.
The four gulpers’ triggerings in the narrow upwelling front
at about 6 h (after start of mission) are consistent with the
“red profiles” around 6 h in the middle panel. The “red profile” at 2.6 h was less than 300 s (the lockout time interval)
away from the last peak-fluorescence triggering (i.e., the third
black triangle in the lower panel of Fig. 11). Therefore, no triggering occurred at that time. For the “red profile” at 3.5 h,
there was no corresponding gulper triggering. A probable
cause is as follows. On the yo-yo profile immediately preced-
driver did log temperature. The logging process introduced a
small delay (well within the temperature data sample interval
of 0.2 s), so that the temperature values used by the AUV’s
onboard calculation were slightly different from the logged
temperature values. Nonetheless, in Fig. 11 we use the Sea-Bird
CTD temperature log to verify that the AUV’s onboard detection and triggerings in the upwelling front performed as
designed, being aware that the two temperature time series
had slight differences. Using the Sea-Bird CTD temperature
log, we calculate off-line the vertical temperature difference
DTempvert(n) and its horizontal gradient |GradDTemp_vert(n)| on each
yo-yo profile, as plotted in the upper and middle panels of Fig.
11, respectively. In the middle panel, those yo-yo profiles that
meet the conditions for an upwelling front (see Fig. 6) are
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Zhang et al.
AUV sampling across an upwelling front
Fig. 11. In the lower panel, the onboard triggerings of the four gulpers (No. 3, 4, 5, and 6) in the upwelling front are marked by the red diamonds.
The vertical temperature difference DTempvert and its horizontal gradient |GradDTemp_vert| on each yo-yo profile, calculated off-line using the Sea-Bird CTD
temperature log, are plotted in the upper and middle panels, respectively (the horizontal distance separated by 7 yo-yo profiles was about 900 m). Those
yo-yo profiles that meet the conditions for an upwelling front are marked red (middle panel). Those yo-yo profiles that meet the condition for an
upwelling water column are marked green (upper panel).
facilitate off-line verification, we will log the two onboard-calculated variables DTempvert(n) and GradDTemp_vert(n).
Detection and sampling in the upwelling water column
After flying through the upwelling front, the AUV
entered the upwelling water column. Running our classification algorithm, the vehicle recognized the upwelling
water and triggered the remaining three gulpers (No. 7, 8, 9)
in the upwelling water column, as marked by the green
squares in the lower panel of Fig. 11. The AUV triggered
each of the three gulpers at the lower turning point of the
ing that “red profile,” DTempvert = 1.43°C, very close to the
1.5°C threshold (the second condition for qualifying as an
“upwelling front yo-yo profile”—Eq. 5). It is probable that the
onboard-calculated counterpart was slightly higher than the
1.5°C threshold (i.e., not meeting that condition), and consequently there was no qualified “upwelling front yo-yo profile”
at 3.5 h, hence no gulper was triggered. Note that the four
gulpers allocated for the upwelling front had all been triggered
before 6.5 h. Therefore, the “red profiles” after that time point
do not have corresponding gulper triggerings. In the future, to
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Zhang et al.
AUV sampling across an upwelling front
Physical and biological analysis results of the AUVacquired water samples
Physical water properties at the triggerings of the 10
gulpers are shown in the left column of Fig. 12. The 10
water samples’ zooplankton (mussels, barnacles, calanoid
copepods, and podoplean copepods) signals were detected
using the sandwich hybridization assay (SHA) method in a
shore laboratory (Scholin et al. 1999; Harvey et al. 2012), as
shown in the right column. The fluorescence and optical
backscatter signals at the three triggerings in the phytoplankton layer (in the upwelling shadow region) were much
higher than those in the upwelling front and in the
upwelling water column. The three triggerings in the
upwelling water column featured low temperature, high
yo-yo profile to acquire a water sample at a relatively deep
spot in the upwelling water column. The lockout time interval was set to 500 s. Again, we use the Sea-Bird CTD temperature log to verify that the AUV’s onboard detection and
triggerings in the upwelling water column performed as
designed. In the upper panel of Fig. 11, which shows DTempvert(n) calculated off-line using the Sea-Bird CTD temperature log, those yo-yo profiles that meet the condition for an
upwelling water column (see Fig. 5) are marked green. The
downward arrow on the threshDTemp = 1°C green dashed line
(in upper panel) indicates the “ ≤ ” condition in Eq. 2. The
three gulpers’ triggerings in the upwelling water column are
consistent with the “green profiles” counterparts in the
upper panel.
Fig. 12. Physical water properties (measured in situ by the AUV, left column) and zooplankton signals (by using molecular probes in a shore laboratory,
right column) of the water samples from the 10 gulpers. Unit of the zooplankton signals in the right column is optical density (OD) at 450 nm wavelength. (Note: gulper No. 5′s water sample volume was 1/3 smaller than the other gulpers’ water samples.)
947
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AUV sampling across an upwelling front
Comments and recommendations
salinity, high nitrate (NO3–) concentration, and low dissolved oxygen, all consistent with expectations for
upwelling water. Note that the water samples from the three
water types were taken from different depths.
Mussels, calanoid copepods, and podoplean copepods
were most abundant in the three water samples collected
from the phytoplankton layer. Conversely, these organisms
were nonexistent in the three water samples collected from
the upwelling water column. Calanoid copepods were of
moderate to high abundance in the water samples collected
from the upwelling front (except for gulper No. 5’s water
sample, which was smaller in volume than the other
gulpers’ water samples by roughly 1/3). These results are
largely consistent with previous findings from zooplankton
population surveys using the Dorado AUV in Monterey Bay
in 2009 (where the gulpers were triggered at prescribed geographic locations and depths) (Harvey et al. 2012). One
notable difference is that while calanoid copepods were
moderately abundant in the frontal water samples in 2011,
none of those samples contained exceptionally high
calanoid abundance as was present in the frontal water samples in 2009. However, we also note a difference of their
locations: the frontal water samples in 2011 were taken
slightly on the upwelling water column side, but the frontal
water samples in 2009 were taken slightly on the stratified
water column side. Another difference is that barnacles were
present in two of the three water samples (abundant in one
sample) collected from the upwelling water column in 2011,
while the water samples collected from upwelling water
columns in 2009 showed very low to no barnacle signal.
These observations call for further studies involving iterative sampling of upwelling frontal environments and their
bounding water masses.
We have set up a key metric of the vertical temperature
homogeneity in the water column—the vertical temperature
difference between shallow and deep depths (Zhang et al.
2011a). This metric was our basis for differentiating upwelling
and stratified water columns, and detecting the upwelling
front. Based on this metric, in this section we propose a
generic metric that includes more depths, as a better measure
of the vertical temperature homogeneity in the water column.
By post-processing the Dorado AUV data, we demonstrate that
the generic metric improves the robustness of upwelling front
detection.
In the middle panel in Fig. 11, we notice 2 groups of “red
profiles’’ (that meet the conditions of an upwelling front)
around 7.5 h and 8 h, although they did not trigger any gulper
because all the four gulpers allocated for the upwelling front
had been triggered before 6.5 h. Those 2 groups of “red profiles” were actually not due to real upwelling fronts, but were
due to variations of temperature at 5 m and 20 m depths. For
instance, across 7.5 h Temp5m remained largely the same (and
was higher than Temp20m), but Temp20m was higher after 7.5 h
than before 7.5 h. As a consequence, DTempvert = |Temp5m –
Temp20m| was smaller after 7.5 h than before 7.5 h. This caused
a drop of DTempvert across 7.5 h, and accordingly led to false
alarms of upwelling front detection at 7.5 h.
For a more robust evaluation of the vertical temperature
homogeneity in the water column, we propose a generic metric that includes more depths:
DTempvert =
1 N
1 N
∑ Tempdepth_i − N ∑i=1 Tempdepth_i
N i=1
(6)
where i is the depth index and N is the total number of depths
included for calculating DTempvert. Tempdepth_i is the temperature
Discussion
1 N
∑ Tempdepth_i is the average temperature of
N i =1
1 N
those depths. Tempdepth_i − ∑i=1 Tempdepth_i measures the differN
The integration of multiple signal processing algorithms
worked well in the very first deployment on an AUV for
autonomous detection and targeted water sampling in three
water types. This was due to algorithm designs based on
oceanographic insights, having robustly tested elements upon
which to build and improve, data-informed algorithm parameters, and appropriate environmental conditions.
We can apply the presented front detection method to
other oceanographic properties, e.g., salinity fronts and
chlorophyll fronts. The Environmental Sample Processor
(ESP) is an underwater robotic laboratory that conducts
molecular biology analysis on discrete water samples in situ
(Scholin et al. 2009). There is an ongoing effort of incorporating the third generation ESP into the Tethys long-range
AUV (Bellingham et al. 2010). The combination of an AUVborne ESP and the AUV’s capability of autonomously detecting ocean features provides an exciting prospect of conducting molecular biology analysis on targeted water samples in
situ, with no human in the loop.
at the ith depth.
ence (absolute value) between the temperature at each depth
and the depth-averaged temperature. The average difference
DTempvert (averaged over all participating depths) is a measure
of the vertical homogeneity of temperature in the water column, which we call the “vertical temperature homogeneity
index.” The formulation of Eq. 6 draws upon the idea in the
definition of variance in statistics, just replacing the square of
each difference with the absolute value of each difference.
By including multiple depths, the impact of temperature
variation at a single depth on DTempvert is mitigated. Thus
DTempvert defined in Eq. 6 is supposed to be a more robust measure of the water column’s vertical temperature homogeneity
than DTempvert defined in Eq. 1.
In the special case of N = 2 (i.e., only two participating
depths), if depth1 denotes the shallow depth and depth2
948
Zhang et al.
AUV sampling across an upwelling front
denotes the deep depth, and suppose Tempdepth_1 > Tempdepth_2,
then Eq. 6 reduces to
DTempvert =
1
2
{[Temp
+[
=
depth_1
−
AUVs during the April and June 2011 CANON Experiments) is
actually a simplified case of the generic vertical temperature
homogeneity index defined in Eq. 6.
Now we test the upwelling front detection performance
with the newly defined vertical temperature homogeneity
index, using the Dorado AUV Mission 2011.164.05 data set.
Four depths are included for calculating DTempvert by Eq. 6: 5
m, 10 m, 15 m, and 20 m. GradDTemp_vert (the horizontal gradient
of DTempvert) is still calculated by Eq. 3. DTempvert(n) and |Grad(n)| on each yo-yo profile are plotted in the upper and
DTemp_vert
middle panels of Fig. 13, respectively.
The two conditions for detection of an upwelling front are
still expressed in Eqs. 4 and 5. For the first condition, we still
1
(Tempdepth_1 + Tempdepth_ 2 )]
2
1
(Tempdepth_1 + Tempdepth_ 2 ) − Tempdepth_ 2 ]
2
}
(7)
1
(Tempdepth_1 − Tempdepth_ 2 )
2
which is the same as Eq. 1 except for a factor of 1/2. Thus
DTempvert defined in Eq. 1 (used by the Tethys and Dorado
Fig. 13. Using Dorado AUV Mission 2011.164.05 data set, the vertical temperature homogeneity index DTempvert calculated by Eq. 6 is shown in the
upper panel. Its horizontal gradient |GradDTemp_vert| is shown in the middle panel. Those yo-yo profiles that meet the conditions of an upwelling front are
marked red (middle panel).
949
Zhang et al.
AUV sampling across an upwelling front
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set threshTempGrad = 0.2°C, the same as in Materials and procedures.
For the second condition, because DTempvert with four participating depths should be smaller than that involving only two
depths (as in Materials and procedures) due to more averaging,
we set threshDTemp_front = 0.5°C, lower than the 1.5°C threshold
used in Materials and procedures.
In the middle panel of Fig. 13, those yo-yo profiles that
meet the conditions of an upwelling front are marked red. The
upward arrow on the threshTempGrad = 0.2°C dashed line (in middle panel) indicates the “ ≥ ” condition in Eq. 4. The downward arrow on the threshDTemp_front = 0.5°C dashed line (in upper
panel) indicates the “ ≤ ” condition in Eq. 5. The upwelling
front is detected around 6 h, the same as in Fig. 11. Using
DTempvert involving multiple depths (calculated by Eq. 6), the
impact of temperature variation at a single depth on DTempvert
is mitigated and is less likely to cause false alarms of upwelling
front detection. As a result, the “red profiles” around 7.5 h and
8 h are much fewer than in Fig. 11. Thus robustness of
upwelling front detection is improved using the generic vertical temperature homogeneity index.
During the May/June 2012 CANON Experiment in Monterey Bay, the vertical temperature homogeneity index (as
expressed in Eq. 6, using four depths) was used in the
improved upwelling front detection/tracking algorithms
run on the Dorado and Tethys AUVs. In three missions over
three consecutive days, the Dorado AUV accurately
detected and acquired water samples in the upwelling front
(as well as in the upwelling and stratified water columns
away from the front). The Tethys AUV closely tracked the
evolution of an upwelling front over 4 d (Zhang et al.
2012b). Details of those AUV missions will be presented in
future publications.
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Submitted 3 April 2012
Revised 9 October 2012
Accepted 24 October 2012
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