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Mar 2024
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Machine Learning for Improving Surface-Layer-Flux Estimates

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1. caozhuyin 10 Mar 2024
  
  in Public
  
  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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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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caozhuyin
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1. caozhuyin 07 Mar 2024
  
  in Public
  
  现阶段已有多种可靠的实测手段来获取2nC，如温度脉动仪、超声风速计和大口径闪烁仪等[2]，但是这些测量仪器不仅价格昂贵、使用成本高，在复杂下垫面情形还难以架设，从而制约了2nC 的测量。因此，国内外学者逐渐开展了利用较容易获得的常规气象参数对2nC 进行估算的模型研究，利用常规气象参数计算光学湍流参数的过程又称为光学湍流参数化。
  
  通量的获取难度
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caozhuyin

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