基于极端梯度提升回归模型的日间边界层湍流耗散率估计OA

Atmospheric turbulence dissipation refers to the conversion of turbulence kinetic energy into thermal energy.The turbulent dissipation rate is a crucial parameter for quantifying turbulence intensity,mixing,and transport characteristics,and it is also an important indicator in engineering applications such as aviation safety and wind power generation.Radiosonde observation sare widely used for vertical atmospheric profiles of wind,temperature,and humidity.However,because turbulence dissipation occurs at the smallest continuous scales of the atmosphere(millimeter and millisecond scales),radiosondes cannot directly observe the dissipation rate.To overcome this limitation and enrich the vertical profile observations of turbulent dissipation rates,a deep learning approach is developed based on large eddy simulation data of the convective boundary layer.An XGBRegressor model is trained to predict dissipation based on the vertical profiles of key meteorological variables including wind,potential temperature and pressure,as well as their vertical gradients.Model performance is evaluated in terms of feature extraction,nonlinear modeling,and generalization capability.The results demonstrate that the proposed model exhibits decent diagnostic skills that outperform the classic Thorpe diagnostic model for dissipation rates.Furthermore,the model demonstrates good generalization capabilities to process different vertical resolutions other than the training datasets.This machine-learning model provides an alternative approach for profiling turbulence dissipation rates based on radiosonde data,and can be potentially used for the parameterization of turbulence dissipation rates in PBL schemes.