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NWP and Reality

Owing to the non-linear nature of the forecast system, output from NWP models does not behave in a simple or regular way.   Errors can be due to:

• The inherent structure of the NWP model.
• Errors in the initial handling of information and data.
• Propagation and non-linear growth of analysis errors moving downstream.

NWP model programs and systems produce forecasts that are true to the physical representation and parameterisation of the atmosphere and its processes within the NWP model programs and systems but .   But minor inconsistencies in initial data can and do amplify. This  This is the whole point of the ensemble forecast so that uncertainty may be investigated.  But users Users should be aware of how best to deal with error growth and to understand how forecaster modification can improve forecastsforecasts can be improved by later modification.

Model forecasts rely heavily on the data available, particularly the addition of new and up-to-date data that can be added to the model background fields.   Users should be alert to Analysis increments show where there are significant differences between the observed data and background fields (analysis increments) as this points out one particular area .  These point out particular areas where the model expectations may be incorrect - .  However, there may be others that are have not been identified in this way. 

Users then have to must decide whether the data or the background is anomalous and or incorrect and then later assess whether the model has adapted, at least partially, to the latest data.  Adjustment  Adjustment to incorrect data, or non-fitting of correct data, both have the potential to amplify/cause forecast errors.

Finally, meteorological disturbances and the associated energy propagate downstream (downstream spread of influence).  This occurs both within NWP models and in reality.  Anomalies in the analysis will therefore affect forecasts well downstream during the forecast period - some differences weakening away, but others amplifying to yield a very incorrect result.  Some differences will perhaps weaken or amplify to yield some very incorrect results.  Users need to be alert to the assess the development and advance progress of disturbances that may potentially amplify and that arise arising from observed data departing from background fields.