Patel Pratiman, Kalyanam Rajesh, He Liu, Aliaga Daniel, Niyogi Dev
Department of Computer Sciences, Purdue University, 305 N University St, West Lafayette, 47907 IN, USA.
Interdisciplinary Programme in Climate Studies, Indian Institute of Technology Bombay, Powai, Mumbai, 400076 Maharashtra, India.
PNAS Nexus. 2023 Feb 3;2(3):pgad027. doi: 10.1093/pnasnexus/pgad027. eCollection 2023 Mar.
Herein, we introduce a novel methodology to generate urban morphometric parameters that takes advantage of deep neural networks and inverse modeling. We take the example of Chicago, USA, where the Urban Canopy Parameters (UCPs) available from the National Urban Database and Access Portal Tool (NUDAPT) are used as input to the Weather Research and Forecasting (WRF) model. Next, the WRF simulations are carried out with Local Climate Zones (LCZs) as part of the World Urban Data Analysis and Portal Tools (WUDAPT) approach. Lastly, a third novel simulation, Digital Synthetic City (DSC), was undertaken where urban morphometry was generated using deep neural networks and inverse modeling, following which UCPs are re-calculated for the LCZs. The three experiments (NUDAPT, WUDAPT, and DSC) were compared against Mesowest observation stations. The results suggest that the introduction of LCZs improves the overall model simulation of urban air temperature. The DSC simulations yielded equal to or better results than the WUDAPT simulation. Furthermore, the change in the UCPs led to a notable difference in the simulated temperature gradients and wind speed within the urban region and the local convergence/divergence zones. These results provide the first successful implementation of the digital urban visualization dataset within an NWP system. This development now can lead the way for a more scalable and widespread ability to perform more accurate urban meteorological modeling and forecasting, especially in developing cities. Additionally, city planners will be able to generate synthetic cities and study their actual impact on the environment.
在此，我们介绍一种利用深度神经网络和反演建模生成城市形态测量参数的新方法。我们以美国芝加哥为例，将从国家城市数据库和访问门户工具（NUDAPT）获取的城市冠层参数（UCPs）用作天气研究与预报（WRF）模型的输入。接下来，作为世界城市数据分析和门户工具（WUDAPT）方法的一部分，以局部气候区（LCZs）进行WRF模拟。最后，进行了第三次新颖的模拟，即数字合成城市（DSC），其中利用深度神经网络和反演建模生成城市形态测量，随后为局部气候区重新计算城市冠层参数。将这三个实验（NUDAPT、WUDAPT和DSC）与Mesowest观测站进行了比较。结果表明，引入局部气候区可改善城市气温的整体模型模拟。DSC模拟产生的结果与WUDAPT模拟相当或更好。此外，城市冠层参数的变化导致城市区域和局部辐合/辐散区内模拟温度梯度和风速出现显著差异。这些结果首次成功地在数值天气预报（NWP）系统中实现了数字城市可视化数据集。这一进展现在可为更具扩展性和广泛应用的能力铺平道路，以进行更准确的城市气象建模和预报，尤其是在发展中城市。此外，城市规划者将能够生成合成城市并研究它们对环境的实际影响。
