weather stations Drescher and Filchner and
Comparison with European Centre for
Medium-Range Weather Forecasts (ECMWF)
Analysis model results
Markus Brüne
Septemb er 27, 2002
1 Abstract 4
2 Intro duction 4
3 DataAcquisitionandPro cessing 5
4 Datapresentation 6
5 ECMWF DataPro cessing 6
6 ClimatologicalFeaturesofthe StudyArea 7
7 Interpretationofthe MeasuredData 9
7.1 Temp erature . . . 9
7.2 AirPressure . . . 9
7.3 Wind. . . 9
7.4 RelativeHumidity . . . 11
8 ComparisonofmeasureddatawiththeECMWF-analysisdata 13 8.1 Temp erature . . . 13
8.2 AirPressure . . . 13
8.3 Wind. . . 14
9 Conclusions 16
A DatapresentationforDrescher 17
B DatapresentationforFilchner 25
C Comparisonwith theECMWF-analysis data 30
D Sensorsp ecications 40
E References 44
1 Lo cationoftheAWSDrescherandAWSFilchner . . . 5
2 Layoutofanautomaticweather station . . . 6
3 Missingdata oftheAWS DrescherandAWSFilchner . . . 7
4 Frequencywindroses fordierentstations . . . 10
5 Frequencywindroses accordingtothep ositionofobservation . . 11
6 Theoreticalisobars ofMSLP forsup er-geostrophic weather con- ditions . . . 12
7 FrequencywindrosesforDrescherandonlyforsup er-geostrophic weathersituations . . . 12
8 Deviationofwinddirectionb etweenobserveredandECMWFvalue 14 9 PositionoftheDrescherAWSandtheclosestECMWF-Gridp oints 15 10 Dierentinterp olationforthelo cationsofDrescherAWS,Halley andNeumayerstation . . . 15
11 Temp eraturein2mab ove surfacefortheDrescherAWS . . . 17
12 Temp eraturein5mab ove surfacefortheDrescherAWS . . . 18
13 Meansealevelpressure(MSLP)fortheDrescherAWS. . . 19
14 Windvelo cityfortheu-comp onentfortheDrescherAWS . . . . 20
15 Windvelo cityforthev-comp onentfortheDrescher AWS . . . . 21
16 Scalarwindvelo cityfortheDrescherAWS. . . 22
17 RelativeHumidityfortheDrescher AWS . . . 23
18 WindrosesforrelativefrequencyofwinddirectionfortheDrescher AWS . . . 24
19 Temp eraturein2mab ove surfacefortheFilchnerAWS . . . 25
20 Temp eraturein5mab ove surfacefortheFilchnerAWS . . . 26
21 MeansealevelpressurefortheFilchnerAWS . . . 27
22 Windvelo cityfortheu-comp onentandv-comp onenetfortheFilch- nerAWS. . . 28
23 RelativeHumidityfortheFilchnerAWS . . . 29
24 Correlationsb etweentheoriginalmeasureddataoftheDrescher AWSandECMWF-data . . . 30
25 Correlationsb etween theoriginalmeasureddataoftheFilchner AWSandECMWF-data . . . 31
26 Comparisonb etween theplotoftemp erature forDrescherAWS andtheinterp olatedECMWF-data. . . 32
27 Comparison b etween the plot of mean seal level pressure for DrescherAWSandtheinterp olatedECMWF-data . . . 33
28 Comparisonb etweentheplotofzonalwindcomp onentforDrescher AWSandtheinterp olatedECMWF-data . . . 34
29 Comparisonb etween the plotof meridonalwind comp onent for DrescherAWSandtheinterp olatedECMWF-data . . . 35
30 Comparisonb etween theplot of temp erature forFilchnerAWS andtheinterp olatedECMWF-data. . . 36
31 Comparisonb etweentheplotofmeansealevelpressureforFilch- nerAWSandtheinterp olatedECMWF-data . . . 37
32 FrequencywindroseforDrescher AWSandECMWF-data. . . . 38
33 Velo citywindroseforDrescherAWSandECMWF-data . . . 39
Automaticweather stations(AWS) aredue to theextreme coldconditionsan
attractive optiontoget moredense climaticdatafromAntarctica. These data
is needed for weather forecast mo delsor other climatic mo dels. Data sets of
theEurop eanCentreforMedium-RangeWeatherForecasts(ECMWF)analysis
mo del areused for comparisonwith AWS inthe Weddell Sea. This region is
ofparticular interest fortheBRIOS2 (Bremerhaven RegionalIce-Ocean Simu-
lations) mo del which pro duce results related to sea ice pro cesses, water mass
mo dicationsandcirculationpatterns,allleadingto ab etter understandingof
thethermohalineo ceancirculationintheSouthernOcean. Therefore,observa-
tionsatAWS'canb eusedasavalidationoftheECMWFmo delandcanreveal
sources forerrorsforclimatemo delsusingaECMWFforcing.
2 Introduction
The Antarctic continent is probably the most extraordinaryplace for climate
observationsonEarth.Formosttimeoftheyear,thecontinentissurroundedby
seaice. TheextremecoldmakesAntarcticatooneofthemostunrealplacesto
live. Duetotheinaccessibilityandtheroughclimate,runningmannedobserva-
toriesisveryexp ensiveandarduously. Therefore,veryfewstationsop eratethe
entire year, andthelo cations areclosetothe coastlineb ecauseof thedicult
supplysituation. Consequently,thelo cationsmayalwaysb enotappropriateto
collectmeaningfuldata. Butclimatologicaldataisneededformanyapplications
ranging from input data for weather forecasts mo dels to long timeglobalcli-
matemo dels. Hence,theinstallationofunmannedautomaticweather stations
(AWS)isveryattractive togetadensernetworkofmeasuredclimaticrecords.
Esp eciallyclimatemo delsrequiredense reliabledatafortheirvalidation.
Thisrep ortdescrib esthedatasetsoftwoautomaticweatherstations,which
have b een maintainedby the AlfredWegener Institute in Bremerhaven (Ger-
many)intheWeddellSearegion. TheDrescherStationsituatedatthewestern
edgeoftheRiiserLarseniceshelfat72 Æ
52'12S19 Æ
3'54Wstartedtransmitting
dataonthe02.02.1992,whiletheFilchnerAWSatthenorthern marginofthe
Ronneiceshelfat77 Æ
4'16S50 Æ
6'32Wtransmittedfrom01.01.1991untilitwas
dismantledon 30.01.1999(see Fig. 1). Thereason forthis was that due to a
big calvingevent aroundthe13Octob er, 1998 theFilchnerAWS was drifting
onatabulariceb erg.
Thisrep ortfo cusses rstlyonthequestion whetherautomaticweather sta-
tionspro duce usefuland reliabledata, secondlyon thepresentation andinter-
pretationofthedataoftheautomaticweather stationsDrescherandFilchner.
Inaddition,dothemeasuredandtransmitteddatatintothegeneralclimatic
characteristics of theWedellSea region? A comparisonof themeasured data
with the Europ ean Centre for Medium-Range Weather Forecasts (ECMWF)
analysisresultsisalsoconducted. Winddirectionandairpressureareofsp ecial
interests forthe coupled ice-o ceanBRIOS2 (Bremerhaven RegionalIce-Ocean
Simulations)mo del. Seasonal variations of the sea-ice cover in the Southern
Ocean represent oneof themostpronouncedsignalsintheannualcycle ofthe
globalclimatesystem(Timmermannetal.,2002).ButtheBRIOS-2resultswith
data, acomparisonb etween ECMWFand theobserveddata couldexclude or
verifytheECMWFdataas ap ossiblesource oferror.
180˚
210˚
240˚
270˚
300˚
330˚
0˚
30˚
60˚
90˚
120˚
180˚ 150˚
180˚
210˚
240˚
270˚
300˚
330˚
0˚
30˚
60˚
90˚
120˚
180˚ 150˚
AWS Drescher AWS Filchner
Figure1: Lo cationof theAWSDrescherandAWSFilchner
3 Data Acquisition and Processing
The stations were tted with sensors for temp erature, air pressure, relative
humidity(Filchnersince 06.02.1995,Drescher since 18.01.95),windsp eed and
wind direction(seeFig. 2). Additionally,the Drescher Stationmeasureduntil
05.03.1995thesnowheightbutthisdatawasneverrecorded(seeApp endixDfor
technicaldetails or Kottmeierand Lüdemann,1996). Thedata istransmitted
bytheARGOSsystemusingtwop olarorbitingNOAAsatellites. Eachsatellite
needs ab out102minutes for orbitingtheearth, so 20to 28 contacts with the
AWS'sarep ossiblep erdayforb oth. Thedataistransmittedevery200seconds
bya8-bitword.Inarststepthedataisseparatedintothedierentparameters,
outliersare eliminatedandshortgaps areinterp olated. Thereceiveddata sets
are also transformedinto a regular spaced timeseries with a resolution of 3
hours.
summerresearchstationFilchnerandattheDrescherinlet(UniversityofWis-
consin: http://uwamrc.ssec.wisc.edu/gifs/awstower.gif)
4 Data presentation
The fulldatapresentation is provided inApp endix A and B.Each parameter
is plotted witharesolution of3hours, monthlymeans, annualmeans,and an
averagemonthlymean. Therecorded airpressure isreduced tomean sealevel
pressure(MSLP).ThedataintegrityoftheFilchnerAWSforwindandhumidity
isnotappropriateenoughtocalculatemeans,and,therefore plotsforthoseare
needless. FrequencywindrosesoftheDrescherAWSarepresented,additionally
sub divided into meansfor theentire measurementp erio d,average winter and
average summermonths, and classied into windsp eeds exceeding morethan
10ms 1
. As the minimumtemp erature o ccurs in August the winter months
are July, August, and Septemb er while thesummer months, inaccordance to
theclassicaldivisionforthesouthern hemisphere,areDecemb er,January,and
February.
5 ECMWF Data Processing
The ECMWFanalysis datais provided inaregularspaced timeseries witha
resolutionof 6hoursonagridof 1.125degree inlongitudeandlatitude. Only
data forairpressure, temp erature, andthe twowindcomp onentsareused for
thecomparisonwiththemeasureddata. Tointerp olate theparametersforthe
exactp ositionoftheAWS',thefourcornerECMWFgridp ointsoftherevolving
square inwhich theAWSis lo catedare weightedaccording to itsdistance. In
ordertogetcomparabledatasetsonlyeverysixhoursameasuredvalueistaken
intoaccount.
Missing Data of Filchner AWS
1991 1992 1993 1994 1995 1996 1997 1998 1999 1991 1992 1993 1994 1995 1996 1997 1998 1999 1991 1992 1993 1994 1995 1996 1997 1998 1999 1991 1992 1993 1994 1995 1996 1997 1998 1999 1991 1992 1993 1994 1995 1996 1997 1998 1999
relative Humididity wind data
Temperature in 5 m Temperature in 2 m Air Pressure
Missing Data of Drescher AWS
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
relative Humididity wind data
Temperature in 5 m Temperature in 2 m Air Pressure
Figure3: MissingdataoftheAWSFilchnerandAWSDrescher. Thegreyarea
meansnodatahasb een provided.
6 Climatological Features of the Study Area
Antarcticadetached fromanyotherlandmassbytheSouthernOceans,iscov-
ered with 98% ice up to 4 km thick. This truncated p osition on the South
Pole of our glob e leads to sp ecial climate conditions. Unlike the Arctic, the
Antarctic p olarregionshowsastrongcircump olarlowpressure trougharound
the64 Æ
Slatitude. Northofthislatitudewesterly windsdominantesandtothe
southwindsfromtheeastprevail(König-Langloet al.,1998). Asthetemp era-
ture gradientb etween thesouthern midandhigh latitudesis strongestaround
theequinoxes,thelowpressuretroughshiftsfurthestsouthatthesetimes(van
Lo on, 1966). Theinteriorof theAntarcticcontinentwhichreachesanaltitude
or,inwinterabsentinsolation,theveryhighalb edo,andthemostlyunconned
outgoinglong-waveradiationleadto anegativeradiationbudget. Therefore,a
strongsurfaceinversiono ccursesp eciallyduringthewinter. Duetotheorogra-
phyandverysmo othsurface,katabaticwindstransp ortcoldairtothecoastlines
(Parish,1988,ParishandBro omwich,1991).
Generally,dataofautomaticweatherstationsislessreliablethendataofmanned
stations. Esp ecially recording wind data at AWS's, where over years nob o dy
lo oks after it, is unreliable. Dep osits of rimeice at the cup anemometers de-
nominatesthebiggestproblem(Stearnsetal. 1993). Windtunnelexp eriments
showedthaticingextremelyinuencesthemeasurements(Kimuraetal. 2001).
Particularlythe Filchnerdata set showsbig gapsof missingwinddataduring
theAntarcticwinterp erio ds (see Fig. 3). Another sourceoferrors istheaccu-
mulationofsnowwhichreducesthedistanceb etween thesensorsandthesnow
surfaces. Ifthelogarithmicwindproleisassumed,themeasuredwindvelo city
issmallerthantheoneinthedenedmeasurementheightab oveground. Allin
all,theairpressure sensorgive themostreliableresults, it canop erate evenif
thesensorissnow-covered.
7.1 Temperature
Thegeographicalp ositionoftheAWS's,highalb edovalues,andthenothinded
outgoinglong-wave radiation causes anegative radiationbalance. Due to the
strongerp olarfrontandtheAntarcticCircump olarOceanCurrentheattransfer
from mid latitudes is not p ossible like in the Arctic. Both stations showed
consistentlowtemp eratureswiththeminimaaroundAugust. Theannualmean
temp erature at Filchner is -21,1 Æ
C due to a more southern lo cation and the
inuenceof very coldkatabaticwinds,Drescherwithamorenorthern p osition
andmoreaectedbyrelativewarmerairmassesfromnorth-eastshowsahigher
meantemp eratureof-16.5 Æ
C(see App endix A,Figs. 11,12;App endix B,Figs.
19,20)
7.2 Air Pressure
The observed average annual mean sea level pressure at b oth stations varied
b etween 985 and 995 hPa. At Filchner AWS a slightly higher air pressure
is recorded which is due to the more southern lo cation resulting ina greater
distance to the circump olar low pressure trough than the Drescher AWS. In
addition, the Filchner AWS is closer to the thermal south p ole high pressure
area. Minimaofairpressure canb efoundaroundMarchandSeptemb er,esp e-
ciallyat Drescher AWS.As mentioned, theAntartic circump olarlowpressure
troughmovesinasemi-annualcyclewiththefurthestshifttothesoutharound
March and Septemb er (vanLo on,1967). This eectis notso wellrepresented
atFilchner,again,duetothemoresouthernlo cation(seeApp endixA,Fig. 13;
App endeixB,Fig. 21).
7.3 Wind
As mentioned,wind isthemost unreliableparameter. Detailedinterpretation
fortheFilchnerAWSisimp ossibleb ecauseoftheintermitteddatarecording. At
theDrescherAWS,abimo dalwindeldwith highfrequency ofwinddirection
inthe E toNE sector andlower frequency intheS to SWsector is observed.
Thesamekindof patternshows thedataofHalley research stationlo catedin
the same region(see Figs. 4and 5). The highersp eeds tend to b e relatedto
S
E W
N
S
E W
N
S
E W
N
S
0.02
E W
N
a) b)
c) d)
Figure 4: Frequencywindroses for dierent stations. Wind velo cities notex-
ceeding1ms 1
arenegelcted. a)DrescherAWS,b)FilchnerAWS,c) Halley,d)
Neumayer. ThewindroseforFilchnerAWSisbasedonvery fewdataonly(see
Chapter 7.3).
winds fromthe east only (see App endix A, Figs. 14-16,18). To the north of
Drescher AWS low pressure areas migrate circump olareastwards. Due to the
Coriolisforcewindisdivertedtotheleftforthesouthernhemisphereandhence
the resultant geostrophic wind at the southern margin of the cyclone blows
fromtheeast. Katabaticwinds increasethe frequencyof easterliesbut cannot
explainthesouth-western windeld comp onent(Connolley andCattle,1994).
Oneexplanationforthesouthwesterncomp onentcouldb ethatcyclonesmigrate
eastwardsinthesouthernWedellSea.Butownexaminationsab outthelo cation
of the center of low pressure corresp ondent with Jones and Simmonds(1993)
thatcyclonesdonottrackthatfarsouth. Somedepressionsmovefarsouthand
reachtheWeddellsea,butthedensityofcyclolysisisveryhighthere,sothelow
pressure areas do notreach the Antarctic continent (King and Turner 1997).
Another explanationis theso calledsup er-geostrophic wind(see Fig. 6). For
thissituationahighpressureridgeextendsfarsouthintotheWeddellSea. Due
to thecurvature of the isobars centrifugal force o ccur. Considering acyclone
northwestwardsoftheDrescherStationthecentrifugalforceacceleratesthewind
60˚W 60˚S
60˚S 50˚W
40˚W
30˚W 20˚W 10˚W
0˚
10˚E
80˚S
80˚S 70˚S
70˚S Neumayer
Drescher
Halley Filchner
Figure5: Frequencywindroses accordingtothep ositionofobservation. Wind
velo cities notexceeding 1m s 1
are negelcted. Thewind rosefor Filchner is
shown ingrey, b ecausethedatabaseisless reliable
matchrelatively go o dsup ergeostrophic weather conditions are considered the
frequencywindroseforDrescherStationshowsadominantwinddirectionfrom
theSouth-West(see Fig. 7). Thep eakofzonal windsp eed aroundSeptemb er
isremarkable,causedbytheshiftofthecircump olarcyclones tothesouth(see
App endix A,Fig. 14).
7.4 Relative Humidity
Data for relative humidity is either erroneous or missing. For example, an
instrumentchange attheDrescherAWSinJanuary1999caused atremendous
change in the observed values. Nearly 100 % humiditywas recorded for the
p erio dsince 1999.ThevaluesatFilchneraremorecredible,butthetimeseries
is to o short, and to many gaps make it more or less needless. Therefore no
70°W 60°W
50°W
30°W 20°W 10°W
0°E
10°E
20°E
80°S
80°S 70°S
70°S 60°S
60°S 40°W
Filchner
Drescher Halley
Neumayer
H L
L
Figure6:TheoreticalisobarsofMSLPforsup er-geostrophicweatherconditions
SOUTH 0.02
EAST WEST
NORTH
Figure 7: Frequency wind roses for Drescher and only for sup er-geostrophic
weathersituations. Wind velo citiesnotexceeding 1ms 1
areneglected.
analysis data
ForthecomparisonofmeasureddatawiththeECMWFanalysis datathedata
sets weretransformedinorder to makeb oth datasets comparable (see Chap-
ter 5). Wind data for Filchner is nottaken into account for the comparison,
b ecause of the already mentioned erroneous and missing data. Positions of
DrescherStationandthenext ECMWF-gridp ointsareshowninFigure9. For
ab etter interpretation,datasetsofthemannedstationsNeumayer(Germany)
andHalley(UnitedKingdom)arecomparedwiththeECMWFanalysisdataas
well. Generally,theb estcorrelationcanb efoundforairpressure. Highr-square
values canb e foundedforFilchner(0.89)andforDrescher(0.95). Due todata
gaps at Filchner, Drescher generallyshowsab etter correlation (see App endix
C, Figs. 24 and 25). Figure 10showsthe correlation co ecients for dierent
metho ds used to interp olate ECMWF data to the p osition of the stationsat
Drescher, HalleyandNeumayer. Itseems that theinuence of dierentinter-
p olation metho ds is very low. Esp ecially for the BRIOS ice-o cean mo del, it
should b eexaminedifremarkable,whetherdierences canb e foundfor theice
grid p ointclosest to the coast. However,it must b e also considered that the
observations areincorp orated intheECMWF-analysis.
8.1 Temperature
ItseemshattheECMWF-mo deloverestimatesthetemp eratures. Butwithcor-
relationco ecientsof0.76(Drescher)and0.66(Filchner)stillreasonablevalues
are reached. A notable temp erature missmatch for measurement p erio d until
1998 can b e foundat b oth AWS stations. Esp ecially thecold winter temp er-
ature can b e either not predicted by the ECMWF-mo del or the temp erature
sensors malfunctioned in very coldconditions. It is remarkablethat the val-
uesofthesamesensormatchquitego o dwiththeECMWFvaluessince winter
1998atDrescherAWS(see App endix C,Figs. 26and30). But includingdata
from Halleyand Neumayer, theECMWF analysis data tend to overestimates
thetemp erature, particular ifthe temp eraturesare very low. All slop esofre-
gressionsaregraterthan1. ThisECMWF"warming"wasdescrib ed byseveral
authors(Timmermannetal.,2002)andcanb eexplainedbyanunderestimation
ofwintersurface temp eratureofthefrozen WeddellSea.
8.2 Air Pressure
As mentioned,theairpressure isthemostreliablemeasuredparameterofthe
automaticweatherstations. Themeasuredvaluest very go o dwiththemo d-
elled values at Drescher(see App endix C,Fig. 27). AtFilchnertill1994,the
oldsensor seemed tomeasureaslightlyto ohigh airpressure. Causingtheav-
eragemonthlyvaluestodier byab out4hPa(see App endixC,Fig. 31). But
overall,theECMWF-valuestverygo o d(see gure19). Ago o dcoherence b e-
tweenECMWFandobserveddatastillexistsifdatafromNeumayerandHalley
stationsarealsocompared.
Measured wind data of automatic weather stations is accompanied by many
errors. It isdiculttodrawconclusionsfromthewinddatacomparisonofthe
FilchnerAWS.Itisnotablethatthemeridionalwindcomp onentdo esnotmatch
at all with the ECMWF database with avery small correlation co ecient of
r 2
=0.18. The zonal comp onent is reected b etter with ar 2
valueof 0.52 (see
App endixC,Fig. 24). Therelative lownumb erofrecords,duetoinstrumental
failureswhichcanb econsideredforthecorrelationmakesthisresultveryvague.
But even the Drescher data, where nearly ve times more data can b e used,
onlymediumcoherences ofr 2
=0.56(zonalcomp onent)andr 2
=0.58(meridional
comp onent),arenoted. Therelativelowregressionslop ecanb eb estexplained
byinstrumentalfailuresduetoicingandreduceddistancetothesurface. Figures
32and33showfrequency andwindsp eedroses fortheoriginalDrescher data,
theinterp olatedECMWFdata fortheDrescherp osition,fortheice-gridp oint
oftheBRIOS2mo del,andthefoursurroundingECMWF-gridp oints. Another
eectis,thattheaveragedeviationofwinddirectionisnotstableovertime.At
Drescher, tillJanuary1995,thedeviation isnegative, afterwards it isp ositive
(see Fig. 8). Together with the fact that exact at that timethe station was
replaced by a new one,it can b e assumedthat the Drescher Stationwas not
p ointednorthaccurately.
average deviation of wind direction ( o )
time -20
-10 0 10 20 30 40
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Figure 8: Deviation(runningmean of50 days)of wind direction b etween ob-
servered and ECMWF value forDrescher (black), Neumayer(darkgrey), Hal-
ley(lightgrey).
339˚
339˚
340˚
340˚
341˚
341˚
342˚
342˚
-73˚ -73˚
-72˚ -72˚
339˚
339˚
340˚
340˚
341˚
341˚
342˚
342˚
-73˚ -73˚
-72˚ -72˚
AWS Drescher
ECMWF Gitterpunkte
Figure 9: PositionoftheDrescherAWSand theclosest ECMWF-Gridp oints.
Thediamondrepresent thenextice-gridp ointfortheBRIOS2mo del
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
4 point int erpol.
nearest neighbour
SW SE grid point
NW grid point NE grid point
4 point int erpol.
nearest neighbour
NW NE grid point
SW grid point SE grid point
4 point int erpol.
nearest neighbour
SE SW grid point
NE grid point NW grid point
cor re la tion coeffi ci ent
-20 -10 0 10 20 30 40 50 60 70 80
aver age devi ati on of w in d di recti on P T
U
V
delta phi
Figure10: Dierentinterp olationforthelo cationsofDrescherAWS,Halleyand
Neumayerstation. Shownarethecorrelationco ecientformainseallevelpres-
sure(P),temp eraturein2mhigh(T),u-windandv-windcomp onent(U.V).The
rightaxesb elongs tothe averagedeviation ofwinddirection (delta phi). The
sequence of thenext ECMWFgridp ointsreects thedistance to thelo cations
ofobservation.
The quality ofdata sets fromautomaticweather stations is less reliable. The
extremecoldclimateinAntarcticalimitstheuseofthesedata. Itb ecomesclear
thatwithdecreasingdistancestotheSouthPoleicingcausesadeadlossofdata
during thewinter p erio d. Esp eciallywinddata mustused verycarefully. The
mostreliableparameteris airpressure. This rep ortcoversthat winddirection
showsabi-mo dalstructure withdominantwindsfromtheEtoNE.Themost
obviousexplanationforthesouthwestwinddirectionobservedbyDrescherand
Halleystationsisasup er-geostrophic weather situation. Thecomparisonwith
theECMWFanalysis revealedthat mo delledairpressure agreeswell withthe
observations incontrast to the temp erature which deviates moreduring very
cold p erio ds. If wind data is used for interpretation one must keep in mind
that maintenanceof the stationsdo es nothapp en foryearsand themeasured
height decreases due to further snow accumulation. The other result is that
ECMWF-dataforcertainp ositionshavetohandlewithcareb ecauseitmustb e
distinguishedb etween lo cationsoverseaorland.
temperature ( o C) -60 -50 -40 -30 -20 -10 0 10
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-35 -30 -25 -20 -15 -10 -5 0
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 temperature ( o C)
monthly mean
-20 -18 -16 -14
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 temperature ( o C)
annual mean
-30 -20 -10 0 10
1 2 3 4 5 6 7 8 9 10 11 12
temperature ( o C)
1992-2002 average monthly mean d)
c) b) a)
Figure 11: Temp eraturein 2mab ove surface for theDrescher AWS. Data in-
terval 3hours. a): allrecorded values b)monthlymeansc): annualmeans d)
averagemonthlymeans
d) c) b) a)
temperature ( o C) -60 -50 -40 -30 -20 -10 0 10
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-35 -30 -25 -20 -15 -10 -5 0
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 temperature ( o C)
monthly mean
-20 -18 -16 -14
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 temperature ( o C)
annual mean
-25 -20 -15 -10 -5 0
1 2 3 4 5 6 7 8 9 10 11 12
temperature ( o C)
1992-2002 average monthly mean temperature ( o C)
-60 -50 -40 -30 -20 -10 0 10
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-35 -30 -25 -20 -15 -10 -5 0
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 temperature ( o C)
monthly mean
-20 -18 -16 -14
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 temperature ( o C)
annual mean
-25 -20 -15 -10 -5 0
1 2 3 4 5 6 7 8 9 10 11 12
temperature ( o C)
1992-2002 average monthly mean
Figure 12: Temp eraturein 5mab ove surface for theDrescher AWS. Data in-
terval 3hours. a): allrecorded values b)monthlymeansc): annualmeans d)
averagemonthlymeans
d) c) b) a)
MSLP (hPa)
925 950 975 1000 1025
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
975 980 985 990 995 1000 1005
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
MSLP (hPa)
monthly mean
980 985 990 995
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
MSLP (hPa)
annual mean
980 985 990 995
1 2 3 4 5 6 7 8 9 10 11 12
MSLP (hPa)
1995-2002 average monthly mean
MSLP (hPa)
925 950 975 1000 1025
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
975 980 985 990 995 1000 1005
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
MSLP (hPa)
monthly mean
980 985 990 995
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
MSLP (hPa)
annual mean
980 985 990 995
1 2 3 4 5 6 7 8 9 10 11 12
MSLP (hPa)
1995-2002 average monthly mean
Figure13: Meansealevelpressure(MSLP)fortheDrescherAWS.Datainterval
3hours. a): allrecordedvaluesb)monthlymeansc): annualmeansd)average
monthlymeans
wind velocity (m/s -1 )
1992-2002 u-wind component (+west/-east) -20
-10 0 10 20
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-8 -6 -4 -2 0 2
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 -8
-6 -4 -2 0 2
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 wind velocity (m s -1 )
monthly mean
-4.5 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 wind velocity (m s -1 )
annual mean
-4.0 -3.5 -3.0 -2.5 -2.0 -1.5
1 2 3 4 5 6 7 8 9 10 11 12
wind velocity (m s -1 )
1992-2002 average monthly mean a)
d) c) b)
Figure 14: Windvelo cityforthe u-comp onentfor theDrescher AWS. Positive
values represent west wind, negative east wind resp ectively. Data interval 3
hours. a): allrecorded values b) monthlymeansc): annualmeansd) average
monthlymeans
a)
d) c) b)
d) b) wind velocity (m s -1 )
v-wind component (+south/-north) -20
-10 0 10 20
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-6 -4 -2 0 2
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 -6
-4 -2 0 2
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 wind velocity (m s -1 )
monthly mean
-1.5 -1.0 -0.5 0.0 0.5
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 wind velocity (m s -1 )
annual mean
-1.5 -1.0 -0.5 0.0
1 2 3 4 5 6 7 8 9 10 11 12
wind velocity (m s -1 )
1992-2002 average monthly mean wind velocity (m s -1 )
v-wind component (+south/-north) -20
-10 0 10 20
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-6 -4 -2 0 2
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 -6
-4 -2 0 2
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 wind velocity (m s -1 )
monthly mean
-1.5 -1.0 -0.5 0.0 0.5
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 wind velocity (m s -1 )
annual mean
-1.5 -1.0 -0.5 0.0
1 2 3 4 5 6 7 8 9 10 11 12
wind velocity (m s -1 )
1992-2002 average monthly mean
Figure 15: Wind velo cityfor thev-comp onentfor theDrescher AWS. Positive
values representnorthwind,negative southwindresp ectively. Dataintervall3
hours. a): allrecorded values b) monthlymeansc): annualmeansd) average
monthlymeans
a)
d) c) b)
wind velocity (m/s -1 ) 0 10 20 30 40
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
0 2 4 6 8 10
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 0
2 4 6 8 10
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 wind velocity (m s -1 )
monthly mean
1 2 3 4 5 6 7
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 wind velocity (m s -1 )
annual mean
3.0 3.5 4.0 4.5 5.0 5.5 6.0
1 2 3 4 5 6 7 8 9 10 11 12
wind velocity (m s -1 )
1992-2002 average monthly mean
Figure16: Scalarwindvelo cityfortheDrescher AWS.. Data interval3hours.
a): allrecordedvaluesb)monthlymeansc): annualmeansd)averagemonthly
means
rel. hummidity (%) 0 10 20 30 40 50 60 70 80 90 100
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
70 75 80 85 90 95 100
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
rel. hummidity (%)
1992-2002 monthly mean
75 80 85 90 95 100
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
rel. hummidity (%)
1992-2002 annual mean
80 85 90 95 100
1 2 3 4 5 6 7 8 9 10 11 12
rel. hummidity (%)
1992-2002 average monthly mean a)
d) c) b)
Figure 17: Relative Humidityfor the Drescher AWS. Data intervall 3 hours.
a): allrecordedvaluesb)monthlymeansc): annualmeansd)averagemonthly
means
S
E W
N
S
E W
N
S
E W
N
annual mean annual mean < 10 m/s annual mean >10 m/s
S
E W
N
S
E W
N
S
E W
N
S 2%
E W
N
S
E W
N
S
E W
N
summer mean summer mean < 10 m/s summer mean >10 m/s winter mean winter mean < 10 m/s winterl mean >10 m/s
calms : 15.6%
calms : 19.1%
calms : 13.3%
Figure18: Windroses forrelative frequency ofwinddirectionfortheDrescher
AWS.Windsp eedb elow1ms 1
isassumedascalms.
d) c) b) a)
temperature ( o C) -60 -50 -40 -30 -20 -10 0 10
1992 1993 1994 1995 1996 1997 1998 1999
-40 -35 -30 -25 -20 -15 -10 -5 0
1992 1993 1994 1995 1996 1997 1998 1999
-40 -35 -30 -25 -20 -15 -10 -5 0
1992 1993 1994 1995 1996 1997 1998 1999
-40 -35 -30 -25 -20 -15 -10 -5 0
1992 1993 1994 1995 1996 1997 1998 1999
temperature ( o C)
monthly mean
-30 -28 -26 -24 -22
1992 1993 1994 1995 1996 1997 1998 1999
temperature ( o C)
annual mean
-35 -30 -25 -20 -15 -10 -5 0
1 2 3 4 5 6 7 8 9 10 11 12
temperature ( o C)
1991-1999 average monthly mean temperature ( o C)
-60 -50 -40 -30 -20 -10 0 10
1992 1993 1994 1995 1996 1997 1998 1999
-40 -35 -30 -25 -20 -15 -10 -5 0
1992 1993 1994 1995 1996 1997 1998 1999
-40 -35 -30 -25 -20 -15 -10 -5 0
1992 1993 1994 1995 1996 1997 1998 1999
-40 -35 -30 -25 -20 -15 -10 -5 0
1992 1993 1994 1995 1996 1997 1998 1999
temperature ( o C)
monthly mean
-30 -28 -26 -24 -22
1992 1993 1994 1995 1996 1997 1998 1999
temperature ( o C)
annual mean
-35 -30 -25 -20 -15 -10 -5 0
1 2 3 4 5 6 7 8 9 10 11 12
temperature ( o C)
1991-1999 average monthly mean
Figure 19: Temp erature in2mab ove surface for the Filchner AWS. Data in-
tervall 3hours. a): allrecorded valuesb) monthlymeansc): annual meansd)
averagemonthlymeans
a)
d) c) b) temperature ( o C)
-60 -50 -40 -30 -20 -10 0 10
1992 1993 1994 1995 1996 1997 1998 1999
-40 -35 -30 -25 -20 -15 -10 -5 0
1992 1993 1994 1995 1996 1997 1998 1999
-40 -35 -30 -25 -20 -15 -10 -5 0
1992 1993 1994 1995 1996 1997 1998 1999
-40 -35 -30 -25 -20 -15 -10 -5 0
1992 1993 1994 1995 1996 1997 1998 1999
temperature ( o C)
monthly mean
-30 -28 -26 -24 -22
1992 1993 1994 1995 1996 1997 1998 1999
temperature ( o C)
annual mean
-35 -30 -25 -20 -15 -10 -5 0
1 2 3 4 5 6 7 8 9 10 11 12
temperature ( o C)
1991-1999 average monthly mean
Figure 20: Temp erature in5mab ove surface for the Filchner AWS. Data in-
tervall 3hours. a): allrecorded valuesb) monthlymeansc): annual meansd)
averagemonthlymeans
d) c) b) a)
MSLP (hPa)
925 950 975 1000 1025
1991 1992 1993 1994 1995 1996 1997 1998 1999
985 990 995 1000 1005 1010 1015
1991 1992 1993 1994 1995 1996 1997 1998 1999
985 990 995 1000 1005 1010 1015
1991 1992 1993 1994 1995 1996 1997 1998 1999
985 990 995 1000 1005 1010 1015
1991 1992 1993 1994 1995 1996 1997 1998 1999
MSLP (hPa)
monthly mean
985 990 995 1000
1991 1992 1993 1994 1995 1996 1997 1998 1999
MSLP (hPa)
annual mean
985 990 995 1000
1 2 3 4 5 6 7 8 9 10 11 12
MSLP (hPa)
1991-1998 average monthly mean
Figure21: Meansealevelpressure(MSLP)fortheFilchnerAWS.Dataintervall
3hours. a): allrecordedvaluesb)monthlymeansc): annualmeansd)average
monthlymeans
b) a)
wind velocity (m s
v-wind component (+south/-north) -20
-10 0 10 20
1991 1992 1993 1994 1995 1996 1997 1998 1999
-20 -10 0 10
1991 1992 1993 1994 1995 1996 1997 1998 1999
wind velocity (m s -1 )
u-wind component (+west/-east) wind velocity (m s -1 )
v-wind component (+south/-north) -20
-10 0 10 20
1991 1992 1993 1994 1995 1996 1997 1998 1999
-20 -10 0 10
1991 1992 1993 1994 1995 1996 1997 1998 1999
wind velocity (m s
u-wind component (+west/-east)
Figure 22: Windvelo city for the v-comp onent (a) and u-comp onenet (b) for
the Filchner AWS. Positive u-values represent west wind, negative east wind.
Positive v-values represent north wind, negative south wind resp ectively. No
meansarecalculateddue tothelackofdata. hours.
b) a)
rel. hummidity (%)
0 10 20 30 40 50 60 70 80 90 100
1992 1993 1994 1995 1996 1997 1998 1999
55 60 65 70 75 80 85 90
1992 1993 1994 1995 1996 1997 1998 1999
rel. hummidity (%)
1991-1999 monthly mean
Figure23: Relative HumidityfortheFilchnerAWS.Datainterval3hours. a):
all recorded values b) monthly means Other meansare calculateddue to the
lackofdata.
n = 14446
Air Pressure Temperature
U=wind componenet V-wind component r = 0.7569
y =1,0199x+2.1755
2
y =0.9982x+2.1633 r = 0.9518
n = 11028
2
y =0.6405x+0.8796 r = 0.5627
n = 8718
2
y =0.6599x+03536 r = 0.5832
n = 8129
2
AWS pressure (hPa)
ECMWF pressure (hPa) 950
975 1000 1025 1050
950 975 1000 1025 1050 AWS temp ( o C)
ECMWF temp ( o C) -50
-40 -30 -20 -10 0 10
-50 -40 -30 -20 -10 0 10
AWS zonal wind (m s -1 )
ECMWF zonal wind (m s -1 ) -20
-15 -10 -5 0 5 10 15 20
-20 -15 -10 -5 0 5 10 15 20 AWS merridional (m s -1 )
ECMWF meridional wind (m s -1 ) -20
-15 -10 -5 0 5 10 15 20
-20 -15 -10 -5 0 5 10 15 20
Figure 24: Correlations b etween the originalmeasured data of the Drescher
AWSand forthesamep ositioninterp olatedECMWF-data
n = 9263
Air Pressure Temperature
U=wind componenet V-wind component r = 0.6562
y =1.11691x+5.5015
2
y =1.017x+13.038 r = 0.8932 n = 9261
2
y =0.897x+0.5206 r = 0.5206 n = 1823
2
y =0.4924x+17.7312 r = 0.1806
n = 1632
2
AWS pressure (hPa)
ECMWF pressure (hPa) 950
975 1000 1025 1050
950 975 1000 1025 1050 AWS temp ( o C)
ECMWF temp ( o C) -50
-40 -30 -20 -10 0 10
-50 -40 -30 -20 -10 0 10
AWS zonal wind (m s -1 )
ECMWF zonal wind (m s -1 ) -20
-15 -10 -5 0 5 10 15 20
-20 -15 -10 -5 0 5 10 15 20 AWS merridional (m s -1 )
ECMWF meridional wind (m s -1 ) -20
-15 -10 -5 0 5 10 15 20
-20 -15 -10 -5 0 5 10 15 20
Figure25: Correlationsb etweentheoriginalmeasureddataoftheFilchnerAWS
andforthesamep ositioninterp olatedECMWF-data
temperature ( o C)
1992-2002 monthly mean -35
-30 -25 -20 -15 -10 -5 0
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-35 -30 -25 -20 -15 -10 -5 0
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
temperature ( o C)
1992-2002 annual mean -20
-18 -16 -14 -12 -10
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
-20 -18 -16 -14 -12 -10
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
temperature ( o C)
1992-2002 average monthly mean -30
-20 -10 0
1 2 3 4 5 6 7 8 9 10 11 12
-30 -20 -10 0
1 2 3 4 5 6 7 8 9 10 11 12
Figure26: Comparisonb etween theplotoftemp eratureforDrescherAWSand
theinterp olatedECMWF-data(grew)
air Pressure (hPa)
1992-2002 monthly mean 975
980 985 990 995 1000 1005
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
975 980 985 990 995 1000 1005
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
air Pressure (hPa)
1992-2002 annual mean 980
985 990 995
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
980 985 990 995
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
air Pressure (hPa)
1992-2002 average monthly mean 980
985 990 995
1 2 3 4 5 6 7 8 9 10 11 12
980 985 990 995
1 2 3 4 5 6 7 8 9 10 11 12
Figure27: Comparisonb etweentheplotofmeanseallevelpressureforDrescher
AWSand theinterp olatedECMWF-data(grew)
wind velocity (m s -1 )
1992-2002 monthly mean -6
-4 -2 0 2
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-6 -4 -2 0 2
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
wind velocity (m s -1 )
1992-2002 annual mean -5
-4 -3 -2 -1 0
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
-5 -4 -3 -2 -1 0
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
wind velocity (m s -1 )
1992-2002 average monthly mean -5
-4 -3 -2 -1 0
1 2 3 4 5 6 7 8 9 10 11 12
-5 -4 -3 -2 -1 0
1 2 3 4 5 6 7 8 9 10 11 12
Figure28: Comparisonb etween theplotofzonalwindcomp onentforDrescher
AWSand theinterp olatedECMWF-data(grew)
wind velocity (m s -1 )
1992-2002 monthly mean -6
-4 -2 0 2 4
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-6 -4 -2 0 2 4
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
wind velocity (m s -1 )
1992-2002 annual mean -2
-1 0 1 2
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
-2 -1 0 1 2
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
wind velocity (m s -1 )
1992-2002 average monthly mean -2
-1 0 1 2
1 2 3 4 5 6 7 8 9 10 11 12
-2 -1 0 1 2
1 2 3 4 5 6 7 8 9 10 11 12
Figure 29: Comparison b etween the plot of meridonal wind comp onent for
DrescherAWSandtheinterp olatedECMWF-data(grew)
temperature ( o C)
1991-1999 monthly mean -40
-35 -30 -25 -20 -15 -10 -5 0
1991 1992 1993 1994 1995 1996 1997 1998 1999
-40 -35 -30 -25 -20 -15 -10 -5 0
1991 1992 1993 1994 1995 1996 1997 1998 1999
temperature ( o C)
19921-1999 annual mean -30
-28 -26 -24 -22 -20 -18 -16 -14 -12 -10
1991 1992 1993 1994 1995 1996 1997 1998 1999
-30 -28 -26 -24 -22 -20 -18 -16 -14 -12 -10
1991 1992 1993 1994 1995 1996 1997 1998 1999
temperature ( o C)
1991-1999 average monthly mean -40
-30 -20 -10 0
1 2 3 4 5 6 7 8 9 10 11 12
-40 -30 -20 -10 0
1 2 3 4 5 6 7 8 9 10 11 12
Figure30: Comparisonb etween theplotoftemp eratureforFilchnerAWSand
theinterp olatedECMWF-data(grew)
air Pressure (hPa)
1991-1999 monthly mean 985
990 995 1000 1005 1010 1015
1991 1992 1993 1994 1995 1996 1997 1998 1999
985 990 995 1000 1005 1010 1015
1991 1992 1993 1994 1995 1996 1997 1998 1999
air Pressure (hPa)
1991-1999 annual mean 985
990 995 1000 1005
1991 1992 1993 1994 1995 1996 1997 1998 1999
985 990 995 1000 1005
1991 1992 1993 1994 1995 1996 1997 1998 1999
air Pressure (hPa)
1991-1999 average monthly mean 985
990 995 1000
1 2 3 4 5 6 7 8 9 10 11 12
985 990 995 1000
1 2 3 4 5 6 7 8 9 10 11 12
Figure31: Comparisonb etweentheplotofmeansealevelpressureforFilchner
AWSand theinterp olatedECMWF-data(grew)
SOUTH 0.02 EAST WEST
NORTH ECMWF Gitterpunkt 18.000 W 72.000S
SOUTH 0.02 EAST WEST
NORTH ECMWF Gitterpunkt 19.125 W 72.000S
SOUTH 0.02 EAST WEST
NORTH ECMWF Gitterpunkt makeroses.gmt*
S
SOUTH 0.02 EAST WEST
NORTH ECMWF Gitterpunkt 19.125 W 73.125S
SOUTH 0.02 EAST WEST
NORTH ECMWF Eisgitter interpoliert 19.05 W 72.73S
SOUTH 0.02 EAST WEST
NORTH Drescher AWS Daten interpoliert :19.044 W 72.87S
SOUTH 0.02 EAST WEST
NORTH ECMWF AWS Drescher interpoliert :19.044 W 72.87S
Figure 32: Frequency windroseforDrescherAWS,theinterp olatedECMWF-
data for the lo cation of Drescher AWS and the next ice-gird p oint of the
BRIOS2-mo delandsep eratlyforthefoursourundingECMWF-gridp oints
ECMWF Gitterpunkt 18.000 W 72.000S ECMWF Gitterpunkt
19.125 W 72.000S
ECMWF Gitterpunkt 18.000 W 73.125 S ECMWF Gitterpunkt
19.125 W 73.125S
ECMWF Eisgitter interpoliert 19.05 W 72.73S Drescher AWS Daten
interpoliert :19.044 W 72.87S
ECMWF AWS Drescher interpoliert :19.044 W 72.87S
SOUTH 2 EAST WEST
NORTH
SOUTH 2 EAST WEST
NORTH
SOUTH 2 EAST WEST
NORTH SOUTH 2
EAST WEST
NORTH
SOUTH 2 EAST WEST
NORTH
SOUTH 2 EAST WEST
NORTH
SOUTH 2 EAST WEST
NORTH
Figure33: Velo citywindroseforDrescherAWS,theinterp olatedECMWF-data
for thelo cation of Drescher AWSand the next ice-gird p ointof theBRIOS2-
mo deland separatelyforthefoursurrounding ECMWF-gridp oints
AWSFilchner3315
op eratingtime: Jan1990-22.05.94
lo cation: 77.087S50.214WElevationa.s.l.40m
manufacturer: DefenseSystemsInc.,USA,
UniversityHanover,Germany
sensors
Air PressureCompany: Paroscientic, Inc.
Sensor: Digiquartztransducer 215
Accuracy +/-0.2hPa
Resolution: 10bit->15hPa
Range: 900-1053.5hPa
Sensor lo cation: approx. 3mab ove icesurface
Air Temp erature
Company: Yellowsprings
Sensor: 44020
Accuracy +/-0:2
Æ
C
Resolution: 8bit ->0:2 Æ
C
Range: -44
Æ
C-+6 Æ
C
Sensor lo cation: 1. Sensorapprox. 5mab ove icesurface
2. Sensorapprox. 3mab ove icesurface
Wind
Company: R.M.Young
Sensor: WindMonitor-RE
Accuracy Sp eed: +/-0.2kn
Direction: +/-5 Æ
Resulution: Sp eed: 8bit->+/- 0.5kn
Direction: 8bit ->+/-1.5 Æ
Range: Sp eed: 0-127.5ms 1
Direction: 0-360 Æ
Sensor lo cation: approx. 5mab ove icesurface
op eratingtime: 06.02.95-30.03.96
lo cation: 77.071S50.109WElevation a.s.l.40m
AWSFilchner3313
op eratingtime: 11.02.97-30.01.99
lo cation: 77.071S50.109WElevation a.s.l.40m
AWSDrescher 3310
op eratingtime: 18.01.95-24.01.99
lo cation: 72.870S19.048WElevation a.s.l.34m
manufacturer: DefenseSystemsInc.,USA,
UniversityHanover,Germany
sensors
AirPressure
Company: Paroscientic,Inc.
Sensor: Digiquartztransducer 215A-102
Accuracy +/-0.2hPa
Resolution: 16bit->0.05hPa
DataSampling Interval10min
Sensorlo cation: approx. ice surface
AirTemp eratureDierence
Resolution: 12bit->0:125 Æ
C
Range: -100
Æ
C-+412 Æ
C
DataSampling Interval10min
Sensorlo cation: approx. 3mab ove icesurface
VerticalDierence
Resolution: 8bit ->0:0625 Æ
C
Range: -5.125
Æ
C-+10.8125 Æ
C
DataSampling Interval10min
Sensorlo cation: approx. 3mand1mab ove icesurface
Humidity
Company: Vaisala
Sensor: HMP35A
Accuracy 2%relhumidityintherangeof0-90%
3%relhumidityintherangeof90-100%
Resolution: 8bit ->0.4%
Range: 0-102%
DataSampling Interval20min
Sensorlo cation: approx. 3mab ove icesurface
Wind
Company: Belfort
Sensor: Mo del123aerovane
Accuracy Sp eed: +/-0.2kn
Direction: +/-5 Æ
Resolution: Sp eed: 8bit->+/-0.25ms 1
Direction: 8bit->+/-1.4 Æ
Range: Sp eed: 0-64ms
1
Direction: 0-356 Æ
Sensorlo cation: approx. 3mab ove icesurface
op eratingtime: 02.02.92- 05.03.95
lo cation: 72.879S19.021WElevationa.s.l. 35m
manufacturer: Defense SystemsInc.,USA
sensors
AirPressure
Company: Paroscientic,Inc.
Sensor: Digiquartz transducer215AT-073
Accuracy +/-0.2hPa
Resolution: 10bit ->0.1hPa
Range: 920-1022.3hPa
DataSampling: Cycle: 4s,Average12min
Interval12min
Sensorlo cation: approx. 1.5mab oveicesurface
AirTemp erature
Company: Yellowsprings
Sensor: 44020
Accuracy +/-0:2
Æ
C
Resolution: 8bit->0:2 Æ
C
Range: -44
Æ
C-+6 Æ
C
DataSampling: Cycle: 4s,Average12min
Interval12min
Sensorlo cation: 1. Sensor approx. 5mab oveicesurface
2. Sensor approx. 3mab oveicesurface
Wind
Company: R.M.Young
Sensor: Wind Monitor-RE
Accuracy Sp eed: +/-0.2kn
Direction: +/-5 Æ
Resolution: Sp eed: 8bit->+/-0.5kn
Direction: 8bit->+/-1.406 Æ
Range: Sp eed: 0-127.5ms 1
Direction: 0-360 Æ
DataSampling: Cycle: 4s,Average12min
Interval12min
Sensorlo cation: approx. 5mab oveice surface
Snow Height
Company: Campb ellScienticCorp.
Sensor: UltrasonicDepth Gauge,UDG01
Accuracy +/-1cm
Resolution: 15bit ->0.125cm
Range: 0-265cm
DataSampling: Cycle: 15s,Average12min
Interval12min
op eratingtime: 24.01.99-
lo cation: 72.864S19.065WElevationa.
s.l. 35m
manufacturer: Sellmann&Kruse GbR,Germany
sensors
Air Pressure
Company: Paroscientic, Inc.
Sensor: Digiquartztransducer 216B
Accuracy +/-0.2hPa
Resolution: 10bit->0.1hPa
Range: 920-1022.3hPa
DataSampling: Interval200s
Sensor lo cation: approx. icesurface
Air Temp erature
Company: R.M.Young
Sensor: Mo del41342(PTA1000)
Resolution: 10bit->0:1 Æ
C
Range: -50
Æ
C-+50 Æ
C
DataSampling: Interval200s
Sensor lo cation: 1. Sensor approx. 5mab oveice surface
2. Sensor approx. 2mab oveice surface
Humidity
Company: Vaisala
Sensor: HMP35A
Accuracy 2%relhumidityintherangeof0-90%
3%relhumidityintherangeof90-100%
Resolution: 8bit ->0.4%
Range: 0-102%
DataSampling Interval200s
Sensor lo cation: approx. 5mab ove icesurface
Wind
Company: R.M.Young
Sensor: WindMonitor-RE
Resolution: Sp eed: 8bit->+/-0.16ms 1
Direction: 8bit->+/-1.406 Æ
Range: Sp eed: 0-40.8ms 1
Direction: 0-358.33 Æ
DataSampling: Cycle: 4s,Average12min
Interval200s
Sensor lo cation: approx. 5mab ove icesurface
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Mo del,Antarctic Sciences,6,115-122,1994.
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Parish,T.R.,SurfacewindsovertheAntarcticcontinent: Areview,Reviewof
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Parish,T.R. andD.H.Bromwich,Continent-scalesimulationofthekatabatic
windregime,Journal ofClimate,4,135-146,1991.
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