overview of ensemble forecasting steven l. mullen univ. of arizona comet faculty 99 course presented...
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Overview of Ensemble Forecasting
Steven L. Mullen
Univ. of Arizona
COMET Faculty 99 CoursePresented by Steve MullenWednesday, 9 June 1999
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Benefactors• Dave Baumhefner, NCAR
• Joe Tribbia, NCAR
• Ron Errico, NCAR
• Tom Hamill, NCAR
• Harold Brooks, NSSL
• Chuck Doswell, NSSL
• Dave Stensrud, NSSL
• Eugenia Kalnay, NCEP-UO-UM-?
• Steve Tracton, NCEP
• Zoltan Toth, NCEP
• Ron Gelaro, NRL
• Rolf Langland, NRL
• Jeff Anderson, GFDL
• Mike Harrison, UKMO
• Tim Palmer, ECMWF
• Roberto Buizza, ECMWF
• Peter Houtekamer, AES
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Presentation Overview
• Philosophy and Benefits of Ensembles
• Estimate of Initial Uncertainty
• Design of Initial Perturbations for EPS
• Inclusion of Model Uncertainty in EPS
• Ensemble Size
• Integration of EPS and Data Assim System
• Model Validation
• Evaluation and Utility of EPS
• Classroom Activities
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Philosophy and Benefitsof Ensemble Forecasting
• Initial Condition Uncertainty (ICU)
• Probability Density Function (PDF) of initial conditions about “Truth”
• GOAL: predict evolution of PDF
• Gives information on 1st & 2nd moments Forecast uncertainty from dispersion
• Thought to be most applicable to MRF (6-10 day) and seasonal (30-90 day) forecasts
• Beneficial to SRF (06 h-2 day) for QPF
• KEY: IC error versus model error More skillful model, more beneficial PIC
• Now includes dispersion from uncertainty in initial state and model formulations
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Univ Utah Ensemble12 km inner grid
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Univ Utah Ensemble12 km inner grid
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Precipitation Dispersion32 km NSSL Mixed Ensemble
Oct 97-Dec 97
1
2
3
4
5
6
7
8
0 3 6 9 12 15 18 21 24 27 30 33 36
forecast time (h)
rms
(mm
)
12 h
6 h
3 h
1 h
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Perturbation Design
• What is the goal?
1) Robust estimate of PDF? 2) Sample extremes of PDF?3) Make up for deficiency in EPS?
• Requirements1) Properly constrained by estimates
of analysis error2) Equally-likely probability
for each perturbation field• What are some of the attributions of
current perturbation schemes for global ensemble models?
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Dave Baumhefner, in progress
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Ranked Probability Scoreby Model and Perturbation
0.2
0.4
0.6
0.8
24h 48hFcst Time
Grand EnsETA DiffETA BredRSM Bred
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Ranked ProbabilitySkill Score
Relative to Climatology
0.0
0.1
0.2
0.3
0.4
0.5
24h 48hFcst Time
RP
SS
Grand EnsETA DiffETA BredRSM BredETA OpnlMeso ETA
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Perturbation DesignConclusions
• Perturbation methods control dispersion characteristics out to 5-7 days
• SV: linear growth 1-3 days
• Random: classic error growth curve
• Random: project onto SVs 1-5 days
• BV: unique, different than analysis error, but has improved with recent changes
• Perturb strategy is unimportant after 5-7 days, once growth is strongly nonlinear
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Model Uncertainties
• Specification of Subgrid Scale Processes
• GOAL: improve transient variability and increase ensemble
dispersion
• Methodologies / Philosophies1) Fixed during model integration:
different parameterization schemeschange tunable parameters 2) Stochastic element during integration:
to a scheme’s tunable parameters to model tendencies directly
• What are some of the attributes?
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Rank Histogram24 h Rain Totals
24h Rank ECMWF
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Fixed
Stoch
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Stochastic Cb Parameterization
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Model Uncertainties Conclusions
• Increases dispersion
• Changes predictability estimates
• Model validation issues?
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Model Validation
• Major Challenge for Mesoscale LAMs• Inclusion of stochastic dynamics/physics into
model requires consideration ofamplitude spatial scaletemporal scale
• Statistics for model and observations are currently lacking, so need for
long-term model integrationsbetter utilization of obs networkin absence of obs statistics, validate by comparison with explicit models
• GOAL: model PDFs match obs PDFs
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Ensemble Size (N)
• Increased N or finer model resolution
• Partitioning N among perturbed IC’s and different physics parameterizations
• Depend on model, forecast objective etc.
• Choice is not always clearResolution of complex terrain
• Larger N always decreases sampling uncertaintyDiminishing returns N exceeds 10-20
• N sets limits on resolution of PDF1% event requires N of 200 or larger
• Large N warranted for accurate EPSModel with good climateAbility to simulate phenomenonSound perturbation strategy
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EPS and Data Assimilation System
• Current status of Data Assimilation 3DVAR and OI techniques
homogeneousisotropic
flow independent• Kalman filter and 4DVAR can account
for these shortcomingsKalman filter expensive
4DVAR lacks cycling
• Ensemble of perturbed 6h SRFs may provide an alternative to 4DVAR
inexpensivecontains cycling
• Houtekamer and Mitchell (1998) study
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Utility of EPS
• Challenge: convey info in ensemblesReduce flow dimensionality
clusters, EOFs, indices, envelopes User friendly and flexible
wide spectrum of needs and abilities
“problem of day” changes
• Enhance utility by stat. post-processingMLR MOS-techniques
Kalman filteringAI-neural
networks
• Rigorous assessment of stat. significance
• Cost-benefit analysis
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Neural Net Post-ProcessingReliability Diagram 0.25”
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
Forecast Probability
Obs
erve
d Fr
eque
ncy NET
RAW
MOS
NET(MOS)
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Cost-Benefit AnalysisPrecipitation
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Fav SitesReal-Time Ensemble Products
• NCEP MRF Ensembles
CDC Boulderwww.cdc.noaa.gov/~map/maproom/ENS/ens.html
NCEP Ensemble Homepagesgi62.wwb.noaa.gov:8080/ens/enshome.html
Univ of Utahwww.met.utah.edu/jhorel/html/models/model_ens.html
• MOS for MRF Ensembles
Penn Statewww.essc.psu.edu/~rhart/ensemble/ensmos.html
• Short-Range Mixed Ensembles
NSSL/NOAAvicksburg.nssl.noaa.gov/mm5/ensemble/index_all.html
• SAMEX? NCEP ETA/RSM?
Ask Kelvin D. and Steve T., respectively!
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Univ. Utah
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Univ. Utah
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MRF Ensemble MOSfrom Penn State
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NSSL Experiment Ensemble Model Physics/Uncertainty
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FNMOC/UA Products
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Classroom ActivitiesAppropriate for Undergrads
• Probabilistic ForecastingQPF
Use MOS thresholds
MAX-MIN
Credible Interval Forecasts
(e.g. Prob. within 2oF)
Be willing to stumble and be humbled!
• Hands-On NWPBarotropic Model Experiments