Machine Learning Emulation of Urban Land Surface Processes. (11th March 2022) - Meyer, David
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. Plain Language Summary: Climate change and densely populated cities make the task of urban weather and climate prediction more and more critical to our society. In this study, we use machine learning to improve the accuracy and efficiency of models predicting urban weather. We find great potential to use these types of machine learning models both as standalone tools and integrated into complex weather models. Key Points: Ensemble mean of several urban land surface models is accurately emulated using a neural network emulator The emulator reduces computational time and complexity compared to a typical urban land surface model Coupled to a numerical weather model, the emulator produces accurate and stable forecasts