A Case Study of Cumulus Convection Over Land in Cloud‐Resolving Simulations With a Coupled Ray Tracer

Veerman, Menno; Stratum, Bart J. H. van; Heerwaarden, Chiel C. van

doi:10.1029/2022gl100808

Cited by 15 publications

(18 citation statements)

References 33 publications

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“…Apart from directly negative consequences for solar energy applications, these errors become more pronounced the better such models resolve individual clouds, and can feed back to other components of the model. Most notably, heterogeneity in surface irradiance can feed back through surface heat fluxes to alter the cloud field and domain‐mean irradiance (Jakub & Mayer, 2017; Lohou & Patton, 2014; Veerman et al., 2022). Given the aforementioned importance of accurately resolved or forecast irradiance variability, it is imperative to understand on more fundamental level how irradiance variability is characterized and where it originates from.…”

Section: Introductionmentioning

confidence: 99%

Reconciling Observations of Solar Irradiance Variability With Cloud Size Distributions

et al. 2023

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Surface solar irradiance can exceed clear-sky, even extraterrestrial irradiance (e.g., Gueymard, 2017;Yordanov et al., 2015), caused by the scattering of radiation by broken clouds, referred to as cloud enhancement (CE). Simultaneously these clouds cast shadows, creating ever changing and moving patterns of alternating high and low irradiance at the surface, resulting in high ramp rates and heterogeneity in surface heat fluxes. The resulting spatiotemporal variability on the intra-day and local scale poses a challenge for stable integration of solar energy in the electricity grid by being unpredictable (Liang, 2017;Yang et al., 2022), while society will increasingly depend on solar energy in mitigating climate change (Clarke et al., 2022). Furthermore, the global carbon, water, and energy cycles are affected by the heterogeneous distribution of solar irradiance caused by clouds (

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Section: Introductionmentioning

confidence: 99%

Reconciling Observations of Solar Irradiance Variability With Cloud Size Distributions

et al. 2023

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“…To this end, we took the cloud field from our simulation with 1D radiative transfer and performed Monte Carlo ray tracing, as described in Veerman et al. (2022) but with delta‐scaled cloud optical properties. The surface irradiance fields obtained with the ray tracing are shown in Figures 5d–5f.…”

Section: Resultsmentioning

confidence: 99%

“…Veerman et al. (2022) showed for the case of 15 August 2016 that a similar cloud cover is modeled when 3D radiative transfer calculations are used.…”

Section: Resultsmentioning

confidence: 99%

“…Although the modeled cloud structures will never be exactly as observed, the average cloud cover is well simulated for both days. Veerman et al (2022) showed for the case of 15 August 2016 that a similar cloud cover is modeled when 3D radiative transfer calculations are used.…”

Section: Case Description and Model Validationmentioning

confidence: 99%

“…This global radiation field shows cloud enhancements in addition to the cloud shadows and clear sky patches.For comparison, we performed a 3D radiative transfer calculation for this time step. To this end, we took the cloud field from our simulation with 1D radiative transfer and performed Monte Carlo ray tracing, as described inVeerman et al (2022) but with delta-scaled cloud optical properties. The surface irradiance fields obtained with the ray tracing are shown in Figures5d-5f.…”

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An Efficient Parameterization for Surface Shortwave 3D Radiative Effects in Large‐Eddy Simulations of Shallow Cumulus Clouds

Tijhuis

Stratum

Veerman

et al. 2022

J Adv Model Earth Syst

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The amount of solar energy that reaches the earth surface is strongly influenced by the complex interactions between clouds and radiation. Therefore, solar energy partly reaches the surface directly and partly reaches the surface as diffuse radiation after it is scattered in the atmosphere by gases, aerosols and clouds. The total amount of solar energy reaching the surface, also referred to as surface irradiance or global radiation, governs many processes at the surface. It drives the sensible and latent heat fluxes, which supply moisture and energy to boundary layer clouds and thus determine their development. Apart from the surface fluxes, the surface irradiance also influences plant photosynthesis, as diffuse radiation is taken up by the canopy more efficiently than direct radiation (Kanniah et al., 2012). Furthermore, surface irradiance determines the production of renewable energy by solar panels. It is therefore important to have a good model representation of the surface irradiance and the partitioning between direct and diffuse radiation.Currently, clouds as well as radiation are usually parameterized in weather and climate models. Existing parameterizations for radiation generally neglect the horizontal transport of radiation. Radiative transfer is considered

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Observed patterns of surface solar irradiance under cloudy and clear‐sky conditions

Mol,

Heusinkveld,

Mangan

et al. 2024

Quart J Royal Meteoro Soc

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Surface solar irradiance varies on scales as small as seconds or metres. This variability is driven mostly by wavelength‐dependent scattering by clouds, and to a lesser extent by aerosols and water vapour. The highly variable nature of solar irradiance is not resolved by most atmospheric models, yet it affects, most notably, the land–atmosphere coupling and the quality of solar energy forecasting. Characterising variability, understanding the mechanisms, and developing models capable of resolving it accurately requires spatially and spectrally resolved observational datasets of solar irradiance at high resolution, which are rare. In 2021, we deployed a network of low‐cost radiometers in the Field Experiment on submesoscale spatio‐temporal variability in Lindenberg (FESSTVaL, Germany) and Land surface Interactions with the Atmosphere over the Iberian Semi‐arid Environment (LIAISE, Spain) field campaigns to gather data on cloud‐driven surface patterns of irradiance, including spectral effects, with the aim of addressing 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 different spatial scales, ranging from 50 m to 30 km. Spectral irradiance in the visible range varies at similar scales, with significant blue enrichment in cloud shadows, most strongly for cumulus, and red enrichment in irradiance peaks, particularly in the case of semitransparent 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 variability in atmospheric moisture in the boundary layer. With this study, we show that observing detailed spatiotemporal irradiance patterns is possible using a relatively small, low‐cost sensor network, and that such network observations can provide insight and validation for the development of models capable of resolving irradiance variability.

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A Case Study of Cumulus Convection Over Land in Cloud‐Resolving Simulations With a Coupled Ray Tracer

Cited by 15 publications

References 33 publications

Reconciling Observations of Solar Irradiance Variability With Cloud Size Distributions

Reconciling Observations of Solar Irradiance Variability With Cloud Size Distributions

An Efficient Parameterization for Surface Shortwave 3D Radiative Effects in Large‐Eddy Simulations of Shallow Cumulus Clouds

Observed patterns of surface solar irradiance under cloudy and clear‐sky conditions

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