Sensitivity of spatial and temporal precipitation patterns to aerosol loadings during an extreme precipitation event
Aerosols are important modulators of the precipitation-generating process, with their concentrations potentially affecting the precipitation process in extreme events. Existing literature suggests that, through microphysical processes, additional aerosols lead to a larger number of smaller cloud dro...
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| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Environmental Research Communications |
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| Online Access: | https://doi.org/10.1088/2515-7620/adbeb8 |
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| author | Wenjia Cao Robert V Rohli Paul W Miller |
| author_facet | Wenjia Cao Robert V Rohli Paul W Miller |
| author_sort | Wenjia Cao |
| collection | DOAJ |
| description | Aerosols are important modulators of the precipitation-generating process, with their concentrations potentially affecting the precipitation process in extreme events. Existing literature suggests that, through microphysical processes, additional aerosols lead to a larger number of smaller cloud droplets, which eventually redistributes the latent heat and the precipitation process. This research addresses the question of how sensitive the spatial and temporal patterns of heavy precipitation events are to aerosol concentration. National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) final (FNL) data were used as input to the Weather Research and Forecasting (WRF) model, to simulate the case study of the catastrophic 2016 flood in Louisiana, USA, for three aerosol loading scenarios: virtually clean, average, and very dirty, corresponding to 0.1×, 1×, and 10× the climatological aerosol concentration. Overall, for the extreme precipitation event in Baton Rouge, Louisiana, in August 2016, increasing aerosol concentrations were associated with 1) a shifted peak precipitation period; 2) a more intense and extreme precipitation event in a more confined area; 3) greater maximum precipitation. Results are important in improving forecast models of extreme precipitation events, thereby further protecting life and property, and more comprehensively understanding the role of aerosols in heavy precipitation events. |
| format | Article |
| id | doaj-art-12e725a421514faea7efb3e660fc4f8d |
| institution | DOAJ |
| issn | 2515-7620 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Communications |
| spelling | doaj-art-12e725a421514faea7efb3e660fc4f8d2025-08-20T02:56:28ZengIOP PublishingEnvironmental Research Communications2515-76202025-01-017303100610.1088/2515-7620/adbeb8Sensitivity of spatial and temporal precipitation patterns to aerosol loadings during an extreme precipitation eventWenjia Cao0https://orcid.org/0000-0003-4765-3271Robert V Rohli1https://orcid.org/0000-0003-2198-5606Paul W Miller2https://orcid.org/0000-0002-0512-8295Department of Oceanography & Coastal Sciences, College of the Coast & Environment, Louisiana State University , Baton Rouge, 70803, United States of AmericaDepartment of Oceanography & Coastal Sciences, College of the Coast & Environment, Louisiana State University , Baton Rouge, 70803, United States of AmericaDepartment of Oceanography & Coastal Sciences, College of the Coast & Environment, Louisiana State University , Baton Rouge, 70803, United States of AmericaAerosols are important modulators of the precipitation-generating process, with their concentrations potentially affecting the precipitation process in extreme events. Existing literature suggests that, through microphysical processes, additional aerosols lead to a larger number of smaller cloud droplets, which eventually redistributes the latent heat and the precipitation process. This research addresses the question of how sensitive the spatial and temporal patterns of heavy precipitation events are to aerosol concentration. National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) final (FNL) data were used as input to the Weather Research and Forecasting (WRF) model, to simulate the case study of the catastrophic 2016 flood in Louisiana, USA, for three aerosol loading scenarios: virtually clean, average, and very dirty, corresponding to 0.1×, 1×, and 10× the climatological aerosol concentration. Overall, for the extreme precipitation event in Baton Rouge, Louisiana, in August 2016, increasing aerosol concentrations were associated with 1) a shifted peak precipitation period; 2) a more intense and extreme precipitation event in a more confined area; 3) greater maximum precipitation. Results are important in improving forecast models of extreme precipitation events, thereby further protecting life and property, and more comprehensively understanding the role of aerosols in heavy precipitation events.https://doi.org/10.1088/2515-7620/adbeb8weather research and forecasting modellouisianafloodthompson aerosol-aware schemecloud condensation nuclei |
| spellingShingle | Wenjia Cao Robert V Rohli Paul W Miller Sensitivity of spatial and temporal precipitation patterns to aerosol loadings during an extreme precipitation event Environmental Research Communications weather research and forecasting model louisiana flood thompson aerosol-aware scheme cloud condensation nuclei |
| title | Sensitivity of spatial and temporal precipitation patterns to aerosol loadings during an extreme precipitation event |
| title_full | Sensitivity of spatial and temporal precipitation patterns to aerosol loadings during an extreme precipitation event |
| title_fullStr | Sensitivity of spatial and temporal precipitation patterns to aerosol loadings during an extreme precipitation event |
| title_full_unstemmed | Sensitivity of spatial and temporal precipitation patterns to aerosol loadings during an extreme precipitation event |
| title_short | Sensitivity of spatial and temporal precipitation patterns to aerosol loadings during an extreme precipitation event |
| title_sort | sensitivity of spatial and temporal precipitation patterns to aerosol loadings during an extreme precipitation event |
| topic | weather research and forecasting model louisiana flood thompson aerosol-aware scheme cloud condensation nuclei |
| url | https://doi.org/10.1088/2515-7620/adbeb8 |
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