  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.
