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GLAMEPSGLAMEPS::GGrand LLimited AArea MModel EEnsemble PPrediction

SSystem

Overview of activities

EWGLAM – Dubrovnik, October 2007

Trond IversenTrond Iversenwith contributions fromwith contributions from

Inger-Lise Frogner, Edit Hágel, Kai Sattler, Roeland Stappers + more

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is in real time to provide to all HIRLAM and ALADIN partner countries:

an operational, quantitative basis for forecasting probabilities of weather events

in Europe up to 60 hours in advance to the benefit of highly specified as well as

general applications, including risks of high-impact weather.

The GLAMEPS objective

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Expectations from Short Range ensemble prediction

• How certain is today’s weather forecast?

• What are the risks of high-impact events?•Forcasted risk = probability x potential damage (vulnerability)

• Lower predictability of “free flows” as scales decrease; i.e.: higher resolution increases the need for information about spread and the timing of spread saturation

• Predictability of “forced flows” is longer than “free flows”:i.e.: beneficial to separate unpredictable “free flows” from those strongly influenced by surface contrasts: e.g. topography, coast-lines, land-use, etc.

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Basic Ideas in GLAMEPSBasic Ideas in GLAMEPS• An array of LAM-EPS models or model

versions:– Each partner runs a unique sub-set of ensemble members– Partners who run the same model version,

use different lower boundary data, or different initial and lateral boundary perturbations

– Partners who run with DA, produce 5 - 21 ensemble members based on initial and lateral boundary perturbations

(one control with DA + pairs of symmetric initial perturbations)– Partners who do not run DA produce 6-20 ensemble members

(pairs)

• Grid resolution – Now 20km, later: 10km or finer, 40 levels, identical in all model

versions (should be increased to at least 60)

• Forecast range

– 60h (shorter?) - starting daily from 00UT and 12 UT

• A common pan-European integration domain– Or alternatively: a minimum common overlap

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Aspects to considerAspects to consider1. Operational aspects

- In particular data storage and Real-Time distribution2. Constructing initial and lateral boundary perturbations

– Imported global eps-members enhanced w.r.t. resolution, European target, moist physics

– LAM-specific perturbations (SVs, ETKF)3. Lower boundary data perturbations

– Stochastic perturbations– Switch surface schemes– Targetted Forcing Singular Vectors or Forcing Sensitivities

4. Model perturbations– Switching models (e.g. Aladin and Hirlam)– Switching physical packages (e.g. Straco, RKKF, ECMWF-physics)– Stochastic perturbations (EC: Cellular automata.)– Forcing Singular Vectors

5. EPS-calibration and probabilistic validation6. Post-processing, graphical presentation, products7. Further downscaling to meso- and convective scales

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Quality objectiveQuality objective

To operationally produce ensemble forecasts with• a spread reflecting known uncertainties in data and

model;• a satisfactory spread-skill relationship (calibration);

and• a better probabilistic skill than the operational

ECMWF EPS; for• the chosen forecast range of 60 hours (could be

shorter); • our common target domain; and• weather events of our particular interest

(European extremes - probabilistic skill parameters).

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GLAMEPS Common Domain

ALADIN• Resolution:

22km• 320 x 300 x 37

HIRLAM (EPS71) • Resolution 0.2 deg.• 306 x 260 x 40

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GLAMEPS_v0: Laboratory at GLAMEPS_v0: Laboratory at ECMWFECMWF

• To select a small set of model versions which are equally valid but significantly different, – 3 different models:

• ALADIN, HIRLAM-STRACO, HIRLAM RK-KF• To construct initial/lateral boundary perturbations

– New ECMWF TEPS: define TSVs targeted to 3 domainsall TSVs are orthogonal to NH SVs (EPS) and mutually

• TSVs: OT=24h, T159, (not yet diabatic)– Use: 30 TSVs and 50 NH SVs, Gaussian sampling to 20

members + control • Probabilistic estimation (e.g. BMA),

– Not started yet: awaits results• Products; Quality and Value

– INM package based on Magics– Predictability of the day, event risks– Reliability, BSS, ROC, Value, …

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TEPS FOR EUROPEInger-Lise Frogner

GLAMEPS integration domain (HIRLAM version)

Target area north (82N,15W,50N,50E)

Target area central(62N,20W,33N,44E)

Target area south(47N,23W,24N,32E)

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First experimental setup for “TEPS for Europe”

• Singular vectors are computed with:– T159L62 (as opposed to T42 for operational NH SVs at

ECMWF)– 24h optimization time (as opposed to 48h)– Targeted in the vertical to the troposphere– Targeted SVs (TSVs) based on total energy norm– The TSVs are orthogonal NH SVs and mutually orthogonal

• TEPS perturbations are made from 80 SVs: – 10 SVs for each of the three targets

and 50 NH SVs and evolved SVs from EPS.– Includes standard stochastic physics

• Different amplitudes is assigned to the different sets of SVs, to give the desirable spread/skill relation – Presently under development

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EXPERIMENTS

• 21 days in summer 2007:– 20070618-20070624, 20070808-

20070814 and 20070820-20070826

• The amplitude of NH SVs is kept as in EPS for the first experiment: 0.020

• The amplitude of TSVs from the three target areas for the first experiment: 0.008– Under adjustment!

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COST

• TSVs for all three target areas: ca 450 SBUs

• TEPS for Europe: ca 3000 SBUs

A total cost of ~3500 SBUs per run /case

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Problems and obstacles

• Technical issues regarding running with three orthogonal sets had to be solved

• The high resolution of the TSVs caused unforeseen problems. The vertical diffusion scheme in the SV calculations in IFS had to be changed. The original scheme caused spurious growth.

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Example of TSVsLowest level, SV no. 2

NHSV

TSV-north

TSV-central

TSV-south

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Spread/Skill relationshipMSLP, 21 summer cases 2007

___ error of EnsembleMean (EM), EPS

___ error of EM, Norwegian

TEPS___ error of EM, European TEPS

---- spread around EM, EPS

---- spread around EM, Norwegian TEPS

---- spread around EM, European TEPS

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RMS Difference in spread between European TEPS and EPS over the 21

cases

+12h +24h

+36h +48h

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Skill scores MSLP (example) 21 summer cases 2007

• Black: EPS, 50 members• Blue: European TEPS, 20 members• Red: Norwegian TEPS, 20 members

Event: anom < -5 hPaROC area

BSS

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Further work

• Experimentation with the amplitudes of the SVs and TSVs will be carried out. – At the moment we are testing:

Increasing the amplitude from the TSVs and at the same time reduce the amplitude from the SVs, in order to get better spread / skill in the range 0 to 60 hours as well as better scores.

• A winter period of 21 days will also be run.• Scores for more parameters will be calculated:

T850, ff10m, Z500, T2m

• After the tuning of TEPS is satisfactory, LAMEPS will be run with TEPS as initial and boundary conditions

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HIRLAM EPS and ALADIN EPS

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Main characteristics of HIRLAM EPS Kai Sattler

1. Calculates a control from HIRLAM 3d-Var (later 4D Var) + ensemble of perturbations

2. Can be used with downscaled ECMWF EPS:* 50+1 members * 12h cycle frequency * data availability:

* online data * boundary data pool (intermediate storage, hindcast)

3. Old Norwegian TEPS: * 20+1 members * 24h cycle frequency* boundary data pool

4. New “TEPS for Europe” (not fully implemented yet): * 3 target areas * 12h cycle frequency* boundary data pool

5. Ensemble member specific environment settings choice of parameterization schemes

6. Selection of convection/condensation scheme for each perturbed member

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ALADIN EPS

• Scripts for the execution of ALADIN/EPS system for the GLAMEPS domain(s) at ECMWF – (Stjepan Ivatek-Sahdan, ZAMG EPS team, Joao

Ferreira)

• Operational ALADIN EPS (“ALADIN LAEF”) built in Austria at ECMWF machines

• used as starting point for the GLAMEPS-v0 laboratory

• So far tested: Downscaling ECMWF EPS for ALADIN.

• quasi-operational manner. Joao Ferreira (“Downscaling ECMWF EPS with ALADIN”)

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Costs

EPS_71T_15: Coarse test domain

EPS_71_20: Full 0.2 deg. dom: 60-70 SBU/48hfcst

• 22km: around 80 SBU/54h fcst

HIRLAM

ALADIN

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Further R&D in parallelFurther R&D in parallelTo gradually increase sources of spread, quality and

reliabilityInclude lower boundary perturbations and other types of model

perturbations, e.g.:– vary model coefficients– Targeted Forcing SVs or Forcing Sensitivities, – weak 4D-Var perturbed tendencies– Stochastic physicsInclude alternative initial/lateral boundary perturbations– ETKF generalized breeding, – HIRLAM and ALADIN LAM SVs, Pdf-estimation, presentation, validation etc. – BMA,– Products – Validation

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ALADIN SVsEfit Hágel, Richard Mladek

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Case study – 27 August 2007, 00 UTC

• ALADIN singular vectors were computed• GLAMEPS domain was used for the

computations, but the target (optimization) area itself was smaller: 56N/34S/2W/40E (see on figure in green) Opt. area

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Case study – 27 August 2007, 00 UTC

• ALADIN SVs - choices for case study:• Norms: total energy norm (initial and final time)

– Optimization area: 56N/34S/2W/40E – Optimization time: 12 and 24 hours– Vertical optimization: level 1 - 46 (all levels)– Resolution: 22 and 44 km– LBC Coupling: every 3 hours (ARPEGE) Opt. area

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Case study – ALADIN SVs• Leading singular values

for the experiments with different optimization time and resolution

• Lowest singular values with 44 km resolution and 12 hours optimization time

• Highest singular values with 22 km resolution and 24 hours optimization time

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Case study – ALADIN SVs

ALADIN leading singular vector at T+0h (top) and evolved singular vector at T+12h and T+24h (bottom) for wind v at model level 22.

44 km 22 km 44 km 22 km

0h

12h/24h

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HIRLAM SVsJan Barkmeijer, Roel Stappers

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Hirlam Case I101 (Fine)

• Analysis June 28th 2006 at 3 UTC• Resolution 0.2 x 0.2 degrees• Optimization time 12 hours• Dry total energy norm• No projection operator• Vertical diffusion in TL-model• No diabatic processes in TL-model• Leading singular value 6.6

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Temperature and wind field of the leading singular vector at model level 19 (500 hPa) at initial (left) and final time (right) using the same temperature contour interval and unit wind vector.

Initial Evolved

Temp.

Wind

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Vertical energy distribution of the leading singular vector for the wind (black) and temperature field (red) at initial (left) and final time( right). Notice the difference in scaling of the horizontal axis.

Initial Evolved

wind

Temp.

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SV-spectrum Fine (0.2x0.2), coarse (0.4x0.4)

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GLAMEPS_v1: distributed GLAMEPS_v1: distributed laboratorylaboratory

To set up a first phase suite tested in distributed mode (2008)based on experience from _v0

Run in hindcast mode use and use ECMWF for data exchange /storage

• Run ECMWF TEPS• Store necessary LBC-data on accessible ECMWF disk for < 24 h• Each partner downloads data and run a set of predefined

LAM-EPS members• Each partner to store selected results on ECMWF disk• A set of probabilistic products made in batch-mode• Each partner to download the entire produc suite • Store on mars for future quality evaluation / validation

/calibration

NB: The success of GLAMEPS relies critically on dedicated partners.

Most probably we need funding for supporting staff and storage at ECMWF.

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ECMWF and GLAMEPSECMWF and GLAMEPSOperationally produce enhanced value intial/lateral boundary perturbations

–“TEPS for Europe” as a Prediction Application Facility (PAF)?

Data exchange central in RT operation–A selected set of data from TIGGE-list copied to ECMWF in RT each LAM-EPS.–At an agreed time, all partners can download the set of GLAMEPS members.

Archiving–Archiving EPS and TEPS for use by GLAMEPS –Archiving GLAMEPS raw data and products

Use software developed at ECMWF for–Selected probabilistic products, –Probabilistic verification and validation

Calibrate and validate the entire GLAMEPSDevelop and maintain

–Prototype codes and scripts for downloading by partners, –Testing and quasi-operationalization in research mode,

Further co-operate with ECMWF staff, scientifically and operationally.

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Thank You!