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Meteorological identification and observation are crucial in production activities closely related to meteorology, such as agricultural production. Currently, some machine learning methods applied in meteorological identification exhibit low richness of semantic features and weak transferability in pre-trained models, leading to insufficient feature extraction capabilities. Additionally, these models often have relatively simple classification layers, and they tend to train as a holistic model. To address the aforementioned shortcomings, this paper proposes a mechanism of fused training, constructing a meteorological identification model based on the enhanced fusion of EVA02 and Linear Support Vector Classification (LinearSVC). The model fine-tunes the pre-trained EVA02 backbone network, fully stimulating the qualitative transformation of transfer learning&#x2019;s high-level semantic abstractions and meteorological data representations. This enhances feature extraction capabilities. Additionally, the model integrates the Nystroem method with the LinearSVC algorithm for classification, further improving classification accuracy and the robustness of the model on small datasets. Through simulation experiments, the model achieves F1-scores on the public datasets MWD and WEAPD that surpass the current state-of-the-art methods by 0.75% and 9.89%, respectively, demonstrating the effectiveness of the proposed method.

A Study on Meteorological Recognition with a Fusion Enhanced Model Based on EVA02 and LinearSVC