Interoperability at INMExperience with the SREPS
system
J. A. García-MoyaNWP – Spanish Met Service INM
SRNWP Interoperability WorkshopECMWF – 14-15 January 2008
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Ensemble for Short RangeSurface parameters are the most important ones for weather forecast.Forecast of extreme events (convective precip, gales,…) is probabilistic.
Short Range Ensembles can help to forecast these events.Forecast risk (Palmer, ECMWF Seminar 2002) is the goal for both Medium- and, also, Short-Range Prediction.
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Errors of the short-range forecast
Due to model formulation.Due to simplifications in parameterisation schemes.Due to uncertainty in the initial state.
Special for LAMs, due to errors in lateral boundary conditions.Due to uncertainties in soil fields (soil temperature and soil water content, …).
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SREPS I
Multi-model approach (Hou & Kalnay 2001).Stochastic physics (Buizza et al. 1999).
Multi-boundaries:From few global deterministic models.From global model EPS (ECMWF).SLAF technique (Hou & Kalnay 2001).
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Multi-model
Hirlam (http://hirlam.org).
HRM from DWD (German Weather Service).
MM5 (http://box.mmm.ucar.edu/mm5/).
UM from UKMO (Great Britain Weather Service).
LM (COSMO Model) from COSMO consortium (http://www.cosmo-model.org).
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Multi-Boundaries
From different global deterministic models:ECMWFUM from UKMO (UK Weather Service)
GFS from NCEPGME from DWD (German Weather Service)
CMC (Canadian Met Centre, ongoing work)
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SREPS at INM
Mummub: Multi-model Multi-boundaries72 hours forecast two times a day (00 & 12 UTC).Characteristics:
5 models.4 boundary conditions.2 latest ensembles (HH & HH-12).
20 member ensemble every 12 hoursTime-lagged Super-Ensemble of 40 members every 12 hours.
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All models X bcs
Whole Area
Zoom over Spain
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Multi-boundariesWhat can we get from using multi-boundaries from deterministic high resolution global models?
Full advantage of Variational Data Assimilation Schemes in global modelsThe best possible data for boundary conditionsMore spread for the short-range than using one single-model EPS
What can we loose using data from different operational centres?
Daily transmission of very large amount of data (full resolution global model fields).Dependence of different priorities at the different operational centres.
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What do we need?
Operational priority at the centres.DWD versus UKMO
Development and maintenance of the interoperability software.Human resources for monitoring.Clear European Policy of commercial rights for using operational NWP data from other centres
Improved exchange of NWP data among Met Services in Europe
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Single BC’s Ensembles ( 4 members each)
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Single model Ensembles (4 members each)
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SLAF (Scaled Lagged Average Forecast)
Integration area 194x181x40.17.5 N – 62.5 N / 28.25 W – 20.0 E.0.25º lat x lon horizontal resolution.ECMWF forecast as boundary files.Forecast up to 48 hours every 6 hours.Test Period of 20 days: 2002120200-2002122418
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SLAFSLAF (AN K • ( AN - FCHH ), 0 k 1)
Four different Ensembles with 9 members each.EXP Initial Files Boundaries
SLAF1 Errors H+06 No perturb.SLAF2 Errors H+48 No perturb.SLAF3 No perturb. Errors H+48SLAF4 Errors H+48 Errors H+48SLAF5 Errors H+24 Errors H+24
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Errors H+06
SLAF (AN K • ( AN - FCHH ), 0 k 1)Experiments:
Control SFCAN = AN00SF1 and SF2 AN = AN00 1 • (AN00-FC06)SF3 and SF4 AN = AN00 3/4 • (AN00-FC12)SF5 and SF6 AN = AN00 1/2 • (AN00-FC18)SF7 and SF8 AN = AN00 1/4 • (AN00-FC24)
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Errors H+24
SLAF (AN K • ( AN - FCHH ), k=cte. )Experiments:
Control SFCAN = AN00S11 and S12 AN = AN00 1.5 • (AN00-FC12)S13 and S14 AN = AN00 1 • (AN00-FC24)S15 and S16 AN = AN00 0.75 • (AN00-FC36)S17 and S18 AN = AN00 0.50 • (AN00-FC48)
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Errors H+48
SLAF (AN K • ( AN - FCHH ), k=cte. )Experiments:
Control SFCAN = AN00S11 and S12 AN = AN00 2.5 • (AN00-FC12)S13 and S14 AN = AN00 2 • (AN00-FC24)S15 and S16 AN = AN00 1.5 • (AN00-FC36)S17 and S18 AN = AN00 1 • (AN00-FC48)
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Spread: BC’s vs. IC’s
SLAF2 – Pert IC
SLAF3 – Pert BC
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Spread (cont.)
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MM5 Multi-physics (5 members)
Same initial and boundary fields.Different parameterizations schemes
Convection (Grell, Kain-Fritsch, Betts-Miller)Boundary layer (MRF, ETA).Shallow convection “on” and “off”.
Two months of parallel test (20060101 – 20060228)
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MSLP H+48
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Cooperation in EuropeINM ARPA SIM DWD
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COSMO-SREPS (Short-Range Ensemble Prediction System) short range (up to 3 days)
16 LM runs at 10 km
LM at 25 km on IFS
IFS – ECMWF globalb
y I
NM
Sp
ain LM at 25 km on GME
GME – DWD global
LM at 25 km on UM
UM – UKMO global
LM at 25 km on NCEP
AVN – NCEP global
P1: conv. scheme (KF)
P2: turb. parameter 1
P3: time filter
P4: turb. parameter 2
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ARPA SIM DWD
UNCERTAINTY INTRODUCED BY LATERAL BOUNDARIES(Ensemble ‘LBC’) transfer of uncertainty across scales in an 'ensemble chain' COSMO-DE is nested into the COSMO-SREPS (16 members, ARPA-SIM, Bologna) COSMO-SREPS uses a COSMO model version with 10km grid spacing and perturbed physics COSMO-SREPS is nested into the INM ensemble (INM, Spain) 16 members in total
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Verification
Verification exercise, April-June 2006:
Calibration: with synoptic variables Z500, T500, PmslResponse to binary events: reliability and resolution of surface variables 10m surface wind, 6h and 24h accumulated precipitation
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Pmsl- ECMWF
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Pmsl- Obs
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ECMWF
Reliab. - 24 h Acc. Precip H+54 (1,5,10,20) mm
Reliab. - 6 h Acc. Precip H+24 (1,5,10,20) mm
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ObservationsReliab. - 6 h Acc. Precip H+24 (1,5,10,20) mm
Reliab. - 24 h Acc. Precip H+54 (1,5,10,20) mm
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ROC curves – 24 h Acc Precip (1, 5, 10 & 20 mm)
ECMWF Observations
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ECMWF - AnalysisR
OC
10 m wind H+24 (10,15,20) m/s
Reliab
ilit
y
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Observations10 m wind H+24 (10,15,20) m/s
Reliab
ilit
yR
OC
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European Climate Networks
24 hours acc. precipitation (from early morning).From Spain, Germany, France, UK, …More than 12,000 observations every day.
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Reliability & Sharpness
Good reliability according tothresholds (base rate)forecast length
NoUnder-samplin
g
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BMA500 hPa Temperature (T500) & Geopotential (Z500)
3, 5 and 10 days of training period3 months of calibration (April, May and June of 2006) 24, 48 and 72 hours forecast
10m Wind speed (S10m)3, 5, 10 and 25 days of training period1 month for S10m (April 2006)24, 48 and 72 hours forecast
Calibration using TEMP and SYNOP obs over Europe
Calibration and bias correction: BMA
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RESULTS
MULTIMODEL
BMA 3 T. DAYS
BMA 5 T. DAYS
BMA 10 T. DAYS
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RESULTS
MULTIMODEL
BMA 3 T. DAYS
BMA 5 T. DAYS
BMA 10 T. DAYS
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RESULTS
MULTIMODEL
BMA 3 T. DAYS
BMA 5 T. DAYS
BMA 10 T. DAYS
BMA 25 T. DAYS
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RESULTS
BMA 3 T. DAYS
BMA 5 T. DAYS
BMA 10 T. DAYS
BMA 25 T. DAYS
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HRM T500 td 3 H+72
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
DATE
WE
IGH
T HAV
HEC
HGM
WEIGHTS TIME SERIES
HIRLAM T500 td 3 H+72
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
DATEW
EIG
HT
IAV
IEC
IGM
IUK
LM T500 td 3 H+72
0,000
0,100
0,200
0,300
0,400
0,500
0,600
0,700
0,800
0,900
1,000
DATES
WE
IGH
TS LAV
LEC
LGM
LUK
MM5 td 3 H+72
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
DATE
WE
IGH
T
MAV
MEC
MGM
MUK
UM T500 td 3 H+72
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
DATE
WE
IGH
T
UAV
UEC
UGM
UUK
HRM HIRLAM LOKAL MODEL
MM5 UM
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HIRLAM td 3 H+48
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
DATE
WE
IGH
T
IAV
IEC
IGM
IUK
HIRLAM T500 td 3 H+72
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
DATE
WE
IGH
T
IAV
IEC
IGM
IUK
WEIGHTS TIME SERIES
HIRLAM td 3 H+24
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
DATE
WE
IGH
T
IAV
IEC
IGM
IUK
HIRLAM
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HIRLAM T500 td 3 H+72
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
DATE
WE
IGH
T
IAV
IEC
IGM
IUK
WEIGHTS TIME SERIES
HIRLAM td 5 H+72
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
DATE
WE
IGH
T
IAV
IEC
IGM
IUK
HIRLAM td 10 h+ 72
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
DATE
WE
IGH
T
IAV
IEC
IGM
IUK
HIRLAM
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WEIGHTS TIME SERIES
HIRLAM td 3 H+24
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
DATE
WE
IGH
T
IAV
IEC
IGM
IUK
Z500 td 3 H + 24
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
DATE
WEI
GH
T
IAV
IEC
IGM
IUK
HIRLAM
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Conclusions
A Multi-model-Multi-boundaries Short Range Ensemble Prediction System (MMSREPS), has been develop at the INM-Spain.Taking advantage of using IC’s & BC’s from different high resolution global models.
Nice spread in the short-range, but …Daily transmission of very large amount of data (full resolution global model fields).Dependence of different priorities of the different operational centres.
Good cooperation across Europe:UKMO, DWD, COSMO INMINM ARPA – SIM DWD
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Weaknesses of the interoperability project
Development and maintenance of interface software.Commercial policy of NWP data across Europe.Easy exchange of software among Met Services.
No model is better than others every time and everywhere
Improved exchange of NWP data and software among Met Services in Europe
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Analysis ECMWF December, 27 1999 at 18 UTC
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ECMWF - OPR Comparison H+42
HIRLAM ECMWF
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ECMWF - OPR Comparison H+30
HIRLAM ECMWF
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ECMWF - OPR Comparison H+18
HIRLAM ECMWF
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ECMWF - OPR Comparison H+6
HIRLAM ECMWF