OCS Study

BOEM 2013-01110

U.S. Department of the Interior

Bureau of Ocean Energy Management

Gulf of Mexico OCS Region

Meteorological and Wave Measurements

for Improving Meteorological and Air

Quality Modeling

OCS Study

BOEM 2013-01110

Published by

U.S. Department of the Interior

Bureau of Ocean Energy Management

Gulf of Mexico OCS Region

New Orleans, LA

July 2013

Meteorological and Wave Measurements

for Improving Meteorological and Air

Quality Modeling

Authors

Clinton P. MacDonald

Alison E. Ray

Paul T. Roberts

Charley A. Knoderer

Ludovic Bariteau

Chris W. Fairall

William J. Gibson

Jeffrey E. Hare

Prepared under BOEM Contract

M08PC20057

by

Sonoma Technology, Inc.

1455 N. McDowell Blvd., Suite D

Petaluma, CA 94954-6503

iii

DISCLAIMER

This report was prepared under contract between the Bureau of Ocean Energy Management 

(BOEM) and Sonoma Technology, Inc. (STI). This report has been technically reviewed by the 

BOEM and has been approved for publication. Approval does not signify that the contents 

necessarily reflect the view and policies of the BOEM, nor does mention of trade names of 

commercial products constitute endorsement or recommendation for use. It is, however, exempt

from review and in compliance with BOEM editorial standards. 

REPORT AVAILABILITY

To download a PDF file of this Gulf of Mexico OCS Region report, please visit the U.S. 

Department of the Interior, Bureau of Ocean Energy Management, Environmental Studies 

Program Information System website and search on OCS Study BOEM 2013-01110. 

This report can be viewed at select Federal Depository Libraries and obtained from the National 

Technical Information Service by using the contact information below. 

U.S. Department of Commerce

National Technical Information Service

5301 Shawnee Road

Springfield, Virginia 22312 

Phone: (703) 605-6000, 1-800-553-6847 

Fax: (703) 605-6900 

Email: orders@ntis.gov

CITATION

MacDonald C.P., Ray A.E., Roberts P.T., Knoderer C.A., Bariteau L., Fairall C.W., Gibson 

W.J., Hare J.E. 2013. Meteorological and wave measurements for improving meteorological and 

air quality modeling. U.S. Dept. of the Interior, Bureau of Ocean Energy Management, Gulf of 

Mexico OCS Region, New Orleans, LA. OCS Study BOEM 2013-01110. 102 pp. 
iv

ACKNOWLEDGMENTS

The authors of this report thank several BOEM employees for their contributions to this project. 

In particular, C. H. Huang, the Contract Officer’s Technical Representative and science advisor; 

Herma Banks, the Contract Officer; and Ron Lai and Dirk Herkoff (retired), senior technical 

advisors. The authors also thank LSU staff for their assistance with equipment installation and 

operations. In addition, the authors are extremely grateful to Chevron for providing use of its 

platform, ST-52, for hosting the equipment, and for its in-kind contributions of the transportation 

of project staff and equipment to and from the platform. Finally, the authors appreciate the 

support of Scientific Review Board members who helped design the measurement program: 

Allen White and Richard Crout from NOAA and Kristina Kasoros, also from NOAA (retired).

SUMMARY

A unique partnership of corporate, government, and university researchers collaborated to 

develop a marine environmental observation program on an offshore platform in the Gulf of 

Mexico. The primary goals of this project were to provide data to (1) improve our understanding 

of boundary layer processes and air-sea interaction over the Gulf of Mexico; (2) improve 

regional-scale meteorological and air quality modeling; and (3) provide a framework for 

advanced offshore measurements to support future needs, such as emergency response, 

exploration and lease decisions, wind energy research and development, and meteorological and 

air quality forecasting. In October 2010, meteorological and oceanographic sensors were 

deployed for an extended period (approximately 18 months) on a Chevron service platform (ST 

52, 90.5W, 29N) to collect boundary layer and sea surface data sufficient to support these goals. 

This project is important because of the large industrial presence in the Gulf, the large regional 

population nearby, and the recognized need for precise and timely dispersion forecasts. 

Observations from this project consist of surface meteorology; marine boundary layer winds; 

vertical profiles of atmospheric temperature, relative humidity, and liquid water; cloud base 

heights; atmospheric boundary layer height; ocean temperature; ocean surface temperature; 

ocean wave height and frequency; downwelling (shortwave) solar and infrared (longwave) 

radiation; and lower atmospheric boundary layer momentum and heat fluxes. This project has

collected an unprecedented set of measurements over the Gulf of Mexico that captures the range 

of meteorological and oceanographic interactions and processes that occur over all seasons.
v 

TABLE OF CONTENTS

FIGURES ...................................................................................................................................... vii

TABLES ........................................................................................................................................ xi

1. Introduction ............................................................................................................................... 1

2. Project Objectives ...................................................................................................................... 3

2.1 Data to Improve Predictions of Boundary Layer Parameters Using COARE ............... 3

2.2 Data to Improve Applicability of COARE through the Use of 

Routine Measurements ................................................................................................... 3

2.3 Data to Further Improve Mixing Height Schemes Used By Models ............................. 4

2.4 Data to Improve Regional-Scale Meteorological Model Predictions ............................ 4

3. Instruments and Their Deployment, Operations, and Maintenance .......................................... 5

3.1 Surface Meteorological Measurements ........................................................................ 11

3.1.1 Background ..................................................................................................... 11

3.1.2 Approach ......................................................................................................... 11

3.2 Oceanographic Measurements .......................................................................................... 14

3.2.1 Background ..................................................................................................... 14

3.2.2 Approach ......................................................................................................... 14

3.3 Direct Flux Measurements ........................................................................................... 17

3.3.1 Background ..................................................................................................... 17

3.3.2 Approach ......................................................................................................... 17

3.4 Vertical Profiles of Temperature, Wind, and Humidity Measurements ....................... 21

3.4.1 Background ..................................................................................................... 21

3.4.2 Approach ......................................................................................................... 21

3.5 Mixing Height Measurements ...................................................................................... 24

3.5.1 Background ..................................................................................................... 24

3.5.2 Approach ......................................................................................................... 24

3.6 Siting and Infrastructure ............................................................................................... 26

3.6.1 Siting ............................................................................................................... 26

3.6.2 Communications and Data Collection ............................................................ 27

3.6.3 Power .............................................................................................................. 28

3.6.4 Interference ..................................................................................................... 28

3.6.5 Hurricanes ....................................................................................................... 28

3.7 Instrument Preparation and Operations ........................................................................ 29

3.7.1 Pre-Deployment Interface and Testing ........................................................... 29

3.7.2 Shipping .......................................................................................................... 30

3.7.3 In-Field Testing After the Initial Installation .................................................. 31

3.7.4 Onsite Maintenance and Operations ............................................................... 32

4. Data Availability ..................................................................................................................... 35

5. Data Processing and Quality Control ...................................................................................... 41
vi 

6. Data Formats ........................................................................................................................... 51

7. Preliminary Data Analyses ...................................................................................................... 55

7.1 January 11, 2011, Case Study ...................................................................................... 55

7.2 Measured and Modeled Vertical Wind Profiles ........................................................... 59

7.3 Observed and COARE-Calculated Fluxes ................................................................... 61

7.3.1 Flux System Description ................................................................................. 62

7.3.2 Method ............................................................................................................ 64

7.3.3 Meteorological Data Evaluation ..................................................................... 66

7.3.3.1 Air Temperature .............................................................................. 67

7.3.3.2 Relative Humidity ........................................................................... 68

7.3.3.3 Sea Temperature ............................................................................. 68

7.3.3.4 Wind Speed and Direction .............................................................. 69

7.3.4 Data Quality Control and Filtering ................................................................. 71

7.3.5 Comparison of Direct Covariance Flux to COARE-Calculated Flux ............. 74

7.3.5.1 Wind Stress ..................................................................................... 75

7.3.5.2 Sensible Heat Flux .......................................................................... 76

7.3.5.3 Latent Heat Flux .............................................................................. 76

7.3.5.4 Discussion ....................................................................................... 76

7.3.6 COARE Data Analysis Summary ................................................................... 81

8. Conclusions and Recommendations ........................................................................................ 83

8.1 Measurement-Related Recommendations ........................................................................ 83

8.2 Data Analysis-Related Recommendations ........................................................................ 84

Literature Cited ............................................................................................................................. 85
vii 

FIGURES

Figure 1. Location of Chevron Platform, ST-52 (SPLL1). ........................................................ 8

Figure 2. The ST-52 platform complex with approximate equipment locations. ...................... 8

Figure 3. Instruments mounted on the platform roof of ST-52B. .............................................. 9

Figure 4. Locations of the IR skin temperature sensor on mid-deck and shortwave and 

longwave radiation sensors on upper deck roof of ST-52B. ...................................... 9

Figure 5. Catwalks connecting the three platforms.. ................................................................ 10

Figure 6. Location of the underwater float temperature sensor.. ............................................. 11

Figure 7. Shortwave and longwave radiation and surface meteorological tower on 

Chevron’s ST-52B oil platform in the Gulf of Mexico. ........................................... 13

Figure 8. Wind speed (WS), wind direction (WD), infrared (longwave) radiation (IR), 

downwelling (shortwave) radiation (DR), temperature (T), and relative humidity 

(RH) from March 2, 2012, through March 6, 2012. ................................................. 14

Figure 9. Components of floating temperature sensor. ............................................................ 16

Figure 10. Float temperature on Chevron’s ST-52B oil platform in the Gulf of Mexico. ........ 16

Figure 11. Sea surface temperature, skin temperature, and float temperature from March 2, 

2012, through March 6, 2012. .................................................................................. 17

Figure 12. Flux package located under the AB Bridge. . .......................................................... 19

Figure 13. Specific humidity, CO2 concentration, and air temperature from the AB Bridge 

flux package on October 5, 2010. ............................................................................. 20

Figure 14. Microwave radiometer mounted on the platform deck. ............................................ 22

Figure 15. Temperature, relative humidity, and liquid water from the microwave radiometer 

from January 11, 2011. ............................................................................................. 22

Figure 16. ASC mini-sodar on Chevron’s ST-52B oil platform in the Gulf of Mexico. ........... 23

Figure 17. Mini-sodar wind profiles from January 11, 2011. .................................................... 23

Figure 18. Vaisala CL31 ceilometer located on Chevron’s ST-52B oil platform in the Gulf 

of Mexico. ................................................................................................................. 25
viii

Figure 19. Ceilometer backscatter, mixed layer height, and cloud height from January 11, 

2011. ......................................................................................................................... 25

Figure 20. Wave data images posted to a website in real time. ................................................. 28

Figure 21. Picture of Connex boxes on Chevron’s ship. ............................................................ 31

Figure 22. Major periods of missing, invalid, or suspect data. ................................................ 40

Figure 23. Mini-sodar winds from February 10, 2012. Wind barbs are colored by vertical 

wind speed.. .............................................................................................................. 43

Figure 24. Surface wind (top) and precipitation (bottom) data on February 10, 2012. . .......... 44

Figure 25. Microwave radiometer temperature sounding (blue), Lake Charles temperature 

sounding (red), and Slidell temperature sounding (green) for January 11, 2011, 

1200 UTC (left) and January 12, 2011, 0000 UTC (right). ...................................... 45

Figure 26. Microwave radiometer temperature sounding (blue), Lake Charles temperature 

sounding (red), and Slidell temperature sounding (green) for December 6, 2011, 

1200 UTC (left) and December 7, 2011, 0000 UTC (right). .................................... 46

Figure 27. Visible satellite imagery over the northern Gulf of Mexico for January 11, 2011, 

at 1431 UTC. ............................................................................................................ 47

Figure 28. Ceilometer backscatter with cloud bases, cloud tops, and mixing heights in 

meters above platform level (apl). ............................................................................ 47

Figure 29. Surface meteorological data from the platform deck for June 23 through June 28, 

2011, including (top to bottom) wind speed (WS) and wind direction (WD); 

infrared radiation (IR) and downwelling radiation (DR); relative humidity (RH) 

and pressure (P); and temperature (T) and rain (RAIN).. ......................................... 48

Figure 30. Skin temperature from the infrared temperature sensor (red line) and fixed sea 

surface temperature from LSU for 11/15/2010 through 12/9/2010. ......................... 49

Figure 31. Wave heights and maximum wave heights for September 14 through September 

18, 2011. ................................................................................................................... 50

Figure 32. Illustration of unfiltered (blue) and filtered (red) specific humidity data from the 

AB Li-Cor 7500 sensor. ............................................................................................ 50

Figure 33. Mini-sodar wind data on January 11, 2011. ............................................................. 56

Figure 34. Microwave radiometer temperature, relative humidity, and liquid water data on 

January 11, 2011. Times are in UTC. ...................................................................... 57

Figure 35. Visible satellite image taken at 1431 UTC on January 11, 2011. ............................. 57
ix

Figure 36. Ceilometer backscatter data, cloud base and top heights, and mixing heights on 

January 11, 2011. ...................................................................................................... 58

Figure 37. COARE-calculated sensible heat flux (Hs) and air-sea temperature (T) difference 

(top), and COARE-calculated latent heat (Hl) and air-sea specific humidity (Q) 

difference (middle), and COARE-calculated streamwise stress and wind speed 

(bottom). . ................................................................................................................. 58

Figure 38. Seasonal averages of measured (sodar) versus model (NAM) winds. ..................... 60

Figure 39. Seasonal average diurnal profile of boundary layer winds as measured by the 

mini-Sodar. ............................................................................................................... 61

Figure 40. Location of the AB and AC flux packages on the platform. .................................... 62

Figure 41. Close-up view of the AB flux package. .................................................................... 63

Figure 42. Meteorological conditions from the platform during 2011. ..................................... 65

Figure 43. Meteorological conditions from the platform during 2012. ..................................... 66

Figure 44. Time series of air temperatures for the two sonic anemometers (blue and red), 

and the temperature measurements from the roof of the B platform (magenta and 

black). ....................................................................................................................... 67

Figure 45. Relative humidity from STI (magenta) and LSU (black) instruments. .................... 68

Figure 46. Time series of air and sea temperatures from STI (blue) and LSU (magenta) 

instruments. ............................................................................................................... 69

Figure 47. Wind speed residuals as a function of STI wind direction (1 m/s bins). 

Horizontal dash lines indicate ±0.5 m/s. ................................................................... 70

Figure 48. Wind direction residuals as a function of STI wind direction (5-degree bins). 

Horizontal dash lines indicate ± 0.5 degrees. ........................................................... 71

Figure 49. A, B, and C platform orientation relative to true north............................................. 72

Figure 50. U (blue) and W (red) spectra from the AB flux package at 0 and 6 UTC on 

August 9, 2011. ......................................................................................................... 73

Figure 51. Illustration of unfiltered (blue) and filtered (red) specific humidity data from the 

AB Li-Cor 7500 sensor. ........................................................................................... 74

Figure 52. Top panel: BULK (calculated) stress (Taub) versus measured streamwise stress 

(Tauc) for the AB and AC bridges combined. Lower panel: Same flux, but binaveraged and plotted as a function of 10-m neutral wind speed. .............................. 78
x 

Figure 53. Top panel: BULK (calculated) sensible heat flux (Hsb) versus measured 

sensible heat flux (Hsc) for the AB and AC bridges. Lower panel: Same flux, 

but divided by wind speed and plotted as a function of sea-air temperature 

difference. ................................................................................................................. 79

Figure 54. Top panel: BULK (calculated) latent heat flux (Hlb) versus measured latent heat 

flux (Hlc) for the AB and AC bridge. Lower panel: Same flux, but divided by 

wind speed and plotted as a function of sea-air specific humidity difference. ......... 80
xi

TABLES

Table 1. Measurements taken during the field study ................................................................ 6

Table 2. Platform description .................................................................................................. 27

Table 3. On-platform (internal) and off-platform (external) data sources used during data 

validation .................................................................................................................. 33

Table 4. Data completeness statistics by instrument type ...................................................... 36

Table 5. Data capture statistics by instrument type ................................................................ 37

Table 6. Major downtimes of the instruments ........................................................................ 38

Table 7. On-platform (internal) and off-platform (external) data sources used during data 

validation .................................................................................................................. 42

Table 8. Data file names ......................................................................................................... 52

Table 9. Fields located in data files ........................................................................................ 53

Table 10. Parameter names and descriptions ............................................................................ 53

Table 11. Flux package sensor heights and sampling rates ...................................................... 63

1 

1. INTRODUCTION

Researchers and technicians from Sonoma Technology Inc. (STI), University of Colorado at 

Boulder (CU), the National Oceanic and Atmospheric Administration (NOAA), and Louisiana 

State University (LSU), with scientific input from the Bureau of Ocean Energy Management 

(BOEM) and a Scientific Review Board,

1

 developed an atmospheric boundary layer 

environmental observations program on an offshore platform in the Gulf of Mexico. 

Specifically, in October 2010, meteorological and oceanographic sensors were deployed for 

approximately 18 months on a Chevron service platform about 12.4 miles (20 km) south of 

Terrebonne Bay, Louisiana, to collect atmospheric boundary layer and sea surface data. The 

ocean depth at this location was 20.5 m. Observations from this project consist of surface 

meteorology; marine boundary layer winds; vertical profiles of atmospheric temperature, relative 

humidity, and liquid water; cloud base heights; atmospheric boundary layer height; ocean 

temperature; ocean surface temperature; ocean wave height and frequency; downwelling 

(shortwave) solar and infrared (longwave) radiation; and lower atmospheric boundary layer 

momentum and heat fluxes. The primary goals of this program were to collect and deliver data 

to support future work to (1) improve our understanding of boundary layer processes and air-sea 

interaction over the Gulf of Mexico; (2) improve small and regional-scale meteorological and air 

quality modeling; and (3) provide a framework for advanced offshore measurements to support 

future needs such as emergency response, exploration and lease decisions, wind energy research 

and development, and meteorological and air quality forecasting. 

This project is important because of the large industrial presence in the Gulf, the sizeable 

regional population nearby, and the recognized need for precise and timely pollutant forecasts. It

yielded an unprecedented set of boundary layer measurements over the Gulf of Mexico that 

captured a range of meteorological and oceanographic interactions and processes which occur 

over all seasons. 

This report provides information on the 

• Project objectives (Section 2) 

• Instruments, and their deployment and operations (Section 3) 

• Data availability (Section 4) 

• Data processing and quality control (Section 5) 

• Data formats (Section 6) 

• Preliminary data analyses (Section 7)2

• Conclusions and recommendations (Section 8) 

 

1 The Scientific Review Board consisted of three members: Allen White and Richard Crout from NOAA, and 

Kristina Kasoros from NOAA (retired). 

2 While the primary objective of this program was to provide data for future analysis and modeling, preliminary data 

analyses were completed. 

3 

2. PROJECT OBJECTIVES 

The overall objective of this study was to collect meteorological data that can be used to better 

characterize the atmospheric boundary layer over the Gulf of Mexico. This information will be 

used in future work to improve meteorological and air quality modeling along the Gulf Coast. A 

critical component of the study was to fill a data gap between surface (platform) measurements 

and observations at about 200 m msl as identified during the BOEM Atmospheric Boundary 

Layer (ABL) Study (MacDonald et al. 2004). As part of the ABL study, the project team and 

BOEM also identified other measurements and analyses needed to further improve modeling. 

This study addressed these measurement needs by providing the data necessary for subsequent 

research and analysis. Details on specific project objectives are described in the following

paragraphs. 

2.1 DATA TO IMPROVE PREDICTIONS OF BOUNDARY LAYER PARAMETERS

USING COARE3

During the 1998–2001 BOEM field program (Vaisala Meteorological Systems and Sonoma 

Technology 2002), no direct flux measurements were made from which to verify the Coupled 

Ocean Atmosphere Response Experiment (COARE) output. In addition, a few parameters (wave 

height and frequency, downwelling shortwave and longwave solar radiation, and precipitation) 

required by COARE were calculated rather than measured. Therefore, the accuracy of the 

COARE algorithm was never verified against observations, and only limited improvements were 

made to COARE for application in the Gulf of Mexico. In this study, all parameters required by 

COARE were directly measured to reduce uncertainty in the COARE calculations. In addition,

direct measurements of boundary layer parameters were made. Wind profiles were measured in 

the lowest few hundred meters; fluxes and turbulence measurements occurred at a fixed height. 

These direct measurements of boundary layer parameters can be compared to the calculated 

parameters and thus will lead to improvements to COARE. These measurements can also be 

used to develop and improve other boundary layer algorithms used in prognostic models. 

2.2 DATA TO IMPROVE APPLICABILITY OF COARE THROUGH THE USE OF 

ROUTINE MEASUREMENTS

Not all meteorological parameters used by COARE are routinely measured at standard sites, thus 

these parameters must be calculated by COARE. For example, Coastal-Marine Automated 

Network (CMAN) stations do not measure sea surface skin temperature, shortwave downwelling 

radiation, and longwave downwelling radiation, or precipitation, resulting in uncertainty in the 

COARE output for these types of sites. To reduce this uncertainty, this study included 

measurements similar to those commonly available at routine sites (like CMAN sites) along with 

direct measurements of all required inputs and many of the outputs of COARE. Subsequent 

calculations of boundary layer parameters (fluxes, etc.) using the routinely available data can be

 

3 COARE is the acronym for the Coupled Ocean Atmosphere Response Experiment. However, based on current 

convention, its use throughout this report refers to the algorithm developed during that experiment, the COARE 

marine boundary layer algorithm (Fairall et al. 1996; Fairall et al. 2003). COARE uses routine oceanographic 

and meteorological measurements to calculate boundary layer parameters such as stress, heat, and momentum 

fluxes.
4 

compared to the measurements made during this study. Future refinements to the COARE 

algorithm based on the findings can increase its applicability to data collected at other sites.

2.3 DATA TO FURTHER IMPROVE MIXING HEIGHT SCHEMES USED BY MODELS

Much work has been done to improve meteorological and air quality modeling in an offshore 

environment, such as the Gulf of Mexico’s. For example, BOEM-sponsored work (Hanna et al. 

2006) has been adopted in the widely used U.S. Environment Protection Agency (EPA) 

CALMET/CALPUFF modeling system. However, there are still issues with models’ ability to 

properly represent the evolution of boundary layer heights, especially in coastal areas. The 

measurements that were collected in this study can be used to validate and improve schemes 

used by CALMET and other frequently used models to estimate mixing height.

2.4 DATA TO IMPROVE REGIONAL-SCALE METEOROLOGICAL MODEL 

PREDICTIONS

Past analyses have shown significant overwater aloft wind differences between weather forecast 

models and observed aloft winds over the Gulf of Mexico (Knoderer et al. 2004). During this

study, we collected data that can be used to evaluate forecast models and set the stage for 

working with the National Center for Environmental Prediction (NCEP) to better understand and 

address any model biases. The results will also provide BOEM guidance for incorporating

regional-scale model predictions in BOEM’s modeling efforts.
5 

3. INSTRUMENTS AND THEIR DEPLOYMENT, OPERATIONS, AND 

MAINTENANCE 

To collect the data necessary to address the objectives presented in Section 2, a wide range of 

highly sophisticated instruments and supporting equipment were deployed on Chevron Oil 

Platform ST-52 about 12.4 miles (20 km) off the coast of Louisiana. The instruments, which are 

listed in Table 1, were operated from October 4, 2010, through April 1, 2012. A map showing 

the location of the platform is shown in Figure 1. Figure 2 shows the platform and the locations 

where the instruments were mounted. Figures 3 through 6 show the instruments on the platform 

complex. The microwave radiometer, minisodar, ceilometer, and surface meteorological tower 

were mounted on the main platform’s (ST-52B) upper deck, which is at approximately 37 m msl. 

The sea skin temperature sensor was mounted on a rail of a mid-level deck on the southern 

corner of platform B at approximately 25 m msl. The deck extended over the water farther than 

the lower decks, yielding an unobstructed view of the water. Two sets of flux instruments were 

used; an instrument package was mounted vertically downward from each of the two catwalks

that connect the platform complex. The first package was mounted under the bridge between the 

A and B platforms at approximately 11 m msl, and the other was mounted beneath the bridge 

that connects the A and C platforms at approximately 14 m msl. Depending on wind direction, 

this placement provided flux measurements that were often free of platform effects on the 

horizontal and vertical motion and were within the atmospheric surface layer. A floating

temperature sensor was mounted on the platform’s southeast leg; the sensor floated at 

about -0.2 m msl. Wave height and frequency measurements were taken adjacent to the

southwest leg of platform B by LSU as part of a separate project (WAVCIS), but the data have 

been made available to this project.

Details of the measurements follow the figures and are organized by atmospheric or 

oceanographic feature measured. 
6 

Table 1. 

Measurements taken during the field study

Parameter

Instrument and Measurement Characteristics

Instrument

Manufacturer

Measurement 

Height(s)

Above Mean 

Sea Level (m)

Vertical Resolution 

(m) Frequency

Upper-air

Wind 

Profiles

Mini-sodar

Atmospheric 

Systems 4000

57–237 10 Sub-hourly

Temperature 

Profiles

Microwave 

radiometer

Radiometrics 

MP-3000A

87–10,000

50 below 500 m

100 between 500 

m and 2000 m

250 above 2000 m

Sub-hourly 

and hourly

Humidity 

Profiles

Microwave 

radiometer

Radiometrics 

MP-3000A

87–10,000

50 below 500 m

100 between 500 

m and 2000 m

250 above 2000 m

Sub-hourly

Mixing 

Heights

Ceilometer

Vaisala CL31

39–7,737

90–10,000

5

90–100

Sub-hourly 

and hourly

Cloud Base 

Height

Ceilometer

Vaisala CL31

39–7,737

90–10,000

5

90–100

Sub-hourly 

and hourly

Near Surface

Winds

Vane and 

propeller 

anemometer

RM Young 5305

48 NA Sub-hourly 

and hourly

Temperature Thermometer

Vaisala HMP45AC 46 and 47 NA Sub-hourly and hourly

Precipitation Texas Instruments 

TR525 38 NA Sub-hourly and hourly

LSU 

Temperature

Thermometer

Vaisala HMP155 46 and 47 NA Hourly

Humidity Hygrometer

Vaisala HMP155 46 and 47 NA Sub-hourly and hourly

LSU 

Humidity

Hygrometer

Vaisala HMP155 46 and 47 NA Hourly

LSU 

Pressure

Barometer

Vaisala PTB101

14 but 

corrected to 

msl

NA Hourly

Downwelling 

Shortwave 

Radiation

Pyranometer

Eppley PSP 39 NA Sub-hourly 

and hourly

Downwelling 

Longwave 

Radiation

Pyrgeometer

Eppley PIR 39 NA Sub-hourly 

and hourly

LSU 

Visibility Belfort C100 39 NA Hourly
7 

Table 1. Measurements taken during the field study (continued)

Parameter

Instrument and Measurement Characteristics

Instrument

Manufacturer

Measurement 

Height(s)

Above Mean 

Sea Level (m)

Vertical 

Resolution (m) Frequency

Flux

Flux 

(AB catwalk)

Sonic anemometer

Gill WindMasterPro,

Infrared hygrometer 

with gas analyzer

Li-Cor LI-7500

10.2, 10.8a NA Sub-hourly

Flux 

(AC catwalk)

Sonic anemometer

Gill WindMasterPro,

Infrared hygrometer 

with gas analyzer

Li-Cor LI-7500

11.6, 12.6b NA Sub-hourly

Ocean

Wave 

measurements 

Digiquartz Pressure 

Transducer

Paroscientific 

25A-158

ADCP Current Meter

RD Instruments 

Workhorse Sentinel

Sea level but 

variable 

depending 

on tides and 

waves

NA Sub-hourly

Fixed-mount 

underwater 

temperature

Thermistor

YSI

-0.60; but 

variable 

depending 

on tides and 

waves

NA Sub-hourly 

and hourly

Underwater 

temperature

Float sensor

STI/LSU/NOAAdeveloped-0.2 NA Sub-hourly 

and hourly

Sea skin 

temperature

I.R. Thermometer

Everest Interscience, 

Inc. and Heitronics 

KT15.85-IIP 

IR sensorc

Sea level but 

mounted at 

~25 m msl

NA Sub-hourly 

and hourly

a Measurements taken at 2238 UTC August 5, 2012.

b Measurements taken at 2243 UTC August 5, 2012.

c Everest sensor was replaced with Heitronics on February 24, 2012.
8 

Figure 1. Location of Chevron Platform, ST-52 (SPLL1).

Figure 2. The ST-52 platform complex with approximate equipment locations. Most equipment is 

located on platform ST-52B in the foreground; ST-52A is on the right and ST-52C is in the 

background.
9 

Figure 3. Instruments mounted on the platform roof of ST-52B. 

Figure 4. Locations of the IR skin temperature sensor on mid-deck and shortwave 

and longwave radiation sensors on upper deck roof of ST-52B. 
10 

Figure 5. Catwalks connecting the three platforms. This photo was 

taken from a mid-level deck of platform ST-52B. Flux 

measurements were taken at about the middle of each 

catwalk.
11 

Figure 6. Location of the underwater float temperature sensor. Only the top of the 

housing can be seen; the sensor is floating in a vertical pipe mounted on 

the leg of the platform. The LSU sea-state measurements are made in 

close proximity to this location.

3.1 SURFACE METEOROLOGICAL MEASUREMENTS 

3.1.1 Background

An important element of this project is the collection of measurements for use in the COARE 

marine boundary layer algorithm (Fairall et al. 2003). The COARE algorithm can be used to 

calculate fundamental boundary layer scaling parameters such as the surface roughness length, 

the friction velocity, and the Monin-Obukhov length, in addition to the latent heat flux and the 

sensible heat flux. From these parameters, the mixing depth (h) and the vertical profiles of wind 

speed, temperature, and water vapor mixing ratio can be estimated for input to transport and 

dispersion models. 

3.1.2 Approach

We collected an “ideal” set of measurements for use by COARE. Note that when COARE does 

not have the ideal set of measurements, it calculates them, leading to greater uncertainty in the 

results. Our measurement strategy was to enable comparisons of observed boundary layer 

measurements to COARE-calculated boundary layer data, using both the ideal and minimum sets 

of measurements required by COARE. These results can then be used to refine the COARE 
12 

algorithm for the Gulf of Mexico region and improve its output for future model applications that 

use data from sites with the minimum set measurements required by COARE (such as 

measurements collected by NWS buoys). Initial COARE evaluation and refinement work was 

performed in this study and results are presented in Section 7. 

The COARE-required measurements that we collected consisted of 

• wind speed, 

• air temperature, 

• relative humidity, and

• measurement heights for the surface meteorological measurement instruments. 

The heights of the sensors are needed for input into the COARE. These parameters consist of

measurement heights for wind, temperature, and relative humidity; and depth of the sea skin 

temperature sensor.

The additional measurements made to produce the ideal data set of additional measurements for 

COARE consisted of 

• downwelling shortwave and longwave solar radiation, 

• precipitation, 

• sea skin temperature, and

• wave height and frequency. 

A tipping bucket rain gauge was installed on the platform deck. It measured rainfall as total 

rainfall per minute so that an hourly rainfall rate could be determined. An Eppley Precision 

Spectral Pyranometer (PSP) First Class Radiometer was installed on a mounting bracket atop a 

short (3 m) tower on the platform deck to measure shortwave downwelling radiation (see 

Figure 7). Next to it an Eppley Precision Infrared Radiometer (PIR) Pyrgeometer was installed 

to measure longwave radiation. In addition, as part of a separate project, LSU measured wave 

height, wave frequency, and water current and took duplicate meteorological measurements on 

the platform deck. The LSU meteorological measurements consisted of wind speed, wind gusts, 

wind direction, air temperature, sea level barometric surface pressure, and visibility. Note that 

the LSU meteorological data use the NWS system of measurement convention of taking 

measurements for 2 minutes at the top of the hour and reporting the measurement as an hourly 

value, while the primary meteorological data were collected as continuous (once per 10 second) 

one-minute averages and accumulated into hourly averages. The data from the LSU 

measurements are included in the data delivery. Figure 7 shows the surface meteorological 

tower containing the anemometers and the temperature, relative humidity, downwelling 

shortwave and infrared (longwave) radiation, and skin temperature sensors. Figure 8 shows a 

sample of the surface meteorological data.
13 

Figure 7. Shortwave and longwave radiation and surface meteorological tower on Chevron’s 

ST-52B oil platform in the Gulf of Mexico.
14 

Figure 8. Wind speed (WS), wind direction (WD), infrared (longwave) radiation (IR), downwelling 

(shortwave) radiation (DR), temperature (T), and relative humidity (RH) from March 2, 2012, 

through March 6, 2012.

3.2 OCEANOGRAPHIC MEASUREMENTS 

3.2.1 Background

As described in the project objectives, there is a need to improve the applicability of COARE 

when using only routine measurements as inputs, especially measurements collected at standard 

buoys. To that end, measurements collected by standard buoys, such as wave height and period 

and underwater temperature, were collected along with other oceanographic measurements.

3.2.2 Approach

We obtained measurements of wave spectra, wave height, wave period, and near-surface water 

temperature that were being collected as part of a separate study (WAVCIS) being conducted by 

LSU. LSU made these measurements available for this study. The oceanographic sensors are as 

follows: a Digiquartz pressure transducer (Paroscientific model 245A-158), an ADCP current 

meter (RD Instruments model Workhorse Sentinel, 600 KHz transducer with a 50 m pressure 

sensor), and an electronic thermometer (locally made with a YSI Thermistor bead). The pressure 
15 

sensor and the ADCP sensors provided for the measurements of directional waves, near-bottom 

to near-surface water column current speed and current direction (in 1-m bins), and the two water 

level measurements. The near-surface water temperature and pressure sensors were positioned 

approximately 4.5 m below mean water level. This depth minimized the depth-attenuation of the 

high-frequency wave signals during relatively calm conditions but was deep enough to measure 

most of the large wave conditions that typically occurred during frontal passages. The ADCP 

sensor was mounted about 2.1 m off the bottom of the platform (under water) and projected 

about 1.5 m out from the rig leg, so that the beams from the ADCP cleared the sides of the 

platform.

A custom floating thermometer was fabricated to measure near-surface water temperature (see 

Figure 9). A 20-ft (6.1 m) length of 4-in (0.1 m) PVC pipe was capped at both ends, perforated 

with a series of slots that ran the length of the pipe, sheathed with anti-foul copper foil, and 

mounted on a leg of the platform perpendicular to the surface of the sea. One end of a coiled 

(1/4-in; 0.635 cm) air hose, with a 2-conductor 24 gauge wire inserted through it, was connected 

to the top of the pipe. The other end was attached to a copper float consisting of a sealed 20-cmlong copper tube that has a thermistor potted inside of it. The float allows the sensor to remain 

suspended approximately 20 cm below the surface. The perforations in the pipe are made of 

fairly narrow vertical slots throughout the pipe applied to dampen the wave action, resulting in a 

somewhat smaller change in the water level while still allowing for a flushing action to occur 

within the pipe. The air coil was sealed at both ends so that it too provided some floatation while 

facilitating the movement of the float up and down the length of the pipe. The mounted float

sensor is shown in Figure 10; sample data are shown in Figure 11. 

An IR thermometer was installed to measure the surface of the sea using a non-intrusive 

measurement method. An Everest Interscience, Inc., 4000L Sea-Therm Infrared Temperature 

sensor fitted with an insulated baffle was initially installed. It proved not to be waterproof 

despite being redesigned and replaced twice by the manufacturer. The data from this sensor are

suspect because the sensor frequently displayed significant temperature excursions. The sensor 

was sent back to the manufacturer for a third modification, but the engineer was unable to 

complete the work. Ultimately, a Heitronics KT15.85-IIP IR sensor was purchased and installed 

on the platform. This sensor operated very well. In addition, as part of the oceanographic 

sensors, a thermistor mounted on the tower leg at a fixed position of approximately -1 m msl

provided a secondary near-surface sea temperature measurement. 
16 

Figure 9. Components of floating temperature sensor. 

Figure 10. Float temperature on Chevron’s ST-52B oil platform in the Gulf of Mexico.
17 

Figure 11. Sea surface temperature, skin temperature, and float temperature from March 2, 2012, 

through March 6, 2012.

3.3 DIRECT FLUX MEASUREMENTS

3.3.1 Background

The transfer of momentum, heat, and mass between the atmosphere and ocean surface is 

accomplished by turbulent processes. Atmospheric mixing processes in the lower marine 

boundary layer (that portion of the atmosphere closest to the sea surface) are driven by 

mechanical and thermal forcing, and these turbulent eddies extend from the surface up to the 

height of the boundary layer (typically between 500 m and 2000 m). The energy contained 

within these eddies cascades down to smaller and smaller time and spatial scales; it is within this 

large breadth of scales that the turbulence is able to make energy transfers to and from the 

surface of the water. These fundamental transfer processes determine the boundary conditions at 

the sea surface, and any effort to model atmospheric dispersion requires determination of the 

source strength (or sink strength) of heat and momentum at the sea surface. Direct measurement 

of turbulence statistics and fluxes required the use of specialized instrumentation and analysis 

techniques, which are briefly outlined below. 

3.3.2 Approach

Turbulent flux measurements were taken with an ultrasonic anemometer/thermometer (sonic 

anemometer). The operating principle of this instrument exploits the fact that a sound wave is 

carried with a moving fluid (the wind), and the small-scale wind fluctuations along three axes 

can be computed from the time-of-flight of an ultrasonic pulse. The frequency of wind velocity 

measurement was 10 times per second (Hz), and this response rate enabled measurement of the 

turbulence and turbulent fluxes on time and spatial scales sufficient to calculate the statistics with 

a high degree of accuracy. The sonic anemometer also enabled a measurement of small-scale 

temperature fluctuations (the speed of sound is proportional to the temperature of the medium), 

and we computed turbulent air temperature statistics from the time-flight estimates. By 

computing the covariance of the various measured combinations of vertical velocity, horizontal 
18 

velocity, and temperature, we made direct measurements of the fluxes of momentum (which 

have two components—streamwise and cross-stream) and sensible heat. The sonic anemometer 

also served to provide an additional measurement of the mean wind speed and direction. 

Latent heat flux measurements were taken by using a fast-response infrared hygrometer and gas 

analyzer on time and spatial scales coincident with those of the sonic anemometer. This 

instrument was deployed as close to the sonic anemometer as possible without disturbing the 

flow of air through the sampling volume. This device operates on the principle that water vapor 

absorbs infrared radiation at a well-defined frequency. By simultaneously measuring the 

absorption of radiation at an adjacent non-absorbing reference frequency, the instrument is able 

to determine small-scale (10-Hz) fluctuations in absolute humidity. Latent heat flux was 

calculated by computing the covariance of the vertical velocity fluctuations from the sonic 

anemometer and the absolute humidity measurements from the hygrometer. 

The same principle of the attenuation of a selected frequency of infrared light was used for the 

measurement of small-scale fluctuations of carbon dioxide (CO2) in the near-surface atmosphere. 

The gas analyzer thus enabled estimates of the turbulent fluxes of moisture and CO2. Although 

not requested by BOEM, CO2 measurements were part of the flux measurement package and 

were included. These measurements may be helpful for other potential applications related to 

climate change. Figure 12 shows the flux package installed under the AB Bridge. A second flux 

package was installed under the AC Bridge. Figure 13 provides a sample of the data collected.

The direct covariance or eddy-correlation flux measurement technique was used to calculate 

fluxes. In essence, turbulent fluctuations of vertical velocity were computed with streamwise 

horizontal velocity fluctuations (to obtain wind stress or momentum flux), with the temperature 

fluctuations (to obtain the sensible heat flux), and with the small-scale fluctuations of H2O and 

CO2 (to obtain the latent heat flux and CO2 flux, respectively). By averaging over a time scale 

sufficient to capture statistics on length scales as large as the marine boundary layer, while 

carefully avoiding averaging through potential complicating factors such as mesoscale 

atmospheric disturbances, we computed the turbulent fluxes and other statistics. Details on the 

fluxes are provided in Section 7. 
19 

Figure 12. Flux package located under the AB Bridge. The flux 

package includes a Gill sonic anemometer and a 

Li-Cor hygrometer and CO2 gas analyzer.

Sonic Anemometer

LI-COR Hygrometer 

and Gas Analyzer
20 

Figure 13. Specific humidity, CO2 concentration, and air temperature from the AB

Bridge flux package on October 5, 2010.
21 

3.4 VERTICAL PROFILES OF TEMPERATURE, WIND, AND HUMIDITY 

MEASUREMENTS

3.4.1 Background

Vertical profiles of temperature, wind, and humidity are needed for understanding and 

characterizing boundary layer processes and for model evaluation, validation, initialization, and 

nudging. The profiles can be used to quantify the time variability and evolutionary processes of 

atmospheric stability and buoyancy generated by variations in both temperature and moisture; 

boundary layer depth; vertical wind shear; and transport and dispersion processes throughout the 

depth of the boundary layer. 

3.4.2 Approach

We used a Radiometrics microwave profiler (radiometer) to measure vertical profiles of 

temperature and humidity and used an Atmospheric Systems Corporation (ASC) mini-sodar to 

measure wind profiles. 

The microwave profiler is a hyperspectral microwave receiver (radiometer). This instrument 

observes microwave energy emitted by the atmosphere, and Planck’s law allows conversion of 

the microwave energy to brightness temperatures. Algorithms convert radiometer observations 

to temperature, humidity, and liquid water profiles. The profiles were measured from about 50 

to 10,000 m apl. The vertical resolution of the data from the microwave radiometer is about 

50 m below 500 m apl, 100 m between 500 and 2000 m apl, and 250 m above 2000 m apl. The 

radiometer mounted to the platform deck is shown in Figure 14, and a sample of the data 

collected is shown in Figure 15. 

The mini-sodar consists of a single phased-array antenna that uses acoustic pulses (i.e., chirps or 

beeps) to measure the profile of the three-dimensional wind vector in the lower atmospheric 

boundary layer (Crescenti 1997). The phased-array mini-sodar antenna consists of a phased 

array of emitters (speakers), which acts to steer the acoustic pulses so that the individual 

components of the wind (two horizontal and one vertical; or u, v, and w) can be resolved. After 

each pulse, the mini-sodar “listens” for the backscattered sound and determines the wind speed 

from the Doppler shift in the acoustic frequency. 

Background noise measurements taken on the selected platform, ST-52B, indicated the need to 

provide additional soundproofing for the instrument to prevent “clutter” or interference by 

ambient noise. Atmospheric Systems Corporation (ASC) designed a soundproof enclosure in 

which to encase the sodar. The mini-sodar (and its attendant soundproofing material) mounted 

to the platform deck is shown in Figure 16, and a sample of the data is shown in Figure 17. 
22 

Figure 14. Microwave radiometer mounted on the platform 

deck.

Figure 15. Temperature, relative humidity, and liquid water from the microwave radiometer from 

January 11, 2011.

Microwave Radiometer

Strong RH 

gradient 

near the top 

of the cloud 

layer

Increased 

levels of liquid 

water in the 

cloud layer
23 

Figure 16. ASC mini-sodar on Chevron’s ST-52B oil platform in the Gulf of Mexico. 

Figure 17. Mini-sodar wind profiles from January 11, 2011.
24 

3.5 MIXING HEIGHT MEASUREMENTS

3.5.1 Background

The surface-based mixed layer (mixing height) is the portion of the planetary boundary layer 

above the surface through which vigorous vertical mixing of heat, moisture, momentum, and 

pollutants occurs (Holzworth 1972). Mixing heights are important to air quality because they 

control the vertical dispersion of pollutants, which directly influences their concentrations. Air 

quality data analysis and modeling benefit greatly from temporally and spatially resolved 

observations of boundary layer depth, especially over the Gulf of Mexico, where meteorological 

models tend not to accurately represent mixing heights (MacDonald et al. 2003). 

Historically, mixing depths have been estimated from hourly surface temperature observations, 

twice daily National Weather Service rawinsonde data (Holzworth 1972), and observational 

models that use rawinsonde data (Berman et al. 1997). However, in the offshore environment, 

rawinsondes are too expensive to use over an extended period of time and do not provide the 

continuous data needed for improving air quality and meteorological modeling. Other viable 

alternatives include radar wind profilers and ceilometers. 

3.5.2 Approach

A Vaisala CL31 ceilometer was deployed to collect data from which subhourly mixing heights 

were estimated. Figure 18 shows the ceilometer on the platform. The selection of this 

ceilometer for inclusion in this study was based upon its performance in a previous near-shore 

study in which it showed its ability to detect mixing heights in a marine environment (Gilroy 

2008), and because it had algorithms to automatically detect mixing heights. It had the added 

advantage of providing cloud-height data. The CL31 works by vertically emitting an eye-safe 

laser beam and detecting the reflection of the beam by particles with a receiver. The continuous 

reflectivity (or backscatter) data were available from about 2 to 7700 m apl; for boundary layer 

characterization, the sensitivity was adequate up to 3000 m apl. Subhourly boundary layer 

heights with a vertical resolution of 5 m were derived from the backscatter data using the 

algorithm in the manufacturer’s software. Sample data from the ceilometer used in the current 

study are shown in Figure 19. 
25 

Figure 18. Vaisala CL31 ceilometer located 

on Chevron’s ST-52B oil platform 

in the Gulf of Mexico.

Figure 19. Ceilometer backscatter, mixed layer height, and cloud height from 

January 11, 2011.

Ceilometer

Cloud bases

Cloud tops 

and mixing 

heights
26 

3.6 SITING AND INFRASTRUCTURE

This section describes how our team conducted this study in a safe manner; obtained the use of a 

platform suitable for project objectives; developed the infrastructure needed to support operation 

of the instruments; and delivered, installed, and operated the instruments. 

3.6.1 Siting

We selected a platform by identifying numerous potential sites that could meet the scientific 

objectives of the project. Once potential sites were identified, we were able to work with 

platform operators to determine which platforms could accommodate the equipment for up to 

two years. With a refined list of potential platforms, we determined which platforms were best 

suited for the project considering logistical and scientific issues. In particular, based on 

experience, we sought to find a platform with the following attributes: 

• Space for the equipment.

• A platform deck lower than 50 m msl. This criterion was critical to achieving 

the science objectives, including measurements of fluxes, and to satisfy 

COARE requirements for measurements below 50 m to calculate boundary 

layer parameters.

• Readily available power.

• Platform operators on site. Platform operators were very helpful in providing 

support for small maintenance items, such as rebooting computers. This 

helped increase data recovery while conserving resources. 

• Little or no ambient noise to interfere with the mini-sodar. There was some 

noise identified, so we mitigated it using sound-insulating methods.

• No large structures (building, cranes, etc.) in locations that could have 

interfered with measurements.

• Ability to mount equipment to the deck. 

• No active wells or space for the equipment at a distance from wells that 

required little or no equipment modifications to meet explosion-proofing 

requirements.

• Space to house staff during installation, maintenance, and deinstallation visits. 

This feature limited time spent traveling to and from the platform during 

multi-day trips. 

After narrowing the list of site candidates by the criteria listed above, we visited a platform (see 

Table 2) and subsequently obtained approval from the Scientific Review Board and BOEM to 

proceed with conducting measurements on the platform. 
27 

Table 2. 

Platform description

Platform name Chevron Platform, ST-52B, South of Terrebonne Bay, 

Louisiana

WAVCIS name CSI-06. LSU operates wave measurement and surface 

meteorological instruments on this platform.

National Data Buoy Center (NDBC)

name

Station SPLL1

Coordinates 28o52',-90o29' (see Figure 1)

Platform deck (roof) height 37 m msl 

Catwalk height

~15 m msl. Note: we mounted the flux instruments on 

booms below the catwalks to help mitigate flow distortion 

issues and to obtain atmospheric surface-layer 

measurements (see Figure 12).

Water depth 20.5 m

Power available? Yes, but we augmented existing power with solar power.

Wave measurements already available? Yes.

Water temperature? 

Yes, but the data were collected at ~2 m below the sea 

surface; we added skin temperature and near-surface 

temperature.

Existing surface meteorology? Yes, but at about 42 m msl; we added flux 

measurements below the catwalk at ~10 to 12 m msl.

Active wells?

Although active wells are in the complex, this platform 

was for crew quarters, eliminating the need for 

explosion-proofing the equipment.

Available for at least 12 months? Yes.

Internet communications available? Yes, but we had to add bandwidth to accommodate the 

additional data.

Routine access available? Yes.

3.6.2 Communications and Data Collection

Reliable communications with the equipment on the platform were required to ensure high data 

recovery rates and minimize costly trips to the platform. Two-way Internet allowed equipment 

to be remotely monitored using a Virtual Network Computing (VNC) connection. It was 

frequently possible to debug and solve instrument problems remotely and to determine what 

equipment should be brought to the platform to effect repairs. 

Using an Internet connection, we automatically pushed most of the data daily from the oil 

platform to file transfer protocol (FTP) servers. When the data were uploaded, an automatic 

process took the data in their raw form and stored them in a Microsoft® SQL Server® database, 

effectively combining all data into a single data set. Another automatic process generated 

images of the data and uploaded them to a website. These processes provided us with an 

efficient way to check data and catch problems that occurred. An example of the website used is 

shown in Figure 20. 
28 

Figure 20. Wave data images posted to a website in real time. 

3.6.3 Power

Optimally, we would have operated the equipment on a platform that already had power 

available. However, the power on the selected platform was limited, so we used a battery 

system. Solar panels were used to charge the batteries for surface meteorology and flux 

packages. The microwave radiometer and ceilometer operated on platform power, as did the 

chargers for the batteries for the rest of the instrument systems.

3.6.4 Interference

Audible noise can interfere with the proper operation of the mini-sodar. To help mitigate this 

problem, we encased the mini-sodar in an additional sound-dampening enclosure. 

3.6.5 Hurricanes

Hurricanes were a real concern in the Gulf of Mexico. Mitigating the negative effects of 

hurricanes during this measurement program included the following: 
29 

• The equipment was able to withstand hurricane-force winds. For example, we 

secured the equipment to the decks with welded steel grids rated to withstand 

hurricane-force winds and used special mounting brackets for all instruments. 

• We purchased the required special equipment insurance for offshore areas and 

for hurricanes. 

• We conducted in-depth meteorological forecasting before any platform visits. 

3.7 INSTRUMENT PREPARATION AND OPERATIONS

The objectives of routine instrument operations were to ensure high-quality data and high data 

recovery rates. Operations were divided into two main elements for this project: (1) predeployment instrument interface and testing, and (2) routine operations. 

3.7.1 Pre-Deployment Interface and Testing

All instruments were shipped to LSU. At LSU’s facilities, STI, LSU, and CU personnel 

assembled the complete, integrated measurement system, including all power sources, 

computers, data management systems, and communications. In addition, special mounting 

brackets were fabricated for each instrument. The instruments were tested as a complete system

over several weeks to ensure that they met manufacturer specifications and that all systems, from 

data collection to data delivery and archiving, worked together properly. During this testing 

phase, several issues were resolved. For example: 

• The microwave radiometer computer had to be replaced with a Cappuccino 

DC computer to accommodate platform power. 

• A replacement flux system was purchased because one Li-Cor Li-7500 and 

one sonic anemometer supplied in-kind by NOAA did not work, and NOAA 

did not have another set available for loan. 

• The microwave radiometer, ceilometer, and mini-sodar had resource conflicts 

on the computer; these problems were resolved.

• The IR skin sensor was not waterproof. 

• The modems supplied in-kind by NOAA did not work because they did not 

transfer data fast enough to handle flux measurements. NOAA mistakenly 

stated that they had been used for this purpose before, so considerable effort 

was spent trying to get them to work beyond their specifications. New 

modems were purchased.

• The new modems were damaged by a power surge and had to be replaced. 

• The program supplied by NOAA for the solar radiation sensor had to be 

rewritten because it didn’t work with the current version of the Campbell data 

logger. 
30 

• Only beta versions of programs for the microwave radiometer and ceilometer 

were available for remote processing of the data, and no documentation was 

available; procedures were developed. 

• The new mini-sodar had pauses in its pinging, unexplained reboots, and 

speaker failures. The problems turned out to be subparts from a new supplier 

that the manufacturer was using; the parts were replaced.

• The sound-dampening enclosure for the mini-sodar was not delivered on 

schedule by the manufacturer; when it arrived, the sections could not be 

broken down as specified to allow it to be moved to the platform roof. The 

enclosure was modified so that it could be taken apart into small sections. In 

addition, the enclosure did not dampen the sound enough, so a secondary 

sound-dampening blanket had to be purchased and delivered and the enclosure 

modified so that the blanket could be installed. 

• Chevron updated its rules on shipping containers, and new crates had to be 

fabricated to house the equipment inside of Connex boxes. 

3.7.2 Shipping 

The equipment for this project was transported to the oil platform by a small ship (see 

Figure 21). For this project, considerable planning and coordination was needed to ensure that 

the equipment arrived on schedule and in good condition. Before shipping, all equipment was 

placed in crates in Connex boxes. The most fragile equipment was individually crated. 
31 

Figure 21. Picture of Connex boxes on Chevron’s ship.

3.7.3 In-Field Testing After the Initial Installation 

After installation of the integrated measurement system on the platform, the system was tested to 

ensure that all equipment, including power sources, computers, data management systems, and 

communications, worked properly. In particular, installation staff completed the following infield testing for each instrument: 

• Leveled the instruments and determined their orientation, when appropriate 

• Anchored all instruments to the platform deck or meteorological tower 

• Tested data loggers to verify programming, time synchronization, sensor input 

configuration, and data storage function 

• Verified data file creation for all instruments

• Verified data collected against internal and external data sources, including 

the LSU meteorological sensors and surface meteorological data from nearby 

Houma, Louisiana
32 

• Synchronized clocks on all instruments based on The Official U.S. Time 

website (NIST and USNO 2013) 

• Verified data transfers to the host computer on oil platform 

• Verified proper operation, data storage, data retrieval, and communications to 

and from STI servers

3.7.4 Onsite Maintenance and Operations

Maintenance of the instruments was critical to successful operations. For this reason, STI’s team 

conducted maintenance and made emergency site visits as needed. During platform visits, staff 

performed the following maintenance: 

• Visually inspected all instruments and cabling 

• Verified that all instruments were collecting reasonable data for the current 

weather conditions

• Inspected all instrument bases to ensure that they remained bolted to the 

platform deck

• Verified all instrument orientations and levels

• Backed up data files

• Verified disk space on all instruments/data loggers 

• Verified that the mini-sodar speakers were operating

• Cleaned the window/lens on the ceilometer 

• Cleaned the domes on the solar radiation sensors 

• Cleaned the radome, temperature/relative humidity screen, superblower filter, 

rain sensor board, and infrared thermometer (IRT) window on the microwave 

radiometer

• Cleaned the temperature/relative humidity radiation shield on the surface 

meteorological tower

• Cleaned the infrared hygrometer/CO2 gas analyzer lenses. Midway through 

the project, an automatic wash system was installed to clean these lenses. 

• Visually inspected the wave measurement, underwater temperature, infrared 

temperature (skin temperature), and sea float temperature sensors

• Repairs were made on an as-needed basis and included such items as 

replacing defective temperature sensors, replacing wind speed bearings, or 

repairing connections.

In addition, data quality checks were performed to ensure that the data collected were complete 

and reasonable. Each day, a meteorologist at STI’s Weather Operations Center compared the 

surface and upper-air data from the site with external data sources as a quality control measure, 
33 

allowing for the identification of any operational or equipment problems. Table 3 shows the data 

and instrument checks that were performed. In addition, a meteorologist compared data 

collected at the site and checked for data reasonableness.

Table 3. 

On-platform (internal) and off-platform (external) data sources used during data validation

Instrument Parameter Internal Data Checks External Data Checks

Mini-sodar Winds Check for consistency 

with surface wind data

Check for consistency with wind data 

from nearby buoys, rawinsondes, and 

numerical models (North American 

Mesoscale Model [NAM], Global 

Forecast System [GFS], etc.)

Microwave 

radiometer

Temperature 

profiles and 

humidity profiles

Check for consistency 

with surface temperature 

and relative humidity data

Check for consistency with 

temperature and relative humidity 

data from nearby buoy, rawinsondes, 

and numerical models (NAM, GFS, 

etc.)

Ceilometer Mixing heights 

and cloud heights

Check for consistency 

with microwave 

radiometer temperature 

data

Check for consistency with cloud 

data, satellites, and rawinsondes

Wind vane and 

cup anemometer Winds

Check for consistency 

with mini-sodar winds

Check for consistency 

with LSU winds

Check for consistency with wind data 

from nearby buoys, coastal surface 

meteorological stations, and 

numerical models (NAM, GFS, etc.)

Thermistor and 

hygrometer

Temperature and 

relative humidity

Check for consistency 

with LSU temperature and 

relative humidity

Check for consistency with wind data 

from nearby buoys, coastal surface 

meteorological stations, and 

numerical models (NAM, GFS, etc.)

Barometer Pressure None

Check for consistency with pressure 

data from nearby buoys, coastal 

surface meteorological stations, and 

numerical models (NAM, GFS, etc.)

Pyranometer

Downwelling 

shortwave 

radiation

Check for consistency 

with cloud data obtained 

from the ceilometer

Check for consistency with cloud 

data, satellites, and rawinsondes

Pyrgeometer

Downwelling 

longwave 

radiation

Check for consistency 

with cloud data

Check for consistency with cloud 

data, satellites, and rawinsondes

Sonic 

anemometer

Winds and 

temperature

Check for consistency 

with surface wind and 

temperature data

Check for consistency 

between AB and AC 

sonic winds and 

temperatures

Check for consistency with wind 

and temperature data from nearby 

buoys
34 

Table 3. On-platform (internal) and off-platform (external) data sources used during data validation

(continued)

Instrument Parameter Internal Data Checks External Data Checks

Infrared 

hygrometer and 

gas analyzer

Specific humidity 

and CO2

concentration

Check for consistency 

between AB and AC 

bridge-specific 

humidities and CO2

concentrations

Check for consistency with relative 

humidity data from nearby buoys 

and CO2 levels from other stations

Wave 

measurements

Wave height, 

wave period, and 

water depth

Check for consistency 

with surface wind data

Check for consistency with wave 

data from nearby buoys

Fixed sea 

surface 

thermometer

Sea surface 

temperature

Check for consistency 

with the sea float 

temperature and the skin 

temperature

Check for consistency with sea 

surface temperature data from 

nearby buoys

Sea float 

thermometer

Sea float 

temperature

Check for consistency 

with the sea surface 

temperature and the skin 

temperature

Check for consistency with sea 

surface temperature data from 

nearby buoys

Infrared 

thermometer Skin temperature

Check for consistency 

with the sea surface 

temperature and the sea 

float temperature

Check for consistency with sea 

surface temperature data from 

nearby buoys
35 

4. DATA AVAILABILITY

The data collection began on October 1, 2010, and was completed on April 1, 2012. The 

instruments were operated with great success and provided very robust offshore data that met the 

project objectives. Tables 4 and 5 summarize key data recovery statistics for data completeness 

and data capture.

The most stringent metric for data recovery statistics is data completeness, which is the 

percentage of valid data points after rigorous data quality control divided by the total number of 

records possible determined using the instrument installation date, frequency of measurement, 

and operations end date. For example, an instrument that produces hourly averaged data would 

have a denominator of 720 observations per 30-day month. A data completeness of 50% would 

mean that 360 observations that were collected are valid and ready for use in data analysis. The 

installation date for all instruments was October 4, 2010, except for the sea float temperature 

sensor, which was installed on June 24, 2011. The end date was April 1, 2012. Therefore, for 

instruments that reported hourly values, the total number of possible records was 548 days * 24 

hours = 13,174. For instruments that report more than one value per hour, such as the sodar, the 

total number of possible records was calculated as appropriate. As summarized in Table 4, the 

data completeness rates are mostly above about 83% and some are as high as 100%. The notable 

exception is the skin temperature sensor, which had a data completeness of 44%. In addition, 

data completeness for the flux sensors on each bridge was 39% for the AB bridge and 20% for 

the AC bridge due primarily to flow distortions created by the oil platforms; when flow 

distortions occurred data were made invalid. When the fluxes from the two bridges are 

combined, the data completeness improved to 55%. 
36 

Table 4. 

Data completeness statistics by instrument type

Instrument Number of Valid 

Data Points

Number of 

Data Points 

Received

Data 

Accumulation 

Frequency

Data 

Completeness 

(%)

Mini-Sodar Winds 379,042 430,730

15 minutes, 

multiple 

heights

88

Microwave Radiometer 

Temperature and 

Humidity

267,672 316,176

2.5 minutes, 

multiple 

heights

85

Ceilometer Mixing 

Heights

34,846 52,696 15 minutes 66

Ceilometer Cloud 

Heights

46,908 52,696 15 minutes 89

Winds 10,954 13,174 60 minutes 83

LSU Windsa 10,971 13,174 60 minutes 83

Ambient Temperature 10,923 13,174 60 minutes 83

LSU Ambient 

Temperaturea 10,902 13,174 60 minutes 83

Relative Humidity 12,519 13,174 60 minutes 95

Pressure 12,519 13,174 60 minutes 95

Downwelling (shortwave) 

Radiation 12,549 13,174 60 minutes 95

Infrared (longwave) 

Radiation 12,373 13,174 60 minutes 94

Precipitation 12,519 13,174 60 minutes 95

Skin Temperature 5,815 13,174 60 minutes 44

Sea Float Temperature 5,653 5,901 60 minutes 96

LSU Sea Temperature 13,128 13,174 60 minutes 100

Wave Data (Including 

Wave Height, Wave 

Period, and Water 

Depth)

13,128 13,174 60 minutes 100

AC Bridge Flux Sensors 15,154 74,742 10 minutes 20

AB Bridge Flux Sensors 28,992 74,742 10 minutes 39

Combined Flux Sensors 41,062 74,742 10 minutes 55

a LSU operates a separate set of surface meteorology instruments.

A less stringent metric for the data recovery statistics is data capture, which is the percentage of 

total data points collected (regardless of validity) divided by the total number of records possible 

determined using the instrument installation date, frequency of measurement, and operations end 

date. As summarized in Table 5, the data capture rates are mostly above about 85%, and some 

are as high as 100%. The notable exception is the AC bridge flux data, which had a data rate 

capture of 74%. Also, before December 3, 2010, because of problems with the lenses being 

covered in salt and instrument failures, the flux data were not considered in the data capture 

calculations. 
37 

Table 5. 

Data capture statistics by instrument type

Instrument Number of Data 

Points Collected

Number of Data 

Points Possible

Data 

Accumulation 

Frequency

Data 

Capture (%)

Mini-Sodar Windsa 50,325 52,696 15 minutes 95

Microwave Radiometer 

Temperature and Humidity 267,672 316,176

2.5 minutes, 

multiple 

heights

85

Ceilometer Mixing Heights 34,846 52,696 15 minutes 66

Ceilometer Cloud Heights 46,908 52,696 15 minutes 89

Winds 12,519 13,174 60 minutes 95

LSU Winds 11,417 13,174 60 minutes 87

Ambient Temperature 12,519 13,174 60 minutes 95

LSU Ambient Temperature 11,417 13,174 60 minutes 87

Relative Humidity 12,519 13,174 60 minutes 95

Pressure 12,519 13,174 60 minutes 95

Downwelling (shortwave) 

Radiation 12,549 13,174 60 minutes 95

Infrared (longwave) Radiation 12,549 13,174 60 minutes 95

Precipitation 12,532 13,174 60 minutes 95

Skin Temperature 12,529 13,174 60 minutes 95

Sea Float Temperatureb 5,901 6,791 60 minutes 87

LSU Sea Temperature 13,128 13,174 60 minutes 100

Wave Data (Including Wave 

Height, Wave Period, and 

Water Depth)

13,128 13,174 60 minutes 100

AC Bridge Flux Sensors 55,169 74,742 10 minutes 73

AB Bridge Flux Sensors 65,652 74,742 10 minutes 87

a For this instrument and data capture statistic, the number of points possible equals ~548 days x 24 

hours x 4, for which the 4 accounts for 15-minute data frequency.

b The sea float sensor was installed on June 24, 2011.

Major instrument downtimes and major periods of suspect or invalid data and the reasons for the 

missing data are presented in Table 6 and are summarized in graphical form in Figure 22. 

Missing data periods in the table are cross-referenced with the information in Figure 22. Text on 

missing data periods that require additional information follows the table. 
38 

Table 6. 

Major downtimes of the instruments

Case Downtime Instrument(s) Affected Problem

1 11/01/2010 to 12/04/2010 AC flux instruments Radio modem failure

2 12/19/2010 to 01/12/2011 AC flux instruments Radio modem failure

3 03/25/2011 to 04/30/2011 AC flux instruments Radio modem failure

4 05/02/2011 to 06/16/2011 Microwave radiometer Power surge/ V-band 

receiver failure

5 05/03/2011 to 05/17/2011 Ceilometer Power surge turned 

instrument off

6 05/03/2011 to 05/18/2011 All flux instruments Power surge turned 

instruments off

7 05/18/2011 to 05/24/2011 Mini-sodar Battery failure

8 05/27/2011 to 05/30/2011

Ceilometer, computer, surface 

meteorology, skin temperature, 

and all flux instruments

Power surge turned 

instrument off

9 06/09/2011 to 06/12/2011 Mini-sodar Battery failure

10 06/09/2011 to 06/14/2012 Surface meteorology 

instruments Battery failure

11 06/28/2011 to 07/25/2011 Sea float temperature sensor Sensor failure

12 09/03/2011 to 09/05/2011 Surface meteorology and skin 

temperature

Power shut off for Tropical 

Storm Lee

13 09/03/2011 to 09/09/2011

Mini-sodar, ceilometer, 

microwave radiometer, and flux 

instruments

Power shut off for Tropical 

Storm Lee

14 09/05/2011 to 09/05/2011 Wave data Power shut off for Tropical 

Storm Lee

15 12/23/2011 to 01/03/2012 Surface meteorology and flux 

instruments

Power surge turned 

instruments off

16 12/23/2011 to 01/20/2012 Microwave radiometer and 

ceilometer

Power surge turned 

instruments off

17 02/26/2012 to 03/04/2012 Mini-sodar Battery failure

18 02/26/2012 to 04/01/2012 Ceilometer Blower failure

Note that the flux instruments required a wash system to be installed when data quality issues 

were observed after several weeks of operations. The wash system was installed on December 3, 

2010. Data before this time period were deemed invalid. Additional downtimes were 

encountered with the AC bridge flux instruments, due primarily to radio modem failures. 

An Everest IR sensor was used to measure skin temperature initially because of its purported 

ability to correct for black body radiation from the sky; however, this sensor was not suitably 

weatherproof as promised by the manufacturer. As a result, the data from this sensor were 

labeled as suspect even when the data appear to be oceanographically reasonable. In addition, 

the majority of the time, the data values were reading either extremely low (~-40°C) or high 

(~100°C) and were invalidated. After replacing the Everest sensor twice, on February 24, 2012, 

a Heitronics KT15.85-IIP IR sensor was installed to measure skin temperature. Data 

completeness from this replacement instrument was 100%. The Heitronics KT15.85-IIP IR skin 
39 

temperature data were corrected to account for black body radiation from the sky that was 

reflected off the ocean surface. The sky black body radiation was measured by an upwardlooking KT15.85-IIP IR sensor mounted inside the microwave radiometer. 
40

Figure 22. Major periods of missing, invalid, or suspect data. Numbers refer to the case numbers in Table 6.
41 

5. DATA PROCESSING AND QUALITY CONTROL

Data quality assurance and control is a key component of producing a data set ready for use by 

data analysts and modelers. STI meteorologists who understand the data collected as well as the 

instruments used to collect the data quality-assured and validated the data using STI’s in-house 

data quality control programs (SurfDat and GraphXM). During the data validation process, the 

meteorologists identified inconsistent observations (outliers) and assigned quality control (QC) 

codes to each data point to indicate its validity. Several stages, or “Levels,” in the data 

validation process were used: 

• Level 0.0 Raw, non-quality-controlled data.

• Level 0.5 Data that were subjected to automatic QC screening by software. 

• Level 1.0 Data that were subjected to quantitative and qualitative reviews for 

accuracy, completeness, and internal consistency. 

• Level 2.0 Data that were subjected to quantitative and qualitative reviews for 

external consistency.

The following steps were used to bring data to Level 2.0 validation. The Level 0.0 data were 

obtained from the platform daily via the Internet. Backup copies of the Level 0.0 data were 

automatically made and archived. In addition, backup data were obtained via a portable hard 

drive during each site visit, and any missing data were added to the database. 

Data were manually reviewed by experienced meteorologists. The reviewers carefully examined 

plots of the data, looking for outliers, and evaluated the reasonableness of the data. The 

reviewers flagged the resulting data as “valid,” “suspect,” “invalid,” or “missing,” using the 

appropriate QC codes: 

• 0 = Valid

• 7 = Suspect

• 8 = Invalid with a data value of -980 

• 9 = Missing with a data value of -999 

Reviewers used internal and external sources of data to help them determine the validity of the 

observations. Internal data sources included other parameters that were measured by the same 

instrument, collocated data sources, and other manually-generated data (e.g., instrument 

performance logs and site operator logs). Table 7 lists internal and external data sources and 

gives a brief explanation of how we use them to QC the data. Examples of external data include 

the NOAA buoy data, National Weather Service (NWS) upper-air and surface weather charts, 

and satellite images. An example of external data use is the comparison of NOAA buoy winds to 

platform winds as a reasonableness check.
42 

Table 7. 

On-platform (internal) and off-platform (external) data sources used during data validation

Instrument Parameter Internal Data Checks External Data Checks

Mini-sodar Winds Compared with surface 

wind data

Compared with wind data from 

nearby buoys, rawinsondes, and 

numerical models (NAM, GFS, etc.)

Microwave 

radiometer

Temperature 

profiles and 

humidity profiles

Compared with surface 

temperature and relative 

humidity data

Compared with temperature and 

relative humidity data from nearby 

buoy, rawinsondes, and numerical 

models (NAM, GFS, etc.)

Ceilometer Mixing heights 

and cloud heights

Compared with 

microwave radiometer 

temperature data

Compared with cloud data, 

satellites, and rawinsondes

Wind vane and 

cup 

anemometer

Winds

Compared with minisodar winds

Compared with LSU 

winds

Compared with wind data from 

nearby buoys, coastal surface 

meteorological stations, and 

numerical models (NAM, GFS, etc.)

Thermistor and 

hygrometer

Temperature and 

relative humidity

Compared with LSU 

temperature and relative 

humidity

Compared with wind data from 

nearby buoys, coastal surface 

meteorological stations, and 

numerical models (NAM, GFS, etc.)

Barometer Pressure None

Compared with pressure data from 

nearby buoys, coastal surface 

meteorological stations, and 

numerical models (NAM, GFS, etc.)

Pyranometer

Downwelling 

shortwave 

radiation

Compared with cloud 

data obtained from the 

ceilometer

Compared with cloud data, 

satellites, and rawinsondes

Pyrgeometer

Downwelling 

longwave 

radiation

Compared with cloud 

data

Compared with cloud data, 

satellites, and rawinsondes

Sonic 

anemometer

Winds and 

temperature

Compared with surface 

wind and temperature 

data

Compared between AB

and AC sonic winds and 

temperatures

Compared with wind and 

temperature data from nearby 

buoys

Infrared 

hygrometer and 

gas analyzer

Specific humidity 

and CO2

concentration

Compared between AB

and AC bridge-specific 

humidities and CO2

concentrations

Compared with relative humidity 

data from nearby buoys and CO2

levels from other stations

Wave 

measurements

Wave height, 

wave period, and 

water depth

Compared with surface 

wind data

Compared with wave data from 

nearby buoys

Fixed sea 

surface 

thermometer

Sea surface 

temperature

Compared with the sea 

float temperature and 

the skin temperature

Compared with sea surface 

temperature data from nearby 

buoys
43 

Table 7. On-platform (internal) and off-platform (external) data sources used during data validation

(continued)

Instrument Parameter Internal Data Checks External Data Checks

Sea float 

thermometer

Sea float 

temperature

Compared with the sea 

surface temperature and 

the skin temperature

Compared with sea surface 

temperature data from nearby 

buoys

Infrared 

thermometer

Skin 

temperaturea

Compared with the sea 

surface temperature and 

the sea float 

temperature

Compared with sea surface 

temperature data from nearby 

buoys

a The skin temperature data were corrected to account for black-body radiation from the sky.

The quality-controlled data were then stored on DVDs in the data file formats agreed upon by 

BOEM and STI on March 9, 2011. Examples of the quality control performed for each 

instrument follow.

Figure 23 shows an example of sodar wind data that were invalidated due to precipitation 

interference. This figure shows the mini-sodar wind data colored by vertical wind speed. The 

vertical wind speeds of about 5.8 m/s (as indicated by blue color on wind barbs) in Figure 23 

show the fall speed of the rain drops. In addition, the wind speeds and directions for 

precipitation events were inconsistent with the surface wind observations shown in Figure 24. 

Therefore, these winds were invalidated.

Figure 23. Mini-sodar winds from February 10, 2012. Wind barbs are colored by vertical wind 

speed. Times are in UTC.
44 

Figure 24. Surface wind (top) and precipitation (bottom) data on February 10, 2012. 

Bristles point in the direction toward which the wind is blowing.

The microwave radiometer data were reviewed every day by an experienced meteorologist. In 

addition, at the end of the study, selected soundings were compared to soundings taken at Lake 

Charles, Louisiana, and Slidell, Louisiana. Figures 25 and 26 show four comparisons for 

January 11, 2011, at 1200 UTC; January 12, 2011, at 0000 UTC; December 6, 2011, at 

1200 UTC; and December 7, 2011, at 0000 UTC. In general, the soundings show good 

agreement. Because the microwave radiometer was over water and the rawinsondes were over 

land, inversions over the water were typically weaker, as shown in Figure 26. 
45 

Figure 25. Microwave radiometer temperature sounding (blue), Lake Charles temperature sounding 

(red), and Slidell temperature sounding (green) for January 11, 2011, 1200 UTC (left) and 

January 12, 2011, 0000 UTC (right).

Strong inversion 

weakens by 0000 

UTC
46 

Figure 26. Microwave radiometer temperature sounding (blue), Lake Charles temperature sounding 

(red), and Slidell temperature sounding (green) for December 6, 2011, 1200 UTC (left) and 

December 7, 2011, 0000 UTC (right).

The ceilometer mixing heights and cloud heights were generated by applying an automated 

algorithm developed by Vaisala to the raw data. In addition, the cloud heights were compared to 

satellite data to determine reasonableness. Figure 27 shows a satellite image for January 11, 

2011, at 1431 UTC, revealing a broad area of low clouds over the Gulf of Mexico. Figure 28 

shows the ceilometer backscatter data collected on that day with the cloud heights and mixing 

heights overlaid. The cloud bases and tops observed in the ceilometer are consistent with the 

low clouds present in the visible satellite imagery.

Comparing the Slidell 

rawinsonde 

temperature profile to 

the microwave 

radiometer shows 

much better 

agreement
47 

Figure 27. Visible satellite imagery over the northern Gulf of Mexico for 

January 11, 2011, at 1431 UTC.

Figure 28. Ceilometer backscatter with cloud bases, cloud tops, and mixing heights in meters above 

platform level (apl).
48 

Figure 29 shows an example of the surface meteorological data for the period from June 23, 

2011, through June 28, 2011, located on ST-52B. This is an example of using collocated data 

sources to validate data. The rainfall is consistent with the wind direction shift and the 

decreasing downwelling radiation. 

Figure 29. Surface meteorological data from the platform deck for June 23 through June 28, 2011, 

including (top to bottom) wind speed (WS) and wind direction (WD); infrared radiation (IR) 

and downwelling radiation (DR); relative humidity (RH) and pressure (P); and temperature 

(T) and rain (RAIN). Times are in UTC.

Figure 30 shows an example of the skin temperature and sea surface temperature data. The skin 

temperature was invalidated at these times because there were periods where the measured skin 

temperature values would drop from roughly 28°C to -26°C. These variations were caused by 

water in the sensor’s electronics.

Rainfall coincident with reduced 

downwelling radiation and wind 

direction shift
49 

Figure 30. Skin temperature from the infrared temperature sensor (red line) and fixed sea surface 

temperature from LSU for 11/15/2010 through 12/9/2010. Times are in UTC.

Figure 31 shows an example of the wave height and maximum wave heights for September 14 

through September 18, 2011. Comparisons of the wave height and peak wave height were done 

to check the internal consistency of the data.

Skin Temperature

LSU Sea Surface

Temperature

(fixed)
50 

Figure 31. Wave heights and maximum wave heights for September 14 through September 

18, 2011.

Figure 32 shows an example of quality control that was completed on the specific humidity data. 

The Automatic Gain Control (AGC) of the Li-Cor 7500 was used in complement with other data 

filters to remove bad specific humidity data. In this case, the low humidity values were caused 

by the wash system that was developed to keep the lenses of the Li-Cor 7500 clean. These data 

points were made invalid. 

Figure 32. Illustration of unfiltered (blue) and filtered (red) specific humidity data 

from the AB Li-Cor 7500 sensor.

Section 7 provides additional information on the flux data processing and quality control. 

0 100 200 300 400

-150

-100

-50

0

50

Year 2011 (UTC)

Specific humidity (g/kg)

 

AB unfiltered

AB filtered

STi
51 

6. DATA FORMATS

The accompanying DVD contains the quality-controlled data collected during this study in 

comma-separated format (CSV), except for the ceilometer backscatter data, which is in its native 

format. The time standard for the data is Coordinated Universal Time (UTC). This section 

describes the file naming convention and the file formats. 

Table 8 lists the data file names, where

• <YYYY> = four-digit year

• <Y> = one-digit year

• <MM> = two-digit month

• <DD> = two-digit day 

• <HH> = two-digit hour 

• <MM> = two-digit minute

• <SS> = two-digit second 

• <x> = level number (for radiometer)

Table 9 lists the fields in the data files (except for the radiometer, ceilometer, and flux data files).

A description of the microwave radiometer file formats can be found in Sections 6.3.5, 6.3.6, and 

6.3.7 (pages 65 through 66) of the Radiometrics Profiler Operator’s Manual4

. Note, the 

radiometer data are stored in three files: Level 0 files (raw sensor data in volts), Level 1 files 

(brightness temperatures), and Level 2 files (profile retrievals). 

The flux data formats are provided in Section 7. 

The ceilometer file format can be found in Chapter 5 (page 58) of the Vaisala CL31 Users Guide

(Vaisala 2004). 

 

4 Please contact Radiometrics at (303) 449-9192 for the operator’s manual.
52 

Table 8. 

Data file names

Parameter Instrument File Name

All Data 

in File or 

Individual 

Files

Data 

Accumulation 

Frequency

Wind speed and direction Mini-sodar Mini-sodar.csv All

15 minutes, 

multiple 

heights

Profiles of temperature, 

relative humidity, and liquid 

water and additional meta 

data associated with these 

measurements

Microwave 

Radiometer 

<YYYY>-<MM>-

<DD>_<HH>-<MM>-

<SS>_lv<x>.CSV

Example: 2010-11-

10_235900_lv2.CSV

Individual 

Files

2.5 minutes, 

multiple 

heights

Backscatter data Ceilometer 

A<Y><MM><DD><HH>.da

t 

Example: A0111023.dat

Individual 

Files

16 seconds, 

multiple 

heights

Mixing and cloud height Ceilometer CeilometerMHCH.csv All 15 minutes

Wind speed and direction

Temperature

LSU wind speed and 

direction

LSU temperature

Relative humidity

LSU pressure

Downwelling (shortwave) 

radiation

Infrared (longwave) radiation

Precipitation

Surface 

Meteorolog

y 

SurfaceMeteorology.csv All 60 minutes

Wave height

Wave period

Water depth

Pressure 

Transducer

and ADCP 

Current 

Meter

Wave.csv All 60 minutes

AB and AC bridge sonic 

winds

AB and AC bridge sonic 

temperature

AB and AC bridge specific 

humidity

AB and AC bridge CO2

concentrations

AB and AC bridge heat flux 

and stress (covariance 

streamwise (u1

w1), 

covariance crossstreamwise (v1

w1), and 

combine AB and AC bridge 

covariance streamwise) 

Sonics and 

Licors

ST52_10minflux_2010.r1

ST52_10minflux_2011.r1

ST52_10minflux_2012.r1

All 10 minutes
53 

Table 9. 

Fields located in data files

Field Description

Site Name Site identifier

Data Type Instrument

Parameter Name Parameter description

DateTime(UTC) Date and time in UTC

Height Height in meters above ground level (m apl)

Value Data value

Units Parameter measured (units for parameter measured)

QC Code QC code for parameter

QC Level QC level for parameter

Table 10 lists the parameter names and descriptions. See Section 7 for a description of the flux 

data files.

Table 10. 

Parameter names and descriptions

Parameter Description

Temperature Air temperature (°C) 

LSU-Temperature LSU air temperature (°C) 

Wind Speed Scalar-averaged wind speed (m/s)

Wind Direction Vector-averaged wind direction (°)

LSU Wind Speed Scalar-averaged wind speed (m/s)

LSU Wind Direction Vector-averaged wind direction (°)

Sigma Theta Wind direction standard deviation (°)

Downwelling Radiation Downwelling radiation (W/m2)

Infrared Radiation Infrared radiation (W/m2)

Pressure Atmospheric pressure (mb)

Relative Humidity Relative humidity (%)

Rain Precipitation (mm)

Wave Height Wave height (m)

Maximum Wave Height Maximum wave height (m)

Wave Period Wave period (sec)

Peak Wave Period Peak wave period (sec)

Water Depth Water depth (m)

Sea Skin Temperature Infrared skin temperature (°C)

Sea Surface Temperature – Fixed Fixed sea surface temperature (°C)

Sea Surface Temperature – Float Floating sea surface temperature (°C)

Mixing Height Mixing height (m apl)

Cloud Height Cloud height (m apl)
54 

An electronic copy of this report and the validated data collected during this reporting period are 

provided on a DVD that accompanies this report. The DVD is labeled as follows:

Format: Microsoft® Office 2010, ASCII text, Vaisala Ceilometer format.

Meteorological and Wave Measurements

[October 1, 2010 through April 1, 2012] 

BOEM Contract #M08PC20057 

STI-908059-5458

Prepared for the 

Bureau of Ocean Energy Management

September 2012 
55 

7. PRELIMINARY DATA ANALYSES 

The goal of this project was to collect meteorological data to better characterize the atmospheric 

boundary layer over the Gulf of Mexico. However, to demonstrate data use and set the stage for 

future analyses, preliminary data analyses were completed as part of this project. In particular, 

we

1. Performed a preliminary case study analysis to investigate the relationship between largescale meteorology and boundary layer conditions at the platform. 

2. Investigated how well the NAM (North American Mesoscale Model) predictions compare to 

the measured vertical wind profiles collected by the mini-Sodar. 

3. Investigated how well COARE-calculated fluxes compare to the measured heat and 

momentum fluxes.

These preliminary analyses are presented below.

7.1 JANUARY 11, 2011, CASE STUDY

This case study illustrates how data collected from this study could be used to better understand 

the relationships among the large-scale weather patterns, vertical temperature and relative 

humidity structures, mixing heights, boundary-layer winds, and energy fluxes. 

On January 11, 2011, a cold front moved south over the Gulf of Mexico. The front brought cold 

air over the Chevron platform. The northerly winds associated with this cold front ranged from 

about 6 to 10 m/sec. As measured by the mini-sodar (see Figure 33), the winds were 

homogeneous in direction and speed through at least the lowest 200 m of the boundary layer. 

This cold air boundary began to arrive at the platform at about 0300 UTC, at which time the 

layer of cold air was measured to be confined to the lowest ~400 m of the boundary layer (see 

the temperature data from the microwave radiometer in Figure 34). As the day went on, the 

layer of cold air deepened, reaching about 1 km by 1700 UTC according to the radiometer 

temperature data. Nearly saturated air (i.e., air with relative humidity near 100%) was observed 

within the boundary layer (see the relative humidity data from the microwave radiometer in 

Figure 34). Above the boundary layer, the air was dry (i.e., RH ~30%). The saturated air within 

the boundary layer resulted in thin stratus clouds over the Gulf as captured by the visible satellite 

image taken at 1431 UTC (see Figure 35). As measured by the ceilometer, the bases of these 

clouds were about 300 m msl in the morning and then elevated to about 500 m msl in the late 

afternoon (see Figure 36). In addition, the ceilometer indicates that these clouds were quite thin 

(~100 m). The tops of the clouds were also the top of the mixed layer as measured by the 

ceilometer (see Figure 36). There was good correlation amongst the mixing and cloud heights 

and temperature structure throughout the day; as the cold layer deepened in the afternoon, the 

cloud and mixing heights increased accordingly. 

During this period, COARE-calculated5 sensible heat flux was moderate to strong upward and 

ranged from about 70 w/m2 in the early morning to about 150 w/m2 in the middle of the day (see 

Figure 37). The strong sensible heat flux was driven by large air-sea temperature differences, 

 

5 Measured fluxes were not available on this day.
56 

which increased throughout the morning until midday as the cold air moved over the warm Gulf 

waters. Late in the day, as the ocean warmed the air above, the air-sea temperature difference 

decreased, as did the sensible heat flux. The spikes in sensible heat flux observed at 500 and 

1330 UTC were driven by increases in wind speeds at those times. The COARE-calculated 

latent heat flux was relatively weak upward and ranged from about 175 w/m2 in the morning to 

about 200 w/m2 in the middle of the day (see Figure 37). The relatively small latent heat flux 

was because of a moist boundary layer that caused relatively small vertical gradients in specific 

humidity.

Figure 33. Mini-sodar wind data on January 11, 2011.
57 

Figure 34. Microwave radiometer temperature, relative humidity, and liquid water 

data on January 11, 2011. Times are in UTC.

Figure 35. Visible satellite image taken at 1431 UTC on January 11, 2011.
58 

Figure 36. Ceilometer backscatter data, cloud base and top heights, and 

mixing heights on January 11, 2011.

Figure 37. COARE-calculated sensible heat flux (Hs) and air-sea temperature 

(T) difference (top), and COARE-calculated latent heat (Hl) and airsea specific humidity (Q) difference (middle), and COAREcalculated streamwise stress and wind speed (bottom). Times are 

in UTC. An additional explanation of the calculations and variables 

used is provided in Section 7.3.5. 
59 

7.2 MEASURED AND MODELED VERTICAL WIND PROFILES

This analysis illustrates how data collected from this study could be used to better understand the 

wind characteristics within the lowest 200 m over the Gulf of Mexico and how well models 

represent these characteristics. We calculated and compared seasonal scalar-average winds 

collected by the mini-sodar and platform “surface” winds to predictions provided by the North 

American Model for October 2, 2010, through September 30, 2011. The data processing 

consisted of the following elements:

• Sodar data were 15-minute averages.

• Near-platform deck level winds were hourly averages and were combined 

with the sodar winds to create a complete profile from ~45 to ~180 m asl. 

• NAM model predictions were for 0000, 0600, 1200, and 1800 UTC. The 

0000 and 1200 UTC predictions were model initializations, and the 0600 and 

1800 UTC predictions were 6-hr forecasts.

• Logarithmic interpolation between vertical model data was applied (dashed

lines in Figure 38).

• Inverse distance interpolation among horizontal grid squares was applied to 

estimate model winds at the platform location.

As shown in Figures 38 and 39, we can conclude the following: 

• The surface and sodar measurements characterize the actual winds from 40 to 

~200 m asl for an entire year over the Gulf. This provides unique information 

regarding winds in the Gulf. 

• At 150 m asl, the average winds exceed 13 mph in all seasons except summer. 

This is higher than expected and is higher than predicted by NAM. 

• There is diurnal variability in the observed winds; the stronger winds tend to 

occur in the afternoon and overnight hours. 

• The average model winds do not represent actual conditions. For example, (1) 

the model winds are often slower than the observed, and (2) the model winds 

do not always capture the vertical structure. For example, in the winter, there 

is a wind speed jet between about 100 and 150 m, which is not captured by the 

model. 
60 

Figure 38.Seasonal averages of measured (sodar) versus model (NAM) winds.
61 

Figure 39. Seasonal average diurnal profile of boundary layer winds as measured by the miniSodar.

7.3 OBSERVED AND COARE-CALCULATED FLUXES

As part of this field effort with STI and LSU, the NOAA Earth System Research Laboratory 

(ESRL) Physical Sciences Division (PSD) Air-Sea Interaction Group and the University of 

Colorado provided a turbulent flux system composed of two sonic anemometers and two Li-Cor 

7500 gas analyzers that were deployed on Chevron Platform ST-52B. 

As part of this project, we conducted a preliminary analysis that compares the Coupled Ocean 

Atmosphere Response Experiment (COARE) algorithm calculations to the eddy-correlation flux 

measurements. The flux instruments used for this analysis are described in Section 7.3.1. 

Section 7.3.2 presents the methods used for the analysis. Results and discussion are provided in 

Sections 7.3.3 to 7.3.5. A COARE data analysis summary is given in Section7.3.6. 
62 

7.3.1 Flux System Description

The NOAA PSD turbulent flux system consists of two sets of each of the following components: 

a Gill WindMaster Pro sonic anemometer and a Li-Cor 7500 fast CO2/H2O analyzer. One flux 

“package” was located on the AB bridge of the platform and one was located on the AC bridge

(see Figures 40 and 41). AB refers to the instruments on the AB bridge (i.e., AB sonic), and AC

refers to the instruments on the AC bridge. 

Figure 40. Location of the AB and AC flux packages on the platform.
63 

Figure 41.Close-up view of the AB flux package.

Table 11 shows sampling rates and deployment heights of the “flux” sensors.

Table 11. 

Flux package sensor heights and sampling rates

Sensor Sampling Rate (Hz) Height (msl)

AB sonic 10 10.2

AB Li-Cor 10 10.8

AC sonic 10 11.6

AC Li-Cor 10 12.6
64 

7.3.2 Method

The data used for the data analysis are from January 1, 2011, to December 31, 2011. Data from 

December 3, 2010, to December 31, 2010; and from January 1, 2012, to May 5, 2012, were also 

processed and delivered to BOEM, but were not used in this comparison. For illustration, the 

10-minute average time series of wind speed, wind direction, air temperature, sea surface 

temperature, incident shortwave and longwave radiation, relative humidity, and atmospheric 

pressure are shown for 2011 and 2012 in Figures 42 and 43. Data gaps are due to instrument or 

acquisition system failures. About 90% of the meteorological data are available for 2011, and

about 72−84% of the raw data are available for the flux packages (AC and AB, respectively).

For this analysis, the following data processing steps were taken: 

• Covariance fluxes and mean meteorological states were calculated over a 

10-minute time period. The sign convention for fluxes is positive upward (i.e. 

away from the surface). 

• All meteorological data sources were compared and evaluated to select an 

ideal data set to compute fluxes from the COARE algorithm.

• Covariance fluxes were quality controlled (QC) via numerous filters (more 

details follow) and compared against BULK fluxes (Fairall et al. 1996). 
65 

Figure 42. Meteorological conditions from the platform during 2011. From the top, panels 

show wind speed (ms

-1), wind direction in degrees relative to true north, air 

temperature (blue) and surface sea temperature (°C) (red), incident shortwave 

(blue) and longwave (red) radiations (Wm-2), relative humidity (%), and 

barometric pressure (mb).

01/01 01/31 03/01 03/31 04/30 05/30 06/29 07/29 08/28 09/27 10/27 11/26 12/31 0

10

20

WS (ms-1)

ST52 - Surface meteorological data

01/01 01/31 03/01 03/31 04/30 05/30 06/29 07/29 08/28 09/27 10/27 11/26 12/31 0

200

WD (deg)

01/01 01/31 03/01 03/31 04/30 05/30 06/29 07/29 08/28 09/27 10/27 11/26 12/31 0

20

40

Temp (degC)

Ta Ts

01/01 01/31 03/01 03/31 04/30 05/30 06/29 07/29 08/28 09/27 10/27 11/26 12/31

0

500

1000

Radiation (Wm-2)

01/01 01/31 03/01 03/31 04/30 05/30 06/29 07/29 08/28 09/27 10/27 11/26 12/31 0

50

100

RH (%)

01/01 01/31 03/01 03/31 04/30 05/30 06/29 07/29 08/28 09/27 10/27 11/26 12/31

1010

1015

1020

1025

BP (mbar)

Year 2011 (UTC)
66 

Figure 43. Meteorological conditions from the platform during 2012. From the top, panels 

show wind speed (ms

-1), wind direction in degrees relative to true north, air 

temperature (blue) and surface sea temperature (°C) (red), incident shortwave 

(blue) and longwave (red) radiations (Wm-2), relative humidity (%), and the 

barometric pressure (mb).

7.3.3 Meteorological Data Evaluation

In this section, we briefly review the surface observations available and select the best data set

for the COARE algorithm.

01/01 01/31 03/01 03/31 04/30 0

10

20

WS (ms-1)

ST52 - Surface meteorological data

01/01 01/31 03/01 03/31 04/30 0

200

WD (deg)

01/01 01/31 03/01 03/31 04/30 0

20

40

Temp (degC)

Ta Ts

01/01 01/31 03/01 03/31 04/30

0

500

1000

Radiation (Wm-2)

01/01 01/31 03/01 03/31 04/30 0

50

100

RH (%)

01/01 01/31 03/01 03/31 04/30

1010

1015

1020

1025

BP (mbar)

Year 2012 (UTC)
67 

7.3.3.1 Air Temperature

Figure 44 shows a time series of air temperature measurements for 2011. As noted during 

deployment, the sonic anemometers were offset from one another by about 5°C. However, 

because fluxes are based on the variations instead of the means, this offset does not affect the 

turbulent fluxes. We also note that the LSU temperature measurement ran about 1°C lower than 

the STI unit in the first half of the year, but then measured about 2°C higher after replacement.

Figure 44. Time series of air temperatures for the two sonic anemometers (blue and red), and the 

temperature measurements from the roof of the B platform (magenta and black). 
68 

7.3.3.2 Relative Humidity

Figure 45 shows relative humidity measurements from the STI and LSU collocated instruments. 

Because the LSU instrument behaved abnormally several times before its replacement in fall 

2011, we used the STI measurement for the COARE flux calculations.

Figure 45. Relative humidity from STI (magenta) and LSU (black) instruments. 

7.3.3.3 Sea Temperature

Figure 46 shows a time series of air and sea temperatures from an instrument at approximately 1 

m below mean water level (LSU sensor) and from a floating thermistor (STI sensor) at a depth of 

approximately 20 cm deployed in July of 2011. Initially, the instruments were within 0.5°C of 

each other at night (no corrections applied), then drifted apart from each other as time passed (up 

to 1°C at night by the end of the year). For the BULK fluxes, we used the LSU instrument (1 m 

below mean water level) for the first half of the year, then the floating thermistor (~20 cm depth) 

for the second half of the year. 
69 

Figure 46. Time series of air and sea temperatures from STI (blue) and LSU (magenta) instruments. 

7.3.3.4 Wind Speed and Direction

Figure 47 shows 10-m wind speed residuals (instrument to compare—for example, sonic wind 

speed—minus STI wind speed) as a function of true wind direction. Similarly, wind direction 

residuals as a function of true wind direction are plotted in Figure 48. Both STI and LSU wind 

speed measurements were within 0.5 m/s of each other and wind direction measurements were 

within 5 degrees. Therefore, we used the STI sensor for the COARE algorithm. We also note 

various flow distortion effects from the platform. Additional details are given in Section 7.3.4, 

Data Quality Control and Filtering. 
70 

Figure 47. Wind speed residuals as a function of STI wind direction (1 m/s bins). Horizontal 

dash lines indicate ±0.5 m/s.
71 

Figure 48. Wind direction residuals as a function of STI wind direction (5-degree bins). Horizontal 

dash lines indicate ± 0.5 degrees. 

7.3.4 Data Quality Control and Filtering

In this section, we describe the main criteria used to quality control the flux data. We began with 

wind direction because it is one of the main factors affecting the flux observations due to 

distortion from the platform structure. At about 60 degrees, the wind blew straight into the AC 

flux package; however, at this angle, the A platform blocked the AB flux location. At 

150 degrees, the wind blew straight into the AB location; the AC flux package was obstructed by 

the A platform (see Figure 49). This can be observed as deflections in wind directions (Figure 

48) and decelerations in wind speeds (Figure 47). 
72 

Figure 49. A, B, and C platform orientation relative to true north.
73 

Using the information from Figures 47, 48, and 49, we selected the following wind sectors as 

good potential wind directions for flux observations: AB (80° to 170°), (240° to 310°); AC (10° 

to 80°), (220° to 280°). Using these “bins” results in about 60% of usable data for year 2011 (by 

combining AB and AC data). 

Automatic screening criteria were applied to the flux data. Figure 50 shows examples of U and 

W spectra in good wind sectors for two different hours. The spectra fit well with the f-2/3 inertial 

subrange line (blue and red solid lines) for a particular hour, then behaved strangely for a 

consecutive hour. This could be due to distortion effects or noise. To filter out suspect values, 

we used the inertial dissipation method and computed u*, T*, q*, and then used those variables 

as filters to constrain our direct covariance calculations. 

Figure 50. U (blue) and W (red) spectra from the AB flux package at 0 and 6 UTC on 

August 9, 2011. The black dashed line indicates a slope of -2/3. 

The standard deviations of the 10 Hz data within the ten-minute averaging were also used to

filter the data. As an example, the standard deviation of the Automatic Gain Control (AGC) of 

the Li-Cor 7500 was used in conjunction with other filters (such as the mean of the AGC and the 

standard deviation of the water vapor) to remove invalid data. Also, the timespans when the 

optics were routinely cleaned by the automated washing system (see Figure 51) were rejected. 

Li-Cor observations with an AGC standard deviation greater than four were rejected. 

10-3 10-2 10-1 100 101

10-4

10-3

10-2

10-1

100 AB Su (blue) and AB Sw (red) for 2 hours

freqency (Hz)

f*Sx(f)
74 

Figure 51. Illustration of unfiltered (blue) and filtered (red) specific humidity data from the AB 

Li-Cor 7500 sensor. The low values indicate the occurrence of washing or rain. 

7.3.5 Comparison of Direct Covariance Flux to COARE-Calculated Flux

This section provides a summary of the analysis that compares observed and COARE-calculated 

sensible, latent, and momentum fluxes. 

Bulk estimates of air-sea fluxes were computed using the COARE bulk algorithm, version 3.0 

(Fairall et al. 2013). Cool-skin and warm-layer corrections were applied within the algorithm to 

correct the measured sea temperature to interfacial temperature. Direct wave measurements 

were not used in the code at this point in the analysis. Direct covariance latent heat fluxes were 

corrected for the density Webb effect. When computing the sensible heat flux, the humidity 

contribution to sonic temperature was removed using the bulk latent heat flux. No other 

corrections were applied for that comparison. After applying all corrections and filtering, about 

50% (180 days) of the data for 2011 remained. 
75 

The scalar fluxes and stress components can be parameterized as

HS = ρa cpCh U (TS − Ta)

Hl = ρa LvCe U (qS − qa)

τi = ρa Cd U (usi − ui)

where ρa is the air density, cp is the specific heat of air at constant pressure, and Lv is the latent 

heat of vaporization. Cd, Ch, and Ce are the transfer coefficients for stress, sensible heat, and 

latent heat, respectively. U is the average value of the wind speed relative to the sea surface, 

Ta is the air temperature, qa is the water vapor mixing ratio, and ui is one of the horizontal wind 

components. The remaining parameters are the sea surface skin temperature TS, the interfacial 

water vapor mixing ratio qS, and the surface current usi. Note that sea surface skin 

temperature TS (Tsea in figures below) was estimated from the LSU temperature sensor (1 m 

below mean water level) before installation of the floating thermistor (~20 cm depth), and using 

the 20 cm thereafter (for additional information, see Section 7.3.3.3, Sea Temperature). The 

measured skin temperature was not used in this preliminary analysis. Qs (Qsea in the figures 

below) is the calculated interfacial water vapor mixing ratio and is computed from the saturation 

mixing ratio for pure water at the sea skin temperature:

qs = 0.98 qsat (Ts) 

where the dimensionless constant, 0.98, accounts for the reduction in water vapor pressure 

caused by a typical salinity of 34 parts per thousand. 

To compare the measured versus calculated fluxes, we have created two types of figures: one to 

show the flux as a function of its corresponding scalar differences 

(e.g., TS − Ta for sensible heat), and the other to separate the data from the AB and AC bridges. 

Plot details and findings for wind stress, sensible heat flux, and latent heat flux follow. 

7.3.5.1 Wind Stress

We plotted the 10-minute average BULK (calculated) stress (Taub) and the measured streamwise 

stress (Tauc) as a function of 10-m neutral wind speed so that effects of stability are removed 

(Figure 52, lower panel). The data have been averaged in wind speed bins with 1 m/s bin widths. 

The streamwise measured stresses were averaged together when coincident wind sectors were 

found for both the AB and AC bridges. To determine whether the stress measured on one bridge 

was more biased than the stress measured on the other bridge compared to COARE-calculated 

stress, we have also plotted the scatter plots of calculated stress (Taub) versus measured 

streamwise stress (Tauc) for the AB and AC bridges (Figure 52, top panel). 

Figure 52 shows that (1) measured stress is usually much less than calculated stress, and (2) both 

bridges appear to observe similar stress, so the discrepancy between measured and calculated 

stress is not because one measurement system is more biased than the other one.
76 

7.3.5.2 Sensible Heat Flux

We plotted the BULK (calculated) sensible heat flux (Hsb) and the measured sensible heat flux

(Hsc) as a function of the temperature difference (TS − Ta) to look at possible temperature biases 

that could affect the measured flux. Hsc and Hsb were also normalized by wind speed to remove 

the wind speed dependency (Figure 53, lower panel). The data have been averaged in 

temperature bins with 0.5°C bin widths. Similarly, to determine whether flux from one bridge 

was more biased than the other compared to calculated flux, we also plotted the scatter plots of 

calculated sensible heat flux (Hsb) versus measured sensible heat (Hsc) for the AB and AC 

bridges (Figure 53, top panel). 

Figure 53 shows that (1) measured sensible heat flux is usually much less than calculated 

sensible heat flux, and (2) besides some outliers, observations from both bridges appear similar.

7.3.5.3 Latent Heat Flux

We plotted the BULK (calculated) latent heat flux (Hlb) and the measured latent heat flux (Hsc) 

as a function of the specific humidity difference (qS − qa). The fluxes were also normalized by 

wind speed (Figure 54, lower panel). The data have been averaged in specific humidity bins 

with 0.5 g/kg bin widths. We also plotted the scatter plots of BULK latent heat flux (Hlb) versus 

measured latent heat (Hlc) for the AB and AC bridges (Figure 54, top panel). 

Figure 54 shows that (1) measured latent heat flux is usually much less than calculated latent 

heat flux, and (2) the AC measured latent heat flux is noisier than the AB bridge latent heat flux, 

which might slightly affect the measured flux versus calculated flux comparison. However, it 

does not explain the large disagreement between the measured and calculated latent heat flux.

7.3.5.4 Discussion

Several possibilities have been investigated as to why the fluxes do not compare well, but no 

physical reasons have yet explained the differences. Additional analysis will be required to fully 

characterize the data. For example:

The BULK meteorological data inputs need to be further evaluated. 

Regional wave and sea surface temperature effects (cool skin/warm layer) need to be researched

to determine their influence on flux estimates in the Gulf of Mexico. 

No obvious flow distortion effects were identified, but additional analysis and comparison to 

data from nearby sources will be required to evaluate flow tilt limits and the wind 

speed/direction data. However, this relates to mean state variables and the turbulence variables 

could behave differently. Flow distortion is more of a concern for stress rather than sensible and 

latent heat flux.

After further analysis, corrections may need to be applied to the latent heat flux (Li-Cor specific 

humidity). The Li-Cor 7500 signal has higher mean humidity (~10-15%) compared to the 

surface sensor measurements. Scaling the turbulent fluctuations based upon the mean ratio of 
77 

the Li-Cor humidity measurements and the surface humidity measurements may help reduce the 

discrepancies between the BULK and covariance latent heat fluxes. 

The discrepancies between the BULK and covariance latent heat fluxes could also be real and 

show that the COARE algorithm needs to be refined for use in the Gulf of Mexico. Perhaps 

investigating offshore flow versus inland flow might help explain some of the differences. 

Additional analysis of inter-comparison of instruments used in this study might also be 

beneficial.
78 

Figure 52. Top panel: BULK (calculated) stress (Taub) versus measured streamwise 

stress (Tauc) for the AB and AC bridges combined. Lower panel: Same 

flux, but bin-averaged and plotted as a function of 10-m neutral wind 

speed.
79 

Figure 53. Top panel: BULK (calculated) sensible heat flux (Hsb) versus measured 

sensible heat flux (Hsc) for the AB and AC bridges. Lower panel: Same 

flux, but divided by wind speed and plotted as a function of sea-air 

temperature difference.
80 

Figure 54. Top panel: BULK (calculated) latent heat flux (Hlb) versus measured latent heat 

flux (Hlc) for the AB and AC bridge. Lower panel: Same flux, but divided by wind 

speed and plotted as a function of sea-air specific humidity difference.
81 

7.3.6 COARE Data Analysis Summary

A preliminary analysis comparing the COARE flux estimates with direct measurements was

performed for data collected from the ST-52 platform. Preliminary results show discrepancies 

between the BULK and covariance fluxes that require extensive analysis to be fully understood. 

The differences may be due to the platform distortion effects or may be real. The COARE 

algorithm may need to be updated for the Gulf of Mexico region. Further study is needed to 

characterize offshore compared with inshore influences and to optimize the COARE algorithm 

for wave effects. 

83 

8. CONCLUSIONS AND RECOMMENDATIONS

The overall objective of this study was to collect meteorological and oceanographic data that will

later be used to better characterize the atmospheric boundary layer over the Gulf of Mexico. 

This information will be used in future work to improve meteorological and air quality modeling 

along the Gulf Coast. This project was successful in yielding an unprecedented set of 

atmospheric boundary layer and oceanographic measurements covering about 18 months over 

the Gulf of Mexico, which represents a range of air-sea interactions and processes that occur 

over all seasons. All data have been quality-controlled, are ready for use in data analysis and 

modeling, and are available on DVD. 

From this study we concluded that long-term, unmanned operations of highly sophisticated 

instruments on an active oil platform are feasible and can provide high quality data, provided that 

the following key elements are addressed:

1. Sodar instruments require both electronic and physical noise reduction systems to obtain 

high-quality measurements from lower-boundary-layer winds in the busy and noisy 

environment of an operating oil platform. 

2. Fast-response specific humidity instruments require a daily lens cleaning to remove salt 

deposits. 

3. Catwalks placed between platforms are useful for capturing near-surface flux 

measurements.

4. A mini-shelter cooled with only dilute anti-freeze can be used to house computers if an 

air-conditioned shelter is not available. 

5. Constant-depth, near-surface ocean temperature measurements can be made without a 

buoy using a system mounted to a platform leg. 

6. Two-way Internet is needed to monitor instruments and their data so that problems can be 

quickly identified and resolved. 

In addition, preliminary analysis has shown that model predictions of boundary-layer processes 

do not always agree with the observations from this study; thus, these measurement data provide 

an excellent source of information to use for evaluating and improving meteorological models. 

Based on lessons learned during the study, preliminary data analysis, and discussions with other 

experts in the field, we provide the recommendations outlined below. 

8.1 MEASUREMENT-RELATED RECOMMENDATIONS

• Investigate the integration of robust boundary-layer offshore measurements

into a long-term network for various applications. 

• Extend this type of study to other locations in the Gulf (deep water) or other 

offshore locations. Because air-sea interactions can be highly dependent on 

wave conditions, the current data taken in shallow water may not be 

representative of conditions in deep water. 
84 

• Investigate critical heights for flux measurements, including measurement 

locations for mean state variables for the COARE model (e.g., surface-layer 

depth). 

• Conduct measurements to better characterize the influence of the platform 

heating and cooling cycles on the on-deck measurements and how the cycles 

influence the bulk flux calculations. 

8.2 DATA ANALYSIS-RELATED RECOMMENDATIONS

• Evaluate the spatial representativeness of the measurements by comparing 

routine measurements taken on the platform in this study to other nearby 

measurements, such as those collected at nearby buoys. This information will 

help guide the applicability of findings from future analyses to other areas in 

the Gulf. 

• Characterize offshore atmospheric boundary layer conditions and processes; 

investigate the influence of large- and local-scale meteorology and ocean 

conditions on atmospheric boundary processes; and characterize air-sea 

processes under storm conditions. There is currently limited information on 

the above processes. The data collected in this study can help us better 

characterize and understand meteorological conditions and processes in the 

Gulf.

• Evaluate the NAM and other meteorological models’ abilities to accurately 

represent the atmospheric and oceanographic conditions, including the various 

processes (e.g., warm-ocean, cool-skin, fluxes, wind profiles). Models are 

relied upon for a range of applications (e.g., emergency response, wind 

energy, forecasting, etc.); thus, accuracy is important. Preliminary analysis 

suggests that the NAM may not always represent boundary layer conditions 

over the Gulf, but more investigation is needed. 

• Further compare COARE bulk flux estimates with measurements, including 

how each calculation in COARE influences the estimates. Preliminary 

analysis showed that the COARE bulk flux estimates and measurements do 

not always agree, but more analysis is required to determine when and why 

they do and do not agree. 
85 

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The Department of the Interior Mission

As the Nation’s principal conservation agency, the Department of the Interior 

has responsibility for most of our nationally owned public lands and natural 

resources. This includes fostering the sound use of our land and water 

resources; protecting our fish, wildlife, and biological diversity; preserving the 

environmental and cultural values of our national parks and historical places; 

and providing for the enjoyment of life through outdoor recreation. The 

Department assesses our energy and mineral resources and works to ensure 

that their development is in the best interests of all our people by encouraging 

stewardship and citizen participation in their care. The Department also has a 

major responsibility for American Indian reservation communities and for 

people who live in island communities. 

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The Bureau of Ocean Energy Management (BOEM) promotes energy 

independence, environmental protection, and economic development through 

responsible, science-based management of offshore conventional and 

renewable energy.