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

University of Cantabria
Carmen Sordo
University of Cantabria
Antonio S. Cofiño
University of Cantabria
In this work we describe a simple statistical method to validate seasonal forecasts,
comparing them with random predictions. This method provides an estimation of the statistical
significance of the skill and, hence, allows us to find out predictable situations where the
seasonal system significantly outperforms a random forecast. We also analyze the advantages
of post-processing the predictions with some appropriate statistical downscaling method. <strong>The</strong>
technique is applied to precipitation and temperature forecasts considering two regions with
different seasonal behavior: Peru (in the tropics) and Spain (mid-latitudes). Results show high
predictability over Peru during El Niño periods. Here, the use of a downscaling method clearly
improves the forecast skill. Over Spain the forecast signal is much weaker, but some
predictability related to El Niño and La Niña events is found.
Finally, some sensitivity studies are presented. On the one hand, we compare raw station
data with high resolution gridded interpolated data. On the other hand, different temporal
aggregation patterns are used for the analog downscaling method (daily, weekly and monthly),
comparing the different results obtained.
For this work we have used data from the DEMETER multimodel project.
Tests for evaluating rank histograms from ensemble forecasts
Speaker: Ian Jolliffe
Ian Jolliffe
University of Exeter
ian@sandloch.fsnet.co.uk
Cristina Primo
ECMWF
Rank histograms are often plotted in order to evaluate the forecasts produced by an
ensemble forecasting system - an ideal rank histogram is `flat'. It has been noted previously
that the obvious test of `flatness', the well-known χ2 goodness-of-fit test, spreads its power
thinly and hence is not good at detecting specific alternatives to flatness, such as bias or
over/under-dispersion. Other tests, which focus their power and are therefore more successful
in detecting such alternatives, will be discussed and illustrated.
<strong>The</strong> talk will emphasise new tests, which decompose the overall χ2 statistic, but the
Cramér-von Mises family of tests will also be described.
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