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The CPC Consolidation Forecast

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  1. The CPC Consolidation Forecast David Unger Dan Collins, Ed O’ Lenic, Huug van den Dool NOAA/NWS/NCEP/Climate Prediction Center

  2. 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

  3. 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)

  4. Ensemble Regression

  5. 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

  6. 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

  7. 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

  8. Skill May Initial TimeCalibrated CFS Vs. Consolidation

  9. U.S. Temperature and Precipitation Consolidation • CFS • Canonical Correlation Analysis (CCA) • Screening Multiple Linear Regression(SMLR) • OCN - Trends.

  10. SON Consolidation Forecast

  11. Performance CRPSS RPSS - 3 HSS Bias (C) % Cover CCA+SMLR CFS CFS+CCA+SMLR, Wts. All – Equal Wts. Official

  12. Future Work • Add more tools and models • Improve weighting method • Trends are too strong • Improve method of mixing statistical and dynamical tools

  13. END

  14. 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)

  15. SST Consolidation • CFS – 42 members (29%) • Constructed Analog (CA) – 12 members (18%) • CCA – 1 member (17%) • MKV – 1 member (36%)

  16. 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)