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Tracking the influence of cloud condensation nuclei on summer diurnal precipitating systems over complex topography in Taiwan

Yu‐Hung Chang, Wei‐Ting Chen, Chien‐Ming Wu, Chien‐Ming Wu, Christopher Moseley, Chia-Chun Wu, Chia-Chun Wu

2021Atmospheric chemistry and physics18 citationsDOIOpen Access PDF

Abstract

Abstract. This study focuses on how aerosols, serving as cloud condensation nuclei (CCN), affect the properties of diurnal precipitation under the weak synoptic weather regime over complex topography, which is a common summertime environmental regime in Taiwan. Semi-realistic large-eddy simulations (LESs) were carried out using TaiwanVVM and driven by idealized observational soundings. We perform object-based tracking analyses, which diagnose both the spatial and temporal connectivity of convective systems, aiming to reduce the variability in convection and align the aerosol effects on the mature stage of the convective life cycle. In the hotspot areas of strong orographic locking processes, the precipitation initiation is postponed significantly when the CCN concentration is increased from the clean scenario to the normal scenario, which prolongs the development of local circulation and convection. For this organized regime, the occurrence of the tracked extreme diurnal precipitating systems is notably enhanced. Also, the 99th percentile of the maximum rain rate, cloud depth, and in-cloud vertical velocity during the lifetime of the diurnal precipitating systems increases by 9.4 %, 4.4 %, and 1.3 %. This study demonstrates that the design of semi-realistic LESs, as well as the object-based tracking analyses, is useful to investigate the responses of orographically driven diurnal convective systems to ambient conditions.

Topics & Concepts

Diurnal cycleEnvironmental scienceOrographic liftConvectionPrecipitationAtmospheric sciencesCloud condensation nucleiClimatologyAerosolMeteorologyPredictabilityGeologyPhysicsQuantum mechanicsAtmospheric aerosols and cloudsMeteorological Phenomena and SimulationsClimate variability and models
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