Numerical models and data assimilation
systems have improved enormously over recent years so that today's
3-day forecast is as good as a 1-day forecast 20 years ago. Despite
this, an NWP forecast looking a few days ahead can frequently be
quite wrong, and even 1-day forecasts can occasionally have large
errors. The reason for this lies in the chaotic nature of the atmosphere,
which means that very small errors in the initial conditions can
lead to large errors in the forecast, the so-called butterfly effect.
This means that we can never create a perfect forecast system because
we can never observe every detail of the initial state of the atmosphere.
Tiny errors in the initial state will be amplified such that after
a period of time the forecast becomes useless. This sensitivity
varies from day to day, but typically we can forecast the main
weather patterns reasonably well up to about three days ahead.
Beyond that uncertainties in the forecasts can become large.
To cope with this uncertainty, we use Ensemble Forecasts. Instead
of running just a single forecast, the model is run a number of
times from slightly different starting conditions. The complete
set of forecasts is referred to as the ensemble and individual
forecasts within it as ensemble members. The initial differences
between ensemble members are very small so that if we compared
members with observations it would be impossible to say which members
fitted the observations better. All members are therefore
equally likely to be correct, but when we look several days ahead
the forecasts can be quite different. Some days the forecasts from
different ensemble members are all quite similar, which gives us
confidence that we can issue a reliable forecast. On other days
the members can differ radically and then we have to be more cautious.
As an illustration of the sensitivity, the following charts show
an example of two equally valid 4-day forecasts of the surface
pressure (isobars) from an ensemble forecast. Differences at the
start of the forecast, in the top row, are so small that we cannot
tell which is more accurate, but the forecasts below are very different!
(In reality, of course, the weather systems over the British Isles
at Day 4 would probably have originated further west at the start
of the forecast, but it illustrates how very similar ensemble members
grow apart during the forecast.)
Forecast A on the left predicts a deep area of low pressure
over Ireland bringing strong winds and rain to much of the British
Isles; forecast B on the right predicts that the high pressure
over the Atlantic will be much stronger and does not develop
the low at all, and thus suggests fine weather although with
a cool northerly wind and the risk of showers in the S and E.
Clearly in this situation a forecaster who has access to only
a single model forecast is in danger of issuing a forecast which
could go seriously wrong. By using this sort of information from
an ensemble with many members, Met Office forecasters are able
to assess the range of possible scenarios and issue advice on
the probabilities and risks associated with them. Ensemble Prediction
is thus all about Risk Management in weather forecasting.
operational medium-range ensemble forecasting, the Met Office makes
use of the Ensemble Prediction System (EPS) run by the European
Centre for Medium-Range Weather Forecasts (ECMWF). ECMWF is
an international organisation supported by many European states,
including the UK, and specialises in NWP for medium-range prediction.
ECMWF does not issue weather forecasts itself, but distributes
its products to the National Meteorological Services of its member
states, including the Met Office, for use in production of weather
As part of the THORPEX programme, the Met Office is carrying out
research into the possible use of multi-model ensemble techniques
for medium-range weather forecasting, with the emphasis on improving
the forecasting of high-impact weather.
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|The ECMWF Ensemble
The ECMWF EPS consists of 51 forecasts
run twice daily using the ECMWF global forecast model with a horizontal
resolution of around 80km. One member, called the control forecast,
is run directly from the ECMWF analysis, our best guess at the
initial state of the atmosphere. Initial conditions for the other
50 members are created by adding small "perturbations" to this
analysis. These perturbations are designed to identify those regions
of the atmosphere which are most likely to lead to errors in the
forecast on each particular occasion. Small random variations in
the model itself are also introduced to allow for some of the approximations
which have to be made in how the model represents the atmosphere.
The charts below show an example of surface pressures
(isobars) for all 51 members of the ECMWF ensemble for a sample
4-day forecast from November 2003. On many occasions the atmosphere
is much more predictable than this, but this illustrates the level
of uncertainty that regularly occurs in forecasts only a few days
Clearly an ensemble forecast contains a huge amount of information
which we need to condense for both forecasters and end-users! Below
we describe some of the ways we can do this, including the use of
More about the EPS from
the ECMWF user guide
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uncertainty in forecasts
Forecast uncertainty results
In June 2006 we asked for feedback on how uncertainty
in the forecast could be presented. More than a thousand respondents
completed a short questionnaire which showed a variety of
options for presenting uncertainty in the five-day temperature
a summary of the findings
Ensemble prediction allows the uncertainty
in forecasts to be assessed quantitatively. This uncertainty
can be passed on to users of the forecast in several ways.
For example, we can provide a range of possible
values for a forecast parameter (such as temperature or windspeed)
such that we know how confident we are that the actual value will
fall within that range.
For the scientist this is very similar to putting an error bar
on the forecast. The example below shows how maximum and minimum
temperatures for each day can be given a range of uncertainty.
The full length of each vertical line represents the 95% confidence
range, while the central bar represents a 50% confidence range.
The horizontal line across this bar is the mid-point of the distribution,
and may be used to estimate the most likely temperature.Thus for
the first night we can be 95% certain the minimum temperature will
be between 8 and 13 Celsius, and 50% certain it will be between
about 11 and 12 Celsius.
Alternatively we can estimate the probability of certain events
happening, for example of the temperature falling below freezing
or the wind speed reaching gale force. Probability forecasts
can help users to assess the risks associated with particular
weather events which are important to them.
information on uncertainty in forecasts
examples of how uncertainty can be presented in forecasts
about probability forecasts
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| The Met Office Ensemble
Post-Processing System (Previn)
Ensemble forecasts from ECMWF are post-processed
to produce a wide variety of chart displays to aid forecasters,
and also high-quality probability forecasts which can be supplied
to customers. Two examples illustrate how these can be used.
A daily task for our weather forecasters is to assess
the most likely developments of major weather systems for several
days ahead - most of our forecast products depend on them getting
this right! Among the most important systems for NW Europe are
Atlantic low pressure centres (cyclones). To aid the forecasters
in assessing the most likely positions and movements of lows, cyclone
tracks predicted by all the ensemble members are plotted on a single
chart. The example below for a 3-day forecast in February 2004
shows that it is most likely that a low pressure will move northeastwards
to the west of the British Isles. However for anyone interested
in risks, it is also worth noting that there is a small chance
that the low will pass further south over Scotland, bringing rain
and wind much further south across the country. This is just one
example of the types of charts used to summarise ensemble forecasts
The ensemble provides useful estimates of probabilities
for many weather events, but these can often be further improved
by statistical post-processing. Calibrated probability forecast
data are generated daily for over 300 sites worldwide. The
graph below shows an example of a calibrated 5-day forecast of
the relative probabilities of different temperatures at Heathrow
Airport for midday on 28th February 2004.
Clearly the most likely temperature is around zero Celsius, with
a 27% probability of temperature below freezing (right). However
there is also a real possibility of quite mild temperatures above
6 Celsius. On a balance of probabilities, information like this
was used by forecasters on this occasion to issue early warnings
of a cold spell of wintry weather several days ahead. In the
event Heathrow experienced temperatures below freezing overnight
with light snow, rising to a maximum
around 5 Celsius and falling rapidly to 1 Celsius in heavy snow
at 1720. On this occasion the balance of probabilities provided
good guidance, and this should normally been the case. However
the lower probability events should also be expected to occur on
some, fewer, occasions. Had the temperature actually been 8-12
Celsius the warning would have seemed excessive, but would still
have been fully justified on the basis of the evidence available
at the time it was issued.
These site-specific probability forecasts are verified routinely to monitor performance and
demonstrate the capability of statistical post-processing.
| Short-range ensemble
Current operational use of ensembles is
restricted to application of the ECMWF EPS for medium-range prediction.
Important uncertainty can also occur in short-range forecasts. Usually
at short-range, up to 3 days ahead the general weather pattern is
well forecast by a single model run, but there can still be uncertainty
in the resulting fine details of the weather, for example in the amount,
location or timing of rainfall. On rare occasions there can also be
significant uncertainty in the large-scale weather patterns, and these
occasions can be particularly important as they may be associated
with severe weather developments. Research is currently being undertaken
to investigate whether ensembles designed specifically for short-range
use can help in quantifying the uncertainty in these areas.
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