Agricultural food production and natural ecological systems depend on a range of seasonal climate indicators that describe seasonal patterns in climatological conditions. This paper proposes a probabilistic forecasting framework for predicting the end of the freeze-free season, or the time to a mean daily near-surface air temperature below 0 $^\circ$C (here referred to as hard freeze). The forecasting framework is based on the multi-model seasonal forecast ensemble provided by the Copernicus Climate Data Store and uses techniques from survival analysis for time-to-event data. The original mean daily temperature forecasts are statistically post-processed with a mean and variance correction of each model system before the time-to-event forecast is constructed. In a case study for a region in Fennoscandia covering Norway for the period 1993-2020, the proposed forecasts are found to outperform a climatology forecast from an observation-based data product at locations where the average predicted time to hard freeze is less than 40 days after the initialization date of the forecast on October 1.
翻译:本文提出一个概率预测框架,用于预测无冻结季节的结束,或预测平均每天近地空气温度低于0. ⁇ circ$C(这里称为硬性冻结)的时间;预测框架以哥白尼气候数据仓库提供的多模型季节性预报共同值为基础,使用生存分析得出的时间-活动数据技术进行生存分析;最初的平均每日温度预测在统计上是事后进行的,在时间-活动预测之前对每个模型系统进行了平均和差异的校正;在对1993-2020年期间覆盖挪威的芬诺斯卡尼亚地区进行的一项个案研究中,发现拟议的预测在10月1日预测的起始日期之后不到40天的地点,超出观测数据产品得出的气候预报。