Machine learning for estimating parameters of a convective-scale model : a comparison of neural networks and random forests

Kurzfassung/Abstract

Errors and inaccuracies in the representation of clouds in convection-permitting
numerical weather prediction models can be caused by various sources, in�cluding the forcing and boundary conditions, the representation of orography,
and the accuracy of the numerical schemes determining the evolution of hu�midity and temperature. Moreover, the parametrization of microphysics and
the parametrization of processes in the surface and boundary layers do have
a significant influence. These schemes typically contain several tunable pa�rameters that are either non-physical or only crudely known, leading to model
errors and imprecision. Furthermore, not accounting for uncertainties in these
parameters might lead to overconfidence in the model during forecasting and
data assimilation (DA).
Traditionally, the numerical values of model parameters are chosen by manual
model tuning. More objectively, they can be estimated from observations by the so-called augmented state approach during the data assimilation [7].
Alternatively, the problem of estimating model parameters has recently been
tackled by means of a hybrid approach combining DA with machine learning,
more specifically a Bayesian neural network (BNN) [6]. As a proof of concept,
this approach has been applied to a one-dimensional modified shallow-water
(MSW) model [8].
Even though the BNN is able to accurately estimate the model parameters and
their uncertainties, its high computational cost poses an obstacle to its use in
operational settings where the grid sizes of the atmospheric fields are much
larger than in the simple MSW model. Because random forests (RF) [2] are
typically computationally cheaper while still being able to adequately represent
uncertainties, we are interested in comparing RFs and BNNs. To this end,
we follow [6] and again consider the problem of estimating the three model
parameters of the MSW model as a function of the atmospheric state.