DetectingdeepmoistconvectionwithSentinel2

StavrosDafis,PhDstudentPolytechnicSchoolofParis,LaboratoryofDynamicMeteorology(LMD),France

NationalObservatoryofAthens(NOA-IERSD),Greece

Deep moist convection (DC) is associated with hazardous weather phenomena such astorrentialrainfallandflashfloods,severeconvectivewindgusts,largehailandtornadoes.Thelatentheatreleaseinsidedeepconvectivecloudsplayacrucialroleinseveralphenomena,forexample,theintensificationofhurricanesandcyclonesingeneral.SeveralstudieshaveshownthatDCandovershootingcloudtopspenetratetheloweststratosphereandallowtheexchangeofgasesfromthetropospheredeepintothestratosphere.Foralltheaforementionedreasons,IbelievetheSentinelconstellationofsatellitesprovideauniqueopportunitytomonitorDCaroundtheglobe,regardlessthegroundemissivitywhichisamajorproblem inother remotesensing techniquesusing themicrowaveand infraredspectrum.Inaddition,infraredchannelsfromgeostationarysatellitesmayprovidemisleadinginformation about the actual areas coveredwithDC due to high-level clouds (e.g., Cirruscanopy),letalonethelowerspatialresolutioncomparedtoSentinel.IhavedevelopedaverysimplescriptfordetectingDCwiththeSENTINEL-2L1CdatasetswhichcanbemodifiedfortheL2AandSentinel3datasetsaswell.IwilldescribetheperformanceofthealgorithminacasestudyinGreeceinApril2019.ThereisapotentialforthisscripttobefurtheroptimizedandevaluatedinthenearfutureandIwouldbeinterestedtouseexternalsourcestoprovidethebestpossibleresults.Themaincodeofthescript:

-----------------------------------------------------------------------function S (a , b) { return a - b }; let gain = 2.5; var MIDCL = S(B08, B09) var DC = S(B10, B12) var LOWCL = S(B11, B10) return [MIDCL, DC, LOWCL].map(a => gain * a);-----------------------------------------------------------------------

withMIDCLrepresentingmid-levelcloudiness(cumuluscongestus,altostratus,nimbostratus,etc)andthincirrus,LOWCLshowinglow-levelcloudiness(shallowcumulus,stratocumulus,stratus,etc)andDCshowingareaswithdeepconvectivecloudsatdifferentstages.WhenwedetectDCweexpectheavyrainfallbelow,andhighprobabilityofsevereweather(hail,severewindsand/ortornadoes).Ihavechosenacasestudy(Fig.1&2)forperformingasimpleverificationofthealgorithmbyusingthecompositereflectivityoftwogroundradarsinGreeceon14April2019(Fig.3).

Fig.1.TheoutputoftheDCalgorithmforNorthGreece(lat=40.1961&lng=21.8860)on14April2019at09:29UTC,showingwithgreencolortheareascoveredwithdeepconvection(thunderstorms),withredmid-levelclouds(precipitatingornot)andwithmagenta/bluelow-levelcloudiness.On14April2019,thesynopticandthermalliftofunstableairmassesoverGreececreatedseveralthunderstorms,someofthemproducingexcessiverainfallandhail.Sentinel2passedwestofGreeceat09:29UTCprovidingagoodsampleofthethunderstormsinNorthGreece(Fig.1&2).InFig.3theradarsof3-ΔcompanywhichisresponsibleforthehailsuppressionoperationsinGreece,showedhighreflectivityvalueswhereDCwasdetectedfromSentinel.

Fig.2.TheoutputoftheDCalgorithmforNorthGreece(Chalkidiki-40.2486&lng=23.1576)on 14 April 2019 at 09:29 UTC, showing with green color the areas covered with deepconvection (thunderstorms), with red mid-level clouds (precipitating or not) and withmagenta/bluelow-levelcloudiness.ByusingthedifferencebetweenthechannelsB10andB12wecanhaveinformationaboutDC andwe can discriminate cirrus clouds at the cloud tops of thunderstorms from thosedevelopingelsewherebyusingthedifferencebetweenchannelsB09andB08,whichshowalsothemiddle-levelcloudiness(Fig.2–eg.Nikitiatthecenterofthefigure).TheB10andB12 channels give information about very cold cloud tops in the upper troposphere,containinglargequantitiesofice,whilethechannelsB09andB08giveusinformationaboutbothiceandsnowinmid-troposphere.

Fig. 3. Composite radar reflectivity (dBz) inNorthGreeceon 14April 2019 at 09:28UTC,showingwithgreencolorprecipitatingareas(lightrain/drizzle),withblueintenserainfall,andwith orange/pink/red areaswith heavy rain and/or hail. The red box corresponds to thedomainsinFig.1andtheblueboxtothesamedomaininFig.2with1-minutelag.IfweonlyneedtoshowDC,amaskisprovidedbelowwithonlythedetectedDCpixels(eg.Fig.4):functionS(a,b){returna-b};letgain=3.0;varMIDCL=[0.8,0.1,0.1];varDC=S(B10,B12)varLOWCL=[1.0,0.2,0.4];return[MIDCL,DC,LOWCL].map(a=>gain*a);

Fig.4.AmaskfortheDCalgorithmforNorthGreece(lat=40.1961&lng=21.8860)on14April2019at09:29UTC,showingwithgreencoloronlytheareascoveredwithdeepconvection(thunderstorms).ThedomainisalmostsimilarasinFig1andtheredboxinFig.3.Asmentionedearlier,thereisagreatpotentialtodevelopamorerobustalgorithmdetectingDC and precipitating areas using Sentinel data. Such a product will be invaluable fornowcastingsevereweathereventsandwillcomplementotherremotesensingdatasetsfromspaceborneplatformswhichhavemuchlowerspatialresolution.Thankyoufortakingmyparticipationintoconsideration.