Articles | Volume 23, issue 4
https://doi.org/10.5194/acp-23-2439-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-2439-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Microphysical processes of super typhoon Lekima (2019) and their impacts on polarimetric radar remote sensing of precipitation
Yabin Gou
Hangzhou Meteorological Bureau, Hangzhou 310051, China
Department of Geoscience and Remote Sensing, Delft University of
Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA
Hong Zhu
Hangzhou Meteorological Bureau, Hangzhou 310051, China
Lulin Xue
Research Applications Laboratory (RAL), National Center for Atmospheric Research, Boulder, CO 80307, USA
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Several recent studies have reported complete cloud glaciation induced by airborne-based glaciogenic cloud seeding over plains. Since turbulence is an important factor to maintain clouds in a mixed phase, it is hypothesized that turbulence may have an impact on the seeding effect. This hypothesis is evident in the present study, which shows that turbulence can accelerate the impact of airborne glaciogenic seeding of stratiform clouds.
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This study aims to investigate how cloud seeding affects snowfall in Australia's Snowy Mountains. By running simulations with different setups, we found that seeding impact varies greatly with weather conditions. Seeding increased snow in stable weather but sometimes reduced it in stormy weather. This helps us to better understand when seeding works best to boost water supplies.
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The possible mechanism of effective ice growth in the cloud-top generating cells in winter orographic clouds is explored using a newly developed ultra-high-resolution cloud microphysics model. Simulations demonstrate that a high availability of moisture and liquid water is critical for producing large ice particles. Fluctuations in temperature and moisture down to millimeter scales due to cloud turbulence can substantially affect the growth history of the individual cloud particles.
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By releasing soluble aerosols into the convective clouds, cloud seeding potentially enhances rainfall. The seeding impacts are hard to quantify with observations only. Numerical models that represent the detailed physics of aerosols, cloud and rain formation are used to investigate the seeding impacts on rain enhancement under different natural aerosol backgrounds and using different seeding materials. Our results indicate that seeding may enhance rainfall under certain conditions.
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The role of dust aerosols as ice-nucleating particles is well established in the literature, whereas their role as cloud condensation nuclei is less understood, particularly in polluted desert environments. We analyze coincident aerosol size distributions and cloud particle imagery collected over the UAE with a research aircraft. Despite the presence of ultra-giant aerosol sizes associated with dust, an active collision–coalescence process is not observed within the limited depths of warm cloud.
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This paper describes observations taken from a research aircraft during a field experiment in the western Atlantic Ocean during January and February 2020. The plane made 11 flights, most 8-9 h long, and measured the properties of the atmosphere and ocean with a combination of direct measurements, sensors falling from the plane to profile the atmosphere and ocean, and remote sensing measurements of clouds and the ocean surface.
Yingzhao Ma, Xun Sun, Haonan Chen, Yang Hong, and Yinsheng Zhang
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A two-stage blending approach is proposed for the data fusion of multiple satellite precipitation estimates (SPEs), which firstly reduces the systematic errors of original SPEs based on a Bayesian correction model and then merges the bias-corrected SPEs with a Bayesian weighting model. The model is evaluated in the warm season of 2010–2014 in the northeastern Tibetan Plateau. Results show that the blended SPE is greatly improved compared with the original SPEs, even in heavy rainfall events.
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This study employs a parcel–DNS (direct numerical simulation) modeling framework to accurately resolve the aerosol–droplet–turbulence interactions in an ascending air parcel. The effect of turbulence, aerosol hygroscopicity, and aerosol mass loading on droplet growth and rain formation is investigated through a series of in-cloud seeding experiments in which hygroscopic particles were seeded near the cloud base.
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Short summary
This article investigates the complex precipitation microphysics associated with super typhoon Lekima using a host of in situ and remote sensing observations, including rain gauge and disdrometer data, as well as polarimetric radar observations. The impacts of precipitation microphysics on multi-source data consistency and radar precipitation estimation are quantified. It is concluded that the dynamical precipitation microphysical processes must be considered in radar precipitation estimation.
This article investigates the complex precipitation microphysics associated with super typhoon...
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