Interoperability at INM Experience with the SREPS system

Interoperability at INMExperience with the SREPS

system

J. A. García-MoyaNWP – Spanish Met Service INM

SRNWP Interoperability WorkshopECMWF – 14-15 January 2008

ECMWF, 14-15 January 2008

SRNWP Interoperability Workshop

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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.



3

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).



6

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




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ObservationsReliab. - 6 h Acc. Precip H+24 (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


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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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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Questions

[email protected]



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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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HIRLAM ECMWF



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HIRLAM ECMWF



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HIRLAM ECMWF

Interoperability at INM Experience with the SREPS system

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