BAMOS
Jun 2018
Figure 2. Typical data coverage for a run of the European Centre for medium-range Weather Forecasting (ECMWF) operational
global model: a) radiosonde coverage; and b) coverage from the Advanced Microwave Sounding Unit (AMSU) on polar-orbiting
satellites with over 600,000 observations covering the Northern and Southern Hemispheres equally. (Source: ECMWF web site)
This approximation (or discretisation) introduces resolved and
unresolved scales: the resolved scales are those larger than
a few multiples of the spacing of the discrete points, while
the unresolved scales are those scales smaller than that. The
physical processes on scales close to or smaller than the grid
spacing cannot be properly resolved by such a discretisation,
and must be represented in terms of the resolved quantities.
In other words, the effects of physical processes too small to
be resolved explicitly must be incorporated through empirical
laws or parameterised. For example, in most models, convective
clouds are part of the unresolved scales and their effects, which
are critical to the success of the forecast, must be added in
the computer model through rules that reflect the underlying
physics and the state of the atmosphere on the resolved scales.
Much of the predictive skill of the computer models hinges
on how well the unresolved physics is represented by the
parameterisations.
Numerical weather prediction is not the only way forecasts
are made. Forecasts for a few minutes to a few hours, called
nowcasts, are usually based on the statistical extrapolation
of observations from radar, satellite, lightning networks and
conventional instruments. Nowcasts are made most often
for rain and thunderstorms. Increasingly, observation-based
nowcasts are blended with short-range computer model
forecasts to generate short-term forecasts.
Forecasters interpret the model predictions and nowcasts,
and communicate this information as weather forecasts and
warnings to the general public and specific users. At times,
forecasters use their local knowledge to adjust the objective
forecasts or to make specialised forecasts for a particular place.
An example of the former is fog prediction for the aviation
industry, while an example of the latter is fire weather forecasts
for firefighters. National weather agencies around the world
share their model predictions in real-time, and in addition to
the output from the suite of models operating on the Bureau’s
supercomputer, forecasters in Australia have access to the
predictions from at least four international computer models.
3.
The Importance of Observations and the
Initial State
Making a weather forecast requires a detailed knowledge of the
current state of the atmosphere, land surface and ocean. (From
a technical mathematical perspective, weather prediction is
an initial-value problem.) The current state of the atmosphere
is known as the analysis. The main sources of input data for
numerical weather forecasts are radiosondes (weather balloons)
and satellites, as well as commercial aircraft, floating buoys,
ships, winds derived from the measured motion of clouds (called
cloud drift winds), radars and surface-based measurements
from automatically recording manual and automated weather
stations. Figure 2 shows the typical data coverage used in
making a forecast with the ECMWF (European Centre for
Medium-range Weather Forecasting) operational global
numerical weather prediction model. The chart for radiosonde
soundings (TEMP) shows the poor coverage over ocean areas
and the Southern Hemisphere (Figure 2a). In contrast, the chart
for observations from the Advanced Microwave Sounding Unit
(AMSU), now carried on several polar orbiting satellites, shows
over 600,000 observations available for the 1200 UTC analysis,
and the coverage in the Southern Hemisphere just the same as
the Northern Hemisphere (Figure 2b).
The analysis blends the observations with short-term computer
model forecasts for the current time, taking into account
estimates of the respective errors in the observations and
computer model forecasts. This process of estimating the
current state is known as data assimilation, and it is one of
the most important reasons for the great improvement in the
predictive skill of numerical weather forecasts.
Weather forecasts can be highly sensitive to small changes in
the analysis. The observations used to construct the analysis
are affected by instrumental and sampling errors. In most
circumstances these initial errors result in forecast errors
which can amplify in time. Moreover, the computer models
themselves have errors as they are based on approximate forms
of the governing laws of physics.
In the 1960s, recognition of this sensitivity of the forecast
to small changes in the initial conditions led the American
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