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Assimilation of Radar Information in the Alpine Model of MeteoSwiss. Daniel Leuenberger and Andrea Rossa MeteoS wiss. Introduction. Radar information is gaining importance in mesoscale data assimilation Latent Heat Nudging (LHN): Assimilation method for precipitation information - PowerPoint PPT Presentation
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Radar in aLMo
Assimilation of Radar Information in the Alpine Model of MeteoSwiss
Daniel Leuenberger and Andrea RossaMeteoSwiss
Dani
el.L
euen
berg
er@
Met
eoSw
iss.c
hSR
NWP
– COS
T-71
7 Lis
bon,
8.O
ctob
er 2
003 2
Radar in aLMoIntroductionRadar information is gaining importance in mesoscale data assimilationLatent Heat Nudging (LHN): Assimilation method for precipitation informationTrigger model precipitation where radar detects precipitation (heating), supress it elsewhere (cooling)4DDA, yet computationally very efficientConceptionally simpleAssimilation of non-prognostic variables not straight forwardHeuristic approach of weighting observations and model
Dani
el.L
euen
berg
er@
Met
eoSw
iss.c
hSR
NWP
– COS
T-71
7 Lis
bon,
8.O
ctob
er 2
003 3
Radar in aLMoRadar ObservationsSwiss radar network: 3 C-Band Doppler RadarsBest estimate of surface rain: preprocessed (e.g. clutter reduction, vertical profile corrections)Resolution: 2 x 2 km2, 5 min
Radar Quality Map
Dani
el.L
euen
berg
er@
Met
eoSw
iss.c
hSR
NWP
– COS
T-71
7 Lis
bon,
8.O
ctob
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003 4
Radar in aLMoReal Case StudyCase
System of severe convection over SwitzerlandTriggered around 22h30 UTC over the Massif CentralDevelopment ahead of weak cold frontModerately unstable environment as observed by the Swiss radiosonde Payerne at 00UTC (CAPE ~250 J/kg)Strong wind shear (~ 30 m/s at 6000m)
SimulationsOperational Alpine Model (aLMo) of MeteoSwiss (x=7km)Convection ParametrizationStarted 21.8.00 00 UTC from GME of DWDCTRL (no forcing)LHN during 6hLHN+ during 3h, free forecast afterwards
Dani
el.L
euen
berg
er@
Met
eoSw
iss.c
hSR
NWP
– COS
T-71
7 Lis
bon,
8.O
ctob
er 2
003 5
Radar in aLMo
010203040506
CTRL LHN Radar
Case Study of the 21.8.2000 StormHourly Sums of Precipitation: Forcing during 6h
Dani
el.L
euen
berg
er@
Met
eoSw
iss.c
hSR
NWP
– COS
T-71
7 Lis
bon,
8.O
ctob
er 2
003 6
Radar in aLMo
01020304
1h Free Forecast
05
2h Free Forecast
06
3h Free Forecast
Case Study of the 21.8.2000 StormHourly Sums of Precipitation: Forcing during 3h
CTRL LHN+ Radar
Dani
el.L
euen
berg
er@
Met
eoSw
iss.c
hSR
NWP
– COS
T-71
7 Lis
bon,
8.O
ctob
er 2
003 7
Radar in aLMoFindings IModel is able to assimilate radar observationsGood impact in analysis, sfc winds in line with observationsSome impact in free forecast up to 03hModel loses information quickly, i.e. storm dies too earlyWhy is the model not able to maintain storm?
Environment not representative?Model resolution ?
Try to find reasons by means of idealized simulations
Dani
el.L
euen
berg
er@
Met
eoSw
iss.c
hSR
NWP
– COS
T-71
7 Lis
bon,
8.O
ctob
er 2
003 8
Radar in aLMoIdealized SimulationsSetup
Environment from Payerne sounding of 21.8.00 00UTCFine mesh (x = 1km), no CPS, no soil model, no radiationTrigger convection with warm bubble: No storm development
wind shearPayerne profile
KW profile
Payerne sounding
KW sounding
Klemp Wilhelmson EnvironmentLarge amount of CAPE (~ 1200 J/kg)Moderate wind shearFavorable for splitting supercell storms
Dani
el.L
euen
berg
er@
Met
eoSw
iss.c
hSR
NWP
– COS
T-71
7 Lis
bon,
8.O
ctob
er 2
003 9
Radar in aLMoOSSE SetupReference run
Convection initiated with warm bubbleModel sfc rain serves as „artificial radar observations“
LHN AnalysisSame environment as reference runNo warm bubble initiationLHN during 3h (artificial rain rates from reference run)
LHN ForecastLHN during first 30, 60 min Free run afterwards
Dani
el.L
euen
berg
er@
Met
eoSw
iss.c
hSR
NWP
– COS
T-71
7 Lis
bon,
8.O
ctob
er 2
003 10
Radar in aLMoInsertion Frequency of Precipitation Input
LHN linearly interpolates between subsequent observationsExamine relevance of insertion frequency t to LHN Analysis
t = 10minlinear interpolation
t = 1min
Dani
el.L
euen
berg
er@
Met
eoSw
iss.c
hSR
NWP
– COS
T-71
7 Lis
bon,
8.O
ctob
er 2
003 11
Radar in aLMoLHN Analysis (LHN during 3h)
“OBS“ t = 10min
t = 4mint = 1min
Dani
el.L
euen
berg
er@
Met
eoSw
iss.c
hSR
NWP
– COS
T-71
7 Lis
bon,
8.O
ctob
er 2
003 12
Radar in aLMoDomain Sum of LH Nudging Increment
Dani
el.L
euen
berg
er@
Met
eoSw
iss.c
hSR
NWP
– COS
T-71
7 Lis
bon,
8.O
ctob
er 2
003 13
Radar in aLMoLHN Forecast (t = 1min)
“OBS“
Free forecast after 30 min
Analysis (LHN during 3h)
Free forecast after 1h
Dani
el.L
euen
berg
er@
Met
eoSw
iss.c
hSR
NWP
– COS
T-71
7 Lis
bon,
8.O
ctob
er 2
003 14
Radar in aLMoSensitivity to Horizontal Grid Spacing
Analysis (LHN during 3h)Free forecast after 1h
x = 2km
x = 5km
Dani
el.L
euen
berg
er@
Met
eoSw
iss.c
hSR
NWP
– COS
T-71
7 Lis
bon,
8.O
ctob
er 2
003 15
Radar in aLMoFindings IILHN capable of analysing and initiating supercell stormGood temporal sampling of the observed phenomena is importantRepresentative large-/mesoscale environment importantEven a poorly resolved forcing is able to initiate and maintain storm evolution in appropriate environmentSupercell storm very stable dynamics: are findings ‚portable‘ to other situations?
Dani
el.L
euen
berg
er@
Met
eoSw
iss.c
hSR
NWP
– COS
T-71
7 Lis
bon,
8.O
ctob
er 2
003 16
Radar in aLMoOutlookReal-case study
Reduction of grid-size to 2kmStudy impact of errors in radar dataMore cases
Idealized OSSESensitivity of vertical forcing distributionAssimilation of model 3D latent heating fieldsAssimilation of horizontal windsConsider case which is less driven by dynamics
Dani
el.L
euen
berg
er@
Met
eoSw
iss.c
hSR
NWP
– COS
T-71
7 Lis
bon,
8.O
ctob
er 2
003 17
Radar in aLMo
Thank you for your attention !
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