资源环境科技发展态势分析平台

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 x 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.