In this study, we take the 0-6 h probabilistic quantitative precipitation forecasts (PQPFs) as an example to illustrate how to evaluate the economic value (EV) of EPFs and offer an example for users to understand how to optimize their decision-making using EPFs. The PQPFs are generated from the ensemble prediction system (EPS) of the Local Analysis and Prediction System (LAPS) operated at the Central weather bureau (CWB) in Taiwan. With the cases of typhoon-associated rainfall prediction, the LAPS EPS has a good spread-skill relationship and good discrimination, and can provide skillful PQPFs. A total of 148 cases of 0-6-h PQPFs based on all typhoon cases in Taiwan during 2008 and 2009 were used to evaluate the EV of users.
It is commonly assumed that users' cost-loss ratio are explicitly known in the EV analysis. In addition, it has been shown that for a perfectly reliable forecast system (i.e., without forecast bias), the optimal probability threshold to maximize the EV for a particular user is equal to his or her cost-loss ratio. Unfortunately, information of users' cost or loss (and thus the cost-loss ratio) cannot be obtained explicitly in some cases. For example, farmers may wonder whether they should harvest their crops before the arrival of typhoon accompanied by heavy rainfall. Compared with normal harvesting, the action of harvesting in advance does not seem to need any cost; however, it may lead to a hidden loss since unripe crops are sold at a lower price. Furthermore, the farmers may also wonder what percentage of crops should be harvested in order to minimize losses if they harvest crops in advance. This example implies that the cost and loss of farmers can be derived based on the experience of preventive actions through the EV analysis. Although the farmers' expected expense based on the LAPS PQPFs will vary with the harvest percentage, the maximum EV provided by the LAPS remains the same regardless of the harvest percentage. In addition, the "full harvest" action yields minimum long-term average losses.