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Keyword: cloud microphysics parameterization ×Clear all

3 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Potential and Limitations of Machine Learning for Modeling Warm‐Rain Cloud Microphysical Processes

   Seifert and Rasp · Journal of Advances in Modeling Earth Systems · 2020

   Neural networks were trained to represent warm-rain droplet processes using superdroplet benchmarks; they predicted rates well but did worse than an established scheme when used to solve the equations over time.

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   1. UncheckedNeural networks trained on simulated data predicted warm-rain process rates skilfully when compared with held-out testing data.“The resulting models make skillful predictions for the process rates when compared to the testing data.”
   2. UncheckedWhen used to solve the rate equations over time, the neural-network warm-rain models gave results not as good as an established warm-rain scheme.“However, when solving the ordinary differential equations, the solutions are not as good as those of an established warm‐rain parameterization.”
   3. UncheckedNeural networks did worse than an established scheme when run forward in time, which may point to ill-posedness in common two-moment warm-rain schemes.“This deficiency can be seen as a limitation of the machine learning methods that are applied, but at the same time, it points toward a fundamental ill‐posedness of the commonly used two‐moment warm‐rain schemes.”