GLAMEPS: Grand Limited Area Model Ensemble Prediction System Plans and ideas for a contribution to a

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GLAMEPSGrand Limited Area Model Ensemble
Prediction SystemPlans and ideas for a
contribution to a European-wide contribution to
TIGGE LAM

• Trond Iversen
• met.no Univ. Of Oslo
• HIRLAM MG-member

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The GLAMEPS objective
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.
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Expectations from SR ensemble prediction

• How certain is todays weather forecast?
• What are the risks of high-impact events?
• Forcasted risk probability x potential damage
  (vulnerability)
• Predictability of free flows decreases with
  decreasing scales
• i.e. higher resolution increases the need for
  information about spread and the timing of spread
  saturation
• Predictability of forced flows may be longer than
  for free flows
• i.e. it can give benefits to separate
  unpredictable features from
• those strongly influenced by surface contrasts
• e.g. topography, coast-lines, land-use, etc.

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Ideas for GLAMEPS

• An array of LAM-EPS models or model versions
• Each partner runs a unique model version
• Partners who run the same model version,
• use different lower boundary data,
• or different ways of producing initial and
  lateral boundary perturbations
• Each partner runs between 5 and 21 ensemble
  members based on initial and lateral boundary
  perturbations
• (one control pairs of symmetric initial
  perturbations)
• Grid resolution
• Now 20km, later 10km or finer, 40 levels,
  identical in all model versions
• Forecast range
• 60h - starting daily from 12 UT (should be more
  frequent?)
• A common pan-European integration domain
• Or alternatively a minimum common overlap

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A proposed commondomain(HIRLAM-version)

• HIRLAM (EPS71)
• NLON306
• NLAT260
• NLEV40
• SOUTH- 20.427
• WEST- 46.475
• NORTH31.373
• EAST14.525
• POLAT- 40.0
• coordinates of South pole
• POLON22.0
• NPBPTS2
• Number of passive boundary points

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A proposed commondomain(ALADIN-version)

• ALADIN
• 309x309 points
• center -4,52
• resolution 20 km
• rotation 340 (-20)

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A proposed commondomain

• Basic (lat,lon) (68N,40W)
• Size
• 0.2 deg 301 x 381
• Lower left corner i -40, j -260
• upper right corner i 260, j 120
• - 0.1 deg 601 x 761
• Lower left corner i -80, j -520
• upper right corner i 520, j 240

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AlternativelyProposed minimum common overlap of
domains

• Basic (lat,lon) (68N,40W)
• Size
• 0.2 deg 211 x 233
• Lower left corner i 30, j -136
• upper right corner i 240, j 96
• - 0.1 deg 421 x 465
• Lower left corner i 60, j -272
• upper right corner i 480, j 192

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Quality 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
• our common target domain and
• weather events of our particular interest
• (probabilistic skill parameters).

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First Phasea GLAMEPS laboratory at ECMWF

• 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
• ECMWF TEPS / EPS (build on met.no LAMEPS)
• Can be adjusted by defining e.g 3 different
  targets
• Ensemble calibration (partly build on INM SREPS)
• spread-skill-relation, ensemble size
• Probabilistic estimation (e.g. BMA),
• Predictability of the day
• Quality Reliability, BSS, ROC, Value,

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Operational

• To set up a first phase suite at ECMWF (SPNOGEPS)
• Over 1-2 years, this suite should gradually
  become distributed to partners, and run in RT by
  the end of 2008
• NB The success of GLAMEPS relies critically on
  dedicated partners for this!! (Is KNMI ready?)
• To use ECMWF for data exchange in RT
• (NB Selected data set for forecasting,
• re. TIGGE-LAM recommendations)
• To use ECMWF to provide a default set of
  graphical presentations

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Further RD in parallel

• Through research to gradually increase ensemble
  size and error-sources
• Include lower boundary perturbations and other
  types of model perturbations
• vary model coefficients
• Targeted Forcing SVs or Forcing Sensitivities
  (KNMI, met.no),
• weak 4D-Var perturbed tendencies?
• .
• Include alternative intial/lateral boundary
  perturbations
• ETKF generalized breeding (SMHI),
• HIRLAM and ALADIN LAM SVs (KNMI, SMHI, HMS),
• Pdf-estimation, presentation, etc.
• BMA, (KNMI, INM)
• Products

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Thank you for your attention
Mother of Pearl clouds over Oslo, January 2002
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Aspects to consider

• Operational aspects
• Constructing initial and lateral boundary
  perturbations
• Each LAM-version uses its own data-assimilation
• Imported global eps-members
• Calculate model-specific perturbations (SVs,
  ETKF, SLAF,)
• Lower boundary data perturbations
• Stochastic perturbations
• Switch surface schemes
• 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
• EPS-calibration and probabilistic validation
• Post-processing, graphical presentation, products
• Further downscaling to meso- and convective scales