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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


Skill may initial time calibrated cfs vs consolidation
Skill May Initial TimeCalibrated CFS Vs. Consolidation


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)