Machine Learning for Estimating Parameters of a Convective-Scale Model: A Comparison of Neural Networks and Random Forests

MCML Authors

Mohammad Hossein Shaker

→ Group Eyke Hüllermeier
 Artificial Intelligence and Machine Learning

Eyke Hüllermeier

Prof. Dr.

Principal Investigator

Artificial Intelligence and Machine Learning

Abstract

Errors and inaccuracies in the representation of clouds in convection-permitting numerical weather prediction models can be caused by various sources, including the forcing and boundary conditions, the representation of orography, and the accuracy of the numerical schemes determining the evolution of humidity 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 parameters 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).<br>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].<br>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.

inproceedings

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Computational Intelligence 2022

32nd Workshop on Computational Intelligence of the VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik. Berlin, Germany, Dec 01-02, 2022.

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Authors

S. Legler • T. Janjic • M. H. Shaker • E. Hüllermeier

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Links

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Research Area

 A3 | Computational Models