Ensembles and Probabilistic Forecasting

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Ensembles and Probabilistic Forecasting
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Probabilistic Prediction

• Because of forecast uncertainties, predictions
  must be provided in a probabilistic framework,
  not the deterministic single answer approach that
  has dominated weather prediction during the last
  century.
• Interestinglythe first public forecasts were
  probabilistic

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Ol Probs
Cleveland Abbe (Ol Probabilities), who led the
establishment of a weather forecasting division
within the U.S. Army Signal Corps. Produced the
first known communication of a weather
probability to users and the public in 1869.
Professor Cleveland Abbe, who issued the first
public Weather Synopsis and Probabilities on
February 19, 1871
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The Trend to Deterministic Forecasts During the
Later 19th and First Half of the 20th Centuries.
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Foundation for probabilistic prediction

• The work of Lorenz (1963, 1965, 1968)
  demonstrated that the atmosphere is a chaotic
  system, in which small differences in the
  initializationwell within observational error
  can have large impacts on the forecasts,
  particularly for longer forecasts.
• Not unlike a pinball game.

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• Similarly, uncertainty in our model physics also
  produces uncertainty in the forecasts.
• Lorenz is a series of experiments demonstrated
  how small errors in initial conditions can grow
  so that all deterministic forecast skill is lost
  at about two weeks.
• Talked about the butterfly effect

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• The Lorenz Diagramchaos
• Is not necessarily random

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Probabilistic NWP

• To deal with forecast uncertainty, Epstein (1969)
  suggested stochastic-dynamic forecasting, in
  which forecast errors are explicitly considered
  during model integration, but this method was not
  computationally practical.
• Another approach, ensemble prediction, was
  proposed by Leith (1974), who suggested that
  prediction centers run a collection (ensemble) of
  forecasts, each starting from a different initial
  state. The variations in the resulting forecasts
  could be used to estimate the uncertainty of the
  prediction. But even the ensemble approach was
  not tractable at this time due to limited
  computer resources.

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Ensemble Prediction

• Can use ensembles to provide a new generation of
  products that give the probabilities that some
  weather feature will occur.
• Can also predict forecast skill!
• It appears that when forecasts are similar,
  forecast skill is higher.
• When forecasts differ greatly, forecast skill is
  less.
• To create a collection of ensembles one can used
  slightly different initializations or different
  physics.

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Ensemble Prediction

• By the early 1990s, faster computers allowed the
  initiation of global ensemble prediction at NCEP
  and ECMWF (European Centre for Medium Range
  Weather Forecasts).
• During the past decade the size and
  sophistication of the NCEP and ECMWF ensemble
  systems have grown considerably, with the
  medium-range, global ensemble system becoming an
  integral tool for many forecasters. Also during
  this period, NCEP has constructed a higher
  resolution, short-range ensemble system (SREF)
  that uses breeding to create initial condition
  variations.

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NCEP Global Ensemble System

• Begun in 1993 with the MRF (now GFS)
• First tried lagged ensembles as basisusing
  runs of various initializations verifying at the
  same time.
• For the last ten years have used the breeding
  method to find perturbations to the initial
  conditions of each ensemble members.
• Breeding adds random perturbations ( and -) to
  an initial state, let them grow, then reduce
  amplitude down to a small level, lets them grow
  again, etc.
• Give an idea of what type of perturbations are
  growing rapidly in the period BEFORE the
  forecast.
• Does not include physics uncertainty.
• Coarse spatial resolution..only for synoptic
  features.

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NCEP Global Ensemble

• At 00Z
• T254L64 high resolution control out to 7 days,
  after which this run gets truncated--just larger
  scales and is run out to 16 days at a T170L42
  resolution
• T62 control that is started with a truncated T170
  analysis
• 10 perturbed forecasts each run at T62 horizontal
  resolution. The perturbations are from five
  independent breeding cycles.
• At 12Z
• T254L64 control out to 3 days that gets truncated
  and run at T170L42 resolution out to 16 days
• Two pairs of perturbed forecasts based on two
  independent breeding cycles (four perturbed
  integrations out to 16 days).

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U.S. Navy Also Has A Global Ensemble System Using
NOGAPS
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NCEP Short-Range Ensembles (SREF)

• Resolution of 32 km
• Out to 87 h twice a day (09 and 21 UTC
  initialization)
• Uses both initial condition uncertainty
  (breeding) and physics uncertainty.
• Uses the Eta and Regional Spectral Models and
  recently the WRF model (21 total members)

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SREF Current System
Model Res (km) Levels Members Cloud
Physics Convection RSM-SAS 45 28 Ctl,n,p
GFS physics Simple Arak-Schubert RSM-RAS
45 28 n,p GFS physics Relaxed
Arak-Schubert Eta-BMJ 32 60 Ctl,n,p Op
Ferrier Betts-Miller-Janjic Eta-SAT
32 60 n,p Op Ferrier BMJ-moist
prof Eta-KF 32 60 Ctl,n,p Op
Ferrier Kain-Fritsch Eta-KFD 32 60 n,p Op
Ferrier Kain-Fritsch with
enhanced detrainment
PLUS NMM-WRF control and 1 pert. Pair
ARW-WRF control and 1 pert. pair
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UW Short Range Ensemble System
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UW Mesoscale Ensemble System

• Single limited-area mesoscale modeling system
  (MM5)
• 2-day (48-hr) forecasts at 0000 UTC and 12 UTC in
  real-time since January 2000. 36 and 12-km
  domains.

12-km
36-km
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UW Ensemble System

• UW system is based on the use of analyses and
  forecasts of major operational modeling centers.
• The idea is that differences in initial
  conditions of various operational centers is a
  measure of IC uncertainty.
• These IC differences reflect different data
  inventories, assimilation schemes, and model
  physics/numerics and can be quite large, often
  much greater than observation errors.
• In this approach each ensemble member uses
  different boundary conditions--thus finessing the
  problem of the BC restraining ensemble spread.
• Also include physics diversity

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Native Models/Analyses Available
Resolution ( _at_ 45 ?N )
Objective Abbreviation/Model/Source
Type Computational Distributed Analysis
avn, Global Forecast System (GFS),
Spectral T254 / L64 1.0? / L14 SSI National
Centers for Environmental Prediction 55 km 80
km 3D Var   cmcg, Global Environmental
Multi-scale (GEM), Finite 0.9??0.9?/L28 1.25? /
L11 3D Var Canadian Meteorological Centre Diff
70 km 100 km   eta, limited-area mesoscale
model, Finite 32 km / L45 90 km /
L37 SSI National Centers for Environmental
Prediction Diff. 3D Var   gasp, Global
AnalysiS and Prediction model, Spectral T239 /
L29 1.0? / L11 3D Var Australian Bureau of
Meteorology 60 km 80 km jma, Global Spectral
Model (GSM), Spectral T106 / L21 1.25? /
L13 OI Japan Meteorological Agency 135 km 100
km   ngps, Navy Operational Global Atmos. Pred.
System, Spectral T239 / L30 1.0? / L14 OI Fleet
Numerical Meteorological Oceanographic Cntr.
60 km 80 km tcwb, Global Forecast
System, Spectral T79 / L18 1.0? / L11 OI Taiwan
Central Weather Bureau 180 km 80 km   ukmo,
Unified Model, Finite 5/6??5/9?/L30 same /
L12 3D Var United Kingdom Meteorological Office
Diff. 60 km
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Relating Forecast Skill and Model Spread
Mean Absolute Error of Wind Direction is Far Less
When Spread is EXTREME (Low or High)
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Ensemble-Based Probabilistic Products
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Verification
The Thanksgiving Forecast 2001 42h forecast
(valid Thu 10AM)
SLP and winds

• Reveals high uncertainty in storm track and
  intensity
• Indicates low probability of Puget Sound wind
  event

1 cent
11 ngps
5 ngps
8 eta
2 eta
3 ukmo
12 cmcg
9 ukmo
6 cmcg
4 tcwb
13 avn
10 tcwb
7 avn
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Ensemble-Based Probabilistic Products
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Ensemble Prediction in the U.S.