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The CPC Consolidation Forecast. David Unger Dan Collins, Ed O’ Lenic, Huug van den Dool NOAA/NWS/NCEP/Climate Prediction Center. Overview. A regression procedure designed for ensembles. Derive a relationship between the BEST member of an N-member ensemble and the observation:

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the cpc consolidation forecast

The CPC Consolidation Forecast

David Unger

Dan Collins, Ed O’ Lenic,

Huug van den Dool

NOAA/NWS/NCEP/Climate Prediction Center

overview
Overview
  • A regression procedure designed for ensembles.

Derive a relationship between the BEST member of an N-member ensemble and the observation:

Y = a0 + a1fb + ε

ensemble regression
Ensemble Regression
  • Weights represent the probability of a given member being the best.
  • If weights are known, coefficients can be calculated from the ensemble set.

(No need to explicitly identify the best member)

example forecast cfs 1 month lead forecast nino 3 4 sst may 1992
Example ForecastCFS 1-month Lead Forecast Nino 3.4 SST, May, 1992

April Data  June-August Mean SST’s

A series of forecasts

  • Start with the ensemble mean
  • Gradually increase the ensemble spread

K = The fraction of the original model spread

multi model consolidation
Multi Model Consolidation
  • At least 25 years of “hindcast” data
  • Standardize each model (means and standard deviations)
  • Remove trend from models and observations
  • Weight the various models
  • Perform regression
  • Add trends onto the results
nino 3 4 consolidation
Nino 3.4 Consolidation
  • CFS, CCA, CA, MKV

(Statistical and Dynamic models mixed)

  • Lead -2 and Lead -1 are a mix of observations and the one and two-month forecast from the CFS
u s temperature and precipitation consolidation
U.S. Temperature and Precipitation Consolidation
  • CFS
  • Canonical Correlation Analysis (CCA)
  • Screening Multiple Linear Regression(SMLR)
  • OCN - Trends.
performance
Performance

CRPSS

RPSS - 3

HSS

Bias (C)

% Cover

CCA+SMLR

CFS

CFS+CCA+SMLR, Wts.

All – Equal Wts.

Official

future work
Future Work
  • Add more tools and models
  • Improve weighting method
  • Trends are too strong
  • Improve method of mixing statistical and dynamical tools
recursive regression
Recursive Regression
  • Y = a0 + a1fi

a+= (1-α) a+ αStats(F,Y)

Stats(F,Y) represents error statistic based on the most recent case

α = .05

a+= .95a + .05 Stats(F,Y)

sst consolidation
SST Consolidation
  • CFS – 42 members (29%)
  • Constructed Analog

(CA) – 12 members (18%)

  • CCA – 1 member (17%)
  • MKV – 1 member (36%)
advantages
Advantages
  • Ideally suited for dynamic models.
  • Uses information from the individual members (Variable confidence, Clusters in solutions, etc.)

Disadvantages

  • Statistical forecasts are not true Solutions
  • Trends are double counted when they accelerate
  • Weighting is not optimum (Bayesian seems appropriate)
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