OCS Study 

MMS 2005-069

Study Final Report 

for the

Beaufort Sea Meteorological Monitoring and 

Data Synthesis Project 

July 2006 

prepared for the 

Minerals Management Service 

Alaska Outer Continental Shelf Region 

3801 Centerpoint Drive, Suite 500 

Anchorage, Alaska 99503 

Contract 1435-01-00-CT-31067 

prepared by

Hoefler Consulting Group 

3401 Minnesota Dr. Suite 300 

Anchorage, Alaska 99503

907-563-2137

ACKNOWLEDGMENT 

This study was funded by the Minerals Management Service, U.S. Department of Interior, Washington, D.C., 

under Contract Number 1435-01-00-CT-31067. 

DISCLAIMER

This draft report has not been approved for publication. Approval, when given, does not signify that the contents

necessarily reflect the views and policies of the Minerals Management Service, nor does mention of trade names 

or commercial products constitute endorsement or recommendation for use.

Hoefler Consulting Group 

TABLE OF CONTENTS

1 INTRODUCTION.......................................................................................................................1

1.1 BACKGROUND ...............................................................................................................................1

1.2 OBJECTIVES AND HISTORY ...........................................................................................................2

2 ABSTRACT.................................................................................................................................3

3 METHODS ..................................................................................................................................4

3.1 ELEMENTS OF THE STUDY.............................................................................................................4

3.1.1 Collection of Meteorological Data............................................................................................4

3.1.2 Collection of Historical Meteorological Data ...........................................................................4

3.1.3 Development of Meteorological Databases..............................................................................5

3.2 STATION LOCATIONS ....................................................................................................................5

3.3 INSTRUMENTATION .....................................................................................................................13

3.3.1 Wind Speed and Direction ......................................................................................................15

3.3.1.1 Adjusting Wind Speed Height ................................................................................................15

3.3.2 Temperature ............................................................................................................................16

3.3.3 Barometric Pressure ................................................................................................................16

3.3.4 Solar Radiation........................................................................................................................17

3.3.5 Relative Humidity...................................................................................................................17

3.3.6 Data Acquisition and Telemetry .............................................................................................17

3.4 STATION OPERATING HISTORY...................................................................................................18

4 RESULTS ..................................................................................................................................19

4.1 WIND SPEED................................................................................................................................19

4.2 WIND DIRECTION ........................................................................................................................24

4.2.1 Wind Direction by Category...................................................................................................31

4.3 WIND SIGMA ...............................................................................................................................36

4.4 TEMPERATURE ............................................................................................................................39

4.5 BAROMETRIC PRESSURE .............................................................................................................44

4.6 SOLAR RADIATION ......................................................................................................................46

4.7 RELATIVE HUMIDITY ..................................................................................................................48

5 DISCUSSION AND CONCLUSIONS ....................................................................................50

5.1 DATA QUALITY AT NORTHSTAR.................................................................................................50

5.2 ICING AT ENDICOTT AND COTTLE ISLAND..................................................................................52

5.3 OROGRAPHIC EFFECTS................................................................................................................54

5.4 SEA BREEZE EFFECTS .................................................................................................................54

5.5 LATITUDINAL EFFECTS ...............................................................................................................57

5.6 CROSS CORRELATION ANALYSIS................................................................................................58

5.7 CONCLUSIONS .............................................................................................................................64

6 RECOMMENDATIONS..........................................................................................................65

6.1 IMPROVEMENTS TO MONITORING...............................................................................................65

6.2 MONITORING NETWORK EFFICIENCY .........................................................................................65

6.3 STATION COMMUNICATIONS.......................................................................................................65

6.4 FUTURE SITE SELECTION ............................................................................................................66

7 REFERENCES..........................................................................................................................68

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LIST OF TABLES 

TABLE 3-1 METEOROLOGICAL MONITORING STATION COORDINATES .......................................................5

TABLE 3-2 PRIMARY MMS METEOROLOGICAL MONITORING EQUIPMENT ..............................................14

TABLE 3-3 ADJUSTMENT TO SEA-LEVEL PRESSURE..................................................................................16

TABLE 3-4 DATA CAPTURE SUMMARY......................................................................................................18

TABLE 4-1 WIND SPEED SUMMARY (M/S)..................................................................................................19

TABLE 4-2 DESCRIPTIVE STATISTICS FOR WIND SPEED (M/S)...................................................................22

TABLE 4-3 FREQUENCY OF WIND DIRECTION BY CATEGORY (DAYS/YR)................................................31

TABLE 4-4 DURATION OF WIND DIRECTION BY CATEGORY (AVERAGE HOURS)......................................31

TABLE 4-5 WIND SIGMA SUMMARY (o)......................................................................................................36

TABLE 4-6 DESCRIPTIVE STATISTICS FOR WIND SIGMA (o).......................................................................36

TABLE 4-7 TEMPERATURE DATA SUMMARY .............................................................................................39

TABLE 4-8 DESCRIPTIVE STATISTICS FOR TEMPERATURE (oC)..................................................................39

TABLE 4-9 DESCRIPTIVE STATISTICS FOR TEMPERATURE DURING OPEN WATER PERIODS .....................41

TABLE 4-10 DESCRIPTIVE STATISTICS FOR TEMPERATURE DURING ICE PERIODS....................................41

TABLE 4-11 DEGREE DAY STATISTICS .......................................................................................................43

TABLE 4-12 BAROMETRIC PRESSURE DATA SUMMARY (MB)....................................................................44

TABLE 4-13 DESCRIPTIVE STATISTICS FOR BAROMETRIC PRESSURE ADJUSTED TO SEA-LEVEL (MB).....44

TABLE 4-14 RELATIVE HUMIDITY DATA SUMMARY .................................................................................48

TABLE 5-1 VALUE OF WIND SPEED CROSS CORRELATION FUNCTION AT LAG = 0...................................58

LIST OF FIGURES

FIGURE 3-1 PROJECT LOCATION MAP..........................................................................................................6

FIGURE 3-2 METEOROLOGICAL MONITORING STATION MAP......................................................................7

FIGURE 3-3 BADAMI MMS METEOROLOGICAL MONITORING TOWER........................................................8

FIGURE 3-4 COTTLE ISLAND MMS METEOROLOGICAL MONITORING TOWER............................................9

FIGURE 3-5 ENDICOTT MMS METEOROLOGICAL MONITORING TOWER...................................................10

FIGURE 3-6 MILNE POINT MMS METEOROLOGICAL MONITORING TOWER..............................................11

FIGURE 3-7 NORTHSTAR MMS METEOROLOGICAL MONITORING TOWER ...............................................12

FIGURE 4-1 SPATIAL DISTRIBUTION OF AVERAGE WIND SPEED ...............................................................20

FIGURE 4-2 SPATIAL DISTRIBUTION OF MAXIMUM HOURLY WIND SPEED...............................................21

FIGURE 4-3 MEAN ADJUSTED MONTHLY WIND SPEED .............................................................................23

FIGURE 4-4 WIND ROSE WIND SPEED LEGEND (M/S)................................................................................24

FIGURE 4-5 MEAN BADAMI WIND ROSE....................................................................................................25

FIGURE 4-6 MEAN COTTLE ISLAND WIND ROSE .......................................................................................26

FIGURE 4-7 MEAN ENDICOTT WIND ROSE.................................................................................................27

FIGURE 4-8 MEAN MILNE POINT WIND ROSE............................................................................................28

FIGURE 4-9 MEAN NORTHSTAR WIND ROSE .............................................................................................29

FIGURE 4-10 SPATIAL DISTRIBUTION OF WIND ROSES..............................................................................30

FIGURE 4-11 FREQUENCY OF OFFSHORE WINDS .......................................................................................32

FIGURE 4-12 FREQUENCY OF ONSHORE WINDS.........................................................................................33

FIGURE 4-13 FREQUENCY OF SHORE-PARALLEL WINDS ...........................................................................34

FIGURE 4-14 FREQUENCY OF VARIABLE WINDS........................................................................................35

FIGURE 4-15 MEAN MONTHLY WIND SIGMA ............................................................................................38

FIGURE 4-16 MEAN MONTHLY TEMPERATURE .........................................................................................40

FIGURE 4-17 TEMPERATURE HISTOGRAMS FOR NORTHSTAR 2001 - 2002................................................42

FIGURE 4-18 MEAN MONTHLY BAROMETRIC PRESSURE ADJUSTED TO SEA-LEVEL................................45

FIGURE 4-19 MEAN DAILY SOLAR FLUX AT MILNE POINT .......................................................................46

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FIGURE 4-20 MEAN MONTHLY SOLAR RADIATION ...................................................................................47

FIGURE 4-21 MEAN MONTHLY RELATIVE HUMIDITY ................................................................................49

FIGURE 5-1 AERIAL VIEW OF NORTHSTAR SHOWING METEOROLOGICAL STATION LOCATION ...............51

FIGURE 5-2 CLIMATRONICS VS. RM YOUNG MEASURED WIND SPEED ....................................................53

FIGURE 5-3 FREQUENCY RATIO OF OFFSHORE (E - NE) TO ONSHORE (W - SW) WINDS BY MONTH.........56

FIGURE 5-4 FREQUENCY RATIO OF E TO NE TO W TO SW WINDS VS. LATITUDE.....................................57

FIGURE 5-5 MONTHLY CORRELATION OF FREQUENCY RATIO OF E – NE TO W – SW WINDS VS.

LATITUDE............................................................................................................................................57

FIGURE 5-6 CROSS CORRELATION OF WIND SPEED BETWEEN BADAMI AND OTHER STATIONS AT 

VARIOUS LAGS....................................................................................................................................59

FIGURE 5-7 CROSS CORRELATION OF WIND SPEED BETWEEN ENDICOTT AND OTHER STATIONS AT 

VARIOUS LAGS....................................................................................................................................60

FIGURE 5-8 CROSS CORRELATION OF WIND SPEED BETWEEN COTTLE ISLAND AND OTHER STATIONS AT 

VARIOUS LAGS....................................................................................................................................61

FIGURE 5-9 CROSS CORRELATION OF WIND SPEED BETWEEN MILNE POINT AND OTHER STATIONS AT 

VARIOUS LAGS....................................................................................................................................62

FIGURE 5-10 CROSS CORRELATION OF WIND SPEED BETWEEN NORTHSTAR AND OTHER STATIONS AT 

VARIOUS LAGS....................................................................................................................................63

FIGURE 6-1 MAP OF EXISTING AND POTENTIAL STATIONS........................................................................67

LIST OF APPENDICES 

A Historical Wind Data Inventory and Map 

B Yearly Graphs 

C Composite Monthly Wind Roses 

D Descriptive Statistics 

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1 INTRODUCTION 

This report presents the results of the 2001- 2005 Nearshore Beaufort Sea Coast Meteorological 

Monitoring Project commissioned by the U.S. Department of the Interior Minerals Management 

Service (MMS). This work was performed by HCG, Inc., d.b.a., Hoefler Consulting Group 

(HCG) under MMS Contract No. 1435-01-00-CT-31067. 

1.1 Background 

MMS predicts that in the near future oil and gas development will expand in the nearshore region 

of the Beaufort Sea. In the Outer Continental Shelf (OCS) areas of the Bering Sea and of the 

continental U.S., MMS maintains networks of offshore buoys to collect long-term surface wind 

data, but such a system is absent in the Beaufort Sea. Data sets collected prior to this study were 

not suitable for use in MMS models because the data sets were too limited (e.g. 3 month offshore 

exploration projects) or the distance from the area of interest was too great (e.g. Barrow or Barter 

Island). 

Research by Dr. Thomas Kozo in the 1980’s demonstrated that Arctic regional circulation 

models based upon upper air pressure fields are inaccurate predictors of surface winds within 20-

30 km of the Beaufort Sea Coast. The two major effects predicted to explain the differences are 

1) the existence of an Arctic sea breeze effect [Kozo, 1982] and 2) orographic effects caused by 

the Brooks Range on Alaska’s eastern Beaufort Coast [Kozo and Robe, 1986]. Kozo suggests 

that the sea breeze effect influences an area centered along the coastline that is approximately 40 

km in width, while orographic effects of the Brooks Range influence an area extending at least 

50 km offshore from Camden Bay to Mackenzie Bay. 

The Arctic sea breeze effect described by Kozo [1982] occurs during the summer, when ice-free 

conditions occur and daylight is almost constant in the Arctic. The long days lead to a land-sea 

thermal imbalance, with the land always being warmer. This causes the upper air surface to 

slope seaward, causing offshore pressure to rise and inducing a shoreward wind (east to northeast in the Beaufort). In contrast to the well-known sea breeze effect of the lower and midlatitudes, this wind is consistently shoreward. Since the sun does not set for long periods while 

the ocean is ice-free, there is never a reversal of temperature gradients resulting in a lack of 

seaward breezes that are commonplace at coastal areas at lower latitudes.

The effects of the Brooks Range are somewhat more complex than the Arctic sea breeze effect. 

Due to the stable atmospheric boundary layer typical in the Arctic, air flow around the Brooks 

Range almost always presents less of an obstacle than air flow over it. This leads to changes in 

wind speed and direction relative to that predicted in models. The exact nature of the diverted 

flow will depend on the incoming direction of the large-scale wind. 

Model results presented by Kozo and Robe [1986] predict that for the dominant wind direction 

(northeast), winds should be supergeostrophic in the area west of 142°W, peaking at 162% of 

geostrophic speed in eastern Camden Bay and declining to 151% north of Mikkelsen Bay and 

140% north of Prudhoe Bay. Winds in Camden Bay were diverted from NE to ENE, while little 

diversion occurred at the other sites. Wind speed was predicted to increase moving eastward 

from Prudhoe Bay to Camden Bay when winds came from the east, northeast, west, and 

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northwest. Of the five wind conditions modeled by Kozo and Robe (E, NE, N, NW, and W), 

only north winds were predicted to be stronger at Prudhoe Bay than at Camden Bay, while in all 

other cases the reverse was true. 

1.2 Objectives and History

The originally stated goals of this study were to collect historical meteorological data from the 

Beaufort Sea region, subject to immediate development, and to collect new meteorological data 

from the deployment of new meteorological stations. 

The study was intended to provide a comprehensive time-series of wind data for MMS modelers 

and researchers for use in their ongoing modeling of the nearshore Beaufort Sea. Meteorological 

data were collected at both offshore and nearshore locations to provide data for MMS models, 

such as the Oil Spill Risk Analysis (OSRA), Coastal Zone Oil Spill (COZOIL), oil weathering, 

and nearshore circulation models. 

The historical meteorological data sets were identified, collected, and delivered to MMS as an 

electronic database in November 2005. 

This database represents the most comprehensive collection of weather observations ever 

assembled for the Beaufort Sea coast, containing all valid hourly data collected by any station 

operating along the U.S. or Canadian coastline from 1985 to October 2005. This collection of 

over 1.2 million station-hours of data should prove to be a valuable resource for MMS and the 

public in modeling the meteorology of this region. 

For this study, HCG operated a total of five nearshore meteorological monitoring stations 

spanning about 100 kilometers along the Beaufort Sea coast from western Simpson Lagoon to 

Mikkelsen Bay. Four of the five meteorological stations were located at BP facilities: the 

Badami storage pad, Endicott’s Satellite Drilling Island (SDI), the top of the Northstar living 

quarters (PLQ) on Seal Island, and Milne Point F-Pad. These four stations began collecting data 

January 1, 2001. Another station on the east end of Cottle Island was added to the study 

beginning August 21, 2002. 

Data collection began at four of the stations on January 1, 2001. The monitoring stations are still 

operating at the time of this writing, and will likely continue to collect data until September 

2006. These stations all collected a variety of parameters, including wind speed and direction, 

wind sigma (a measure of turbulence), temperature, relative humidity, solar radiation, and 

barometric pressure. 

Over the life of this project HCG prepared 18 Quarterly Data Reports for MMS, which 

summarize the stations operating history, equipment and collected data in detail. The primary 

concern of this report is to focus on the most pertinent data and results. As this was 

foundationally a study to aid in modeling wind fields to predict oil spill movements, the main 

emphasis is on the wind speed and direction data. Variables of secondary interest; temperature, 

wind sigma, barometric pressure, solar radiation and relative humidity, are included but given 

less thorough treatment. 

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2 ABSTRACT 

This report summarizes meteorological data collected from January 2001 to June 2005 from five 

nearshore meteorological monitoring stations spanning a 100 km stretch of the Beaufort Sea 

coast centered on Prudhoe Bay. 

The data are generally similar between most of the sites, although statistically significant 

differences are shown to exist between different sites and between seasons. The station at 

Badami is the most distinctive due to a weakened sea breeze effect and orographic effects of the 

Brooks Range, which appear to influence the eastern edge of the study area. 

The data support the sea breeze and orographic effects theorized by Dr. Thomas Kozo in the 

1980’s, while providing more detail in the area around Prudhoe Bay. The data indicate that the 

sea breeze effect is stronger in May and June than in August. 

Northstar is not a good location for a meteorological monitoring station due to several 

obstructions on the island. However, the Milne Point and Cottle Island stations are 

representative of the area, especially the Cottle Island station.

It was discovered over the course of this project that 3-cup anemometers will pack with snow 

and under-report wind speed in mid-winter at some Arctic coastal sites. It was proven through 

simultaneous operation that propeller-style wind sensors are a far more reliable means of 

collecting wind data in this region. Therefore, the use of propeller-style wind instruments is 

recommended in all future studies where icing might be an issue. 

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3 METHODS 

3.1 Elements of the Study 

This study included four key elements: 

(1) Collection of new meteorological data 

(2) Collection of historical wind data 

(3) Development of meteorological databases for the collected data 

(4) Analysis of the collected data. 

These four tasks were conducted as follows.

3.1.1 Collection of Meteorological Data 

A primary element of this study was the collection of new meteorological data from the 

nearshore Beaufort Sea region of Alaska. The study included a total of five meteorological 

monitoring stations. Four new meteorological stations were established for this study at Badami, 

Endicott, Northstar, and Milne Point in January 2001. To address concerns about wind 

interferences at Northstar, a fifth meteorological station was installed on Cottle Island in August 

2002. The observed parameters for this study were wind speed, wind direction, air temperature, 

barometric pressure, relative humidity, and solar radiation. All parameters were monitored 

continuously. Data collection will likely continue through September 2006. 

The meteorological monitoring sites were selected to measure nearshore Beaufort Sea winds in 

the vicinity of proposed offshore oil and gas development. Four of the stations were located at 

BP Exploration (Alaska) Inc. oil and gas exploration and production facilities. All five sites 

were situated in flat areas near sea level. The Badami and Milne Point stations are “nearshore 

stations” located on the mainland. The Endicott, Northstar, and Cottle island stations are 

“offshore stations”, located on islands. A map of the five meteorological sites is presented in 

Figure 3-2. Photographs of the five site locations are presented in Figure 3-3 through Figure 3-7. 

3.1.2 Collection of Historical Meteorological Data 

Historical Beaufort Sea wind speed and direction data from all available sources were compiled 

into a single database with consistent formats. Historical wind data collected near the Beaufort 

Sea coast between 1985 through 2005 were obtained from public and private sources. The 

historical data gathering effort included a literature search of the known studies in the Beaufort 

Sea, Alaska region. A total of 32 sets of historical wind data were collected for this region. Two 

additional private-domain wind data sets were identified, but could not be readily obtained in an 

electronic format. 

Many of the historical data sets consisted of only a few months of data. Only wind data, which 

was collected approximately every hour, was used in the historical database. Data sets were 

limited to an area stretching from Barrow to Herschel Island (just beyond the Alaska-Canada 

border). An updated historical database was presented to MMS on April 2005. This historical 

database was entitled the “Nearshore Beaufort Sea Historical Wind Database”. A listing and a 

map of the historical wind data sets included in this database is included in Appendix A. 

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3.1.3 Development of Meteorological Databases 

A system of data management was developed to support the collection of the new and historical 

meteorological data. The historical wind data was compiled into a “Nearshore Beaufort Sea 

Historical Wind Database”, as previously described. The newly collected data was compiled 

into the primary “Beaufort Sea Meteorological Monitoring and Data Synthesis Project 

Database”. Each of these databases was developed in a Microsoft Access format. 

Throughout the study period, newly collected meteorological data from the five stations are 

downloaded every working day to an Anchorage-based server. The data are then reviewed and 

posted to a web-enabled database that is available for public access. Public access to this 

database is available on the Beaufort Sea Meteorological Monitoring and Data Synthesis Project 

website at http://www.resdat.com/mms/. 

3.2 Station Locations 

This study includes five meteorological monitoring stations located along the Beaufort Sea coast 

of Alaska. Four of the meteorological stations are situated at oil and gas exploration and 

production facilities owned by BP Exploration (Alaska) Inc. (BPXA), and one station is located 

on land owned by the state of Alaska. The host facilities are the Badami Development Project, 

Endicott Production Facility, F-Pad in the Milne Point Unit, and Northstar Production Facility. 

The fifth station is located on Cottle Island, which is owned by the state of Alaska. These 

meteorological stations span across 100 kilometers of the Beaufort Sea coastline. Maps of the 

study area and the station locations are shown in Figure 3-1 and Figure 3-2, respectively. Table 

3-1 provides the coordinates for each station. 

Table 3-1 Meteorological Monitoring Station Coordinates 

Site Latitude Longitude 

Badami 70° 08.171’N 147° 00.522’W

Endicott 70° 19.370’N 147° 51.895’W

Northstar 70° 29.428’N 148° 41.901’W

Cottle Island 70° 29.920’N 149° 05.571’W

Milne Point 70° 30.402’N 149° 39.725’W

The meteorological monitoring sites were selected to measure the nearshore winds along the 

Beaufort Sea coast in the vicinity of proposed offshore oil and gas development. The sites were 

located in a manner consistent with the Environmental Protection Agency (EPA) Prevention of 

Significant Deterioration (PSD) criteria for surface meteorological data collection. Data 

collection at the Badami, Endicott, Milne Point, and Northstar stations began on January 1, 2001 

and at Cottle Island on August 21, 2002. 

All five sites are situated in flat areas near sea level. The Badami and Milne Point stations are 

located on the mainland, Endicott and Northstar stations are located on man-made islands, and 

the Cottle Island station is located on a natural island. Photographs of each meteorological 

station are provided in Figure 3-3 through Figure 3-7. 

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Figure 3-1 Project Location Map

Study Area

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Figure 3-2 Meteorological Monitoring Station Map 

 Nuiqsut 

Cottle 

Island

NationalPetroleumReserve-Alaska 

0 10 20 miles

Bada

● Kuparuk Airport 

● Deadhorse Airport 

 Prudhoe Bay ●

Prudhoe 

Bay

Endicott

Milne Point Northstar

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Figure 3-3 Badami MMS Meteorological Monitoring Tower

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Figure 3-4 Cottle Island MMS Meteorological Monitoring Tower 

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Figure 3-5 Endicott MMS Meteorological Monitoring Tower 

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Figure 3-6 Milne Point MMS Meteorological Monitoring Tower 

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Figure 3-7 Northstar MMS Meteorological Monitoring Tower 

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The Endicott, Northstar, and Cottle Island stations are located on islands and considered

“offshore” stations. The Endicott monitoring station is located on Endicott’s Satellite Drilling 

Island (SDI), which is connected to shore by a causeway. SDI is located about three kilometers 

offshore from the mouth of the Saganavirktok River. The Northstar monitoring station is located 

at the Northstar Production Facility on Seal Island, a two hectare (five acre) gravel island 10 

kilometers from the coast. The Cottle Island monitoring station is located on an undeveloped 

island five kilometers from the coast. 

The Badami and Milne Point stations are located on the mainland, and are considered “onshore” 

stations. To the south of the Badami and Milne Point stations is tundra, with numerous lakes 

scattered throughout the region. Vegetation in the area consists of mosses, lichens, grasses, and 

low-growing Arctic tundra bushes. Milne Point is located on the coast, a few meters from the 

ocean. Badami is somewhat inland, separated from the Beaufort Sea by two kilometers of flat, 

treeless tundra. 

3.3 Instrumentation 

This section briefly describes the meteorological sensors used in this monitoring program. All 

five sites use identical instrumentation. All instruments meet or exceed the stringent EPA PSD 

requirements for range accuracies, thresholds, response times, resolutions, damping ratios, and 

other performance measures. The meteorological monitoring program collects hourly data for 

the following parameters at each monitoring location: 

z Wind speed (meters/second [m/s]); 

z Wind direction (degrees [°]); 

z Wind direction standard deviation (wind sigma [σθ]); 

z Air temperature, motor-aspirated shield (degrees Celsius [°C]); 

z Air temperature, motor-aspirated shield, backup (degrees Celsius [°C]); 

z Barometric pressure (millibar [mbar]); 

z Solar radiation (watts/m2 [W/m2]); and 

z Relative humidity, motor-aspirated shield (percent [%]). 

Each site also records the minimum and maximum instantaneous temperature and the maximum 

instantaneous wind speed during the previous hour. All parameters are collected as hourly 

averages, except barometric pressure which is recorded at the start of each hour. All measured 

parameters (except barometric pressure and solar radiation) were audited and calibrated semiannually. 

The mean hourly temperature, minimum instantaneous temperature, and maximum instantaneous 

temperature will be very similar most of the time. The mean hourly temperature is defined as the 

average temperature recorded for every second of the specified one-hour interval. The minimum 

instantaneous temperature is defined as the lowest temperature recorded in the one-second 

measurements throughout the specified one-hour interval. The maximum instantaneous 

temperature is defined as the highest temperature recorded in the one-second measurements 

throughout the specified one-hour interval. 

Wind speed, wind direction, and wind sigma are measured at a height of approximately 10 m at 

Badami, Cottle Island and Endicott, and at approximately 14 m and 23 m at Milne Point and 

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Northstar, respectively. Temperature and relative humidity are measured at a height of 2 meters 

at Badami, Cottle Island, and Endicott, and at 11 m and 19 m at Milne Point and Northstar, 

respectively. 

Barometric pressure is measured at a height of 1 to 2 m at Badami, Cottle Island, Endicott, and 

Milne Point, at 19 m at Northstar. Solar radiation is measured at a height of approximately 5 m at 

Badami, Cottle Island, and Endicott, and at 11 m and 21 m at Milne Point and Northstar, 

respectively. 

A listing of each parameter and sampling method used during the monitoring program is 

provided in Table 3-2. 

Table 3-2 Primary MMS Meteorological Monitoring Equipment 

Parameter 

Manufacturer/ 

Model Operating Range Method 

Wind Speed Climatronics 

F460 0.2 to 60 m/s Three-cup anemometer 

assembly 

Wind 

Direction 

Climatronics 

F460 0o

 to 360o

Vane, potentiometer voltage 

output is proportional to 

wind direction 

Wind 

Sigma 

Campbell 

Scientific 

CR10X-XT 

--- 

DAS calculated, 15-minute 

root mean square values 

averaged to 1-hour values 

Temperature Climatronics 

100093-2 -50°C to +50°C 

Platinum 4-wire probe and 

thermistor in a motoraspirated shield 

Barometric 

Pressure 

Campbell 

Scientific 105 600 to 1,060 mb Silicon capacitive pressure 

sensor 

Relative 

Humidity 

Campbell 

Scientific HMP45C 

-40°C to +60°C 

(0% - 100% RH) 

Capacitive polymer H chip 

in a motor-aspirated shield 

Solar 

Radiation 

Campbell 

Scientific 

LI200X 

400 to 1,100 

nanometers 

Silicon photovoltaic 

detector mounted in cosinecorrected head 

Datalogger 

Campbell 

Scientific 

CR10X-XT 

-55°C to +85°C 

1.0-second scans, processed 

to hourly averages recorded 

on the hour 

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3.3.1 Wind Speed and Direction 

Wind speed and direction are measured continuously using Climatronics F460 Wind Sensors and 

RM Young Model 05305-AQ Wind Monitors. The Climatronics sensor uses a three-cup 

anemometer to measure wind speed. The cup rotation produces a signal frequency that is 

proportional to wind speed and is recorded by the datalogger. RM Young sensors are deployed 

at Cottle Island and Endicott and measure wind speed using a propeller anemometer. The 

propeller rotation produces a signal frequency which is proportional to wind speed. These signal 

frequencies are recorded by the datalogger. 

Both Climatronics and RM Young wind direction sensors consists of a wind vane with a 

360-degree potentiometer for a signal transducer. The wind speed sensor and wind direction 

sensor are separate instruments on the Climatronics assembly. The standard deviation of the 

wind direction (wind sigma [σθ]) is computed by the Campbell CR10X-XT datalogger using the 

EPA-preferred Yamartino (1984) method. 

3.3.1.1 Adjusting Wind Speed Height 

Wind speed is measured at a height of 10 meters at Badami, Cottle Island and Endicott, but the 

Milne Point and Northstar towers were raised to avoid interfering obstacles (this was more 

successful at Milne Point than Northstar). The sensor heights are 14 and 23 meters at Milne 

Point and Northstar, respectively. 

Theoretically, wind speed is equal to zero at the ground, then increases logarithmically with 

height. The rate of increase depends upon the surface roughness (z0). If the surface roughness is 

known, and if the wind speed M1 at height z1 is known, then wind speed M2 at height z2 can be 

estimated using the formula: 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = ⋅ ln( / )

ln( / )

1 0

2 0

2 1

z z

z z M M

Heights are in meters and speeds in m/s. At Milne Point we assume a surface roughness of 0.005 

meters, which is defined by the Davenport-Wieringa roughness length classification as 

“smooth”, representing surfaces such as beaches pack ice, morass, and snow covered fields. The 

formula then tells us that the wind speeds observed at Milne Point should be multiplied by 0.958 

to estimate the wind speed at 10 meters. 

Adjusting the height at Northstar is more problematic, since the general area around the station 

has a surface roughness length between 0.0002 (“sea”) and 0.005 (“smooth”), but the area 

around it is >2, or “chaotic”, representing city centers or irregular forests with scattered 

clearings. This is further complicated by what should be considered to be “ground height”. For 

example, in a forest the tree tops are considered “ground height”, but Northstar has many 

structures taller than the wind sensor, leaving the ground height ambiguous. 

Assuming that the surface of the island is effectively ground height and the surface roughness is 

0.005, it is estimated that the wind speed should be multiplied by 0.929 to approximate the wind 

speed at 10 meters. However, if a surface roughness of 2 is applied, then the multiplier falls to 

0.659. 

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All data in the historical database, on the website, in the appendices, and in the wind roses 

represents the raw unadjusted data. The summary tables in Section 4.1 have been adjusted by 

0.958 for Milne Point for 0.929 at Northstar, the most conservative estimate. 

3.3.2 Temperature 

The stations measure temperature at different heights. The stations at Endicott, Badami and 

Cottle Island rest on the ground, whereas the Milne Point and Northstar stations are mounted 

atop raised structures. The Endicott and Cottle Island temperature probes are located at a height 

of 2.0 m above ground level; the Badami temperature probes are located at 2.6 m. At Milne 

Point and Northstar, the probes are mounted at 11 m and 19 m above ground level, respectively. 

Two separate temperature probes are located at each site: a Climatronics Temperature Sensor 

Model 100093-2 and a Campbell Model HMP-45C instrument, which is a slightly modified 

version of the Vaisala HMP45 dual temperature/relative humidity probe. The Campbell Model 

HMP-45C instrument has a similar temperature measurement range as the Climatronics probe, 

but is not certified for accuracy below -40°C. Data from the Campbell probe are being used to 

back up and verify data from the primary Climatronics probe. 

3.3.3 Barometric Pressure 

Pressure is measured using a Campbell CS105 (Vaisala PTB-101B) Barometric Pressure Sensor 

housed inside the Campbell datalogger enclosure. The barometric pressure sensor takes an 

hourly instantaneous reading. No user-serviceable parts are present on the sensor. 

Barometric pressure varies significantly from site to site, but some of this variation is due to the 

height of the sensor. The Badami, Endicott, Milne Point, and Cottle Island stations have their 

sensors mounted at approximately 2 meters, while the Northstar station measures barometric 

pressure at 21 meters. The exact height above sea level at the base of each tower is unknown, 

but estimates of the total elevation of the sensors and the corresponding estimated adjustment to 

sea-level are shown in Table 3-3. The pressure was corrected using the formula: 

100

g z P ⋅ ⋅ ∆ ∆ = ρ

∆P is the change in pressure, ρ is density (1.225 kg/m3

), g is the force of gravity (9.8m/s

2

), and 

∆z is the change in height in meters. Presssure units of Pascals are converted to millibar by 

dividing by a factor of 100. All data in the historical database, on the website, in the appendices 

and in the wind roses represents the raw, unadjusted data. The adjustments shown in Table 3-3 

have been added to the summary tables in Section 4.5 where noted. 

Table 3-3 Adjustment to Sea-Level Pressure

Monitoring 

Station Elevation (m) Adjustment (mbar) 

Badami 17 +2.0

Cottle Is 5 +0.6 

Endicott 5 +0.6

Milne Pt 7 +0.8 

Northstar 24 +2.9

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3.3.4 Solar Radiation 

A Campbell LI200X Silicon Pyranometer, manufactured by Li-Cor, measures solar radiation at 

each of the sites. The pyranometer measures sun plus sky wavelengths between 400 and 1,100 

nanometers (daylight spectrum). The instrument has an absolute error in natural daylight of plus 

or minus 5 percent. Occasional artificial light sources (facility lighting, flaring) have caused 

false readings at some sites. Suspect solar radiation readings are especially noted at Northstar 

due to the nearby facility flare. 

3.3.5 Relative Humidity 

Relative humidity is measured at the same heights as the temperature probes using a Campbell 

Model HMP-45C, which is essentially a Vaisala HMP-45A dual temperature/relative humidity 

probe. The probe uses a capacitive polymer H chip for the relative humidity measurement and 

operates in a -40°C to +60°C temperature range. 

3.3.6 Data Acquisition and Telemetry 

A Campbell Scientific Model CR10X-XT, 12-channel datalogger located at the base of each 

tower, monitors all instruments. At all sites except Northstar, the enclosure/datalogger is situated 

at ground level. At Northstar, the enclosure is located at the base of the tower on the roof of the 

PLQ. The datalogger collects a continuous stream of data from the instruments and then stores 

hourly averages, peaks, and/or instantaneous readings in the datalogger storage module. The 

data are then downloaded from the storage modules to the HCG office in Anchorage via cellular 

connection on a daily basis. 

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3.4 Station Operating History 

The network of five meteorological monitoring stations generally performed well during the four 

and one-half years of the study (January 2001 - June 2005). However, the study was not without 

some periods of lost data due to equipment failures, equipment damage, station audits, and other 

factors. Frozen anemometers, broken or corroded wind vanes and broken relative humidity 

sensors were the most common equipment problems. The Northstar station, in particular, had 

significant periods of missing data due to wind direction sensor damage and repeated relative 

humidity sensor failures. 

Meteorological data collected during this study were validated using guidelines set forth in 

EPA’s On-site Meteorological Program Guidance for Regulatory Modeling Applications (EPA, 

1995) and screened based on EPA's suggested screening criteria (EPA, 2000). The data 

validation criteria are also outlined in the Minerals Management Service Meteorological 

Monitoring Plan (Hoefler, 2001). The EPA data validation screening criteria were not effective 

in identifying an error in wind direction measurements at Northstar. As a result, additional wind 

direction validation procedures were developed and implemented in December 2002 to notify 

HCG of any errors in wind direction data collection. A Certified Consulting Meteorologist 

(CCM) from HCG performed a final review of the collected data for accuracy. 

Data flagged under the EPA or HCG criteria were carefully examined, but were generally not 

removed unless the values were outside the normal range of variation, the values become almost 

constant for an unidentified reason, maintenance activity has occurred at the site, instruments 

have been damaged, or if the flags continue uninterrupted for an extended period without 

explanation. The Quarterly Data Reports cover these procedures in greater detail, and provide 

discussion of all significant occurrences of lost or questionable data. 

Data recovery from the stations was quite good, especially considering the remoteness of the 

sites and the hostile conditions. Data recovery is expressed as a percentage equal to the number 

of valid hourly measurements divided by the total number of hours. Data recovery for each 

parameter at all sites was above 90%, with the exception of Northstar’s wind direction and wind 

sigma measurements. This performance complies with EPA’s rigorous PSD air quality modeling 

data capture requirements. Data capture for each major meteorological parameter at each station 

is summarized in Table 3-4. 

Table 3-4 Data Capture Summary

Monitoring 

Station Wind Speed 

Wind 

Direction Wind Sigma Temperature 

Barometric

Pressure 

Badami 99.8% 99.0% 99.0% 99.8% 99.9%

Cottle Island 99.6% 93.4% 93.4% 99.9% 99.9%

Endicott 94.1% 91.4% 91.4% 93.2% 99.3%

Milne Point 99.4% 98.1% 98.1% 99.9% 100.0%

Northstar 97.0% 81.9% 81.9% 99.6% 99.6%

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4 RESULTS 

This section of the report presents summary tables and general discussion of descriptive statistics 

for wind speed, wind direction, wind sigma, temperature, barometric pressure, solar radiation 

and relative humidity for the study period January 2001 to June 2005. 

Because Cottle Island station began acquiring data in August of 2002, it has a smaller data set 

than the other stations. Throughout this section, the confidence interval given for the mean is the 

95 percent confidence interval. 

Supplementary information is contained within the Appendices. Yearly data graphs, composite 

monthly wind roses, and descriptive statistics with histograms are provided in Appendices B, C 

and D, respectively. 

4.1 Wind Speed 

Wind speed data at Milne Point and Northstar have been adjusted in Table 4-1, Table 4-2, and 

Figure 4-1 through Figure 4-3 to account for the monitoring height of the anemometer, as 

described in Section 3.3.1.1. Graphs of annual wind speeds can be found in Appendix B. Figure 

4-1 provides a map of the spatial distributions of average wind speeds and Figure 4-2 provides a 

map of the highest recorded instantaneous wind speeds at each site. 

Badami had the highest average wind speed and the highest hourly wind speed was recorded at 

Northstar. It should be noted that Cottle Island wind speeds were measured only from August 

21, 2002 and maximum instantaneous wind speeds were not recorded at Cottle Island until 

January 2003. 

Table 4-1 Wind Speed Summary (m/s)

Hourly Mean 

Wind Speed 

Instantaneous 

Wind Speed Monitoring 

Station Mean Max Mean Max 

Badami 6.0 27.9 8.0 35.5

Cottle Island 5.7 25.0 7.6 26.7 

Endicott 5.3 23.7 7.2 30.6

Milne Point 5.4 24.9 7.2 33.8 

Northstar 5.1 24.9 7.5 36.2

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Figure 4-1 Spatial Distribution of Average Hourly Wind Speed 

NOTES: The Cottle Island station began operation on 8/21/02. 

Wind Speed is measured at a height of 10 meters at Badami, Cottle Island, and Endicott, and at 14 meters and 23 meters at Milne Point and NWind speeds at Northstar and Milne Point have been adjusted to approximate the local wind speed at 10 meters. 

Site Latitude Longitude

Milne Point 70° 30.402’N 149° 39.725’W

Cottle Island 70° 29.920’N 149° 05.571’W

Northstar 70° 29.428’N 148° 41.901’W

Endicott 70° 19.370’N 147° 51.895’W

Badami 70° 08.171’N 147° 00.522’W

Endicott 

5.3 m/s

5.7 m/s

Cottle Island Northstar 

5.1 m/s

Milne Point 

5.4 m/s 

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Figure 4-2 Spatial Distribution of Maximum Hourly Wind Speed 

NOTES: The Cottle Island station began operation on 8/21/02. 

Wind Speed is measured at a height of 10 meters at Badami, Cottle Island, and Endicott, and at 14 meters and 23 meters at Milne Point and respectively. Wind speeds at Northstar and Milne Point have been adjusted to approximate the local wind speed at 10 meters. 

Site Latitude Longitude

Milne Point 70° 30.402’N 149° 39.725’W

Cottle Island 70° 29.920’N 149° 05.571’W

Northstar 70° 29.428’N 148° 41.901’W

Endicott 70° 19.370’N 147° 51.895’W

Badami 70° 08.171’N 147° 00.522’W

Endicott 

30.6 m/s

26.7 m/s

Cottle Island Northstar 

36.2 m/s

Milne Point 

33.8 m/s 

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Descriptive statistics for wind speed are presented in Table 4-2 below. The low mean wind 

speed at Endicott may be partially due to anemometer icing problems (see Section 5.2). 

Northstar’s wind speed may have been biased due to interference from on-site obstacles (see 

Section 5.1). The higher wind speed observed at Badami corroborates the findings of Kozo 

[1986], whose model indicates higher wind speeds closer inland toward the Brooks Range under 

most conditions (see Section 1.1). 

Table 4-2 Descriptive Statistics for Wind Speed (m/s)

Badami Cottle Island Endicott Milne Point Northstar 

Mean 5.98 5.71 5.29 5.40 5.13

95% Confidence 

Interval (Mean) 5.93 – 6.02 5.66 – 5.76 5.25 – 5.32 5.37 – 5.43 5.10 –5.15 

Standard Deviation 3.98 3.76 3.52 3.33 3.00 

Median 5.00 4.86 4.40 4.67 4.61

Maximum 27.87 24.96 23.69 24.91 24.92

Annual variations in wind speed are presented in Figure 4-3. Wind speed is not particularly 

seasonal compared to the other variables. Endicott shows a marked drop in December, probably 

due to anemometer error (see Section 5.2). Badami wind speed measurements appear to reflect a 

real cycle that is not observed at the other sites, with lower wind speeds during July through 

September and higher wind speeds in winter. 

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Figure 4-3 Mean Adjusted Monthly Wind Speed

0.01.02.03.04.05.06.07.08.0

Jan Feb Mar Apr May June July Aug Sept Oct 

Wind Speed (m/s)

Badami Cottle Island Endicott Milne Point Northstar

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Hoefler Consulting Group 

4.2 Wind Direction 

Four and one-half year wind roses for each site are provided in Figure 4-5 through Figure 4-9. 

Wind Roses depict the frequency of occurrence of winds in each of 16 direction sectors (every 

22.5°) and six wind speed classes (shown in Figure 4-4) for a given location and time period. 

The wind roses were generated using the Lakes Environmental Software, WRPLOT View. 

Monthly composite wind roses are provided in Appendix C. Figure 4-10 provides a map of the 

spatial distributions of wind roses for the five meteorological stations. 

Discussion of the results can be found in Sections 5.3 through 5.5. 

Figure 4-4 Wind Rose Wind Speed Legend (m/s) 

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Figure 4-5 Mean Badami Wind Rose 

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Figure 4-6 Mean Cottle Island Wind Rose 

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Figure 4-7 Mean Endicott Wind Rose 

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Figure 4-8 Mean Milne Point Wind Rose 

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Figure 4-9 Mean Northstar Wind Rose 

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Figure 4-10 Spatial Distribution of Wind Roses 

NOTES: The Cottle Island station was only in operation 8/21/02-12/31/02 

Wind Speed and direction are measured at a height of 10 meters at Badami, Cottle Island, and Endicott, and at 14 meters and 23 meters at Milne Point and Northstar, respectively. 

Site Latitude Longitude

Milne Point 70° 30.402’N 149° 39.725’W

Cottle Island 70° 29.920’N 149° 05.571’W

Northstar 70° 29.428’N 148° 41.901’W

Endicott 70° 19.370’N 147° 51.895’W

Badami 70° 08.171’N 147° 00.522’W

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4.2.1 Wind Direction by Category 

An in-depth categorical summary of wind direction and duration for the five stations was 

performed. The wind direction categories were onshore (from between 310-90°), offshore (130-

27°), and shore parallel (270-310° and 90-130°). The results of the analysis are presented in 

Table 4-3, Table 4-4, and Figure 4-11 through Figure 4-14. 

Table 4-3 shows the average number of days per year the wind blew from one of these directions 

for more than 16 hours. A day was considered "variable" if no one condition persisted for 16 

hours or more. Wind direction was not counted for calm periods (wind speed <0.51m/s). 

Table 4-3 Frequency of Wind Direction by Category (Days/Yr) 

Meteorological 

Station Offshore Onshore Shore Parallel Variable 

Badami 107 129 23 106

Endicott 64 159 21 122

Milne Point 62 163 35 106

Cottle Island 60 169 25 111

Northstar 44 191 34 97

Onshore winds were the most common at every site; all five stations spend about half of an 

average year in the onshore wind direction category. Note that onshore winds were more 

frequent the further out into the Beaufort Sea each station was. Offshore winds, by contrast 

became more common as one moved farther from the ocean. Shore parallel winds were the 

rarest condition at all sites. Badami displays a much stronger offshore wind component 

compared to the other stations. This is discussed further in Sections 5.4 and 5.5. 

Table 4-4 shows the average uninterrupted hourly duration of winds by category. Only wind 

conditions which persisted for 3 hours or more were counted towards duration. 

Table 4-4 Duration of Wind Direction by Category (Average Hours)

Meteorological 

Station Offshore Onshore Shore Parallel Calm 

Badami 21 24 9 20

Endicott 18 33 9 48

Milne Point 18 31 11 21

Cottle Island 16 31 8 15

Northstar 15 44 9 44

Onshore winds are not only the most common condition, but also the most persistent. Like 

frequency, the duration of onshore winds increase seaward. Offshore winds increased in 

duration as one moved landward. The high duration of calm conditions at Endicott may be a 

result of the anemometer icing problems (see Section 5.2). 

The annualized data in Table 4-3 mask the highly seasonal nature of wind direction. The 

following four graphs display the average days each condition occurs each month. The 

frequency of offshore winds is the most exceptionally seasonal, becoming almost non-existent in 

June. Onshore winds by contrast are most dominant in the summer. Shore-parallel winds are 

most common in the fall, while variable conditions are most likely in the late summer/early fall.

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Figure 4-11 Frequency of Offshore Winds

02468

10121416

Jan Feb Mar April May June July Aug Sept Oct N

Days per Month

Badami Cottle Is Endicott Milne Pt Northstar

Page 32 
Figure 4-12 Frequency of On-Shore Winds

05

10152025

Jan Feb Mar April May June July Aug Sept Oct N

Days per Month

Badami Cottle Is Endicott Milne Pt Northstar

Page 33 
Figure 4-13 Frequency of Shore-Parallel Winds

01234567

Jan Feb Mar April May June July Aug Sept Oct N

Days per Month

Badami Cottle Is Endicott Milne Pt Northstar

Page 34 
Figure 4-14 Frequency of Variable Winds

02468

101214161820

Jan Feb Mar April May June July Aug Sept Oct N

Days per Month

Badami Cottle Is Endicott Milne Pt Northstar

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4.3 Wind Sigma 

Table 4-5 provides a summary of wind sigma data for the five stations for the period of January 

2001 to June 2005. Wind sigma is measured at a height of 10 meters at Badami, Cottle Island, 

and Endicott, and at 14 meters and 23 meters at Milne Point and Northstar, respectively. There 

is no sound method for adjusting wind sigma for height. Annual wind sigma graphs are provided 

in Appendix B, Annual Graphs. 

Table 4-5 Wind Sigma Summary (o)

Monitoring 

Station Mean Max 

Badami 6.7 74.8

Cottle Island 6.4 83.1 

Endicott 7.6 77.1

Milne Point 6.5 81.7 

Northstar 13.3 98.2

The most striking property of the wind sigma data is that the mean at Northstar (13.3) is 71 

percent larger than the second highest average wind sigma (Endicott). This is indicative of 

turbulence generated in an obstacle-rich environment at Northstar (see Figure 5-1). 

Descriptive statistics for wind sigma, presented in Table 4-6 below, show that the mean wind 

sigma is statistically different between the different stations, although this is probably more of an 

artifact of the station’s local environments rather than the area at large. Endicott is not nearly as 

prone to interference as Northstar, but the presence of a large number of structures on the same

island as the station to the south and east no doubt increases the wind sigma above background. 

The sites with the lowest means are Milne Point, Badami, and Cottle Island, reflecting the 

relatively uncluttered environment at these sites. Cottle Island is the only site without man-made 

obstacles nearby. We expect the true value for the region is in the 6.4o – 6.7o range. 

Table 4-6 Descriptive Statistics for Wind Sigma (o) 

Badami Cottle Island Endicott Milne Point Northstar 

Mean 6.68 6.45 7.76 6.51 13.27

95% Confidence 

Interval (Mean) 

6.62 – 

6.74 

6.39 – 

6.51 

7.50 – 

7.62 

6.46 – 

6.56 

13.12 – 

13.41 

Standard Deviation 6.06 4.69 5.91 5.41 13.63 

Median 5.05 5.32 5.45 4.85 8.79

Minimum 0.22 0.19 0.00 0.20 0.00

Maximum 74.80 83.10 77.10 81.70 98.20

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Mean annual variation in wind sigma is shown in Figure 4-15 for all sites but Northstar. 

Northstar was excluded because its wind sigma is not reflective of nature (as discussed in 

Section 5.1) and detracts from the seasonal pattern shown at the other sites. While there is 

variation between sites, wind sigma follows a similar pattern of troughing in February and March 

and peaking in the late summer or fall. 

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Figure 4-15 Mean Monthly Wind Sigma

0123456789

10

Jan Feb Mar Apr May June July Aug Sept Oct 

Month

Wind Sigma (Degrees)

Badami Cottle Island Endicott Milne Point

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4.4 Temperature 

Table 4-7 summarizes temperature measurements during the study period. Seasonal temperature 

variation was pronounced in the region, ranging between -45°C and 26°C. For the majority of

the year, temperatures are below freezing. Mean temperatures are above freezing from about 

June 14 to September 19, with the average year having 100 days above freezing and 265 days 

below. 

Descriptive statistics for the sites are shown in 

Table 4-8. Annual variations in temperature are presented in Figure 4-16. Graphs of hourly 

average temperatures for the period of January 2001 to June 2005 are presented in Appendix B, 

annual graphs. 

Table 4-7 Temperature Data Summary 

Monitoring Temperature (°C) 

Station Min Mean Max 

Badami -45.3 -11.3 26.1

Cottle Island -45.1 -11.3 18.2 

Endicott -42.2 -11.0 19.1

Milne Point -43.6 -11.4 22.0 

Northstar -41.3 -11.2 19.9

Table 4-8 Descriptive Statistics for Temperature (oC) 

Badami Cottle Island Endicott Milne Point Northstar 

Mean -11.26 -11.29 -11.00 -11.43 -11.22

95% Confidence 

Interval (Mean) 

-11.39 – 

-11.12 

-11.46 – 

-11.13 

-11.14 – 

-10.87 

-11.56 – 

-11.30 

-11.35 – 

-11.10 

Standard Deviation 13.93 12.82 12.84 13.01 12.32 

Median -10.57 -9.51 -9.31 -10.79 -10.33

Minimum -45.28 -45.13 -42.16 -43.6 -41.28

Maximum 26.05 18.17 19.09 21.95 19.93

Mean annual temperature was not found to be significantly different among the sites. By 

contrast, the standard deviation does depend on the distance of the station from the shore, owing 

to the damped diurnal cycle at the offshore sites. 

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Figure 4-16 Mean Monthly Temperature

-30-25-20-15-10

-5

05

10

Jan Feb Mar Apr May June July Aug Sept Oct 

Mean Hourly Temperature (oC)

Badami Cottle Island Endicott Milne Point Northstar

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Temperature histograms at every site, such as the example shown in Figure 4-17, show an 

interesting bimodal distribution. The data indicates two distributions which have different means 

and shapes. Two distinct thermal regimes appear to exist in the area. During open water 

periods, a relatively mild coastal regime exists with the temperature not deviating significantly 

from 0 oC. During closed ice periods the climate becomes colder and more “continental” with 

wider swings in temperature. These two distinct temperature regimes are shown independently 

in Appendix D. 

Summary tables for each period are shown in Table 4-9 and 

Table 4-10. Open water was approximated to exist from June through October. Once these two 

periods are separated, a significant difference in the mean temperatures between the offshore and 

onshore sites is revealed, with the onshore sites being significantly warmer in the open water 

periods and significantly cooler in the ice season than the offshore sites. 

Table 4-9 Descriptive Statistics for Temperature During Open Water Periods 

(oC, June-October) 

Badami Cottle Island Endicott Milne Point Northstar 

Mean 1.74 0.95 0.69 0.96 0.56

95% Confidence 

Interval (Mean) 

1.63 – 

1.86 

0.85 – 

1.05 

0.60 – 

0.78 

0.87 – 

1.06 

0.48 – 

0.64 

Standard Deviation 7.07 4.87 5.52 5.85 5.10

Median 1.77 1.06 0.89 0.92 0.71

Minimum -29.94 -23.64 -25.93 -25.25 -23.77

Maximum 26.05 18.17 19.09 21.95 19.93

Table 4-10 Descriptive Statistics for Temperature During Ice Periods 

(oC, November-May) 

Badami Cottle Island Endicott Milne Point Northstar 

Mean -19.64 -19.13 -19.14 -19.38 -18.73

95% Confidence 

Interval (Mean) 

-19.77 – 

-19.51 

-19.29 – 

-18.98 

-19.27 – 

-19.01 

-19.51 – 

-19.26 

-18.86 – 

-18.62 

Standard Deviation 10.37 9.85 9.86 9.70 9.34

Median -19.99 -20.06 -19.48 -19.97 -19.28

Minimum -45.28 -45.13 -42.16 -43.60 -41.28

Maximum 11.55 6.62 9.18 7.86 6.65

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Figure 4-17 Temperature Histograms for Northstar 2001-2002 

-40 -36 -32 -28 -24 -20 -16 -12 -8 -4 0 4 8 12 16 20

Temperature (o

C)

Full Year 

-40 -36 -32 -28 -24 -20 -16 -12 -8 -4 0 4 8 12 16 20

Temperature (oC)

Ice Season (November – May) 

-40 -36 -32 -28 -24 -20 -16 -12 -8 -4 0 4 8 12 16 20

Temperature (o

C)

Open Water Season (June – October) 

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Table 4-11 gives total and monthly mean degree day statistics for degree freezing days, degree 

above freezing days, and heating degree days. The table shows the mean year July – June 

because the “freezing year” and “heating year” during which freezing and heating degree days 

are accumulated begins July 1. Degree freezing days measure the daily negative difference 

between the mean temperature and 0°C. Degree above freezing days measure the daily positive 

difference between the mean daily temperature and 0°C. Degree heating days measure the daily 

negative differences between the mean temperature and 18.3°C (65°F), a value set by the U.S. 

National Oceanic and Atmospheric Administration to approximate the level of energy used to 

keep inhabited structures warm. 

Table 4-11 Degree Day Statistics 

Month 

Degree 

Freezing Days 

Degree Above 

Freezing Days 

Heating 

Degree Days 

July 0 157 411

August 0 138 429

September 11 49 510

October 203 0 771

November 482 0 1,031

December 673 0 1,240

January 750 0 1,318

February 754 0 1,266

March 771 0 1,339

April 475 0 1,024

May 168 0 736

June 2 35 516

Total 4,290 379 10,591

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4.5 Barometric Pressure 

Table 4-12 provides a summary of the barometric pressure at each site over the study period. 

Descriptive statistics for barometric pressure are presented in Table 4-13. Where noted, pressure 

has been adjusted to sea-level as described in Section 3.3.3 in order to provide a more 

meaningful comparison. 

Sea-level pressure ranged from 982 mb to 1,057 mb. After adjusting pressure to sea-level, Milne 

Point had the highest mean pressure, followed by Badami. The difference in means between 

Endicott and Cottle Island was not statistically significant. Northstar has a lower mean pressure

than the other sites, even after adjusting the readings to sea-level. 

Table 4-12 Barometric Pressure Data Summary (mb)

Monitoring Pressure Sea-Level Pressure 

Station Min Max Mean Min Max Mean 

Badami 980 1054 1014 982 1056 1016

Cottle Island 982 1051 1015 983 1052 1015

Endicott 982 1055 1015 983 1056 1015

Milne Point 983 1056 1017 984 1057 1017

Northstar 979 1052 1012 982 1055 1015

Table 4-13 Descriptive Statistics for Barometric Pressure Adjusted to Sea-Level (mb) 

Badami Cottle 

Island Endicott Milne Point Northstar 

Mean 1016.0 1015.3 1015.4 1017.5 1014.9

95% Confidence 

Interval (Mean) 

1015.9 – 

1016.1 

1015.2 – 

1015.4 

1015.3 – 

1015.5 

1017.4 – 

1017.6 

1014.8 – 

1015.0 

Standard Deviation 10.8 10.9 10.8 10.8 10.8

Median 1016.0 1015.6 1015.6 1017.8 1014.9

Minimum 982.0 982.6 982.6 983.8 981.9

Maximum 1056.0 1051.6 1055.6 1056.8 1054.9

Barometric pressure shows a consistent seasonal pattern at all sites, as shown in Figure 4-18. It 

remains relatively low from July through December, then rapidly increases, peaking in March. 

The mean monthly pressure in March is higher than November by 13 to 14 mb at all sites. 

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Figure 4-18 Mean Monthly Barometric Pressure Adjusted to Sea-Level

995

1000100510101015102010251030

Jan Feb Mar Apr May June July Aug Sept Oct 

Barometric Pressure (mb)

Badami Cottle Island Endicott Milne Point Northstar

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4.6 Solar Radiation 

As expected, solar flux is enormously seasonal in the study area, as shown in Figure 4-19 and 

Figure 4-20. Sensor output is mean hourly W/m

2

. However, for a summary of mean solar flux 

by month, it is also worth considering mean total daily flux since this captures not only the 

seasonal variation in intensity, but also in duration of daylight. Because there is very little site-tosite variation, Figure 4-19 shows only Milne Point. 

Since any sea-breeze effect in the area would be driven by the difference in the heat rate of land 

and sea, it would seem that such an effect would be much stronger earlier in the summer. For 

instance, the mean daily solar flux in June at Milne Point is roughly 2.5 times higher than in 

August and roughly 4.5 times higher than in September. 

Figure 4-19 Mean Daily Solar Flux at Milne Point 

0

1,000

2,000

3,000

4,000

5,000

6,000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Mean Daily Solar Flux (

W-hr/m2

)

Figure 4-20 shows the mean monthly solar flux for each site. Solar flux is quite unevenly 

distributed about the summer equinox, probably due to significantly increased cloudiness during 

the open-water period. Cottle Island appears to experience a cloudier July than the other sites, 

although it should be noted that there is a shorter time series at Cottle Island than at the other 

sites, so some variability should be expected.

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Figure 4-20 Mean Monthly Solar Radiation

0

306090

120150180210240270

Jan Feb Mar Apr May June July Aug Sept Oct 

Solar Radiation (watts/m2)

Badami Endicott Milne Point Northstar Cottle Island

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4.7 Relative Humidity 

Table 4-14 provides a summary of the relative humidity at each site over the study period. 

Relative humidity generally ranged between 70 and 90 percent. There was little real difference 

between the various sites, except that the Northstar relative humidity appears to remain higher in 

the winter. Instrument error for relative humidity is ±3 percent, so no real certainty exists that 

any of these sites display significantly different means for relative humidity. 

Table 4-14 Relative Humidity Data Summary

Badami Cottle Island Endicott Milne Point Northstar 

Mean 84% 85% 85% 85% 86%

Standard Deviation 10% 9% 9% 9% 9%

Minimum 30% 31% 39% 30% 45%

Maximum

1 100% 100% 100% 100% 100%

Relative humidity shows a consistent seasonal pattern at all sites, as shown in Figure 4-21. The 

humidity is higher during the months of May through October, likely because of the presence of 

surface water. 

 1

 Actual sensor readings occasionally exceeded 100% due to the instrument error of ±3%.

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Figure 4-21 Mean Monthly Relative Humidity

707580859095

Jan Feb Mar Apr May June July Aug Sept Oct 

Relative Humidity(%)

Badami Cottle Island Endicott Milne Point Northstar

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5 DISCUSSION AND CONCLUSIONS 

5.1 Data Quality at Northstar 

The data quality at Northstar station is markedly poor compared with that of the other four MMS 

stations. Despite the ideal geographical location, the number of large obstacles makes it 

impossible to have confidence that the wind data collected is representative of the larger region. 

Figure 5-1 shows the problematic structural environment surrounding the Northstar station. The 

current site is overshadowed by other structures and does not sit at 10 meters. Wind speed is 

probably close to accurate, but wind direction and wind sigma data are unreliable due to the 

presence of a process module, drill rig and crane, all of which exceed the height of the 

meteorological tower. 

The meteorological station was installed on top of the permanent living quarters (PLQ) in 

December 2000 with wind instrumentation at 23 meters. The large building in Figure 5-1 is the 

South Process Module, which is approximately 36 meters tall and located 100 meters north of the 

monitoring tower. The drill rig and crane are taller than the process module and mobile. When 

the process module was installed on August 10, 2001, alternative station sites on the island were 

investigated but none could be identified. 

The Northstar station has also experienced unusual equipment corrosion, which led to data 

problems when a corroded tail fell off of the wind vane.

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Figure 5-1 Aerial View of Northstar Showing Meteorological Station Location

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5.2 Icing at Endicott and Cottle Island

Measured winter wind speeds are biased at Endicott and, to a lesser degree, Cottle Island due to 

rime ice impeding and occasionally stopping the anemometer. The measured winter wind speeds 

at these sites should be viewed with this caveat in mind, although icing will obviously not be 

problematic during warmer months. 

Endicott and Cottle Island wind speed was primarily measured by a 3-cup anemometer, which 

computes wind speed by the number of revolutions per second the sensor makes. The mass of 

the cups increases as snow and ice freeze onto the cups. Therefore the same amount of wind 

stress will cause the iced-over cups to experience fewer revolutions. As the anemometer became

more sluggish, it became possible for the instrument to completely freeze. Six site visits were 

made to repair or replace frozen anemometers. Additionally, there were occasions the 

anemometer became unstuck of its own accord, or was merely slowed (these periods could not 

be detected without redundant equipment). 

Heating the cups would not have been effective even if line power were available on-site. This is 

because heat applied to the main body of the instrument would not radiate effectively down the 

long, thin, spinning arms connecting the sensor body to the cups. 

While the 3-cup anemometer design is the most popular for weather stations, another design 

using a propeller is also common. We theorized that this design would prove to be more 

resistant to icing for a number of reasons. First, and most importantly, there were no cups which 

could fill up with snow and ice. Second, the propeller-style anemometer rotates 2.5 times faster 

at any given wind speed than the 3-cup design. Lastly, the anemometer shaft is horizontal as 

opposed to vertical, making it harder for blowing snow to accumulate around the shaft. 

We conducted an intercomparison of wind sensors at Endicott and Cottle Island by installing RM 

Young O5305AQ propeller anemometers as redundant wind sensors in October 2004. Endicott’s 

system was removed in December 2005, but Cottle Island’s continues to operate. The three-cup 

and propeller anemometers gave identical wind speeds most of the time, but in the winter there 

were periods when the propeller anemometer gave substantially higher readings than the colocated 3-cup anemometer. Wind speed measurements from the two systems at Endicott for the 

month of December 2004 are shown as an example in Figure 5-2. Since it is extremely unlikely 

that an anemometer could over-read wind speed, the fault almost certainly lies with the 3-cup 

anemometer. 

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Figure 5-2 Climatronics vs. RM Young Measured Wind Speed

02468

101214161820

12/1/04 12/4/04 12/7/04 12/10/04 12/13/04 12/16/04 12/19/04 12/22/04 12/25/04 1

Date

Wind Speed (m/s)

ClimatronicsRM Young

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5.3 Orographic Effects 

The Badami monitoring station sits relatively further inland compared to the other sites. The 

greater mean velocity of winds and the greater proportion of easterly winds (as opposed to ENE) 

at Badami corroborate the predictions of Kozo [1986], suggesting that orographic effects 

influence the eastern part of the study area. Kozo’s model results indicate that perturbations of 

wind speed and direction due to orographic effects should become greater as one moves east. If 

further monitoring efforts are conducted, more emphasis should be put on the eastern side of the 

area of interest, where more spatial variability is expected. 

5.4 Sea Breeze Effects 

Kozo [1980] predicted the existence of a unidirectional onshore sea breeze effect within a 40 km

area centered along and running parallel to the Arctic coastline during the summer. This 

phenomenon is driven by a thermal gradient between the relatively warm land and cold sea. 

Unlike coastal areas at lower latitudes, this flow does not reverse at night due to 24-hour sunlight 

during the Arctic summer. 

The frequency and duration analyses discussed in Section 4.2.1 show that most winds at all sites 

are either onshore and seaward as winds parallel to the shoreline represent a minor component. 

At all sites, onshore winds dominate seaward winds, both in terms of frequency and average 

duration. This appears to support the influence of a sea-breeze effect on the measured winds; 

however it is difficult to differentiate sea breeze effects from those of greater synoptic conditions 

since both favor winds from the E and NE. 

The wind roses in Section 4.2 show that there is a predominance of onshore winds from the E, 

ENE, and NE (34° to 101°), while seaward winds from the W, WSW and SW (214° to 281°) 

represent a secondary, yet significant component2

. The dominance of landward winds over that 

of the seaward winds can be represented as the ratio of the frequency of landward winds to the 

frequency of seaward winds (i.e., the number of hours when winds are from the E to NE divided 

by the number of hours winds are from the W to SW). 

The ratio of landward to seaward wind frequencies at Badami is at least half that of the ratios 

observed at the other sites. Other onshore stations at Deadhorse and Kuparuk also display more 

frequent offshore (W to SW) winds compared to the coastal sites. Figure 5-3 shows the seasonal 

dominance of onshore breezes at the MMS stations, Kuparuk, and Deadhorse3

. As expected, the 

sea breeze effect is most pronounced at the sites closest to the coastline and is most evident 

during the summer months. 

While Kozo’s research used only August data, Figure 5-3 shows that the effect is most

pronounced in June. In late May and most of June, snow cover over land has diminished 

lowering the land-surface albedo. This allows for a greater transformation of solar energy into 

thermal energy over the land-surface than the ocean, which remains covered with reflective snow 

 2

 Note that the definition of “seaward” and “landward” winds in this section is distinct from the “onshore” and

“offshore” winds discussed in Section 4.2.1 3

 These data sets are from the period 1/1/01-12/13/04 for the Deadhorse and Kuparuk airports. These sites sit 12

km and 20 km inland, respectively.

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and ice during this period. This can exaggerate the land-sea thermal gradient leading to a 

magnified sea breeze effect. June also has the highest daily solar flux, as shown in Figure 4-19. 

Mean daily solar flux in June is 2.5 times greater than in August, and 4.5 times greater than in 

September. 

The MMS data is in agreement with Kozo’s statement that there is a seasonally dependent sea 

breeze effect in coastal regions. However, because the MMS stations are at or situated relatively

close to the coast, more offshore data would need to be gathered before any prediction could be 

made about the distance offshore that the effect might reach. 

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Figure 5-3 Frequency Ratio of Offshore (E - NE) to Onshore (W - SW) Winds by Month

0369

12151821

Jan Feb Mar Apr May Jun Jul Aug Sep Oct 

Frequency ratio of offshore (E - NE) to onshore (W - SW) windsBadami

Cottle IsEndicottMilne PoNorthstaDeadhorKuparuk

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5.5 Latitudinal Effects 

Differentiating a summer sea breeze effect from Arctic climatology is difficult since both favor 

winds from the E – NE. However, it is worth noting that the dominance of winds from the E – 

NE correlates very well to latitude, but poorly to distance from shore. 

As shown in Figure 5-4, the ratio of E – NE winds to W – SW winds can be predicted in the area 

of study as a function of decimal degrees north. The eight stations graphed are the five MMS 

stations, Prudhoe Bay (1995-99), Deadhorse (2001-04), and Kuparuk (2001-04). While this 

linear correlation is strong (R2

 = 0.92), the correlation of the wind frequency ratio to distance 

offshore is quite weak by comparison (R2

 = 0.33). 

Figure 5-4 Frequency Ratio of E to NE to W to SW Winds vs. Latitude 

y = 2.67x - 185.7

R2 = 0.92

1

1.4

1.8

2.2

2.6

70.1 70.2 70.3 70.4 70.5 70.6

Deg N

Frequen

c

y ratio of E-NE to WS

W winds

There is strong seasonality to this correlation. Looking at the R2 values by month at the five 

MMS stations, it is clear that the correlation weakens substantially during the summer. 

However, it is not replaced by a better correlation to distance offshore. 

Figure 5-5 Monthly Correlation of Frequency Ratio of E – NE to W – SW Winds vs. 

Latitude 

0.0

0.2

0.4

0.6

0.8

1.0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

R2

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5.6 Cross Correlation Analysis 

Correlation patterns among the various stations were examined to investigate the relationship of 

wind speed at the various stations. Values of the cross correlation function were determined for 

various time lags for each set of two stations. A summary of the cross correlation function 

between simultaneous readings (lag = 0) is shown in Table 5-1. 

Table 5-1 Value of Wind Speed Cross Correlation Function at Lag = 0 

Badami Endicott Milne Point Northstar Cottle Island

Badami 1.00 0.848 0.815 0.778 0.868

Endicott 0.848 1.00 0.880 0.847 0.885

Milne Pt 0.815 0.880 1.00 0.905 0.915

Northstar 0.778 0.847 0.905 1.00 0.882

Cottle Is 0.868 0.885 0.915 0.882 1.00

The most notable observations about the Table 5-1 are: 

• Badami has relatively distinct winds from the other stations. 

• Cottle Island is most representative of the other stations. 

• Cottle Island and Milne Point have the strongest mutual correlation (0.915). 

• Otherwise, the overall wind speed correlations are fairly good, with an average cross 

correlation value of 0.862. 

It is worth re-emphasizing here that the Cottle Island station has a slightly shorter data set than 

the other stations. 

Figure 5-6 through Figure 5-10 show the wind speed cross correlation results by station at lag 

-12 through +12 (hours). As an example, a graph of Badami versus Endicott would plot the 

correlation between the wind speed at Badami at time t and the wind speed at Endicott at time t +

k, where k is the lag. 

One of the most interesting features of the graphs is that the peaks are not symmetric about lag 0. 

Wind speed at Milne Point at time t actually correlates best with wind speed at Endicott at t+1. 

Wind speed at Milne Point at time t also correlates equally well with wind speed at Northstar at t

and t+1. Most of the cross correlation curves seem to “lean” one direction or the other. A curve 

leaning to the right, as for Milne Point versus Endicott, indicates that variations in wind speed at 

Milne Point tend to precede those at Endicott. For a curve leaning to the left, the reverse is true. 

Overall, a suggestion exists of west-to-east wind speed patterns over the recorded time period. 

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Figure 5-6 Cross Correlation of Wind Speed between Badami and Other Stations at Various L

0

0.10.20.30.40.50.60.70.80.9

1

-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 

Lag (hours)

Cross-correlation function value (CCF)

v Endicott v Milne Point v Northstar v Cottle Island

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Figure 5-7 Cross Correlation of Wind Speed between Endicott and Other Stations at Various L

0

0.10.20.30.40.50.60.70.80.9

1

-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1234 567 8 

Lag (hours)

Cross-correlation function value (CCF)

v Badami v Milne Point v Northstar v Cottle Island

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Figure 5-8 Cross Correlation of Wind Speed between Cottle Island and Other Stations at Vari

Lags

0

0.10.20.30.40.50.60.70.80.9

1

-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 

Lag (hours)

Cross-correlation function value (CCF)

v Badami v Endicott v Milne Point v Northstar

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Figure 5-9 Cross Correlation of Wind Speed between Milne Point and Other Stations at Various La

0

0.10.20.30.40.50.60.70.80.9

1

-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 

Lag (hours)

Cross-correlation function value (CCF)

v Endicott v Northstar v Badami v Cottle Island

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Figure 5-10 Cross Correlation of Wind Speed between Northstar and Other

Stations at Various Lags 

0

0.10.20.30.40.50.60.70.80.9

1

-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1234 567 8 

Lag (hours)

Cross-correlation function value (CCF)

v Badami v Endicott v Milne Point v Cottle Island

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5.7 Conclusions 

The primary goal of this study was to collect scientifically sound meteorological data for the 

nearshore Beaufort Sea region. The data collected from the new monitoring stations are among 

the most extensive meteorological data collected from this region to date. The data set available 

through June 2005 will provide valuable data for researchers and modelers. 

Wind data collected at Northstar are suspect due to wind field perturbations caused by the nearby 

structures overshadowing the station (drill rig, process module, and crane). The Northstar wind 

rose lacks a northerly component due to the process module blocking wind from the north. Wind 

speed should have been less affected by the obstacles than wind direction and turbulence (wind 

sigma). 

Cottle Island was originally installed to address concerns about the quality of data being 

collected at Northstar. The data collected at Cottle Island since August 2002 suggest the station 

correlates very well with all other stations except Badami. Table 5-1 shows that wind speed at 

Cottle Island and Milne Point correlate particularly well, with a correlation coefficient of 0.915. 

Northstar is also similar to Milne Point, although it is about twice as far as Cottle Island. The 

Northstar station includes interference by the process module, drill rig, flare and crane, and is 

often difficult to visit for station servicing or repairs due to its island location. These factors 

suggest that Northstar may also be a redundant station with compromised wind data. 

Based on the previous discussion, there is support for dropping one or two stations from the 

study for future monitoring. Meteorological monitoring at Northstar should be discontinued due 

to data quality concerns. Monitoring for the western part of the study area should be continued 

at Cottle Island and/or Milne Point. Both sites have distinct advantages. Cottle Island is further 

offshore, has fewer obstacles, and is more representative of Northstar. Milne Point offers easier 

access than Cottle Island and has on-site power. 

The four and one-half years of data collected during the study have also shown that nearshore 

stations are sufficiently representative of conditions eight kilometers offshore and within the 

study area.

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6 RECOMMENDATIONS

6.1 Improvements to Monitoring 

As discussed in Section 5.2, propeller-style wind sensors are far more resistant to icing than 

three-cup anemometers. We have shown that 3-cup anemometers can under-read wind speed 

during the winter months and, in some circumstances, freeze up entirely. This may represent a 

source of bias that may affect many historical data sets as well, as the use of 3-cup anemometers 

is very common. We strongly recommend using only propeller-style anemometers for future 

monitoring in this or any other region where icing is likely to occur. 

Redundant sensors save money and improve data capture. The cost of site visits in this area is 

very high relative to the cost of equipment and on many occasions site visits proved to be 

impossible. Redundant sensors should be installed for all important measurements, especially 

wind speed and direction. Since propeller-style wind sensors combine wind speed and direction 

sensors into one instrument, our ideal configuration would consist of a horizontal crossbar with a 

propeller-style wind sensor at either end and positioned at the 10 meter height. 

6.2 Monitoring Network Efficiency 

Mid-winter monitoring is costly and far less useful for oil-spill modeling. Winter site visits are 

difficult and designing a power system allowing for daily cell-phone calls for uploading data 

requires the installation of a wind turbine. Wind turbines are more expensive than solar panels 

and need to be replaced almost annually. 

Large savings could result from emphasizing the warmer part of the year. Solar capacity should 

be boosted (from 40W to 85W, for example) and wind turbines eliminated. Communications 

would be programmed to shut down at the end of November, and resume in mid-March. The 

station would continue to record data but would not turn on its cell phone (or radio, as discussed 

below). When the station resumed communicating it would upload all of the data it had 

collected during the mid-winter. If station repairs were required, they would be deferred until 

spring. 

Semi-annual audits and calibrations were probably excessive. We would recommend a single 

annual calibration in the spring. For remote site access close to the road system, transport via 

snowmobile in April/May is a low-cost, effective option with access by small plane a preferred 

alternative. Helicopter access is very expensive relative to the other costs. Boat access during 

open water periods has shown potential and is low-cost, but has proven to be unreliable due to 

unpredictable sea conditions. 

6.3 Station Communications 

The best way to communicate with the stations would be to use spread-spectrum radios to 

transmit data to Ethernet-connected hubs. This would conserve power, eliminate the charges 

associated with cell phones or satellite communications, and allow for much more frequent 

uploads. This could be used to create a real-time monitoring network. 

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Communications are by far the biggest power drain on the meteorological systems and 

alternative modes of communication that use minimal power makes station design and 

maintenance less expensive. A freewave spread-spectrum radio draws 500 mA of power 

compared to 3000 mA used for cell phone communications. More importantly, a freewave 

spread-spectrum radio would use only 5 mA during standby compared to 300 mA for a cell 

phone. 

With a range of 50 miles, any conceivable offshore site could transmit to an Ethernet port at a 

facility somewhere on the North Slope. This type of data transmission could be used to create a 

real-time monitoring network, updating a website hourly, for instance. 

If the above suggestion for concentrating on the warmer part of the year is put into effect, the 

stations could potentially transmit hourly data from April through October and be scheduled to 

transmit data on a weekly, or less frequent, basis throughout the winter. 

6.4 Future Site Selection 

Northstar is not a good location for a meteorological station due to several obstructions detailed 

in Section 5.1. It is recommended that data collection at the site be discontinued. Other sites, 

such as Cottle Island, provide data that correlates well enough with the Northstar station that the 

differences may have more to do with the structures interfering with wind than with the 

geographical distance between the two stations. 

For any future monitoring in the region, there are two possible objectives to be considered. One 

would be to gather more research data to help computer modelers better understand the weather 

systems along this section of Beaufort Sea coast, which are near industrial activity. The other 

objective would be to establish a semi-permanent monitoring network to provide data in the 

event of an oil spill. 

If improving current understanding of the climatology of the region is an objective, then more 

data from eastern sites is recommended. Badami clearly stands out from the other four stations, 

and Kozo and Robe’s model results [1986] suggest that perturbations of wind speed and 

direction due to orographic effects should become greater as one moves east. If further 

monitoring efforts are conducted, more emphasis should be put on the eastern side of the area of 

interest where more spatial variability is expected. Tigvariak Island and Flaxman Island would 

be ideal sites in this regard. Both can both be accessed by plane, are offshore, and could transmit 

to Badami by spread-spectrum radio. Endicott would be maintained as a monitoring site along 

with one other station; preferably Cottle Island but possibly Milne Point if cost is a primary 

consideration. If the suggestions noted above are put into effect, the annual cost of running these 

sites would be considerably reduced. 

It may also be worth considering extending the study area to the west to the Colville Delta since, 

1) oil and gas development is expanding in that direction, 2) the area is of special ecological 

interest, and 3) there is a paucity of data in this region (Nuiqsut airport is over 20 km inland). 

The ideal location would be the Helmricks homestead (“Colville Village”). Colville Village 

currently hosts a small weather station, but it does not collect research-quality data. 

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If the main goal is to focus on areas where a spill is most likely to occur, the focus should be on 

the existing infrastructure. In this case, instilling a network using Endicott and Cottle Island 

stations, and perhaps adding another station at West Dock, are recommended. West Dock is 

offshore, close to the center of oil infrastructure, and about half-way between Cottle Island and 

Endicott. An additional advantage is that this would co-locate meteorological monitoring 

equipment with the High Frequency Radar sites MMS is using to track surface currents in the 

area. 

Figure 6-1 shows the existing sites as well as the potential sites and radio links mentioned above. 

Figure 6-1 Map of Existing and Potential Stations 

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7 REFERENCES 

Kozo, T.L., 1982. “An Observational Study of Sea Breezes Along the Alaskan Beaufort Sea 

Coast: Part I” Journal of Applied Meteorology, Vol. 21, No. 7, pp.891-905. 

Kozo, T.L. and R.Q. Robe, 1986. An Observational Study of Sea Breezes Along the Alaskan 

Beaufort Sea Coast: Part I, J. Geophys. Res., Vol. 91, No. C11, pp.13011-13032. 

Stull, R. B., 2000. Meteorology for Scientists and Engineers, Brooks/Cole Pub., Pacific Grove, CA. 

U.S. Environmental Protection Agency, 1987. Ambient Monitoring Guidelines for Prevention of 

Significant Deterioration (PSD). U.S. EPA OAQPS. Research Triangle Park, NC. EPA-450/4-87-

007. 

U.S. Environmental Protection Agency (EPA), 1995. On-Site Meteorological Program 

Guidance for Regulatory Modeling Applications. Revised. Office of Air Quality Planning and 

Standards, Research Triangle Park, NC. EPA-450/4-87-013. Revised August 1995. 

U.S. Environmental Protection Agency (EPA), 2000. Meteorological Monitoring Guidance for 

Regulatory Modeling Applications. Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. EPA-454/R-99-005. 

Yamartino, R.J., 1984. A Comparison of Several "Single-Pass" Estimators of the Standard 

Deviation of Wind Direction. J. Climate Appl. Meteor., Vol. 23, pp. 1362-1366. 

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APPENDIX A 

Historical Wind Data Inventory and Map 

(The database is included on the CD in the back of this report)
Station ID Name Latitude Longitude Data Start Data End 

1 Aurora Wellsite 70.109 -142.785 9/12/87 9/12/88 HCG Histo5 Barter Island DEW #2 70.133 -143.583 11/4/90 10/9/96 HCG Histo6 Belcher Wellsite 70.275 -141.513 9/1/88 8/31/89 HCG Histo7 Betty Pingo 70.28 -148.896 5/27/94 1/1/04 HCG Histo8 Cabot Wellsite 71.324 -155.216 10/31/91 2/29/92 HCG Histo10 Diamond Wellsite 71.333 -161.68 8/31/91 10/5/91 HCG Histo12 Fireweed Wellsite 71.088 -152.603 10/14/90 12/20/90 HCG Histo13 Galahad Wellsite 70.561 -144.96 9/13/91 10/14/91 HCG Histo14 Herschel Island, Y.T. 69.567 -138.917 10/16/86 8/11/01 HCG Histo15 Komakuk Beach, Y.T. 69.583 -140.183 1/1/85 6/30/93 HCG Histo16 Kuparuk Airport 70.317 -149.583 2/4/91 12/31/04 HCG Histo17 Kuparuk DS-1F 70.29 -149.68 1/1/91 6/30/02 HCG Histo18 Kuvlum Wellsite #2 70.31 -145.538 7/19/93 8/30/93 HCG Histo19 Kuvlum Wellsite #3 70.327 -145.404 8/31/93 9/30/93 HCG Histo20 Lonely DEW 70.917 -153.233 1/1/85 3/25/88 HCG Histo23 Nuiqsut 70.218 -150.993 4/9/99 12/31/04 HCG Histo24 Oliktok #1 70.5 -149.9 1/1/85 6/6/94 HCG Histo25 Oliktok #2 70.5 -149.883 1/1/90 9/26/95 HCG Histo26 Phoenix Wellsite 70.717 -150.428 9/2/86 9/9/87 HCG Histo27 Prudhoe Bay 70.25 -148.333 6/3/87 6/14/99 HCG Histo28 Wild Weasel Wellsite 70.229 -145.499 9/30/93 11/10/93 HCG Histo29 West Dock 70.381 -148.561 7/14/95 1/1/04 HCG Histo111 Badami 70.136 -147.009 1/1/01 3/1/05 MetStat W222 Endicott 70.323 -147.865 1/1/01 3/1/05 MetStat W333 Milne Point 70.507 -149.662 1/1/01 3/1/05 MetStat W444 North Star 70.49 -148.698 1/1/01 3/1/05 MetStat W499 Cottle Island 70.499 -149.093 8/21/02 3/1/05 MetStat W555 McCovey 70.528 -148.187 11/30/02 2/9/03 MetStat W26616 Kotzebue 66.886 -162.604 1/1/84 12/31/99 UAA 27401 Barter 70.133 -143.633 1/1/84 12/31/04 UAA NCA27406 Deadhorse 70.2 -148.467 1/1/85 12/31/04 UAA, HCG27502 Barrow 71.284 -156.778 1/1/84 12/31/04 UAA, HCG

Historical Wind Data Inventory 
Historical Meteorological Station Location Map 

Herschel Island 

Komakuk Beach 

Belcher Wellsite 

Barter Island 

Wild Weasel 

Wellsite 

Oliktok 

Prudhoe Bay 

Milne Point 

Kuparuk Airport 

Endicott Island 

Northstar 

Kuparuk DS1F 

Phoenix Wellsite 

Fireweed Wellsite 

Cabot Wellsite 

Barrow 

Diamond Wellsite 

Lonely 

Nuiqsut 

Pingo 

Badami 

Galahad Wellsite 

Kuvlum Wellsites 

Prudhoe Bay, Betty Pingo 
APPENDIX B 

Yearly Graphs 
 2001 Hourly Average Wind Speeds

048

1216202428

Jan-2001Feb-2001Mar-2001Apr-2001May-2001Jun-2001Jul-2001Aug-2001Sep-2001Oct-2001Nov-2001

Wind Speed (m/s)

Badami Endicott Milne Point Northstar
 2002 Hourly Average Wind Speeds

048

1216202428

Jan-2002Feb-2002Mar-2002Apr-2002May-2002Jun-2002Jul-2002Aug-2002Sep-2002Oct-2002Nov-2002

Wind Speed (m/s)

Badami Cottle Island Endicott Milne Point Northstar
 2003 Hourly Average Wind Speeds

048

1216202428

Jan-2003Feb-2003Mar-2003Apr-2003May-2003Jun-2003Jul-2003Aug-2003Sep-2003Oct-2003Nov-2003

Wind Speed (m/s)

Badami Cottle Island Endicott Milne Point Northstar
 2004 Hourly Average Wind Speeds

048

1216202428

January-2004 February-2004 March-2004April-2004May-2004 June-2004July-2004 August-2004 September-2004 October-2004 November-2004 D

Wind Speed (m/s)

Badami Cottle Island Endicott Milne Point Northstar
 1st Half 2005 Hourly Average Wind Speeds

048

1216202428

Jan-2005 Feb-2005 Mar-2005 Apr-2005 May-2005 Jun-200

Wind Speed (m/s)

Badami Cottle Island Endicott Milne Point Northstar
 2001 Hourly Average Temperatures

-45-40-35-30-25-20-15-10

-5

05

1015202530

Jan-2001 Feb-2001 Mar-2001 May-2001 Jun-2001 Jul-2001 Aug-2001 Oct-2001 No

Temp (°C)

Badami Endicott Milne Point Northstar
 2002 Hourly Average Temperatures

-45-40-35-30-25-20-15-10

-5

05

1015202530

Jan-2002 Feb-2002 Mar-2002 May-2002 Jun-2002 Jul-2002 Aug-2002 Oct-2002 No

Temp (°C)

Badami Endicott Milne Point Northstar Cottle Island
 2003 Hourly Average Temperatures

-45-40-35-30-25-20-15-10

-5

05

1015202530

Jan-2003 Feb-2003 Mar-2003 May-2003 Jun-2003 Jul-2003 Aug-2003 Oct-2003 No

Temp (°C)

Badami Endicott Milne Point Northstar Cottle Island
 2004 Hourly Average Temperatures

-45-40-35-30-25-20-15-10

-5

05

1015202530

Jan-2004 Feb-2004 Mar-2004 Apr-2004 Jun-2004 Jul-2004 Aug-2004 Oct-2004 No

Temp (°C)

Badami Endicott Milne Point Northstar Cottle Island
 1st Half 2005 Hourly Average Temperatures

-45-40-35-30-25-20-15-10

-5

05

1015202530

Jan-2005 Feb-2005 Mar-2005 May-2005 Ju

Temp (°C)

Badami Endicott Milne Point Northstar Cottle Island
2001 Hourly Barometric Pressure

970980990

1000101010201030104010501060

January-2001 February-2001 March-2001April-2001May-2001 June-2001July-2001 August-2001 September-2001 October-2001 November-2001 D

Barometric Pressure (mb)

Badami Endicott Milne Point Northstar
2002 Hourly Barometric Pressure

970980990

10001010102010301040105010601070

January-2002 February-2002 March-2002April-2002May-2002 June-2002July-2002 August-2002 September-2002 October-2002 November-2002 D

Barometric Pressure (mb)

Badami Cottle Island Endicott Milne Point Northstar
2003 Hourly Barometric Pressure

970980990

1000101010201030104010501060

January-2003 February-2003 March-2003April-2003May-2003June-2003July-2003 August-2003 September-2003 October-2003 November-2003 De

Barometric Pressure (mb)

Badami Cottle Island Endicott Milne Point Northstar
2004 Hourly Barometric Pressure

970980990

1000101010201030104010501060

January-2004 February-2004 March-2004April-2004May-2004 June-2004July-2004 August-2004 September-2004 October-2004 November-2004 De

Barometric Pressure (mb)

Badami Cottle Island Endicott Milne Point Northstar
1st Half 2005 Hourly Barometric Pressure

970980990

1000101010201030104010501060

January-2005 February-2005 March-2005 April-2005 May-2005 June-2005

Barometric Pressure (mb)

Badami Cottle Island Endicott Milne Point Northstar
2001 Hourly Average Relative Humidity

35404550556065707580859095

100

January-2001 February-2001 March-2001April-2001May-2001 June-2001July-2001 August-2001 September-2001 October-2001 November-2001 D

Relative Humidity (%)

Badami Endicott Milne Point Northstar
2002 Hourly Average Relative Humidity

35404550556065707580859095

100

January-2002 February-2002 March-2002April-2002May-2002 June-2002July-2002 August-2002 September-2002 October-2002 November-2002 D

Relative Humidity (%)

Badami Cottle Island Endicott Milne Point Northstar
2003 Hourly Average Relative Humidity

35404550556065707580859095

100

January-2003 February-2003 March-2003April-2003May-2003June-2003July-2003 August-2003 September-2003 October-2003 November-2003 D

Relative Humidity (%)

Badami Cottle Island Endicott Milne Point Northstar
2004 Hourly Average Relative Humidity

35.040.045.050.055.060.065.070.075.080.085.090.095.0

100.0

January-2004 February-2004 March-2004April-2004May-2004June-2004July-2004 August-2004 September-2004 October-2004 November-2004 De

Relative Humidity (%)

Badami Cottle Island Endicott Milne Point Northstar
1st Half 2005 Hourly Average Relative Humidity

35404550556065707580859095

100

January-2005 February-2005 March-2005 April-2005 May-2005 June-2005

Relative Humidity (%)

Badami Cottle Island Endicott Milne Point Northstar
2001 Wind Sigma

0.00

20.00

40.00

60.00

80.00

100.00

120.00

January-2001 February-2001 March-2001April-2001May-2001 June-2001July-2001 August-2001 September-2001 October-2001 November-2001 Decem

Wind Sigma

Badami Endicott Milne Point Northstar

NOTE: Wind Sigma is measured at a height of 10 meters at Badami, Cottle Island, and Endicott, and at 14 meters and 23 meters at Milne Poi
2002 Wind Sigma

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

January-2002 February-2002 March-2002April-2002May-2002 June-2002July-2002 August-2002 September-2002 October-2002 November-2002 Decem

Wind Sigma

Badami Cottle Island Endicott Milne Point Northstar

NOTE: Wind Sigma is measured at a height of 10 meters at Badami, Cottle Island, and Endicott, and at 14 meters and 23 meters at Milne Poi
2003 Wind Sigma

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

January-2003 February-2003 March-2003April-2003May-2003 June-2003July-2003 August-2003 September-2003 October-2003 November-2003 Dece

Wind Sigma

Badami Cottle Island Endicott Milne Point Northstar

NOTE: Wind Sigma is measured at a height of 10 meters at Badami, Cottle Island, and Endicott, and at 14 meters and 23 meters at Milne Poi
2004 Wind Sigma

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

January-2004 February-2004 March-2004April-2004May-2004 June-2004July-2004 August-2004 September-2004 October-2004 November-2004 Decem

Wind Sigma

Badami Cottle Island Endicott Milne Point Northstar

NOTE: Wind Sigma is measured at a height of 10 meters at Badami, Cottle Island, and Endicott, and at 14 meters and 23 meters at Milne Poi
1st Half 2005 Wind Sigma

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

January-2005 February-2005 March-2005 April-2005 May-2005 June-2005

Wind Sigma

Badami Cottle Island Endicott Milne Point Northstar

NOTE: Wind Sigma is measured at a height of 10 meters at Badami, Cottle Island, and Endicott, and at 14 meters and 23 meters at Milne Poi
2001 Hourly Average Solar Radiation

0

100

200

300

400

500

600

700

800

900

January-2001 February-2001 March-2001April-2001May-2001 June-2001July-2001 August-2001 September-2001 October-2001 November-2001 Decem

Solar Radiation (watts/m

2

)

Badami Endicott Milne Point Northstar
2002 Hourly Average Solar Radiation

0

100

200

300

400

500

600

700

800

900

January-2002 February-2002 March-2002April-2002May-2002 June-2002July-2002 August-2002 September-2002 October-2002 November-2002 Decem

Solar Radiation (watts/m

2

)

Badami Cottle Island Endicott Milne Point Northstar
2003 Hourly Average Solar Radiation

0

100

200

300

400

500

600

700

800

900

January-2003 February-2003 March-2003April-2003May-2003 June-2003July-2003 August-2003 September-2003 October-2003 November-2003 Decem

Solar Radiation (watts/m

2

)

Badami Cottle Island Endicott Milne Point Northstar
2004 Hourly Average Solar Radiation

0

100

200

300

400

500

600

700

800

December-2003 January-2004 February-2004 March-2004April-2004May-2004 June-2004July-2004 August-2004 September-2004 October-2004 Novem

Solar Radiation (watts/m

2

)

Badami Cottle Island Endicott Milne Point Northstar
1st Half 2005 Hourly Average Solar Radiation

0

100

200

300

400

500

600

700

800

January-2005 February-2005 March-2005 April-2005 May-2005 June-2005

Solar Radiation (watts/m

2

)

Badami Cottle Island Endicott Milne Point Northstar
APPENDIX C 

Composite Monthly Wind Roses 
Minerals Management Service 

Meteorological Monitoring 

Site Badami 

Date Composite of all January data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Badami 

Date Composite of all February data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Badami 

Date Composite of all March data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Badami 

Date Composite of all April data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Badami 

Date Composite of all May data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Badami 

Date Composite of all June data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Badami 

Date Composite of all July data 2001-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Badami 

Date Composite of all August data 2001-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Badami 

Date Composite of all September data 2001-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Badami 

Date Composite of all October data 2001-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Badami 

Date Composite of all November data 2001-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Badami 

Date Composite of all December data 2001-2004 

Company MMS 

Unit M/S 

 
Minerals Management Service 

Meteorological Monitoring 

Site Northstar 

Date Composite of all January data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Northstar 

Date Composite of all February data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Northstar 

Date Composite of all March data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Northstar 

Date Composite of all April data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Northstar 

Date Composite of all May data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Endicott 

Date Composite of all June data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Northstar 

Date Composite of all July data 2001-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Northstar 

Date Composite of all August data 2001-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Northstar 

Date Composite of all September data 2001-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Northstar 

Date Composite of all October data 2001-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Northstar 

Date Composite of all November data 2001-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Northstar 

Date Composite of all December data 2001-2004 

Company MMS 

Unit M/S 

 
Minerals Management Service 

Meteorological Monitoring 

Site Endicott 

Date Composite of all January data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Endicott 

Date Composite of all February data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Endicott 

Date Composite of all March data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Endicott 

Date Composite of all April data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Endicott 

Date Composite of all May data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Endicott 

Date Composite of all June data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Endicott 

Date Composite of all July data 2001-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Endicott 

Date Composite of all August data 2001-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Endicott 

Date Composite of all September data 2001-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Endicott 

Date Composite of all October data 2001-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Endicott 

Date Composite of all November data 2001-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Endicott 

Date Composite of all December data 2001-2004 

Company MMS 

Unit M/S 

 
Minerals Management Service 

Meteorological Monitoring 

Site Milne Point 

Date Composite of all January data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Milne Point 

Date Composite of all February data 2001- 2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Milne Point 

Date Composite of all March data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Milne Point 

Date Composite of all April data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Milne Point 

Date Composite of all May data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Milne Point 

Date Composite of all June data 2001-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Milne Point 

Date Composite of all July data 2001-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Milne Point 

Date Composite of all August data 2001-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Milne Point 

Date Composite of all September data 2001-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Milne Point 

Date Composite of all October data 2001-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Milne Point 

Date Composite of all November data 2002-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Milne Point 

Date Composite of all December data 2001-2004 

Company MMS 

Unit M/S 

 

 
Minerals Management Service 

Meteorological Monitoring 

Site Cottle Island 

Date Composite of all January data 2003-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Cottle Island 

Date Composite of all February data 2003-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Cottle Island 

Date Composite of all March data 2003-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Cottle Island 

Date Composite of all April data 2003-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Cottle Island 

Date Composite of all May data 2003-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Cottle Island 

Date Composite of all June data 2003-2005 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Cottle Island 

Date Composite of all July data 2003-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Cottle Island 

Date Composite of all August data 2002-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Cottle Island 

Date Composite of all September data 2002-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Cottle Island 

Date Composite of all October data 2002-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Cottle Island 

Date Composite of all November data 2002-2004 

Company MMS 

Unit M/S 
Minerals Management Service 

Meteorological Monitoring 

Site Cottle Island 

Date Composite of all December data 2002-2004 

Company MMS 

Unit M/S 

 
APPENDIX D 

Descriptive Statistics 
List of Abbreviations:

BAD Badami 

COT Cottle Island 

END Endicott 

MPU Milne Point Unit 

NOR Northstar 

Pres Barometric pressure 

RH Relative humidity 

Solar Solar radiation 

StDev Standard deviation 

T Ice Temperatures from November through May (closed sea ice period) 

T OW Temperatures from June through October (open sea water period) 

Temp Temperature 

WS Wind speed 

WSig Wind sigma, the standard deviation of the wind direction 
-48-43-38-33-28-23-18-13 -8 -3 2 7 12 17 22 27 32

95% Confidence Interval for Mu

-11.5 -11.0 -10.5

95% Confidence Interval for Median

Variable: B

-10.9300 13.8309-11.3943Maximum3rd QuartileMedian1st QuartileMinimumNKurtosisSkewnessVarianceStDevMeanP-Value:A-Squared:

95% Confidence In

95% Confidence I

95% Confidence 

Anderson-Darling 

Descriptive Statistics
-41 -36 -31 -26 -21 -16 -11 -6 -1 4 9 14 19

95% Confidence Interval for Mu

-11 -10 -9

95% Confidence Interval for Median

Variable: EN

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum-11.1368 12.7521 -9.6500

Anderson-Darling 

95% Confidence I

95% Confidence Int

95% Confidence Int

Descriptive Statistics
-49 -44 -39 -34 -29 -24 -19 -14 -9 -4 1 6 11 16

95% Confidence Interval for Mu

-11 -10 -9

95% Confidence Interval for Median

Variable: CO

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum-11.4564 12.7084 -9.8500

Anderson-Darling 

95% Confidence I

95% Confidence Int

95% Confidence Int

Descriptive Statistics
26 31 36 41 46 51 56 61 66 71 76 81 86 91 96101106

95% Confidence Interval for Mu

84.0 84.5 85.0 85.5

95% Confidence Interval for Median

Variable: BA

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 83.960 10.133 85.200

Anderson-Darling N

95% Confidence In

95% Confidence Inte

95% Confidence Inte

Descriptive Statistics
31 36 41 46 51 56 61 66 71 76 81 86 91 96101106111

95% Confidence Interval for Mu

85.0 85.5 86.0

95% Confidence Interval for Median

Variable: 

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 84.943 9.089 85.700

Anderson-Darling 

95% Confidence 

95% Confidence I

95% Confidence In

Descriptive Statistics
28 33 38 43 48 53 58 63 68 73 78 83 88 93 98

95% Confidence Interval for Mu

83.0 83.5 84.0

95% Confidence Interval for Median

Variable: 

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 82.798 9.651 83.600

Anderson-Darling 

95% Confidence 

95% Confidence I

95% Confidence In

Descriptive Statistics
0 50100 200 300 400 500 600 700 800

95% Confidence Interval for Mu

0 50 100

95% Confidence Interval for Median

Variable: B

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum102.748168.763 6.981

Anderson-Darling 

95% Confidence 

95% Confidence I

95% Confidence In

Descriptive Statistics
0 50100 200 300 400 500 600 700 800

95% Confidence Interval for Mu

0 50 100

95% Confidence Interval for Median

Variable: E

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 99.454163.237 7.820

Anderson-Darlin

95% Confidenc

95% Confidence 

95% Confidence 

Descriptive Statistics
0 50100 200 300 400 500 600 700 800

95% Confidence Interval for Mu

0 50 100

95% Confidence Interval for Median

Variable: M

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 93.840152.507 6.753

Anderson-Darling 

95% Confidence 

95% Confidence I

95% Confidence In

Descriptive Statistics
0 40 90140 240 340 440 540 640 740

95% Confidence Interval for Mu

10 60 110

95% Confidence Interval for Median

Variable: N

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 97.378158.949 10.399

Anderson-Darling 

95% Confidence 

95% Confidence I

95% Confidence In

Descriptive Statistics
10 60 110 210 310 410 510 610 710

95% Confidence Interval for Mu

0 10 20 30 40 50 60 70 80 90

95% Confidence Interval for Median

Variable: CO

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 86.323148.310 2.947

Anderson-Darling No

95% Confidence Int

95% Confidence Inter

95% Confidence Inter

Descriptive Statistics
981 991 1001 1011 1021 1031 1041 1051 1061

95% Confidence Interval for Mu

1014.7 1014.8 1014.9 1015.0

95% Confidence Interval for Median

Variable: EN

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum1014.67 10.721015.00

Anderson-Darling N

95% Confidence Int

95% Confidence Inte

95% Confidence Inter

Descriptive Statistics
982 992 1002 1012 1022 1032 1042 1052 1062

95% Confidence Interval for Mu

1016.0 1016.5 1017.0

95% Confidence Interval for Median

Variable: M

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum1016.56 10.731016.00

Anderson-Darling 

95% Confidence 

95% Confidence In

95% Confidence Int

Descriptive Statistics
980 990 1000 1010 1020 1030 1040 1050 1060

95% Confidence Interval for Mu

1011.9 1012.0 1012.1

95% Confidence Interval for Median

Variable: N

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum1011.88 10.701012.00

Anderson-Darling 

95% Confidence 

95% Confidence In

95% Confidence In

Descriptive Statistics
984989994999 1009 1019 1029 1039 1049

95% Confidence Interval for Mu

1014.54 1014.64 1014.74 1014.84 1014.94 1015.04

95% Confidence Interval for Median

Variable: C

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum1014.57 10.771015.00

Anderson-Darling 

95% Confidence 

95% Confidence In

95% Confidence Int

Descriptive Statistics
980 990 1000 1010 1020 1030 1040 1050 1060

95% Confidence Interval for Mu

1013.80 1013.85 1013.90 1013.95 1014.00 1014.05

95% Confidence Interval for Median

Variable: B

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum1013.82 10.761014.00

Anderson-Darling 

95% Confidence I

95% Confidence In

95% Confidence Int

Descriptive Statistics
2 7 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82

95% Confidence Interval for Mu

5.0 5.5 6.0 6.5

95% Confidence Interval for Median

Variable: BA

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 6.6228 6.0223 5.0300

Anderson-Darling N

95% Confidence In

95% Confidence Int

95% Confidence Inte

Descriptive Statistics
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75

95% Confidence Interval for Mu

5.5 6.5 7.5

95% Confidence Interval for Median

Variable: END 

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 7.4964 5.8626 5.4100

Anderson-Darling No

95% Confidence Int

95% Confidence Inter

95% Confidence Inter

Descriptive Statistics
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80

95% Confidence Interval for Mu

5.0 5.5 6.0 6.5

95% Confidence Interval for Median

Variable: MPU 

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 6.4560 5.3727 4.8200

3

Anderson-Darling Norm

95% Confidence Interv

95% Confidence Interva

95% Confidence Interval 

Descriptive Statistics
2 7 121722273237424752576267727782879297

95% Confidence Interval for Mu

8.5 9.5 10.5 11.5 12.5 13.5

95% Confidence Interval for Median

Variable: NOR 

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum13.117013.5254 8.7200

Anderson-Darling Nor

95% Confidence Inte

95% Confidence Interv

95% Confidence Interva

Descriptive Statistics
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80

95% Confidence Interval for Mu

5.5 6.0 6.595% Confidence Interval for Median

Variable: COT W

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 6.3854 4.6455 5.3000

32

 

64255

    

8

   

Anderson-Darling Norma

95% Confidence Interva

95% Confidence Interval 

95% Confidence Interval f

Descriptive Statistics
39.5 47.0 54.5 62.0 69.5 77.0 84.5 92.0 99.5

95% Confidence Interval for Mu

85.3 85.8 86.3 86.8

95% Confidence Interval for Median

Variable: EN

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 85.395 8.829 86.500

Anderson-Darling No

95% Confidence Int

95% Confidence Inter

95% Confidence Inter

Descriptive Statistics
45.5 53.0 60.5 68.0 75.5 83.0 90.5 98.0

95% Confidence Interval for Mu

86.5 86.6 86.7 86.8 86.9 87.0

95% Confidence Interval for Median

Variable: N

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 86.504 8.659 86.700

Anderson-Darling 

95% Confidence I

95% Confidence In

95% Confidence Int

Descriptive Statistics
-40.5 -30.5 -20.5 -10.5 -0.5 4.5 9.5 14.519.5

95% Confidence Interval for Mu

-11.0 -10.5 -10.0

95% Confidence Interval for Median

Variable: NO

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum-11.3465 12.2390-10.6000

Anderson-Darling N

95% Confidence Int

95% Confidence Inte

95% Confidence Inter

Descriptive Statistics
-42.6-36.6-30.6-24.6-18.6-12.6 -6.6 -0.6 5.4 11.4 17.4 23.4

95% Confidence Interval for Mu

-11.5 -11.0 -10.5

95% Confidence Interval for Median

Variable: MP

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum-11.5575 12.9216-11.1100

Anderson-Darling 

95% Confidence I

95% Confidence In

95% Confidence Int

Descriptive Statistics
-28 -23 -18 -13 -8 -3 2 7 12 17 22 27

95% Confidence Interval for Mu

1.65 1.75 1.85

95% Confidence Interval for Median

Variable: BA

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 1.6328 6.9958 1.6899

Anderson-Darling N

95% Confidence In

95% Confidence Int

95% Confidence Inte

Descriptive Statistics
-25.0 -20.0 -15.0 -10.0 -5.0 0.0 5.0 10.0 15.0

95% Confidence Interval for Mu

0.58 0.68 0.78 0.88 0.98

95% Confidence Interval for Median

Variable: EN

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 0.6017 5.4577 0.8150

Anderson-Darling N

95% Confidence In

95% Confidence Inte

95% Confidence Inte

Descriptive Statistics
-24.0 -19.0 -14.0 -9.0 -4.0 1.0 6.0 11.0 16.0 21.0

95% Confidence Interval for Mu

0.85 0.95 1.05

95% Confidence Interval for Median

Variable: MP

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 0.8704 5.7852 0.8604

Anderson-Darling 

95% Confidence I

95% Confidence Int

95% Confidence Int

Descriptive Statistics
-23.0 -18.0 -13.0 -8.0 -3.0 2.0 7.0 12.0 17.0

95% Confidence Interval for Mu

0.45 0.55 0.65 0.75

95% Confidence Interval for Median

Variable: NO

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 0.4762 5.0400 0.6449

Anderson-Darling 

95% Confidence 

95% Confidence In

95% Confidence In

Descriptive Statistics
-22.4-18.4-14.4-10.4 -6.4 -2.4 1.6 5.6 9.6 13.6 17.6

95% Confidence Interval for Mu

0.85 0.95 1.05 1.15

95% Confidence Interval for Median

Variable: CO

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 0.8549 4.8035 0.9520

Anderson-Darling N

95% Confidence In

95% Confidence Int

95% Confidence Inte

Descriptive Statistics
-44 -40 -36 -32 -28 -24 -20 -16 -12 -8 -4 -0 4 8

95% Confidence Interval for Mu

-20.24 -20.14 -20.04 -19.94 -19.84 -19.74 -19.64 -19.54 -19.44

95% Confidence Interval for Median

Variable: BA

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum-19.7720 10.2765-20.1793

Anderson-Darling N

95% Confidence Int

95% Confidence Inte

95% Confidence Inter

Descriptive Statistics
-41.0-36.0-31.0-26.0-21.0-16.0-11.0 -6.0 -1.0 4.0 9.0

95% Confidence Interval for Mu

-19.7 -19.6 -19.5 -19.4 -19.3 -19.2 -19.1 -19.0

95% Confidence Interval for Median

Variable: E

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum-19.2728 9.7671-19.6600

Anderson-Darling 

95% Confidence 

95% Confidence I

95% Confidence In

Descriptive Statistics
-42.5-37.5-32.5-27.5-22.5-17.5-12.5 -7.5 -2.5 2.5 7.5

95% Confidence Interval for Mu

-20.2 -20.1 -20.0 -19.9 -19.8 -19.7 -19.6 -19.5 -19.4 -19.3 -19.2

95% Confidence Interval for Median

Variable: M

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum-19.5069 9.6179-20.1200

Anderson-Darling 

95% Confidence 

95% Confidence In

95% Confidence In

Descriptive Statistics
-40.5 -35.5 -30.5 -25.5 -20.5 -15.5 -10.5 -5.5 -0.5 4.5

95% Confidence Interval for Mu

-19.5 -19.4 -19.3 -19.2 -19.1 -19.0 -18.9 -18.8 -18.7 -18.6

95% Confidence Interval for Median

Variable: N

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum-18.8558 9.2571-19.4700

Anderson-Darling 

95% Confidence 

95% Confidence In

95% Confidence In

Descriptive Statistics
-45 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10

95% Confidence Interval for Mu

-20.0 -19.5 -19.0

95% Confidence Interval for Median

Variable: C

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum-19.2877 9.7369-20.2300

Anderson-Darlin

95% Confidenc

95% Confidence 

95% Confidence 

Descriptive Statistics
0.5 3.0 5.5 8.0 10.5 15.5 20.5 24.3

95% Confidence Interval for Mu

5.0 5.5 6.0

95% Confidence Interval for Median

Variable: 

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 5.9392 3.9552 4.9536

Anderson-Darlin

95% Confidenc

95% Confidence 

95% Confidence 

Descriptive Statistics
0.6 2.6 4.6 6.6 8.6 10.612.6 16.6 20.6 23.6

95% Confidence Interval for Mu

4.4 4.9 5.4

95% Confidence Interval for Median

Variable: E

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 5.2505 3.4931 4.3560

Anderson-Darling 

95% Confidence 

95% Confidence I

95% Confidence In

Descriptive Statistics
0.75 3.25 5.75 8.2510.75 15.75 20.75 25.75

95% Confidence Interval for Mu

4.8 4.9 5.0 5.1 5.2 5.3 5.4 5.5 5.6 5.7

95% Confidence Interval for Median

Variable: M

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 5.6053 3.4510 4.8340

Anderson-Darling 

95% Confidence I

95% Confidence In

95% Confidence Int

Descriptive Statistics
0.75 3.25 5.75 8.2510.75 15.75 20.75 25.75

95% Confidence Interval for Mu

4.9 5.0 5.1 5.2 5.3 5.4 5.5 5.6

95% Confidence Interval for Median

Variable: N

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 5.4894 3.2061 4.9250

Anderson-Darling 

95% Confidence I

95% Confidence In

95% Confidence Int

Descriptive Statistics
0.50 3.00 5.50 8.00 10.50 15.50 20.50 24.25

95% Confidence Interval for Mu

4.8 5.3 5.8

95% Confidence Interval for Median

Variable: C

A-Squared:P-Value:MeanStDevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum 5.6644 3.7261 4.8070

Anderson-Darling 

95% Confidence 

95% Confidence In

95% Confidence Int

Descriptive Statistics