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# The CPC Consolidation Forecast - PowerPoint PPT Presentation

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

David Unger

Dan Collins, Ed O’ Lenic,

Huug van den Dool

NOAA/NWS/NCEP/Climate Prediction Center

• 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

• 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 ForecastCFS 1-month Lead Forecast Nino 3.4 SST, May, 1992

April Data  June-August Mean SST’s

A series of forecasts

K = The fraction of the original model spread

• 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

• 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

Skill May Initial TimeCalibrated CFS Vs. Consolidation

U.S. Temperature and Precipitation Consolidation

• CFS

• Canonical Correlation Analysis (CCA)

• Screening Multiple Linear Regression(SMLR)

• OCN - Trends.

CRPSS

RPSS - 3

HSS

Bias (C)

% Cover

CCA+SMLR

CFS

CFS+CCA+SMLR, Wts.

All – Equal Wts.

Official

• Add more tools and models

• Improve weighting method

• Trends are too strong

• Improve method of mixing statistical and dynamical tools

• 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)

• CFS – 42 members (29%)

• Constructed Analog

(CA) – 12 members (18%)

• CCA – 1 member (17%)

• MKV – 1 member (36%)

• Ideally suited for dynamic models.

• Uses information from the individual members (Variable confidence, Clusters in solutions, etc.)