文章基本信息

标题：Machine Learning Emulation of Urban Land Surface Processes
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作者：David Meyer ; Sue Grimmond ; Peter Dueben 等
期刊名称：Journal of Advances in Modeling Earth Systems
电子版ISSN：1942-2466
出版年度：2022
卷号：14
期号：3
页码：n/a-n/a
DOI：10.1029/2021MS002744
语种：English
出版社：John Wiley & Sons, Ltd.
摘要：Abstract Can we improve the modeling of urban land surface processes with machine learning (ML)? A prior comparison of urban land surface models (ULSMs) found that no single model is “best” at predicting all common surface fluxes. Here, we develop an urban neural network (UNN) trained on the mean predicted fluxes from 22 ULSMs at one site. The UNN emulates the mean output of ULSMs accurately. When compared to a reference ULSM (Town Energy Balance; TEB), the UNN has greater accuracy relative to flux observations, less computational cost, and requires fewer input parameters. When coupled to the Weather Research Forecasting (WRF) model using TensorFlow bindings, WRF‐UNN is stable and more accurate than the reference WRF‐TEB. Although the application is currently constrained by the training data (1 site), we show a novel approach to improve the modeling of surface fluxes by combining the strengths of several ULSMs into one using ML.