Determination of Mixing Layer Height and ASOS:...

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DeterminationofMixingLayerHeightandASOS:

Testbed,AlgorithmsandNetwork

RubenDelgado,BelayDemozAtmosphericLidarGroup

JointCenterforEarthSystemsTechnologyUniversityofMaryland,BaltimoreCounty

InternationalCooperativeforAerosolPrediction(ICAP)8th WorkingGroupMeeting:

LidarDataandItsUseinModelVerificationandDataAssimilationJuly13,2016

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•Inversionalgorithms,optical,chemicalandphysicalpropertiesofatmosphericaerosols,gases,andclouds.

•BoundaryLayerDynamics(AirQualityandWindEnergy)

•ContinentalandintercontinentalplumetransporttoEasternUSandCaribbean.

•AOD-PM2.5EstimatorDevelopmentfromGround,SatelliteObservations,NWFandGlobalModels

•Newremotesensingtechnologiesforatmosphericobservations.

UMBCAtmosphericLidarGroupResearchAreas

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MixingLayerHeight(MLH)

• Diagnosticvariableatmospherictransportanddispersionforecastingmodels.

• WithoutrealisticMLHmodelshavelargeerrorsthatresultininadequatepublicprotectionagainstunhealthyairquality.

• NationalResearchCouncilhasrecommendeda“networkofnetworks”1• After60yearsofremotesensingresearch,itisastoundingthatthePBLisnotmeasuredregularlythroughoutitsdiurnalcycle

1- NRC.2009.ObservingWeatherandClimatefromtheGroundUp:ANationwideNetworkofNetworks.Washington,DC:NationalAcademyPress.

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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

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MLHAlgorithms

• LidarandwindprofilersMLHcanprovidecontinuoustemporalresolutionatmosphericprofilesforverificationandvalidationofforecastsandmodels,onwhetherthephysicsanddynamicspackagesarecorrectinmodels.

*Compton etal.(2013),J.Atmos.Ocean.Tech.,doi:10.1175/JTECHD-12-00116.1

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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.

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JointNOAA/ARLNOAA/NCEPFieldStudy

LidarmeasurementshelpedtoidentifyproblemswithautomaticPBLHcalculationfromaircraftprofiles(ACARS).

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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

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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.

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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.

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PeakDetectionMethodUsingHaarWaveletTransform

BowtieandVerticalScanrange-correctedintensityprofilesandhorizontalwindspeedanddirection.

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K-meansAlgorithm• Initializetheseeds(clusters)• Calculatethedistancefromeachpointtoeachcluster• Assigneachpointtotheclosestcluster• Redefinetheclustersasthecentroidofpointsassigned• Repeattheprocessuntiltheintra-clustervariancenolonger

decreases

InitialConditions• Twoclustersused,assigntophalfoftheprofiletooneclusterand

lowerhalftotheothercluster

ClusterAnalysis

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ConvergentTest

• CalculateEuclideandistancebetweeneachpointtotheclusterandintra-clustervariance

• Thealgorithmstopswhentheintra-clustervariancearenolongerdecreasing• TheMLHisdefinedastheheightwheretheclustertransitions

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BadGood

• Pros:fasterthanrandomseeding,resultsareconsistenteverytime• Cons:resultisnotaccurateifthere’smissingdatainonesingleprofile• ValidationandSensitivityofAlgorithmiscurrentlyevaluatedwithPECAN

Elastic,Raman,DopplerLidar,MicrowaveRadiometerandSoundingsdatasets

ClusterAnalysis

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Update:SavingCeilometerDatafromthe

AutomatedSurfaceObservingSystem(ASOS)

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Ceilometers!!!

ThermodynamicProfilingTechnologiesWorkshop12-14April,2011

NRCstudy:ObservingWeatherandClimatefromtheGroundUp:ANationwideNetworkofNetworks(2009)

Regional Testbed

ASOSData:Motivation

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www.umbc.edu17

• GOAL:DescribehowASOSceilometerbackscatterdatawouldbeusedifNWScouldprovideit.

• WhatvaluewouldthedataprovidetotheNation?Neededtoanswerthefollowingquestions:

– WhatdataareavailablefromtheCL31?– Whatisthequalityofthedata?– Howoftenisthedataavailable?– Howwouldthedatabesavedwithoutoperationalinterference.

• Listtheavailableapplicationsforbackscatterdata• Describetheresearchthatisunderwayorrequired• Listchallengesforresearch-to-operations(RTO)• Chartacourseofactiontoachievegoals

ASOSCeilometerWorkshop:NWS/Sterling,VA;March22,2012

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Chet Schmitt (OPS 22): ASOS Ceilometer Workshop, NWS/Sterling, VA. March 22, 2012

ASOSCeilometerSites

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ASOS(Instrumentation/Issues)

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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

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StepsRequiredBeforeWeCanStart…ASOSCL31DataPollingatNWS- Sterling,VAStep1:CollectandevaluateCOTSceilometer’sprofiledatainalocalnetwork[Completed].

Step2:EvaluatemethodsofPollingASOSceilometersforprofiledatawithoutinterferingwithASOSfunctions

Ceilometerprofilesat1minresolutionwerecollectedformonthsusingadatalogger

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Nointerferenceobservedthatcouldbetracedtotheinstallationofthedataloggerontheceilometer!

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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?

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CL31:CaseStudiesList

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UMBCMPLCL31

CHM 15k

HUCHM 15kCL31 &51

CT12

NWSCL31MPL

NCWCPCL31 ES

CT12

HUCT12

SERCCT12

GSFCMPL

SigmaMPL

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CurrentCL31PBLProjectSitesMap• MLHprocessingassessmentandevaluationinprogress.CollaborationwithNASAMPLNeT (E.Welton andJ.Lewis)

• UMBCSmogBlog:MonitoringofMLandotherAQissues

• Ceilometerinter-comparisoninprogress• NWSengagementinASOS

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• 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

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PossibleASOSuseinAQ

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PECAN:CL31NetworkDemonstrationforSevereStormResearch

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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.

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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.

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PECAN: 3June2015BoreCase

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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

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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

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EndofPresentation

Thankyou!

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Extra

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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)

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• CL31datastatistics:• Cloudheightstatisticsabove12Kft needstobereported

~30%- Not reported

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CloudStatistics@12Kft+

BACK

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GoodComparisonDay:SuccessinmultiplelayerswithQIof3.

BadComparisonDay:NotePBLHmissingafter1200UTC.

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BL-view/UMBC)

Selectioncriteriaforcomparison?

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PBL(LCLasfiler)

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GoodPBLComparisonDay:• ExcellentmorningPBLevolution• LCLfilter removesday-nighttransitionsissues

BaddayComparisonDay:• PBL– definitionissue• LCLfilterdoesn’thelp

AmongthePBLMethodscompared:Liu&Liang,Hicksetal2015;Waveletbased;RiB – Richardsonnumber,HeffterBL-View(Vaisala)

“Bad Day” “Good Day”

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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

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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-<joel.dreessen@maryland.gov

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)

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Peak-basedThreshold

TheMLHisdefinedasthehighestpointconnectedtothegroundintheprofile