Correcting weather and climate models by machine learning nudged
historical simulations
Abstract
Due to limited resolution and inaccurate physical parameterizations,
weather and climate models consistently develop biases compared to the
observed atmosphere. These biases are problematic for forecasting on
timescales from medium-range weather to centennial-scale climate. Using
the FV3GFS model at coarse resolution, we propose a method of machine
learning corrective tendencies from a hindcast simulation nudged towards
an observational analysis. We show that a random forest can predict the
nudging tendencies from this hindcast simulation using only the model
state as input. This random forest is then coupled to FV3GFS, adding
corrective tendencies of temperature, specific humidity and horizontal
winds at each timestep. The coupled model shows no signs of instability
in year-long simulations and has significant reductions in short-term
forecast error for 500hPa height, surface pressure and near-surface
temperature. Furthermore, the root mean square error of the annual-mean
precipitation is reduced by about 20%.