arXiv subjects

Menno A. Veerman

Publications and source records attributed to Menno A. Veerman.

5 recordsLinked to original sources

arXiv2026

Evaluating local climate in global storm-resolving models with the Köppen-Geiger classification

Global storm-resolving models aspire to become digital twins of the Earth, delivering information at the local scale at which humans experience climate. We evaluated how well two such models, ICON and IFS-FESOM, reproduce the climate as classified by the Köppen-Geiger system, using 30-year (2020-2049) simulations from the nextGEMS project at 9~km global resolution under SSP3-7.0 scenario. Both models capture the global distribution of the five main climate categories, encouraging given the infancy of storm-resolving climate modelling. Substantial regional biases nonetheless remain. Both underestimate tropical rainforest (Af) extent due to insufficient dry-month precipitation in Amazonia and equatorial Africa. ICON almost eliminates hot arid desert (BWh) across Australia through excessive precipitation, while IFS-FESOM reproduces it well. The two models show opposing biases along the temperate--continental boundary: IFS-FESOM winters are too cold in western Europe, ICON winters too warm. Substituting observed temperature or precipitation into the model fields reveals that precipitation errors dominate misclassification, while temperature biases play a secondary role confined to mid-latitude climate zone boundaries. Under climate change, the two models and CMIP6 projections agree on the direction of climate zone shifts: expansion of tropical savanna and hot desert at the expense of subarctic, tundra, and ice cap zones. However, inter-model differences in present-day climate exceed the 30-year climate change signal for many zones, calling for caution in regional projections and adaptation planning. Our results expose where local-scale climate representation still falls short of the digital twin ambition, while confirming that storm-resolving models already perform well across many regions. We propose Köppen-Geiger classification as a standard diagnostic to help track further progress.

physics.ao-ph↗

arXiv2025

Machine Learning-based Denoising of Surface Solar Irradiance simulated with Monte Carlo Ray Tracing

Simulating radiative transfer in the atmosphere with Monte Carlo ray tracing provides realistic surface irradiance in cloud-resolving models. However, Monte Carlo methods are computationally expensive because large sampling budgets are required to obtain sufficient convergence. Here, we explore the use of machine learning for denoising direct and diffuse surface solar irradiance fields. We use Monte Carlo ray tracing to compute pairs of noisy and well-converged surface irradiance fields for an ensemble of cumulus cloud fields and solar angles, and train a denoising autoencoder to predict the well-converged irradiance fields from the noisy input. We demonstrate that denoising diffuse irradiance from 1 sample per pixel (per spectral quadrature point) is an order of magnitude faster and twice as accurate as ray tracing with 128 samples per pixel, illustrating the advantage of denoising over larger sampling budgets. Denoising of direct irradiance is effective in sunlit areas, while errors persist on the edges of cloud shadows. For diffuse irradiance, providing additional atmospheric information such as liquid water paths and solar angles to train the denoising algorithm reduces errors by approximately a factor of two. Our results open up possibilities for coupled Monte Carlo ray tracing with computational costs approaching those of two-stream-based radiative transfer solvers, although future work is needed to improve generalization across resolutions and cloud types.

physics.ao-ph↗

arXiv2022

Shallow cumulus convection over land in cloud-resolving simulations with a coupled ray tracer

We present simulations of cumulus convection over land with shortwave radiation computed by a Monte Carlo ray tracer coupled to the flow solver. Ray tracing delivers very realistic in-cloud heating rates and global horizontal irradiance fields. The necessary performance of the ray tracer has been enabled by the raw power of GPU computing and from techniques for accelerating data lookup and ray tracer convergence. We used a case study over a grassland in the Netherlands to compare simulations with a coupled ray tracer to those with a conventional two-stream solver, and to test ray tracer convergence. We demonstrate that the simulated cloud evolution is insensitive across a wide range of samples sizes in the ray tracer. Furthermore, simulations with a coupled ray tracer produce surface irradiance patterns that resemble observations and that strongly feed back to the evolution of clouds via locally enhanced surface heat fluxes.

physics.ao-ph↗

arXiv2021

Record high solar irradiance in Western Europe during first COVID-19 lockdown largely due to unusual weather

Spring 2020 broke sunshine duration records across western Europe. The Netherlands recorded the highest surface irradiance since 1928, exceeding the previous extreme of 2011 by 13 %, and the diffuse fraction of the irradiance measured a record low percentage (38 %). The coinciding irradiance extreme and a reduction in anthropogenic pollution due to COVID-19 measures triggered the hypothesis that cleaner-than-usual air contributed to the record. Based on analyses of ground-based and satellite observations and experiments with a radiative transfer model, we estimate a 1.3 % (2.3 W m$^{-2}$) increase in surface irradiance with respect to the 2010-2019 mean due to a low median aerosol optical depth, and a 17.6 % (30.7 W m$^{-2}$) increase due to several exceptionally dry days and a very low cloud fraction overall. Our analyses show that the reduced aerosols and contrails due to the COVID-19 measures are far less important in the irradiance record than the dry and particularly cloud-free weather.

physics.ao-ph↗

arXiv2020

Predicting atmospheric optical properties for radiative transfer computations using neural networks

The radiative transfer equations are well-known, but radiation parametrizations in atmospheric models are computationally expensive. A promising tool for accelerating parametrizations is the use of machine learning techniques. In this study, we develop a machine learning-based parametrization for the gaseous optical properties by training neural networks to emulate a modern radiation parameterization (RRTMGP). To minimize computational costs, we reduce the range of atmospheric conditions for which the neural networks are applicable and use machine-specific optimised BLAS functions to accelerate matrix computations. To generate training data, we use a set of randomly perturbed atmospheric profiles and calculate optical properties using RRTMGP. Predicted optical properties are highly accurate and the resulting radiative fluxes have average errors within \SI{0.5}{\flux} compared to RRTMGP. Our neural network-based gas optics parametrization is up to 4 times faster than RRTMGP, depending on the size of the neural networks. We further test the trade-off between speed and accuracy by training neural networks for the narrow range of atmospheric conditions of a single large-eddy simulation, so smaller and therefore faster networks can achieve a desired accuracy. We conclude that our machine learning-based parametrization can speed-up radiative transfer computations whilst retaining high accuracy.

physics.ao-ph↗