Quantifying the Turbulent Entrainment‐Mixing Processes Based on Z‐LWC Relationships of Cloud Droplets
Abstract Turbulent entrainment‐mixing processes profoundly influence the relationship between radar reflectivity factor and liquid water content (Z‐LWC) of cloud droplets. However, quantification of the entrainment‐mixing mechanisms based on the Z‐LWC relationship is still lacking. To address this g...
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Geophysical Research Letters |
| Online Access: | https://doi.org/10.1029/2024GL111457 |
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| _version_ | 1832593449355837440 |
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| author | Shi Luo Chunsong Lu Yangang Liu Haoran Li Fengwei Zhang Jingjing Lv Lei Zhu Xiaoqi Xu Junjun Li Xin He Ying He Sinan Gao Xinlin Yang Juan Gu Xuemin Chen Haining Sun |
| author_facet | Shi Luo Chunsong Lu Yangang Liu Haoran Li Fengwei Zhang Jingjing Lv Lei Zhu Xiaoqi Xu Junjun Li Xin He Ying He Sinan Gao Xinlin Yang Juan Gu Xuemin Chen Haining Sun |
| author_sort | Shi Luo |
| collection | DOAJ |
| description | Abstract Turbulent entrainment‐mixing processes profoundly influence the relationship between radar reflectivity factor and liquid water content (Z‐LWC) of cloud droplets. However, quantification of the entrainment‐mixing mechanisms based on the Z‐LWC relationship is still lacking. To address this gap, 12,218 entrainment‐mixing cases are simulated using the Explicit Mixing Parcel Model. We examine the variations of the parameters in the power‐law relationship Z = aLWCb, and the relationship between parameter b and homogeneous mixing degree (ψ), a measure quantifying entrainment‐mixing processes. The results indicate that parameter b distributes within the range of 1–2, with a positive correlation between parameter b and ψ. The b‐ψ relationship is fitted, which connects the Z‐LWC relationship for various entrainment‐mixing types. The results suggest the potential for employing a remote sensing approach to investigate the entrainment‐mixing mechanisms of non‐precipitating small cumulus/stratocumulus clouds, thereby overcoming the limitations of traditional observational studies that rely solely on aircraft observations. |
| format | Article |
| id | doaj-art-0afe764f0584451ba175cead1c6ede14 |
| institution | Kabale University |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-0afe764f0584451ba175cead1c6ede142025-01-20T13:05:57ZengWileyGeophysical Research Letters0094-82761944-80072025-01-01521n/an/a10.1029/2024GL111457Quantifying the Turbulent Entrainment‐Mixing Processes Based on Z‐LWC Relationships of Cloud DropletsShi Luo0Chunsong Lu1Yangang Liu2Haoran Li3Fengwei Zhang4Jingjing Lv5Lei Zhu6Xiaoqi Xu7Junjun Li8Xin He9Ying He10Sinan Gao11Xinlin Yang12Juan Gu13Xuemin Chen14Haining Sun15College of Aviation Meteorology Civil Aviation Flight University of China China Meteorological Administration Key Laboratory for Aviation Meteorology Chengdu ChinaChina Meteorological Administration Aerosol‐Cloud and Precipitation Key Laboratory and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC‐FEMD) Nanjing University of Information Science and Technology Nanjing ChinaEnvironmental and Climate Sciences Department Brookhaven National Laboratory Upton NY USAState Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing ChinaSichuan Province Meteorological Bureau Chengdu ChinaChina Meteorological Administration Aerosol‐Cloud and Precipitation Key Laboratory and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC‐FEMD) Nanjing University of Information Science and Technology Nanjing ChinaChina Meteorological Administration Aerosol‐Cloud and Precipitation Key Laboratory and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC‐FEMD) Nanjing University of Information Science and Technology Nanjing ChinaNanjing Joint Institute for Atmospheric Sciences Nanjing ChinaChina Meteorological Administration Aerosol‐Cloud and Precipitation Key Laboratory and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC‐FEMD) Nanjing University of Information Science and Technology Nanjing ChinaChina Meteorological Administration Aerosol‐Cloud and Precipitation Key Laboratory and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC‐FEMD) Nanjing University of Information Science and Technology Nanjing ChinaChina Meteorological Administration Aerosol‐Cloud and Precipitation Key Laboratory and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC‐FEMD) Nanjing University of Information Science and Technology Nanjing ChinaGuangzhou Institute of Tropical and Marine Meteorology of China Meteorological Administration GBA Academy of Meteorological Research Guangzhou ChinaCollege of Aviation Meteorology Civil Aviation Flight University of China China Meteorological Administration Key Laboratory for Aviation Meteorology Chengdu ChinaCollege of Aviation Meteorology Civil Aviation Flight University of China China Meteorological Administration Key Laboratory for Aviation Meteorology Chengdu ChinaChina Meteorological Administration Aerosol‐Cloud and Precipitation Key Laboratory and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC‐FEMD) Nanjing University of Information Science and Technology Nanjing ChinaCollege of Aviation Meteorology Civil Aviation Flight University of China China Meteorological Administration Key Laboratory for Aviation Meteorology Chengdu ChinaAbstract Turbulent entrainment‐mixing processes profoundly influence the relationship between radar reflectivity factor and liquid water content (Z‐LWC) of cloud droplets. However, quantification of the entrainment‐mixing mechanisms based on the Z‐LWC relationship is still lacking. To address this gap, 12,218 entrainment‐mixing cases are simulated using the Explicit Mixing Parcel Model. We examine the variations of the parameters in the power‐law relationship Z = aLWCb, and the relationship between parameter b and homogeneous mixing degree (ψ), a measure quantifying entrainment‐mixing processes. The results indicate that parameter b distributes within the range of 1–2, with a positive correlation between parameter b and ψ. The b‐ψ relationship is fitted, which connects the Z‐LWC relationship for various entrainment‐mixing types. The results suggest the potential for employing a remote sensing approach to investigate the entrainment‐mixing mechanisms of non‐precipitating small cumulus/stratocumulus clouds, thereby overcoming the limitations of traditional observational studies that rely solely on aircraft observations.https://doi.org/10.1029/2024GL111457 |
| spellingShingle | Shi Luo Chunsong Lu Yangang Liu Haoran Li Fengwei Zhang Jingjing Lv Lei Zhu Xiaoqi Xu Junjun Li Xin He Ying He Sinan Gao Xinlin Yang Juan Gu Xuemin Chen Haining Sun Quantifying the Turbulent Entrainment‐Mixing Processes Based on Z‐LWC Relationships of Cloud Droplets Geophysical Research Letters |
| title | Quantifying the Turbulent Entrainment‐Mixing Processes Based on Z‐LWC Relationships of Cloud Droplets |
| title_full | Quantifying the Turbulent Entrainment‐Mixing Processes Based on Z‐LWC Relationships of Cloud Droplets |
| title_fullStr | Quantifying the Turbulent Entrainment‐Mixing Processes Based on Z‐LWC Relationships of Cloud Droplets |
| title_full_unstemmed | Quantifying the Turbulent Entrainment‐Mixing Processes Based on Z‐LWC Relationships of Cloud Droplets |
| title_short | Quantifying the Turbulent Entrainment‐Mixing Processes Based on Z‐LWC Relationships of Cloud Droplets |
| title_sort | quantifying the turbulent entrainment mixing processes based on z lwc relationships of cloud droplets |
| url | https://doi.org/10.1029/2024GL111457 |
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