Data to initialise the model

At a major global weather forecasting centre, data from many sources are collected and fed into the model. This process is called initialisation. Figure 5.4 illustrates some of the sources of data for the forecast

Surface Observations (23158)

Satellite soundings (33640)

Figure 5.4 Some of the sources of data for input into the UK Met Office global weather forecasting model on a typical day. Surface observations are from land observing stations (manned and unmanned), from ships and from buoys. Radiosonde balloons make observations at up to 30 km altitude from land and from ship-borne stations. Satellite soundings are of temperature and humidity at different atmospheric levels deduced from observations of infrared or microwave radiation. Satellite cloud-track winds are derived from observing the motion of clouds in images from geostationary satellites. The number of observations of each type is given in brackets.

Satellite soundings (33640)

Figure 5.4 Some of the sources of data for input into the UK Met Office global weather forecasting model on a typical day. Surface observations are from land observing stations (manned and unmanned), from ships and from buoys. Radiosonde balloons make observations at up to 30 km altitude from land and from ship-borne stations. Satellite soundings are of temperature and humidity at different atmospheric levels deduced from observations of infrared or microwave radiation. Satellite cloud-track winds are derived from observing the motion of clouds in images from geostationary satellites. The number of observations of each type is given in brackets.

beginning at 0000 hours Universal Time (UT) on 20 May 2008. To ensure the timely receipt of data from around the world a dedicated communication network has been set up, used solely for this purpose. Great care needs to be taken with the methods for assimilation of the data into the model as well as with the data's quality and accuracy.

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Radiosonde balloons (1586)

Satellite cloud-track winds (7779) Figure 5.4 Continued

Satellite cloud-track winds (7779) Figure 5.4 Continued

Year

Figure 5.5 Errors (root mean square differences of forecasts of surface pressure in hPa compared with analyses) of UK Met Office forecasting models for the north Atlantic and Western Europe since 1966 for 24-hour (blue), 48-hour (green) and 72-hour (red) forecasts compared with assuming no change (purple). Note that 1 hPa = 1 mbar.

Year

Figure 5.5 Errors (root mean square differences of forecasts of surface pressure in hPa compared with analyses) of UK Met Office forecasting models for the north Atlantic and Western Europe since 1966 for 24-hour (blue), 48-hour (green) and 72-hour (red) forecasts compared with assuming no change (purple). Note that 1 hPa = 1 mbar.

used for initialisation (see box) or in the resolution of the model (the distance between grid points), the resulting forecast skill has increased. For instance, for the British Isles, three-day forecasts of surface pressure today are as skilful on average as two-day forecasts of ten years ago, as can be seen from Figure 5.5.

When looking at the continued improvement in weather forecasts, the question obviously arises as to whether the improvement will continue or whether there is a limit to the predictability we can expect. Because the atmosphere is a partially chaotic system (see box below), even if perfect observations of the atmospheric state and circulation could be provided, there would be a limit to our ability to forecast the detailed state of the atmosphere at some time in the future. In Figure 5.6 current forecast skill is compared with the best estimate of the limit of the forecast skill for the British Isles (similar results would be obtained with any other mid latitude situation) with a perfect model and near-perfect data. According to that estimate, the limit of significant future skill is about 20 days ahead.

Figure 5.6 Potential improvements in forecast skill if there were better data or a better model. The ordinate (vertical axis) is a measure of the error of model forecasts (it is the root mean square differences of forecasts of the 500 hPa height field compared with analyses). Curve (a) is the error of 1990 UK Met Office forecasts as a function of forecast range. Curve (b) is an estimate showing how, with the same initial data, the error would be reduced if a perfect model could be used. Curve (c) is an estimate showing the further improvement which might be expected if near-perfect data could be provided for the initial state. After a sufficiently long period, all the curves approach a saturation value of the average root mean square difference between any forecasts chosen at random.

Forecast skill varies considerably between different weather situations or patterns. In other words some situations are more 'chaotic' (in the technical sense in which that word is used - see box below) than others. One way of identifying the skill that might be achieved in a given situation is to employ ensemble forecasting in which an ensemble of forecasts is run from a cluster of initial states that are generated by adding to an initial state small perturbations that are within the range of observational or analysis errors. The forecasts provided from the means of such ensembles show significant improvement compared with individual forecasts. Further, ensemble forecasts where the spread amongst the ensemble is low possess more skill than those where the spread in the ensemble is comparatively high (Figure 5.8).3

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