Machine Learning Emulation of Urban Land Surface Processes

• URL: http://arxiv.org/abs/2112.11429v2
• Date: Wed, 22 Dec 2021 15:42:21 GMT
• Title: Machine Learning Emulation of Urban Land Surface Processes
• Authors: David Meyer, Sue Grimmond, Peter Dueben, Robin Hogan, Maarten van Reeuwijk
• Abstract summary: We develop an urban neural network (UNN) trained on the mean predicted flux from 22 urban land surface models (ULSMs) at one site. 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 parameters. Although the application is currently constrained by the training data (1 site), we show a novel approach to improve the modeling of surface flux by combining the strengths of several ULSMs into one using ML.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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