Chapter 7 - Ensemble methods.pdf

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Example with 4 ensemble members and 5 intervals, for variable P For this location and time t forecast , the observed P (X obs ) is lower than any of the forecast Ps and the observation is thus in I 1 .
• Follow the same process for all other pairs of observations and forecasts at this time. • Calculate the total number of observations in each of the five intervals, or ranks, and plot a histogram of the frequency. • This provides a graphical view of how the ensemble of forecasts relates to the observations. • A non-uniformity in the distribution reveals systematic errors in the ensemble.
Page 1 and 2: Chapter 7 Ensemble methods
Page 3 and 4: The essence of ensemble prediction
Page 5 and 6: Deterministic vs. ensemble predicti
Page 7 and 8: Historical context • Since the 19
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: Rank histograms • Also called ver
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

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