For a forecast provider, predictability means striking a balance between the needs of an end user to make decisions and the limitations of what is scientifically possible. Scientifically the best information is probabilistic, normally generated from ensemble forecasts, but to make effective use of this information we need to understand the decision-making process of the user. This chapter will discuss some of the issues related to the calculation of relevant probabilities, and how to transmit that information to users and help them with decision-making.

Introduction

Predictability is not a new issue for forecast providers, such as the UK Met Office. Forecasters have always dealt with uncertainty, usually describing it subjectively with terms such as ‘mainly in the north-west’, or ‘a risk of patchy fog affecting the airfield, but you should get in OK’. The second example here immediately shows an understanding by the forecaster of the decision which the pilot has to make, and many forecasters' daily jobs involve providing bespoke services to individual customers. By understanding those customers' businesses, forecasters are able to provide them with information on some of the risks and uncertainties which impinge on their activities and affect their decision-making, and tune their forecasts accordingly. Nevertheless, two major changes in recent years are altering the way we deal with forecast uncertainty. First, new methods, such as ensemble prediction, are improving the ability of forecast providers to assess uncertainty quantitatively, in an objective and verifiable fashion.