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Advanced Ensemble Regression Techniques for Climate Prediction Consolidation

This document explores a regression procedure tailored for ensemble forecasts, deriving a relationship between the best ensemble member and actual observations. It highlights the use of ensemble regression to calculate coefficients from ensemble sets without needing to identify the best member explicitly. With a focus on historical data and multi-model consolidation techniques, it improves forecasting accuracy for Nino 3.4 SST and U.S. temperature and precipitation. Future directions aim to enhance weighting methods and integrate statistical and dynamical modeling tools.

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Advanced Ensemble Regression Techniques for Climate Prediction Consolidation

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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)

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