Chapter 7 - Ensemble methods.pdf

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Benefit of ensemble method • 1) The mean of the ensemble of forecasts is generally more accurate than the forecast from an individual member, for a large number of forecasts. • 2) The spread or variance or dispersion of the ensemble members can be related to the uncertainty in the ensemble mean. • 3) The probability distribution (or density) function (PDF) can provide information about extreme events. • 4) Quantitative probabilistic products can be moreeffectively employed in decision-support systems
Historical context • Since the 1960’s it has been shown that combining human forecasts from different forecasters produces a group-mean forecast that is superior – “consensus forecasting”. • Forecasting paradigm – When models agree…good confidence in forecast – When models disagree…low confidence
Page 1 and 2: Chapter 7 Ensemble methods
Page 3 and 4: The essence of ensemble prediction
Page 5: Deterministic vs. ensemble predicti
Page 9 and 10: Compromises with ensemble predictio
Page 11 and 12: High predictability Example of diff
Page 13 and 14: Defining the ensemble members • I
Page 15 and 16: Verification of ensemble prediction
Page 17 and 18: An example of a real reliability di
Page 19 and 20: Rank histograms • Also called ver
Page 21 and 22: • Follow the same process for all
Page 23 and 24: Quantitatively interpreting • Man
Page 25 and 26: Calibration of ensembles • Calibr
Page 27 and 28: Time lagged ensemble • Advantage:
Page 29 and 30: Short-range ensemble prediction wit
Page 31 and 32: The ensemble mean • These fields
Page 33 and 34: 12 h Spaghetti plots: 36 h 84 h 552
Page 35 and 36: Probability of exceedance plots The
Page 37 and 38: “Stamp maps”
Page 39 and 40: An “electricity-gram” - an exam
Page 41 and 42: • Decision to protect will have c
Page 43: • Such an approach to decision ma

Chapter 7 - Ensemble methods.pdf

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