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NAVAL 

POSTGRADUATE 

SCHOOL 

MONTEREY, CALIFORNIA 


THESIS 


SENSIBLE HEAT FLUX RELATED TO VARIATIONS IN 
ATMOSPHERIC TURBULENCE KINETIC ENERGY ON 
A SANDY BEACH 

by 

Jessica S. Koscinski 
June 2017 

Thesis Advisor: Jamie MacMahan 

Second Reader: Qing Wang 


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SENSIBLE HEAT FLUX RELATED TO VARIATIONS IN ATMOSPHERIC 
TURBULENCE KINETIC ENERGY ON A SANDY BEACH 

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6. AUTHOR(S) Jessica S. Koscinski 

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Naval Postgraduate School 

Monterey, CA 93943-5000 

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13. ABSTRACT (maximum 200 words) 


Two field experiments were conducted in the surf zone of the Monterey Bay to explore the 
relationship between surf zone sea spray and sensible heat flux. Nine flux tripod towers with 
instrumentation designed to measure atmospheric wind speed and direction, temperature and relative 
humidity, as well as thermistor and pressure sensors in the surf zone, were deployed throughout the two 
experiments. Data analysis showed that the ocean temperature was warmer than the air temperature and 
the environment was mildly unstable throughout most of the two experiments. The total data set was 
broken into 15-minute averages and limited to onshore winds over 3m/s and a stability range of 

-5<C<0.5. £ being the non-dimensional height under Monin-Obukhov Similarity Theory. Sensible 
heat flux was calculated using the eddy covariance method and also using the CO ARE 3.5 model, 
validated for the open ocean, and the results were compared. The model under-predicted measured results 
by over 50%. Sea spray sensible heat was then calculated and added to the model results; the new 
comparison showed that the model was nearly the same as the measured results with sea spray sensible 
heat added. 


14. SUBJECT TERMS 

Sensible heat flux, turbulence kinetic energy, surf zone 

15. NUMBER OF 
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Approved for public release. Distribution is unlimited. 


SENSIBLE HEAT FLUX RELATED TO VARIATIONS IN ATMOSPHERIC 
TURBULENCE KINETIC ENERGY ON A SANDY BEACH 


Jessica S. Koscinski 

Lieutenant Commander, United States Navy 
B.A., Buffalo State College, 2005 


Submitted in partial fulfillment of the 
requirements for the degree of 


MASTER OF SCIENCE IN METEOROLOGY AND 
PHYSICAL OCEANOGRAPHY 

from the 

NAVAL POSTGRADUATE SCHOOL 
June 2017 


Approved by: Jamie MacMahan 

Thesis Advisor 


Qing Wang 
Second Reader 


Peter Chu 

Chair, Department of Oceanography 



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IV 



ABSTRACT 


Two field experiments were conducted in the surf zone of the Monterey Bay to 
explore the relationship between surf zone sea spray and sensible heat flux. Nine flux 
tripod towers with instrumentation designed to measure atmospheric wind speed and 
direction, temperature and relative humidity, as well as thermistor and pressure sensors in 
the surf zone, were deployed throughout the two experiments. Data analysis showed that 
the ocean temperature was warmer than the air temperature and the environment was 
mildly unstable throughout most of the two experiments. The total data set was broken 
into 15-minute averages and limited to onshore winds over 3 m/s and a stability range of 
-5 <^<0.5, L, being the non-dimensional height under Monin-Obukhov Similarity 
Theory. Sensible heat flux was calculated using the eddy covariance method and also 
using the COARE 3.5 model, validated for the open ocean, and the results were 
compared. The model under-predicted measured results by over 50%. Sea spray sensible 
heat was then calculated and added to the model results; the new comparison showed that 
the model was nearly the same as the measured results with sea spray sensible heat 
added. 


v 


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vi 



TABLE OF CONTENTS 


L INTRODUCTION.1 

II. THEORETICAL BACKGROUND.9 

A. REYNOLDS NUMBER.9 

B. MONIN-OBUKHOV SIMILARITY THEORY.9 

C. DRAG COEFFICIENT.10 

D. FOOTPRINT ANALYSIS.11 

III. METHODS.13 

A. EXPERIMENTAL LOCATIONS.13 

B. INSTRUMENTATION.14 

C. DATA PROCESSING.17 

IV. RESULTS.19 

A. ENVIRONMENT.19 

B. ROUGHNESS.22 

C. STABILITY.23 

D. FOOTPRINT.25 

E. TURBULENCE KINETIC ENERGY.26 

F. SENSIBLE HEAT FLUX.29 

V. SUMMARY AND CONCLUSIONS.33 

LIST OF REFERENCES.35 

INITIAL DISTRIBUTION LIST.39 


vii 























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LIST OF FIGURES 


Figure 1. Normalized TKE Budget Terms. Source: Garratt (1992).7 

Figure 2. Drag Coefficient. Source: Shabani et al. (2016).11 

Figure 3. Experimental Locations.13 

Figure 4. Instruments and Tower Setup during the October Experiment.15 

Figure 5. Wind Speed vs. Direction.19 

Figure 6. Hourly Wind Speed and Air Temperature.20 

Figure 7. Air-Ocean Temperature Difference.21 

Figure 8. Roughness Reynolds Number.22 

Figure 9. Thermal Stability.24 

Figure 10. Footprint Analysis.25 

Figure 11. The Terms of the TKE Budget.27 

Figure 12. Zonally separated Buoyancy Flux.28 

Figure 13. Contributions of Sand, Surf Zone and Ocean outside Surf Zone.28 

Figure 14. Sensible Heat Flux.30 

Figure 15. Sensible Heat Flux from October Experiment.31 

Figure 16. Sensible Heat Flux from CLASI Experiment.32 


IX 



















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x 



LIST OF TABLES 


Table 1. Instrumentation for October Deployment.16 

Table 2. Instrumentation for CLASI Deployment.16 


xr 





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LIST OF ACRONYMS AND ABBREVIATIONS 


CLASI 

DMB 

ESN 

ESS 

MAB 

MAR 

MOST 

RSMAS 

SHF 

TKE 


Coastal Land-Air-Sea-Interaction 
Del Monte Beach 
Elkhorn Slough North 
Elkhorn Slough South 
Manaresa Beach 
Marina Beach 

Monin-Obukhov Similarity Theory 
Rosenstiel School of Marine and Atmospheric Science 
sensible heat flux 
turbulence kinetic energy 



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xiv 



ACKNOWLEDGMENTS 


I would like to thank my thesis advisor, Jamie MacMahan, whose knowledge, 
motivation and assistance made this possible. Thank you also to Qing Wang, my second 
reader, an air-sea expert, and Ed Thornton, a legend in the field of nearshore and my third 
set of eyes. To LCDR Darin Keeter, who got me started on this path and who has been a 
friend and mentor, thank you. Thanks to my cohort, Adam, Andrew, Chris A., Chris B., 
Eric and Kevin. I wouldn’t have made it through these two and a half years without you. 
Finally, thank you. Tucker, for always having time during your busy Ph.D. process to 
help me with whatever MATLAB problem or stupid question I had. 


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xvi 



I. INTRODUCTION 


Atmospheric and oceanic factors in the littoral region are of increasing 
importance to the U.S. Navy. The “littoral” in simple definition, refers to a coastal region 
or a shore and geographically it is a coastline zone between extreme high and low tides 
(Vego 2014). Two of the primary anti-access/area-denial capabilities in the littorals are 
land-based aircraft and unmanned aerial vehicles. One advantage of using these platforms 
in the littorals is the enhanced probability of avoiding detection and achieving surprise if 
they approach targets at low altitudes or over land (Vego 2014). Another example of a 
need for atmospheric data in the littorals is the occurrence of atmospheric ducting. 
Ducting can trap electromagnetic energy and can both prevent it from reaching a sensor 
and allow it to travel farther than had it not been in a duct. Ducting occurs in conditions 
of temperature and water vapor vertical gradients such as in the littorals where large 
diurnal land/sea temperature differences are common (Vego 2014). Unfortunately current 
numerical models, such as the Coupled Ocean/Atmosphere Mesoscale Prediction System 
(COAMPS) run at the Naval Research Laboratory in Monterey, California, struggle with 
modeling land/ocean boundaries. The nearshore processes that influence those boundaries 
are not well parameterized because of insufficient data in coastal areas including the surf 
zone. To mitigate shortfalls, the Office of Naval Research provided funding for research 
into the nearshore processes that might improve numerical modeling. The goal of this 
thesis is to show that there are differences in the magnitude of sensible heat flux and 
turbulence kinetic energy (TKE) in the surf zone as compared to the open ocean. 
Measurements of wind speed and direction as well as air and ocean temperature are used 
to determine those differences. 

In the marine environment, sensible heat is transferred from the water to the 
atmosphere when the ocean is warmer than the air above it. Sensible heat flux is driven 
by wind speed and air-sea temperature differences and was defined by Andreas (1992) as: 

H=pCC mo U w {T w -T a \ 


1 


where p a is air density in kg/m 3 , C is specific heat of air in kJ/kg.°C, U 10 is the 10 m 

T T °C 

wind speed in m/s, w is sea surface temperature and "is 10 m air temperature in 

C 

and mo is the transfer coefficient for sensible heat where: 


C = 


(C 1C V 

"HN 10 V^DIO ^DN 10 / 


. 1/2 


l-rc^c^^ao/z) (2) 

where C H io, C D io, and C E io are bulk transfer coefficients for momentum with subscripted 
N denoting neutral stability, k = 0.4 is the von Karman constant, L is the Obukhov length 
in meters, and ^(10/Z) = 21n[(l+([l-16(10/Z)] 1/4 ) 2 )/2] is a stability correction 
(Andreas 1992). Latent heat flux contributes to the overall heat flux and is defined as: 

H, — A-^10^10 (Pvs — Pva) ’ (3) 

where L v = 2,260 kJ/kg is the latent heat of vaporization of water, p is water vapor 
density of air in equilibrium with sea water and p va is water vapor density of the air 
(Andreas 1992). 

Sensible heat flux over the open ocean has been studied for decades, starting with 
Pond et al. (1971), who collected data from the M/V Flip and determined that sensible 
heat flux was a small part of the total heat flux at a fixed value of 1.3 mW cm' 2 . Mahrt 
et al. (2012), recently measured sensible heat flux values of up to 60 W m" 2 for winds 
over 20 ms' and up to 20 W m' for high winds, even in near-neutral conditions. 

The production of sea spray adds an additional layer of sensible heat transfer to 
the atmosphere as aerosols are expelled into the air at the temperature of the ocean 
surface. These aerosols are able to release their sensible heat into the atmosphere to the 
point of equilibrium and reenter the ocean before evaporating (Mahrt et al. 2012). Wave 
energy dissipation and wind speed contribute directly to the generation and transport of 
sea spray. The primary means of aerosol generation is wave breaking (Neele et al. 1998, 
Van Eijk et al. 2011). It was shown that the aerosol emission from a coastal wave¬ 
breaking zone can be reasonably predicted, and that there is a simple relationship 
involving wave energy dissipation and the amount of aerosol produced (Chomka and 

Petelski 1997). Wind provides the means of transport of aerosols away from the origin of 

2 



generation (Neele et al. 1998, De Leeuw et al. 2000, Van Eijk et al. 2006, Andreas et al. 
2015, Andreas 2016). 

For wave breaking in the surf zone, Andreas (2016) measured spray 
concentrations on a rocky shore that were 2-3 orders of magnitude larger than 
concentrations found in the open ocean for droplets smaller than 30/i/w . This is largely 
because a contributor to sea spray aerosols in the surf zone in white water, that is, water 
that has been aerated and roughened into froth by mechanical processes. White water 
coverage is ubiquitous in the surf zone which includes breaking waves, more so than in 
the open ocean where white caps are the primary source of white water (Andreas 2016). 
Andreas (2016) surmised that the concentration of aerosol spray he was recording came 
from the surf zone and not from the waves crashing on the rocks because the rocky area 
was outside of the footprint region. Near surface aerosol concentrations (Co) found in this 
experiment for wind speeds of 6—lOm/s and for droplet sizes of ~ 1—1 00/im were 

from ~10 5 m 3 /jm 1 for smaller droplets, to ~10 2 m 3 /jm 1 for the largest droplets. For 
this calculation of nearshore aerosol concentrations: 


CM = C(z,/;X— r , ' <r " >< “‘ /,> , (4) 

Z 

where C is the measured aerosol concentration at height z above mean sea level, r Q is the 
initial radius of the droplet, A m = H m / 2 is the significant wave amplitude and is half of 
the significant wave height (H 1 / 3 ), t 7 g (r 0 ) is the terminal fall speed of the droplet, 

u t —(w'u' +tvV ) is the frictional velocity, and f s is related to the turbulent 
diffusivity of the droplets given by: 


fM »>“*) = 


1 


1 + 2 [r, g (r 0 )/<Tj (5) 

where cr w is the standard deviation of the vertical velocity fluctuations of the air 
(Andreas 2016). Similar results were described by de Leeuw et al. (2000) who reported a 


3 





surf zone aerosol size distribution of 0.16 —47//W and from modeled results by Neele et 

al. (1998) who reported surf produced aerosol flux values of ~10 . 

Once the concentration of aerosols in an area of interest is known, the sensible 
and latent heat exchange with the atmosphere can be calculated as: 


4 dF 

= P,c„X T ,„~TJ[l-exp(-r f /r r )]C-»r 0 3 —) 


sjp w ps v iv eq J 


( 6 ) 


and 


H = p L 

Ljp 1 w v 


r(r f ) 


4 7rr 0 dF 


3 dr 


(7) 


o7 


where p w is water density, is the specific heat of sea water, 7^ is the temperature of 

Ay 3 

a saline droplet when it reaches equilibrium, with the atmosphere, t = —-— is the time 

required for a droplet to fall a certain distance in still air, and t t is the time for a droplet 

to undergo 63% of its potential temperature change. It is assumed that r » t t for 

droplets with initial radii less than 100 to 200 pm for wind speeds up to 20 ms' 1 (Andreas 
dF 

1992). -in this equation is the sea spray generation function for the open ocean and is 

dn 


calculated as: 


with: 



( 8 ) 


[/,„= U + +^(7)1 

k z L L 


(9) 


where 


U, 


mo 


is the 10 m neutral wind speed, and U is wind speed (Andreas 2016). 


4 











4 dF 

It is noted that >^r 3 /3)(—) is the volume flux spray (Andreas 1992). 


De Leeuw et al. (2000) give a sea spray generation function for the surf zone: 


dF(r) _ U dN{r) 

K dr W W K dr W 

where W is the width of the surf zone, and: 


(10) 




dr 


dN(r,z) 
~dr r 


)s U rf dz 


o ~ (11) 

is the height integrated size dependent number concentrations of sea spray aerosols 
produced in the surf zone. They also defined a single equation describing the surf source 

dF 

function for winds up to 9 ms' 1 as —— = 2.2 x e° 23x1/10 x (2r) _165 when applied to particles 

dr 

with diameters between 1.6 and 20 pm (Aubinet et al. 2012). The sensible heat flux can 
be calculated using the eddy covariance of buoyancy flux as applied to sonic anemometer 
measurements, as: 


H = p c w@ = 

s rap s 


Pa^a 

R0 


c w 0 

p * 


( 12 ) 


where 6 s is the sonic temperature, p a i s the dry atmospheric pressure, R is the universal 
gas constant, and m a is the dry air molar mass. Buoyancy flux can also be measured 
using vertical profiles of wind speed and temperature (e.g., Akylas and Tombrou 2005). 


A more practical way to determine buoyancy flux is through use of the TKE 
budget. TKE can be separated into the forces that are sources or sinks of turbulence 
including buoyancy flux, shear production, turbulent transport by pressure fluctuations, 
dissipation and flux divergence. The TKE budget as explained by Srivastava and Sarthi 
(2002) is: 


£ ( ^r)_^v|£_^_ e= o, 

' t t Z \ t 

Term 1 Term 2 Term 3 Term 4 


(13) 


5 













where g is the acceleration of gravity, 0 V is the virtual potential temperature, u,v and w 
are the horizontal and vertical components of the wind, and primes denote perturbation 
from the mean, denoted by an over bar, is height above the surface, e is the dissipation 


and 


e = 


~(u' 2 +v' 2 +w' 2 ) 

2 


is the total TKE. 


Term 1 is the buoyancy flux, which describes the tendency of an air parcel to 
move vertically in response to its density difference from its surrounding environment. 
Term 2, the shear production, describes the mechanical generation of turbulence. Term 3 
is flux divergence, which describes the differential transport of TKE by turbulent eddies. 


t j 

Term 4, dissipation, is a sink that reduces turbulence. Pressure transport, — — — — is 

dz 

omitted from this budget, as it is known to be very difficult to measure directly (Nilsson 
et al. 2015). Equation (13) also assumes stationary and horizontal homogeneity of the 
mean and TKE fields. 


Terms in the TKE budget take precedence based on the stability of the 
atmosphere. The accepted scheme as introduced by Garratt (1992) shows that 1) in the 
neutral near surface and stable cases, shear production balances dissipation; 2) in the 
unstable case, in the mid to upper boundary layer, TKE is maintained by the combination 
of turbulence and pressure transport (see Figure 1). 


6 




Turbulent kinetic energy and stability parameters 



Terms in the TKE budget as a function of height, normalized in the case of the clear daytime ABL. 
Profiles based on observations and model simulations. B is the buoyancy term, D is dissipation, S is 
shear generation, and T is the Transport term. 


Figure 1. Normalized TKE Budget Terms. Source: Garratt (1992). 


7 













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8 



II. THEORETICAL BACKGROUND 


A. REYNOLDS NUMBER 

A first order determination of the likelihood of turbulence involves looking at the 
general flow of the air in the footprint area. The Reynolds Number is a ratio of the inertial 
force (— U 2 / L) to the viscous force {-vU / X 2 ) where v is the kinematic viscosity and 
R e = UL/ o. A fluid is capable of developing turbulence when the Reynolds Number is 
large, i.e., when the inertial term overwhelmingly dominates the viscous term (American 
Meteorological Society 2012). Expanding on this idea, the Roughness Reynolds 
z 

number R, = 0 describes the roughness of the airflow at the boundary where z 0 is 
v 

the surface roughness. Over the ocean, z 0 can be described by the Chamock relation 
aul 

z 0 = —— , a being the Charnockparameter, 0.01 <a< 0.02 (Andreas 2012). 
g 

B. MONIN-OBUKHOV SIMILARITY THEORY 

The Monin-Obukhov Similarity Theory (MOST), developed by A.S. Monin and 
V. M. Obukhov in 1954, describes turbulent vertical fluxes and how they are related to 
the mean profiles in the atmospheric surface layer (Akylas and Tombrou 2005). The 
atmospheric surface layer, as defined in MOST, is the bottom of the boundary layer 
where surface turbulent fluxes and stress vary by less than 10% of their magnitude 

z 

(Stull 1988). The theory states that atmospheric stability is defined as C, = — where z is 

L 

the height above the surface, much less than the height of the boundary layer, and L is the 

3rrt 

—u t l 

Obukhov length, given by L = = where T v is the virtual temperature (Akylas and 

kgw'0 v 

Tombrou 2005). Experimental results show that while MOST fits experimental data well 
at or near neutral conditions (i^—>0), it is not valid in stable (^>0) or very unstable 
(C<-10) conditions (Akylas and Tombrou 2005). 


9 






C. DRAG COEFFICIENT 

Using the methods of Andreas et al. (2012) and Vickers et al. (2013), the 10 m 
neutral wind is calculated by 


where: 



(14) 


^ h [0=2\n 


(l+x^ . (1+x^ 

m +in m 


—2tan x [ x )+ tt / 2 ; 


C<o 


(15) 


and: 


C> o, (is) 

where x - (1 - a ^) w , a x = 16 , and a 2 = 5 (Vickers et al. 2013). 

A drag coefficient is often required when relating fluxes to mean properties of the 
flow at a single level (Garratt 1992) and is a dimensionless ratio of the component of 
force parallel to the direction of flow exerted on a body by a fluid to the kinetic energy of 
the fluid multiplied by a characteristic surface area of the body (American 
Meteorological Society 2012). The neutral drag coefficient is traditionally calculated as 


" [In(z/z 0 )] 2 ’ (17) 

where £= 0 (Garratt 1992). Using the eddy covariance method, the 10 m neutral drag 
coefficient was simplified by (Andreas et al. 2012) as: 




C = ( V 

^DN\Q \jj ) 


(18) 


Shabani et al. (2016) noted a well-defined dependence of the wind drag 
coefficient (Cd) on the wind direction where the largest Cd was found to correspond to 
onshore winds and the lowest Cd corresponded to alongshore winds (see Figure 2). Based 
on his results, it is clear that there must be an understanding of where the instrument’s 
measurements originate. 


10 







Measured drag coefficients (C DN i 0 ), measured at a height of 5m above mean water, vs. the 
angle (0) between the wind and wave directions. 


Figure 2. Drag Coefficient. Source: Shabani et al. (2016). 

D. FOOTPRINT ANALYSIS 

The footprint area is the field of view of the flux sensor that includes all effective 
sources and sinks and the footprint is defined as the contribution of all sources and sinks 
to the measured vertical flux (Aubinet et al. 2012). The footprint function <j) is contained 
in the integral equation of diffusion (Aubinet et al. 2012): 

r=ljG.?)QCy')d(?)' (19) 

where y is the quantity being measured at location y ? and Q(y') is the source/sink 
strength in the surface volume R . 


11 

























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12 



III. METHODS 


A. EXPERIMENTAL LOCATIONS 

Field experiments were conducted in the Monterey Bay, which is classified as a 
Mediterranean environment under the Koppen-Geiger climate classification. More 
specifically, it is a temperate zone with a warm, dry summer (Peel et al. 2007) where the 
water temperature is often warmer than the air temperature. The prevailing wind is from 
the Northwest, and due to the orientation of Monterey Bay, it crosses both ocean water in 
the harbor, and land (see Figure 3). 

The first portion of the experiment was conducted from 2 October to 5 November 
2015 on Del Monte Beach in Monterey, California. The site is a sandy beach with 
moderate to gentle slope and sand dominantly quartz and feldspar, with a medium to fine 
texture (Hohenstein et al. 1965). The beach lies in the lee of the Monterey peninsula and 
is protected from southwesterly to westerly waves and swell (see Figure 3). 



a) Tower locations From: Google Earth. From North to South, Manresa State Beach (MAN), Elkhorn 
Slough North (ESN), Elkhorn Slough South (ESS), Marina State Beach (MAB), Del Monte Beach (DMB1, 
DMB2, DMB3, October), b) Prevailing wind direction, North to Northwest (315°T is shown by the red 
line). Images from Google Earth. 

Figure 3. Experimental Locations 


13 






The second portion of the experiment was conducted in June 2016 in 
collaboration with the Rosenstiel School of Marine and Atmospheric Science (RSMAS) 
as a part of the Coastal Land-Air-Sea Interaction (CLASI) experiment. Four separate 
locations with different environmental conditions were chosen for this experiment. The 
northernmost location was Manresa State beach (MAN), which is moderately protected 
from northwesterly waves and swell by the outcropping of Santa Cruz Peninsula, 
California (see Figure 3). This is a gently sloping beach with coarse sand, backed by a 
steep bluff. Two beach sites were located on the middle of Monterey Bay on the north 
and south sides of Elkhorn Slough (see Figure 3). Elkhom Slough North (ESN) is a 
gently sloping beach with medium sand backed by a low dune and moderately shielded 
from southerly waves and swell by a jetty, not shown. Elkhorn Slough South (ESS) is a 
gently sloping beach with medium sand and is not significantly blocked in any direction. 
Further to the South, Marina State beach (MAB) and Del Monte Beach (DMB) are 
located in southern Monterey Bay (see Figure 3). MAB has a steeply sloping beach with 
medium to coarse sand and is backed by a steep dune. 

B. INSTRUMENTATION 

Data acquired during the October experiment were collected from a 6m tower on 
the beach and instrument strings in the surf zone (see Figure 4). Instrument locations on 
the tower and specifications are given in Table 1. Wind speed and direction were 
measured using 3 dimensional ultrasonic anemometers placed at six evenly spaced 
intervals on the tower. Temperature and relative humidity were measured at the 
anemometer levels using an integrated probe system. Surface temperature was measured 
using a downward looking infrared radiometer and total radiation transfer was measured 
using a bi-directional net radiometer. The tower instruments were powered by one 12V 
marine battery that experienced some power degradation resulting in loss of data (see 
Table 1). All tower data were recorded on a CR6 measurement and control datalogger. 
The ocean temperature was measured using an array of thermistors at varying depths. 
However, due to the shallow depth of the location, the temperatures were nearly 
isothermal and were averaged for simplicity. Water level height was obtained from 

National Data Buoy Center station MTYC1 in 6-minute averages. 

14 




Ultrasonic Anemometer 
Voltage and Serial Output 
Model 81000 


CNR4 net radiometer 


SI-lll-PW 
Infrared Radiometer 


HC2S3-LTemperature and 
Relative Humidity Probe 


CR6 Measurement and 
Control Datalogger 




Wo 


RBRsoloT | Temperature Logger 


From bottom center clockwise, Apogee SI-111 PW Infrared Radiometer, RBR Solo-T Temperature 
Logger, RM Young Ultrasonic Anemometer Model 81000, CR6 measurement and control datalogger, 
Campbell Scientific HC253-L Temperature and Relative Humidity Probe, Kipp & Zonen CNR4 Net 
Radiometer. Sources: Campbell Scientific (b), RBR, Young, Campbell Scientific (a), Campbell Scientific (c), 
Kipp and Zonen. 

Figure 4. Instruments and Tower Setup during the October Experiment 


During CLASI, one 3-D ultrasonic anemometer and one temperature/relative 
humidity sensor were mounted on each of seven 6m towers and deployed along the shore 
of the Monterey Bay according to the configuration in Table 2. The battery packs on 
these towers were augmented with two battery recharging SP10 10 W solar panels to 
mitigate data loss. 


15 













Table 1. Instrumentation for October Deployment 


Instrument 

Sampling Rate 

Level 

% Loss 

(6) RM Young Ultrasonic Anemometer 

Model 81000 

20Hz 

1 

26.82 

2 

20.03 

3 

24.52 

4 

22.02 

5 

46.01 

6 

45.89 

(6) Campbell Scientific HC253-L Temperature and 
Relative Humidity Probe 

1Hz 

1 

12.95 

2 

11.91 

3 

11.91 

4 

11.91 

5 

35.69 

6 

35.69 

(1) Apogee SI-111-PW Infrared Radiometer 

1Hz 

1.5 

11.91 

(1) Kipp & Zonen CNR4 Net Radiometer 

1Hz 

1.5 

12.95 

(1) RBR Solo-T Temperature Logger 

1Hz 

N/A 

10.45 


Levels given are 1) 1.19 m, 1.5) 1.69 m, 2) 2.19, 3) 3.19 m, 4) 4.19 m, 5) 5.19 m, 6) 6.19 m. 


Table 2. Instrumentation for CLASI Deployment 


Instrument 

Sampling 

Rate 

Location 

% Loss 

(7) RM Young Ultrasonic 
Anemometer 

Model 81000 

20Hz 

DMB1 

17.5893 

DMB2 

5.9226 

DMB3 

6.1310 

ESN 

1.1012 

ESS 

1.1905 

MAB 

1.2202 

MRB 

1.2798 

(7) Campbell Scientific HC253-L 
Temperature and Relative 

Humidity Probe 

1Hz 

DMB1 

17.5893 

DMB2 

5.9226 

DMB3 

6.1310 

ESN 

1.1012 

ESS 

1.1905 

MAB 

1.2202 

MRB 

1.2798 

(12) RBR Solo-T Temperature 
Logger 

1Hz 

ESN, ESS, MAB, MRB 

0 

(5) RBR Solo-D Pressure Logger 

1Hz 

ESN, ESS, MAB, MRB, 
DMB 

0 


Locations include MAN, ESN, ESS, MAB, DMB1, DMB2, DMB3. 


16 





c. 


DATA PROCESSING 


Raw data were processed according to the methods described by Aubinet et al. 
(2012), including despiking and cross-correlation of the time series data to align all 
signals to the same time base. Winds were rotated into a shore-normal frame of reference. 

All data outside of ±140° T were filtered out to remove offshore winds and the influence 
of the instrument tower. Wind speeds less than 3 m/s were removed, as they were not 
considered to contribute significantly to atmospheric instability. Stability was then 
limited to -2 < C, < 0.5 (October) and —5 < C, < 0.5 (CLASI) for all calculations. Once 
preprocessing was complete, the data were compressed into 15-minute averages, 
wherever 15 full minutes of data were available. 


17 


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18 



IV. RESULTS 


A. ENVIRONMENT 

To understand the state of the environment during the two field experiments, 
various analyses of the data were performed. First, wind speed and direction were 
examined to determine the most appropriate ranges for use. The along-shore winds were 
plotted vs. cross-shore winds for the October experiment (see Figure 5). The lowest wind 
speeds (<3 m/s) and offshore winds, which are also generally lower, were not considered 
to be major contributors to heat flux and were removed (see Figure 5), this limitation was 
also applied to the CLASI data 


10 

a) 


b) 

1 

0 

-10 

4 

r 


c) io 


d) 

j 

£ 0 
> 

-JO 

e)0 

i 

l 

f) 

M 

0 

-10 

J 


A 


-10 0 10 -10 0 10 
u (m/s) 


150 
100 
50 = 

O 

0 £ 
b 

-50 I 

| 5 

| -100 

1-150 



Cross-shore (u) winds plotted vs. along shore (v) winds are color coded by wind direction. The plot 
on the left are the unconstrained data and the plot on the right are data that have been confined to 
speeds of >3 m/s and wind direction between +140°T. Measurement height levels are a) 1.19 m, b) 
2.19 m, c) 3.19 m, d) 4.19 m, e) 5.19 m, and f) 6.19 m. 


Figure 5. Wind Speed vs. Direction 


19 






















To find out when higher winds were occurring, temporal variations of the wind 
and temperature from the flux towers, averaged over all six height levels for the October 
experiment and over all seven locations for the CLASI experiment, were plotted (see 
Figure 6). Wind speed was color-coded by hour of the day and, for both experiments, the 
highest wind speeds occurred in the mid to late afternoon and corresponded to relatively 
warm temperatures (see Figure 6). 



Time series of wind speed (colored dots) and air temperature (black line), averaged over all 
respective instruments for a) October and b) CLASI and color-coded by the hour of day. 

Figure 6. Hourly Wind Speed and Air Temperature 


20 






















Finally, using air temperature data from the towers, averaged as described above, 
and ocean temperature from the RBR Solo-T thermistors, averaged, the ocean and air 
temperatures were plotted in time series for both experiments (see Figure 7). The air- 
ocean temperature difference was plotted on the same figure to show that for over 90% of 
the CLASI experiment and over 65% of the October experiment, the ocean temperature 
was warmer than the air temperature (see Figure 7). This air-sea temperature difference 
indicates thermal instability and upward sensible heat flux from the ocean to the 
atmosphere. 



Year Day 


20 

b) 


15 


O 

a> 


C3 

1= 

CU 

CL 

E 

QJ 


10 

5 

O 


-5 


-10 







Ocean 

-Air 

-Air Ocean Difference 

^WVA/'A/ u ' VAJWLAr V vw vv 





148 150 152 154 156 158 160 162 164 166 168 

Year Day 


Averages of measured air and ocean temperatures are shown as the blue and red lines, the 
difference is shown in black and the black horizontal lines at zero are to show the cutoff 
for air warmer than ocean. The ocean is warmer than the air (a) 68.42% of the time for the 
October experiment and (b) 90.69% of the time for the CLASI experiment. 


Figure 7. Air-Ocean Temperature Difference 


21 







B. ROUGHNESS 


As an initial estimate of the aerodynamic properties of our environment and 
following the methods of Andreas et al. (2012), Roughness Reynolds Numbers ( R ,) were 
calculated and plotted for each vertical level on the October tower (see Figure 8). Cutoffs 
for aerodynamically smooth flow at a value of 0.135 and aerodynamically rough flow at a 
value of 2.5 (Andreas et al. 2012) were used to separate the roughness regime of the flow. 
Very little of the data were below the smooth limit and in fact, 95.89% of the R t values 
were in either the transitional or the rough regimes, and 53.36% were above the rough 
line and therefore in the aerodynamically rough regime. 



b) 

CC 10 5 


• R. 

oAverage R. 



U N10 (m/S > 


The horizontal lines show the aerodynamically smooth limit (at 0.135) and the aerodynamically 
rough limit (at 2.5) (Andreas et al. 2012). a) The colored circles are means at each level within 
U N10 in 1 ms' 1 bins, b) The magenta circles are the average R* for all levels within a bin the black 
bars are the 95% confidence interval. 


Figure 8. Roughness Reynolds Number 


22 
















c. 


STABILITY 


Expecting some instability in the atmosphere based on our environmental 
conditions, stability values for both experiments were calculated using the Monin- 
Obukhov parameter, C, and analyzed using a histogram distribution. The range of stability 
that best represents our data set for the October experiment is -2 < C, < 0.5 (see Figure 9a) 
and is increased to —5 < C, < 0.5 for the CLASI experiment (see Figure 9b). The ranges 
confirm that the environment in both experiments was mildly unstable with CLASI being 
more unstable due to the more frequent occurrence of warm water under cool air. 


23 


a) 


300 


200 


li 



L.Ji i 


if) 

CD 

£ 100 

<1) 
k- 

3 

O o 1 

o _ 10 .9 . 8 _ 7 _6 -5 - 4 - 3 - 2-10 1 2 3 

° 100r 

CD 

_q 

E 

3 


50 


ladll jilllll ll alfelMla 1 



1 

1.19m 

1 

2.19m 


3.19m 


4.19m 

1 

5.19m 

1 

6.19m 


- 1.5 


-1 


- 0.5 


0.5 



Histogram distribution of stability values (£") for a) October and b) CLASI. The top plot shows the total 
data set; the values at -11 and 3 include all points below or above them respectively. The bottom plot is 
the distribution of ^ used in the analysis. 


Figure 9. Thermal Stability 


24 
















D. FOOTPRINT 


Footprint analyses were conducted to identify the origin of the sampled turbulent 
eddies. This analysis was performed for the towers on Del Monte Beach including the 
October experiment, DMB1, DMB2 and DMB3. The probability density function curves 
of the footprint analysis for the six levels from 1.19 m to 6.19 m as well as a 10 m 
reference were plotted vs. the cross-shore influence, which is the distance seaward from 
the instrument tower (see Figure 10). The footprint analysis for the other CLASI towers 
(MAB, ESS, ESN and MRB) is closely represented by the 6.19m curve in Figure 10. This 
shows that for the lowest level, the area of origin begins very close to the tower but as the 
height increases, the area of origin moves offshore. 



Probability distribution function of footprint analysis for a) October and b) CLASI vs. cross-shore 
influence, or distance seaward from the instrument at the origin. 

Figure 10. Footprint Analysis 


25 











The footprint analysis was then used to separate the measured data into three 
zones including the sand, the surf zone and the area outside of the surf zone. A 70% cut 
off was chosen to separate the data into the zones. If 70% of the area under the footprint 
curve was inside one of those designated regions, the data were considered to have 
originated in that region; a process made more challenging by the changing tides. Data 
not falling into one of the three categories was kept for comparison. 

E. TURBULENCE KINETIC ENERGY 

To determine the cause of turbulence in the environment during the experiments, 
turbulence kinetic energy budget terms was calculated by rearranging Equation (11): 


a v ' dz dz 


( 20 ) 


Now the three terms on the left, buoyancy flux, shear and flux divergence balance 
dissipation on the right. The buoyancy flux appears to be several orders of magnitude 
larger than both the shear production and the flux divergence, meaning that it is likely the 
primary balancing force to dissipation and therefore the main influence on TKE (see 
Figure 11). Due to the difficult nature of estimating wind shear, there is potential error in 
these calculations but there is reasonable confidence in the results. Because the 
calculations required two height levels, this method was not used for the CLASI 
experiment; results shown are for the October experiment. 


26 







1.69m 
■2.69m 
3.69m 
■ 4.69m 
5.69m 


- xlCT 15 
0 or* 
o 
c 
CD 

U) 0 

0 
> 

b -5 

x 

^ -10 



* •. •• 


280 290 300 

Year Day 


2 



_1 280 290 300 

Year Day 


The TKE budget, the four main terms are plotted in time series for the length of the October experiment. 


Figure 11. The Terms of the TKE Budget 


Buoyancy flux was separated into footprint-based contributions as previously 
described. Points that did not meet the 70% criteria were still considered in the overall 
buoyancy flux (see Figure 12). For the lowest level at 1.69 m, the sand is the highest 
contributor at 45%, vs. 29% surf and 26% ocean. For each of the other levels the surf 
zone is the highest contributor, 36% vs. 33% sand and 31% ocean at 2.69 m, 39% vs. 
31% sand and 30% ocean at 3.69 m, 42% vs. 29% sand and 29% ocean for 4.69 m, and 
40% vs. 29% sand and 31% ocean for 5.69 m (see Figure 13). 


27 


















X 

13 


>> 

O 

c 

03 

O 1 

□□ 


0,5 






4 

* 


'll 

H »< 


Surf Zone 
Sand 

Outside Surf Zone 


i l I 1 t ** i‘ i 

• * A i*l I. / • 

«. LV I* Vivo i* 


0.5 


>A*A il di(r« 

280 290 300 


• 

• 




■ 

4 




. ff 




f 

t 




f 

i 

* 

4 ' i 

i. ! f : 

[\. i ■: g.. 

• 

i 

f 

A |l! 1 

* 

t 

4 : S 

1 - 4# 

i.'-i i 

|-< i j ■ .] 

• i *\ 

t 

* 

* »t*' 


280 290 300 

Year Day 


280 290 300 


Buoyancy flux values are separated into their footprint based contributions from the sandy beach, the 
surf zone and the ocean outside of the surf zone. Levels from top left to bottom right are 1.69 m, 2.69 
m, 3.69 m, 4.69 m and 5.69 m. 


Figure 12. Zonally separated Buoyancy Flux 



Measurement Level (m) 


Figure 13. Contributions of Sand, Surf Zone and Ocean outside Surf Zone 


28 





































F. 


SENSIBLE HEAT FLUX 


It was assumed that the initial droplet size was on the order of 0.16-47 fjm based 
on the findings of De Leeuw et al. (2000). Using that size range, sensible heat from the 
surf zone sea spray was calculated with the methods of Andreas et al. (2015), as: 

H L ^, = alf L ( 21 ) 

and 

H sjp = P H s ~(<x-r)H L , (22) 

where a,/3 and y are small turning coefficients, determined to be 2.46, 15.15 and 1.77 

respectively (Andreas et al., 2015), H L = J II L (r 0 )dr 0 and II s = J ^ H£r 0 )dr 0 are the 
integrated latent and sensible heat exchanges (Andreas et al. 2016). 

Calculated sensible heat values were separated into footprint-based zones in the 
manner described above. Points that did not meet the 70% criteria were still considered in 
the overall sensible heat flux. Measured and zoned sensible heat flux is plotted against 
wind speed for both the October and CLASI experiments (see Figure 14). In both cases, 
the sand contributed more sensible heat flux at low wind speeds. That is because the land 
is warmer than the ocean and at those lower wind speeds, the environment is more stable 
and the footprint is closer to the tower, therefore covering more of the sand than the surf 
zone. The sand contribution is overtaken by the contribution from the surf zone at higher 
wind speeds where instability has increased and the footprint has moved outward. 
Contributions from the area outside of the surf zone were close to zero because, in order 
for the sensors to receive measurements from that area, the environment would have to be 
much more stable. That stability would in turn decrease the amount of sensible heat flux 
available for measurement. In both cases, the measured sensible heat flux is greater than 
that predicted by the model. 


29 


120 

100 




80 


A 60 


5 

v 

CL 

o 

nj 

CL. 


40 

20 


0 


-20 

0 


• all 

• surf 

• outside 

• sand 




5 10 15 0 5 lO 15 

U (m/s) u < m/s > 


Sensible heat flux separated into the contributions from the sand, the surf zone and outside the surf 
zone plotted vs. wind speed (U) for a) October and b) CLASI. 

Figure 14. Sensible Heat Flux 


Noting the large number of points in the surf zone for each experiment, sensible 
heat was again calculated using equations 4-11 to include the influence of sea spray. The 
results were plotted against the CO ARE 3.5 model calculations and color-coded based on 
the air-water temperature difference. The slopes of the linear regression lines, plotted for 
the October experiment data are 0.50 without sea spray and 1.16 with sea spray included 
(see Figure 15). For the CLASI experiment, the slopes of the linear regression lines were 
0.48 without sea spray and 1.08 with spray included (see Figure 16). This means that the 
COARE 3.5 model, which is designed for the open ocean, is underestimating the sensible 
heat flux in the surf zone by over 50%. With the sea spray sensible heat flux included 
however, the estimate is much more accurate. The COARE 3.5 model was chosen for its 
ease of use and functionality. Other model results were not investigated in this effort but 
further comparisons would benefit this field of study. 


30 











COARE3.5 SHF (no spray) 


60 


60 


1 


40 

20 

0 


-20 






/***# 

* 


J 


■ 

* ** 

[i 


* x* 

7^. * 

•tv / 
* * ' 









-20 0 20 40 60 


measured SHF 


> 

ro 

a 

Cfl 


N 

W 

+ 


40 


X 20 


LO 


□C 

< 

O 

O 


0 


-20 






#* 





// 

/ / 



m 

* J 

< $£ * 

fer 

• * ** 

i 


/mr'i i r ' 

/sHgpjv, 

/ r M 1 * • ■ 
■ 

/ / 4 vvy 

* ( 

f 

* * 



*! 








-20 0 20 40 60 


0 

o 


-1 


-2 


-3 


measured SHF 


Measured vs. modeled sensible heat flux without influence from sea spray on the right and with sea spray 
included on the left. The white circles represent the averages for 5 Wm' 2 wide bins. The solid line is 1:1 
and the dashed line is the linear regression. The slopes of the regression lines are 0.4957 with no spray and 
1.16 with spray included. 


Figure 15. Sensible Heat Flux from October Experiment 


31 


air-water 








COARE3.5 SHF (no spray) 



measured SHF 



measured SHF 


o 

o 


-1 


I- 

<1 


-2 


-3 


Measured vs. modeled sensible heat flux without influence from sea spray on the right and with sea spray 
included on the left. The solid line is 1:1 and the dashed line is the linear regression. The white circles 
represent the averages for 5 Wm-2 wide bins. The slopes of the regression lines are 0.4784 with no spray 
and 1.08 with spray included 


Figure 16. Sensible Heat Flux from CLASI Experiment 


32 


air-water 










V. SUMMARY AND CONCLUSIONS 


Wind, temperature and relative humidity were measured along the coast of the 
Monterey Bay during two field experiments conducted in October 2015 and June 2016 
(CLASI). In both experiments, the winds of interest were onshore from ±140°7\ The 
water temperature was warmer than the air temperature for 68.42% of the October 
experiment and 90.69% of the CLASI experiment suggesting thermal instability in the 
atmospheric surface layer during the experiment periods. 

Using Reynolds Roughness number as a measure of surface roughness regimes, 
we found that the atmosphere was in a transient to rough regime for over 95% of the 
October experiment. Further analysis using the Monin-Obukhov stability parameter £ 
showed that the atmospheric surface layer was in the neutral to slightly unstable stability 
regime for both experiments. 

Instability in the atmosphere, coupled with onshore winds created a footprint that 
allowed the instrument arrays to collect data originating in the sand, the surf zone and the 
area just beyond the surf zone. Sensible heat flux calculated directly from the data 
collected during the October and CLASI experiments was separated into its contributions 
from those zones and further confined based on wind speed and direction and 
atmospheric stability. The refined data were then compared to COARE 3.5 model 
estimate of sensible heat flux. The model under predicted sensible heat flux for the surf 
zone by over 50% for both experiments. It was believed that the missing sensible heat 
flux was a result of sea spray aerosols. While the size and volume flux of these aerosols 
are not known exactly, there are various estimates, as explained by Cavaleri et al. (2012). 

There is strong uncertainty about the actual global production of sea-spray 
aerosols. The present estimates (see de Leeuw et al. 2011) span two orders 
of magnitude, between 0.02 and 1 A~ 1014 kg yr-1, of whose small range 
(<1 p m), 20% or more, is expected to be organic matter. As breaking 
waves are especially abundant in the surf zone, Monahan (1995) 
speculated that many more sea-spray aerosols per unit area and time 
would be generated over the surf zone than in the open ocean, and these 
have been observed to be transported over tens of kilometers. (Cavaleri 
et al. 2012) 


33 


Knowing that there is a large amount of sea spray in the surf zone, the sea spray 
sensible heat flux was added to the model estimate of sensible heat flux. With that 
addition, the calculated total sensible heat flux (interfacial and sea spray heat fluxes) 
compared much better with observations with the estimated values being slightly larger. 
The assumptions made during this analysis may involve some uncertainties due to the 
lack of sea spray measurements. However, the results are qualitatively informative. 

Sensible heat flux from sea spray aerosols significantly increases the total sensible 
heat flux in the surf zone and therefore cannot be ignored when parameterizing sensible 
heat in numerical modeling. In this set of experiments, only the 70% footprint coverage 
was considered but other distributions should be explored for completeness. This effort 
focused on the surf zone but the footprint area could be moved or expanded based on 
different stability and height factors to examine other parts of the coastal environment. 


34 



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38 



INITIAL DISTRIBUTION LIST 


1. Defense Technical Information Center 
Ft. Belvoir, Virginia 

2. Dudley Knox Library 
Naval Postgraduate School 
Monterey, California 


39