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要旨

This article examines the potential to improve numerical weather
prediction (NWP) by estimating upper and lower bounds on predictability
by re-visiting the original study of Lorenz (1982) but applied to the
most recent version of the European Centre for Medium Range Weather
Forecasts (ECMWF) forecast system, for both the deterministic and
ensemble prediction systems (EPS). These bounds are contrasted with an
older version of the same NWP system to see how they have changed with
improvements to the NWP system. The computations were performed for the
earlier seasons of DJF 1985/1986 and JJA 1986 and the later seasons of
DJF 2010/2011 and JJA 2011 using the 500-hPa geopotential height field.
Results indicate that for this field, we may be approaching the limit of
deterministic forecasting so that further improvements might only be
obtained by improving the initial state. The results also show that
predictability calculations with earlier versions of the model may
overestimate potential forecast skill, which may be due to insufficient
internal variability in the model and because recent versions of the
model are more realistic in representing the true atmospheric evolution.
The same methodology is applied to the EPS to calculate upper and lower
bounds of predictability of the ensemble mean forecast in order to
explore how ensemble forecasting could extend the limits of the
deterministic forecast. The results show that there is a large potential
to improve the ensemble predictions, but for the increased
predictability of the ensemble mean, there will be a trade-off in
information as the forecasts will become increasingly smoothed with
time. From around the 10-d forecast time, the ensemble mean begins to
converge towards climatology. Until this point, the ensemble mean is
able to predict the main features of the large-scale flow accurately and
with high consistency from one forecast cycle to the next. By the 15-d
forecast time, the ensemble mean has lost information with the anomaly
of the flow strongly smoothed out. In contrast, the control forecast is
much less consistent from run to run, but provides more detailed
(unsmoothed) but less useful information.