OCS Study 

MMS 2000-075 

Meteorology of the 

Northeastern Gulf of Mexico: 

Data from 1995 to 1997 

Final Report 

32-

+ 

Albany 

Hatt}isburg + . .+p~e~ + t0 

3 

Mo 

Tallahassee t 

+f Pan City t0 

9 New - 0 

t 

t 

2 111- 

27-

+ Land Based 

C-MAN 

Buoy 2 

4 Upper Air 

-90 -89 -80 -87 -86 -B5 -84 -83 -82 

U.S. Department of the Interior 

Minerals Management Service 

Gulf of Mexico OCS Region 
OCS Study 

MMS 2000-075 

Meteorology of the 

Northeastern Gulf of Mexico: 

Data from 1995 to 1997 

Final Report 

Authors 

M.A. Yocke 

C.A. Emery 

P. Roberts 

C. MacDonald 

D. Ladner 

J . Prouty 

A. Barnett 

Prepared under MMS Contract 

1435-01-97-CT-30826 

by 

ENVIRON International Corporation 

101 Rowland Way, Suite 220 

Novato, California 94945 

and 

Sonoma Technology Inc . 

AeroVironment Environmental Service Inc. 

Published by 

U .S. Department of the Interior 

Minerals 1management Service New Orleans 

Gulf of Mexico OCS Region October 2000 



ABSTRACT 

During this three-year study entitled "Meteorology of'the Northeastern Gulf of Mexico" 

meteorological data for the 1995-1997 period from all available sources (National Weather 

Service, National Data Buoy Center, stations funded by Minerals Management Service, and 

other private sources) spanning the region of the Northeastern Gulf of Mexico (NEGOM) were 

collected . Estimates of the temporal/ frequency contents and spatial scales of variation from 

these wind measurements were prepared. Humidity, air and sea-surface temperature 

measurements for the same period throughout the NEGOM were also analyzed and maps of 

the mean and variance fields, and estimate the temporal and spatial scales of variation were 

prepared. Calculations of mean and variance fields of surface wind stress and heat flux over 

the NEGOM were made and maps prepared of the quantities to estimate wind curl and vertical 

vorticity means and variability after effects of sea breezes were identified via filtering 

techniques . These data were used to study winter cyclogenesis and cold frontal passage in the 

NEGOM. Assessments were made of frontal passage and the modification of air masses 

affecting the local fields of temperature, humidity, pressure and other relevant meteorological 

parameters . Temporal and spatial variation scales were analyzed as functions of atmospheric 

and climatological types . Prognostic meteorological model output was also archived for the 

same period to supplement the observational data base for the NEGOM region . A computerbased "Expert" software system was developed that allows rapid, real-time access to the 

information and products developed in this study and aid in interpretation of current and 

forecast meteorological conditions . 

v 

TABLE OF CONTENTS 

Page 

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi 

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii 

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv 

1 . INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1 

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1 

Study Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-2 

Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-2 

Data Period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3 

Deliverables and Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3 

2 . OBSERVATIONAL DATABASE DEVELOPMENT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1 

Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ?-1 

Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1 

Observational Data Archival and Database Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2 

Gridded Eta Model Archive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7 

Acquiring Copies of the NEGOM Meteorological Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-8 

3 . ETA MODEL OUTPUT DATABASE DEVELOPMENT . . . . . . . . . . . .. . . . . . . . . . . . . . . . . 3-1 

Availability of Prognostic Meteorological Model Results for the NEGOM Region . . . 3-1 

Usefulness of Eta Model Output for Calculating Derived Quantities . . . . . . . . . . . . . . . . . . . . 3-2 

Scope of Eta Model Output Archival and Analysis Efforts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3 

4. METEOROLOGICAL TYPECASTING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1 

Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1 

Previous Studies of Meteorological Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1 

Revised Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-7 

Daily Typecasting for 1995-1997 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-11 

vii 
5. METEOROLOGICAL DATA REDUCTION AND ANALYSIS . . . . . . . . . . . . . . . . . . . . 5-1 

Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1 

Statistical Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-2 

Issues Associated with Derived Quantities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-7 

Methodology for Calculating Derived Parameters from Observed Data . . . . . . . . . . . . . . .5-11 

Calculation of Derived Parameters from Eta Model Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-15 

6. SYNTHESIS AND CONCEPTUAL MODELS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6-1 

Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1 

Discussion of Frequency of Weather Types by Season . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1 

Discussion of Statistical Differences Between Seasons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-3 

Discussion of Statistical Differences Between Weather Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-4 

Discussion of Time-series Statistical Differences Between Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v-7 

Analysis of Frontal Passages and Cyclogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G-9 

7. DESCRIPTION OF AND USER'S GUIDE TO THE 

EXPERT SOFTWARE SYSTEM (NEGOMES) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7-1 

Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1 

NEGOMES Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1 

NEGOMES Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-2 

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-2 

How to Use the NEGOMES Main Program Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-5 

Summary/Station Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-6 

How to Use the Stations Statistics Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-7 

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-) 

How to Select a Station Using the Map Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-9 

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-10 

How to Use the Transect Statistics Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-10 

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7-12 

How to Locate Transects Using the Map Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-13 

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-14 

How to Use the Gridded Statistics Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-14 

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-16 

How to Use the Data Display Screens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-16 

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-16 

How to Use the Eta Surface Data Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-17 

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-17 

How to Use the Eta 3-D Data Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-18 

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-19 

How to Use the Eta Surface Statistics Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1) 

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-20 

How to Use the Eta Data Display Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-?0 

viii 
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-?2 

How to Use the Trajectory Evaluation Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-22 

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R-1 

Appendix A: Seasonal mean and variability (standard deviation) by 

meteorological type of key observed and derived meteorological 

variables from coastal and ocean-based observation sites and Eta 

grid points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1 

Appendix B: Time Series Analyses for Wind Speed and Temperature for 

16 Selected NEGOM Offshore and Coastal Observation Stations . . . . . . . . . . . . . . . . . . . . . . B-1 

Appendix C: Filtered (12-hour Block Filter) Time Series Analyses for 

Wind Speed for 16 Selected NEGOM Offshore and Coastal 

Observation Stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .C-1 

CD-ROMs: NEGOMES - Release Candidate Version 1 .0 (Set of 5 CDS) 

ix 

LIST OF FIGURES 

Figure 2-1 . Location of land-based, buoy, C-MAN, and upper meteorological 

monitoring sites in the MMS NEGOM study area . 

(Note : coastline may not be accurately depicted) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . _-J 

Figure 4-1 . Representative examples of eight synoptic weather types for 

New Orleans, Louisiana (from Muller and Wax, 1977). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-4 

Figure 4-2. The frequency of occurrence for four weather types over 

New Orleans, Louisiana from 1961 through 1990 based on 

the data provided by the Louisiana Office of State Climatology . . . . . . . . . . . . . . . . . . . . . . . . . 4-5 

Figure 4-3 . The frequency of occurrence for Continental Index 

(weather types CH + FOR),Tropical Index 

(GR + FGR + GTD + GH), and Storminess Index 

(FOR + FGR + GTD). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-6 

Figure 4-4. Midwest Continental High (MCH), high pressure generally 

centered west of the Mississippi River and north of the 

Texas/Mexico border . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-12 

Figure 4-5 . Eastern Continental High (ECH), high pressure generally 

centered east of the Mississippi River and west of the eastern 

seaboard and from just north of the Gulf Coast to the 

US/Canada border . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-13 

Figure 4-6. Bermuda High (BH), high pressure generally centered in 

the Atlantic Ocean from Florida to Maine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1-4 

Figure 4-7. Midwest Low (MLOW), low centered east of the Rockies 

with or without a north/south-orientated front west of 

New Orleans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-15 

Figure 4-8. Gulf front or trough N/S (GFNS), north/south-orientated 

front or trough between New Orleans and Tampa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-16 

Figure 4-9. Gulf front or trough E/W (GFEW), east/west-orientated 

front or trough located in the Gulf region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-17 

Figure 4-10. East Coast Low (ELOW), low pressure system east of the 

Mississippi River with west/east-orientated isobars over the 

Gulf region and no front over the Gulf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-18 

Figure 4-11 . Gulf High (GH), high surface pressure centered in the Gulf, 

south of the Northeast Gulf from Florida to Texas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-19 

Figure 4-12 . No Gradient (NOGRAD), no surface pressure gradient in the 

Gulf region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-20 

Figure 4-13 . Hurricane, Tropical Storm, or Depression (TS), a tropical storm 

in the Gulf region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-21 

Figure 4-14 . Decision tree flowchart describing the general methodology 

used to select a synoptic category . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-Z2 

Figure 5-1 . An example of a gridded Eta model statistics map (this 

example shows gridded mean surface temperatures for 1996 

and the ECH type meteorology) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-18 

xi 
Figure 5-2 . Example of gridded mean winds (vectors and contoured speed) 

for the Midwest Continental High category over the entire data period . . . . . . . 5-19 

Figure 5-3 . Example of time series plots of temperature for the 

Elgin AFB/Valparais site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5-20 

Figure 5-4 . Example of time series plots of temperature for the Mid Gulf site . . . . . . . . . . . . 5-21 

Figure 5-5 . Location of deep water transects in the NEGOM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . _5-22 

Figure 5-6 . Location of coastal water transects in the NEGOM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-23 

Figure 7-la. Flow chart depicting the sequencing of various screens within 

the MMS NEGOMES (observed data) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-3 

Figure 7-lb . Flow chart depicting the sequencing of various screens within 

the MMS NEGOMES (Eta model data) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-4 

Figure 7-2 . Main Program Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-5 

Figure ?-3 . Station Statistics Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . , . . . . . . . 7-6 

Figure 7-4 . Selecting a Station Using the Map Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-8 

Figure 7-5 . Transect Statistics Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-10 

Figure 7-6 . Selecting a Transect Using the Map Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-12 

Figure 7-7 . Gridded Statistics Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7-13 

Figure 7-8 . Data Display Screens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7-15 

Figure 7-9 . Eta Surface Data Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7-16 

Figure 7-10. Eta 3-D Data Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-17 

Figure 7-11 . Eta Surface Statistics Screen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7-18 

Figure 7-12 . Eta Data Display Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7-20 

Figure 7-13 . Trajectory Evaluation Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7-22 

xii 
LIST OF TABLES 

Table 2-1 . Station data obtained from National Climatic Data Center (NCDC) . . . . . . . . . . . . 2-3 

Table 2-2 . Station data obtained from National Data Buoy Center (NDBC) . . . . . . . . . . . . . . . . 2-4 

Table 2-3 . Access database structure for observational data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7 

Table 3-1 . Summary of available meteorological grid modeling archives . . . . . . . . . . . . . . . . . . . 3-4 

Table 3-2 . Configuration of the 1996-97 Eta ACCESS database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-5 

Table 4-1 . Synoptic weather type frequency (in percent) at New Orleans, 

Louisiana from 1961 through 1990 

(Data Source : Louisiana Office of State Climatology) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-7 

Table 4-2 . Relationship between isobar spacing and 10-m geographic wind speed . . . . . . . 4-10 

Table 4-3 . Frequency of occurrence (days) for each synoptic 

category by season and year. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-11 

Table 5-1 . Example of a station statistics table : (this case shows statistics for 

station 42001, for all years, ECH meteorology types and the winter season) . 5-5 

Table 5-2 . Surface meteorological data sites used in the time series 

analysis for the 1995-1997 data period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-6 

Table 5-3 . Example of Laplacian and vorticity calculations using 

two differencing methodologies at 1200 LST, July 14, 1996 . . . . . . . . . . . . . . . . . . . 5-10 

Table 5-4 . List of deep and coastal transects, and site IDs that define each . . . . . . . . . . . . . . . . 5-17 

Table 6-1 . 1996-1997 annual and seasonal frequencies of occurrence for each 

meteorological regime defined in Section 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-3 

Table 6-2 . Annual and seasonal mean and variability (standard deviation) 

of key observed and derived meteorological variables from 

coastal and ocean-based observation sites and Eta grid points . . . . . . . . . . . . . . . . . . . 6-4 

Table 6-3 . Annual mean and variability (standard deviation) by 

meteorological type of key observed and derived 

meteorological variables from coastal and ocean-based 

observation sites and Eta grid points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-6 

Table 6-4. Diurnal land-sea breeze influences at surface meteorological 

data sites based on interpretation of time series analysis 

for the 1995-1997 data period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-8 

xiii 

ACKNOWLEDGMENTS 

Many persons deserve acknowledgments for completion of this phase of the study, including 

staff at ENVIRON International Corp., subcontractors Sonoma Technology Inc., and 

AeroVironment Environmental Services Inc., the MMS Contracting Officer's Technical 

Representative, and the Scientific Review Board (SRB) members . 

Several persons contributed substantially to the writing of this report, but all participants made 

contributions to the ideas, results, and accomplishments to date of the study . Below we 

recognize those who made significant contributions to the study (in no particular order) : 

ENVIRON International Corporation 

Mark Yocke 

Chris Emery 

Till Stoeckenius 

Gary Wilson 

Annette Taylor 

Mark Grondona 

Sonoma Technology, Inc. 

Paul Roberts 

Clinton MacDonald 

Donna Ladner 

Jeff Prouty 

AVES 

Alex Barnett 

David Bush 

Lydia Chu 

LSU 

S .A . Hsu 

MMS 

Alexis Lugo-Fernandez 

SRB 

Richard McNider - UAH 

Allan Clarke - FSU 

xv 

1 . INTRODUCTION 

BACKGROUND 

There is renewed interest in oil and gas extraction activities in the Northeastern Gulf of 

Mexico (NEGOM). While indications are that the resources to be recovered are likely to be 

natural gas rather than oil, the threat of an oil spill in the NEGOM from these activities 

remains a great concern . Further, the National Environmental Policy Act (NEPA) mandates 

multidisciplinary environmental assessments of major Federal actions, such as oil and gas 

leases and related activities in the NEGOM. Such assessments could benefit from a 

meteorological database, especially in regards to oceanographic studies, oil spill trajectory 

estimates, air quality/plume dispersion calculations, and operational meteorological forecasts 

in the NEGOM. 

The Minerals Management Service (MMS) contracted with ENVIRON International 

Corporation (ENVIRON) and its team to conduct a meteorological study of the NEGOM and 

to assemble and analyze such a database. ENVIRON's team was composed of Sonoma 

Technology Inc., AeroVironment Environmental Services, Evans Hamilton Inc ., and Dr. S .A . 

Hsu of Louisiana State University . 

The research conducted for this study resulted in a database useable for ongoing and future 

MMS studies in the NEGOM. In addition, the meteorological and oceanographic science 

communities should benefit from an improved understanding of atmospheric-oceanic 

interactions in this area. The database and software system products from this study may be 

used as a type of "handbook" to be utilized in the future by planners and analysts in the case 

of new industrial activities in the area, as well as on the occasion of monitoring and predicting 

conditions associated with non-routine or accidental hazardous events . 

Knowledge acquired through the variety of analyses performed in this study will improve 

understanding of wind field patterns and sea breeze structures, atmospheric boundary layer 

behavior and structures, wind stress patterns on the sea surface and effects on ocean currents, 

cold air outbreaks, and boundary layer moisture fluxes across the land-sea interface. Further, 

the database and software products will be useful for several ongoing and proposed 

oceanographic studies in the NEGOM. One such study is the 1996-97 "Northeastern Gulf of 

Mexico Inner Shelf Circulation Study" program, designed to provide analyses of sea surface 

currents in the same area . It is anticipated that the raw data and products from this NEGOM 

meteorological study could be used to reconcile wind and stress patterns with trajectory data 

from "Northeastern Gulf of Mexico Inner Shelf Circulation Study" drifters and supply 

valuable information concerning the state of the atmosphere for any period of interest during 

that study . 

1-1 
STUDY OBJECTIVES 

The objectives of this study were : 

1) To collect wind data from available sources (National Weather Service, National Data 

Buoy Center, stations funded by Minerals Management Service, and other private 

sources) spanning the NEGOM, and estimate the temporal/frequency contents and 

spatial scales of variation from these measurements; 

2) To collect humidity, and air and sea-surface temperature measurements throughout the 

NEGOM from these same institutions, prepare maps of the mean and variance fields, 

and estimate the temporal and spatial scales of variation; 

3) To calculate mean and variance fields of surface wind stress and heat flux over the 

NEGOM and prepare maps of these quantities, to estimate wind curl and vertical 

vorticity means and variability with and without effects of sea breezes (i .e . with and 

without filtering techniques), and to use these data to the maximum extent possible to 

study winter cyclogenesis and cold frontal passage in the NEGOM; 

4) To assess how frontal passage and the modification of air masses affect the local fields 

of temperature, humidity, pressure and other relevant meteorological parameters ; 

5) To estimate the transport of water-vapor and latent heat across water-land boundaries 

or coastlines and the properties of the atmospheric boundary layer, and to evaluate the 

temporal and spatial variation scales analyzed as functions of atmospheric and 

climatological forcing ; 

6) To collect, archive, and analyze the gridded output of a prognostic meteorological 

model (the Eta model) for the same time periods as the observational database . 

7) To develop an ACCESS relational database containing both observed data, derived 

parameters, and gridded Eta model output for a two-year period (1996-1997). 

8) To develop an "expert" software system to provide the users of the NEGOM 

meteorological database with a simple way to access and display the database and the 

data analysis products of the study . 

The study was divided into two phases : (1) data collection followed by (2) data reduction, 

analysis, interpretation, and data synthesis, and report preparation. 

STUDY AREA 

The study area was defined as the region enclosed within the latitudes of 28 °-32 ° N and the 

longitudes 82°-90°W . Figure 2-1 shows the geographical location of the study area. 

1-2 
DATA PERIOD 

The data collection period of interest for this study was Jan 1, 1996 through December 31, 

1997. 1995 observational data were also included in the ACCESS database because they were 

collected at the beginning of the study to test archival and analysis methods . 1995 Eta results 

were not available from NCAR. The ACCESS data base containing the 1995 observational 

data is available on magnetic tape from MMS, but 1995 observational data were not included 

in the NEGOMES software system. 

DELIVERABLES AND PRODUCTS 

In addition to this report and a prior interim report, a number of other deliverables were 

produced in this study . We acquired data from many agencies and processed more than 200 

Gigabytes of meteorological data . From this raw data, we produced an immense relational 

database (Access) containing more than 5 .2 Gigabytes of meteorological data . Users can 

easily access this data and rapidly sort and prepare reports of the data subsets utilizing MS 

Access97 software. The Access data set is available on magnetic tape cartridge from MMS or 

the National Climatic Data Center (NCDC) . The ACCESS database contains all observational 

data and Eta model output for the study period . 

In addition, we produced and delivered a software system called the North East Gulf of 

Mexico Expert System (NEGOMES). This software allows its users to browse the statistical 

and analysis products of the study . The system's graphical user interface capabilities allow 

users to easily access, select, sort, view, and navigate data maps and other tabular and 

graphical data products. The expert system also allows the user to quickly forecast and view 

projections of wind trajectories based on the stored wind statistics . The expert software 

system and supporting data are available from the MMS on a 5 CD-ROM set. The supporting 

NEGOMES database consists of nearly 3 Gigabytes of processed meteorological data and 

graphical displays that includes : 

" 1996-1997 surface observation statistics ; 

" 1996-1997 aloft observation statistics ; 

" 1996-1997 gridded observation statistics ; 

" 1996-1997 transect data derived from observed surface data ; 

" 1996-1997 Eta gridded surface data; 

" 1996-1997 Eta gridded 3-D data; 

" 1996-1997 gridded Eta surface statistics . 

1-3 

2 . OBSERVATIONAL DATABASE DEVELOPMENT 

OVERVIEW 

The study team conducted a survey of available measurement and modeling databases to 

compile into the MMS NEGOM meteorological database . The survey revealed that routine 

measurement data could be obtained only from the National Climatic Data Center (NCDC) 

and the National Data Buoy Center (NDBC) . Meteorological data were also available from 

Louisiana State University for a special monitoring network on Breton Island . 

NCDC was contacted to establish an agreement that NCDC would be the final repository of 

the MMS NEGOM meteorological database . A letter was prepared for NCDC documenting 

the agreement, which also explained the goals of the project, the agency funding the study, the 

project management staff, and a description of the data structures that would make up the final 

database. 

Many organizations were contacted to inquire about the existence and availability of 

meteorological data for the study period . Ultimately, the MMS NEGOM observational 

meteorological database was populated using all 1995-1997 surface and upper air data in the 

NEGOM study area, purchased from the NCDC, 1995-1997 C-MAN and buoy data 

downloaded from the NDBC web site, and 1995-1996 Breton Island surface data obtained 

from Louisiana State University (LSU) . The database was created using Microsoft Access. 

This Access database was used as a basis for development of systems and programs to conduct 

statistical and diagnostic analyses . Recall that, while the study period is officially 1996-1997, 

we also collected 1995 data early in the study to test analysis and archival methods to be used 

later on the 1996-1997 data. 

DATA ACQUISITION 

Potential Routine Observational Data Sources 

The following government agencies, and private companies were contacted for monitored 

meteorological data collected within the study area (28°N to 32°N, 82°W to 90°V) for 1995, 

1996, and 1997 : 

" National Climatic Data Center (NCDC) 

" National Data Buoy Center (NDBC) 

" USGS Center for Coastal Geology 

" Breton Island (National Park Service) 

" Shell Oil Company 

" Chevron 

" Marathon 

" Conoco 

2-1 
" Exxon 

" Mobil 

" Freeport-McMoran 

" Amoco 

" Texaco 

" ORYX (Sun) 

" British Petroleum 

" U.S . Navy, Office of Naval Research 

" U.S . Navy, Fleet Numerical, Naval Oceanographic Office 

" NWS Cooperative Data 

" Southern Regional Climate Center 

" Neptune Sciences, Inc . 

" Evans-Hamilton, Incorporated 

" Ocean Weather 

" University of Alabama - Dauphin Island Campus 

" Pennsylvania State University 

" Louisiana State University (LSU) 

Only the NCDC, NDBC, and LSU were able to provide substantive and coherent 

meteorological data for the study region during the period of interest . The data specifically 

obtained are described later. 

OBSERVATIONAL DATA ARCHIVAL AND DATABASE STRUCTURE 

Data Acquired 

Table 2-1 lists the data obtained from NCDC sites in the study area, with the date range of 

valid data for each. Table 2-2 lists the data obtained from NDBC sites in the study area, with 

the date range of valid data for each. Figure 2-1 displays the locations of all measurement 

sites within the analysis domain . Two sites are located outside this area : buoy 42002 to the 

west of 91'W, and Rawindsonde observation (RAOB) site 13889 near Jacksonville, FL. 

2-2 
Table 2-1 . 

Station data obtained from National Climatic Data Center (NCDC) . 

Station ID Station Name Station Type Range of Observables 

13858 Eglin AFB/Valparais Upper Air 95-97 RAOB 

13861 Waycross/Ware Co . Upper Air 1/95 RAOB 

13889 Jacksonville International Upper Air 95-97 RAOB 

53813 Slidell Municipal Upper Air 95-97 RAOB 

93805 Tallahassee Regional Upper Air 95-97 RAOB 

722055 Ocala Muni (AW08) Land Based 95-97 wind, t, Td, vsb 

722120 Cross City Land Based 95-6/96 wind, t, Td 

722130 Waycross/Ware Co . Land Based 95-96 wind, t, Td, vsb 

722135 Alma/Bacon Land Based 95-97 wind, t, Td, p, vsb 

722140 Tallahassee Regional Land Based 95-97 wind, t, Td, p, vsb 

722146 Gainesville Regional Land Based 95-97 wind, t, Td, p, vsb 

722160 Albany Municipal Land Based 95-97 wind, t, Td, p, vsb 

722166 Valdosta Regional Land Based 95-97 wind, t, Td, p, vsb 

722210 Eglin AFB/Valparais Land Based 95-97 wind, t, Td, p, vsb 

722215 Crestview/Bob Sikes Land Based 95-97 wind, t, Td, p, vsb 

722223 Pensacola Regional Land Based 95-97 wind, t, Td, p, vsb 

722225 Pensacola NAS Land Based 95-97 wind, t, Td, p, vsb 

722226 Whiting Field NAS-North Land Based 95-97 wind, t, Td, p, vsb 

722230 Mobile/Bates Field Land Based 95-97 wind, t, Td, p, vsb 

722235 Mobile Downtown Land Based 95-97 wind, t, Td, vsb 

722245 Panama City/Bay Co . Land Based 95-97 wind, t, Td, vsb 

722246 Duke Field/Eglin Aux Land Based 95-97 wind, t, Td, p, vsb 

722267 Troy Municipal Land Based 95-97 wind, t, Td, p, vsb 

722268 Dothan Municipal Land Based 95-97 wind, t, Td, vsb 

722269 Cairns AAF/Ozark Land Based 95-97 wind, t, Td, p, vsb 

722275 Andalusia/OPP Airport Land Based 95-97 wind, t, Td, p, vsb 

722276 Schell AHP Land Based 95 wind, t, Td, vsb 

722307 Golden Tri Land Based 95-97 wind, t, Td, vsb 

722309 Grand Isle Land Based 95-97 wind, t, Td, vsb 

722310 New Orleans Int'1 Airport Land Based 95-97 wind, t, Td, p, vsb 

722316 New Orleans NAS Land Based 95-97 wind, t, Td, p, vsb 

722348 Pine Belt Regional AWOS Land Based 95-97 wind, t, Td, vsb 

747685 Gulfport-Biloxi Land Based 95-97 wind, t, Td, p, vsb 

747686 Kessler AFB/Biloxi Land Based 95-97 wind, t, Td, p, vsb 

747750 Tyndall AFB Land Based 95-97 wind, t, Td, p, vsb 

747770 Hurlbert Field Land Based 95-97 wind, t, Td, p, vsb 

747810 Moody AFB/Valdosta Land Based 95-97 wind, t. Td, n, vsb 

wind = wind speed and direction p = surface pressure 

vsb = visibility t = surface air temperature 

Td = dew point temperature RAOB = rawindsonde observation 

2-3 
Table 2-2 . 

Station data obtained from National Data Buov Center 

ame 

42001 Mid Gulf Buoy 95- 97 wind, t, p, wave 

42002 West Gulf Buoy 95- 97 wind, t, p, sst, wave 

42003 East Gulf Buoy 95- 9/96 wind, t, p, sst, wave 

42007 OTP Buoy 95- 97 wind, t, p, sst, wave 

42036 West Tampa Buoy 95- 97 wind, t, p, sst, wave,vsb 

42039 Pensacola S Buoy 95- 97 wind, t, p, sst, wave 

42040 Mobile South Buoy 95- 97 wind, t, p, Td, sst, wave 

BURL1 Southwest Pass, LA C-Man 95- 97 wind, t, p 

BUSL1 Bullwinkle Block 65 C-Man 8/95 - 10/96 wind, t, p, sst 

CDRF1 Cedar Key, FL C-Man 95- 97 wind, t, p 

CSBF1 Cape San Blas, FL C-Man 95- 97 wind, t, p, Td 

DPIA1 Dauphin Island, FL C-Man 95- 97 wind, t, p, sst 

GDIL1 Grand Isle, LA C-Man 95- 97 wind, t, p, sst, dp 

KTNF1 Keaton Beach. FL C-Man 95- 97 wind, t, n 

wind = wind speed and direction 

t = surface air temperature 

Td = dew point temperature 

p = surface pressure 

vsb = visibility 

sst = sea surface temperature 

wave = wave height, dominant period, mean period 

Observational Data Screening and Validation 

NCDC Data 

NCDC performed extensive validation measures at their facilities before the data are made 

available to the public . All NCDC data were subjected to a four step quality review process 

that included: 

- Checking the data for readability and inventorying ; 

- Converting the data into the DATSAV2 format; 

- Processing the data through quality control programs that identify class (systematic) 

and sports (intermittent) errors and developing methods for correcting the data ; 

- Processing the data through a series of merge programs to produce a monthly file, and 

then a yearly file . 

2-4 
NDBC Data 

NDBC performs extensive validation measures at their facilities before the data are made 

available to the public . The NDBC data were collected at remotely operating sites (buoys and 

C-MAN stations). NDBC routinely conducts real-time operational checks, including : 

Transmission parity error, range limit, and time continuity checks ; 

Relational checks, such as examining the wind gust to wind speed ratio to check the 

quality of both measurements; 

Checks to ensure that the battery voltage is adequate for barometric pressure 

measurements . 

After data arrives at NDBC, stricter range and time continuity limit checks are performed. 

These checks include : 

Comparison of measurements from duplicate sensors to ensure that they track together ; 

A man-machine mix of quality checks, such as graphical procedures that relate wind 

speed and spectral wave energy ; 

The use of time series plots, spectral wave curves, and computerized weather maps to 

detect errors . 

Breton Island Data 

We confirmed that the Breton Island data had been routinely subjected to Level I data 

validation checks by LSU. These checks are similar to those performed by NCDC on its data 

set . 

Data Processing and Further Data Validation 

We conducted additional data processing and validation to ensure that the reported data are 

valid and comparable to those collected by federal, state and local air pollution agencies . 

These procedures met the requirements and guidelines of the Environmental Protection 

Agency ; e .g ., Appendices A and B of 40 CFR 58 ; Quality Assurance Handbook for Air 

Pollution Measurement Systems, Volumes I and II (1994a, 1994b) . 

After the data were read from the original source media (9-track tapes, floppies, or 8-mm 

cassettes) and transferred to a workstation, they were processed by customized FORTRAN 

programs that parsed and indexed the data into the specified categories . These parsed files 

were then loaded into the Microsoft Access database tables according to their categories . 

Various queries and screening procedures were set up in the database to identify outliers, 

which were flagged for review . These flagged data were reviewed by both data aides and the 

database manager (Level I data review). 

2-5 
The Level I data were output both electronically and in hard copy for further review . This 

second level of review (Level II) compared the data from adjacent stations for consistency 

(parameter-to-parameter comparisons for consistency and trends). This data review was 

performed by personnel familiar with the interrelationships of air quality and meteorological 

parameters, and potential local influences at the sites . The Level II validated data represent 

the "final data" produced for this project. 

Observational Data Reformatting 

The NCDC data did not contain decimal points when received . The decimal points were 

added into the data in accordance with the USAFETAC Climatic Database Users Handbook 

Number 4 . Additionally, the NDBC data were reformatted as follows: 

- Missing data codes were converted to -999 to be consistent with the NCDC data; 

- The data units were changed to be consistent with the NCDC units . For example, 

temperatures in degrees Kelvin were converted to degrees Celsius, and visibility units 

in miles were converted to meters . 

Observational Database Structure 

The MMS NEGOM data were organized into ten Access tables by data type . This structure 

allows for efficient data querying for screening, validation, and reformatting, and for 

subsequent data analyses . The structure of the Access tables is described in Table 2-3 . 

2-6 
Table 2-3. 

Access database structure for observational data. 

TABLE NAME VARIABLES 

Station Information : ID, name, type, latitude, longitude, elevation, call letters 

Temperature and Pressure: ID, date, hour, dewpoint, temperature, sea level pressure, 

altimeter setting 

Winds: ID, date, hour, direction, speed 

Wind Gust: ID, date, hour, gust 

Sea Surface -Temperature : ID, date, hour, sea surface temperature 

Sea Surface - Wave : ID, date, hour, significant height, dominant period, mean 

period 

Sky Conditions : ID, date, hour, ceiling, cloud type, cloud cover 

Precipitation : : ID, date, hour, period of precipitation, precipitation 

Visibility : ID, date, hour, visibility 

Upper Air: ID, date, hour, (height, pressure, temperature, speed, 

direction, relative humidity) (repeat for each altitude) (standard 

encoding) 

GRIDDED ETA MODEL ARCHIVE 

In addition to the observational data, we also acquired from the National Center for 

Atmospheric Research (NCAR) the output of a prognostic meteorological model (the Eta 

model) for the 1996-1997 study period (NCAR,1998). The Eta model is run twice each day 

by the National Center for Environmental Prediction (NCEP) to support routine NWS weather 

forecasting activities . NCAR is responsible for archiving the output of the twice daily Eta 

runs made by NCEP . 

Our Access database contains gridded Eta model output at three hour intervals for the entirety 

of 1996 and 1997 (note : there are missing data in the Eta archived received from NCAR). 

These three-hourly gridded fields contain wind, temperature, and moisture information, have a 

horizontal spatial resolution of about 40 km, vertical resolution of tens of meters near the 

ground, and are available at the surface and at several aloft layers . The Eta data were 

organized into four Access tables by data type as shown in Table 3-2. The development of the 

Eta model output database is described in greater detail in Chapter 3 . 

2-7 
ACQUIRING COPIES OF THE NEGOM METEOROLOGICAL DATABASE 

The entire observational database and gridded Eta model output archives and the Access 

database format developed under this study has been submitted to MMS on magnetic tape 

cartridges along with this report . Furthermore, it has been submitted to NCDC as per our 

agreement. NCDC will distribute the data to interested parties as per agreement with the 

MMS. 

2-8 
32 

Albany 

Hatti4sburg + +fDc~lhan + +O 

31- 

M o 

Tallahassee 

'- ~+ + { Pan City t0 

New-0 

t `" 

2 vyT 

28-

27-

+ Land Based 

t C-MAN 

26- " Buoy 

O Upper Air 

-91 -90 -89 -88 -87 -86 -SS -84 -83 -82 

Figure 2-1 . Location of land-based, buoy, C-MAN, and upper meteorological monitoring 

sites in the MMS NEGOM study area. (Note: coastline and state boundaries may 

not be accurately depicted) . 

2-9 

3. ETA MODEL OUTPUT DATABASE DEVELOPMENT 

Background 

Offshore meteorological data sites are widely spaced and limited in number . Therefore, 

calculation of some parameters required in this study that involve spatial gradients (wind stress 

curl and vertical vorticity) were difficult and suspect (discussed at greater length below). 

Also, one of the intended uses of the data archive produced in this study is the analysis of 

LATEX drifter data. Ideally, concurrent gridded winds and temperatures for the entire 1996-

1997 drifter program would be very useful in distinguishing between wind-driven and currentdriven drifter motion. So, in addition to aiding in calculation of wind stress, wind stress curl, 

and vertical vorticity statistics, it was concluded that the best available gridded meteorological 

model output should be included within the study's data archive and in the expert system to 

more directly aide in the reconciliation of LATEX drifter data. 

Thus, archived prognostic meteorological (weather) forecast model results were used to add 

spatial resolution afforded by solution of the governing primitive equations. Most recent 

prognostic meteorological models assimilate large volumes of observational data for 

initialization, so initial and early forecast fields are relatively faithful to observed conditions . 

For these fields, the prognostic models can be viewed as offering interpolation between 

observation points derived from detailed and sophisticated descriptions and solutions of the 

governing physics . Extraction of useful prognostic model products from model output 

archives has become more common in air quality/meteorological studies because it effectively 

capitalizes on the huge ongoing investments by U.S . government agencies in their daily 

forecasting simulations . Most importantly, prognostic model results should substantially 

enhance the information available to the users of this NEGOM database and expert data 

software system . 

Typical output from a modern prognostic model for a two year period represents a very large 

volume of data (more than 65 gigabytes) ; the task involved manipulation, parsing, and 

analyses of hundreds of very large gridded model output archives . 

AVAILABILITY OF PROGNOSTIC METEOROLOGICAL 

MODEL RESULTS FOR THE NEGOM REGION 

We conducted a review of gridded meteorological model results archived by various agencies . 

The results of this review are summarized in Table 3-1 . It was concluded that the 

NOAA/NCAR Eta model archives offered the greatest potential benefit to this study . 

Since late 1995, NOAA's National Center for Environmental Prediction (NCEP) has been 

exercising the Eta model several times daily for routine weather forecasting purposes . The 

National Center for Atmospheric Research (NCAR) began archiving these NCEP runs in late 

3-1 
1997. NCAR also reconstructed Eta model output archives for the earlier NCEP runs made 

between October 1995 and late 1997. 

The Eta model is an operational forecast model run every 12 hours to supply forecast fields 

out to 48 hours for the entire North American Continent, with special emphasis on 

precipitation forecasting. The model is initialized during a 12-hour "spinup" mode using a 

three-dimensional variational analysis, which assimilates wind, temperature, humidity, 

radiance, and precipitable water data from satellites (polar and GOES), rawinsondes, profilers, 

radars, and instrumented commercial aircraft . Output from the assimilation/initialization 

process include vertical profiles of wind, temperature, moisture, cloud water, turbulent kinetic 

energy, absolute vorticity, and bulk values such as precipitable water, convective available 

potential energy, stability and lifted indices, etc . Mixing height can be diagnosed from 

temperature and turbulence fields . 

While the NCAR archive does include both initialization and forecast fields, only the 3-hourly 

initialization fields were of value to this particular study . The fields are available on a 40 km 

grid in a Lambent Conformal projection, with coverage across the entirety of North America 

and surrounding oceans (extending across the northern half of Mexico -- see Figure 7-12 for 

depiction of Eta's horizontal grid resolution) . This database supplies ample coverage across 

the northern Gulf of Mexico . Additionally the Eta archive offers vorticity fields as well ; 

which drastically simplified the development of the various derived quantities dependent on 

surface wind and pressure patterns . The Eta model attempts to represent three-dimensional 

characterization of the atmosphere by solution of the Navier-Stokes equations of motion; 

which should improve our understanding of conditions in the NEGOM where there is shortage 

of three-dimensional data. With information extended in the vertical, certain analyses are 

greatly simplified yet more accurate, such as the estimation of latent heat flux across sea-land 

boundary (an especially difficult analysis with limited surface and sounding data), boundary 

layer characterization, etc . Finally, in contrast to other information sources such as satellites, 

there are fewer issues associated with the quality of information over/near land versus open 

ocean in prognostic model analysis fields, except as related to how well model grid and 

surface parameterizations resolve the land-sea interface. 

Thus, the Eta analysis fields represent a valuable supplemental source of meteorological 

information within a single consistent dynamic- and energy-balanced arrangement. Spatial 

resolution is improved over the observation network to 40 km, and temporal resolution of 3 

hours is comparable to the observation data. 

USEFULNESS OF ETA MODEL OUTPUT FOR CALCULATING 

DERIVED QUANTITIES 

One of the thrusts of the subject study, besides the compilation and statistical analysis of 

directly measured meteorological parameters in the northeast Gulf of Mexico, was to 

determine and analyze spatial and temporal patterns of several derived quantities (i .e ., stress, 

fluxes, and boundary layer parameters) . However, we found that the calculation of derived 

3-2 
quantities is often difficult for the Gulf due to the sparsity of necessary measurement data, 

particularly any parameters dependent upon humidity or spatial gradients of winds . Some 

parameters can be grossly calculated but may not lend themselves to a substantive level of 

analysis due to data sparsity and complexities associated with mid-gulf versus shoreline 

measurements . In particular, the determination of vorticity, stress curl, and heat fluxes are 

problematic . Future measurement studies aimed at this issue would likely require horizontal 

spacing between measurement instruments of less than 100km. 

Chapter 5 discusses the manner in which the Eta model gridded output were used in this study 

to improve the calculation of derived surface and aloft meteorological parameters . Generally 

speaking, the Eta modeling output database provided us with wind and thermodynamic 

information with consistent temporal coverages and spatial scales on a regular orthogonal grid. 

SCOPE OF ETA MODEL OUTPUT ARCHIVAL AND ANALYSIS EFFORTS 

" Acquired 1996-1997 archived gridded NOAA/NCAR Eta model initialization fields 

" Calculated geostrophic vorticity, wind stress curl, and boundary layer parameters for 

each grid point within our study domain; calculated mean and variance statistics 

of these parameters (see Chapter 5 for a discussion of the calculation procedures 

for these derived parameters) . 

" Archived gridded, three-hourly surface and upper level winds, temperatures, RH, 

mixing depth, vorticity, etc. within the NEGOM ACCESS meteorological 

database . Table 3-2 summarizes the structure and organization of the Eta 

Access database and statistics tables . 

" Included this Eta model archive within the expert software system product of this study 

(NEGOMES). 

3-3 
Table 3-1 . 

Summary of available meteorological grid modeling archives . 

SOURCE ~ MODEL ~ GRID RESOLUTION T COMMENTS 

U.S . Navy NOGAPS 240 km `95 and `96 data only 

U.S . Air Force Relocatable Window 

Model 

50 km Limited Number of 

days in `95 & `96 

NOAA GCIP/ESOP-95 NA Contours of 

Observed Data 

NOAA NMC 180 km Monthly averages 

NOAH NGM 180 km <95 & <96 

NOAH/NCEP/ 

NCAR 

Eta 40km `96 &`97 

Purdue Univ. Eta 80 km `95-'97 but no 

surface wind data 

archived 

NCEP/NCAR MMS Reanalysis 240 km `95-'97 

NOAA Spectral Model 40 km typical User-specific runs 

ENVIRON/ASTER RAMS 16 km 28 days, summer `93 

U.S . Navy COAMPS 40 km typical User-specific runs 

3-4 
Table 3-2 . 

Configuration of the 1996-97 Eta ACCESS database. 

Table Name Variables on each record 

Eta ID Point ID 

Latitude 

Longitude 

X (easterly distance from southwest corner) 

Y (northerly distance from southwest corner) 

Level (Surface, 1000 mb, 850 mb) 

Met Type Year 

Julian Date 

Month 

Day 

Hour 

Season 

Meteorological Type 

Surface Data Point ID 

Year 

Julian date 

Hour 

Elevation above sea level 

Wind speed (derived) 

Wind direction (derived) 

U-component wind 

V-component wind 

Air temperature at 2 m 

Surface temperature 

Drag coefficient 

Specific humidity 

Sensible heat flux 

Latent heat flux 

Geostrophic vorticity (derived) 

Wind stress curl (derived) 

Moisture flux (derived) 

Ekman pumping velocity (derived) 

Bulk Richardson number (derived) 

Convective boundary layer height (derived) 

3-5 
Table 3-2 . 

Configuration of the 1996-97 Eta ACCESS database. (continued) 

3D Data Point ID 

Year 

Julian date 

Hour 

Height above sea level 

U-component wind 

V-component wind 

Temperature 

Specific Humidity 

File Name I Description 

Sfc-1996.mdb 1996 surface data 

Sfc-1997 .mdb 1997 surface data 

stats-1996 .mdb 1996 mean and variance of surface data 

stats-1997 .mdb 1997 mean and variance of surface data 

Sfc-stats .mdb 1996+1997 mean and variance of surface 

data 

3D-1996 .mdb 1996 data at 1000 mb, 850 mb 

3D-1997 .mdb 1997 data at 1000 mb, 850 mb 

3-6 
4. METEOROLOGICAL TYPECASTING 

PURPOSE 

One of the goals of this project, besides the compilation and delivery of a complete three-year 

(1995-1997) observational database of meteorological parameters in the NEGOM, is to 

augment the raw database with statistical summaries of the data and to analyze the role of 

synoptic-scale weather features and patterns in defining regional-scale forcing . Such analyses 

will provide valuable resources into concurrent and future oceanographic studies, as well as 

the likely transport of pollutant plumes from accidental releases off-shore. In light of interand intra-annual meteorological variability in the NEGOM, it was useful to provide statistical 

measures on various temporal scales, such as season, year, and data period (1995-1997), as 

well as stratified by meteorological regime. 

The NEGOM Expert System described in Chapter 7 not only provides a simple interface tool 

to access both the raw data and the statistical summaries, but also can be used in a forecast 

mode to estimate likely air and water parcel trajectories based upon the three-year climatology. 

A sequence of probable parcel paths can be determined from statistical information generated 

for a particular location, season, and meteorological regime that agrees most closely with 

current and forecast weather patterns . Caution should be exercised in using this climatological 

database since the period involved is quite short, and it includes an "El Nino" episode. 

Understanding systematic synoptic weather analyses required us to develop procedures for 

identifying key meteorological patterns in the NEGOM so that (1) each day in the database can 

be classified into a particular regime for the purpose of generating pattern-specific statistics for 

the meteorological parameters, and (2) so that a user of the Expert System can select one or 

more of these patterns to match current and forecast conditions, thereby displaying the most 

appropriate statistical fields . This section describes the procedure by which ten synoptic-scale 

weather patterns were selected to describe the range of conditions observed during the 1995-97 

data period . General guidance is also provided for users of the Expert System in choosing the 

most appropriate pattern from common weather and forecast charts . 

PREVIOUS STUDIES OF METEOROLOGICAL CATEGORIES 

The selection of weather regimes began with a review of literature sources regarding the 

synoptic weather types previously identified for NEGOM region . Only two studies were 

revealed by our search : Muller (1977) and Muller and Willis (1983) 

Because there were no continuous 30-year weather observations over the Gulf, Muller (1977) 

employed a 30-year normal statistics from New Orleans, Louisiana as a first approximation. 

Using the day-to-day weather at a National Weather Service station at Moisant Airport, and on 

the basis of atmospheric circulation patterns, he organized the data into relatively few types to 

provide an environmental and economic baseline inventory for the surrounding region . The 

4-1 
synoptic weather situation at 0600 CST on each day from 1961 through 1990 was classified 

into one of eight all-inclusive synoptic weather types (see Table 4-1 and Figure 4-1) . Brief 

descriptions of each of Mullers' eight synoptic-type are as follows (for more detail, see Muller 

and Willis, [1983]). 

- Pacific High (PH): Following a "Pacific" cold front, this weather type is normally fair 

and mild with west to northwest winds . Ahead of the front, stronger southerly winds 

may prevail . 

- Continental High (CH) : This weather type is associated with polar or arctic outbreaks . 

North or northeasterly winds will prevail over a large section of the Gulf. 

- Frontal Overrunning (FOR): This cloudy and rainy type occurs frequently when cold 

fronts become stationary along the Gulf coast or over the northern or central Gulf. For 

its effect on the Gulf, see Hsu ( 1992 and 1993 ) . 

- Coastal Return (CR) : The wind around the high pressure over east Canada will bring 

southeasterly and southerly flow over the Gulf. 

- Gulf Return (GR) : Similar to the CR type, the wind direction is mainly from the 

southeast and south but the wind speed is higher due to the developing low-pressure 

system over north Texas. 

- Frontal Gulf Return (FGR) : When a cold front is approaching the Gulf from the 

northwest, the return flow ahead of the front becomes increasingly turbulent and 

stormy. 

- Gulf High (GH) : When a high-pressure system prevails over the Gulf, calmer weather 

will be the result . 

- Gulf Tropical Disturbance (GTD): Any tropical weather system such as a hurricane 

prevails over the Gulf. 

The frequency of occurrence of each weather type at New Orleans, Louisiana was compiled 

for a 30-year normal (i .e ., from 1961 through 1990 ) (see Table 4-1) . For brevity, four types 

are plotted in Figure 4-2 . It can be seen that the frequency of GR peaks in April; GH in July ; 

CH in October; and FOR in January . This sequence mirrors the progression of the seasons. 

From a climatological point of view, three climatic indices are presented in Figure 4-3 . The 

Continental Index includes types CH and FOR, indicating a measure of the frequency of 

continental polar air masses with northerly winds and cooler, drier air . As expected, the 

Continental Index peaks mainly in the fall and winter . On the other hand, the Tropical Index 

consists of maritime tropical air, which includes types GR, FGR, GTD, GH, indicating more 

southerly flow with warm and moist air from the Gulf. As shown in Figure 4-3, the Tropical 

Index peaks during the summer and is lowest during late fall and winter, which is opposite of 

the Continental Index . The third index in Figure 4-3 is the Storminess Index, which includes 

type FOR, FGR and GTD. On the average, it is highest in mid-winter in association with 

frontal activity . 

4-2 
The following synoptic climatology derives from this review : 

( 1 ) From May through mid-August, more than 50% of the time, the northeastern Gulf of 

Mexico is dominated by the tropical weather with winds mainly from the south. From 

mid-September through mid-February, more than 50% of the time this region is influenced by 

the continental weather types with winds from the north. 

(2) The march of the seasons can be represented by four major weather types over the 

northeastern Gulf. The Gulf Return type peaks in April for Spring, Gulf High in July for 

summer, Continental High in October for fall, and Frontal Overrunning in January for winter . 

(3) Climatological indices for the study area are also investigated . It is found that they can 

be grouped into three indices : Continental, Tropical and Storminess . As expected, the 

Continental Index peaks mainly in the fall and winter, Tropical Index in summer, and 

storminess Index in winter caused mainly by atmospheric frontal activity . 

Allowing for differences in observational databases, these findings are generally consistent 

with earlier studies (Franceschini [1953], Mueller [1977], and Muller and Willis [1983]) . 

4-3 
.r 

f 

^ ~ ~ f f , ~~ ~ ~~( 

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FOR 71 CR 

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' 

FGR 

` ~ I ! f , 1, 

t, 1` 

~ O \ ~~ ~U 

- 

w 

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_- GH GTD 

~' '- ~~~5 \ ~ S 

I 1 

Figure 4-1 . Representative examples of eight synoptic weather types for New Orleans, 

Louisiana (from Muller and Wax, 1977) . 

4-4 
FALL 

CH, 

OCT 

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N 

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a 

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0 

W Z 

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WINTER 

FOR, 

- JAN 

SPRING 

FOR 1` 

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S 

~---y, L 

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'. 

APR R~ 

z 

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SUMMER 

FOR, 

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MONTH 

Figure 4-2 . The frequency of occurrence for four weather types over New Orleans, 

Louisiana from 1961 through 1990 based on the data provided by the Louisiana 

Office of State Climatology . 

4-5 
Q 

J 

2 

Q 

W 

O 

3 

W 

Z 

H 

a0 

r 

Zz 

Up 

W~ 

WT MCD 

LLM T 

W~ 

a0 

w 

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z 

MONTH 

Figure 4-3 . The frequency of occurrence for Continental Index (weather types CH + 

FOR),Tropical Index (GR + FGR + GTD + GH), and Storminess Index 

(FOR + FGR + GTD) . 

4-6 
Table 4-1 . 

Synoptic weather type frequency (in percent) at New Orleans, Louisiana 

from 1961 through 1990 (Data Source : Louisiana Office of State Climatology) . 

Month PH CH FOR CR GR FGR GH GTD 

Jan 4 23 37 7 11 13 5 0 

Feb 6 23 31 7 11 16 6 0 

Mar 6 21 24 9 21 16 3 0 

Apr 5 20 13 10 31 15 6 0 

May 4 20 13 12 28 15 7 1 

Jun 0 17 7 15 27 11 19 5 

Jul 0 6 4 16 20 8 36 10 

Aug 0 14 7 21 16 8 25 9 

Sep 1 29 13 19 14 7 6 12 

Oct 3 42 16 16 10 8 3 2 

Nov 4 30 23 12 15 13 2 1 

Dec 4 26 32 6 12 16 4 0 

Annual 3 23 18 13 18 12 10 3 

Note: PH stands for Pacific High weather type 

CH for Continental High 

FOR for frontal overrunning 

CR for Coastal Return 

GR for Gulf Return 

FGR for Frontal Gulf Return 

GH for Gulf High, and 

GTD for Gulf Tropical Disturbance (see Muller and Wax, 1977). 

REVISED CATEGORIES 

A preliminary classification was performed for the year 1994 using Muller's weather typing 

scheme . Through this effort, it was realized that Muller's eight weather types, which were 

focused on New Orleans, needed to be refined to more specifically address surface wind and 

pressure gradient patterns in the NEGOM. Specifically, the following changes were 

developed : 

" The Coastal Return classification was redefined as a regime dominated by high 

pressure over or off-shore of the eastern U.S . -- Eastern Continental High and 

Bermuda High ; 

" The Frontal Overrunning classification was separated into three regimes dominated 

by low pressure over the eastern U.S . -- Eastern Low, Gulf Front East/West, and 

Gulf Front North/South ; 

" The Gulf High classification was separated into two regimes dominated by high 

pressure over the Gulf -- Gulf High and No Gradient (the difference being defined 

in terms of the location and breadth of the central highest pressure). 

4-7 
" The Frontal Gulf Return classification was dropped as it is covered by the Gulf 

Return category. 

" The names of the remaining original classifications were changed to be more 

descriptive in terms of the dominant system defining each regime . 

These changes resulted in ten new synoptic weather types . Two project team members then 

independently reviewed NWS daily weather maps for each day of the three year (1995-1997) 

NEGOM database, and assigned each day to a specific classification based on pressure 

contour maps and wind flow patterns at 1200 UTC (0600 CST) . After reconciliation of the 

two independent reviews, the criteria or "rules" defining each of the ten regimes were 

amended and solidified . The review confirmed that ten synoptic patterns would adequately 

represent the various surface conditions in the Northeast Gulf provided that an accurate and 

comprehensive description of each was available to future users of the Expert System. 

Examples of these final ten synoptic patterns are shown in the attached figures (4-4 through 

4-13) . 

Another important step was to define each synoptic category in terms of the key synoptic 

features that are routinely and easily identifiable to the database users. Therefore, we 

identified the key features in terms of isobar orientation commonly plotted on surface weather 

charts . The criteria for each synoptic feature are described below and are included, in brief, 

in the figure captions shown in Figure 4-14 . These general guidelines will allow expert 

software system users to distinguish, based on routinely available weather maps, between the 

position and orientation of synoptic high and low pressure systems that influence the wind 

flow in the NEGOM. 

Midwest Continental High (Figure 4-4) : A high-pressure system is generally centered west 

of the Mississippi River and north of the Texas/Mexico border . In the Northeast Gulf the 

isobars under this pattern are orientated from the northeast to the southwest. Winds in the 

Northeast Gulf under this pattern are from the north by northwest with anti-cyclonic 

curvature following the flow. 

Eastern Continental High (Figure 4-5): A high pressure system is generally centered east 

of the Mississippi River and west of the eastern seaboard and from just north of the Northeast 

Gulf Coast to the US/Canada border . In the Northeast Gulf the isobars under this pattern are 

orientated from the east to the west. Winds in the Northeast Gulf under this pattern are from 

the northeast with anti-cyclonic curvature following the flow . 

Bermuda High (Figure 4-6) : A high-pressure system is generally centered in the Atlantic 

Ocean from Florida to Maine. In the Northeast Gulf the isobars under this pattern are 

orientated from the south to the north or from the southeast to the northwest. Winds in the 

Northeast Gulf are from the east by southeast under this synoptic pattern, with anti-cyclonic 

curvature following the flow . This pattern often coexists with a Midwest Low. If this is the 

4-8 
case, the user must decide which pattern is closest to the Northeast Gulf and select that 

pattern. 

Midwest Low (Figure 4-7) : A low-pressure system is generally located east of the Rockies 

with or without a north/south orientated front located west of New Orleans. In the Northeast 

Gulf the isobars under this pattern are orientated from the south to the north. Winds in the 

Northeast Gulf under this synoptic pattern are from the southeast with cyclonic curvature 

following the flow. This pattern often coexists with a Bermuda High. If this is the case, the 

user must decide which pattern is closest to the Northeast Gulf and select that pattern. 

Gulf Front or Trough N/S (Figure 4-8): A north/south-orientated front or trough exists 

between New Orleans and Tampa. Winds in the Northeast Gulf to the west of the front are 

northwesterly and winds to the east of the front are southeasterly under this pattern . This 

pattern may often be accompanied by other patterns such as the Midwest Low or Bermuda 

High. If the winds on both sides of the front are from the same direction (indicating a weak 

front or trough), and the wind directions are consistent with the other accompanying pattern, 

then the Gulf Front or Trough N/S pattern should be ignored and the other accompanying 

pattern should be chosen. 

Gulf Front or Trough E/W (Figure 4-9): An east/west-orientated front or trough is located 

in the Northeast Gulf region within 50 km of the coastline . Winds are northerly on the 

northern side of the front and southerly on the southern side of the front . Other patterns may 

often coexist with this pattern. If the winds on both sides of the front are from the same 

direction (indicating a weak front or trough), and the wind directions are consistent with the 

other observed synoptic pattern, then the Gulf Front or Trough E/W pattern should be 

ignored and the other accompanying pattern should be chosen . 

East Coast Low (Figure 4-10): A low-pressure system is generally located east of the 

Mississippi River with east/west-orientated isobars over the Northeast Gulf region and no 

front over the Northeast Gulf. Winds in the Northeast Gulf are westerly under this synoptic 

pattern with cyclonic curvature following the flow. 

Gulf High (Figure 4-11) : High surface pressure is centered in the Gulf, south of the 

Northeast Gulf Coast from Florida to Texas, and usually associated with a weak pressure 

gradient . Winds in the Northeast Gulf have a southerly component under this pattern, and 

are generally weak and anti-cyclonic. 

No Gradient (Figure 4-12): No other patterns are present and no surface pressure gradient 

is evident in the Northeast Gulf region . Winds in the Northeast Gulf are calm or light and 

variable under this pattern. 

Hurricane, Tropical Storm, or Depression (Figure 4-13): A hurricane, tropical storm or 

tropical depression exists in the Gulf region . Winds in the Northeast Gulf are variable . 

4-9 
When choosing a synoptic pattern NEGOMES users should use the wind observations as a 

guideline . For example, if the user suspects that an eastern continental high is present, but 

the winds in the Gulf are not northerly, as expected, but from a significantly different 

direction, then the user should attempt to find another category appropriate to the wind 

direction and pressure pattern . If, however, the user suspects that the synoptic category is an 

eastern continental high and northerly winds are observed, then the user should choose the 

Eastern Continental High with confidence . 

As mentioned above in the synoptic pattern description, there are times when more than one 

pattern may be present, in which case the user must choose the feature that has the greater 

influence on the winds in the Northeast Gulf. For example, an east/west-orientated front may 

exist over the Northeast Gulf region with a strong continental high located east of the 

Mississippi and to the north of the Gulf. If the high pressure system dominates the pressure 

pattern and the wind flow, and if the front has little influence on the wind (i .e ., no wind shift 

across the frontal boundary), it is appropriate to classify the pattern as the Eastern Continental 

High and not the Gulf Front or Trough E/W. 

At times it was difficult to decide which pattern is having a greater influence on the winds in 

the northeast Gulf. Under these circumstances we favored the upstream feature (generally the 

feature to the west) rather than the downstream feature (generally to the east). For example, 

if the Midwest Low and the Bermuda High were simultaneously observed and both appeared 

to be having an equal influence on the winds in the northeast Gulf, then we chose the 

upstream Midwest Low rather than the Bermuda High. 

After categorization of the synoptic condition we estimated transport times . The estimation 

of transport times required an estimation of wind speeds over the northeast Gulf. Because the 

wind speeds could not be estimated from the synoptic patterns alone, the surface pressure 

gradients were used. Guidelines for wind speeds based on the surface pressure gradients 

were estimated using geostrophic balances and logarithmic vertical wind profiles, and are 

shown in the Table 4-2 . For example, with a surface pressure gradient of 4 mb per 400 km, 

the surface wind speed at 10 meters over the water is about 10 m/sec . Likewise, a surface 

pressure gradient of 4 mb per 800 km would result in a 10 meter wind speed of about 5 

m/sec . 

Table 4-2. 

between isobar snacinR and 10-m geographic wind 

4 mb isobar contours Wind Speed (m/sec) 

200 20 

400 10 

600 7 

800 5 

1000 4 

1500 3 

2000 2 

4-10 
DAILY TYPECASTING FOR 1995-1997 

Results of synoptic typecasting for each day of the three year period 1995-97 are shown in 

Table 4-3 . Cumulative days for each of the ten categories are given for each season and for 

the entire year. Overall, daily synoptic meteorology was found to be dominated during this 

period by the three high pressure categories (Midwest, eastern and Bermuda high), followed 

in order by no-gradient, the frontal categories, and finally by the various lows (Midwest, 

eastern, and tropical) . The daily assignments were used in the calculation of statistics by 

meteorological category, season, and year. 

Table 4-3 . 

Frequency of occurrence (days) for each synoptic category by season and year . 

Period MCH ECH BH MLOW GFNS GFEW ELOW GH NG TS 

Spring 95 20 23 18 4 11 2 2 5 4 1 

Summer 10 24 30 0 5 0 1 5 14 2 

95 

Fall 95 7 32 11 1 7 4 1 5 15 9 

Winter 95 21 34 15 1 7 3 2 2 5 2 

1995 58 113 74 6 30 9 6 17 38 14 

Spring 96 14 18 26 1 9 4 4 8 6 1 

Summer 9 24 25 0 6 3 0 5 19 0 

96 

Fall 96 4 23 18 2 3 7 3 12 20 0 

Winter 96 9 37 18 0 7 6 2 5 7 1 

1996 36 102 87 3 25 20 9 30 52 2 

Spring 97 12 27 16 3 12 4 1 7 8 0 

Summer 4 28 14 2 8 6 4 17 8 0 

97 

Fall 97 6 21 6 0 6 11 5 2 31 4 

Winter 97 20 32 7 4 10 6 6 1 6 0 

1997 42 108 43 9 36 27 16 27 53 4 

4-11 
Figure 4-4 . Midwest Continental High (MCH), high pressure generally centered west of 

the Mississippi River and north of the Texas/Mexico border . In the Northeast 

Gulf the isobars are approximately orientated northeast to southwest and winds 

are from the north by northwest. The highest frequencies of occurrence for 

this type are in the Spring and Summer 

4-12 
Figure 4-5 . Eastern Continental High (ECH), high pressure generally centered east of the 

Mississippi River and west of the eastern seaboard and from just north of the 

Gulf Coast to the US/Canada border . In the Northeast Gulf the isobars are 

approximately orientated east to west and winds are from the northeast. The 

highest frequencies of occurrence for this type are in the Winter. 

4-13 
Figure 4-6 . Bermuda High (BH), high pressure generally centered in the Atlantic Ocean 

from Florida to Maine. In the Northeast Gulf the isobars are approximately 

orientated southeast to northwest or south to north and winds are from the 

southeast. The highest frequencies of occurrence for this type are in the Spring 

and Summer 

4-14 
Figure 4-7 . Midwest Low (MLOW), low centered east of the Rockies with or without a 

north/south-orientated front west of New Orleans. In the Northeast Gulf the 

isobars are orientated south to north and the wind has a southerly component. 

The highest frequencies of occurrence for this type are in the Winter and 

Spring 

4-15 
Figure 4-8 . Gulf front or trough N/S (GFNS), north/south-orientated front or trough 

between New Orleans and Tampa. In the Northeast Gulf winds to the west of 

the front are northwesterly and winds to the east of the front are southeasterly . 

The highest frequencies of occurrence for this type are in the Spring 

4-16 
Figure 4-9. Gulf front or trough E/W (GFEW), east/west-orientated front or trough located 

in the Gulf region . In the Northeast Gulf winds are northerly on the northern 

side of the front and southerly on the southern side of the front. The wind 

directions are highly variable . The highest frequencies of occurrence for this 

type are in the Fall 

4-17 
Figure 4-10 . East Coast Low (ELOW), low pressure system east of the Mississippi River 

with west/east-orientated isobars over the Gulf region and no front over the 

Gulf. In the Northeast Gulf the wind has a westerly component. The 

frequencies of occurrence for this type are similar in all seasons 

4-18 
Figure 4-11 . Gulf High (GH), high surface pressure centered in the Gulf, south of the 

Northeast Gulf from Florida to Texas. In the Northeast Gulf winds have a 

southerly component. The highest frequencies of occurrence for this type are 

in the Summer and Fall . 

4-19 
Figure 4-12 . No Gradient (NOGRAD), no surface pressure gradient in the Gulf region . In 

the Northeast Gulf winds are calm or light and variable . The highest 

frequencies of occurrence for this type are in the Summer and Fall. 

4-20 
Figure 4-13 . Hurricane, Tropical Storm, or Depression (TS), 

region . In the Northeast Gulf winds are variable 

occurrence for this type are in the Fall . 

a tropical storm in the Gulf 

. The highest frequencies of 

4-21 
Is there a high, low, or front 

influencing the winds in the 

gulf? Low and front or 

High and front or 

High, low, and front or 

I '\~\ low only 

High Only 

None High and Low 

no front 

I s there a north/south-orientated 

front or trough between Tampa YES Synoptic category is 

and New Orleans with a the Gulf Front North/ 

significant wind shift across I South I 

the front or trough'? 

NO 

Is there an east/west-orientated yES 

front within 50 km of the NE. Synoptic category is 

Gulf coast with a significant the Gulf Front East/ 

wind shift across the frontal I West I 

bounds '? 

NO 

Is a high or law having a 

High greater influencing on the winds 

in the Gulf? If bath a high and 

low arc present and having an 

equal influence, choose the 

upstream or feature to the west . 

Low 

Is the low centered nosh of the YES 

Gulf with approximately south Synoptic category is 

to north orientated isobars ove the Midwest Low 

NO 

Is the low centered north of the YES 

Gulf with approximately west 

Synoptic category is 

to east orientated unbars over 

the Northeast Gulf? the Eastern Low 

NO 

YES 

] 

Is the low in the Gulf! 

Synoptic category is 

NQ the Hurricane, TS, or 

Depression 

Is the high located in the Gul( 

and/or over Florida'? 

NO 

Synoptic category is YES Am the isobars in the Northeast 

the Midwest Gulf approximately orientated 

Continental High northeast to southwest? 

NO 

YES 

1 

Arc the isobars in the Northeast 

Synoptic category is , 4 YES Gulf approximately orientated 

the Eastern east to west? 

Continental High 

NO 

Are the isobars in the Northeast 

Synoptic category is Gulf approzimently orientated 

the Bermuda High sough to nosh' Is the a high centemd south of 

the Northeast Gulf anywhere 

NO YES from Florida to Texas'? 

Synoptic category is 

the Gulf High NO 

YES Is the a high centered over the 

iynoptic category is Northeast Gulf! 

No Gradient 

O 

Synoptic case is not defined 

here . Use synoptic picture 

Is there a pressure gradient 

examples and observed wind 

in the Northeast Gulf! 

to choose appropriate category 

~ 

I 

- 

Figure 4-14. Decision tree flowchart describing the general methodology used to select a 

synoptic category. 

4-22 
5 . METEOROLOGICAL DATA REDUCTION AND ANALYSIS 

OVERVIEW 

After compilation of a historical three-year observational database, we conducted various 

analyses of directly measured and derived quantities that may ultimately be useful to multidisciplinary scientific studies, forecasters, and engineers . These analysis products were 

developed using the three-year observational database . In addition, we also collected and 

archived the output of a prognostic meteorological grid model, the Eta model. This output 

was extracted from NCAR's Eta model archives . No attempt was made in this study to 

reconcile any differences between the two separate data bases (i .e ., the observational and Eta 

model databases) . 

The general tasks performed were : 

" Means and variances, stratified by season, meteorological regime, year, and total data 

period (1995-97) were determined for directly measured quantities such as winds, 

temperature (air and/or sea, depending on the site), humidity, and pressure . 

" Means and variances, stratified by season, meteorological regime, year, and total data 

period (1995-97) were determined for quantities derived from directly measured 

parameters (where data allowed) ; these included heat flux, boundary layer depth, boundary 

layer stability, moisture and latent heat flux across the land-sea boundary, stress, stress 

curl, and Ekman pumping velocity . 

" Time series of winds and temperature for all marine sites and selected coastal stations were 

generated for the entire data period . All time series data were reduced and analyzed using 

accepted, state-of-the-art time series techniques, including the calculation of frequency 

spectra and autocorrelation functions . These same analysis techniques were applied to 

filtered versions of the raw time series that removed significant diurnal modes such as sea 

breeze circulations . 

" Using all available data, distribution maps were prepared for mean and variance wind 

patterns, mean and variance wind stress, mean and variance air and sea surface 

temperature, and mean and variance humidity . Distribution maps were stratified by 

season, meteorological category, year, and total data period . 

" Vertical vorticity and wind stress curl were estimated, mapped, and interpreted under 

different thermal stratification conditions . 

" Bulk formulae were utilized to estimate surface layer water and latent heat transport across 

the sea-land boundary ; statistics of these fluxes were calculated for various temporal and 

meteorological regime stratifications . 

5-1 
The basic properties and stability associated with the atmospheric boundary layer were 

estimated using theoretical relationships (bulk parameterization); statistics of these 

parameters were developed for various stratifications (season, meteorological category, 

etc.) that allowed for their variations to be determined as a function of atmospheric 

forcing. 

Some of the meteorological parameter statistics were directly calculated from the observational 

database that we assembled; the observed parameters included : 

" wind speeds and velocity 

" surface temperature 

" sea-surface temperature 

" humidity 

" pressure . 

Other parameters, called derived parameters were calculated from observed parameters for 

offshore (overwater) sites . The derived parameters included: 

" humidity at buoy sites 

" wind stress 

" wind stress curl 

" heat fluxes 

" moisture fluxes 

" boundary layer depths 

" boundary layer stability 

" Ekman pumping velocity . 

In addition, most of these parameters are also predicted by the Eta model, so statistics were 

calculated for both observed and derived parameters using the gridded Eta model output . 

Next we discuss the statistical calculation and sorting methods for all parameters, including 

observed, derived, and predicted parameters. Then we discuss the issues with and calculation 

methods for the derived parameters. 

STATISTICAL ANALYSES 

Statistical Data Summaries 

Summaries of statistical sample mean and sample variance for observed and derived 

meteorological parameters were prepared for each surface observing site listed in Table 2-2 

and Table 2-3 . For the Eta model output, statistics were calculated at each model grid node 

(See Figure 5-1) . 

Sample mean is defined as follows : 

5-2 
N~ 

= -

1 

E Pi 

P i-1 

where : 

= mean of parameter p sample set 

NP = number of samples for parameter p 

p; = value of sample I for parameter p. 

Sample variance is defined as follows : 

N 

2 1 ( 1 

6P = - 

~ \~7i - 

it 

P)2 

NP 2 i--1 

where: 

QP = variance of parameter p sample set 

NP = number of samples for parameter p 

p; = value of sample I for parameter p 

= mean of parameter p sample set. 

Observed data were segregated and organized into five Access database tables : 

1 . By site 

2 . By site and meteorological type 

3 . By site and season (winter = December-February; spring = March-May; summer=JuneAugust; fall =September-November) 

4 . By site and meteorological type and season 

5 . By site and season and year 

The sample mean and variance statistics were computed using all of the data in each of the 

above five tables for the following observed and derived parameters : 

" temperature (C) 

" barometric sea-level pressure (mb) 

" relative humidity ( % ) 

" wind speed (m/s) 

" magnitude of u and v components of the wind vector (m/s) 

" wind direction (degrees from north) 

" sea surface temperature (C)' 

" surface heat flux (W/m2)` 

" convective PBL depth (m)' 

5-3 
" bulk Richardson number' 

" horizontal moisture flux across the land-sea boundary2 

" horizontal latent heat flux across the land-sea boundary2 

" wind stress3 

" stress curl4 

" Ekman pumping4 

Notes : 

' Only sites with sea surface temperature (buoy and C-MAN) 

Z Coastal sites only 

3 Buoy sites only 

° Transects only 

Access data tables were prepared of the mean and variance of each parameter and each of the 

five data groups listed above. Also tabulated were the number of non-missing values used in 

the statistical calculations, and number of possible values (based on hourly sampling) . The 

number of possible hourly values was computed as twenty-four times the number of days in 

the table cell . Thus, for the first table (by site), the number of possible hourly data points in 

the 1996-1997 data period was 17,544 . 

Since the number of statistical tables we have produced is very large, they are not presented in 

hard copy form in this report . The companion Access database and expert system contain all 

the data and can be easily downloaded and viewed on a PC type computer. However, we 

present an example of a station statistics table in Table 5-1 . 

Spatial Interpolation Of Observed Data Statistics 

Meteorological data for the study region are available at a very limited number of irregularly 

spaced observing locations . A spatial interpolation procedure was employed to estimate values 

of temperature, sea surface temperature, relative humidity, wind speed direction, and wind 

stress over a regularly spaced array of grid nodes consisting of 19 nodes in the east-west 

direction and 13 nodes in the north-south direction. The grid was equally spaced in a latitudelongitude coordinate system with east-west and north-south spacings of 0.5 degrees each 

(approximately 900 km by 700 km). Given the sparse coverage of data sites, spatial 

interpolation was performed using a standard software package for "Kriging" interpolation ; 

interpolation weights were automatically applied to each data point based on a variogram 

developed by the software describing the spatial variability in the data. This approach is wellsuited to and commonly applied to interpolation irregularly-spaced environmental data sets . A 

linear model was used for the variogram with scale factor C = 6 .67 and length parameter 

A = 4.12. The variogram was assumed to be isotropic with no nugget effect . "Ordinary" 

Kriging was used (i .e ., no drift was specified) . Calculations were carried out with a PC-based 

spatial mapping software package (Surfer 6.0, Golden Software Inc.) . A data completeness 

criterion of 50% of all possible hours (assuming measurements made every hour) was applied : 

any sites not meeting this criterion were excluded from the spatial interpolation. 

5-4 
Table 5-1 . 

Example of a station statistics table: (this case shows statistics for station 42001, 

for all years, ECH meteorology types and the winter season) . 

Station ID = 42001 

Station Name = MID GULF 

Longitude = -89 .65 

Latitude = 25 .43 

Weather Pattern = ECH - Eastern Continental High 

Year = R11 Years 

Season = winter 

Meteorological Parameter Mean Variance 

------------------------------------------------------

Rir Temperature (°C) 22 .93 5 .61 

Sea Temperature (°C) 23 .03 1 .10 

Pressure (mb) 1021 .00 15 .86 

Wind Speed (m/s) 7 .41 5 .71 

U Winds (m/s) -5 .70 8 .29 

U Winds (m/s) -1 .46 17 .72 

Surface Heat Flux (W/m2) 35 .54 638 .42 

Bulk Richardson Number .D2 1 .07 

Planetary Boundary Layer (m) 546 15854 

Wind Stress (kg/m2s) .980 .00#9 

Drag Coefficient .Dft12 _DOBi1 

IF-- ~PW7 

Sample Size 

429 

429 

924 

929 

429 

929 

852 

928 

852 

752 

752 

CIOSE 

A typical example of gridded mean winds is shown in Figure 5-2 . The balance of the gridded 

mean fields plots are provided as part of the Expert software system. 

Time Series Analysis Methods 

Time series of hourly scalar wind speed, magnitude of the north-south wind component, and 

surface temperature observations collected at sixteen marine, coastal, and inland locations 

listed in Table 5-2 were analyzed for their statistical behavior . 

5-5 
Table 5-2 . 

Surface meteorological data sites used in the 

time series analvsis for the 1995-1997 data period . 

°Io Non-Missing Values 

Site Location (based on wind speed) 

Buoy 42001 Mid Gulf 89 

Buoy 42002 West Gulf 92 

Buoy 42007 OTP 99 

Buoy 42036 West Tampa 76 

Buoy 42039 Pensacola S 99 

Buoy 42040 Mobile South 99 

Land Based 722210 Eglin AFB/Valparais 85 

Land Based 722225 Pensacola NAS 94 

Land Based 747686 Kessler AFB/Biloxi 88 

Land Based 747750 Tyndall AFB 89 

C-MAN Buoy BURL1 Southwest Pass, LA 96 

C-MAN Buoy CDRF1 Cedar Key, FL 99 

C-MAN Buoy DPIA1 Dauphin Island, FL 96 

C-MAN Buoy GDIL1 Grand Isle, LA 99 

C-MAN Buoy KTNF1 Keaton Beach, FL 99 

BRETON Breton Island 69 

Since the time series analysis methods used here assume complete data, missing values were 

first filled in with means computed for the season and hour corresponding to the time of each 

missing observation . Assuming normally distributed data, this procedure avoids introduction 

of biases in the mean diurnal pattern. Furthermore, for sparse, randomly occurring missing 

values, biases in spectral analyses should be minimal. Therefore, sites in Table 5-2 represent 

locations with data completeness of at least 70 percent; sites with more extensive missing data 

were not included in the analysis although this requirement was relaxed somewhat at the buoy 

sites to avoid losing data from these critical locations. 

Autocorrelation functions and periodograms were computed for both the raw time series and a 

filtered series in which the 24-hour diurnal cycle has been removed using a Buys-Ballot filter . 

The Buys-Ballot filter simply subtracts the mean diurnal cycle from the raw time series . In 

other words, a time series consisting of a repeated sequence of 24 values corresponding to the 

mean for each hour of the day is subtracted from the raw time series to create the filtered 

series . 

5-6 
Autocorrelations for lags n=1,2, . . ., N/2 hours where N is the length of the time series were 

computed and plotted using the S-PLUS acf function . 

Smoothed periodograms were computed using the S-PLUS spectrum function . This function 

first removes any trend from the raw time series (using a least squares fit) and rescales to a 

zero mean. A split cosine data taper of ten percent is then applied to each end of the 

detrended series . A mixed radix fast Fourier transform algorithm is then applied to the series 

to estimate the raw periodogram which is smoothed by applying a sequence of running 

averages (a sequence of two modified Daniell windows of length 25 and 27). The smoothed 

periodogram is expressed in decibels . 

We generated the time series analysis plots for each of the sixteen stations listed in Table 5-2. 

Sample time-series statistics plot are presented in Figure 5-3 and 5-4; Figure 5-3 shows the 

results for a site that is influenced by diurnal land-sea breeze flow reversal ; Figure 5-4 is for 

an offshore site beyond the furthest extent of land-sea breeze influences . The balance of the 

time series figures are available in the Appendices B and C, and in the NEGOMES Expert 

Software System which is a companion deliverable of this study . 

ISSUES ASSOCIATED WITH DERIVED QUANTITIES 

One of the thrusts of the subject study, besides the compilation and statistical analysis of 

directly measured meteorological parameters in the northeast Gulf of Mexico, was to 

determine and analyze spatial and temporal patterns of several derived quantities (i .e ., stress, 

fluxes, and boundary layer parameters) . As described above, the calculation of derived 

quantities was often difficult for the Gulf due to the sparsity of necessary measurement data, 

particularly any parameters dependent upon humidity or spatial gradients of winds. Some 

parameters could have been grossly calculated but did not lend themselves to a substantive 

level of analysis due to data sparsity and complexities associated with mid-gulf versus 

shoreline measurements ; in particular, the determination of vorticity and stress curl were 

problematic. 

This section presents a review of the specific problem areas, and closes with our final 

diagnostic approach of applying parametric relationships to the existing surface observation 

database. The importance of obtaining archived gridded meteorological model output is also 

stressed; these are regarded as a highly valuable resource to provide the data coverage and 

variables needed for an adequate assessment of derived parameters. 

Humidity-based Parameters 

Most derived quantities that characterize the boundary layer depend on surface-level humidity 

measurements. The number of data buoys measuring dew point in the northeastern Gulf was 

recently increased in the last half of 1997 . Nevertheless, surface humidity data were routinely 

available from only a single buoy in the Gulf of Mexico for the database years of 1995-97 . It 

was therefore essential to identify available methodologies from which to derive either 

5-7 
humidity or, alternatively, related boundary layer parameters directly. We identified three 

possible resources (Emery, 1998) : (1) satellite soundings of moisture, (2) recently developed 

empirical relationships between surface layer temperature and moisture fluxes, and (3) 

archived NWS Eta prognostic meteorological model fields . 

In order to utilize satellite data, potentially significant additional resources would have been 

needed to procure vertical sounder archives and process the data for the 3 year database . In 

return, a rather high spatial (20 km), but low temporal (6-hourly), resolution surface humidity 

database could have been developed for much of the northeastern Gulf. The Eta model 

initialization archives included 3-hourly analysis fields on a 40 km grid in a Lambert 

Conformal projection grid, with coverage across the entirety of North America and 

surrounding oceans (extending across the northern half of Mexico) . The major drawback to 

this source of information was the enormity of the data volume ; as with the satellite data 

described above. Significant resources were used to procure the Eta archives and process the 

data for the 1996-97 MMS database, but the usefulness of the Eta model information far 

surpassed that of the surface observation dataset. 

Hsu (1998) presents a compelling relationship between air and sea-surface temperature 

differences and the Bowen ratio (the ratio of sensible to latent heat flux from the surface) in 

the Gulf of Mexico during unstable conditions . Based upon theoretical considerations, the airsea temperature difference can be linked to the latent heat flux to some extent . Using data 

from Buoy 42040 during a cold air outbreak in December 1996, Hsu finds a linear correlation 

between the air-sea temperature difference and the air-sea mixing ratio difference with a high 

correlation coefficient of 0 .98 ; these findings are then extended to a non-linear relationship 

between air-sea temperature difference and the Bowen ratio under unstable conditions and 

seems to also work reasonably well under neutral and weekly stable conditions . This 

relationship was extended to additional buoy data from four sites between 1993 and 1997 . 

Different coefficients are derived for near shore areas as well, based on data from the Dauphin 

Island C-MAN site . 

Once the Bowen ratio is known, surface humidity may be calculated, and heat fluxes, surface 

layer stability, and boundary layer depth can be determined at each site containing air and seasurface temperature data. This approach, therefore, is limited to estimations of humidity 

based on available hourly buoy temperature data at each site, and did not improve the spatial 

or temporal resolution beyond those of the temperature observations . Nevertheless, we 

pursued the latest developments of Hsu (1998) in relating sea-air temperature differences to the 

Bowen ratio, as it appeared to be a promising technique to derive boundary layer quantities 

that would otherwise require knowledge of humidity at each measurements site . 

Wind-based Parameters 

We identified several problems associated with determining some of the derived quantities 

from the wind observations . Particularly, we investigated the feasibility of using pressure 

measurements at buoys and C-MAN sites to define rhomboid patterns (i .e ., parallelograms 

5-8 
with oblique angles and unequal adjacent sides) from which the pressure Laplacian may be 

calculated and geostrophic vorticity and stress curl can be derived. Originally, only two 

rhomboids could be defined that had a sufficient number of measurement sites to calculate 

these quantities . This problem was remedied in part by procuring data from three additional 

buoys located along the 28'N parallel . 

A Laplacian, being a second-order spatial derivative, requires a rhomboidal configuration of at 

least 5 measurement sites . A simple differencing formula suggested by Hsu (1992) was 

initially planned to be used in this project; however, its simplicity rests in the assumption that 

the various sites are about equally spaced, and that the sites are aligned orthogonally . In the 

current study, the distances between sites were quite variable for the few highly nonorthogonal rhomboids that could be constructed . To understand the sensitivity of the 

rhomboid approach to which differencing technique is used, vorticity was calculated using the 

formula initially chosen (Hsu, 1992), and then with a revised formula to account for the large 

disparity among inter-site distances (although it still did not account for the non-orthogonality 

in each rhombus). An example of the results are shown in Table 5-3 for a particular time for 

two rhomboids . Note that both the Laplacian and vorticity values change by an order of 

magnitude, and the signs flip, denoting a change from cyclonic to anticyclonic vorticity . 

Furthermore, there was no consistent "scale" of the derived quantities among each rhombus . 

For example, one rhombus may characterize the scale of vorticity across 40,000 km2, whereas 

another might extend over several more orders of magnitude, while containing a portion of the 

first area. The fact that the vorticity from the smaller rhombus was consistently higher than 

the vorticity from the larger rhombus indicated that rhombus scale was playing a role in the 

calculations . Similar differences were noted in the seasonal and annual averages. However, 

for the seasonal calculations, an additional problem of data availability arose. For the three 

years of hourly pressure data, only 10-25 % of the total number of hours contained sufficient 

data at all sites to calculate the two-dimensional spatial gradients . 

A simplification to the rhombus methodology was also presented in the analyses of Hsu 

(1992), in which it was determined that an equally robust metric could be based on simply the 

temperature (pressure) difference between a single shoreline site and a deep water buoy (i .e ., a 

transect methodology) . This stems from the fact that in the winter season, the along-shore 

component of temperature difference is quite small compared to the large temperature 

differences orthogonal to the shore, especially between deep water and the shoreline. It was 

realized that a similar analysis could be carried out for the NEGOM, however, transects 

needed to be developed that run orthogonal to the gulf shelf break both east and west of the 

Desoto Canyon area due to its influence on atmospheric baroclinicity. It was also suggested 

that transects be developed to characterize outer gulf and near-shore vorticity separately. The 

transects methodology was fully adopted in lieu of the use of fully two-dimensional rhomboids 

in calculating Laplacian fields ; we worked closely with Dr. Hsu in developing the most 

appropriate transects as a function of sub-surface topography and data availability . 

5-9 
Hence, while Laplacian calculations were made from the buoy data alone, the quality of the 

results remained highly uncertain and dependent upon the methodology employed. Also, 

spatial and temporal coverage was greatly limited. It became obvious from these tests that an 

improved set of wind information on a more regular grid would be a valuable component of 

the NEGOM meteorological database, certainly in regards to the reconciliation of stress 

estimates with currents and transport patterns of LATEX drifters in 1996-97. 

Table 5-3 . 

Example of Laplacian and vorticity calculations using 

two differencing methodologies at 1200 LST, July 14, 1996. 

Rhombus Initial Revised Initial Revised 

centered on site Laplaci an Laplacian Vorticity Vorticity 

42007 5.4 x 10-g -2 .8 x 10-8 6 .1 x 10-4 -2 .7 x 10-4 

42040 4 .8 x 10-1° -1 .7 x 10-9 5 .4 x 10-6 -2 .0 x 10'5 

Usefulness of Eta Model Output for Estimating Derived Parameters 

As has been described, the derivation of wind stress, heat flux, boundary layer parameter 

estimates from observed data alone was complicated by the scarcity of stations . For example, 

many of the diagnostic calculations listed above were derived from meteorological data taken 

at buoy and C-MAN stations located in the NEGOM. The locations of these sites are 

presented in Figure 2-1 . Furthermore, the derivations of certain parameters required 

measurements of parameters that are not always reported by Buoy or C-Man stations . (The 

methodology to derive these quantities using alternative approaches is described later in this 

section). 

The Eta model fields archived by NCAR (described earlier) offered a very attractive source of 

surface (and aloft) information. Since these output are gridded, they drastically simplify the 

development of the various derived quantities dependent on spatial surface wind and pressure 

patterns. Wind and thermodynamic output have consistent temporal coverage and are resolved 

on a regular 40 km orthogonal grid. Since the Eta model initializations assimilated much of 

the observed data in the area, the Eta model analysis output can be viewed as a very 

sophisticated spatial and temporal data interpolation scheme. 

Eta model output fields are generated on a finer spatial (and in some cases time) scales than 

available from a monitoring network. This is very useful when it is necessary to extrapolate 

analyses beyond the spatial limits of the monitoring network, or to interpolate analyses to finer 

scales within the network. This is particularly true in the vertical, where modeled fields allow 

for much more spatial and temporal detail in the vertical than available from measurements. 

Moreover, the theoretical expressions employed by Eta models automate the estimation of 

5-10 
some derived parameters, particularly in defining the structures and variations of the 

atmospheric boundary layer, heat and mass fluxes, and sea breeze circulations . 

METHODOLOGY FOR CALCULATING DERIVED PARAMETERS FROM 

OBSERVED DATA 

The first and most critical step in the process of calculating the various derived parameters was 

to ensure that all over-water sites possessed hourly humidity data when sufficient temperature 

and pressure data were available . This step drew from the relationships developed by Hsu 

(1998) that link air-sea temperature differences to the Bowen ratio. Once Bowen ratio was 

known, it was a rather simple matter to determine humidity parameters. Two basic humidity 

measures were calculated in this way: specific humidity was calculated since it was needed for 

other derivations such as heat flux ; and dewpoint temperature was needed to fill in missing 

humidity values for buoy and C-MAN sites in the raw observational database . 

At over-water sites without humidity measurements, the Bowen ratio was first calculated for 

all hours with valid air and sea-surface temperature (T and TS in °C, respectively): 

B = a( TS -T)b 

where the constants a = 0.077, b = 0.70 for buoy sites, and a = 0.087, b = 0.74 for CMAN sites (as determined by Hsu, 1998). The ambient specific humidity qQ was determined 

from a relationship defining surface latent heat flux as a function of moisture and temperature 

differences between the sea and air surface: 

T-TS.+O.OIz 

Qa - q.~ + 2500B 

(S/S) 

where z is measurement probe height (5 m for buoys 42039, 42040, and 42036, and 10 m for 

all other buoys and C-MAN sites) . The sea-surface specific humidity was assumed to be 

saturated, and is therefore a function of ambient pressure p (mb) and sea-surface temperature 

only : 

r 7.5 Ts 1 

q ,S. = 0.62 e

.` , 

e.~ 

= 6.1 x 10~ 257.2 +TSJ 

P 

Specific humidity was determined as well for all land sites that report dewpoint Td (°C), using 

equations similar to above : 

5-11 
7.5 Td 

j 

qQ = 0.62 e, e = 6.1x10 zs7.z " rd 

P 

The horizontal moisture flux orthogonal to the coastline F9 was determined at selected coastal 

sites based on the north-south component of the observed wind v (m/s) and the water vapor 

concentration cw (kg/m3) : 

Fq = v-cW (kg m-ZS-') 

where 

cw = 1.2 qQ 273 p ) 

( 1013 .25 T 

The 1 .2 factor in the equation above represents a standard atmospheric density, which was 

used throughout these analyses wherever density was needed. The range of this value is not 

large under most circumstances, and is relatively small compared to the uncertainty associated 

with the assumptions necessarily made in these analyses . The horizontal latent heat flux F,, 

(J/kg) was calculated from F9 by simply multiplying the latter by the latent heat of vaporization 

(2 .5 x 106 J/kg). 

The total surface heat flux H was only determined for conditions in which the sea surface was 

warmer than the ambient air temperature . The equation utilized the Bowen ratio previously 

calculated, and therefore heat flux was only determined for over-water sites : 

H = 1 .2 Cp CT V ( TS- ~ 1 + 0.07 (Wm -2) 

B 

where cP is the specific heat of dry air at constant pressure (1004 J K-' kg'), CT is the bulk heat 

transfer coefficient under convective conditions (taken from Hsu [1997]), and V is total wind 

speed (m/s) . From this, the convective boundary layer depth was estimated for offshore sites 

using an empirical relationship developed by Hsu (1997) : 

CBL = 369 + 6004(-W/01 (m) 

5-12 
where 

w '6~ = H (m Kls) 

1 .2 cP 

There are a number of parameters to represent the stability regime of the surface boundary 

layer . We used the bulk Richardson number since it could be directly determined from wind 

speed and surface-to-probe gradients in temperature: 

Rb - ZT (T.~ 

- 

S 

where g is the gravitational acceleration constant (9 .8 m/s2) and TS must be expressed in units 

of K in the denominator . 

Calculation of wind stress required the knowledge of a drag coefficient. Since the focus of the 

current study was on wind stress on the ocean surface, we limited the calculation of wind 

stress to over-water stations . As pointed out by Hsu (1995), it is difficult to define the drag 

coefficient, or alternatively the friction velocity u*, because they depend on more unknown 

variables than there are equations to interrelate them . Therefore, Hsu developed a 

parameterization for the over-water drag coefficient Cd based on wave information that is 

routinely available from buoy sites . Effectively, the significant wave height HS (m) and the 

dominant wave period TP (s) were used as surrogates for a space- and time-variable surface 

roughness length . The resulting equation was: 

2 0.4 

Cd H S It - In - 

2 .6 

2 9 nV TP I 

The vertical component of the wind stress tensor was then determined for all over-water 

stations from the following relationship : 

i = 1 .2CdVz 

The wind stress curl may be determined by combining the horizontal components of the 

vertical stress with the geostrophic vorticity equation. Evaluating vorticity requires the 

5-13 
calculation of a pressure (or temperature) Laplacian, and as discussed above, this second-order 

two-dimensional spatial derivative requires at least five data points . We elected to evaluate the 

vorticity using a transect method, which reduced the number of data points to three, aligned 

along a direction that usually yields the largest gradient (i .e ., orthogonal to the gulf shore 

line) . This approach maximized the total number of possible configurations as well as the 

coverage of stress curl estimates over the Gulf. 

Hsu (1992) utilized a Laplacian of temperature to diagnose geostrophic vorticity during 

wintertime cold-air outbreaks over the central and western Gulf. Since our focus was more 

broad in determining stress curl for all seasons of the three-year database, we elected to use 

pressure so that diurnal temperature tides at land sites were not introduced (although a slight 

diurnal pressure wave of usually less than 1-2 mb was typically present) . The large land-based 

temperature variation over the day was often sufficient to cause a semi-diurnal reversal in the 

geostrophic vorticity for all scales of transects. We did not see this when using pressure . 

Transects were defined by three sites that are roughly equally spaced along a consistent 

direction . In this way, the one-dimensional pressure Laplacian was simplified using pressure 

from the three sites and an average separation distance D (m): 

pzp = 

1 Dz (pi + pZ - 2po) 

where p, and pZ are pressures (Pa) at the transect endpoints, and po is the pressure at the 

central (usually coastal) site . The wind stress curl was then determined from 

OxT = C f ° OZ P 

Here, the drag coefficient was taken from the buoy site defining the deep water endpoint of the 

transect, or the nearest buoy to the transect if no data were available . The total wind speed Vo 

(m/s) was taken from the central site in the transect, and the Coriolis parameter f was set to 

7 .3 x 10-5 s-', which represents the value at 30°N. From the stress curl, it was a simple matter 

of deriving the Ekman pumping velocity : 

W = 

Oxi 

assuming the density of seawater p,, is about 1030 kg /M3 . 

5-14 
Transect Definitions 

Several transects were defined for the calculation of pressure Laplacian. The general rules in 

developing these were: (1) three sites define a transect, one in the outer Gulf, one near shore, 

and one inland ; (2) the spacing between each of the three sites should be fairly uniform; and 

(3) the three sites should form a straight line orthogonal to the shoreline . Two sets of 

transects were defined, coastal and deep water, with the central point of the former located on 

or near the coast (typically C-MAN sites) and the Gulf point located near the shelf break, and 

the central point of the latter at the shelf break and the Gulf point at a "deep water" buoy. 

Many more transects were defined than were feasible when calculating actual hourly pressure 

Laplacian values, because some of the land based endpoint sites contained significant data 

gaps. Since no viable alternatives were found, these transects were dropped from the analysis . 

Figures 5-5 and 5-6 present the locations of the deep water and coastal transects, respectively . 

Note that in the figures, dashed lines represent original transect definitions that had to be 

dropped due to data sparsity . Table 5-4 provides a listing of stations defining each transect in 

these analyses, by station ID. 

CALCULATION OF DERIVED PARAMETERS FROM ETA MODEL OUTPUT 

After Eta model output was obtained from NCAR, data were extracted for the study region 

and selected output parameters . The extracted data were reorganized into Access data tables, 

and indexed by meteorological type and season . To be consistent with the derived quantities 

in the observational database, several variables were derived from the Eta analyses, including 

bulk Richardson number (stability measure of the boundary layer), convective boundary layer 

depth, vorticity, wind stress curl, and Ekman pumping velocity . Since the Eta fields contain 

only the east-west (u) and north-south (v) components of the winds, the total wind speed and 

direction were first calculated and supplied to the data tables . Surface fluxes of sensible and 

latent heat were also supplied in the Eta fields . The surface moisture flux at each grid point 

was calculated from the surface latent heat flux by simply multiplying by the latent heat of 

vaporization . 

The bulk Richardson number was estimated using an equation similar to that used for the 

observational database, but using vertical gradients specific to the Eta output . In essence, the 

value of height z in the equation was increased from 10 to 50 to account for the fact that the 

Eta model provides temperatures at 2 m above the surface and wind speeds at 10 m (zero wind 

speed is assumed at the surface) . Also, the convective boundary layer heights were 

determined using the same equation as developed for the observational database, with values of 

total surface heat flux (latent + sensible) taken directly from the Eta output fields . 

The calculation of wind stress curl from the Eta wind fields was significantly improved 

relative to the observational database. First, the geostrophic vorticity field was explicitly 

calculated from the full two-dimensional pressure Laplacian. At each grid node, vorticity was 

calculated from 

5-15 
1 v2 

P 

1 .2 f 

where atmospheric density (1 .2 kg/m3), coriolis (fl, and pressure (p) were defined previously . 

The Laplacian was calculated using simple difference equations, where for point (i,j), 

where Ox is the grid point spacing (40 km). Since the values for pressure must be available at 

points I + 1, I-1, j + 1, and j-1 in the above equation, the derived vorticity along the border of 

the domain was marked as missing. 

Wind stress curl was calculated similarly to the observational data, 

VxT = 1 .2CdV~ 

where the drag coefficient at each point was provided in the model output fields . Finally, 

Ekman pumping velocity was estimated using the stress curl as described for the observational 

database. 

Within the Eta output data there were occasional periods of missing data. These data were 

given a value of -999. If a missing value was required in the computation of a derived 

variable, that missing value was propagated to the derived variable . After all derived variables 

were calculated for the entire 1996-97 period, summary mean and variance statistics were 

computed for all model and derived surface variables using the same stratifications as for the 

observed database (e .g ., by site, by season, by meteorological type, etc .) . Although no 

statistics were derived for the upper air data, the 3-hourly 1000 and 850 mb winds, 

temperature, and humidity are available in the Eta database for every grid point in the 

NEGOM domain. 

5-16 
Table 5-4. 

List of deep and coastal transects, and site IDs that define each. 

Transect ID Outer Water Site Coastal Si te Inland Site 

Deep Water 

1D 42001 42040 722267 

21) 42003 42039 722267 

3D 42003 42036 722135 

Coastal 

1C BUSL1 GDIL1 747685 

2C BUSL 1 BURL 1 42007 

3C 42040 722225 722275 

4C 42039 722210 722267 

5C 42039 722245 722269 

6C 42039 CSBF1 722140 

7C 42036 CSBF 1 722269 

8C 42036 KTNF 1 722135 

9C 42036 CDRF 1 722146 

5-17 
Figure 5-1 . An example of a gridded Eta model statistics map (this example shows gridded 

mean surface temperatures for 1996 and the ECH type meteorology) . Each 

colored square represents the data for one grid square . 

5-18 
M1VISMet Mean Wed Speed (mls) 

ST!! : 0 mls 1911,11-z", V 

0 S 10 13 

Figure 5-2 . Example of gridded mean winds (vectors and contoured speed) for the Midwest 

Continental High category over the entire data period (1995-1997) . The mean 

wind velocity in each grid square is depicted by an arrow whose length 

is proportional to mean speed and whose direction is the vector averaged wind 

direction . 

5-19 

lldet Type : Midwest Cooodnevntal High 
Wind Speed (mls) 1996 - 1997: Station 722210 

N 

A 

W 

d 

0 

d 

N 

E 

2 0 

O 

D 

0 5000 10000 15000 

Time in hours 

1 14 1 00 1006 10000 

Lag 

100 20 10 7 5 

Wtrvdvngth in Mows 

Figure 5-3 . Example of time series plots of temperature for the Elgin AFB/Valparais site . 

Note the power spectrum peaks at 12 and 24 hours, reflecting diurnal, land-sea 

breeze influences .(ACF is an abbreviation for autocorrelation function which is a 

measure of the correlation between the successive observations of a parameter 

at one site as a function of lag time). 

5-20 
Wind Speed (m/s) 1996 - 1997: Station 42001 

0 

lop 0 

u 

C 

O 

O 

O 

m 

n m 

O 

0 5400 10000 15000 

Time in hours 

1 10 100 1000 1OCAp 

Lag 

1W 20 10 7 5 

Wavelength in Hours 

Figure 5-4. Example of time series plots of temperature for the Mid Gulf site . Note the 

absence of power spectrum peaks at 12 and 24 hours, no reflecting diurnal, landsea breeze influences. (ACF is an abbreviation for autocorrelation function which 

is a measure of the correlation between the successive observations of a 

parameter at one site as a function of lag time). 

5-21 
32 

i +t + + f 

31 

f' U 

30- 

f i f 

t 

T 

I 

I 

I I 

28- 1p 2D 

I 

I 

I 

I 

I 

27- I 

i 

I 

I 

I 

I 

I 

26- I 

91 -90 -89 -88 -87 -t36 -85 -is4 -s3 -82 

Figure 5-5 . Location of deep water transects in the NEGOM. Dashed line represents original 

definition that was dropped from the analysis due to lack of observation data . 

(Note : + indicates land-based stations, squares are C-MAN stations, circles are 

buoys, and diamonds are upper air sites.) 

5-22 
31t-- 

*,` + t 

i 

i 

~~ 6 4 5 

t 

,' 3C 

.5.~ ~ . 

V 7C 

2C 

9C 

-90 -89 -88 -87 -86 -85 -84 -83 -82 

Figure 5-6 . Location of coastal water transects in the NEGOM. Dashed line represents 

original definition that was dropped from the analysis due to lack of observation 

data. 

(Note: + indicates land-based stations, squares are C-MAN stations, circles are 

buoys, and diamonds are upper air sites.) 

5-23 

6. SYNTHESIS AND CONCEPTUAL MODELS 

OVERVIEW 

In Chapter 4 we describe 10 prevalent weather types that encompass the bulk of 

meteorological conditions in the study area . These weather types were designed so that 

investigators can determine the dominant synoptic weather pattern currently affecting or 

forecasted to affect weather conditions in the region, and then determine the climatologicallyaveraged conditions that accompany such patterns. The observational and Eta model Access 

databases have been organized so that data or the data statistics can be retrieved by weather 

type (as well as by season) . 

The weather typing approach is also a convenient starting point for developing a conceptual 

understanding of surface meteorological phenomena in the NEGOM. Firstly, general flow 

patterns and tendencies can be more easily discerned when grouped by the forcing synopticand mesoscale pressure and frontal patterns. Secondly, physical reasoning is easily applied in 

understanding the differences in meteorological statistics between weather types. For 

example, the "No Gradient" category can be expected to exhibit lower average wind speeds, 

higher average temperatures, and greater wind direction variability than most other categories 

because physical reasoning suggests more chaotic light winds, higher pressure, and subsidence 

in this category. Third, since frontal passage is an integral component of our weather typing 

scheme, frontal passages can be described as sequences of weather types; so corresponding 

meteorological parameter statistics by weather type can be assembled to assess how frontal 

passage, air mass evolution, and cyclogenesis affect local fields in the NEGOM. 

In this chapter, we present discussions of the consistency and correlations between conceptual 

models of the conditions associated with each weather type by season, and the statistics 

calculated on the observed database by weather type and season . For this investigation, the 

seasons were defined as follows : 

" Winter months -- December, January, and February 

" Spring months -- March, April, and May 

" Summer months -- June, July, and August 

" Fall months -- September, October, and November 

DISCUSSION OF FREQUENCY OF WEATHER TYPES BY SEASON 

Table 6-1 summarizes the annual and seasonal frequency of occurrence of each meteorological 

pattern type for the period 1996-1997. Review of these statistics reveals the following notable 

characteristics : 

The East Continental High pressure pattern (ECH) was, by far, the most frequent 

pattern during all seasons . 

6-1 
" The high pressure patterns (ECH, MCH, BH, NG, plus GH) dominated 80% of the 

year; the fall and winter had the lowest total frequencies (78%) and the spring had the 

highest (89%) . 

" The No Gradient (NG) pattern frequency peaked in the fall . 

" The Gulf High (GH) pattern frequency peaked in the summer. 

" The East-West frontal pattern (GFEW) occurred most frequently in the fall . 

" The North-South frontal pattern (GFNS) occurred most frequently in the spring . 

" The Mid-continental low pressure (MLOW) and Eastern low pressure (ELOW) patterns 

rather infrequently affected the weather in the NEGOM. 

" The tropical storm (TS) pattern occurred very infrequently in all seasons, especially in 

the summer season. 

Generally speaking, the statistics confirm that the NEGOM was dominated by fair weather 

conditions, which was infrequently punctuated in all season by frontal passage. Tropical 

storms are infrequent, and did not occur once during the summer months of both years (which 

seems counter-intuitive) . 

Most of these findings are consistent with expectation based on common-sense meteorological 

reasoning and longer-term historical climatology. However, it must be remembered that a 

strong El Nino event marked the second half of 1997, and the related eastern movement of the 

Southern Oscillation in the Pacific was signaled in late 1996. El Nino events are known to 

suppress western Atlantic tropical storm development in the summer and fall months following 

onset . 

6-2 
Table 6-1 . 

1996-1997 annual and seasonal frequencies of 

occurrence for each meteorological regime defined in Section 4. 

-r- : 

MET, TAPE ANNUAL ' 

. 

FtEQ. 

SPRING ' 

FtEQ. 

SUMMER 

FREQ. 

FALL 

FREQ. `' 

WIHTEIt 

FYtEQ. 

MCH 11 14 7 5 16 

I ECH 29 25 29 24 38 

BH 18 23 23 13 14 

MLOW 2 2 1 1 2 

GFNS 8 12 8 5 9 

GFEW 6 4 4 10 7 

ELOW 3 3 2 4 4 

GH 8 8 12 8 3 

NG 14 8 15 28 7 

TS 1 1 0 2 71:::] 

DISCUSSION OF STATISTICAL DIFFERENCES BETWEEN SEASONS 

Table 6-2 provides a summary of the mean and variability of key meteorological parameters in 

the NEGOM for 1996-97 by season . Review of these results lead to the following points : 

Winds are strongest in the winter, and lighter and more variable in the summer. 

Generally winds are from the east, yet progressively turn from southerly in the summer 

to northeasterly in the winter . 

Surface air temperature follows the expected season patterns . 

Annually, sea-surface temperature (SST) are greater than air temperatures (unstable 

regime) . In spring and summer, SST is less than the air temperature (stable regime), 

but in the fall and winter, unstable conditions prevail as off-shore flow moves cooler 

continental air over the Gulf. 

Geostrophic vorticity is slightly positive annually, suggesting the dominance of 

cyclonic curvature of surface-level winds; negative vorticity (anti-cyclonic curvature) is 

evident in spring and summer, likely due to the dominance of high pressure regimes. 

6-3 
Variability in vorticity is larger in the winter, possibly due to the larger frequency of 

transient systems moving through the area, while it is least variable in the summer with 

the establishment of constant and stationary high pressure systems . 

Wind stress curl follows the vorticity patterns since the two are directly related. 

It is important to note that the mean vorticity and wind stress curl reported in these tables are 

rather low (about 1 order of magnitude smaller than the rotational rate of the Earth, or the 

coriolis effect) . This suggests that the means for each meteorological type are influenced by 

both cyclonic and anti-cyclonic regimes on a day-to-day basis . Since they are near-zero, the 

vorticity means are probably not statistically significant, and so the reader should not place 

much significance on the actual values . 

Table 6-2 . 

Annual and seasonal mean and variability (standard deviation) of key observed and derived 

meteorological variables from coastal and ocean-based observation sites and Eta grid points . 

OsMv$u/DEuvIn 

PARAMEM 

AtvivvAL 

MEAN/SD 

SPRING 

Minx/SD 

Summm 

MEAN/SD 

FALL 

MEAN/SD 

WIN= 

MEAN/SD 

Wind Speed (m/s) 5.3/2.4 5.6/2.5 4.2/2.1 5.6/2.5 6.1/2.6 

Wind Direction (dept 90 / 59 112 / 55 169 / 66 64 / 57 43 / 57 

Surf. Temp. (deg. C) 23.8/3.1 22.7/3.5 29.7/1.5 25.1/4.1 17.5/3.5 

Humidity (%) 77 / 10 80 / 11 77 / 7 75 / 11 78 / 13 

SST (deg. C) 24.4 / 2.0 22.5 / 2.5 29.2 / 1 .3 25.7 / 2.9 19.5 / 1.4 

Wind Stress Curl from 

Eta' 

0.6/25.0 -0.2/19.3 0.2/12.1 1.1/24.3 1.4/44.3 

Vorticit from Eta2 0.6/83.5 -3.9/77.9 -1.6/69.1 5.3/76.7 2.7 / 110.8 

'Values in units of 10-' kg M-2 s-Z 

Z Values in units of 10-6 s-1 

DISCUSSION OF STATISTICAL DIFFERENCES BETWEEN WEATHER TYPES 

Table 6-3 provides a summary of the mean and variability of key meteorological parameters in 

the NEGOM for 1996-97 by meteorological pattern type . Review of these results lead to the 

following points : 

The strongest winds are associated with the low pressure regimes and transient 

systems, e .g ., the GFNS and the more quick-moving high pressure systems such as 

MCH. 

The wind directions faithfully reflect the typing procedure (since the approach relied 

heavily upon recognizing the patterns in wind directions) ; the frontal and No Gradient 

6-4 
types are associated with the largest variability in direction, while the Tropical Storm 

type has lower than expected variability (potentially a result of the very few TS cases in 

the database). 

Surface air temperatures by meteorological type generally reflect the seasons in which 

each type dominate; the maximum variability is seen for MCH, and is probably related 

to the fact that under this condition offshore flow is bringing post-frontal continental air 

into the NEGOM, the temperature of which varies significantly by season (this is 

consistent with the temperature variability shown by type and season, provided in 

Appendix A) . Less variance is seen for on-shore types and TS, due to consistent 

temperatures within the tropical maritime air mass. 

Humidity is lower for off-shore flow regimes, and higher for on-shore regimes as well 

as those types that occur in single seasons . 

Negative voriticity (anti-cyclonic curvature) is associated with high pressure regimes 

(GH, NG, BH), but also includes MLOW; positive vorticity is seen for low-pressure 

regimes but also includes MCH and ECH. The highest variability is associated with 

the most transient systems. 

While the meteorological categories were defined by the presence and influence of high and 

low pressure systems in or near the NEGOM, the actual flow patterns in the NEGOM were 

often characterized by curvature in opposite sense to the pressure regime. For example, in the 

MLOW case, a low pressure center is located over the mid-U.S ., with a front extending 

southward approaching the Gulf. Air flow in the NEGOM often curves anti-cyclonically 

(from easterly to southeasterly) well ahead of such fronts ; furthermore, MLOW commonly 

exists with the BH condition. Also, for MCH and ECH categories, high pressure systems 

induce off-shore flow that often curves cyclonically from northerly to westerly once well 

beyond the coastline. 

The reader is referred to Appendix A for additional detail, where these statistics by 

meteorological pattern are further broken down by season . 

6-5 
Table 6-3 . 

Annual mean and variability (standard deviation) by meteorological type of key observed 

and derived meteorological variables from coastal and ocean-based observation sites and Eta grid points . 

,pARAMMM 

. . 

METFAROLOGICAL TYPE ,' 

MCP ` ECH ~BH MIAW GFNS G FEW ELOW GH ' NG TS 

Wind Speed (m/s) 6.5/2.8 5.5/2.5 5.4/2.2 5.9/2.8 6.7/3.1 4.8/2.5 6.4/2.7 4.8/2.1 3.5/1.8 8.6/3.0 

I Wind Direction (deg.) 347/51 68/53 147/49 162/54 296/77 141/79 291/47 213/51 172/77 30/46 

Surf. Temp. (deg. C) 18.7/4.5 23.3/3.9 24.7/ 

2.3 

24.8/2.0 22.1/3.1 25.8/2.3 23.4/2.7 25.4/2.4 26.7/2.8 27.0/1 .2 

Humidity (%) 69/14 74/12 86/8 88/8 80/13 83/8 71/11 83/9 75/10 82/5 

SST (deg.C) 22.1/2.1 24.3/2.3 23.8/1.9 23.5/2.2 22.7/1.6 25.5/1 .8 24.3/1.5 25.0/1.8 26.9/1.9 26.8/0.7 

Wind Stress Curl 

from Eta` 

2.5/62.8 0.6/20.2 -0.3/ 

22.1 

-1.0/ 

10.7 

1 .5/23.9 0.9/12.4 0.5/31 .4 -0.5/ 

13.4 

0.2/22.9 5.4/32.0 

Vorticity from Eta2 8.2 

/116.4 

4.0/73.2 

~ 

-5 .9/ 

~ 73.2 

-6.3/ 

~ 83.5 

9.2 

1 /105.5 

5.2/77.3 

~ 

1 .7 

~ /118.3 

- 

1 11.0/80.9 

-5.51 

1 75.4 

29.3 

1 /90.1 

'Values in units of 10-' kg M-2 s-Z 

'Values in units of 10' s-1 
DISCUSSION OF TIME-SERIES STATISTICAL DIFFERENCES BETWEEN SITES 

Time series were conducted for the sixteen stations listed in Table 5-2 to discern diurnal 

patterns in the data. Two scalar parameters that would likely be influenced by diurnal 

oscillations were selected for analysis -- surface temperature and wind velocity component (the 

component orthogonal to the coast line) . Although wind direction is also likely affected, time 

series analyses for this parameter were not feasible it this is a vector quantity . The results of 

these analyses are graphically depicted in the figures in Appendix B. We summarize key 

features of these results in Table 6-4 . 

Two obvious conclusions can be drawn from these results . First, the effects of land-sea breeze 

recirculation and flow reversals are not felt far offshore ; certainly less than 80 km and 

probably no further than about 50 km . This finding is consistent with prior analyses of coastal 

meteorology in the Gulf of Mexico . Second, the land-sea breeze effect is much less pronounce 

offshore Louisiana; probably due to the predominance of swamps and a poorly defined 

coastline . 

As discussed in Chapter 5, we also filtered the observed meteorological data to remove the 

diurnal signals . This was accomplished using a 12 block averaging method. The effectiveness 

of this filtering can be seen in the filtered results for selected stations that exhibited strong 

diurnal, as shown in the figures of Appendix C. These results confirm that the 12-hour block 

filtering effectively removed the diurnal land-sea breeze components of the data stream . Thus, 

the filtered data are available for use by investigators that wish to examine meteorological 

conditions absent diurnal influences. 

6-7 
Table 6-4. 

Diurnal land-sea breeze influences at surface meteorological 

data sites based on interoretation of time series analysis for the 1995-1997 data period . 

Approximate Discernable 

Distance from Diurnal Influence 

Site Location Shoreline (km) (wind speed/temps .) 

Buoy 42001 Mid Gulf 300 none/none 

Buoy 42002 West Gulf 250 none/none 

Buoy 42007 OTP 25 weak/weak 

Buoy 42036 West Tampa 150 none/none 

Buoy 42039 Pensacola S 80 none/none 

Buoy 42040 Mobile South 80 none/none 

Land Based 722210 Eglin < 10 strong/strong 

AFB/Valparais 

Land Based 722225 Pensacola NAS <10 strong/strong 

Land Based 747686 Keesler <10 strong/strong 

AFB/Biloxi 

Land Based 747750 Tyndall AFB <10 strong/strong 

C-MAN Buoy Southwest Pass, <10 very weak/weak 

BURL I. LA 

C-MAN Buoy Cedar Key, FL < 10 weak/moderate 

CDRF 1 

C-MAN Buoy DPIA1 Dauphin Island, <10 very weak/moderate 

FL 

C-MAN Buoy GDILI Grand Isle, LA <10 very weak/moderate 

C-MAN Buoy Keaton Beach, FL <10 moderate/moderate 

KTNF 1 

BRETON Breton Island 25 none/weak 

6-8 
ANALYSIS OF FRONTAL PASSAGES AND CYCLOGENESIS 

During the 1996-97 period, about a dozen cyclogenesis events occurred in the northern Gulf of 

Mexico, during which times low pressure centers rapidly deepened over the Gulf and exited to 

the northeast. These cases were found to occur between late fall and early spring . About 

another dozen or so events also occurred during this period in which low pressure centers over 

the Gulf coast states (Arkansas, Louisiana, Mississippi, Alabama, and Georgia) intensified via 

the strong baroclinic zones that set up along associated frontal systems extending southward 

into the Gulf. 

A comprehensive description of the role that the NEGOM plays in frontal passage and 

cyclogenesis is not feasible on a case-by-case basis because of the unique characteristics of 

each event. The most significant of these characteristics include : 

the role of larger-scale synoptic forcings ; 

orientation of the frontal systems (north-south vs . east-west) ; 

type of fronts (warm, cold, stationary); 

speed of the low/frontal systems, and the tendency for stalling/stationarity ; and 

locations within the NEGOM. 

However, a more generalized description (or conceptual model) of NEGOM cyclogenesis has 

been developed by reviewing the events occurring in the 1996-97 period to characterize the 

process as a progression or sequence of the meteorological types identified in Section 4 . A 

specific example of a representative frontal passage/cyclogenesis event has been selected as a 

model to establish the temporal progression of meteorological types. The period analyzed was 

January 4-10, 1997 . 

Two conditions appear to be necessary for cyclogenesis to occur in this region : (1) a strong 

cold-core upper level low or trough above or just to the west of the Gulf; and (2) a preexisting 

front (commonly a stationary front aligned east-west near the NEGOM coast) defining the 

boundary between cold continental air and warm tropical maritime air, often associated with a 

weak surface low pressure center . The following sequence of events for January 4-10, 1997 

was found to be typical of cyclogenesis in the NEGOM: 

JanuarY4 (Bermuda Him In the NEGOM winds are light from the south and 

southeast and temperatures are in the low 70's over the water; a strong frontal system 

is approaching the Mississippi Valley, extending to the southwest from a very deep low 

pressure system over the upper midwest. 

January 5 (Midwest Low) : As the low enters the Great Lakes area its northeastsouthwest oriented front approaches the NEGOM with a preceding heavy squall line ; 

anti-cyclonic curvature increases for the southerly winds over the NEGOM. 

January 6 (Gulf Front East-West) : The front stalls over the NEGOM as the low 

pressure center exits the northeast U.S . ; the front aligns east-west just south of the 

6-9 
NEGOM coastline and is associated with a 15 degree (F) temperature gradient . Winds 

are southerly to the south of the front and northerly to the north of the front . 

January 7 (Gulf Front East-West) : The front remains stationary over the NEGOM, but 

a wave (weak low pressure system) forms on the front south of Texas preceding a very 

deep and low-latitude upper air trough over the southwestern U.S. ; southerly winds 

strengthen to 15 knots south of the front and heavy precipitation falls along the Gulf 

coast . 

January 8 (Gulf Front East-West) : The stationary front moves slightly south, leading to 

northeast winds over much of the NEGOM under the East Coast High regime, and the 

weak low pressure wave retrogrades to the west in response to the amplifying upperlevel trough now centered over the southern Rocky Mountains ; heavy precipitation in 

the southeast U.S . continues. 

January 9 (Gulf Front North-South) : The low pressure wave suddenly deepens and 

moves eastward into the NEGOM, driven by the advancing upper level trough now 

over Mississippi Valley; this reorients the front to a north-south configuration, and a 

25-degree (F) temperature gradient across the front increases the baroclinicity of the 

storm system . Winds are southerly to the east of the front, and are northwesterly to 

the west of the front. The heaviest precipitation moves to the eastern seaboard. 

January 10 (Gulf High or No Gradient) : As the frontal system exits into the Atlantic, 

more quiescent conditions prevail over the NEGOM; winds are light and variable with 

a tendency for northwesterly directions, temperatures range in the high 50's and low 

60's over the Gulf waters . 

6-10 
7. DESCRIPTION OF AND USER'S GUIDE TO THE EXPERT SOFTWARE SYSTEM 

(NEGOMES) 

OVERVIEW 

The MMS NEGOM Expert Software System (NEGOMES) is a graphical-user-interfaced 

(GUI) software package written in VisualBasic v6 .0 . NEGOMES allows the various 

meteorological observations and statistics, derived parameters, gridded ETA model output and 

other products of this study to be easily accessed and viewed. NEGOMES automates the 

process of navigating through the multitude of ACCESS data tables, statistical tables, and 

graphics files. The point-and-click windows style screens are arranged with buttons, dropdown menus, and dialogue boxes that most personal computer (PC) users are familiar with, 

and guide the user through selections to the desired data tables or plots . The NEGOMES 

includes user-interactive screens that allow for the construction of parcel trajectories under 

specific meteorological conditions and seasons, based on statistically-derived wind 

information . The Help function within the NEGOMES software package also includes 

much of the same information presented in this section. 

NEGOMES software and its associated databases are disseminated on a set of 5 CDROMs 

available from the MMS. The associated NEGOMES database is comprised of nearly 3 

Gigabytes of information that includes : 

" 1996-1997 surface observation statistics 

" 1996-1997 aloft observation statistics 

" 1996-1997 gridded observation statistics 

" 1996-1997 transect data derived from observed surface data 

" 1996-1997 Eta gridded surface data 

" 1996-1997 Eta gridded 3-D data 

" 1996-1997 gridded Eta surface statistics 

NOTE: When running NEGOMS, users should be sure the insert into their computer CD 

drive the CDROM containing the data appropriate for the NEGOMS data display requested. 

NEGOMES FUNCTIONS 

A NEGOMES user can select from a number of functions : 

" View ACCESS database containing observational data 

" View ACCESS database containing Eta meteorological model output fields 

" View tabulated observed parameter statistics for user-selected measurement stations 

" View time-series plots or surface wind-rose plots for user-selected measurement 

stations 

7-1 
" View maps of gridded observed parameters statistics 

" View maps of gridded Eta model surface or 3-D output 

" View maps of gridded Eta model output statistics 

" View tabulated statistics of derived parameters along transects 

" Perform trajectory evaluation for hypothetical air parcel releases 

" Print or export certain viewed tables, maps, or data files. 

The users can select to view data, tables or products derived from the entire two-year 

NEGOMS database or for an individual year (1996 or 1997), an individual season, or an 

individual weather type. The users can easily navigate from one screen to another and direct 

the flow to the program by point-and-click action on buttons and pull-down menus. The user 

can select stations or geographical locations by direct entry of station names, entry of 

Latitude/Longitude coordinates, and pointing-and-clicking on map locations . 

Viewing the ACCESS database required that a copy of ACCESS '97 also be installed on the 

host computer. 

NEGOMES OPERATION 

After launching the NEGOMES software session, the user should load the data CDROM 

appropriate his/her requests . Figure 7-1 displays a flowchart of the NEGOMES, indicating 

the screens that are presented as one progresses through various paths, beginning with initial 

selections in the Main Menu Screen. The purposes and operations of each individual screen 

are described next. Description of the technical concepts, parameters, statistical measures, 

data products, and data stratifications referred to in this section are provided in Chapters 1 

through 5 . 

SUMMARY 

The NEGOMES main program screen allows users to access various statistical data produced 

during the Northeast Gulf of Mexico study . To view graphical and tabular data and statistics, 

simply : (1) click on the appropriate button and then, (2) select the data you wish to view from 

the selection screen that appears next. The Perform Trajectory Evaluation button takes users 

to an interactive program that allows various trajectory scenarios to be plotted using data from 

the study . 

7-2 
.101 xI 

of 

~-we .~ s~ e,e-ds7a ha 

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i 

Ek uts .,0~a E+a7D EwS~MV~ci 

~~' Ferfami~qck~ryEwhiatin Ew 

Yw 

Figure 7-la . Flow chart depicting the sequencing of various screens within the MMS 

NEGOMES (observed data) . 

7-3 
Figure 7-lb. Flow chart depicting the sequencing of various screens within the MMS 

NEGOMES (Eta model data) . 

7-4 
1 ~ .~~ . . 

File Options Help 

MMS Northeast Gulf of Mexico 

Expert System 

View Graphical and Tabular Statistics 

Station Statistics Trenseck Statistics Gridded Statistics 

Eta Surface Data Eta 3D Data Eta Statistics I 

Perfor m Trajectory Evaluation I Exit 

Figure 7-2 . Main Program Screen 

HOW TO USE THE NEGOMES MAIN PROGRAM SCREEN 

Viewing the MMS Database 

Select File I MMS Database from the menu bar to open Microsoft Access and load the MMS 

database file . If your system does not have Microsoft Access '97 installed, you will not be 

able to view the database file directly . The user should select and load the CDROM 

containing the observational data appropriate for his/her data request (data content is printed 

on the CDROM labels) . 

Viewing the Eta Database 

Select File I Eta Database from the menu bar to open Microsoft Access and load the Eta 

database file . If your system does not have Microsoft Access '97 installed, you will not be 

able to view the database file directly . The user should select and load the CDROM containing 

the Eta output appropriate for his/her data request (data content is printed on the CDROM 

labels). 

Exiting NEGOMES 

Select File I Exit from the menu bar, or click the "X" button in the top right corner of the 

main screen to exit the program. 

Changing Settings 

Select Options I Settings from the menu bar to view the Settings screen . This screen is used 

to set the location of files needed to run NEGOMES. 

7-5 
Viewing General Program Information 

Select Help I About from the menu bar to open the splash screen that is seen at startup. 

Locating Help Information 

Select Help I Using from the menu bar to open the NEGOMES .HTML file you are currently 

reading . To print out the User's Guide, just right-click and select Print from the pop-up 

menu . 

Station- 

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4200' = " Fill) GULF ~ 59 .65 2~59 3 

Loc at e wi t h Map. . . ~ Locate av :kh Lan 8: Lak 

Seaaon--

3d' 't~~'Inlef 

f1 f 

t 

Meteorological ParameterEl rag Coefficient f 

i 

Diap lay Data Close 

Figure 7-3 . Station Statistics Screen 

SUMMARY/STATION STATISTICS 

The Station Statistics screen is used to retrieve a specific monitoring station's data set. The 

specified data set is viewed by clicking the Display Data button . The display can be viewed as 

a tabular summary of data values, a time series image, or as a wind rose image . The displays 

available are based on the selection of Weather Pattern, Year, and Season . 

7-6 
Certain combinations of Station, Weather Pattern, Year, and Season have either no data or 

insufficient statistical information available. In these situations, the Meteorological Parameter 

selection box will indicate "No Data Available ." 

HOW TO USE THE STATION STATISTICS SCREEN 

Selecting a Station 

Use one of the following three options to select a station: 

1) Choose either a Station ID or a Station Name from the appropriate drop-down selection 

box . When one of these displays is changed, the remaining station information is 

automatically updated. 

2) Manually enter longitude and latitude values and click on the Locate with Lon & Lat 

button . NEGOMES will then determine the closest station to these values and will display the 

pertinent information for this station. Longitude and latitude coordinates are expressed as 

degrees north latitude and degrees east longitude (e.g ., buoy 42001 coordinates are -89 .65 

degrees longitude and 25 .93 degrees latitude). 

3) Click on the Locate with Map button . The Map screen appears, displaying the MMS 

monitoring stations as red dots on a map of the Gulf of Mexico (see "Selecting a Station Using 

the Map Screen"). Select the appropriate station location on the Map screen, which will 

return you to the Station Statistics screen . All station information will be updated to reflect 

the station selected . 

Note: Due to missing data and/or sample size limitations, statistics may be unavailable for 

certain combinations of season, year, or parameter. In these case, the parameter selection box 

will be blank . For example, certain weather types occur very infrequently or not occur at all 

during some seasons, so statist 

Selecting a Weather Pattern 

Ten major weather patterns have been identified in the Gulf of Mexico . Statistics have been 

gathered on each weather pattern and for all weather patterns combined . Choose a weather 

pattern using the drop-down selection box. The displayed image of the United States will then 

be updated to show the main characteristics of the chosen weather pattern. 

Selecting a Year 

Statistics have been gathered in the Northeast Gulf of Mexico for 1996 and 1997 and for both 

years combined. Select a year (or all years) by using the drop-down selection box. 

Selecting a Season 

Statistics have been gathered in the Northeast Gulf of Mexico for each of the four seasons and 

for all seasons combined. Select a season (or all seasons) by using the drop-down selection 

box. 

7-7 
Selecting a Meteorological Parameter 

Select the meteorological parameter to be viewed by using the drop-down selection box. Wind 

speed and time series statistics are displayed in graphical images . Other parameters are 

presented in a tabular data format . Select All Parameters to display variables that are only 

presented in a tabular data format . The contents of the drop-down selection box change 

according to the selected station, weather pattern, year, and season. Certain combinations 

have no statistics available . In these cases, the Meteorological Parameter drop-down selection 

box will display the message "No Data Available" and the Display Data button will be 

disabled . 

Displaying Station Statistics 

After selecting a value for all of the selection options, click on the Display Data button to open 

a screen with the statistics displayed either as a graphical image, or in a tabular data format . 

To see another set of statistics, the user can leave the display screen open, change the selection 

parameters, and click on the Data Display button again. Note : time-series displays are 

available for only the 16 stations listed in Table 5-2. 

Exiting the Station Statistics Screen 

Click the Close button at the bottom of the screen, or click the "X" button in the top right 

corner of the Station Statistics screen to exit the screen 

i 

-T-7- -- , ----- ,--~ ~------------ 

-- - - ---- --- 

---- 

- 

Figure 7-4 . Selecting a Station Using The Map Screen 

7-8 
SUMMARY 

The Map screen enables the user to select a particular monitoring station in the Northeast Gulf 

of Mexico Study . Using the mouse, the user selects a red dot which represents a station. 

When the Close button is clicked, the user is returned to the Select Statistics screen and the 

information regarding the newly selected station is updated. 

HOW TO SELECT A STATION USING THE MAP SCREEN 

Display Stations 

Stations are indicated by red dots on the map. The station display can be turned off by 

toggling the Stations button in the lower left corner of the Map screen . 

Display Transects 

Transects are indicated by colored lines that connect the red dots on the map. The transect 

display can be turned off by toggling the Transects button in the lower left corner of the Map 

screen . 

Display Grid Lines 

Latitude and longitude grid lines are indicated by dashed lines . The grid line display can be 

turned off by toggling the Grid button in the lower left corner of the Map screen . Longitude 

and latitude coordinates are expressed as degrees north latitude and degrees east longitude 

(e.g., buoy 42001 coordinates are -89.65 degrees longitude and 25 .93 degrees latitude) . 

Reading the Status Bar 

The status bar at the bottom of the Map screen is divided into four sections : (1) station ID, (2) 

station name, (3) longitude, and (4) latitude . The status bar displays information for the 

station currently selected on the map. 

Exiting the Map Screen 

Click the Close button or click the "X" button in the top right corner of the Map screen to exit 

the screen . 

7-9 
-Year------- T S '~ ;1~ 

,III Years 

E- -- 

._ . . . Season ------ ------____~_ _________~ ~~ 

i 

All '_ ;ea~on :> 

Meteorological Parameter --

i 

E All F'arariiFter :-: 

DisGlay Data ~ Close 

Figure 7-5 . Transect Statistics Screen 

SUMMARY 

The Transect Statistics screen is used to select a data set describing statistical information 

retrieved from a particular monitoring station . The data set is then viewed by clicking the 

Display Data button . 

HOW TO USE THE TRANSECT STATISTICS SCREEN 

Selecting a Transect 

Use one of the following two options to select a transect: 

1) Choose a Transect ID or a Transect name from the drop-down selection boxes provided. 

2) Use a map to select a transect. Click the Locate with Map button to display the available 

stations as red dots on a map of the Gulf of Mexico . After making a selection from the Map 

screen, the user is returned to the Transect Statistics screen and all transect information is 

updated to reflect the transect selected. 

7-10 
Selecting a Weather Pattern 

Ten major weather patterns have been identified in the Gulf of Mexico. Statistics have been 

gathered on each of these weather patterns as well as on all weather patterns combined. 

Choose a weather pattern using the drop-down selection box. After the weather pattern is 

updated, the image of the United States is updated to show the main characteristics of the 

chosen weather pattern. 

Selecting a Year 

Statistics have been gathered in the Northeast Gulf of Mexico for 1996 and 1997 and for both 

years combined. Select a year (or all years) by using the drop-down selection box. 

Selecting a Season 

Statistics have been gathered in the Northeast Gulf of Mexico for the four seasons, as well as 

for all seasons combined. Select a season (or all seasons) by using the drop-down selection 

box. 

Selecting a Meteorological Parameter 

Select which meteorological parameter you wish to view by choosing the parameter from the 

Meteorological Parameter drop-down selection box. The contents of the drop-down selection 

box change according to the selected transect, weather pattern, year, and season . For some 

combinations of these no statistics exist. In these cases, the Meteorological Parameter dropdown selection box will display the message "No Data Available" and the "Display Data" 

button will be disabled . 

Misplaying Transect Statistics 

After selecting a value for all of the selection options, click on the Display Data button to open 

a screen with the statistics displayed as either a graphical image or in a tabular data format. 

To see another set of statistics, the user can leave the display screen open, change the selection 

parameters, and click on the Data Display button again. 

Exiting the Transect Statistics Screen 

Click the Close button at the bottom of the screen to exit the Transect Statistics screen . The 

user will be returned to the Main Program screen . 

7-11 
3tl 

& 27 

Stations ~ Tr,~nsectsGrid rTrans2D ~ From station 42003 I to 42039 to 722267 

Figure 7-6 . Selecting a Transect Using The Map Screen 

SUMMARY 

The Map screen can be used to select a particular transect (group of three monitoring stations) 

in the Northeast Gulf of Mexico Study. Using the mouse, the user selects a colored line which 

represents a transect. When the Close button is clicked, the user is returned to the Transect 

Statistics screen, and the information regarding the newly selected transect is updated. 

7-12 
HOW TO LOCATE TRANSECTS USING THE MAP SCREEN 

Display Stations 

Stations are indicated by red dots on the map. The station display can be turned off by 

toggling the Stations button in the lower left corner of the Map screen . 

Display Transects 

Transects are displayed by colored lines that connect the red dots on the map. The transect 

display can be turned off by toggling the Transects button in the lower left corner of the Map 

screen . 

Display Grid Lines 

Latitude and longitude grid lines are indicated by dashed lines . The grid line display can be 

turned off by toggling the Grid button in the lower left corner of the Map screen . 

Reading the Status Bar 

The status bar at the bottom of the screen is divided into four sections: transect ID, transect 

description, and the two station end points of the transect . The Status bar displays information 

for the transect currently selected on the map. 

Exiting the Map Screen 

Click the Close button or click the "X" button in the top right corner of the Map screen to exit 

the screen . 

Figure 7-7 . Gridded Statistics Screens 

Display Daka 

7-13 
SUMMARY 

The Gridded Statistics screen is used to select a data set describing statistical information 

retrieved from a particular grid point. View the data set by clicking the Display Data button . 

HOW TO USE THE GRIDDED STATISTICS SCREEN 

Selecting a Weather Pattern 

Ten major weather patterns have been identified in the Gulf of Mexico . Statistics have been 

gathered on each weather pattern and for all weather patterns combined . Choose a weather 

pattern using the drop-down selection box. The displayed image of the United States will then 

be updated to show the main characteristics of the chosen weather pattern. 

Selecting a Year 

Statistics have been gathered in the Northeast Gulf of Mexico for 1996 and 1997 and for both 

years combined. Select a year (or all years) by using the drop-down selection box. 

Selecting a Season 

Statistics have been gathered in the Northeast Gulf of Mexico for the four seasons, as well as 

for all seasons combined. The user can select a season (or all seasons) by using the dropdown selection box. 

Selecting a Meteorological Parameter 

Select the meteorological parameter to be viewed by using the drop-down selection box. The 

contents of the drop-down selection box change according to the selected station, weather 

pattern, year, and season . Certain combinations have no statistics available. In these cases, 

the Meteorological Parameter drop-down selection box will display the message "No Data 

Available," and the Display Data button will be disabled . 

Displaying Gridded Statistics 

After selecting a value for all of the selection options, click on the Display Data button to open 

a screen with the statistics displayed as a graphical image. To see another set of statistics, the 

user can leave the display screen open, change the selection parameters, and click on the Data 

Display button again. 

Exiting the Gridded Statistics Screen 

Click the Close button at the bottom of the screen to exit the screen . 

7-14 
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ather Pattern - ECH - Eastern Continental High 

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Speed 

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WinOS (m/s) -1 .46 17 .72 

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lk Ric~arASOn Number .02 1 .07 

anetary Boundary Layer (a) 546 15854 

nd Stress (kg/w25) .Q980 .O W9 

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Figure 7-8 . Data Display Screens (accessible from the station gridded statistics screens by 

clicking the display data button -- note : time-series are not available for all 

stations ; when time series and wind rose data are available, they are listed in the 

Meteorological Parameter drop-down list as "TS" and "Wind Rose.") . 

7-15 
SUMMARY 

The Data Display Screens present data in tabular or graphical format . Multiple display screens 

can be open at one time . 

HOW TO USE THE DATA DISPLAY SCREENS 

Printing a Data Display Screen 

Print the information displayed on a selected screen by clicking the Print button . The image 

or tabular data display will be sent to the default printer, using the default printer settings . 

Closing a Data Display Screen 

Click the Close button at the bottom of the screen, or click the "X" button in the top right 

corner of the screen to close a particular display screen . 

Date 

January 1996 

`?1 2 3 4 5 6 

7 8 9 10 11 12 73 

14 15 16 17 78 19 20 

21 22 23 24 25 26 27 

28 29 30 37 1 2 3 

d 5 6 7 8 9 70 

Display Data Save Date 

~- Hour -

1-11000 (1 -2b'idnight 

I 

E ¬ 

Meteorological Parameter-

.41r Temp, ~ rn 

Close. 

Figure 7-9. Eta Surface Data Screen 

SUMMARY 

The Eta Surface Data screen is used to select a data set describing surface information for a 

particular date, time, and meteorological parameter . View a color contour or wind direction 

plot of the data set by clicking the Display Data button . Save the data set to a comma 

separated value ( .csv) file by clicking the Save Data button and specifying a filename . 

7-16 
HOW TO USE THE ETA SURFACE DATA SCREEN 

Selecting a Date 

Eta model data have been generated for 1996 and 1997. Select a month by clicking on the 

arrow buttons and a day by clicking on a date on the calendar. 

Selecting a Time 

Eta model data have been generated at three-hour intervals . The user can select a time by 

using the Hour drop-down selection box. 

Selecting a Meteorological Parameter 

Select the meteorological parameter to be viewed or saved by using the drop-down selection 

box. Selecting Wind Direction will produce an arrow plot which is color coded by wind 

speed . Selecting any other parameter will generate a color contour plot when the Display Data 

button is clicked . 

Exiting the Eta Surface Data Screen 

Click the Close button at the bottom of the screen to exit the screen . 

a 

~- Date r Hour E 

E 

~ J 1996 

007 X12 M Tight) 

E ! anua ry 

37 ` ` 2 3 4 5 6 Pressure bevel 

7 8 9 10 71 72 73 I 

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27 22 23 24 25 26 27 --' 

28 29 30 31 1 2 ., -' 

4 5 6 7 8 9 10 - Meteorological Parameter 

i 

( 3 

jAir Temperature 

Display D ata ~ Save Data Close 

Figure 7-10 . Eta 3-D Data Screen 

SUMMARY 

The Eta 3D Data screen is used to select a data set describing model information for a 

particular date, time, pressure level, and meteorological parameter . View a color contour or 

7-17 
wind direction plot of the data set by clicking the Display Data button . Save the data set to a 

comma separated value ( .csv) file by clicking the Save Data button and specifying a filename. 

HOW TO USE THE ETA 3D DATA SCREEN 

Selecting a Date 

Eta model data have been generated for 1996 and 1997 . Select a month by clicking on the 

arrow buttons and a day by clicking on a date on the calendar. 

Selecting a Time 

Eta model data have been generated at three-hour intervals. The user can select a time by 

using the drop-down selection box. 

Selecting a Pressure Level 

Eta model data have been generated for four pressure levels : 1000, 950, 900, and 850 mb. 

The user can select a pressure level by using the drop-down selection box. 

Selecting a Meteorological Parameter 

Select the meteorological parameter to be viewed or saved by using the drop-down selection 

box. Selecting "Wnd Direction" will produce an arrow plot which is color coded by wind 

speed . Selecting any other parameter will generate a color contour plot when the Display Data 

button is clicked. 

Exiting the Eta 3D Data Screen 

Click the Close button at the bottom of the screen to exit the screen . 

1 17.16 117 

Weather Patterns 

Year - 

1-1I1"T'eafS T 

Season 

1 NII ' ~ e,~s,rr 

Meteorological 

Air Temp,~2 Wff m - Mean 

Display Data Save Data Cla 

Figure 7-11 . Eta Surface Statistics Screen 

7-18 
SUMMARY 

The Eta Surface Statistics screen is used to select a data set describing surface statistics for a 

particular weather pattern, year, season, and meteorological parameter. View a color contour 

or wind direction plot of the data set by clicking the Display Data button . Save the data set to 

a comma separated value (.csv) file by clicking the Save Data button and specifying a 

filename. 

HOW TO USE THE ETA SURFACE STATISTICS SCREEN 

Selecting a Weather Pattern 

Ten major weather patterns have been identified in the Gulf of Mexico. Statistics have been 

gathered on each weather pattern and for all weather patterns combined. Choose a weather 

pattern using the drop-down selection box . The displayed image of the United States will then 

be updated to show the main characteristics of the chosen weather pattern. 

Selecting a Year 

Eta model data statistics have been generated for 1996 and 1997, as well as for both years 

combined. Select a year (or all years) by using the drop-down selection box. 

Selecting a Season 

Eta model data statistics have been generated for the four seasons, as well as for all seasons 

combined. The user can select a season (or all seasons) by using the drop-down selection box. 

Selecting a Meteorological Parameter 

Select the meteorological parameter to be viewed or saved by using the drop-down selection 

box. Selecting "Wind Direction" will produce an arrow plot which is color coded by wind 

speed. Selecting any other parameter will generate a color contour plot when the Display Data 

button is clicked. 

Exiting the Eta Surface Statistics Screen 

Click the Close button at the bottom of the screen to exit the screen. 

7-19 
. 

Air Temp, Su 

. 

rF - Mean (*KJ 

32 

liver 

3 005E+0 

30 to 

}-r 2 .944E+02 

. . 

, . __ , . . ~ . . 3 , 

. . .. 

F ~ 2, 883E + 02 h 

r .t~ 

''y , t f1 } 

r 

2.821 E+02 

98 -96 .94 -92 ~90 .88 -86 .84 -82 -80 Save Data 

Stations Transecrs ` Grid n: -81~4966 ;-19.A394 ~avgTsfc: 3 .003E+02 ~ Print Close 

Figure 7-12 . Eta Data Display Screen 

SUMMARY 

The Eta Data Display Screen presents data in graphical format as color contours or wind 

direction arrows . Only one display screen may be open at one time . 

HOW TO USE THE ETA DATA DISPLAY SCREEN 

Changing a Data Scale 

The upper and lower bounds of the color scale may be adjusted by entering new values in the 

text boxes at either end of the legend . Initially these are automatically set to the maximum and 

minimum values found in the data set. 

7-20 
Display Stations 

Stations are indicated by red dots on the map. The station display can be turned off by 

toggling the Stations button in the lower left corner of the Map screen . 

Display Transects 

Transects are indicated by colored lines that connect the red dots on the map. The transect 

display can be turned off by toggling the Transects button in the lower left corner of the Map 

screen . 

Display Grid Lines 

Latitude and longitude grid lines are indicated by dashed lines . The grid line display can be 

turned off by toggling the Grid button in the lower left corner of the Map screen . 

Reading the Status Bar 

The status bar at the bottom of the Map screen is divided into three sections : (1) longitude, (2) 

latitude, and (3) data field name and value. The status bar displays information for the Eta 

model grid cell currently selected on the map. 

Save the Data to a File 

Save the data set to a comma separated value ( .csv) file by clicking the Save Data button and 

specifying a filename . 

Printing a Data Display Screen 

Print the information displayed on a selected screen by clicking the Print button . The image 

or tabular data display will be sent to the default printer, using the default printer settings . 

Closing a Data Display Screen 

Click the Close button at the bottom of the screen, or click the "X" button in the top right 

corner of the screen to close a particular display screen . 

7-21 
Figure 7-13 . Trajectory Evaluation Screen 

SUMMARY 

The Trajectory Evaluation screen allows the user to plot trajectories in the Northeast Gulf of 

Mexico region and see the potential transport direction. The trajectories are plotted based on 

statistics gathered for various weather patterns and seasons in each grid square . By plotting 

the trajectories one after another, the user can get an idea of the possible distance a particle 

can travel in specific weather conditions . The resulting plot can be saved to a file as well as 

printed. 

HOW TO USE THE TRAJECTORY EVALUATION SCREEN 

Selecting a Weather Pattern 

Ten major weather patterns have been identified in the Gulf of Mexico . Statistics have been 

7-22 
gathered on each weather pattern and for all weather patterns combined. Choose a weather 

pattern using the drop-down selection box. The displayed image of the United States will then 

be updated to show the main characteristics of the chosen weather pattern. 

Selecting a Season 

Statistics have been gathered in the Northeast Gulf of Mexico for each of the four seasons and 

for all seasons combined. Select a season (or all seasons) by using the drop-down selection 

box. 

Selecting a Time Increment 

Select the appropriate Time Increment button on the right side of the screen to draw 

trajectories in either 6 hour or 12 hour increments . Be sure to select the desired time 

increment before clicking on the map to plot a trajectory . 

Plotting Trajectories 

After selecting the desired weather pattern season, and time increment, draw a trajectory by 

clicking on the map where you would like the trajectory to start . If a point outside the map 

region is clicked, no trajectory will be plotted. 

Removing Trajectories 

Remove the last trajectory drawn by clicking the Undo Trajectory button located on the right 

side of the screen . Clicking this button numerous times will undo trajectories one at a time in 

reverse order . Click the Reset Trajectories button located on the right side of the screen to 

remove all trajectories . 

Viewing Detailed Information About the Trajectories Drawn on the Screen 

The Trajectory Evaluation screen is too small to display all of the information about the 

trajectories . To determine the weather pattern and season used to draw a trajectory, click the 

Show History button on the right side of the screen . The information for all trajectories will 

be shown in a window . 

Tracking Elapsed Time 

The Elapsed Time display at the top right side of the screen shows a summation of the time 

corresponding to each trajectory . To reset the elapsed time to 0, click the button to the right 

of the display. 

Saving Trajectories 

Save a trajectory image to a bit map file by clicking the Save Map As button on the lower 

right side of the screen . A dialog box will prompt the user for a file name. The trajectory 

history information will be saved with the image. 

Printing Trajectories 

Print a trajectory image by clicking the Print Map button on the lower right side of the 

7-23 
screen . The default printer setting will be used to print the image . The trajectory history 

information will also be printed with the image. 

Viewing Current Weather Conditions 

Click the Current Conditions button on the bottom right side of the screen to obtain assistance 

in selecting a weather pattern based on the current weather conditions . If the user has an 

Internet browser installed, the browser will be started and a URL of a weather site will be 

loaded so the user can compare the available weather patterns to the current weather pattern. 

The user will be able to switch between the NEGOMES program and the browser window if 

both windows cannot fit on the monitor screen . 

Viewing Stations and Transects on the Map 

Click the Stations toggle button on the bottom left side of the screen to view the location of the 

monitoring stations . The stations are indicated by red dots on the map. Click the Transects 

toggle button at the bottom left side of the screen to view the location of the transects . The 

transects appear as colored lines on the map. 

Exiting the Trajectory Evaluation Screen 

Click the Close button at the bottom of the screen, or click the "X" button in the top right 

corner of the Trajectory Evaluation screen to exit the screen . The user will be returned to the 

Main Program screen . 

7-24 
REFERENCES 

Emery, C.A. 1998 . Alternative sources of wind and humidity information for the meteorology 

of the Northeast Gulf of Mexico Study memorandum to A. Lugo-Fernandez, 27 January, 

1998. 

EPA. 1994a, 1994b. Quality Assurance Handbook for Air Pollution Measurement Systems, 

Volumes I and II . 40 CFR 58. 

Florida A & M College. 1988. Meteorological Database and Synthesis for the Gulf of Mexico, 

U.S . Mineral Management Service, MMS 88-0064, Gulf of Mexico OCS Regional 

Office, New Orleans, LA. 

Franceschini, G.A. 1953. The Distribution of the Mean Monthly Wind Stress over the Gulf of 

Mexico, Texas A&M University Oceanography and Meteorology Working Collection. 

Hsu. S. A. 1988. Coastal Meteorology, Academic Press. 

Hsu, S.A. 1992. Effects of Surface Baroclinicity on Frontal Overrunning Along the Central 

Gulf Coast. J. Appl. Meteor., 31 : 900-907. 

Hsu. S . A. 1993. The Gulf of Mexico - a breeding ground for winter storms, Mariners Weather 

Log, 37 (2),4- 11 . 

Hsu, S .A. 1995. A Proper Wind-Stress Drag Coefficient Formulation for Computer Modeling 

of Seas and Coastal Regions . In Computer Modeling of Seas and Coastal Regions II. 

C.A. Brebbia, L.Traversoni, and L.C . Wrobel, Eds. Computational Mechanics 

Publications, Southhampton, UK, and Boston MA. 

Hsu, S .A. 1997. Estimating Overwater Convective Boundary Layer Height from Routine 

Meteorological Measurements for Diffusion Applications at Sea. J. Appl. Meteor., 36: 

1245-1248. 

Hsu, S .A . 1998. A Relationship Between the Bowen Ratio and Sea-Air Temperature Difference 

Under Unstable Conditions at Sea. Coastal Studies Institute, Louisiana State University, 

Baton Rouge, Louisiana 70803 . 

Lewis, J. K. and S . A. Hsu. 1992. Mesoscale air-sea interactions related to tropical and 

extratropical storms in the Gulf of Mexico, J. Geophys. Res., 97 ( C2 ), 

2215-2228. 

Muller, R.A. and C.L. Wax. 1977. A comparative synoptic climatic baseline for coastal 

Louisiana. Geoscience and Man, 18:121-129. 

R-1 
Muller, R. A. 1977. A synoptic climatology for environmental baseline analysis: 

New Orleans . J. Appl. Meteor., 16, 20-33 . 

Muller, R. A. and J. E. Willis . 1983 . New Orleans weather 1961 - 1980 : A climatology 

by means of synoptic weather types. Misc. Pub. 83 - 1, School of Geoscience, Louisiana 

State University, Baton Rouge. 

R-2 
APPENDIX A 

Seasonal mean and variability (standard deviation) by meteorological type of key observed and 

derived meteorological variables from coastal and ocean-based observation sites and Eta grid 

points . The following parameters and units are provided : 

Wind Speed (m/s) 

Wind Direction (deg) 

Surface Temp. (C) 

Humidity ( % ) 

SST (C) 

Wind Stress Curl from Eta (in units of 10-' kg m-' s-') 

Vorticity from Eta (in units of 10-6 s-') 

WINTER MCH ECH BH MLOW GFNS GFEW ELOW GH NG TS 

Wind Speed : 7 .6 / 2 .8 5 .9 / 2 .3 5 .4 / 2 .3 6 .7 / 2 .2 7 .5 / 3 .3 5 .3 / 2 .5 6 .9 / 3 .2 4 .9 / 2 .3 3 .7 / 2 .1 NA / NA 

Wind Direction : 344 .0 / 44 .7 62 .0 / 46 .9 144 .2 / 50 .6 172 .8 / 55 .0 304 .7 / 79 .3 110 .6 / 68 .4 297 .3 / 47 .6 231 .4 / 57 .2 325 .3 / 89 .8 NA / NA 

Surface Temperature : 13 .4 / 4 .4 6 .4 / 4 .1 20 .7 / 2 .1 21 .6 / 1 .1 18 .4 / 3 .7 20 .2 / 2 .3 16 .6 / 3 .2 18 .6 / 3 .6 15 .7 / 4 .1 NA / NA 

Humidity : 69 .6 / 13 .9 3 .5 / 14 .2 87 .5 / 9 .7 86 .1 / 10 .5 81 .5 / 13 .6 90 .2 / 8 .6 65 .6 / 12 .3 80 .6 / 15 .9 73 .7 / 16 .3 NA / NA 

SST : 19 .0 / 1 .5 9 .2 / 1 .3 19 .8 / 1 .3 19 .5 / 0 .3 19 .7 / 1 .2 19 .9 / 1 .2 18 .9 / 1 .5 19 .7 / 1 .4 18 .8 / 1 .2 NA / NA 

Vorticity : 14 .8 / 166 .4 3 .9 / 81 .9 -7 .4 / 84 .7 -15 .8 / 107 .6 13 .2 / 133 .4 6 .1 / 74 .2 7 .2 / 233 .3 -10 .5 / 97 .8 -3 .3 / 113 .2 NA / NA 

Wind Stress Curl : 3 .9 / 108 .5 0 .7 / 25 .1 -0 .2 / 24 .1 -1 .4 / 9 .6 2 .5 / 37 .7 2 .3 / 28 .1 2 .7 / 65 .2 -0 .5 / 30 .3 1 .6 / 59 .1 NA / NA 

SPRING MCH ECFi BH MLOW GFNS GFEW ELOW GH NG TS 

Wind Speed : 6 .7 / 2 .9 5 .2 / 2 .4 5 .6 / 2 .1 5 .6 / 2 .7 6 .6 / 3 .2 5 .9 / 3 .2 7 .5 / 3 .5 4 .9 / 1 .9 3 .8 / 1 .7 8 .9 / 2 .5 

Wind Direction : 351 .2 / 55 .0 67 .3 / 57 .1 140 .8 / 43 .6 54 .7 / 56 .2 288 .6 / 84 .0 117 .9 / 73 .6 289 .9 / 49 .6 202 .4 / 41 .0 135 .1 / 64 .1 61 .0 / 49 .6 

Surface Temperature : 19 .0 / 5 .2 21 .9 / 4 .5 24 .1 / 2 .6 23 .6 / 1 .9 21 .7 / 2 .3 24 .3 / 2 .6 19 .9 / 3 .3 24 .9 / 2 .6 23 .9 / 3 .9 19 .5 / 1 .5 

Humidity : 65 .4 / 15 .0 73 .0 / 13 .8 89 .9 / 7 .3 89 .7 / 6 .4 84 .8 / 13 .7 88 .8 / 7 .9 70 .3 / 11 .3 86 .3 / 9 .6 79 .5 / 14 .8 91 .4 / 2 .2 

SST : 21 .1 / 2 .4 22 .3 / 2 .9 22 .8 / 2 .5 22 .0 / 2 .0 21 .2 / 1 .0 22 .9 / 2 .4 21 .0 / 1 .9 23 .5 / 2 .4 23 .6 / 2 .8 19 .6 / 0 .2 

Vorticity : 3 .1 / 95 .8 -0 .3 / 74 .2 -7 .3 / 68 .7 -8 .2 / 81 .0 4 .9 / 97 .3 -0 .4 / 93 .8 0 .5 / 78 .0 -14 .2 / 77 .5 -14 .3 / 63 .4 30 .4 / 94 .8 

Wind Stress Curl : 0 .4 / 17 .5 -0 .1 / 16 .0 -0 .3 / 32 .7 -1 .5 / 11 .4 0 .7 / 16 .9 0 .2 / 5 .1 -0 .4 / 39 .5 -0 .9 / 9 .2 -0 .8 / 8 .3 -2 .2 / 19 .5 

SUMMER MCH ECH BH MLOW GFNS GFEW ELOW GH NG TS 

Wind Speed : 9 .0 / 1 .9 4 .3 / 2 .2 4 .2 / 1 .9 NA / NA 4 .6 / 2 .1 4 .3 / 2 .2 4 .7 / 2 .0 4 .7 / 2 .0 3 .4 / 1 .6 7 .3 / 3 .2 

Wind Direction : 342 .5 / 66 .0 83 .3 / 64 .8 173 .8 / 59 .2 NA / NA 246 .9 / 57 .3 221 .0 / 81 .5 270 .7 / 49 .0 209 .3 / 52 .5 177 .0 / 75 .1 224 .5 / 53 .0 

Surface Temperature : 30 .1 / 1 .3 29 .0 / 1 .9 30 .1 / 1 .1 NA / NA 27 .6 / 1 .9 29 .6 / 1 .5 30 .1 / 1 .8 30 .0 / 1 .2 30 .2 / 1 .2 29 .3 / 1 .2 

Humidity : 72 .6 / 9 .6 75 .4 / 8 .1 78 .8 / 4 .8 NA / NA 72 .0 / 9 .7 80 .0 / 7 .0 76 .3 / 7 .4 81 .1 / 6 .2 75 .4 / 6 .0 80 .9 / 5 .5 

SST : 29 .6 / 0 .9 28 .8 / 1 .5 29 .4 / 0 .9 NA / NA 27 .2 / 1 .4 29 .2 / 1 .2 29 .4 / 1 .4 29 .1 / 1 .3 29 .6 / 1 .1 28 .1 / 0 .8 

Vorticity: -6 .3 / 63 .7 4 .5 / 64 .0 -2 .8 / 65 .8 NA / NA 1 .3 / 74 .1 3 .1 / 76 .4 -6 .2 / 76 .8 -9 .8 / 72 .5 -4 .2 / 69 .1 -0 .5 / 80 .4 

Wind Stress Curl : 0 .1 / 3 .0 0 .5 / 12 .2 -0 .3 / 5 .6 NA / NA -0 .2 / 6 .9 0 .3 / 9 .1 -0 .3 / 6 .4 -0 .1 / 5 .7 0 .2 / 21 .1 2 .0 / 21 .2 

W 

FALL MCH ECH BH MLOW GFNS GFEW ELOW GH NG TS 

Wind Speed : 5 .2 / 2 .7 6 .1 / 2 .7 5 .9 / 2 .1 6 .0 / 2 .8 6 .4 / 2 .9 4 .4 / 2 .2 8 .0 / 2 .3 4 .4 / 1 .9 3 .4 / 1 .7 13 .3 / 2 .1 

wind Direction : 351 .2 / 52 .0 66 .3 / 44 .6 146 .5 / 46 .9 167 .9 / 51 .0 306 .0 / 72 .6 116 .1 / 89 .1 309 .7 / 35 .3 223 .5 / 61 .9 230 .8 / 80 .5 36 .0 / 18 .4 

Surface Temperature : 22 .9 / 4 .8 24 .3 / 4 .7 26 .7 / 2 .9 26 .7 / 2 .2 25 .3 / 3 .4 26 .2 / 3 .2 21 .6 / 3 .2 27 .6 / 2 .7 27 .2 / 4 .0 23 .9 / 0 .8 

Humidity : 68 .6 / 12 .9 73 .2 / 10 .9 83 .0 / 9 .4 85 .3 / 8 .4 77 .4 / 11 .0 80 .2 / 10 .5 62 .5 / 16 .1 84 .6 / 6 .8 72 .4 / 12 .1 87 .3 / 3 .6 

SST : 24 .9 / 2 .9 25 .3 / 3 .0 25 .6 / 2 .8 25 .8 / 2 .8 26 .1 / 2 .6 26 .0 / 2 .9 23 .5 / 1 .3 26 .5 / 2 .8 27 .3 / 2 .9 25 .9 / 0 .1 

Vorticity : 7 .4 / 82 .3 6 .4 / 73 .2 -3 .0 / 70 .0 -2 .0 / 81 .6 11 .3 / 87 .4 9 .9 / 72 .0 15 .3 / 109 .6 -5 .2 / 74 .6 -3 .2 / 74 .5 147 .5 / 124 .2 

Wind Stress Curl : 2 .9 / 58 .4 1 .0 / 24 .7 -0 .1 / 13 .1 -0 .3 / 10 .0 1 .3 / 17 .9 0 .9 / 8 .5 0 .8 / 38 .0 0 .1 / 5 .6 0 .1 / 1 .5 22 .9 / 76 .7 

APPENDIX B 

Time Series Analyses for Wind Speed and Temperature for 

16 Selected NEGOM Offshore and Coastal Observation Stations . 

Temperature (C) 1996 - 1997: Station 42001 

U 

o 

0 5000 low 15000 

TimO v hours 

N 

O 

U 

4 

2 

o 

n .o 

v 

too 20 10 7 5 

Wavalnrqth in lkwrc 

B-3 

1 t0 100 1000 14000 

Lag 
Temperature (C) 1996 - 1997: Station 42002 

0 5000 1 Op00 15000 

Time n hours 

h 

4 

2 

R 

m 

0 

100 20 10 7 5 

WarvoloVh in Pbuurs 

B-4 

> 10 100 1000 10000 

L" 
Temperature (C) 1996 - 1997: Station 42007 

6 

O 

N 

0 5400 10000 15000 

Time n hours 

N 

U t 

4 

a 

0 

100 70 10 7 5 

Wavelength in Hwoung 

B-5 

1 10 100 1000 10000 

Lag 
Temperature (C) 1996 - 1997 : Station 42036 

U 

H wn 

Vi 

LL G 

h 

Q 

0 50017 10000 15000 

Time n hours 

10 100 1000 10000 

Lag 

O 

1pp 20 10 7 5 

WxvalvnVh in Hours 

B-6 
Temperature (C) 1996 - 1997: Station 42039 

v 

0 

U ev 

< p 

T 

Q 

0 5pp0 10000 15000 

Time in hours 

10 100 1000 10004 

x-89 

5 

F) 

mo 

n 

0 

100 20 10 7 5 

Wavelength in Fbura 

B-7 
Temperature (C) 1996 - 1997 : Station 42040 

~Uy G 

Z 

h 

4 

2 

9 

m a n 

0 

0 5400 10000 15000 

Time n hours 

100 20 10 7 5 

Wavelength in Hotas 

B-g 

1 10 100 1000 10000 

Lag 
Temperature (C) 1996 - 1997: Station 722210 

U 

0 

0 5000 10000 15000 

Time n hours 

N 

u. O 

U 

N 

Q 

10 100 1000 10001D 

"`y 

o 

0 

tpp zp 10 7 5 

Wwval*rqth in HM" 

B-9 
Temperature (C) 1996 - 1997: Station 722225 

v g 

0 

r- o 

0 SQ00 10000 15000 

Tirne n hours 

N 

IL Q 

N 

Q 

10 100 1000 10000 

Lag 

0 

100 20 10 7 5 

Worvelwnqlh in Fburs 

B-10 
Temperature (C) 1996 - 1997 : Station 747686 

V 

O 

F C 

0 5ppp 10000 15000 

Time n hours 

C 

U 

N 

Q 

10 100 1000 100100 

`W 

C 

O 

n 

b 

o 20 10 z s 

Wavelvrgfh in HOUiz 

B-11 
Temperature (C) 1996 - 1997: Station 747750 

U F, 

O 

O 

0 5000 10000 15000 

Time qi hours 

N 

LL O 

N 

Q 

10 100 1000 10000 

Lag 

o 

W 

O 

100 20 10 7 s 

Wwval*Vh in Hours 

B-12 
Temperature (C) 1996 - 1997: Station BRETON 

0 5000 10000 15000 

Time -n hotus 

N 

,1 0 

U t 

h 

Q 

2 

ts o 

m 

0 

100 10 10 7 

Wirvslvnqih in "bum 

B-13 

1 ~0 100 1000 10000 

Lag 
Temperature (C) 1996 - 1997: Station BURL1 

0 5000 10000 15000 

Tim* r+ hours 

N 

,L a 

R 

E 

2 

o 

a -

0 

100 2(f 10 7 5 

Wavrlenqfh in Hours 

B-14 

t t0 100 1000 10000 

Lag 
Temperature (C) 1996 - 1997: Station CDRF1 

V 

0 

H 

0 5000 10000 15000 

Time n hours 

~Uy C 

N 

Q 

10 100 1000 10000 

Lag 

o 

b 

100 20 10 7 5 

Wavol"h in Fours 

8-15 
Temperature (C) 1996 - 1997: Station DPIA1 

U 

O 

O 

H 

~a Q 

U t 

4 

2 

R 

m o 

a 

b 

O 

0 5000 10000 15000 

Time in hours 

100 20 10 7 5 

Wrvelwgfh in Fburz 

B-16 

t 10 100 1000 10000 

Lad 
Temperature (C) 1996 - 1997: Station GDIL1 

U 

0 

0 5000 10000 15000 

Time o hours 

U 

4 

2 

:8 

a 

m 

0 

100 20 10 7 5 

Waval"h in Hours 

B-17 

1 10 100 1000 10000 

Lag 
Temperature (C) 1996 - 1997: Station KTNF1 

0 o 

p 5400 1(X7Q0 15000 

7irn4 n hours 

N 

~(yJ O 

2 

h 

Q 

10 100 1000 10000 

Lag 

o 

0 

tpp 20 10 7 5 

Wuvolon9th in Hbw% 

B-ig 
Wind Speed (m/s) 1996 - 1997 : Station 42001 

E o 

0 

0 5000 1(7006 15000 

Time on hours 

0 

2 0 

O 

O 

> to 100 1000 10000 

Lag 

o 

a m 

0 

100 20 10 7 5 

WRValpr,qlh in Fbuxs 

B-19 
Wind Speed (m/s) 1996 - 1997 : Station 42002 

s 

O 

0 5400 10000 15000 

Timq n hours 

a 

C 

4U 

O 

O 

10 1D0 1000 10090 

Lag 

o 

°a 

0 

100 20 10 7 5 

Wavelonplh in Fbas 

B-20 
Wind Speed (m/s) 1996 - 1997: Station 42007 

m 

0 

0 54010 10000 15000 

Tirno n hpura 

6 

U 

G 

O 

O 

10 100 1000 10000 

Lag 

o 

o 

0 

1D0 20 10 7 5 

Wavelsrqth in Hours 

8-21 
Wind Speed (m/s) 1996 - 1997 : Station 42036 

o 

N 

O 

0 5000 10000 15000 

Time n hours 

0 

u 

U t c 

0 

0 

10 100 1000 10000 

Lag 

2 o 

a 

o 

a 

100 20 10 7 5 

WavalvVh in Hours 

8-22 
Wind Speed (m/s) 1996 - 1997 : Station 42039 

m 

a 

m 

0 

u y 

~t o 

0 

0 

n 

a 

O 

0 5000 10000 15000 

Time -n hours 

1 10 100 1000 10000 

Lag 

100 20 10 7 5 

Wwvol*Vh in Hours 

B-23 
Wind Speed (m/s) 1996 - 1997: Station 42040 

R 

O 

N 

O 

0 

u 

U ? 

< o 

0 

0 

a 

a 

a 

D 

0 5000 10000 15000 

Time in hours 

1 10 100 1000 1 0000 

Lag 

100 20 10 7 5 

Wavmlen4th in Hours 

B-24 
Wind Speed (mls) 1996 - 1997 : Station 722210 

N 

r 

O 

0 

d 

N 

Q 

R 

o 

m 

G 

1 1Q 100 1000 1000 

Lag 

100 20 10 7 5 

Wavalsnqth in Fbws 

B-25 

0 5000 10000 15000 

Time m hours 
Wind Speed (mls) 1996 - 1997: Station 722225 

o 

0 

0 5000 10000 15040 

Time -n hours 

0 

(] O 

Z O 

ell 

4 

1 10 100 1000 10000 

Lag 

R 

o 

a m 

o 

0 

100 20 10 7 5 

WavNonqih in Hours 

B-26 
Wind Speed (m/s) 1996 - 1997: Station 747686 

m 

N 

O 

c 

U 'r 

c o 

0 

0 

10 100 10110 14004 

Lag 

vN 

o 

w 

O 

100 20 10 7 5 

Wxvelpnplh in HOws 

0 5000 1000 15000 

Time n hours 
Wind Speed (m/s) 1996 - 1997 : Station 747750 

A 

O 

O 

0 5000 10000 15000 

Time n hours 

C 

Q 'T O 

N 

Q 

1 10 100 1000 10000 

Lag 

o 

m 

O 

100 20 10 7 S 

Wavelength in Hour% 

B-20 
Wind Speed (m/s) 1996 - 1997 : Station BRETON 

N 

O 

0 

ILL 

U + 

0 

0 

0 5000 10000 15000 

Time n hours 

10 100 1000 100oo 

Lag 

a 

0 

tOD 20 10 7 5 

Wavelength in Hours 

B-29 
Wind Speed (m/s) 1996 - 1997 : Station BURl1 

0 

m 

C 

1 C 

O 

O 

a 

15 -

m 

n 

b 

O 

0 5000 14000 15000 

Time -n hours 

~o goo 1000 ioooo 

Lag 

100 20 10 7 5 

Wavela,ylh in Fours 

B-30 
Wind Speed (m/s) 1996 - 1997: Station CDRF1 

eo 

0 

0 

U y 

t p 

O 

O 

0 500p 10000 15000 

Time n hours 

1o 1oo 1ooo 1oooo 

Lag 

o 

0 

100 20 10 7 5 

Wervslanplh in Hags 

B-31 
Wind Speed (m/s) 1996 - 1997: Station DPIA1 

°-

0 

a 

0 

U a 

0 

0 

o 

m 

m° o 

0 

0 5400 10000 15000 

Time n hours 

1 t0 100 1000 10000 

Lag 

100 20 10 7 5 

Wave l"h in Hours 

B-32 
Wind Speed (m/s) 1996 - 1997: Station GDIL1 

N 

N 

O 

w 

a 

o 

0 

0 

0 5000 10000 15000 

Time n hours 

t0 100 1000 10000 

Lag 

2 

R 

E 

a 

0 

100 20 10 7 5 

Wavelength in Hws 

B-33 
Wind Speed (m/s) 1996 - 1997 : Station KTNF1 

N 

O 

O 

W 

O 

O 

O 

O 

2 2 

m 

O 

O 

17 

1 10 100 1000 10000 

Lap 

100 24 10 7 5 

WrvpIKglh in Hours 

B-34 

0 5000 1 Op00 15000 

Time n hours 
APPENDIX C 

Filtered (12-hour Block Filter) Time Series Analyses for 

Wind Speed for 16 Selected NEGOM Offshore and Coastal Observation Stations . 

Filtered Wind Speed (m/s) 1996 - 1997 : Station 42001 

0 

N 

O 

G 

U ? 

t O 

O 

O 

0 5004) 10000 15000 

Time n hours 

1p IUD 1040 100t7Q 

L89 

o 

0 

100 20 10 i 5 

Wavelength in Haitx 

C-3 
Filtered Wind Speed (mls) 1996 - 1997 : Station 42002 

C 

< G 

O 

C 

0 5000 100OU 15000 

Tim* n hours 

1p 100 1000 10000 

Lag 

o 

mo 

0 

100 20 10 7 5 

Wavelsrpth in Hours 

C-4 
Filtered Wind Speed (m/s) 1996 - 1997: Station 42007 

0 5000 10000 15000 

Tima wi hours 

0 

uC~ 

O 

a 

0 

10 10D 1000 10000 

Lag 

b 

100 20 10 

W:rvalorqlh in Mcvxs 

C-5 
Filtered Wind Speed (m/s) 1996 - 1997 : Station 42036 

0 

N 

0 5000 10000 15000 

Time -n hours 

m 

0 

u 

a 

0 

10 1D0 100 10000 

Lag 

a 

0 

1110 20 10 7 5 

Wavai"h in Hours 

C-6 
Filtered Wind Speed (m/s) 1996 - 1997: Station 42039 

0 50110 100043 15000 

Time n hours 

O 

o 

0 

0 

1 10 100 1000 10000 

Lag 

a m 

0 

100 20 10 7 5 

Wnvelanplh in Haas 

C-7 
Filtered Wind Speed (mls) 1996 - 1997: Station 42040 

0 

0 5000 loom 15000 

Tirnq n hours 

O 

U r 

< O 

O 

O 

10 100 1000 10600 

Lag 

o 

m 

0 

104 20 10 7 5 

Wwvslv,qth in Hws 

C-8 
Filtered Wind Speed (m/s) 1996 - 1997 : Station 722210 

0 

O 

O 

4 

O 

N 

w 

O 

1 10 100 1000 10000 

Lag 

100 20 10 7 5 

Wxvai*npAh in Nam 

C-9 

0 5000 10000 16000 

limn ri hours 
Filtered Wind Speed (mls) 1996 - 1997: Station 722225 

s 

N 

O 

N 

0 

u 

o 

0 

0 

o 

2S 

0 

0 5000 10000 16000 

Time m hours 

t 10 100 1000 10000 

Lag 

100 20 10 7 5 

Waivel"h in Hours 

C-10 
Filtered Wind Speed (m/s) 1995 - 1997 : Station 747686 

m 

4 

0 5000 10000 15000 

Time m hours 

m 

0 

u 

U 

o 

0 

0 

1 10 100 1000 10pOp 

Lag 

2 

~no 

10 

I DO 20 1(1 7 5 

Wrvr+l"h in Mars 

C-11 
Filtered Wind Speed (m/s) 1996 - 1997: Station 747750 

a 

N 

W 

cc 

u 

0 

0 

0 

d 5400 10000 15000 

Tima ~n hours 

tp 100 1000 low 

Lag 

N 

r 

~1 O to 

G 

goo ao 10 7 s 

Wwvol"h in Hours 

G12 
Filtered Wind Speed (m/s) 1996 - 1997: Station BRETON 

0 5000 10000 15000 

Time n hours 

0 

U O 

C 

O 

G 

10 100 1000 10000 

Lag 

2 a 

o 

0 

100 20 10 7 5 

VYorvml"h in Hours 

C-13 
Filtered Wind Speed (m/s) 1996 - 1997 : Station 6URL1 

f_~ o 

O 

x 

a 

C 

U 

o 

0 

0 

10 Boa 1000 10000 

Lag 

o 

a m 

0 

100 20 10 7 5 

Wwv-plvVh in Hours 

G14 

0 5000 ,0000 150oo 

Time n hours 
Filtered Wind Speed (m/s) 1996 - 1997: Station CDRF1 

mN 

O 

h 

0 

u 

o 

0 

0 

o 

o 

0 

0 5000 10000 15000 

Time n hours 

1 10 100 1000 10090 

Lag 

100 20 10 7 5 

WavaIpnqih in Hood 

G15 
Filtered Wind Speed (m/s) 1996 - 1997 : Station DPIA1 

N 

,` N 

N 

0 5000 10000 15000 

Time n hours 

O 

` G 

O 

C 

o 100 1000 10000 

Lag 

o 

w O 

O 

100 20 10 7 5 

Wwalongth in Hours 

C-16 
Filtered Wind Speed (m/s) 1996 - 1997: Station GDIL1 

0 5000 10000 15000 

Time in hours 

m 

O 

` G 

O 

O 

10 100 1000 10000 

Lag 

0 

o 

0 

104 20 tU 7 5 

Waval"h in lours 

C-17 
Filtered Wind Speed (m/s) 1996 - 1997: Station KTNF1 

m0 50Q0 10000 15000 

Tirna 4n hours 

O 

u 

o 

0 

c 

10 100 1000 10000 

Lag 

R 

~G p 

O 

100 ?0 10 7 5 

Wirvalanplh in Boors 

C-18 
op The Department of the Interior Mission 

`' 4 , p 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 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 

M4qCH 3 ~~p9 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 territories 

under U.S . administration . 

The Minerals Management Service Mission 

As a bureau of the Department of the Interior, the Minerals Management Service's (MMS) primary 

s~ p responsibilities are to manage the mineral resources located on the Nation's Outer Continental Shelf 

(OCS), collect revenue from the Federal OCS and onshore Federal and Indian lands, and distribute 

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those revenues . 

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Moreover, in working to meet its responsibilities, the Offshore Minerals Management Program 

administers the OCS competitive leasing program and oversees the safe and environmentally sound 

exploration and production of our Nation's offshore natural gas, oil and other mineral resources. The 

MMS Royalty Management Program meets its responsibilities by ensuring the efficient, timely and 

accurate collection and disbursement of revenue from mineral leasing and production due to Indian 

tribes and allottees, States and the U.S . Treasury. 

The MMS strives to fulfill its responsibilities through the general guiding principles of: (1) being 

responsive to the public's concerns and interests by maintaining a dialogue with all potentially affected 

parties and (2) carrying out its programs with an emphasis on working to enhance the quality of life for 

all Americans by lending MMS assistance and expertise to economic development and environmental 

protection .