How Do Microphysical Processes Influence Large-Scale Precipitation Variability and Extremes?
Abstract
Convection permitting simulations using the Model for Prediction Across Scales-Atmosphere (MPAS-A) are used to examine how microphysical processes affect large-scale precipitation variability and extremes. An episode of the Madden-Julian Oscillation is simulated using MPAS-A with a refined region at 4-km grid spacing over the Indian Ocean. It is shown that cloud microphysical processes regulate the precipitable water (PW) statistics. Because of the non-linear relationship between precipitation and PW, PW exceeding a certain critical value (PWcr) contributes disproportionately to precipitation variability. However, the frequency of PW exceeding PWcr decreases rapidly with PW, so changes in microphysical processes that shift the column PW statistics relative to PWcr even slightly have large impacts on precipitation variability. Furthermore, precipitation variance and extreme precipitation frequency are approximately linearly related to the difference between the mean and critical PW values. Thus observed precipitation statistics could be used to directly constrain model microphysical parameters as this study demonstrates using radar observations from DYNAMO field campaign.
- Authors:
-
[1];
[1];
[2];
[1];
[1]
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Univ. of Science and Technology of China, Hefei (China). School of Earth and Space Sciences
- Publication Date:
- Research Org.:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF)
- OSTI Identifier:
- 1422784
- Grant/Contract Number:
- AGS-1442095; AC05-76RL01830
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Geophysical Research Letters
- Additional Journal Information:
- Journal Volume: 45; Journal Issue: 3; Journal ID: ISSN 0094-8276
- Publisher:
- American Geophysical Union
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES
Citation Formats
Hagos, Samson, Ruby Leung, L., Zhao, Chun, Feng, Zhe, and Sakaguchi, Koichi. How Do Microphysical Processes Influence Large-Scale Precipitation Variability and Extremes?. United States: N. p., 2018.
Web. doi:10.1002/2017GL076375.
Hagos, Samson, Ruby Leung, L., Zhao, Chun, Feng, Zhe, & Sakaguchi, Koichi. How Do Microphysical Processes Influence Large-Scale Precipitation Variability and Extremes?. United States. https://doi.org/10.1002/2017GL076375
Hagos, Samson, Ruby Leung, L., Zhao, Chun, Feng, Zhe, and Sakaguchi, Koichi. Sat .
"How Do Microphysical Processes Influence Large-Scale Precipitation Variability and Extremes?". United States. https://doi.org/10.1002/2017GL076375. https://www.osti.gov/servlets/purl/1422784.
@article{osti_1422784,
title = {How Do Microphysical Processes Influence Large-Scale Precipitation Variability and Extremes?},
author = {Hagos, Samson and Ruby Leung, L. and Zhao, Chun and Feng, Zhe and Sakaguchi, Koichi},
abstractNote = {Convection permitting simulations using the Model for Prediction Across Scales-Atmosphere (MPAS-A) are used to examine how microphysical processes affect large-scale precipitation variability and extremes. An episode of the Madden-Julian Oscillation is simulated using MPAS-A with a refined region at 4-km grid spacing over the Indian Ocean. It is shown that cloud microphysical processes regulate the precipitable water (PW) statistics. Because of the non-linear relationship between precipitation and PW, PW exceeding a certain critical value (PWcr) contributes disproportionately to precipitation variability. However, the frequency of PW exceeding PWcr decreases rapidly with PW, so changes in microphysical processes that shift the column PW statistics relative to PWcr even slightly have large impacts on precipitation variability. Furthermore, precipitation variance and extreme precipitation frequency are approximately linearly related to the difference between the mean and critical PW values. Thus observed precipitation statistics could be used to directly constrain model microphysical parameters as this study demonstrates using radar observations from DYNAMO field campaign.},
doi = {10.1002/2017GL076375},
journal = {Geophysical Research Letters},
number = 3,
volume = 45,
place = {United States},
year = {Sat Feb 10 00:00:00 EST 2018},
month = {Sat Feb 10 00:00:00 EST 2018}
}
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