short-range ensemble prediction system at inm josé a. garcía-moya smnt – inm 27th ewglam &...
TRANSCRIPT
Short-Range Ensemble Prediction
System at INM
José A. García-MoyaSMNT – INM
27th EWGLAM & 12th SRNWP MeetingsLjubljana, October 2005
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Introduction
Surface parameters are the most important ones for weather forecast.
Forecast of extreme events (convective precip, gales,…) is probabilistic even for the short-range.
Short Range Ensemble prediction can help to forecast these events.
Forecast risk (Palmer, ECMWF Seminar 2002) is the goal for both Medium- and, also, “Short-Range Prediction” (quotation is mine).
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Errors in LAMs
Due to the model formulation Multimodel techniques
Due to uncertainties in the initial state Singular vectors, breeding
Due to uncertainties at boundaries From different deterministic global models From a global ensemble
Due to the parameterization schemes Multiphysic Stochastic physic techniques
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Multi-model
Hirlam. HRM
from DWD. MM5 UM
Unified Model from UKMO.
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Multi-Boundaries
From different global deterministic models: ECMWF UM
UKMO AVN
NCEP GME
DWD.
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Ensemble
72 hours forecast four times a day (00, 06, 12 y 18 UTC).
Characteristics: 4 models. 4 boundary conditions. 4 last ensembles (HH, HH-6, HH-12, HH-18).
16 member ensemble every 6 hours Time-lagged Super-Ensemble of 64
members every 6 hours.
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Actual Ensemble
72 hours forecast once a day (00 UTC). Characteristics:
4 models. 4 boundary conditions.
13 (of 16 expected) member ensemble every 24 hours
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Actual Ensemble II
BCs / Model
AVN ECMWF
GME UM
Hirlam X X X X
Hrm X X X X
MM5 X X X X
UM O O O X
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Road Map2003-2004
Research to find best ensemble for the Short Range
Jun 04 – Jun 05
Building Multimodel System
Jun 05-Dec 05
Mummubn/16 members
Daily run non-operational
Mar 06 Mummub 16/16
members
Full operations
Jun 06 Mummub+4lag64 members
First try
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Post-processing
Integration areas 0.25 latxlon, 40 levels Interpolation to a common area
~ North Atlantic + Europe Grid 380x184, 0.25º
Software Enhanced PC + Linux ECMWF Metview + Local developments
Outputs Deterministic Ensemble probabilistic
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Post-processing II
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Monitoring in real time
Intranet web server Deterministic outputs
Models X BCs tables Maps for each couple (model,BCs)
Ensemble probabilistic outputs Probability maps: 6h accumulated
precipitation, 10m wind speed, 24h 2m temperature trend
Ensemble mean & Spread maps EPSgrams (not fully-operational)
Verification
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Monit 1: home
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Monit 2: all models X bcs
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Monit 5: All Prob 24h 2m T trend
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Monit 7: Spread - Emean maps
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Validation ECMWF operational analysis as reference. Verification software
~ ECMWF Metview + Local developments Deterministic scores
Bias & Rms for each member Probabilistic ensemble scores
Rank histograms ROC Spread skill
15 days of comparison (Aug, 17 to 31, 2005).
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Rank histograms
Ensemble members ranked from smallest to greatest value.
Percent of cases which verifying analysis falls in an interval.
First interval, below smallest member.
Last one, above greatest member. Z500, T500, Msl Pressure
H+24, H+48
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Spread Skill
Spread vs Ensemble Mean Error Z500
H+00 to H+72 T500
H+00 to H+72 Msl Pressure
H+00 to H+72
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ROC Curves
10m Wind Speed Thresholds: 10m/s, 15m/s H+24, H+48
24h Accumulated Precipitation Thresholds: 1mm, 5mm, 10mm, 20mm H+24, H+48
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Advantages: Better representation of model errors (SAMEX and
DEMETER). Consistent set of perturbations of initial state and
boundaries. Better results (SAMEX, DEMETER, Arribas et al., MWR
2005). Disadvantages:
Difficult to implement operationally (four different models should be maintained operationally).
Expensive in terms of human resources. No control experiment in the ensemble.
Conclusions for Multimodel
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Future 16 members full-operational Bias removal Calibration: Bayessian Model
Averaging Verification against observations Time-lagged 64 members 4runs/day More Post processing software
(targeting clustering)
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Thanks to…
MetOffice Ken Mylne, Jorge Bornemann
DWD Detlev Majewski, Michael Gertz
ECMWF Metview Team