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Research on meteorological environment analysis and simulated deep learning algorithms supervised by physical and chemical models.

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At 14:00- 15:30, on August 10th 2023 (Thursday)

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By establishing a mathematical description of physical and chemical processes for numerical models, deep learning can also realize the learning and restoration of data from the perspective of mathematical statistics. The author has developed some deep learning methods with pattern data as constraints to achieve a multitude of functions such as generating meteorological environmental data analysis fields and simulating and predicting atmospheric environmental variables. In these deep learning models, the numerical solution process of partial physical and chemical equations can be explicitly presented through deep learning modules, while the physical and chemical parameters and complex processes are implicitly learned through encoding and decoding modules. At the structural level, each module of the algorithm model is matched to the constraint equation set of each process. In this way, a great many functions such as reasonable parameter estimation analysis, internal process analysis, and sensitivity analysis can be achieved together with favorable simulation results.

Guest Information

Dr. Lv Baolei, phD of the Department of Earth Science, Tsinghua University, is a senior engineer. He has engaged in intelligent identification, and data fusion algorithms as well as comprehensive meteorological algorithm, and application development for meteorological observation and meteorological data analysis and application after joining Huayunshengda (Beijing) Meteorological Technology Co., Ltd. in 2017, Dr. Lv was awarded the Young Meteorological Talent of China Meteorological Administration in 2022, with 28 papers published, including 17 SCI papers as the first or corresponding author.