This study presents the first attempt to identify extreme rainfall events based on surrounding sea-level pressure anomalies, using neural network-based classification. Sensitivity analysis was also performed to identify the spatial importance of sea-level pressure anomalies. Three classification models were generated: the first classifies the patterns between extreme and regular rainfall events in the North West of England, the second classifies the patterns between extreme and regular rainfall events in the South East of England, and the third classifies between the patterns of extreme events in the North West and South East of England. All classifiers obtain accuracies between 60 and 65%, with precision and recall metrics showing that extreme events are easier to identify than regular events. Finally, a sensitivity analysis is performed to identify the spatial importance of the patterns across the North Atlantic, highlighting that for all three classifiers the local anomaly sea-level pressure patterns around the British Isles are key to determining the difference between extreme and regular rainfall events. In contrast, the pattern across the mid and western North Atlantic shows no contribution to the overall classifications.

这项研究首次尝试使用基于神经网络的分类，根据周围海平面压力异常来识别极端降雨事件。还进行了敏感性分析，以确定海平面压力异常的空间重要性。生成了三个分类模型：第一个分类了英格兰西北部极端和正常降雨事件之间的模式，第二个分类了英格兰东南部极端和正常降雨事件之间的模式，第三个分类了英格兰西北部极端和常规降雨事件之间的模式。英格兰西北部和东南部的极端事件。所有分类器的准确率都在 60% 到 65% 之间，准确率和召回率指标表明极端事件比常规事件更容易识别。最后，执行敏感性分析以确定横跨北大西洋的模式的空间重要性，强调对于所有三个分类器，不列颠群岛周围的局部异常海平面压力模式是确定极端和常规降雨事件之间差异的关键。相比之下，横跨北大西洋中西部的模式对整体分类没有贡献。