The tenth IMSC, Beijing, China, 2007 - International Meetings on ...

Statistical Methods in CPC Climate Forecast
Speaker: Peitao Peng
Peitao Peng
CPC/NCEP/NOAA
peitao.peng@noaa.gov
Although dynamical models have been increasingly used in climate forecast, statistical
(or empirical) methods still have rooms to play, since dynamical models are far from perfect in
their settings. This presentation will review the status of climate forecast in CPC and introduce
some statistical methods used in operational forecast. Emphasis will be on the methods
developed by CPC scientists, such as the optimum climate normal (OCN) and the constructed
analog (CA). <strong>The</strong> OCN method, taking an average of latest years as the prediction for the
coming year, has shown valuable skills in catching decadal or longer timescale variability. <strong>The</strong>
CA method, approximating current state with a linear combination of historical sates and then
carrying forward in time while persisting the weights assigned to each historical case, is skillful
for forecasting sub-seasonal and seasonal variability.
In addition, our recent effort in consolidating forecasting tools will also be reported
Establishing non-trivial Skill in Seasonal Probability Forecasts: <strong>The</strong> Rise and Fall of Strawmen
Speaker: Leonard Smith
Professor Leonard Smith
London School of Economics, Centre for the Analysis of Time Series (CATS)
lenny@maths.ox.ac.uk
A clear demonstration of the utility for probability forecasts on seasonal time scales
would significantly increase their attractiveness to decision makers who already use ensemble
information on weather timescales. <strong>The</strong> challenges of robust evaluation of probability forecasts
on seasonal scales are illustrated by contrasting the performance of simulation models with
relatively simple data-based statistical models. Several challenges arise from the
small-number statistics available for seasonal lead-times, multi-model multi-initial condition
ensembles must be translated into probability density functions and evaluated based on only
dozens of forecast-verification pairs; this is in contrast to the weather case where the forecast
archive may consist of many hundreds of pairs. When the archive is small, and data are
precious, false confidence in the forecasts is likely due to over-fitting and the poor assignments
of weights to the various model structures. Three methods of weighting models ([i] equally,
[ii]in/out selection which rejects some models completely while assigning equal weights to
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