Best practices for post-processing ensemble climate forecasts, part I: selecting appropriate recalibration methods
Sansom, PG; Ferro, CAT; Stephenson, DB; et al.Goddard, L; Mason, SJ
Date: 15 October 2016
Journal
Journal of Climate
Publisher
American Meteorological Society
Publisher DOI
Abstract
This study describes a systematic approach to selecting optimal statistical
recalibration methods and hindcast designs for producing reliable probability
forecasts on seasonal-to-decadal time scales. A new recalibration method is
introduced that includes adjustments for both unconditional and conditional
biases in the mean and ...
This study describes a systematic approach to selecting optimal statistical
recalibration methods and hindcast designs for producing reliable probability
forecasts on seasonal-to-decadal time scales. A new recalibration method is
introduced that includes adjustments for both unconditional and conditional
biases in the mean and variance of the forecast distribution, and linear
time-dependent bias in the mean. The complexity of the recalibration can
be systematically varied by restricting the parameters. Simple recalibration
methods may outperform more complex ones given limited training data. A
new cross-validation methodology is proposed that allows the comparison
of multiple recalibration methods and varying training periods using limited
data.
Part I considers the effect on forecast skill of varying the recalibration
complexity and training period length. The interaction between these
factors is analysed for grid box forecasts of annual mean near-surface
temperature from the CanCM4 model. Recalibration methods that include
conditional adjustment of the ensemble mean outperform simple bias correction
by issuing climatological forecasts where the model has limited skill.
Trend-adjusted forecasts outperform forecasts without trend adjustment at
almost 75% of grid boxes. The optimal training period is around 30 years for
trend-adjusted forecasts, and around 15 years otherwise. The optimal training
period is strongly related to the length of the optimal climatology. Longer
training periods may increase overall performance, but at the expense of very
poor forecasts where skill is limited.
Mathematics and Statistics
Faculty of Environment, Science and Economy
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