Midlatitude prediction on subseasonal timescales is difficult due to the chaotic nature of the atmosphere and often requires the identification of favorable atmospheric conditions that may lead to enhanced skill (“forecasts of opportunity”). Here, we demonstrate that an artificial neural network (ANN) can identify such opportunities for tropical‐extratropical circulation teleconnections within the North Atlantic (40°N, 325°E) at a lead of 22 days using the network's confidence in a given prediction. Furthermore, layer‐wise relevance propagation (LRP), an ANN explainability technique, pinpoints the relevant tropical features the ANN uses to make accurate predictions. We find that LRP identifies tropical hot spots that correspond to known favorable regions for midlatitude teleconnections and reveals a potential new pattern for prediction in the North Atlantic on subseasonal timescales.

中文翻译：

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可解释的神经网络确定的季节变化下的机会预测

由于大气层的混乱性质，难以预测亚季节时标的中纬度，并且常常需要确定有利的大气条件，这可能会导致技能的提高（“机会预测”）。在这里，我们证明了人工神经网络（ANN）可以利用网络对给定预测的信心，在22天的时间内识别出北大西洋（40°N，325°E）内热带-热带环流遥相关的这种机会。此外，ANN可解释性技术即分层相关性传播（LRP）可以精确定位ANN用于做出准确预测的相关热带特征。