Articles | Volume 13, issue 6
https://doi.org/10.5194/gmd-13-2851-2020
https://doi.org/10.5194/gmd-13-2851-2020
Model evaluation paper
 | 
29 Jun 2020
Model evaluation paper |  | 29 Jun 2020

Impact of scale-aware deep convection on the cloud liquid and ice water paths and precipitation using the Model for Prediction Across Scales (MPAS-v5.2)

Laura D. Fowler, Mary C. Barth, and Kiran Alapaty

Related authors

Process Analysis of Elevated Concentrations of Organic Acids at Whiteface Mountain, New York
Christopher Lawrence, Mary Barth, John Orlando, Paul Casson, Richard Brandt, Daniel Kelting, Elizabeth Yerger, and Sara Lance
EGUsphere, https://doi.org/10.5194/egusphere-2024-715,https://doi.org/10.5194/egusphere-2024-715, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Role of atmospheric aerosols in severe winter fog over Indo Gangetic Plains of India: a case study
Chandrakala Bharali, Mary Barth, Rajesh Kumar, Sachin D. Ghude, Vinayak Sinha, and Baerbel Sinha
EGUsphere, https://doi.org/10.5194/egusphere-2023-1686,https://doi.org/10.5194/egusphere-2023-1686, 2023
Short summary
Impacts of condensable particulate matter on atmospheric organic aerosols and fine particulate matter (PM2.5) in China
Mengying Li, Shaocai Yu, Xue Chen, Zhen Li, Yibo Zhang, Zhe Song, Weiping Liu, Pengfei Li, Xiaoye Zhang, Meigen Zhang, Yele Sun, Zirui Liu, Caiping Sun, Jingkun Jiang, Shuxiao Wang, Benjamin N. Murphy, Kiran Alapaty, Rohit Mathur, Daniel Rosenfeld, and John H. Seinfeld
Atmos. Chem. Phys., 22, 11845–11866, https://doi.org/10.5194/acp-22-11845-2022,https://doi.org/10.5194/acp-22-11845-2022, 2022
Short summary
Comparison and evaluation of updates to WRF-Chem (v3.9) biogenic emissions using MEGAN
Mauro Morichetti, Sasha Madronich, Giorgio Passerini, Umberto Rizza, Enrico Mancinelli, Simone Virgili, and Mary Barth
Geosci. Model Dev., 15, 6311–6339, https://doi.org/10.5194/gmd-15-6311-2022,https://doi.org/10.5194/gmd-15-6311-2022, 2022
Short summary
Trifluoroacetic acid deposition from emissions of HFO-1234yf in India, China, and the Middle East
Liji M. David, Mary Barth, Lena Höglund-Isaksson, Pallav Purohit, Guus J. M. Velders, Sam Glaser, and A. R. Ravishankara
Atmos. Chem. Phys., 21, 14833–14849, https://doi.org/10.5194/acp-21-14833-2021,https://doi.org/10.5194/acp-21-14833-2021, 2021
Short summary

Related subject area

Atmospheric sciences
Advances and prospects of deep learning for medium-range extreme weather forecasting
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024,https://doi.org/10.5194/gmd-17-2347-2024, 2024
Short summary
An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024,https://doi.org/10.5194/gmd-17-2265-2024, 2024
Short summary
cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024,https://doi.org/10.5194/gmd-17-2247-2024, 2024
Short summary
PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024,https://doi.org/10.5194/gmd-17-2053-2024, 2024
Short summary
Deep learning applied to CO2 power plant emissions quantification using simulated satellite images
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 17, 1995–2014, https://doi.org/10.5194/gmd-17-1995-2024,https://doi.org/10.5194/gmd-17-1995-2024, 2024
Short summary

Cited articles

Alapaty, K., Herwehe, J. A., Otte, T. L., Nolte, C. G., Bullock, O. R., Ballard, M. S., Kain, J. S., and Dudhia, J.: Introducing subgrid-scale cloud feedbacks to radiation for regional meteorological and climate modeling, Geophys. Res. Lett., 39, L24809, https://doi.org/10.1029/2012GL054031, 2012. 
Alishouse, J. C., Snider, J. B., Westwater, E. R., Swift, C. T., Ruf, C. S., Vongsathron, S. A., and Ferraro, R. R.: Determination of cloud liquid water content using the SSM/I, IEEE T. Geosci. Remote, 28, 817–822, https://doi.org/10.1109/36.58968, 1990. 
Arakawa, A. and Schubert, W. H.: Interaction of a cumulus cloud ensemble with the large-scale environment, Part I, J. Atmos. Sci., 31, 674–701, https://doi.org/10.1175/1520-0469(1974)031<0674:IOACCE>2.0.CO;2, 1974. 
Arakawa, A. and Wu, C.-M.: A unified representation of deep moist convection in numerical modeling of the atmosphere. Part I, J. Atmos. Sci., 70, 1977–1992, https://doi.org/10.1175/JAS-D-12-0330.1, 2013. 
Bechtold, P., Bazile, E., Guichard, F., Mascart, P., and Richard, E.: A mass-flux convection scheme for regional and global models, Q. J. Roy. Meteor. Soc., 130, 3139–3172, https://doi.org/10.1002/qj.49712757309, 2001. 
Download
Short summary
The cloud liquid and ice water path and precipitation simulated with the Model for Prediction Across Scales are compared against satellite data over the tropical Pacific Ocean. Uniform and variable-resolution experiments using scale-aware convection schemes produce strong biases between simulated and observed diagnostics. Results underscore the importance of evaluating clouds, their optical properties, and radiation budget in addition to precipitation in mesh refinement global simulations.