LES of a real campus finds shielding controls individual building drag, quantified by upstream fetch ratio L_s/H_s and relative height ratio H_s/H.
org/view/journals/mwre/139/12/mwr-d-10-05013.1.xml
3 Pith papers cite this work. Polarity classification is still indexing.
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Machine learning cloud microphysics parameterization achieves stable decade-long online coupling in ICON with performance comparable to the classical graupel scheme while eliminating two tuning parameters.
Identifies morphology-dependent urban heterogeneity scales (~256 m original layout, ~64 m infilled) where resolved and sub-grid flow variability become comparable and tests drag and stress parameterizations for scale-aware urban canopy models.
citing papers explorer
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Building drag and shielding in a realistic urban environment
LES of a real campus finds shielding controls individual building drag, quantified by upstream fetch ratio L_s/H_s and relative height ratio H_s/H.
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From stable online coupling to decade-long climate simulations: A machine learning parameterization for cloud microphysics in ICON
Machine learning cloud microphysics parameterization achieves stable decade-long online coupling in ICON with performance comparable to the classical graupel scheme while eliminating two tuning parameters.
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Multi-scale flow analysis for scale-aware urban-canopy models
Identifies morphology-dependent urban heterogeneity scales (~256 m original layout, ~64 m infilled) where resolved and sub-grid flow variability become comparable and tests drag and stress parameterizations for scale-aware urban canopy models.