arXiv subjects

Chiel van Heerwaarden

Publications and source records attributed to Chiel van Heerwaarden.

4 recordsLinked to original sources

arXiv2023

Observed Patterns of Surface Solar Irradiance under Cloudy and Clear-sky Conditions

Surface solar irradiance varies on scales as small as seconds or meters due to scattering and absorption by the atmosphere. Clouds are the main driver of this variability, but moisture structures in the atmospheric boundary layer and aerosols have an influence too, and depend on wavelength. The highly variable nature of solar irradiance is not resolved by most atmospheric models, yet it affects most notably the land-atmosphere coupling, which in turn can change the cloud field, and the quality of solar energy forecasting. Spatially and spectrally resolved observational datasets of solar irradiance at such high resolution are rare, but they are required for characterising observed variability, understanding the mechanisms, and developing fast models capable of accurately resolving this variability. In 2021, we deployed a spatial network of low-cost radiometers at the FESSTVaL (Germany) and LIAISE (Spain) field campaigns, specifically to gather data on cloud-driven surface patterns of irradiance, including spectral effects, with the aim to address this gap in observations and understanding. We find in case studies of cumulus, altocumulus, and cirrus clouds that these clouds generate large spatiotemporal variability in irradiance, but through different mechanisms and at difference spatial scales, ranging from 50 m to 30 km. Spectral irradiance in the visible range varies at similar spatial scales, with significant blue enrichment in cloud shadows, most strongly for cumulus, and red enrichment in irradiance peaks, particularly in the case of semi-transparent clouds or near cumulus cloud edges. Under clear-sky conditions, solar irradiance varies significantly in water vapour absorption bands at the minute scale, due to local and regional variability in atmospheric moisture.

physics.ao-ph↗

arXiv2023

Reconciling observations of solar irradiance variability with cloud size distributions

Clouds cast shadows on the surface and locally enhance solar irradiance by absorbing and scattering sunlight, resulting in fast and large solar irradiance fluctuations on the surface. Typical spatiotemporal scales and driving mechanisms of this intra-day irradiance variability are not well known, hence even one day ahead forecasts of variability are inaccurate. Here we use long term, high frequency solar irradiance observations combined with satellite imagery, numerical simulations, and conceptual modelling to show how irradiance variability is linked to the cloud size distribution. Cloud shadow sizes are distributed according to a power law over multiple orders of magnitude, deviating only from the cloud size distribution due to cloud edge transparency at scales below 750 meters. Locally cloud-enhanced irradiance occurs as frequently as shadows, and is similarly driven mostly by boundary layer clouds, but distributed over a smaller range of scales. We reconcile studies of solar irradiance variability with those on clouds, which brings fundamental understanding to what drives irradiance variability. Our findings have implications for not only for weather and climate modelling, but also for solar energy and photosynthesis by vegetation, where detailed knowledge of surface solar irradiance is essential.

physics.ao-ph↗

arXiv2022

Logarithmic profiles of velocity in stably stratified atmospheric boundary layers

The universal velocity log law first proposed by von Kármán in the near-wall region of turbulent shear flows is one of the cornerstones of turbulence theory. When buoyancy effects are important, the universal velocity log law is typically believed to break down according to Monin-Obukhov similarity theory (MOST), which has been used in almost all global weather and climate models to describe the dependence of the mean velocity profiles on buoyancy in the atmospheric boundary layer. In contrast to MOST, we propose new logarithmic profiles of near-wall mean velocity in the stably stratified atmospheric boundary layers based on direct numerical simulations and field observations across a wide range of buoyancy effects. We find that buoyancy does not change the logarithmic nature of velocity profiles but instead modifies the slope of the log law in stably stratified conditions.

physics.flu-dyn↗

arXiv2020

Deep-learning enhancement of large scale numerical simulations

Traditional simulations on High-Performance Computing (HPC) systems typically involve modeling very large domains and/or very complex equations. HPC systems allow running large models, but limits in performance increase that have become more prominent in the last 5-10 years will likely be experienced. Therefore new approaches are needed to increase application performance. Deep learning appears to be a promising way to achieve this. Recently deep learning has been employed to enhance solving problems that traditionally are solved with large-scale numerical simulations using HPC. This type of application, deep learning for high-performance computing, is the theme of this whitepaper. Our goal is to provide concrete guidelines to scientists and others that would like to explore opportunities for applying deep learning approaches in their own large-scale numerical simulations. These guidelines have been extracted from a number of experiments that have been undertaken in various scientific domains over the last two years, and which are described in more detail in the Appendix. Additionally, we share the most important lessons that we have learned.

cs.CE↗