Using Machine Learning to Predict Urban Canopy Flows for Land Surface Modeling

Developing urban land surface models for modeling cities at high resolutions needs to better account for the city‐specific multi‐scale land surface heterogeneities at a reasonable computational cost. We propose using an encoder‐decoder convolutional neural network to develop a computationally efficient model for predicting the mean velocity field directly from urban geometries. The network is trained using the geometry‐resolving large eddy simulation results. Systematic testing on urban structures with increasing deviations from the training geometries shows the prediction error plateaus at 15%, compared to errors sharply increasing up to 35% in the null models. This is explained by the trained model successfully capturing the effects of pressure drag, especially for tall buildings. The prediction error of the aerodynamic drag coefficient is reduced by 32% compared with the default parameterization implemented in mesoscale modeling. This study highlights the potential of combining computational fluid dynamics modeling and machine learning to develop city‐specific parameterizations.

开发用于高分辨率城市建模的城市地表模型需要以合理的计算成本更好地考虑城市特有的多尺度地表非均质性。我们提议使用编解码器卷积神经网络开发一种计算高效的模型，以便直接根据城市几何形状预测平均速度场。该网络使用解析几何的大涡模拟结果进行训练。对与训练几何形状偏差逐渐增大的城市结构进行的系统测试表明，预测误差稳定在15%，而在零模型中误差急剧上升至35%。这是因为训练后的模型成功捕捉到了压力阻力的影响，特别是对于高层建筑。与中尺度建模中采用的默认参数化相比，空气动力阻力系数的预测误差降低了32%。这项研究凸显了将计算流体动力学建模与机器学习相结合以开发特定于城市的参数化的潜力。