ABSTRACT: Normalized anomalies in air temperature and precipitation patterns derived from monthly data series collected at 28 in situ meteorological stations in the Adriatic-Ionian region between 1961 and 2015 were mapped by using the self-organizing map (SOM) method—an unsupervised neural network method—and connected with hemispheric indices in the atmosphere. Four indices were used: North Atlantic Oscillation (NAO), East Atlantic (EA), East Atlantic/West Russia (EAWR) and Scandinavian (SCA). Two experiments were conducted: the first combined normalized temperature anomalies with the indices, and the second combined normalized precipitation anomalies with the indices. Precipitation data better fit the SOM model than temperature data, while the SOM model has the best performance at a 3 × 3 matrix size. Region-wide negative precipitation anomalies are related to a combination of positive NAO and EAWR, and negative SCA indices, and vice versa. Positive air temperature anomalies are related to the positive EA index and vice versa. The mapped relations between the NAO index and precipitation anomalies indicate a nonlinear connection, because positive index values resemble 2 different precipitation patterns. The SOM-based nonlinear clustering might be useful in determining and forecasting precipitation and air temperature patterns from hemispheric indices.

中文翻译：

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自组织图得出的亚得里亚海-爱奥尼亚州空气温度和降水模式：与半球指数的关系

摘要：气温和降水量模式的归一化异常，源于28个原位收集的每月数据序列使用自组织图（SOM）方法（一种无监督的神经网络方法），对1961年至2015年亚得里亚海爱奥尼亚州地区的气象站进行了测绘，并与大气中的半球指数联系起来。使用了四个指数：北大西洋涛动（NAO），东大西洋（EA），东大西洋/俄罗斯西部（EAWR）和斯堪的纳维亚（SCA）。进行了两个实验：第一个结合指标的归一化温度异常，第二个结合指标的归一化降水异常。降水数据比温度数据更适合SOM模型，而SOM模型在3×3矩阵大小下具有最佳性能。区域范围内的负降水异常与NAO和EAWR的正值，SCA指数的负值有关，反之亦然。气温正异常与EA指数正相关，反之亦然。NAO指数与降水异常之间的映射关系表明存在非线性关系，因为正指数值类似于2种不同的降水模式。基于SOM的非线性聚类在从半球指数确定和预测降水和气温模式方面可能很有用。