Academic Journal of Environment & Earth Science, 2023, 5(5); doi: 10.25236/AJEE.2023.050509.
Zhenyu Zhang1, Huaqing Peng2, Tingsen Tao2
1Yixing Meteorological Bureau, Yixing, Jiangsu, 214206, China
2Wuxi Meteorological Bureau, Wuxi, Jiangsu, 214135, China
This study conducted an EOF decomposition of the winter minimum temperature anomaly in the Yixing area from 2015 to 2020. The results indicate an increasing trend in the overall minimum temperature in the Yixing area, with the spatial mode corresponding to the geological heat capacity around the stations. Analysis of the field situation of ERA5 reanalysis data reveals that the difference in minimum temperature between neighboring stations is not significant when influenced by the southwest airflow. However, under clear sky conditions, the difference becomes apparent, with surrounding stations located in terrain favorable for cold air accumulation exhibiting anomalously low temperatures. The constructed feed-forward neural network model, implemented in PyTorch, demonstrates that incorporating a topographic factor assessment into the winter low-temperature forecast significantly improves forecast accuracy.
minimum temperature, EOF, synthetic analysis, deep learning
Zhenyu Zhang, Huaqing Peng, Tingsen Tao. Analysis of the difference of winter minimum temperature in Yixing area. Academic Journal of Environment & Earth Science (2023) Vol. 5 Issue 5: 53-59. https://doi.org/10.25236/AJEE.2023.050509.
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