Interest is growing in weather insurance within the agricultural sector but its use has been limited by the difficulty in defining the appropriate weather event and the lack of agreement on how to price the product. In this paper we develop a new insurance pricing method for weather insurance under situations where volume returns depend not only on the occurrence of the weather event, but also its timing. The method is applied to the pricing of weather insurance for ice wine in the Niagara Peninsula of southern Ontario. Because the harvest quantity of grapes for ice wine degrades over time, the strike value on the weather event measured as harvestable hours is random. This random strike, we developed a systematic approach to valuing the insurance using first, the single index model to capture inter-temporal covariance effects, and then a Monte Carlo simulation protocol to estimate the premium. While this study investigated a model unique to ice wine production in particular, the ideas can be extended to a number of other agricultural situations in which weather affects critical timing in the production process.