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Sea surface temperature (SST) is one of the most important physical variables of the ocean, which provides the basic information of the climate system. Accurately SST forecasting system has a comprehensive and essential application. In recent years, AI-based SST forecasting methods have become popular and shown great potential. Based on the convolutional long and short-term memory neural network (ConvLSTM), this paper studies the influence of multi-scale input fields on SST prediction in the northern South China Sea. Multi-dimensional ensemble empirical mode decomposition method (MEEMD) is used to decompose the average daily SST into the spatial eigenmodes of differentiated scales. Input different combinations of eigenmodes into ConvLSTM for training and prediction experiments. Results show that when using all four SST eigenmodes, the RMSE of the predicted SST in 1−7 days is 0.4−0.8℃, decrease 0.2−1.2℃ compared with the original SST alone; the MAPE is 1%−6%, decrease 0.5%−10%; the spatial correlation coefficient is 99.5%−96.5%, improve 0.5%−3.5%. Moreover, the randomized experiments also further proved the method has a high universality. The prediction model based on deep learning needs to select the appropriate training data in order to further improve its prediction accuracy. This paper preliminarily explores the integration of artificial intelligence methods and physical concepts in SST prediction, which can provide some reference for future research.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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