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Keyword: model bias correction ×Clear all

3 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Correcting Weather and Climate Models by Machine Learning Nudged Historical Simulations

   Watt‐Meyer, Brenowitz, Clark et al. · Geophysical Research Letters · 2021

   The authors train a machine-learning correction on a nudged hindcast of a coarse FV3GFS model and couple it to the model, reducing some short-term errors and annual-mean precipitation error by about 20%.

   Unchecked3 claims
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   1. UncheckedA random forest can predict nudging tendencies from a nudged hindcast run with moderate skill, using only the model state as input.“We show that a random forest can predict the nudging tendencies from this hindcast simulation with moderate skill using only the model state as input.”
   2. UncheckedA weather model with a machine-learned correction showed no instability in year-long runs and had lower short-term forecast errors for three variables.“The coupled model shows no signs of instability in year‐long simulations and has significant reductions in short‐term forecast error for 500 hPa height, surface pressure and near‐surface temperature.”
   3. UncheckedAdding a machine-learned correction to a coarse weather model reduced the root mean square error of its annual-mean precipitation by about 20%.“Furthermore, the root mean square error of the annual‐mean precipitation is reduced by about 20%.”