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www.umbc.edu
DeterminationofMixingLayerHeightandASOS:
Testbed,AlgorithmsandNetwork
RubenDelgado,BelayDemozAtmosphericLidarGroup
JointCenterforEarthSystemsTechnologyUniversityofMaryland,BaltimoreCounty
InternationalCooperativeforAerosolPrediction(ICAP)8th WorkingGroupMeeting:
LidarDataandItsUseinModelVerificationandDataAssimilationJuly13,2016
www.umbc.edu
•Inversionalgorithms,optical,chemicalandphysicalpropertiesofatmosphericaerosols,gases,andclouds.
•BoundaryLayerDynamics(AirQualityandWindEnergy)
•ContinentalandintercontinentalplumetransporttoEasternUSandCaribbean.
•AOD-PM2.5EstimatorDevelopmentfromGround,SatelliteObservations,NWFandGlobalModels
•Newremotesensingtechnologiesforatmosphericobservations.
UMBCAtmosphericLidarGroupResearchAreas
www.umbc.edu
MixingLayerHeight(MLH)
• Diagnosticvariableatmospherictransportanddispersionforecastingmodels.
• WithoutrealisticMLHmodelshavelargeerrorsthatresultininadequatepublicprotectionagainstunhealthyairquality.
• NationalResearchCouncilhasrecommendeda“networkofnetworks”1• After60yearsofremotesensingresearch,itisastoundingthatthePBLisnotmeasuredregularlythroughoutitsdiurnalcycle
1- NRC.2009.ObservingWeatherandClimatefromtheGroundUp:ANationwideNetworkofNetworks.Washington,DC:NationalAcademyPress.
www.umbc.edu
June2015CanadianSmokeEvent
Elastic lidar backscatter image shows aerosolsaloft (1.5-3 km) on June 10th. The particles beganto mix causing increased near surface particlepollution.
The 11th shows a homogenous layer, smokemixed with the mixing layer which extends up to1.5 km.
UMBCSmogBlog:http://alg.umbc.edu/usaq
www.umbc.edu
MLHAlgorithms
• LidarandwindprofilersMLHcanprovidecontinuoustemporalresolutionatmosphericprofilesforverificationandvalidationofforecastsandmodels,onwhetherthephysicsanddynamicspackagesarecorrectinmodels.
*Compton etal.(2013),J.Atmos.Ocean.Tech.,doi:10.1175/JTECHD-12-00116.1
www.umbc.edu
JointNOAA/ARLNOAA/NCEPFieldStudy- September,2009
1-Develop an urban meteorological evaluation database to investigate theevolution and spatial variability of the urban atmospheric boundary layer mixingheight.
2-Evaluate various instrument platforms for detecting mixed layer height.
3- Accurate assessment of boundary layer information at finer scales shouldimprove the Nation's ability to assess the effects of a toxic release (in support toHomeland Security).
*Project supported demonstration of NOAA's Real-Time Mesoscale Analysis(RTMA) of PBL information for use by plume dispersion modelers.
www.umbc.edu
JointNOAA/ARLNOAA/NCEPFieldStudy
LidarmeasurementshelpedtoidentifyproblemswithautomaticPBLHcalculationfromaircraftprofiles(ACARS).
www.umbc.edu
DISCOVERAQSummer2011@Beltsville,MD
CL51Ceilometer MicroPulse Lidar(MPL) HowardRamanLidar
Laser:InGaAsPower:8.9mWWavelength:910nmAlgorithm:BL-View
Laser:Nd-YLFPower:25mWWavelength:532nmAlgorithm:Wavelet
Laser:Nd-YAGPower:8WWavelength:355nmAlgorithm:Wavelet
AlgorithmComparison
www.umbc.edu
RecommendationofCeilometerPBLHeightsforAssimilation/VerificationofForecastProducts
UMBC: Belay Demoz, Ruben Delgado, Kevin Veermesch; Howard University: RicardoSakai; NWS: Dennis Atkinson, Michael Hicks, Jason Chasse (Program Manager NextGenAviation Weather at NOAA/NWS/ OS&T)
• UMBC algorithm being used to retrieve MLH from the NWS Vaisala’s CL31 ceilometers, as partof a Proof of Concept CL31 Test bed.
• The algorithm development for MLH from CL31 ceilometers to be implemented at nationwideASOS sites, as support of scientific efforts of the NWS Sterling Field Support Center.
www.umbc.edu
DeterminationofPlanetaryBoundaryLayerHeightwithDopplerWindLidar
QinLiu1,BrianCarroll1,ThomasRieutord2,AlanBrewer3,AdityaChoukulkar3,RubenDelgado1
1UniversityofMaryland,BaltimoreCounty,Baltimore2Météo-France,Toulouse,France
3National Oceanic and Atmospheric Administration
• CollaborationwithNOAAESRL.• Thepurposeofthisstudyistoevaluatetheplanetaryboundarylayer
heightretrievalsfromDopplerwindlidars.• Analysiswasappliedtodatacollectedfromthetwolidar systems
duringtheJuly-August2014DiscoverAQandLUMEXcampaigns.• Thiscomparisonaidsapplicationsinairqualityandwindenergy
forecasting.
www.umbc.edu
PeakDetectionMethodUsingHaarWaveletTransform
BowtieandVerticalScanrange-correctedintensityprofilesandhorizontalwindspeedanddirection.
www.umbc.edu
K-meansAlgorithm• Initializetheseeds(clusters)• Calculatethedistancefromeachpointtoeachcluster• Assigneachpointtotheclosestcluster• Redefinetheclustersasthecentroidofpointsassigned• Repeattheprocessuntiltheintra-clustervariancenolonger
decreases
InitialConditions• Twoclustersused,assigntophalfoftheprofiletooneclusterand
lowerhalftotheothercluster
ClusterAnalysis
www.umbc.edu
ConvergentTest
• CalculateEuclideandistancebetweeneachpointtotheclusterandintra-clustervariance
• Thealgorithmstopswhentheintra-clustervariancearenolongerdecreasing• TheMLHisdefinedastheheightwheretheclustertransitions
www.umbc.edu
BadGood
• Pros:fasterthanrandomseeding,resultsareconsistenteverytime• Cons:resultisnotaccurateifthere’smissingdatainonesingleprofile• ValidationandSensitivityofAlgorithmiscurrentlyevaluatedwithPECAN
Elastic,Raman,DopplerLidar,MicrowaveRadiometerandSoundingsdatasets
ClusterAnalysis
www.umbc.edu
Update:SavingCeilometerDatafromthe
AutomatedSurfaceObservingSystem(ASOS)
www.umbc.edu
Ceilometers!!!
ThermodynamicProfilingTechnologiesWorkshop12-14April,2011
NRCstudy:ObservingWeatherandClimatefromtheGroundUp:ANationwideNetworkofNetworks(2009)
Regional Testbed
ASOSData:Motivation
16
www.umbc.edu17
• GOAL:DescribehowASOSceilometerbackscatterdatawouldbeusedifNWScouldprovideit.
• WhatvaluewouldthedataprovidetotheNation?Neededtoanswerthefollowingquestions:
– WhatdataareavailablefromtheCL31?– Whatisthequalityofthedata?– Howoftenisthedataavailable?– Howwouldthedatabesavedwithoutoperationalinterference.
• Listtheavailableapplicationsforbackscatterdata• Describetheresearchthatisunderwayorrequired• Listchallengesforresearch-to-operations(RTO)• Chartacourseofactiontoachievegoals
ASOSCeilometerWorkshop:NWS/Sterling,VA;March22,2012
www.umbc.edu
Chet Schmitt (OPS 22): ASOS Ceilometer Workshop, NWS/Sterling, VA. March 22, 2012
ASOSCeilometerSites
18
www.umbc.edu
ASOS(Instrumentation/Issues)
19
Problem-2:Limited bandwidth transmission to main frame ASOS computer CT12K CL31
Problem-1:Quality of the lidars Problem-3:
• Inertia• “Operational”!
ß Each ASOS
www.umbc.edu
StepsRequiredBeforeWeCanStart…ASOSCL31DataPollingatNWS- Sterling,VAStep1:CollectandevaluateCOTSceilometer’sprofiledatainalocalnetwork[Completed].
Step2:EvaluatemethodsofPollingASOSceilometersforprofiledatawithoutinterferingwithASOSfunctions
Ceilometerprofilesat1minresolutionwerecollectedformonthsusingadatalogger
20
Nointerferenceobservedthatcouldbetracedtotheinstallationofthedataloggerontheceilometer!
www.umbc.edu
TwoExamples:• PM-studies:ScalingSatellite-measuredAODandPM-Correlations• Nighttimeconvection: PECANexperiment[Elevatedstorm]
Morecasesstudies:•LimitednetworkofCeilometer: Baltimore-Washington-area-Network•CL31vsCT12VsCL51:Anexampleofcomparativedata•CL31datastatistics:Cloudbaseabove12000ft needstobereported•PBLstudy: PBLfromCL31:Multi-algorithmcomparison
MoreonAirQualityApplications•FireandAirquality:Thecaseof9-10June2015•Volcanicashmonitoring: HowcouldASOShelp?
21
CL31:CaseStudiesList
www.umbc.edu
UMBCMPLCL31
CHM 15k
HUCHM 15kCL31 &51
CT12
NWSCL31MPL
NCWCPCL31 ES
CT12
HUCT12
SERCCT12
GSFCMPL
SigmaMPL
22
CurrentCL31PBLProjectSitesMap• MLHprocessingassessmentandevaluationinprogress.CollaborationwithNASAMPLNeT (E.Welton andJ.Lewis)
• UMBCSmogBlog:MonitoringofMLandotherAQissues
• Ceilometerinter-comparisoninprogress• NWSengagementinASOS
www.umbc.edu
• First1-2signalreturnsintheCL-31profilecanbeusedtoinferPMloading.Clearsky,RH<62%used[Munkel etal.(2007):Vermeesch etal.(2011),others]
• “Correctandscale”satelliteopticaldepthmeasurementsforAQstudies[Chuetal.,2013- DISCOVER-AQsite;]– Lietal.2016usedCT25atBeltsvilleBeltsville,Maryland,Vermeeschetal.,2011
23
PossibleASOSuseinAQ
www.umbc.edu
PECAN:CL31NetworkDemonstrationforSevereStormResearch
24
CL31RS-232
DatacollectionRaspberryPI(Linux
board)Cellnetwork
DataProcessing,storageandvisualization
A 24/7 real-time access/op
HU - CL31
Other - CL31
• Plains Elevated Convection At Night http://catalog.eol.ucar.edu/pecanA multi-agency, multi-university field observation over Kansas to investigate the sources of nightime summer elevated convection.
• CL31 (4) were used for realtime network demonstration (see figure below)
• NWS SOO and field sites from Dodge City and Wichita, Kansas were collaborators and allowed siting of two of the CL31s.
• Data collection algorithm and electronics developed and tested.
www.umbc.edu
PECAN:3June2015BoreCase
Pictures taken at FP2
SPOL
DDCFP2 ICT
• Undular Bores are one of the suspected event thattransport moisture upward priming the nightimeatmosphere for destabilization and severe storms.
• An accurate statistics of occurrence and observation islacking, hence PECAN. This is bore case observed earlyon 3 June 2015, during PECAN.
• The CL31 network reveals the spatial evolution andduration of this bore.
www.umbc.edu
PECAN: 3June2015BoreCase
26
CL31 network data from PECAN – no operational instrument is capable of capturing this event in such detail. Equivalent ASOS data is plotted, showing data lost. Analysis of these data sets is ongoing in PECAN.
What we measure
What is saved by ASOS
www.umbc.edu
ASOSprojectMilestonesandFuture• CL31PBLProofofConceptcompleted• Managementapprovaltoproceed• DatacollectionfromASOSdemonstrated• CaseStudiesCompleted
– PBL,PECAN,Fireetc,(severestorm)– demonstrationnetworkcompleted
• Morecasestudy/dataanalysis• WorkingonWMOVolcanicAshexpertteam• BAMSpaperindraft
• AlgorithmAssessment/TestinginASOSOperationalEnvironmentComplete(plannedDecember2017)
• AlgorithmIncorporatedintoASOS*(plannedJune2018)*dependentuponASOSACU/DCPupgradecompletion
Com
plet
ed
InProgress
Futu
re
www.umbc.edu
EndofPresentation
Thankyou!
www.umbc.edu
Extra
www.umbc.edu
Instrumenttypes:CL31,CT12K,CL51
CL31
CT12
CL51
BACK
Currentinstrumentintest• Lufft:CHM15K(UMBC,HU)• Vaisala:CL51,CL31,CT12K
OtherLidars:• HU-RamanLidar• ALVICE(NASARaman)
PlantoworkwithMPLnet (J.Welton onPBLandsuch)
www.umbc.edu
• CL31datastatistics:• Cloudheightstatisticsabove12Kft needstobereported
~30%- Not reported
31
CloudStatistics@12Kft+
BACK
www.umbc.edu
GoodComparisonDay:SuccessinmultiplelayerswithQIof3.
BadComparisonDay:NotePBLHmissingafter1200UTC.
32
BL-view/UMBC)
Selectioncriteriaforcomparison?
www.umbc.edu
PBL(LCLasfiler)
33
GoodPBLComparisonDay:• ExcellentmorningPBLevolution• LCLfilter removesday-nighttransitionsissues
BaddayComparisonDay:• PBL– definitionissue• LCLfilterdoesn’thelp
AmongthePBLMethodscompared:Liu&Liang,Hicksetal2015;Waveletbased;RiB – Richardsonnumber,HeffterBL-View(Vaisala)
“Bad Day” “Good Day”
www.umbc.edu
Summary:CL31comparisontableRoutine Pros Cons CommentsHicks •GoodformorningPBL
•LCLfilterhelpfulinpruning• NWSorigin
•Day-Nighttransachallenge•LCLfilterremoveselevatedNBL
PublishedinBLMHicksetal.,2015;Combinessomeoftheerror-function,Meteorology,andcanrunonarchive.
UMBC •PerformanceasHicksetal.;•Comparedtoradar-SNR•Comparedtoothers
• Day-Nighttransachallenge PublishedinComptonetal.,2013.localsourceandsimilartoHicksetal.AlsousedbyMDEetc.
BL-View • Runsinrealtimenow• welltested/robust(NWSSeattle,Vancouver,EU,etc)• designedfortheceilometer
•1-softwareto1-instrument• Notnetworkcapable• limitstheprofileto4.5km
Severalpapers.Commercialbacking.Costly,inrelativeterms,unlessnegotiated.
Recommendation:• AcombinationofUMBC/Hicksmethodsbeusedoncurrentdata.• Alow-cost,networkcapable,commercialsoftwareisdesirable.• Abilitytoprocessesreal-timeaswellasarchivedataisdesired.
34BACK
http://so2.gsfc.nasa.gov/pix/special/2009/redoubt/redoubt_all.html
HowcouldASOShavehelpedinVolcanicashstudies?
CouldtheCL31haveseentheash?
Stepsused:• OMI/CALIPSOforplumeboundary
• Estimateaerosol“loading”abovebackground.
• LocateifwithinCL31range
• Speculateifitwouldhavebeendetectedandmeasured.
OMI:ColumnSO2
Redoubt– PlumeofMarch23,2009
www.umbc.edu
CL31:FireandSmokepollutionhttp://alg.umbc.edu/usaq/
Black Carbon at the Beltsville site over the week leading up to and through the 6/11 event. Joel Dreessen -MDE-<[email protected]
Redoubt:Lostopportunity
• Plumeof03/23/2009wouldhavebeendetectedbyASOSlidars
• WouldhaveassistedNWS-Alaskaregioninmonitoring.
BACK
DISCOVERAQSummer2011@Beltsville,MD
CL51Ceilometer MicroPulseLidar(MPL) HowardRamanLidar
Laser:InGaAsPower:8.9mWWavelength:910nmAlgorithm:BL-View
Laser:Nd-YLFPower:25mWWavelength:532nmAlgorithm:Wavelet
Laser:Nd-YAGPower:8WWavelength:355nmAlgorithm:Wavelet
AlgorithmComparison
CL31PBLReview
Summary:BLView avgdiff(m)
Hicksavgdiff(m)
UMBCavgdiff(m)
Method
STAB
LE
LiuLiang 710.327 12.066 1221.472RiB 770.776 173.336 1158.482Heffter 761.597 54.004 1243.862AVERAGE 768.476 123.15 1236.075
CONVE
CTIVE LiuLiang 196.37 -604.388 ****
RiB 527.195 -175.566 ****Heffter -299.849 -1151.683 ****AVERAGE 227.076 -732.185 ****
Lidar-sonde differences:
• BL-View data close to the sonde is chosen from the 3-choices.
• UMBC/Hicks lowest reported is chosen
• Explore smarter method for comparing algorithms
NB: PBLH comparisons under ideal conditions – revealed +/- 200m (IHOP2002)
39
Peak-basedThreshold
TheMLHisdefinedasthehighestpointconnectedtothegroundintheprofile