Antarctic Firn Annual Emissivity Trends Antarctic Firn Annual Emissivity Trends at the Ski Hi Automatic Weather Station at the Ski Hi Automatic Weather Station

From in-situ and SSM/I Brightness From in-situ and SSM/I Brightness TemperaturesTemperatures

Alicia KirkAlicia KirkGregory D. McGee, IIGregory D. McGee, II

Travis SmithTravis SmithMentor: Dr Malcolm LeCompte Mentor: Dr Malcolm LeCompte

IntroductionIntroduction

Alicia Kirk, Fayetteville State Alicia Kirk, Fayetteville State University, University,

Gregory D. McGee, II, Mississippi Gregory D. McGee, II, Mississippi Valley State University,Valley State University,

Travis Smith, Elizabeth City State Travis Smith, Elizabeth City State University University

Mentor: Dr Malcolm LeCompte Mentor: Dr Malcolm LeCompte

OverviewOverview

Emissivity Trends at the Ski Hi Automatic Emissivity Trends at the Ski Hi Automatic Weather Station from in-situ and SSM/I Weather Station from in-situ and SSM/I Brightness TemperaturesBrightness Temperatures

Data and MethodsData and Methods ResultsResults ConclusionConclusion Future ResearchFuture Research

PurposePurpose

Determine emissivity trends at Ski Determine emissivity trends at Ski Hi for 1994 to 1998.Hi for 1994 to 1998.

Calculating the emissivity data for Calculating the emissivity data for gaps in data.gaps in data.

AbstractAbstract

Firn is compacted, near-surface snow persisting longer than Firn is compacted, near-surface snow persisting longer than one season but not yet compressed into glacial ice. one season but not yet compressed into glacial ice. Knowledge of firn surface temperature (Knowledge of firn surface temperature (TSTS) trends across the ) trends across the Antarctic ice sheet is useful for documenting and quantifying Antarctic ice sheet is useful for documenting and quantifying change and providing a temporal and spatial context for change and providing a temporal and spatial context for measurements made during the Antarctic International Polar measurements made during the Antarctic International Polar Year (IPY): 2007-2009. Automatic Weather Stations (AWS) Year (IPY): 2007-2009. Automatic Weather Stations (AWS) provide intermittent daily near-surface temperatures (provide intermittent daily near-surface temperatures (TAWS, TAWS, ≈ TS≈ TS) at a limited number of sites on the Antarctic ice sheet, ) at a limited number of sites on the Antarctic ice sheet, while satellite passive microwave radiometers aboard the while satellite passive microwave radiometers aboard the Defense Meteorology Satellite Program (DMSP) Special Defense Meteorology Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) obtain a nearly continuous Sensor Microwave Imager (SSM/I) obtain a nearly continuous daily record of surface brightness temperature (daily record of surface brightness temperature (TBTB) across ) across the entire continent. The Rayleigh-Jeans Approximation the entire continent. The Rayleigh-Jeans Approximation suggests that, suggests that, TBTB should be very nearly equal to the product should be very nearly equal to the product of the surface emissivity (of the surface emissivity (εε) with the actual surface ) with the actual surface temperature, (temperature, (TSTS) although the actual magnitude of ) although the actual magnitude of ε ε is is uncertain.uncertain.

Abstract (cont…)Abstract (cont…)

The ratio of spatially and temporally coincident The ratio of spatially and temporally coincident TBTB and and TAWSTAWS yields an estimate of yields an estimate of εε at a at a specific time and place and can be used to specific time and place and can be used to extrapolate extrapolate TSTS trends across temporal and spatial trends across temporal and spatial gaps in the limited AWS record. The spatial and gaps in the limited AWS record. The spatial and temporal variability of firn emissivity is not well temporal variability of firn emissivity is not well understood but known to be much less variable understood but known to be much less variable than daily than daily TSTS. Tabulating continuous daily ratios . Tabulating continuous daily ratios of of TB/TAWSTB/TAWS yields an approximate firn yields an approximate firn εε trend trend from which from which TSTS data gaps can be filled from data gaps can be filled from TBTB data or vice versa. A least squares technique will data or vice versa. A least squares technique will be used to derive an analytic function providing be used to derive an analytic function providing ε ε variation with Julian datevariation with Julian date..

ObjectiveObjective

The ECSU summer 2007 URE Antarctic Temperature Mapping Team The ECSU summer 2007 URE Antarctic Temperature Mapping Team will create an analytic model of annual emissivity (will create an analytic model of annual emissivity (εε) at the Ski Hi ) at the Ski Hi AWS from 1994-1998 using previously compiled satellite and in-situ AWS from 1994-1998 using previously compiled satellite and in-situ AWS temperature records and satellite microwave brightness AWS temperature records and satellite microwave brightness temperatures. Time permitting, Ski Hi temperatures. Time permitting, Ski Hi εε trends will be compared with trends will be compared with εε trends derived at selected stations on the West Antarctic Ice Sheet trends derived at selected stations on the West Antarctic Ice Sheet (WAIS) bordering an interior region without AWS coverage. The (WAIS) bordering an interior region without AWS coverage. The other AWS stations whose temperature records could provide the other AWS stations whose temperature records could provide the basis for calculating emissivity trends are: Brianna (1994-1997), Byrd basis for calculating emissivity trends are: Brianna (1994-1997), Byrd (1981-99), Elizabeth (1996-99), Erin (1996-99), Patrick (1986-91), (1981-99), Elizabeth (1996-99), Erin (1996-99), Patrick (1986-91), Swithenbank (1998-99), and Theresa (1994-99). Swithenbank (1998-99), and Theresa (1994-99).

(AWS temperatures were obtained from the AWS Project data (AWS temperatures were obtained from the AWS Project data archive at the University of Wisconsin’s Space Science and archive at the University of Wisconsin’s Space Science and Engineering Center (SSEC). Values of Engineering Center (SSEC). Values of TBTB (1994-1998) for the Ski Hi (1994-1998) for the Ski Hi site were obtained from Dr. Chris Schuman at NASA Goddard Space site were obtained from Dr. Chris Schuman at NASA Goddard Space Flight Center.)Flight Center.)

Future work will use AWS and SSM/I Future work will use AWS and SSM/I TBTB data to establish continuous data to establish continuous emissivity and emissivity and TSTS trends at other AWS and across spatial and trends at other AWS and across spatial and temporal data gaps. The 2007 URE work is thus a necessary temporal data gaps. The 2007 URE work is thus a necessary intermediate step toward deriving surface temperature trends across intermediate step toward deriving surface temperature trends across the entire Antarctic ice sheet over the last 28 years.the entire Antarctic ice sheet over the last 28 years.

ToolsTools

Microsoft ExcelMicrosoft Excel World Wide WebWorld Wide Web Microsoft WordMicrosoft Word TI-84 PlusTI-84 Plus Ski Hi AWS DataSki Hi AWS Data

Data Emissivity TrendData Emissivity TrendCalculated Emissivity and Trend 2/22-12/31/94

y = 6E-15x6 - 1E-09x5 + 0.0001x4 - 4.6317x3 + 119988x2 - 2E+09x + 1E+13

0.8200

0.8400

0.8600

0.8800

0.9000

0.9200

0.9400

0.9600

0.9800

01/16/94 03/07/94 04/26/94 06/15/94 08/04/94 09/23/94 11/12/94 01/01/95 02/20/95

Dates

Em

iss

ivit

y

Calculated and Modeled Emissivity Calculated and Modeled Emissivity Trends Trends

Graph for New Equation (1994)

0.8000

0.8500

0.9000

0.9500

1.0000

1.0500

0 50 100 150 200 250 300 350 400

Excel 6th Order Polynom 4yr Avg. Emissivity '94 Emissivity Data

Calculated and Modeled Emissivity Calculated and Modeled Emissivity Trends – 1996Trends – 1996

Graph from New Equaton (1996)

0.8000

0.8500

0.9000

0.9500

1.0000

1.0500

1.1000

0 50 100 150 200 250 300 350 400

Julian Day

Em

issi

vity

Excel 6th Order Polynomial '96 Emissivity Model '96 Emissivity Dta

Calculated and Modeled Emissivity Calculated and Modeled Emissivity Trends - 1997Trends - 1997

Graph for New Equation (1997)

0.8000

0.8500

0.9000

0.9500

1.0000

1.0500

1.1000

0 50 100 150 200 250 300 350 400

Julian Day

Em

issi

vity

'94 6th Order Polynom '97 Model Emissivity '97 Data Emissivity

Calculated and Modeled Emissivity Calculated and Modeled Emissivity Trends - 1998Trends - 1998

Graph for New Equation (1998)

0.8000

0.8500

0.9000

0.9500

1.0000

1.0500

0 50 100 150 200 250 300 350 400

Julian Day

Em

issi

vity

'94 6th Order Polynom '98 Model Emissivity '98 Data EMissivity

ResultsResults

0.899+0.4904*SIN(x*2.33*PI()/365-(0.61*PI()))0.899+0.4904*SIN(x*2.33*PI()/365-(0.61*PI()))

We were able to fill in the gaps of the temperatures We were able to fill in the gaps of the temperatures that were not recorded because of the satellite failure.that were not recorded because of the satellite failure.

ConclusionConclusion At the end of the session we completed the input and manipulation of the At the end of the session we completed the input and manipulation of the

data sets years of 1994 to 1998.data sets years of 1994 to 1998. In order to get temperatures for each year, we had to adjust amplitude, In order to get temperatures for each year, we had to adjust amplitude,

frequency, phase change and y intercept, also known as the offset of the frequency, phase change and y intercept, also known as the offset of the equation. They were adjusted to achieve the lowest possible Chi squared equation. They were adjusted to achieve the lowest possible Chi squared value so that a new equation could be created and the temperatures that value so that a new equation could be created and the temperatures that were not recorded by Automated Weather Stations could be completed were not recorded by Automated Weather Stations could be completed from that equation.from that equation.

Graphs were made to show the change in temperatures for each year and Graphs were made to show the change in temperatures for each year and in correspondence to the emissivity for each year. in correspondence to the emissivity for each year.

Future ResearchFuture Research The continuation of this project would use the The continuation of this project would use the

SSM/I - AWS temperature ratios to construct the SSM/I - AWS temperature ratios to construct the emissivity trends and complete the missing data emissivity trends and complete the missing data points within the record. points within the record.

The future team will have to choose what time The future team will have to choose what time frame to begin the projection, since the projection frame to begin the projection, since the projection maps will have to be produced on a daily or maps will have to be produced on a daily or weekly basis. However, once a template for this weekly basis. However, once a template for this process has been established the time frame for process has been established the time frame for creating the projection maps should be creating the projection maps should be substantially shortened.substantially shortened.

Since Only one weather station was used. To get Since Only one weather station was used. To get the most accurate data, weather stations Noel the most accurate data, weather stations Noel and Theresa will need to be included. and Theresa will need to be included.

AcknowledgementsAcknowledgements

Dr. Linda Hayden; Principal Dr. Linda Hayden; Principal Investigator and Director of Investigator and Director of CERSERCERSER

Dr. Malcolm LeCompte; MentorDr. Malcolm LeCompte; MentorMr. Jeff WoodMr. Jeff Wood

ReferencesReferencesShuman et al (2000): An Empirical Technique for Estimating Near Surface Temperature Trends in Central Greenland from SSM/I Brightness Temperatures: Remote Sensing of the Environment, 51, pp. 245-253, 1995

Center or Remote Sensing of Ice Sheets (CReSIS) 2006Retrieved from http://www.cresis.ku.edu/ on the date of February 25, 2007 

Estimating Firn Emissivity, from 1994 to1998,at the Ski Hi Automatic Weather Station on theWest Antarctic Ice Sheet Using PassiveMicrowave Data; TreAsia Fields Jerome Mitchell Demetrus Rorie; ECSU Undergraduate Reasearch 2006

QUESTIONS???QUESTIONS???