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As the global impact of climate change intensifies, the importance of climate monitoring is increasingly highlighted. This study explores the application of artificial intelligence (AI) in climate monitoring, particularly focusing on its potential and challenges in handling large-scale climate data and enhancing prediction accuracy. This study provides an in-depth exploration of the application of AI in the field of climate monitoring, with particular emphasis on the critical role of deep learning models in improving the accuracy of climate predictions. We specifically analyze the efficient performance of these models in processing large-scale climate data, as well as their advanced capabilities in identifying complex climate patterns and extreme weather events.At the same time, the research also points out the challenges faced by deep learning models in practical applications, including the lack of interpretability of the model and the high demand for computing resources. This paper discusses the implications of these challenges for the field of climate science and analyzes potential avenues to overcome them through technological innovation and algorithmic optimization.Furthermore, it presents a vision for optimizing AI models to better serve the field of climate science in the future.
Chuanming Zeng (Tue,) studied this question.