radar in almo assimilation of radar information in the alpine model of meteoswiss daniel leuenberger...
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Radar in aLMo
Assimilation of Radar Information in the Alpine Model of MeteoSwiss
Daniel Leuenberger and Andrea Rossa
MeteoSwiss
Danie
l.Le
uenberg
er@
Mete
oSw
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chSR
NW
P –
CO
ST-7
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Lis
bon,
8.O
ctober
20
032
Radar in aLMoIntroduction
Radar information is gaining importance in mesoscale data assimilation
Latent Heat Nudging (LHN): Assimilation method for precipitation information
Trigger model precipitation where radar detects precipitation (heating), supress it elsewhere (cooling)
4DDA, yet computationally very efficient
Conceptionally simple
Assimilation of non-prognostic variables not straight forward
Heuristic approach of weighting observations and model
Danie
l.Le
uenberg
er@
Mete
oSw
iss.
chSR
NW
P –
CO
ST-7
17
Lis
bon,
8.O
ctober
20
033
Radar in aLMoRadar Observations
Swiss radar network: 3 C-Band Doppler Radars
Best estimate of surface rain: preprocessed (e.g. clutter reduction, vertical profile corrections)
Resolution: 2 x 2 km2, 5 min
Radar Quality Map
Danie
l.Le
uenberg
er@
Mete
oSw
iss.
chSR
NW
P –
CO
ST-7
17
Lis
bon,
8.O
ctober
20
034
Radar in aLMoReal Case Study
CaseSystem 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
Danie
l.Le
uenberg
er@
Mete
oSw
iss.
chSR
NW
P –
CO
ST-7
17
Lis
bon,
8.O
ctober
20
035
Radar in aLMo
010203040506
CTRL LHN Radar
Case Study of the 21.8.2000 Storm
Hourly Sums of Precipitation: Forcing during 6h
Danie
l.Le
uenberg
er@
Mete
oSw
iss.
chSR
NW
P –
CO
ST-7
17
Lis
bon,
8.O
ctober
20
036
Radar in aLMo
01020304
1h Free Forecast
05
2h Free Forecast
06
3h Free Forecast
Case Study of the 21.8.2000 Storm
Hourly Sums of Precipitation: Forcing during 3h
CTRL LHN+ Radar
Danie
l.Le
uenberg
er@
Mete
oSw
iss.
chSR
NW
P –
CO
ST-7
17
Lis
bon,
8.O
ctober
20
037
Radar in aLMoFindings I
Model is able to assimilate radar observations
Good impact in analysis, sfc winds in line with observations
Some impact in free forecast up to 03h
Model loses information quickly, i.e. storm dies too early
Why is the model not able to maintain storm?
Environment not representative?
Model resolution ?
Try to find reasons by means of idealized simulations
Danie
l.Le
uenberg
er@
Mete
oSw
iss.
chSR
NW
P –
CO
ST-7
17
Lis
bon,
8.O
ctober
20
038
Radar in aLMoIdealized Simulations
Setup
Environment from Payerne sounding of 21.8.00 00UTC
Fine mesh (x = 1km), no CPS, no soil model, no radiation
Trigger convection with warm bubble: No storm development
wind shear
Payerne profile
KW profile
Payerne sounding
KW sounding
Klemp Wilhelmson Environment
Large amount of CAPE (~ 1200 J/kg)
Moderate wind shear
Favorable for splitting supercell storms
Danie
l.Le
uenberg
er@
Mete
oSw
iss.
chSR
NW
P –
CO
ST-7
17
Lis
bon,
8.O
ctober
20
039
Radar in aLMoOSSE Setup
Reference run
Convection initiated with warm bubble
Model sfc rain serves as „artificial radar observations“
LHN Analysis
Same environment as reference run
No warm bubble initiation
LHN during 3h (artificial rain rates from reference run)
LHN Forecast
LHN during first 30, 60 min
Free run afterwards
Danie
l.Le
uenberg
er@
Mete
oSw
iss.
chSR
NW
P –
CO
ST-7
17
Lis
bon,
8.O
ctober
20
03 10
Radar in aLMoInsertion Frequency of Precipitation Input
LHN linearly interpolates between subsequent observations
Examine relevance of insertion frequency t to LHN Analysis
t = 10minlinear interpolation
t = 1min
Danie
l.Le
uenberg
er@
Mete
oSw
iss.
chSR
NW
P –
CO
ST-7
17
Lis
bon,
8.O
ctober
20
03 11
Radar in aLMoLHN Analysis (LHN during 3h)
“OBS“ t = 10min
t = 4mint = 1min
Danie
l.Le
uenberg
er@
Mete
oSw
iss.
chSR
NW
P –
CO
ST-7
17
Lis
bon,
8.O
ctober
20
03 12
Radar in aLMoDomain Sum of LH Nudging Increment
Danie
l.Le
uenberg
er@
Mete
oSw
iss.
chSR
NW
P –
CO
ST-7
17
Lis
bon,
8.O
ctober
20
03 13
Radar in aLMoLHN Forecast (t = 1min)
“OBS“
Free forecast after 30 min
Analysis (LHN during 3h)
Free forecast after 1h
Danie
l.Le
uenberg
er@
Mete
oSw
iss.
chSR
NW
P –
CO
ST-7
17
Lis
bon,
8.O
ctober
20
03 14
Radar in aLMoSensitivity to Horizontal Grid Spacing
Analysis (LHN during 3h)Free forecast after 1h
x = 2km
x = 5km
Danie
l.Le
uenberg
er@
Mete
oSw
iss.
chSR
NW
P –
CO
ST-7
17
Lis
bon,
8.O
ctober
20
03 15
Radar in aLMoFindings II
LHN capable of analysing and initiating supercell storm
Good temporal sampling of the observed phenomena is important
Representative large-/mesoscale environment important
Even a poorly resolved forcing is able to initiate and maintain storm evolution in appropriate environment
Supercell storm very stable dynamics: are findings ‚portable‘ to other situations?
Danie
l.Le
uenberg
er@
Mete
oSw
iss.
chSR
NW
P –
CO
ST-7
17
Lis
bon,
8.O
ctober
20
03 16
Radar in aLMoOutlook
Real-case study
Reduction of grid-size to 2km
Study impact of errors in radar data
More cases
Idealized OSSE
Sensitivity of vertical forcing distribution
Assimilation of model 3D latent heating fields
Assimilation of horizontal winds
Consider case which is less driven by dynamics
Danie
l.Le
uenberg
er@
Mete
oSw
iss.
chSR
NW
P –
CO
ST-7
17
Lis
bon,
8.O
ctober
20
03 17
Radar in aLMo
Thank you for your attention !