OCS Study

BOEM 2020-015

U.S. Department of the Interior

Bureau of Ocean Energy Management

Gulf of Mexico OCS Region

Cooperative Agreement

Coastal Marine Institute

Louisiana State University

Coastal Marine Institute

A Real-time Ocean Observing Station

Off Timbalier Island, Louisiana
OCS Study

BOEM 2020-015

Published by

U.S. Department of the Interior

Bureau of Ocean Energy Management

New Orleans Office

Cooperative Agreement

Coastal Marine Institute

Louisiana State University

Coastal Marine Institute

A Real-time Ocean Observing Station

Off Timbalier Bay, Louisiana

Authors

Chunyan Li

Brian Milan

Eurico D’Sa

Wei Huang

Yixin (Peter) Luo

February 2020

Prepared under BOEM Cooperative Agreement M15AC00015

by

Louisiana State University

Coastal Marine Institute

Baton Rouge, Louisiana 70803
DISCLAIMER

Study collaboration and funding were provided by the US Department of the Interior, Bureau of 

Ocean Energy Management (BOEM), Environmental Studies Program, Washington, DC, under 

Agreement Number M15AC00015. This report has been technically reviewed by BOEM, and it has 

been approved for publication. The views and conclusions contained in this document are those of the 

authors and should not be interpreted as representing the opinions or policies of the US Government, 

nor does mention of trade names or commercial products constitute endorsement or recommendation 

for use.

REPORT AVAILABILITY

To download a PDF file of this report, go to the US Department of the Interior, Bureau of Ocean 

Energy Management Data and Information Systems webpage (http://www.boem.gov/EnvironmentalStudies-EnvData/), click on the link for the Environmental Studies Program Information System 

(ESPIS), and search on 2020-015. The report is also available at the National Technical Reports 

Library at https://ntrl.ntis.gov/NTRL/.

CITATION

Li C, Milan B, Huang W, Luo Y. A real-time observing station off Timbalier Bay, Louisiana. 2020. 

New Orleans (LA): US Department of the Interior, Bureau of Ocean Energy Management. OCS 

Study BOEM 2020-015. Cooperative Agreement No.: M15AC00015. 74 p.
i

Abstract

Offshore real-time oceanographic stations are difficult to establish, operate, and maintain. For one 

reason, the oceanographic conditions are not always favorable for instruments and most installation 

and maintenance work. For example, severe weather, bio-fouling, especially in the subtropical ocean 

environment of the Gulf of Mexico, metal corrosion in seawater environment, difficulty in access 

whether by ship or airplane, and high costs are all important factors. Another reason is that it is very 

difficult to mount any instrument in a fixed position in the ocean for long-term operation: oceans are 

deep, sedimentation can bury a bottom-mounted device over time, and the powers of winds and ocean 

currents make any installation of any equipment a daunting task. Indeed, there are very few real-time 

stations off the Louisiana coast on the continental shelf. The Wave-Current-Surge Information 

System has been operating a few such stations for metocean (meteorology and physical 

oceanography) data over the years. These stations are based on oil and gas platforms and are bulky. 

The stations are based on old computers and technologies and difficult to maintain. This project uses 

new technology: it does not use an onsite computer, it does not need a cooling system, it uses an 

omnidirectional small satellite antenna, and so is reliable and easy to maintain. We are the first users

to work with the company who makes this system and there have been challenges. The system has 

collected 10 months of data and still operating. It would be beneficial if the system is duplicated; this 

would make the overall operation standardized and more cost effective.
ii

Contents

Abstract.................................................................................................................................................. i

List of Figures ..................................................................................................................................... iii

List of Tables ........................................................................................................................................ v

Abbreviations ...................................................................................................................................... vi

1. Introduction ...................................................................................................................................... 7

1.1 Data Need .................................................................................................................................... 7

1.2 Coastal Waves and Currents ....................................................................................................... 7

1.3 Sediment Transport.................................................................................................................... 11

1.4 Objectives................................................................................................................................... 13

1.5 Station ........................................................................................................................................ 14

2. Station Selection, Instrument, and Installation........................................................................... 15

2.1 Site Selection ............................................................................................................................. 15

2.2 Instrument .................................................................................................................................. 17

2.3 Design of the Installation............................................................................................................ 21

2.4 Test Installation and Test Run of the System ............................................................................ 29

2.5 Installation .................................................................................................................................. 31

3. Data Collected ................................................................................................................................ 33

3.1 The Real-time Oceanographic Data .......................................................................................... 33

3.2 The Short-term CTD and Optical Data....................................................................................... 47

3.3 Weather Conditions during the Deployment .............................................................................. 48

4. Data Usage...................................................................................................................................... 57

4.1 Data Submission ........................................................................................................................ 57

4.2 Data Usage for Research: Analysis and Numerical Experiments.............................................. 57

4.3 Data Usage for Research: Model Validation and Subsequent Numerical Experiment.............. 57

4.4 Data Use for Student Projects.................................................................................................... 58

5. Summary......................................................................................................................................... 59

Works Cited ........................................................................................................................................ 60

Appendix 1. Numerical Modeling for the Louisiana Continental Shelf ........................................ 62

Appendix 1 References.................................................................................................................... 73
iii

List of Figures

Figure 1.1. Vector trajectories from CSI 3 (left panel) and CSI 6 (right panel), Jan., 2008..................................... 8

Figure 1.2. Wave period, water level, wind speed, and air pressure measured from CSI 6 and CSI 9 in 2008. .... 9

Figure 1.3. MODIS image showing suspended sediment along the Louisiana coast before and after a winter 

storm. ................................................................................................................................................................... 10

Figure 1.4. Wind vector showing the abrupt increase of wind on Oct. 19, 2011 due to a winter storm. ............... 10

Figure 1.5.a. Relationship between suspended particulate matter (SPM) and optical backscattering obtained at 

various locations during field sampling along the Louisiana coast. The variable anap is the nonalgal particle 

absorption. ........................................................................................................................................................... 12

Figure 1.5.b. Lower panel: SeaWiFS satellite derived imagery of SPM (mg/L) obtained using a regional algorithm 

(figure from D’Sa et al. 2007). .............................................................................................................................. 13

Figure 2.1. WAVCIS Stations and the new station (SS91)................................................................................... 15

Figure 2.2. CSI 5, the originally selected site. ...................................................................................................... 16

Figure 2.3. Final site, Fieldwood Energy on Ship Shoal 91 (SS91) A & B platforms (7 nm offshore)................... 16

Figure 2.4. A nearby station, CSI 6. ..................................................................................................................... 17

Figure 2.5. Custom-made ADCP to reduce the effect of the rig legs by facing the transducer sideways............. 17

Figure 2.6. The new WAVCIS station antenna..................................................................................................... 18

Figure 2.7. Old WAVCIS station antenna............................................................................................................. 18

Figure 2.8. The AWAC ADCP, solar panel, battery, cable, and the breaker installed battery box. ...................... 19

Figure 2.9. The mounting panel installed at the SS91 platform with battery inside the housing on the left and the 

AOS on the right................................................................................................................................................... 20

Figure 2.10. Conductivity-temperature-depth sensor deployed at SS91. ............................................................. 21

Figure 2.11. Engineering drawing (1) for the installation at SS91. ....................................................................... 22

Figure 2.12. Engineering drawing (2) for the installation at SS91. ....................................................................... 23

Figure 2.13. Engineering drawing (3) for the installation at SS91. ....................................................................... 24

Figure 2.14. Engineering drawing (4) for the installation at SS91. ....................................................................... 25

Figure 2.15. Engineering drawing (5) for the installation at SS91. ....................................................................... 26

Figure 2.16. Engineering drawing (6) for the installation at SS91. ....................................................................... 27

Figure 2.17. Conduit installation on one of the rig legs. ....................................................................................... 28

Figure 2. 18. CAD drawing of the clamp for the conduit....................................................................................... 29

Figure 2.19. Test station at Barataria Pass for the AOS System before the actual deployment at SS91............. 29

Figure 2.20. Example velocity data from the entire period of the first test run at the Caminada Pass.................. 30

Figure 2.21. Zoomed in flow velocity data for bins 5, 10, and 20 (shown by 3 different colors). .......................... 31

Figure 2.22. The new station (SS91).................................................................................................................... 32

Figure 2.23. Photo of an installed clamp. ............................................................................................................. 32

Figure 3.2. Time series of battery for the ADCP AOS system before December 6, 2018. ................................... 33

Figure 3.3. Time series of battery for the ADCP AOS system after March 2, 2019.............................................. 34

Figure 3.4. Time series of water depth above the ADCP before December 6, 2018............................................ 34

Figure 3.5. Time series of water depth above the ADCP after March 2, 2019. .................................................... 34

Figure 3.6. Time series of water temperature at the AWAC ADCP unit before December 6, 2018...................... 35

Figure 3.7. Time series of water temperature at the AWAC ADCP unit after March 2, 2019. .............................. 35
iv

Figure 3.8. Time series of heading of the AWAC ADCP unit before December 6, 2018...................................... 36

Figure 3.9. Time series of tilt of the AWAC ADCP unit before December 6, 2018. .............................................. 36

Figure 3.10. Time series of heading of the AWAC ADCP unit after March 2, 2019.............................................. 36

Figure 3.11. Time series of tilt of the AWAC ADCP unit after March 2, 2019....................................................... 37

Figure 3.12. Velocity direction at 1.4 m above the ADCP for August 23–December 6, 2018............................... 37

Figure 3.13. Velocity direction at 2.4 m above the ADCP for August 23–December 6, 2018............................... 37

Figure 3.14. Velocity direction at 3.4 m above the ADCP for August 23–December 6, 2018............................... 38

Figure 3.15. Velocity direction at 4.4 m above the ADCP for August 23–December 6, 2018............................... 38

Figure 3.16. Velocity direction at 5.4 m above the ADCP for August 23–December 6, 2018............................... 38

Figure 3.17. Velocity direction at 6.4 m above the ADCP for August 23–December 6, 2018............................... 38

Figure 3.18. Velocity direction at 7.4 m above the ADCP for August 23–December 6, 2018............................... 39

Figure 3.19. Velocity direction at 8.4 m above the ADCP for August 23–December 6, 2018............................... 39

Figure 3.20. Velocity magnitude at 1.4 m above the ADCP for August 23–December 6, 2018............................ 39

Figure 3.21. Velocity magnitude at 2.4 m above the ADCP for August 23–December 6, 2018............................ 39

Figure 3.22. Velocity magnitude at 3.4 m above the ADCP for August 23–December 6, 2018............................ 40

Figure 3.23. Velocity magnitude at 4.4 m above the ADCP for August 23–December 6, 2018............................ 40

Figure 3.24. Velocity magnitude at 5.4 m above the ADCP for August 23–December 6, 2018............................ 40

Figure 3.25. Velocity magnitude at 6.4 m above the ADCP for August 23–December 6, 2018............................ 40

Figure 3.26. Velocity magnitude at 7.4 m above the ADCP for August 23–December 6, 2018............................ 41

Figure 3.27. Velocity magnitude at 8.4 m above the ADCP for August 23–December 6, 2018............................ 41

Figure 3.28. East velocity component at various depths above the ADCP for August 23–December 6, 2018..... 41

Figure 3.29. North velocity component at various depths above the ADCP for August 23–December 6, 2018. .. 42

Figure 3.30. Velocity direction at 1.4 m above the ADCP for March 2–August 6, 2019. ...................................... 42

Figure 3.31. Velocity direction at 2.4 m above the ADCP for March 2–August 6, 2019. ...................................... 42

Figure 3.32. Velocity direction at 3.4 m above the ADCP for March 2–August 6, 2019. ...................................... 43

Figure 3.33. Velocity direction at 4.4 m above the ADCP for March 2–August 6, 2019. ...................................... 43

Figure 3.34. Velocity direction at 5.4 m above the ADCP for March 2–August 6, 2019. ...................................... 43

Figure 3.35. Velocity direction at 6.4 m above the ADCP for March 2–August 6, 2019. ...................................... 44

Figure 3.36. Velocity direction at 7.4 m above the ADCP for March 2–August 6, 2019. ...................................... 44

Figure 3.37. Velocity direction at 8.4 m above the ADCP for March 2–August 6, 2019. ...................................... 44

Figure 3.38. Velocity magnitude at 1.4 m above the ADCP for March 2–August 6, 2019. ................................... 44

Figure 3.39. Velocity magnitude at 2.4 m above the ADCP for March 2–August 6, 2019. ................................... 45

Figure 3.40. Velocity magnitude at 3.4 m above the ADCP for March 2–August 6, 2019. ................................... 45

Figure 3.41. Velocity magnitude at 4.4 m above the ADCP for March 2–August 6, 2019. ................................... 45

Figure 3.42. Velocity magnitude at 5.4 m above the ADCP for March 2–August 6, 2019. ................................... 46

Figure 3.43. Velocity magnitude at 6.4 m above the ADCP for March 2–August 6, 2019. ................................... 46

Figure 3.44. Velocity magnitude at 7.4 m above the ADCP for March 2–August 6, 2019. ................................... 46

Figure 3.45. Velocity magnitude at 8.4 m above the ADCP for March 2–August 6, 2019. ................................... 47

Figure 3.46. East velocity component at various depths above the ADCP for March 2–August 6, 2019. ............ 47

Figure 3.47. North velocity component at various depths above the ADCP for March 2–August 6, 2019............ 47
v

Figure 3.48. Weather map example 1 of 3 for Tropical Storm Gordon................................................................. 51

Figure 3.49. Weather map example 2 of 3 for Tropical Storm Gordon................................................................. 51

Figure 3.50. Weather map example 3 of 3 for Tropical Storm Gordon................................................................. 52

Figure 3.51. Weather map example for a typical stationary front. ........................................................................ 52

Figure 3.52. Weather map example 1 of 3 for Hurricane Michael. ....................................................................... 53

Figure 3.53. Weather map example 2 of 3 for Hurricane Michael. ....................................................................... 53

Figure 3.54. Weather map example 3 of 3 for Hurricane Michael. ....................................................................... 54

Figure 3.55. Weather map example for a typical cold front. ................................................................................. 54

Figure 3.56. Weather map example for a typical warm front................................................................................ 55

Figure 3.57. Weather map example 1 of 3 for Hurricane Barry............................................................................ 55

Figure 3.58. Weather map example 2 of 3 for Hurricane Barry............................................................................ 56

Figure 3.59. Weather map example 3 of 3 for Hurricane Barry............................................................................ 56

Figure A-1. Model grid for FVCOM....................................................................................................................... 62

Figure A-2. Model grid zoomed in on the Ship Shoal area................................................................................... 63

Figure A-3. Model comparison with observations from the Ship Shoal for the raw east component of the 

horizontal velocity................................................................................................................................................. 63

Figure A-4. Model comparison with observations from the Ship Shoal for the 40-hour low-pass filtered east 

component of the horizontal velocity. ................................................................................................................... 64

Figure A-5. Model comparison with observations from the Ship Shoal for the raw north component of the 

horizontal velocity................................................................................................................................................. 64

Figure A-6. Model comparison with observations from the Ship Shoal for the 40-hour low pass filtered north 

component of the horizontal velocity. ................................................................................................................... 64

Figure A-7. Example flow field for Hurricane Harvey............................................................................................ 65

Figure A-8. Example flow field for Hurricane Harvey (continued)......................................................................... 66

Figure A-9. Example flow field for Hurricane Harvey (continued)......................................................................... 67

Figure A-10. Example flow field for Hurricane Harvey (continued)....................................................................... 68

Figure A-11. Example flow field for Hurricane Harvey (continued)....................................................................... 69

Figure A-12. Example flow field for Hurricane Harvey (continued)....................................................................... 70

Figure A-13. Example flow field for Hurricane Harvey (continued)....................................................................... 71

Figure A-14. Example flow field for Hurricane Harvey (continued)....................................................................... 72

Figure A-15. Example flow field for Hurricane Harvey (continued)....................................................................... 73

List of Tables

Table 3.1. Atmospheric Frontal Events between September 4 and December 9, 2018....................................... 49

Table 3.2. Atmospheric Frontal Events between March 2 and August 6, 2019.................................................... 50
vi

List of Abbreviations

Short Form Long Form

ADCP acoustic Doppler current profiler

AOS automated observing system

AWAC acoustic wave and current sensor 

CDOM colored dissolved organic matter

CTD

CSI

conductivity-temperature-depth sensor

Coastal Studies Institute

FVCOM Finite Volume Community Ocean Model

GCOOS Gulf Coast Ocean Observing System

IOOS integrated ocean observing system

SPM suspended particulate matter 

WAVCIS Wave-Current-Surge Information System
7

1. Introduction

1.1 Data Need

One of the major challenges that we face in the northern Gulf of Mexico coastal area is the need for a 

better and reliable offshore metocean (meteorology and physical oceanography) real-time data 

collection system. Such a system will support the mission of the Bureau of Ocean Energy 

Management (BOEM) and other federal and local agencies engaged in coastal management, 

protection, and restoration, especially along the Louisiana coast. This area has a suite of 

environmental problems that require the acquisition of real-time data for immediate assessment or 

model based assessment and predictions that rely on this kind of data. This need was demonstrated by 

the 2005 hurricanes Katrina and Rita (Rego and Li 2010a 2010b), 2008 hurricanes Gustav and Ike (Li 

et al. 2009a, Li et al. 2010), the 2008 and 2011 Mississippi River flood diversions (Falcini et al. 2012,

Kolker et al. 2014), and the 2010 Deepwater Horizon oil spill. One such system providing this kind of 

data is managed by the Wave-Current-Surge Information System (WAVCIS) at Louisiana State 

University (LSU). 

WAVCIS, established in 1997, has been providing technical and operational services to one of the 

eleven regional associations of the US Integrated Ocean Observing System (IOOS) along the 

Louisiana coast, the Gulf Coast Ocean Observing System (GCOOS). These services are well in line 

with BOEM’s mission to “make decisions about the environmental risks and socioeconomic impacts 

of offshore oil and gas development in federal waters”, which require reliable information for weather 

and ocean conditions around the oil and gas production units. In addition, BOEM’s Marine Minerals 

Program manages non-energy minerals (sand and gravel) on the Outer Continental Shelf (OCS) for 

coastal restoration. “BOEM ensures that the removal of any mineral resource is conducted in a safe 

and environmentally sound manner, and that any potentially adverse impacts on the marine, coastal, 

or human environments are avoided or minimized”. For that purpose, offshore environmental data are 

crucial for the managers, engineers, and researchers. 

WAVCIS has been an important component of BOEM’s Marine Minerals Program; it provides

observational data for wave modeling validations associated with predicting impacts to wave climate 

associated with excavating offshore sediments. In addition to hurricanes, more frequent but less 

severe winter storms can also introduce significant hydrodynamics response (e.g., Feng and Li 2010,

Li 2013, Li et al. 2011a, 2018, and 2019) and related sediment movement. The existing system, 

however, had been aging. The three working stations, Stations 3, 6, and 9, though the only such 

stations in the offshore area of the coastal Louisiana, were experiencing persistent technical problems 

due to a lack of funding, outdated electronic and data logging technology, and aging mechanical 

equipment. The present project was designed to develop a new system using new technology that 

would be more economical to operate and at the same time, more reliable, with better sensors and 

data loggers, as well as data transfer technology. 

1.2 Coastal Waves and Currents

The fate of suspended sediment and related geomorphological changes–such as the land loss,

evolution of dredged underwater pit after sand source excavations, oil spills, and nutrient transport–

depends on the physical conditions of the coastal ocean, particularly waves, currents, and water 

levels. The physical conditions of the ocean, in turn, are affected significantly by the weather, such as. 

hurricane storm surges and winter storm surges during cold and warm front events. The weather 

impact and storm surge effects can be quite pronounced in the Louisiana area because of the low 

profile of the Louisiana coastal plain topography and shelf slope shaped by the Mississippi River and 

suspended sediment load over the geological time scales. According to Cochrane and Kelly (1986), 

the Texas-Louisiana shelf has an almost year-round east to west coastal current, except during the 

summer when the coastal currents may be reversed by wind regime changes. 
8

Our recent analysis of the WAVCIS data has shown that the variability over the general coastal 

current described by Cochrane and Kelly (1986) and many others is highly dependent on the weather. 

Both waves and currents are variable, even though the general trend of the coastal current is mostly 

east to west. Short-time episodic weather impacts, however, can be quite significant, such as oil 

trajectories that are dependent on the history of the flows during an oil spill event. Further, many 

coastal geomorphological features have length scales smaller than the tidal excursion. The width of 

tidal inlets and the mouth of a bay in Louisiana are mostly smaller than the local tidal excursion. An 

episodic reverse of a coastal current could determine if an oil spill will enter into a bay or not. Figure 

1.1 shows the water particle trajectories at various water depth measured from two offshore stations,

Coastal Studies Institute (CSI) 3 and CSI 6, during the first month of 2008, indicating a few flow 

reversals due to wind shifts during winter storms (cold fronts). Such wind shifts can happen also 

during other times of the year (mid-fall to late spring, Walker and Hammack 2000). During the 

passage of cold fronts, increased wind speed usually causes long period of higher waves (Figure 1.2) 

that can cause significant sediment re-suspension (Figure 1.3, corresponding wind vector time series 

during that time period, Figure 1.4).

Figure 1.1. Vector trajectories from CSI 3 (left panel) and CSI 6 (right panel), Jan., 2008. 

The colors and numbers in the inset indicate the distance of the data point from the sensor sitting at bottom. The unit for the 

depth of data with colored lines is in meters above the bottom.

-250 -200 -150 -100 -50 0 50 -40

-20

0

20

40

60

80

100

120

140

Progressive Vector plot of the First 30 Days, 2008

x (km)

y (km)

1.1

1.6

2.1

2.6

3.1

-800 -700 -600 -500 -400 -300 -200 -100 0 -250

-200

-150

-100

-50

0

50

Progressive Vector plot of the First 30 Days, 2008, CSI 6

x (km)

y (km)

3.17

7.17

11.17

15.17

19.17
9

Time (days in 2008)

Figure 1.2. Wave period, water level, wind speed, and air pressure measured from CSI 6 and CSI 9 in 

2008. 

During a couple of strong wind events, wave periods are increased. The titles on top are the y-axis. The horizontal axis is time 

in days in the year 2008 (day 1 is Jan. 1, 2008).

Winter weather in Louisiana is dominated mostly by weather systems from the north and northwest. 

These weather systems are usually characterized by a low pressure center to the north of all the 

southern states, with a line of precipitation in parallel to a cold front trailing from the center of the 

low. The cold front is characterized by an air pressure minimum and a relatively sharp change of 

wind direction (from the southerly winds to northerly winds). The low pressure center forms a 

cyclonic circulation, often followed by a high pressure system behind the cold front, which is moving 

toward the south-southeast, before moving to the northeast. Though the low-pressure center of the 

cyclonic system is usually north of Louisiana and rarely reaches coastal Louisiana, the cold front

formed along a trough of the low-pressure system often does sweep through the area. Another kind of 

cold front is a trough between two high pressure systems (the maritime tropical air and continental 

Arctic air) that is not obviously associated with a northern cyclone. In any case, the pre-frontal wind 

from the southern quadrants brings in relatively warm and wet air from the Gulf of Mexico, providing 

moisture and energy for the formation of thunderstorms and precipitation in general, along the cold 

front (Hsu 1988).

This kind of winter weather also occurs often during late fall and in the spring. The occurrence of 

cold fronts is a way for the atmosphere to exchange heat and redistribute thermal energy between 

high and low latitudes, by which the north-south temperature gradient can remain limited. Cold fronts 

are related to large scale atmospheric processes in which the westerly winds, set up by the north-south 

temperature gradient through geostrophic balance, has an important role. In the process, the 

atmosphere adjusts itself, tending to homogenize the air temperature, and cold air from the north 

needs to break through the barrier of the Jet Stream (the maximum zone of the upper troposphere 

westerly winds) to reach the south. As a result, the weather associated with a cold front can usually be 

identified by the upper level Jet Stream meandering toward the south. The frontal position is usually 

240 250 260 270 280 290

4

6

Wave Period (sec), Red - CSI 6, Blue - CSI 9

240 250 260 270 280 290

-0.2

0

0.2

0.4

Wave Height (m)

240 250 260 270 280 290

0

10

20

Wind Speed (m/s)

240 250 260 270 280 290

1000

1010

1020

Air Pressure (mb)
10

on the east of an upper level trough. As the mid-latitude Jet Stream meanders through the US, the 

associated cold front may sweep through the coastal Louisiana area.

Figure 1.3. MODIS images showing suspended sediment along the Louisiana coast before and after a 

winter storm.

Left: Oct. 16, 2011; right: Oct. 19, 2011. 

Figure 1.4. Wind vector showing the abrupt increase of wind on October 19, 2011 due to a winter storm.

As is documented (e.g., Roberts et. al. 1989, Walker and Hammack 2000, Feng and Li 2010), cold 

fronts occur at roughly weekly intervals. The pre-frontal winds from the southern quadrants bring up 

the water level in the coastal bays, lakes, and lagoons. This is often associated with high surface 

waves due to the long fetch of the southerly winds. As the front passes the area, the winds switch to 

those from the northern quadrants, which push the water level down. The contrast in wind directions 

causes a relatively large swing of the bay water (e.g., Li et al. 2011a, 2011b). As much as 40–50% of 

the water volume can be flushed in and out of a coastal Louisiana Bay during a typical cold front 

event (Feng and Li 2010).
11

The importance of cold front and its impact to coastal waters have been more appreciated by the 

coastal geologist but not by the majority of the physical oceanography community. The importance of 

cold fronts can be argued at least in three aspects: first, they occur frequently in the fall to spring 

seasons almost everywhere in the mid-latitude on our planet. Second, a cold front system has a much 

larger scale than any regional coastal oceanographic problems—it is an atmospheric event that has 

~1000 km scales and thus affects a large area. A cold front can easily stretch over multiple states in 

the US mainland. Third, cold fronts occur every year at 3–10 day intervals (Feng and Li 2010; Li 

2013; Li et al. 2008b, 2011a, 2011b; Li and Chen 2014; Lin et al. 2016). Coastal observing stations 

can be invaluable in providing continuous data that advances our understanding of the coastal 

currents in the area and the implications to coastal geomorphology under these quasi-periodic and 

recurring synoptic scale atmospheric events every year.

1.3 Sediment Transport

Several important issues about the Louisiana coastal water require understanding and quantifying the 

sediment transport, which is highly dependent on the weather and hydrodynamics. For example, the 

evolution of sandy barrier islands, the effectiveness and environmental consequences of coastal 

restoration engineering projects, and the protection of underwater pipelines of oil and gas industry 

where dredging activities to harvest sand can occur in a nearby area. In the offshore environment of a 

large river delta, both sandy deposit and muddy deposits exist. Coastal Louisiana’s low profile land 

and wetlands are protected by many barrier islands. The wetlands, however, are experiencing net loss 

at a rate surpassing other places in the US. These barrier islands are sandy sedimentary environments 

separated from the mainland by estuary or lagoon environments. The barrier islands protect interior 

estuaries and wetlands from weather forcing, waves, and storm surges. A major component of the 

State of Louisiana’s effort to manage coastal land loss is to restore degraded barrier shorelines by 

dredging sand from borrow sites and delivering it where it is needed. Dredging the bottom to harvest 

sand can impact the sea floor and its surrounding environments, and is a subject of study for

oceanographers, engineers, benthic ecologists, archeologists, environmental scientists, and decision 

makers. How a dredged site evolves depends greatly on the physical forcing and hydrodynamics.

About ninety percent of the Mississippi and Atchafalaya rivers’ sediment load is suspended mud, with 

sediment finer than 63 μm in diameter, and only about ten percent is sand (Nittrouer et al. 2008). 

Thus sand resources are quite scarce. Excluding the far-offshore sand deposit sites, the economic sand 

resource near the coast is even more limited. Understanding the spatial and temporal variations of the 

formation of limited sand resources along the Mississippi Deltaic Plain is critical to the success of 

dredging projects. Further, the suspended particulate matter (SPM), a large fraction of which is 

composed of suspended sediments along the river-dominated Louisiana coast, strongly influences the 

light field and thus primary production and the ecology of the region. In addition to the rivers, 

dredging, sedimentation, and physical processes (e.g., currents, waves) will influence its transport and 

budget, necessitating regular monitoring to detect changes due to both natural and human impacts. 

Because in situ measurements of SPM are time consuming and expensive, optical scattering methods 

provide an alternative for more continuous observation of SPM. Field observations have shown good 

relationships between optical backscattering and SPM in various coastal environments (Zawada et al. 

2007), including the Louisiana coast (D’Sa et al. 2007; Figure 1.5 upper panel). Ocean color or 

spectral reflectance of the ocean is, at the first-order of approximation, proportional to optical 

backscattering (Boss et al. 2005) allowing for the development of ocean color algorithms for SPM 

from optical measurements (Figure 1.5 lower panel, D’Sa et al. 2007). The integration of an optical 

backscattering sensor at the selected site would provide an important optical proxy for suspended 

sediments that would be used to monitor changes in SPM in conjunction with physical forcing, as 

well as for examining trends in ocean color observations related to SPM (D’Sa et al. 2007, 2008; 

Zawada et al. 2007).
12

A conceptual model of sediment deposition under both regressive and transgressive environments 

was proposed by Penland et al. (1989). Prograded deltaic headlands are reworked by marine 

processes, often influenced by weather, after river abandonment, resulting in transgression and 

transformation to ebb tidal delta and before becoming an inner-shelf sandy shoal. The majority of the 

northern Gulf of Mexico is dominated by muddy deposits and only a fraction of restoration-quality 

sand is located nearshore. These include Tiger Shoal, Trinity Shoal, and Ship Shoal. However, these 

are located south of Atchafalaya Bay, which makes it too costly to use in most restoration projects. 

On the other hand, discrete sand deposits associated with ancient rivers that flowed across the shelf 

during the earlier stages of shelf and delta evolution did occur close to shore within the OCS area. As 

sea-level rose, these channels were filled with sandy sediment and ultimately buried by more recent 

muddy deposits from the modern rivers. Suter et al. (1987) identified numerous incised fluvial 

systems offshore of the western Louisiana Chenier Plain. These river channels were widely 

distributed along 800 km-long Louisiana coast and were formed in geological history when the sea 

levels fluctuated and the rivers switched lobes. Some of these paleochannels are close to the modern 

shorelines. These channel-filling sands on the continental shelf have been targeted for coastal 

restoration projects, resulting in significant cost savings. In 2014, BOEM contracted a Louisiana State 

University (LSU) team to study the fate of mud-capped dredged pits at several offshore sites, 

including one near Raccoon Island (Figure 1.5) (Zhu et al. in press). In the future, this kind of project 

will rely on environmental data that can provide information for the study of the fate of mud-capped 

dredged pits, the impact to its environment, the evaluation of the appropriateness of existing safety 

distance to nearby pipelines, and management of resources and policy assessment and regulations.

Figure 1.5.a. Relationship between suspended particulate matter (SPM) and optical backscattering 

obtained at various locations during field sampling along the Louisiana coast.

The variable anap is the nonalgal particle absorption.
13

Figure 1.5.b. Lower panel: SeaWiFS satellite derived imagery of SPM (mg/L) obtained using a regional 

algorithm (figure from D’Sa et al. 2007).

1.4 Objectives

Originally, our main objective for this project was to revive and significantly upgrade the old

WAVCIS station CSI 5 for high quality, continuous, and real-time metocean data collection and

sharing. The actual station is not CSI 5 but SS91 or the Station 10 of WAVCIS at (28.92oN, 

90.77oW). The system is more efficient and reliable as new technology for power system (including 

cooling system which is needed for this subtropical marine location). In addition to the real time 

continuous ocean velocity profile observations, we also install an optical backscatter sensor, on a 

short-term basis (not providing real time data). The time-series optical measurements will enhance the 

ocean data collection to support decision making related to environmental conditions and sediment 

transport that could affect environmental conditions in the proposed study area. The optical 

measurements will also support efforts to improve ocean color satellite estimates of these 

environmental parameters in optically complex coastal waters (D’Sa et al. 2006, 2008, 2011, 2014).

With the establishment of this WAVCIS station at SS91, the system no longer uses the old desktop

computer on site like the other existing WAVCIS stations do. Instead, we use a new satellite 

transmission system that is more reliable and without the need of a cooling system that is prone to 

problems. As a result, the power needs will be much less. This change also eliminates all software 

upgrades frequently needed for the desktop Microsoft Windows®-based computers, which caused

security problems and impacted the effective speed of data transfer.

We also eliminated the external data loggers. Our old external data loggers were based on technology 

more than 10 years old. They were not designed for the amount of data traffic with many sensors and 

frequent samplings. As a result, it became slower in data transfer as more sensors were added. 

Cheaper, faster, smaller, and more reliable data loggers integrated with the devices are now available 

and such an upgradecan benefit the entire program. It costs less as we reduce the number of required 

trips to the site for maintenance because with new technology, the system is more reliable and has 

more capacity in data storage.

Specific objectives of the project:

1) Establish a new real-time coastal observing station within the GCOOS network; this new station is 

outside of the Terrebonne-Timbalier bays. The station collects real time oceanography data and

seasonal ocean optics data.
14

2) Collect real-time oceanographic data from this new station using new technology. The station will 

be maintained to have as much continuous data as possible. With the use of new technology we will 

no longer use the on-site Microsoft Windows®-based computer that often presented problems due to 

frequent automated upgrade and unnecessary overhead of various background programs that are not 

needed for the operation of the station.

3) Collect seasonal optical scattering data at the new station to assess temporal variability in

suspended particulate matter and its physical linkages. Water samples will be obtained on site for 

suspended matter concentration.

4) Provide data and results from our analysis that will assist the other projects for mud-capped dredge 

pit evolution and enhance our understanding of mud-capped dredge pit evolution.

5) Help the implementation and improvement of regional coastal ocean models using Finite Volume

Community Ocean Model (FVCOM) incorporating data from both the new station and nearby 

GCOOS stations for a better understanding of the physical factors on the sediment transport, coastal 

currents, water particle trajectories (for application on oil spill and pollutant transport) and evolution 

of dredged pit under various weather conditions. A series of numerical models have been developed 

in our labs and within the regional community funded by the Northern Gulf Institute, National 

Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric 

Administration (NOAA), BOEM, the State of Louisiana, and BP (for oil spill). Our data will be 

available online and accessible by the general public through the GCOOS, NOAA, and WAVCIS 

web pages.

1.5 Station

After a request from BOEM and subsequent discussions, we originally selected the old CSI 5 station 

as the site for the new system, which is located in the proximity of the Ship Shoal sand body offshore 

Louisiana where dredging under BOEM’s purview had been underway. Observations from Station 

CSI 5 were used in BOEM-funded studies to model potential environmental impacts associated with 

dredging (Stone et al. 2009); the results were used by BOEM for decisions and consultations with 

other resource management agencies. The idea was that with CSI 5 being inoperable, there would be 

no long or short- term wave or current observations available during the time when collecting those 

data would be critical: when dredging was actually occurring. In addition, post-dredging data could 

be used to validate any predictive model and inform future management decisions.

However, it was later determined that Station CSI 5 was not the best site for the kind of information 

needed. After an extensive search and discussion, as well as legal requirements (property use permit, 

boarding pass, etc.) with oil and gas companies, we selected a site off Timbalier Bay on Ship Shoal 

(Station 10A for WAVCIS). 
15

2. Station Selection, Instrument, and Installation

2.1 Site Selection

Originally, the site for the new station was selected to be at the old CSI 5 (Figures 2.1, 2.2), which is 

an unmanned platform that was used by Wave-Current-Surge Information System (WAVCIS). It was 

later determined that this station would be too close to shore and far away from the area of interest 

(e.g., Ship Shoal) and a search for a proper site began. For instance, we examined a possibility of 

using a platform from Block 84, 86, 87, or 99. The purpose was to select a relatively new site that 

would not be decommissioned soon, allowing us to collect sufficient data. We also looked into the

possibility of finding a wooden piling, which turned out to be very difficult to find. We identified a 

few locations that all turned out to be unsuitable for the project: most of the potentially good sites 

were to be decommissioned soon. At one point, a US Coast Guard lighthouse was identified as a 

potential site. However, it was later determined that the condition of the lighthouse was not favorable 

and at the same time there was an issue of the ownership. All these challenges delayed the project for 

about a year. By November 2016, we had been working on a boarding agreement with the owner 

(Fieldwood Energy) of the selected platform (Ship Shoal 91, 28.919oN; -90.774oW, Figure 2.3): we 

were in communication with people at Fieldwood Energy SS91, regarding a site visit before putting 

an acoustic Doppler profiler (ADCP) and conductivity-temperature-depth sensor (CTD) at the site.

Figure 2.1. WAVCIS Stations and the new station (SS91).

The boarding agreement with Fieldwood Energy SS91 was approved by both Louisiana State 

University (LSU) and the company on December 12, 2016. After this, we were allowed to visit the 

site for measurements aimed at the design of the installation, and for the actual instrument installation 

after the manufacturing of the large clamps for fixing the conduit for cables. 

The site SS91 at a shallow water depth of ~37 ft is ideal because (1) it is on Ship Shoal where 

dredging sand has been ongoing and (2) there is a need for proper assessment of the fate of a dredged 

pit under various weather and oceanographic conditions and (3) the integrated effect over the years. A 

real-time station for continuous measurements of oceanographic conditions will be valuable. This site 

is close to other WAVCIS stations (CSI 6 and CSI 9, e.g. Figure 2.4). This site is also in the coastal 

current zone throughwhich the Louisiana-Texas coastal current passes. It is also in the area of the 

seasonal hypoxia. The data from this site can be very useful for coastal current model validation. This 

kind of model can be used for many purposes, including the effects of severe weather, hypoxia, and

oil spills, just to name a few.
16

Figure 2.2. CSI 5, the originally selected site.

Figure 2.3. The final site, Fieldwood Energy on Ship Shoal 91 (SS91) A and B platforms (7 nm offshore).
17

Figure 2.4. A nearby station, CSI 6.

2.2 Instrument

To measure the current velocity profiles, we use the Nortek 1 MHz acoustic Doppler profiler (ADCP, 

Figure 2.5)–the 4-beam acoustic wave and current sensor (AWAC). The AWAC ADCP was custom 

made based on our request that the four transducers not be symmetric: they are facing one side so that 

no beam would be interfered with by the oil platform structure. To reduce biofouling, we painted the 

surface of the ADCP with blue color antifouling paint.

Figure 2.5. Custom-made ADCP to reduce the effect of the rig legs by facing the transducer sideways.

The blue color is antifouling paint that applied before the deployment.
18

The ADCP has a secure digital (SD) card for up to 4 GB of data storage. It is connected with the 

satellite transmission box (the Automated Observing System, or AOS Iridium Nortek module). 

This AWAC ADCP at the new WAVCIS Station SS91 is a current profiler and a wave directional 

system in one unit. Traditional WAVCIS stations had ADCPs and separate pressure sensors that

needed to be replaced yearly. Extra cabling was needed, as well. The SS91 ADCP can measure the 

current speed and direction in one-meter thick layers from the sensor depth to the surface. Waves of 

all varieties are measurable; this includes long waves, storm waves, short wind waves, or transient 

waves generated by local ship traffic. The customization of the ADCP heads allows for beams to 

gather data away from influence of rig structure. Traditional ADCP would have only one beam and it 

would be influenced by the oil rig’s structure.

Figure 2.6. The new WAVCIS station antenna.

10” x 8” footprint; no cable needed; Iridium satellite omnidirectional antenna would not lose signal and data during and after 

strong wind events because the movement of the antenna does not affect the connection with the satellites.

Figure 2.7. Old WAVCIS station antenna.

96” x 72” footprint, hundreds of feet of coaxial cable to computer; subject to misalignment from strong winds. 
19

The AOS data collection and/or transmission unit is designed for easy use and can handle harsh 

environments. The data collection unit is equipped with an Iridium satellite communication modem 

for a truly global coverage. It is a very small and light unit–only 10 x 8 inches (Figure 2.6), compared 

to the old WAVCIS antenna of 96 x 72 inches (Figure 2.7). It takes much less deck space, which is 

always very valuable on offshore platforms. In addition, because the old WAVCIS antennas are 

directional, they have to be precisely facing the direction of the geostationary satellite for reliable data 

transmission. In severe weather, the antenna might be moved by strong wind and data connection lost. 

The AOS system is omnidirectional and can be set in any orientation and not affected by strong wind 

during a severe weather. This makes the data transmission more reliable and the data quality higher.

The old WAVCIS ADCPs were all powered by on-site AC power from the oil and gas company that 

provided the platform. This new ADCP is powered by a solar panel-charged battery. This battery is 

securely housed in a weatherproof aluminum electronic box with an automated breaker that will click 

off when power surges or lightning strikes to stop the current going to the instrument and causing 

damage. This kind of box has been used by the US Department of Transportation along highways and 

is very reliable for outdoor use, including offshore deployment. The AWAC ADCP, solar panel, 

battery, cable, and the breaker installed battery box are shown in Figure 2.8. The ADCP is mounted at 

about seven meters below the surface and looking upward so it can record velocity profiles at hourly 

intervals.

Figure 2.8. The AWAC ADCP, solar panel, battery, cable, and the breaker installed battery box. 

The battery box with the breaker and the AOS system are mounted on a panel that is vertically placed 

on the platform for data collection (Figure 2.9).

In addition to the ADCP, we also use a Seabird Electronic (SBE) CTD model SBE 37 SM (Figure 

2.10). This is for the seasonal measurements together with the optical measurements and so there is 

no real-time data transmission from this instrument. 

The optical instrument used is an ECO BBFL2 (WETlabs, Inc.) that is used mainly to measure optical 

backscatter at 700 nm (measurement range 0 to 3 m-1

). The instrument also includes measurements of 

chlorophyll-a fluorescence (measurement range 0 to 30 ug Chl/L) and colored dissolved organic 

matter (CDOM) fluorescence (measurement range 0 to 375 ppb). The instrument has internal memory 
20

capabilities, an anti-fouling wiper with copper faceplate, and internal batteries. The instrument was 

set up for a sampling period of 15 mins (96 samples/day) that could support data collection for a 

period of about four months. However, wiper movement potentially affected by fouling in the 

faceplate could reduce the sampling period. Before the deployment, the instrument was taken on a 

field trip and calibrated for suspended particulate matter and CDOM. Calibration was conducted by 

obtaining a series of optical measurements with the BBFL2 in surface waters in a Gulf of Mexico 

estuary. Concurrently, water samples were collected for measurements of suspended particulate 

matter (SPM; mg/L) and CDOM absorption coefficients (m-1

).

Figure 2.9. The mounting panel installed at the SS91 platform with battery inside the housing on the left

and the AOS on the right. 
21

Figure 2.10. Conductivity-temperature-depth sensor deployed at SS91.

The third piece of equipment deployed at SS91 is a WETLabs Eco scattering sensor. The ECO-w 

comes with an active anti-biofouling design for longer-term mooring deployments. The ECO-w is 

programmed to sample at hourly intervals; data is stored onboard its memory. Time series of the 

optical data are used in conjunction with physical measurements to obtain a mechanistic 

understanding of the seasonal variability of suspended material at the site.

2.3 Design of the Installation

The design of the installation was made by the technicians at the Field Support Center, Coastal 

Studies Institute at LSU. The major pieces include: (1) the conduit, partially submerged, for cable that 

sends data up to the deck unit AOS system for satellite data transmission; (2) the clamps for fixing the 

conduit on one of the platform legs; and (3) a mounting structure for the instrument package (the 

ADCP, CTD, and ECO-w). The engineering drawing for the system is provided in Figures 2.11–2.18.
22

Figure 2.11. Engineering drawing (1) for the installation at SS91. 
23

Figure 2.12. Engineering drawing (2) for the installation at SS91. 
24

Figure 2.13. Engineering drawing (3) for the installation at SS91. 
25

Figure 2.14. Engineering drawing (4) for the installation at SS91. 
26

Figure 2.15. Engineering drawing (5) for the installation at SS91. 
27

Figure 2.16. Engineering drawing (6) for the installation at SS91. 
28

Figure 2.17. Conduit installation on one of the rig legs.
29

Figure 2. 18. CAD drawing of the clamp for the conduit.

2.4 Test Installation and Test Run of the System

Before the actual installation and operation of the new station at SS91, we had two test installations 

and test runs at two inshore stations: one at the Caminada Pass between May 25, 2014 and January 2, 

2016 (we call this the pre-project test), and the other at Barataria Pass between March 16, 2017 and 

April 4, 2018 (we call this project test). Figure 2.19 shows the installation at the latter.

Figure 2.19. Test station at Barataria Pass for the AOS system before the actual deployment at SS91.
30

Figure 2.20. Example velocity data from the entire period of the first test run at the Caminada Pass. 

The upper panel shows the north velocity component from one bin; the lower panel shows the data inventory.

The test installations and operations were a great success. With the first installation as an example, 

from September 15, 2014 to December 31, 2015, data were almost continuous except two gaps when 

the battery went low (Figures 2.20–2.21). At that time, the AOS box used only batteries but was not 

connected to a solar panel yet and the replacement of battery required a manual onsite service. 
31

Figure 2.21. Zoomed in flow velocity data for bins 5, 10, and 20 (shown by 3 different colors). 

From September 15, 2014 to December 31, 2015, data were almost continuous except two gaps when the battery went low.

2.5 Installation

After all the preparations, and some delays due to weather and ship time scheduling issues, the new 

station at SS91 was finally finished with several trips and the AWAC AOS system started recording 

real-time data from August 23, 2018 and still functioning at the time of this report. It should be noted 

that the instrument operated in real time from August 23 to December 9, 2018 when it stopped 

because we were working with the system to optimize it and testing for a better position for the AOS 

box. There were some issues involving the cable: it must be explosion proof, which was a new 

regulation implemented after our proposed work. All the cables had to be remade. After working with 

the company, fixing issues of data transmission, we boarded SS91 again on March 2, 2019 when we 

fixed the problem onsite and reactivated the real-time data transmission for the AWAC ADCP. The 

data have been flowing real time since then. Figures 22–23 show the installations on SS91. We will 

discuss the data obtained so far in the next chapter.
32

Figure 2.22. The new station (SS91). 

Shown in the picture are the locations of the AOS unit before March 2, 2019.

Figure 2.23. Photo of an installed clamp.
33

3. Data Collected

3.1 The Real-time Oceanographic Data

Data Inventory. As described earlier, after the whole system was installed and after a period of tuning 

and testing, the real-time data started to flow on August 23, 2018. The data stream stopped between 

December 6, 2018 and March 2, 2019, after which the system has been operational (real-time data 

collection continuing, Figure 3.1). Our last download of the data was on August 5, 2019 and our 

graphs of the data are all up to this time, even though the data are still being collected in real time. 

Battery Voltage. The data are shown on the Wave-Current-Surge Information System (WAVCIS)

web site and the post-processed data are to be regularly sent to the Gulf Coast Ocean Observing 

System (GCOOS). Between August 23 and December 6, 2018 the AOS used a non-chargeable battery 

and was not yet connected to the solar panel charged battery. As a result, the battery power declined

gradually (Figure 3.2). After March 2, 2019, the solar panel powered battery was connected to the 

system and the battery voltage stayed pretty much stable; no obvious drop was seen (Figure 3.3). The 

oscillation of voltage shown in Figure 3.3. was caused mainly by the nighttime slight drop in voltage. 

Figure 3.1. Data inventory for the SS91 acoustic wave and current sensor (AWAC) acoustic Doppler 

profiler (ADCP) data.

Figure 3.2. Time series of battery for the ADCP AOS system before December 6, 2018.
34

Figure 3.3. Time series of battery for the ADCP AOS system after March 2, 2019.

Figure 3.4. Time series of water depth above the ADCP before December 6, 2018.

Figure 3.5. Time series of water depth above the ADCP after March 2, 2019.

Water Level. The time series for the water level variations or the water depth variation above the 

pressure sensor of the acoustic Doppler profiler (ADCP) shows daily tidal and fortnightly (two-week) 

tidal variations (Figures 3.4–3.5). However, the pressure sensor built in the acoustic wave and current 

sensor (AWAC_ ADCP does not have very high resolution for water level. This is not a major 

problem because the pressure on the ADCP here serves mainly to indicate where the surface is when 
35

the velocity time series is measured. The vertical bins of the horizontal velocity components,

however, have a resolution of 1 m and so anything finer than 1 m does not increase the resolution of 

the position of the vertical bins.

Water Temperature at ~7 m below Surface. The water temperature was measured at the position of 

the AWAC ADCP unit, roughly ~7 m below the surface. It can be seen that during the first time 

period (August 23 and December 6, 2018) the water temperature was overall decreasing (Figure 3.6); 

during the second time period (March 2–August 6, 2019) the water temperature was overall 

increasing (Figure 3.7). These are obviously due to the seasonal variations. 

Figure 3.6. Time series of water temperature at the AWAC ADCP unit before December 6, 2018.

Figure 3.7. Time series of water temperature at the AWAC ADCP unit after March 2, 2019.

Heading and Tilt of the AWAC ADCP. The heading and tilt of the AWAC ADCP showed no variation 

over the entire period, indicating that the unit was securely mounted and there was no movement 

during the entire period (Figures 3.8-3.11), even during Hurricane Barry, which passed the area 

around July 21, 2019. 
36

Figure 3.8. Time series of heading of the AWAC ADCP unit before December 6, 2018.

Figure 3.9. Time series of tilt of the AWAC ADCP unit before December 6, 2018.

Figure 3.10. Time series of heading of the AWAC ADCP unit after March 2, 2019.
37

Figure 3.11. Time series of tilt of the AWAC ADCP unit after March 2, 2019.

Flow Direction, Magnitude, and Vector Components. The horizontal flow velocity was recorded as 

direction and magnitude at 1 m intervals in the vertical. For the first time period (August 23–

December 6, 2018), the time series of the flow direction is shown in Figures 3.12–3.19; the time 

series of the flow magnitude is shown in Figures 3.20–3.27. The decomposed east and north velocity 

components at various depth above the ADCP are shown in Figures 3.28 and3.29. 

For the second period (March 2–August 6, 2019), the time series of the flow direction are shown in 

Figures 3.30–3.37; the time series of the flow magnitude are shown in Figures 3.38–3.45. The 

decomposed east and north velocity components at various depths above the ADCP are shown in 

Figures 3.46 and 3.47.

Figure 3.12. Velocity direction at 1.4 m above the ADCP for August 23–December 6, 2018. 

Figure 3.13. Velocity direction at 2.4 m above the ADCP for August 23–December 6, 2018.
38

Figure 3.14. Velocity direction at 3.4 m above the ADCP for August 23–December 6, 2018.

Figure 3.15. Velocity direction at 4.4 m above the ADCP for August 23–December 6, 2018.

Figure 3.16. Velocity direction at 5.4 m above the ADCP for August 23–December 6, 2018.

Figure 3.17. Velocity direction at 6.4 m above the ADCP for August 23–December 6, 2018.
39

Figure 3.18. Velocity direction at 7.4 m above the ADCP for August 23–December 6, 2018.

Figure 3.19. Velocity direction at 8.4 m above the ADCP for August 23–December 6, 2018.

Figure 3.20. Velocity magnitude at 1.4 m above the ADCP for August 23–December 6, 2018.

Figure 3.21. Velocity magnitude at 2.4 m above the ADCP for August 23–December 6, 2018.
40

Figure 3.22. Velocity magnitude at 3.4 m above the ADCP for August 23–December 6, 2018.

Figure 3.23. Velocity magnitude at 4.4 m above the ADCP for August 23–December 6, 2018.

Figure 3.24. Velocity magnitude at 5.4 m above the ADCP for August 23–December 6, 2018.

Figure 3.25. Velocity magnitude at 6.4 m above the ADCP for August 23–December 6, 2018.
41

Figure 3.26. Velocity magnitude at 7.4 m above the ADCP for August 23–December 6, 2018.

Figure 3.27. Velocity magnitude at 8.4 m above the ADCP for August 23–December 6, 2018.

Figure 3.28. East velocity component at various depths above the ADCP for August 23–December 6,

2018.
42

Figure 3.29. North velocity component at various depths above the ADCP for August 23–December 6,

2018.

Figure 3.30. Velocity direction at 1.4 m above the ADCP for March 2–August 6, 2019. 

Figure 3.31. Velocity direction at 2.4 m above the ADCP for March 2–August 6, 2019.
43

Figure 3.32. Velocity direction at 3.4 m above the ADCP for March 2–August 6, 2019.

Figure 3.33. Velocity direction at 4.4 m above the ADCP for March 2–August 6, 2019.

Figure 3.34. Velocity direction at 5.4 m above the ADCP for March 2–August 6, 2019.
44

Figure 3.35. Velocity direction at 6.4 m above the ADCP for March 2–August 6, 2019.

Figure 3.36. Velocity direction at 7.4 m above the ADCP for March 2–August 6, 2019.

Figure 3.37. Velocity direction at 8.4 m above the ADCP for March 2–August 6, 2019.

Figure 3.38. Velocity magnitude at 1.4 m above the ADCP for March 2–August 6, 2019.
45

Figure 3.39. Velocity magnitude at 2.4 m above the ADCP for March 2–August 6, 2019.

Figure 3.40. Velocity magnitude at 3.4 m above the ADCP for March 2–August 6, 2019.

Figure 3.41. Velocity magnitude at 4.4 m above the ADCP for March 2–August 6, 2019.
46

Figure 3.42. Velocity magnitude at 5.4 m above the ADCP for March 2–August 6, 2019.

Figure 3.43. Velocity magnitude at 6.4 m above the ADCP for March 2–August 6, 2019.

Figure 3.44. Velocity magnitude at 7.4 m above the ADCP for March 2–August 6, 2019.
47

Figure 3.45. Velocity magnitude at 8.4 m above the ADCP for March 2–August 6, 2019.

Figure 3.46. East velocity component at various depths above the ADCP for March 2–August 6, 2019.

Figure 3.47. North velocity component at various depths above the ADCP for March 2–August 6, 2019.

3.2 The Short-term CTD and Optical Data

The conductivity-temperature-depth sensor (CTD) and ECO-w data are not real time; they are saved 

in the internal memory of the instrument. There has not yet been a download from these instruments

because of difficulties in ship time scheduling and the availability of divers for the retrieval of the 

equipment. These instruments operate independent of the real time ADCP AOS system and can be 

redeployed independently in the future.

After retrieving the equipment from its deployment site along the Louisiana coast, we plan to first 

assess the state of the instrument and attempt to retrieve the data from the instrument memory. The 
48

most relevant data is the backscattering time series data. This data will be converted to suspended 

particulate matter (SPM) based on the pre-deployment calibration measurements. The SPM data will 

be analyzed in conjunction with physical measurements (currents, waves, weather) to obtain a 

mechanistic understanding of the variability of suspended material at the study site. We will also 

examine satellite ocean color data during the sampling period and assess spatial variability of SPM in 

the study region. The additional measurements of colored dissolved organic matter (CDOM) and 

chlorophyll fluorescence will contribute to assessing constraints on satellite SPM estimates.

3.3 Weather Conditions during the Deployment

Because the flow conditions in this region are significantly affected by the weather, we compiled the 

severe weather events during these time periods. The data collected so far cover periods from August

2018–December 9, 2018; and March 3, 2019 (still going). For these periods, we have identified 25 

atmospheric events for the 2018 data; and 28 events for the 2019 data (up to August 6). These events 

include two hurricanes (Hurricane Michael in 2018 and Hurricane Barry in 2019), one tropical storm, 

10 stationary fronts, 33 cold fronts, and seven warm fronts. Li et al. (2018) studied the impacts of 76 

atmospheric frontal events that include cold, warm, and cold-warm front combinations, and 

demonstrated that these atmospheric events are important in driving the coastal-estuarine circulations. 

Likewise, these events should also have significant effects on the hydrodynamics of coastal offshore 

inner shelf region. Given the unique data we obtain from this region, including those from our new 

station at SS91, the events identified in Tables 3.1–3.2 can be the subjects of future study for the 

impact to the circulation and sediment transport. For instance, as subjects of study, we have selected 

Tropical Storm Gordon (Figures 3.48–3.50), a typical stationary front (Figure 3.51), Hurricane 

Michael (Figures 3.52–3.54) in 2018, and a cold front (Figure 3.55), a warm front (Figure 3.56), and 

Hurricane Barry (Figures 3.57–3.59) in 2019. We have analyzed the National Oceanic and 

Atomospheric Administration’s (NOAA’s) reanalysis weather data associated with our oceanographic 

data. This is ongoing research.
49

Table 3.1. Atmospheric Frontal Events between September 4 and December 9, 2018

Begin

Y M D UTC

End

M D UTC Type

2018 9 4 15 9 5 12 Tropical Storm Gordon

9 10 18 9 12 3 stationary front

9 27 0 9 28 0 cold front

9 28 3 9 28 21 stationary front

10 10 3 10 10 21 Hurricane Michael

10 10 21 10 11 9 cold front

10 13 6 10 13 15 cold front

10 15 15 10 16 9 cold front

10 20 15 10 21 6 cold front

10 25 6 10 25 15 warm front

10 25 15 10 26 3 cold front

11 1 3 11 2 3 cold front

11 4 12 11 5 0 cold front

11 6 9 11 6 21 cold front

11 8 0 11 9 12 stationary front

11 12 6 11 12 12 warm front

11 12 15 11 13 6 cold front

11 18 21 11 20 6 cold front

11 25 9 11 25 18 warm front

11 25 18 11 26 6 cold front

12 1 12 12 2 12 cold front

12 2 15 12 3 0 cold front

12 3 0 12 3 12 cold front

12 7 18 12 8 9 stationary front

12 9 15 12 9 3 cold front
50

Table 3.2. Atmospheric Frontal Events between March 2 and August 6, 2019

Begin

Y M D UTC

End

M D UTC

2019 3 2 9 3 2 21 Type

2019 3 3 12 3 4 3 stationary front

2019 3 11 9 3 12 21 cold front

2019 3 13 3 3 13 15 cold front

2019 3 14 9 3 15 15 warm front

2019 3 19 3 3 19 18 cold front

2019 3 21 0 3 21 12 cold front

2019 3 25 15 3 26 9 cold front

2019 3 30 18 3 31 12 cold front

2019 4 4 12 4 6 3 cold front

2019 4 8 6 4 9 0 warm front

2019 4 12 0 4 13 6 cold front

2019 4 14 0 4 14 15 cold front

2019 4 18 12 4 19 12 cold front

2019 4 25 3 4 26 3 cold front

2019 5 4 15 5 5 9 cold front

2019 5 8 0 5 8 9 cold front

2019 5 11 3 5 12 21 stationary front

2019 5 31 21 6 1 6 stationary front

2019 6 3 0 6 3 12 stationary front

2019 6 8 3 6 8 21 stationary front

2019 6 9 6 6 10 3 cold front

2019 6 10 9 6 11 6 stationary front

2019 6 13 9 6 13 21 cold front

2019 6 14 12 6 14 18 cold front

2019 7 12 0 7 14 12 warm front

2019 7 23 3 7 24 3 Hurricane Barry

2019 7 26 21 7 27 3 cold front

warm front
51

Figure 3.48. Weather map example 1 of 3 for Tropical Storm Gordon.

The vectors are ground level wind velocity–the inset for the reference vector is in m/s. The blue lines are the contours for sealevel air pressure in millibar (mb). The blue letter H and red letter L indicate the high- and low- pressure centers, respectively. 

These are the same for Figures 3.49–3.59. 

Figure 3.49. Weather map example 2 of 3 for Tropical Storm Gordon.
52

Figure 3.50. Weather map example 3 of 3 for Tropical Storm Gordon.

Figure 3.51. Weather map example for a typical stationary front.
53

Figure 3.52. Weather map example 1 of 3 for Hurricane Michael.

Figure 3.53. Weather map example 2 of 3 for Hurricane Michael.
54

Figure 3.54. Weather map example 3 of 3 for Hurricane Michael.

Figure 3.55. Weather map example for a typical cold front.
55

Figure 3.56. Weather map example for a typical warm front.

Figure 3.57. Weather map example 1 of 3 for Hurricane Barry.
56

Figure 3.58. Weather map example 2 of 3 for Hurricane Barry.

Figure 3.59. Weather map example 3 of 3 for Hurricane Barry.
57

4. Data Use

The data obtained by the new Wave-Current-Surge Information System (WAVCIS) Station SS91 can 

be used for many purposes. For example, they can be used to study the hydrodynamic conditions in 

the area to better understand the sediment transport and evolution of a dredged pit under various tidal 

and weather conditions, especially under repeated severe weather events, which include the 

atmospheric cold fronts, warm fronts, combinations of cold-warm fronts (Li et al. 2018), and tropical 

storms and hurricanes. 

4.1 Data Submission

The real time oceanographic data are to be submitted to GCOOS, which is one of the 11 certified 

regional associations in the US under the Integrated Ocean Observing System (IOOS). GCOOS was 

created as a member-driven entity in 2005 and incorporated as a nonprofit organization in 

2013. In 2018, it was certified by the US Integrated Ocean Observing System and NOAA, 

indicating that GCOOS is meeting federal standards for data gathering and management and 

operates inclusively, transparently and seeks user input to determine system priorities. GCOOS 

is the only regional association of IOOS that covers the entire northern Gulf of Mexico,

encompassing the states of Texas, Louisiana, Mississippi, Alabama, and Florida. WAVCIS has 

been providing QA/QCed data to IOOS/NOAA through GCOOS for almost a decade. The 

submitted data will be in standard forms that meet all the requirements of NOAA. As a member 

of GCOOS, WAVCIS has agreed to submit all data from SS91 of this project to GCOOS for 

broader dissemination and usage.

4.2 Data Usage for Research: Analysis and Numerical Experiments

The data can be used for various research projects. The WAVCIS lab has used the ADCP from the 

test installation and operation in the Caminada Pass and studied the impact of 51 winter storms to the 

exchange of water between Barataria Bay and the Louisiana coastal water. More specifically, multiple 

horizontal ADCPs are used in a long-term deployment to obtain time series data from three inlets of 

Barataria Bay. One of these ADCPs is a Nortek horizontal profiler using the AOS to transmit the real 

time data. The data allows an opportunity to study the impact of 51 atmospheric cold fronts to the 

subtidal exchange flows of the bay with the Louisiana coastal ocean. Analyses demonstrate relations 

between the weather forcing and subtidal ocean response in the “weather band” (3–7 days in period). 

The prefrontal, frontal, and post-frontal winds produce alternating flows into, out of, and then back 

into the bay, forming an asymmetric “M” for low-pass filtered flows. A regression explainable by the 

dynamical balance shows that cold front-induced flows are the most important component in this 

region and the flows can be predicted based on wind vector time series. The effect of air pressure is 

small but correlated with the subtidal currents. A numerical simulation using a validated Finite 

Volume Community Ocean Model (FVCOM) demonstrates that the wind-driven oscillations within 

the bay are consistent with the quasi-steady state with little influence of Coriolis for cold front-related 

wind-driven flows. It is also found that the multiple inlets behave in a way such that the inward 

transport tends to be through the upwind inlet and the downwind inlet tends to have less inward or 

even outward transport. Shallow (deep) water tends to have downwind (upwind) flows. Counter-wind 

flows can develop at bottom. The correlation, energy spectrum, dynamics-based regression of these 

51 storms, and numerical modeling all demonstrate that cold fronts produce the major subtidal flows 

in this micro-tidal system. For a more complete presentation showing the value of the data in 

supporting research, see Li et al. 2019.

4.3 Data Usage for Research: Model Validation and Subsequent Numerical Experiment

The data can also be used for model validation. There is virtually no other source for real-time 

continuous measurements of velocity profiles over the Louisiana continental shelf at this moment 

except the WAVCIS station SS91. The data are useful for model validation for studies on 
58

hydrodynamics, sediment transport, storm surges, hypoxia, and oil spill, just to name a few. In our 

own research, we have also used the SS91 data for model validation and subsequent numerical model 

experiments in a recent effort to determine the transport and coastal currents during and after 

hurricanes Harvey, Michael, and Barry. Appendix 1 provides some relevant information on this 

effort. 

4.4 Data Use for Student Projects

The WAVCIS data have been provided to numerous users, including students throughout the country 

for their projects. For example, the data have been used for student dissertations related to the recent 

BOEM-supported dredged pit projects. In March 2018, Nazanin Chaichitehrani successfully defended 

her dissertation “Numerical Experiment of Sediment Dynamics over a Dredged Pit on the Louisiana 

Shelf” in which she used WAVCIS data for analysis and for model validation. We anticipate more 

students will use the new SS91 station data for dissertations and theses. 
59

5. Summary

In this BOEM-funded project, we have selected a site at SS91, south of Timbalier Bay for the 

installation of a real time ocean observing station. The station is at a shallow water of ~37 ft on Ship 

Shoal, where dredging of sand has been ongoing and where there is a need for proper assessment of 

the fate of a dredged pit under various weather and oceanographic conditions and the integrated effect 

over the years. This is a station based on an oil production unit owned by the Fieldwood Energy. 

We have successfully installed an upward-looking acoustic Doppler profiler (ADCP) at this station 

for real-time data transfer through Iridium Satellite Communications with an omnidirectional antenna. 

This system is much smaller than the old Wave-Current-Surge Information System (WAVCIS)

stations, which used an onsite desktop computer that was subject to hacking and frequent unwanted 

system updates with costly data fees. The hacking and unwanted system updates caused interruptions 

in operation and costly repairs (ship time and labor costs). The new system does not use an onsite 

computer; new technology is used and an internal processor and data logger replace the onsite 

computer. In addition, this system does not need the bulky cooling systems at each of the old stations. 

The old stations also needed heavy and large directional parabolic antennas. 

Our intention is to keep the station running and provide the data to BOEM and Gulf Coastal Ocean 

Observing System (GCOOS). WAVCIS is part of the GCOOS. The data will be crucial for the 

modeling of sediment transport and the fate of dredged pit off Louisiana coast to support the mission 

of BOEM’s Marine Minerals Program in the Outer Continental Shelf region. The data will be also 

provided to the National Oceanic and Atmospheric Administration for archive. They will also be used 

by researchers, students, including people at Louisiana State University and other institutions for 

studying the hydrodynamics and for numerical model predictions of the oceanic conditions that affect 

the fate of sediments and potential impact to any mineral resources. 
60

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Appendix 1. Numerical Modeling for the Louisiana Continental Shelf

A few models based on the Finite Volume Community Ocean Model (FVCOM) have been developed 

at Wave-Current-Surge Information System (WAVCIS) with the SS91 acoustic Doppler profiler

(ADCP) data for comparison and model validation. Here we use one of them as an example 

demonstrating the value of data from SS91. The purpose of the model was to simulate the storm 

current over the shelf for Hurricane Harvey and other severe weather.

Model mesh. The model mesh covers the whole area of the Gulf of Mexico, from 80.7°W to 97.9°W, 

and 18.1°N to 30.7°N. There are two open boundaries. One is located at the edge of Caribbean Sea,

connecting the east border of Mexico and the west edge of Cuba. The other open boundary is located 

at the North Atlantic Ocean with the northern point at the edge of Florida and the southern point at the 

border of Cuba. The mesh contains 122664 nodes and 220493 cells (Figures A-1–A-2). The finest 

resolution is about 20m. There are 40 sigma layers. Time step for the external mode is 1 second. The 

ratio of the internal time step to external time step is set to be 5. 

Figure A-1. Model grid for FVCOM.

Experimental design. Time period of numerical simulation is from July 20, 2017 to September 15, 

2017. The time interval for the output is 30 minutes. The tide is forced at the open boundary with 

spatially variable wind and air pressure forcing for the whole area. Tidal forcing is the combination of 

10 tidal constituents (M2, S2, N2, K2, K1, O1, P1, Q1, MF, and MM). It is predicted by a tide model 

called TMD (Padman and Erofeeva 2004). Wind data and air pressure data are the Climate Forecast 

System Reanalysis (CFSR) data obtained from the National Centers for Environmental Prediction 

(NCEP). The resolution for the wind and air pressure data is 0.5°×0.5°with a time interval of 6 hours. 

Wind data are interpolated into the mesh grid. 

Model-data comparison and Preliminary Results. The preliminary model-data comparison is shown 

in Figures A-3–A-6. It can be seen that the east component of the velocity was well captured by the 

model, but the north component was not. The reason could be the inaccuracy in bathymetry data. The 
63

model is currently being improved. Preliminary results of circulation under Hurricane Harvey is 

shown in Figures A-7–A-15.

Figure A-2. Model grid zoomed in on the Ship Shoal area.

Figure A-3. Model comparison with observations from the Ship Shoal for the raw east component of the 

horizontal velocity.
64

Figure A-4. Model comparison with observations from the Ship Shoal for the 40-hour low-pass filtered 

east component of the horizontal velocity.

Figure A-5. Model comparison with observations from the Ship Shoal for the raw north component of the 

horizontal velocity.

Figure A-6. Model comparison with observations from the Ship Shoal for the 40-hour low pass filtered 

north component of the horizontal velocity.
65

Figure A-7. Example flow field for Hurricane Harvey.
66

Figure A-8. Example flow field for Hurricane Harvey (continued).
67

Figure A-9. Example flow field for Hurricane Harvey (continued).
68

Figure A-10. Example flow field for Hurricane Harvey (continued).
69

Figure A-11. Example flow field for Hurricane Harvey (continued).
70

Figure A-12. Example flow field for Hurricane Harvey (continued).
71

Figure A-13. Example flow field for Hurricane Harvey (continued).
72

Figure A-14. Example flow field for Hurricane Harvey (continued).
73

Figure A-15. Example flow field for Hurricane Harvey (continued).

Appendix 1 References 

Padman L, Erofeeva S. 2004. A barotropic inverse tidal model for the Arctic Ocean. Geophys Res 

Lett. 31:L02303. doi:10.1029/2003GL019003
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development of U.S. Outer Continental Shelf energy and mineral resources in 

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