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Data Analysis and Assimilation
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To produce an accurate weather forecast, precise
knowledge of the current state of the atmosphere
(the 'initial conditions') is needed. This is achieved by
using observations and assimilating those observations
into the model. Many thousand observations are received
each day from a variety of observing types e.g. satellites,
aircraft, ships, buoys, radiosondes and land stations.
Various atmospheric parameters are routinely measured
including temperature, wind speed and direction and
humidity. Observations are assimilated into the model
using a process known as variational analysis.
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| What is data assimilation? |
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There are insufficient observations at any one time to
determine the state of the atmosphere. So if we want a
detailed complete picture, we need additional information.
This is available as knowledge of the behaviour and
probable structure of the atmosphere. For instance the
knowledge of the typical structure of a frontal
depression enables a human to draw an "analysis" of the
atmospheric state, based on scattered observations. To
advance beyond this subjective approach, the behaviour
of the atmosphere is embodied in a computer model. In
particular, knowledge of the evolution with time is
embodied in a forecast model. This enables us to use
observations distributed in time. The model also provides
a consistent means of representing the atmosphere.
Assimilation is the process of finding the model
representation which is most consistent with the
observations.
Usually, data assimilation proceeds sequentially in time.
The model organises and propagates forward the information
from previous observations. The information from new
observations is used to modify the model state, to be as
consistent as possible with them and the previous
information. It is the experience with operational
assimilation for NWP that there is usually more
information in the model state, from previous
observations, than there is in a new batch at a single
synoptic time. Thus it is important to preserve this in
the assimilation process; it is not just a question of
fitting the new data. Since all information has to be
represented within the model, it is important that the
model should be of sufficiently high resolution, with
physically realistic detail, to represent the information
observed. Some research is investigating non-sequential
data assimilation methods, especially four-dimensional
variational assimilation.
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The preceeding paragraphs were taken from Forecasting
Research Scientific Paper 34 by A.C. Lorenc which is
based on a lecture given at the Second WMO Symposium on
Assimilation of Observations in Meteorology and
Oceanography, held in Tokyo, Japan, 13-17 March 1995.
The full paper is available for download as a pdf
document. Download now (278K)
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| Products and uses of assimilation |
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Assimilation produces a convenient, comprehensive,
high-resolution, representation of the atmosphere. It
has been clearly demonstrated that the use of a computer
model is usually better (i.e. leads to better forecasts)
than the subjective human approach. The main practical
use of these assimilated "analyses" is for initialising
numerical weather prediction (NWP) forecasts. They are
also useful for climate and general circulation studies,
for instance in the calculations of fluxes, which make
use of their high resolution and comprehensive coverage.
However it must be remembered that the blend of observed
and modelled information will vary according to the
accuracy and coverage of the observations. So they must
be used with great care for model validation, and
climate change detection.
Very useful secondary products of a data assimilation
system are the statistics on the (mis-)fit of
observations to model. These can be more directly used
for model (in-)validation, and for the monitoring of
observing systems.
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