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2.3.2
Numerical weather prediction
: Forecasts for lead
times in excess of several hours are essentially based
almost entirely on NWP. In fact, much of the
improvement in the skill of weather forecasts over
the past 20 years can be attributed to NWP
computer models, which are constructed using the
equations governing the dynamical and physical
evolution of the atmosphere. NWP models repre-
sent the atmosphere on a three-dimensional grid,
while typical operational systems in 2001 use a hori-
zontal spacing of 50–100 km for large-scale
forecasting and five to 40 km for limited area fore-
casting at the mesoscale. This will improve as more
powerful computers become available.
Only weather systems with a size several times the
grid spacing can be accurately predicted, so phenom-
ena on smaller scales must be represented in an
approximate way using statistical and other tech-
niques. These limitations in NWP models particularly
affect detailed forecasts of local weather elements,
such as cloud and fog and extremes such as intense
precipitation and peak gusts. They also contribute to
the uncertainties that can grow chaotically, ultimately
limiting predictability
2.3.3
Ensemble prediction
: Uncertainty always exists – even
in our knowledge of the current state of the atmos-
phere. It grows chaotically in time, with much of the
new information introduced at the beginning no
longer adding value, until only climatological infor-
mation remains. The rate of growth of this uncertainty
is difficult to estimate since it depends upon the three-
dimensional structure of the atmospheric flow. The
solution is to execute a group of forecasts – an
ensemble – from a range of modestly different initial
conditions and/or a collection of NWP models with
different, but equally plausible, approximations. If the
ensemble is well designed, its forecasts will span the
range of likely outcomes, providing a range of patterns
where uncertainties may grow. From this set of fore-
casts, information on probabilities can be derived
automatically, tailored to users’ needs
Forecast ensembles are subject to the limitations of
NWP discussed earlier. Additionally, since the group
of forecasts are being computed simultaneously, less
computer power is available for each forecast. This
requires grid spacings to be increased, making it more
difficult to represent some severe weather events of
smaller horizontal scale. Together with the limited
number of forecasts in an ensemble, this makes it
harder to estimate probabilities of very extreme and
rare events directly from the ensemble. Moreover it is
not possible to modify the NWP models used to
sample properly modelling errors, so sometimes all
models will make similar errors
2.3.4
Operational meteorologist:
There remains a critical role
for the human forecaster in interpreting the output
and in reconciling sometimes seemingly conflicting
information from different sources. This role is espe-
cially important in situations of locally severe weather.
Although vigorous efforts are being made to provide
forecasters with good quality systems such as inter-
active workstations for displaying and manipulating
the basic information, they still have to cope with
vast amounts of information and make judgements
within severe time constraints. Furthermore, fore-
casters are challenged to keep up to date with the
latest scientific advances.
3. Prediction at seasonal to interannual timescales
3.1 Beyond two weeks, weekly average predictions of
detailed weather have very low skill, but forecasts of
one-month averages, using NWP with predicted
seasurface temperature anomalies, still have signifi-
cant skill for some regions and seasons to a range of
a few months
3.2 At the seasonal timescale, detailed forecasts of
weather events or sequences of weather patterns are
not possible. As mentioned above, the chaotic nature
of the atmosphere sets a fundamental limit of the
order of two weeks for such deterministic predictions,
associated with the rapid growth of initial condition
errors arising from imperfect and incomplete obser-
vations. None the less, in a limited sense, some
predictability of temperature and precipitation anom-
alies has been shown to exist at longer lead times out
to a few seasons. This comes about because of inter-
actions between the atmosphere, the oceans, and the
land surface, which become important at seasonal
timescales
3.3 The intrinsic timescales of variability for both the land
surface and the oceans are long compared to that of
the atmosphere, due in part to relatively large thermal
inertia. Ocean waves and currents are slow in
comparison to their atmospheric counterparts, due
to the large differences in density structure. To the
extent that the atmosphere is connected to the ocean
and land surface conditions, then, a degree of
predictability may be imparted to the atmosphere at
seasonal timescales. Such coupling is known to exist
particularly in the tropics, where patterns of atmos-
pheric convection ultimately important to global scale
weather patterns are quite closely tied to variations in
ocean surface temperature. The most important
example of this coupling is found in the ENSO
phenomenon, which produces large swings in global
climate at intervals ranging from two to seven years
3.4 The nature of the predictability at seasonal timescales
must be understood in probabilistic terms. It is not
the exact sequence of weather that has predictability
at long lead times (a season or more), but rather some
aspects of the statistics of the weather – for example,
the mean or variance of temperature/precipitation