Abstract Wine production is intricately dependant on the evolution of weather conditions in a given year. Therefore, seasonal weather forecasts coupled with empirical wine production models can play a critical role in the short to medium-term management of vineyards and wineries. The implementation of suitable and timely adaptation measures based on predicted wine productions may contribute to risk reduction and improve efficiency. The performance of seasonal forecasts of wine production in the Portuguese Douro & Port wine region (D&P WR) is here assessed for the first time. This application may serve as a case study to be potentially extended to other wine regions. Here, we develop a predictive logistic model of wine production based on monthly mean air temperatures and monthly total precipitation, averaged over the periods of February–March, May–June, and July–September, complemented with an autoregressive component of wine productions. The wine production in the D&P WR during the period 1950–2017 (68 years) is keyed into three classes: low, normal and high production years. The model reveals a correct estimation ratio of approximately 3/4 for the full period, and 2/3 when applied to independent 10%-random subsamples. We then evaluate the performance of the ECMWF 7-month seasonal weather forecasts, issued from February to August, in predicting the meteorological conditions relevant for the wine production in the D&P WR. Overall, the performance is satisfactory for the meteorological predictors. As for the weather forecasts coupled with the wine production model, results reveal that forecasts from May to August are strikingly the best performing, as 1) more observed data is integrated into the empirical model and 2) the skill of seasonal forecasts for summer months is higher. The operational application of these forecasts in the D&P WR is already foreseen. Given the encouraging results, we believe this case study and the established methodology may be tested and adapted to other wine regions worldwide, with obvious benefits for the winemaking sector.

中文翻译：

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杜罗和波特酒产量季节性预测的表现

摘要 葡萄酒生产错综复杂地取决于特定年份的天气条件的演变。因此，季节性天气预报与经验葡萄酒生产模型相结合，可以在葡萄园和酿酒厂的中短期管理中发挥关键作用。根据预测的葡萄酒产量实施适当和及时的适应措施可能有助于降低风险和提高效率。此处首次评估了葡萄牙杜罗和波特酒产区 (D&P WR) 葡萄酒产量季节性预测的表现。此应用程序可作为案例研究，有可能扩展到其他葡萄酒产区。在这里，我们基于月平均气温和月总降水量开发了葡萄酒生产的预测逻辑模型，2 月至 3 月、5 月至 6 月和 7 月至 9 月期间的平均值，并辅以葡萄酒生产的自回归成分。1950 年至 2017 年（68 年）期间 D&P WR 的葡萄酒产量分为三类：低产量、正常产量和高产量年份。该模型显示整个期间的正确估计比率约为 3/4，应用于独立的 10% 随机子样本时为 2/3。然后，我们评估了 ECMWF 从 2 月到 8 月发布的 7 个月季节性天气预报在预测与 D&P WR 葡萄酒生产相关的气象条件方面的表现。总体而言，气象预报器的性能令人满意。至于天气预报加上葡萄酒生产模式，结果表明，从 5 月到 8 月的预测表现最好，因为 1）更多的观察数据被整合到经验模型中，2）夏季月份的季节性预测技巧更高。已经预见到这些预测在 D&P WR 中的业务应用。鉴于令人鼓舞的结果，我们相信这个案例研究和既定的方法可能会被测试并适用于全球其他葡萄酒产区，对酿酒行业有明显的好处。