211 / 218
[
] 209
endorsed by the Executive Council at its fifty-fourth
session in June 2002. It is provided for the informa-
tion of all those with an interest in the scientific
foundations and limitations of weather and climate
forecasting on timescales from minutes and hours
through to decades and centuries.
2. The science of weather forecasting
Dynamical and physical processes within the atmosphere,
and interactions with the surroundings (e.g. land, ocean,
and ice surfaces), determine the evolution of the atmosphere
and, hence, the weather. Scientifically-based weather fore-
casts are possible if the processes are well enough
understood and if the current state of the atmosphere is well
known enough, for predictions to be made of future states.
Weather forecasts are prepared using a largely systematic
approach, involving observation and data assimilation,
process understanding, prediction and dissemination. Each
of these components has, and will continue, to benefit from
advances in science and technology.
2.1 Observations and data assimilation
2.1.1 Over the past few decades, substantial advances in
science have resulted in improved and more efficient
methods for making and collecting timely observa-
tions, from a wide variety of sources including radar
and satellites. Using these observations in scientifi-
cally-based methods has caused the quality of
weather forecasts to increase dramatically, so that
people around the world have come to rely on
weather forecasts as a valued input to many decision-
making processes
2.1.2 Computer-generated predictions are initialised from
a description of the atmospheric state built from past
and current observations in a process called data
assimilation, which uses the NWP model (see para-
graph 2.3.2) to summarize and carry forward in time
information from past observations. Data assimila-
tion is very effective at using the incomplete coverage
of observations from various sources to build a coher-
ent estimate of the atmospheric state. But, like the
forecast, it relies on the NWP model and cannot
easily use observations of scales and processes not
represented by the model
2.1.3 The international scientific community is emphasiz-
ing the still very poorly observed areas as being a
limiting factor in the quality of some forecasts. As a
consequence, there is a continued need for improved
observation systems and methods to assimilate these
into NWP models.
2.2 Understanding of the atmosphere: inherent limi-
tations to predictability
2.2.1 The scientific understanding of physical processes
has made considerable progress through a variety of
research activities, including field experiments, theo
retical work and numerical simulation. However,
atmospheric processes are inherently non-linear and
not all physical processes can be understood or repre-
sented in NWP models. For instance, the wide variety
of possible cloud water and ice particles must be
highly simplified, as are small cumulus clouds that
can lead to rain showers. Continued research effort
using expected improvements in computer technol-
ogy and physical measurements will enable these
approximations to be improved. Even then, it will
still not be possible to represent all atmospheric
motions and processes
2.2.2 There is a wide spectrum of patterns of atmospheric
motion, from the planetary scale down to local turbu-
lence. Some are unstable and are arranged so that
flow is amplified using, for example, energy from
heating and condensation of moisture. This property
of the atmosphere means that small uncertainties
about the state of the atmosphere will also grow, so
that eventually the unstable patterns cannot be
precisely forecast. How quickly this happens depends
on the type and size of the motion. For convective
motions such as thunderstorms, the limit is of the
order of hours, while for large scales of motion it is
of the order of two weeks.
2.3 Weather prediction
2.3.1
Nowcasting:
Forecasts extending from 0 out to 6 to
12 hours are based upon a more observations-inten-
sive approach and are referred to as nowcasts.
Traditionally, nowcasting has focused on the analy-
sis and extrapolation of observed meteorological
fields, with a special emphasis on mesoscale fields
of clouds and precipitation derived from satellite
and radar. Nowcast products are especially valuable
in the case of small-scale hazardous weather
phenomena associated with severe convection and
intense cyclones. In the case of tropical cyclones,
nowcasting is an important detection and subse-
quent short-term prediction approach that provides
forecast value beyond 24 hours in some cases.
However, the time rate of change of phenomena
such as severe convection is such that the simple
extrapolation of significant features leads to a
product that deteriorates rapidly with time – even
on timescales of the order of one hour. Thus,
methods are being developed that combine extrap-
olation techniques with NWP, both through a
blending of the two products and through the
improved assimilation of detailed mesoscale obser-
vations. These are inherently difficult tasks and,
although accuracy and specificity will improve over
coming years, these products will always involve
uncertainty regarding the specific location, timing
and severity of weather events such as thunder and
hail storms, tornadoes and downbursts