This series of Haby Hints investigates problems that cause a forecast to bust. A bust occurs when a certain weather parameter is expected but one or more factors cause the forecast to be wrong. This particular Haby Hint will focus on how time causes forecast problems.

The future can be predicted but it does get "cloudier" as time goes on. The reason the future can not be perfectly predicted is because it is impossible to be "all knowing" of the current conditions and because small scale influences magnify in importance over time.

The reason it is impossible to be all knowing of current conditions is because:

1) It is impossible to sample an "infinite" number of observation points. Since a "point" is undefined because volume can always be made smaller no matter how small it is, there are too many points to sample all of them.
2) Even if there could be an infinite number of points sampled for weather data, the sampling points would take up all the space in the atmosphere.
3) By the time the current conditions are sampled and known, time has progressed into the future. Since time moves forward and the increments between time steps can be infinitely small, "current conditions" is undefined. No matter how precise a time given is, it can always be made more precise.

The further in time a forecast prog product is, the less it can be trusted. Once beyond about the 5 day time period, small errors in the analysis become large ones. This can cause model progs to change substantially from one day to the next (i.e. the 7 day prog looking very different from next days 6 day prog). This is especially true when the atmosphere has a complex flow patterns as compared to a progressive flow pattern.

Time errors can be reduced by the following methods:

1) Study several different forecast models. A consensus of several different quality models on average will do better than using only one model.
2) Study forecast model ensembles. An ensemble is a run of the same model but using differing amounts and types of data. Just small changes to the initial data will result in the future states of the atmosphere looking more and more different over time (the butterfly effect). With ensembles you can see likely scenarios of what could happen instead of just one scenario. These are especially important to study for long term forecasting (5 days or more into the future) but are also helpful in shorter term forecasts.
3) If a model does not initialize well than the output progs will likely be inferior to models that did initialize well. Initialization analysis is the comparison of a model's zero hour forecast to the actual analysis charts.

In conclusion, it impossible to predict the future perfectly BUT in practical terms there will always be ways to improve the predictions.