Decide now or wait for the next forecast?

Weather forecasts, seasonal forecasts and climate projections can in principle help their users make "good" decisions, but using the information they provide in a optimal way is far from easy. Decisions that users may need to make include whether to act now or wait for the next forecast, or select which of a series of lagged forecasts to use, when using a forecast has both costs and benefits. In this presentation, I will provide an overview of some tools that may be used to support sound decision-making based on weather forecasts. I will first present an extension of the cost-loss model applied to weather forecasts to help users "decide when they should decide". This boils down to the question of whether to make a decision now, on the basis of the current weather forecast, or to wait for the next forecast before making the decision. The later forecast is hopefully more accurate, but delaying the decision may lead to higher costs, and may thus not be the optimal choice. An analysis of this problem shows that better decisions can be made if information describing potential forecast changes is made available.  I will next explore a number of ways in which such forecast information can be presented, from changes in forecast values to changes in forecast skill. I will specifically consider unconditional forecast metrics, namely information about forecast changes that can be derived from analysis of historical forecasts. I conclude by arguing that forecast providers should consider presenting forecast change information in order to help forecast users make better decisions.