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The Preliminary Application of Spectral Microphysics in Numerical Study of the Effects of Aerosol Particles on Thunderstorm Development.
- Source :
-
Remote Sensing . Jun2024, Vol. 16 Issue 12, p2117. 18p. - Publication Year :
- 2024
-
Abstract
- Progress in numerical models and improved computational capabilities have significantly advanced our comprehension of how aerosol particles impact thunderstorm clouds. Yet, much of this research has focused on employing bulk microphysics models to explain the impacts of aerosol particles acting as cloud condensation nuclei (CCN) on electrical activities in thunderstorm clouds. The bulk thunderstorm models use mean sizes of particles and terminal-fall velocities. This causes calculation deviation in the electrification simulation, which in turn leads to deviations in the simulation of lightning processes. Developing this further, we established a three-dimensional high-resolution cloud–aerosol bin thunderstorm model with electrification and lightning to provide more accurate microphysics and dynamic fields for studying electrical activities. For evaluating the impacts of aerosol particles, specifically CCN, on the properties of continental thunderclouds, aerosols from both clean and polluted continental environments were selected. Cloud simulations indicate that droplets develop a narrower spectrum in polluted continental conditions, and weakened ice crystal growth increases the number of small ice crystals compared to clean conditions. Smaller droplets and ice crystals result in less effective riming and decreased graupel concentration and mass. Consequently, a significant decrease in large ice particles leads to a weakened process of charge separation under conditions of pollution. As a direct result, there is about a 43% reduction in lightning frequency and a delay of approximately 5 min in the lightning process under polluted conditions. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 16
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 178191732
- Full Text :
- https://doi.org/10.3390/rs16122117