Based on the NECP/NCAR reanalysis dataset, the associations between the number of cold days (NCD) over East Asia (100–150° E, 25–55° N) and Arctic Oscillation (AO)/Arctic warming during 1956–2015 are explored. The results show the NCD was closely associated with AO during 1956–1990 and Arctic warming during 1991–2015. It reveals NCD over East Asia showed a downward trend and a significantly negative correlation with AO in the previous stage, while it presented an upward trend and notably positive association with Arctic warming in the later period. Meanwhile the increase in the earlier-stage AO will often be accompanied by the weakness of the Siberian high (SH), the Ural Mountains Blocking high (UBH), and the East Asian trough (EAT), and a “positive–negative–positive” wave band exist in the upper troposphere, which is linked with weakened northerly wind over East Asia. All these anomalies are unfavorable for the southward transportation of cold air, eventually leading to the decrease in NCD over East Asia. Additionally, when the near-surface temperature over the Arctic rises in the later period, on the one hand, SH reinforces and further results in more NCD over East Asia; on the other hand, the 1000–500 hPa thickness field displays a “north positive–south negative” pattern, which is conducive to the deceleration of the westerlies at mid-latitudes over Eurasia, and further bring about the enhancement of EAT and UBH, favoring the southward intrusion of cold air, finally, more NCD are generated.
In response to the problems of large computational volume and tedious computational process of fuzzy integrated evaluation, and general neural network models without clear water quality training criteria, this paper organically combines fuzzy rules, affiliation function, and neural network, and proposes a comprehensive method for the evaluation of water quality based on a T-S fuzzy neural network. On the three water quality monitoring data of six national key monitoring stations in Taihu Lake Basin, three evaluation methods—the one-factor evaluation method, the fuzzy integrated evaluation method, and the T-S fuzzy neural network evaluation method—were used to comprehensively evaluate water environment quality, and the results showed that the T-S fuzzy neural network method has the advantages of convenient calculation, strong applicability, and scientific results.
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