LASSOmodeling&measurementsupdateLESARMSymbio:cSimula:on&Observa:onWorkflow

WilliamIGustafsonJr.(PI)1,AndyVogelmann(Co-PI)2,ZhijinLi3,4,

XiaopingCheng3,SatoshiEndo2,TamiToto2,HengXiao1

JenniferComstock1

1PNNL,2BNL,3UCLA,4JPL

LASSOWebpage:h"p://www.arm.gov/science/themes/lasso

LASSOe-maillistsignup:h"p://eepurl.com/bCS8s5

TheLASSOVision

n  BridgingthescalegapbetweenARMobservaAonsandmodels

n  UseLEStoaddvaluetoARMobservaAonsn  Self-consistentrepresentaAonoftheatmosphere

n  ProvideadynamicalcontextfortheobservaAons

n  Elucidateunobservableprocesses&properAes

n  GenerateasimulaAonlibraryforresearchersn  EnablestaAsAcalapproachesbeyondsingle-casementality

n  ProvidetoolsnecessaryformodelerstoreproducetheLES

WhatcanyoudowithanObs+LESlibrary?

n  AsanobservaAonalistn  Informinstrumentremotesensingretrievalsn  ConductObservaAonSystemSimulaAonExperiments(OSSEs)n  TestimplicaAonsofradarscanstrategiesorflightpaths

n  AsatheoreAciann  GetesAmatesoffluxes&co-variabilityofvaluesn  TestrelaAonshipsw/ohavingtorunthemodelyourself

n  Asamodelern  KnowaheadofAmewhichdayshavegoodforcingn  Haveco-registeredobservaAonsathigh-resoluAonscalesn  HaveinputsandcorrespondingoutputstotestparameterizaAons

Learningtowalkbeforewerun

n  IniAalimplementaAonisaproof-of-conceptandtargetsshallowconvecAonatSGPn  DeveloptheARMinfrastructurefordoingrouAneLES

n  TestinterfacesandvisualizaAonmethodologiesforaccessingtheLASSOlibrary

n  Long-rangevisionistolaterexpandtomulApleARMlocaAonsandaddiAonalcloudregimes

TheroadtoLESatSGP

GoalofrunningrouAneLESforshallow

convecAonin2017

The2-yrLASSOpilotproject

n  Modularstructureforflexibilityandfuturegrowth

n  Majorfocusonge[ngfuncAonalityinplaceandimprovingitoverAmen  ImplemenAngmethods

thatalreadyworkn  NoAmetoexperiment

withuntestedmethodologies

SGPMegasiteSupportsIntegratedMeasurements-ModelStrategy

n  BoundaryLayerProfilingModules(4locaAons)n  UpgradestoAOSandRamanLidarInstrumentsn  Improvedsoilmoisturenetwork(17locaAons)n  PlanstodeploymulAplescanningcloudradars

SGPMegasiteConcept:anetworkofspaAallydistributedfaciliAesdesignedtocharacterizetheheterogeneityinatmosphericandlandproperAesaroundtheSGPsite.ThisnetworkaddressesarangeofsciencethemesusinganobservaAonsandmodelingstrategy.

MeasurementEnhancements:

TheSouthernGreatPlainsMegasite

Service Layer Credits:

!(

!(

!(

!(

%%%

$+

$+

$+$+

E9 -

Ashton,

KS. 386m

E11 -

Byron,

OK. 360mE12 -

Pawhuska,

OK. 331m

E13 - Central

Facility. 318m

E15 -

Ringwood,

OK. 418m

E31 -

Anthony,

KS. 412m

E32 -

Medford,

OK. 328m

E33 -

Newkirk,

OK. 357m

E34 - Maple

City,

KS. 417m

E35 -

Tryon,

OK. 294m

E36 -

Marshall,

OK. 340m

E37 -

Waukomis,

OK. 379m

E38 -

Omega,

OK. 367m

E39 -

Morrison,

OK. 279m E40 -

Pawnee,

OK. 247m

E41 -

Peckham.

341m

IF4 -

Billings, OK

IF5 -

Garber, OK

IF6 - Deer

Creek, OK

IF8 -

Tonkawa, OK

IF9 -

Billings, OK

IF10 -

Lamont, OK

IF7 -

Nardin, OK

IF12

IF11

¹

$+ IF Locations (Precipitation Radar)

% IF11/12 (Scanning Cloud Radar)

!( EF/Boundary Layer Profiling Site

! EF Locations

! IF Locations

70 km Radius Around CF

0 4020

km

167 - 200

201 - 220

221 - 240

241 - 260

261 - 280

281 - 300

301 - 320

321 - 340

341 - 360

361 - 380

381 - 400+

2011 USGS 10m DEMElevation in meters.

25km

75km

Boundarylayerprofilingsites

9

ImprovedRetrievalswithUncertaintyEs:mates

MWR3C

AERI DopplerWindLidar

MWR3CCadedduetal.

LWP

PWV

AERIoeTurneretal.

Temp

WVMR

DLPROFWINDNewsometal.

WindSpeed&Direc:on

ECOR

Latent&SensibleHeat

SKYRAD

Radia:veFluxes

EnhanceknowledgeofspaAalheterogeneityofstatevariables,fluxes,andsoilproperAes.

Situa:onally-dependentLES–not24/7

n  SimulaAonsdonewhencondiAonsaremetn  “RouAne”insteadof“operaAonal”n  1–2monthlagAmetoaccountforneededVAPsandcomputeAme

n  ConAngentonavailableobservaAons&presenceofShCun  NewcloudclassificaAonschemebySunnyLim

Low clouds Congestus Deep Conv. Altocumulus Altostratus Cirrostratus Cirrus

Ensembleofforcingdatasets

n  Employforcingensemblestoaddressforcinguncertainty

n  TesAng3forcingmethodsduringpilotphase

n  ARMconstrainedvariaAonalanalysis(VARANAL)

n  ECMWF-analysis–basedforcing

n MulA-scaledataassimilaAon(MS-DA)n  BasedonWRFwithGSIusingtwodifferenterrorcovariancescales

n  IniAallyusing3D-VarDAandplantotesthybridEnKFDA

Mul:-scaledataassimila:on

n  Startwithagriddedbackgroundfield,e.g.FNLn  IncorporateARMT,q,andwindprofilesfromboundary/intermediate

faciliAesn  Providesa3-Dvolumeevery3hoursforgeneraAngLESforcing

ARSCLCloudFrac:onCloudFrac:onfromΔx=2kmMS-DAAveragedover25km Averagedover75km

Averagedover150km Averagedover300km

Modelselec:on&configura:on

n  EvaluaAngbothSAM&WRFduringpilotphase

n  TesAngdoubly-periodic&nestedLESdomains

n  IniAallytargeAng25-kmdomain,Δx=100m

n  Currenttestswithmodeltopneartropopause

Databundles

n  DatabundleswillprovidetheprimaryinterfaceforLASSOdatan  ContainobservaAon&modeloutputincomparableforms

n  AppropriateAmesampling

n  Instrumentsimulators

n  Includepre-computedquick-looks,metrics,anddiagnosAcstoassistdatadiscoveryandquickenuseranalysis

n  Include3-Dmodelfields,profilestaAsAcs,model-basedbudgetterms,andGCMparameterizaAonterms.

n  ForcingsandiniAalcondiAonsprovidedforuserstoconducttheirownsensiAvitystudies.

OObbss

.. && CC

aassee

CCllaass

ssiifificcaa

ttiioonn LLEESS IInnppuuttss

&& OOuuttppuuttss

MMeettrriiccss && DDiiaaggnnoossttiiccss

Databundlemetrics

n  LESsimulaAonsassessedusingdiagnosAcsbasedonARMobservaAonsofcloudandenvironmentalproperAes

n  E.g.,Timeseries,Regressionanalyses,Taylordiagrams,Heatmaps,andPhasespacerelaAonships

n  SkillsscoresbasedonthediagnosAcswillquanAfythequalityofthesimulaAonsn  e.g.,LWP,cloudfracAon,temperature,watervaporconcentraAon,

relaAvecloudforcing,verAcalvelocity,etc.

n  Searchablemetricswillassistusersinfindingcasesofinterest.

Datadiscovery

n  SearchonmetricvaluesandselectedcondiAonsn  EvaluaAngwaystosearchbasedonmodel-observaAonagreementandcase

descriptors

n  TesAngmethodstopermitrobustsearchcapabiliAesandon-the-flyeventcomparisonn  E.g.,CassandraNoSQLdatabasen  TieredlevelsofcomplexitywithuserneedsandsophisAcaAon

Dataaccess&sojwaretools

n  Reproducibilityisparamountn Willprovideallworkflowsomware,modelcode,andconfiguraAonstoenableotherstoreproducetheruns

n  DesigningworkflowtorunonbothARMandotherDOEcompuAngfaciliAes

n  Goalofenablingeasierdataaccess/transferfromtheARMArchiveviaGlobus

LASSOTimeline

May2015 Pilotprojectbegan

June2016 IniAalShCusimulaAonsfromspring-summer2015madeavailable•  Ensembleofforcings•  LESsimulaAonsfromSAMandWRF(bulkmicrophysics)•  ObservaAonsincomparableform•  FirstcutatmetricsanddiagnosAcs

January2017 2ndbatchofShCusimulaAonsfromspring-summer2016•  Willincludeinfluenceofboundaryfacilityprofiles•  Bothbulkandspectral-binmicrophysicsversions

April2017 AddiAonaltestcasesforyear-roundshallowcloudcondiAonsBetasomwaresuiteRecommendedconfiguraAonsforongoingsimulaAons

May2017 PilotprojectoverandtransiAontorouAnesimulaAonmode

LearnmoreaboutLASSO

n  Breakoutsession,Wed.7:30–9:30p.m.n  Posters

n  145:Gustafson,TheLASSOWorkflowPilotProject

n  147:Endo,LASSOWorkflow:EnsembleforcingsandLESsensiAvity

n  146:Vogelmann,LASSOWorkflow:model-observaAon“datacubes”

n  148:Comstock,Boundarylayerprofilingmodules…

n  137:Lim,Developmentofcloud-typeclassificaAonalgorithms…

n  139:Kollias,RadarnetworkapproachtocharacterizeShCuatSGP

n  138:Krishna,Large-scaledataanalysisandvis.forARMusingNoSQL

n Website:h"p://www.arm.gov/science/themes/lasso

n  E-maillist:h"p://eepurl.com/bCS8s5