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  1. Mar 2024
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    Machine Learning for Improving Surface-Layer-Flux Estimates
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    1. caozhuyin 10 Mar 2024
      Abstract

      结论：预测结果，好于MOST（MO估计系统地低估了湍流通量的大小，改善了与观测值和减小与观测通量偏离的总幅度。）,不同地点的泛化能力 不足：不含物质通量，预测结果待提升，结果因稳定性而异常，不同季节的泛化能力，运用了不易获得的变量（找到最小观测集）

      This effort demonstrates the potential of ML models to overcome some of the limitations related to the simple regression fits employed to determine the stability correction parameter (only a function of the inverse Obukhov length) and other limitations inherent to the MO formulation of surface-layer fluxes (e.g., assumed homogeneous and stationary conditions).

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