ABSTRACT Atmosphere–ocean teleconnections influence the accumulation and melt of snow in western Canada and can be useful in seasonal forecasting of snowmelt and runoff. Interannual variation in these atmosphere–ocean modes has been shown to influence the accumulation and melt of snow within British Columbia (BC), Canada. We investigate fall mean sea level pressure (MSLP) globally as a predictor of remotely sensed snowmelt dates within BC. We use the last day of continuous snow cover ( ) detected from time series satellite imagery acquired by the Moderate Resolution Imaging Spectroradiometer for the hydrological years 2000–2018. It has been shown that is correlated with continuous snow duration and is also of interest to seasonal forecasters. Global MSLP from the Fifth major global reanalysis produced by the European Centre for Medium-range Weather Forecasts was obtained over hydrological years 1979–2018. An S-mode (time versus location) principal component analysis was carried out on both datasets. The principal component scores were grouped using a k-means clustering routine. Using evolutionary feature selection, the subset of MSLP principal components that provided good linear discrimination of the clusters were found. We explore the atmospheric MSLP principal components that influence the timing of snowmelt over BC and use them to predict the clusters at a seasonal lead time.

中文翻译：

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根据平均海平面压力对加拿大不列颠哥伦比亚省的成簇降雪日期进行分类

摘要 大气-海洋遥相关影响加拿大西部积雪的积累和融化，可用于融雪和径流的季节性预测。这些大气-海洋模式的年际变化已被证明会影响加拿大不列颠哥伦比亚省 (BC) 的积雪和融化。我们在全球范围内调查秋季平均海平面压力 (MSLP)，作为 BC 内遥感融雪日期的预测指标。我们使用从 2000-2018 水文年中分辨率成像光谱仪获取的时间序列卫星图像中检测到的连续积雪的最后一天 ( )。已经表明，这与连续降雪持续时间相关，并且季节性预报员也很感兴趣。欧洲中期天气预报中心进行的第五次主要全球再分析的全球 MSLP 是在 1979-2018 年的水文年中获得的。对两个数据集进行 S 模式（时间与位置）主成分分析。使用 k 均值聚类程序对主成分分数进行分组。使用进化特征选择，找到了对集群提供良好线性区分的 MSLP 主成分子集。我们探索了影响 BC 地区融雪时间的大气 MSLP 主成分，并使用它们来预测季节性提前期的集群。使用 k 均值聚类程序对主成分分数进行分组。使用进化特征选择，找到了对集群提供良好线性区分的 MSLP 主成分子集。我们探索了影响 BC 地区融雪时间的大气 MSLP 主成分，并使用它们来预测季节性提前期的集群。使用 k 均值聚类程序对主成分分数进行分组。使用进化特征选择，找到了对集群提供良好线性区分的 MSLP 主成分子集。我们探索了影响 BC 地区融雪时间的大气 MSLP 主成分，并使用它们来预测季节性提前期的集群。