Articles | Volume 13, issue 6
https://doi.org/10.5194/gmd-13-2851-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-2851-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
29 Jun 2020
Model evaluation paper |
| 29 Jun 2020
| 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
CORRESPONDING AUTHOR
National Center for Atmospheric Research, Boulder, Colorado 80307-3000, USA
Mary C. Barth
National Center for Atmospheric Research, Boulder, Colorado 80307-3000, USA
Kiran Alapaty
Center for Environmental Measurements and Modeling, U.S. Environmental Protection Agency Research Triangle Park, North Carolina 27711, USA
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Christopher Lawrence, Mary Barth, John Orlando, Paul Casson, Richard Brandt, Daniel Kelting, Elizabeth Yerger, and Sara Lance
Atmos. Chem. Phys., 24, 13693–13713, https://doi.org/10.5194/acp-24-13693-2024, https://doi.org/10.5194/acp-24-13693-2024, 2024
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This work uses chemical transport and box modeling to study the gas- and aqueous-phase production of organic acid concentrations measured in cloud water at the summit of Whiteface Mountain on 1 July 2018. Isoprene was the major source of formic, acetic, and oxalic acid. Gas-phase chemistry greatly underestimated formic and acetic acid, indicating missing sources, while cloud chemistry was a key source of oxalic acid. More studies of organic acids are required to better constrain their sources.
Genevieve Rose Lorenzo, Luke D. Ziemba, Avelino F. Arellano, Mary C. Barth, Ewan C. Crosbie, Joshua P. DiGangi, Glenn S. Diskin, Richard Ferrare, Miguel Ricardo A. Hilario, Michael A. Shook, Simone Tilmes, Jian Wang, Qian Xiao, Jun Zhang, and Armin Sorooshian
EGUsphere, https://doi.org/10.5194/egusphere-2024-2604, https://doi.org/10.5194/egusphere-2024-2604, 2024
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Novel aerosol hygroscopicity analysis of CAMP2Ex field campaign data show low aerosol hygroscopicity values in Southeast Asia. Organic carbon from smoke decreases hygroscopicity to levels more like those in continental than in polluted marine regions. Hygroscopicity changes at cloud level demonstrate how surface particles impact clouds in the region affecting model representation of aerosol and cloud interactions in similar polluted marine regions with high organic carbon emissions.
Chandrakala Bharali, Mary Barth, Rajesh Kumar, Sachin D. Ghude, Vinayak Sinha, and Baerbel Sinha
Atmos. Chem. Phys., 24, 6635–6662, https://doi.org/10.5194/acp-24-6635-2024, https://doi.org/10.5194/acp-24-6635-2024, 2024
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This study examines the role of atmospheric aerosols in winter fog over the Indo-Gangetic Plains of India using WRF-Chem. The increase in RH with aerosol–radiation feedback (ARF) is found to be important for fog formation as it promotes the growth of aerosols in the polluted environment. Aqueous-phase chemistry in the fog increases PM2.5 concentration, further affecting ARF. ARF and aqueous-phase chemistry affect the fog intensity and the timing of fog formation by ~1–2 h.
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
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This study constructed an emission inventory of condensable particulate matter (CPM) in China with a focus on organic aerosols (OAs), based on collected CPM emission information. The results show that OA emissions are enhanced twofold for the years 2014 and 2017 after the inclusion of CPM in the new inventory. Sensitivity cases demonstrated the significant contributions of CPM emissions from stationary combustion and mobile sources to primary, secondary, and total OA concentrations.
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
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In the present study, we explore the effect of making simple changes to the existing WRF-Chem MEGAN v2.04 emissions to provide MEGAN updates that can be used independently of the land surface model chosen. The changes made to the MEGAN algorithm implemented in WRF-Chem were the following: (i) update of the emission activity factors, (ii) update of emission factor values for each plant functional type (PFT), and (iii) the assignment of the emission factor by PFT to isoprene.
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
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We calculated the expected concentrations of trifluoroacetic acid (TFA) from the atmospheric breakdown of HFO-1234yf (CF3CF=CH2), a substitute for global warming hydrofluorocarbons, emitted now and in the future by India, China, and the Middle East. We used two chemical transport models. We conclude that the projected emissions through 2040 would not be detrimental, given the current knowledge of the effects of TFA on humans and ecosystems.
Andreas Tilgner, Thomas Schaefer, Becky Alexander, Mary Barth, Jeffrey L. Collett Jr., Kathleen M. Fahey, Athanasios Nenes, Havala O. T. Pye, Hartmut Herrmann, and V. Faye McNeill
Atmos. Chem. Phys., 21, 13483–13536, https://doi.org/10.5194/acp-21-13483-2021, https://doi.org/10.5194/acp-21-13483-2021, 2021
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Feedbacks of acidity and atmospheric multiphase chemistry in deliquesced particles and clouds are crucial for the tropospheric composition, depositions, climate, and human health. This review synthesizes the current scientific knowledge on these feedbacks using both inorganic and organic aqueous-phase chemistry. Finally, this review outlines atmospheric implications and highlights the need for future investigations with respect to reducing emissions of key acid precursors in a changing world.
Yuting Wang, Yong-Feng Ma, Domingo Muñoz-Esparza, Cathy W. Y. Li, Mary Barth, Tao Wang, and Guy P. Brasseur
Atmos. Chem. Phys., 21, 3531–3553, https://doi.org/10.5194/acp-21-3531-2021, https://doi.org/10.5194/acp-21-3531-2021, 2021
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Large-eddy simulations (LESs) were performed in the mountainous region of the island of Hong Kong to investigate the degree to which the rates of chemical reactions between two reactive species are reduced due to the segregation of species within the convective boundary layer. We show that the inhomogeneity in emissions plays an important role in the segregation effect. Topography also has a significant influence on the segregation locally.
Havala O. T. Pye, Athanasios Nenes, Becky Alexander, Andrew P. Ault, Mary C. Barth, Simon L. Clegg, Jeffrey L. Collett Jr., Kathleen M. Fahey, Christopher J. Hennigan, Hartmut Herrmann, Maria Kanakidou, James T. Kelly, I-Ting Ku, V. Faye McNeill, Nicole Riemer, Thomas Schaefer, Guoliang Shi, Andreas Tilgner, John T. Walker, Tao Wang, Rodney Weber, Jia Xing, Rahul A. Zaveri, and Andreas Zuend
Atmos. Chem. Phys., 20, 4809–4888, https://doi.org/10.5194/acp-20-4809-2020, https://doi.org/10.5194/acp-20-4809-2020, 2020
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Acid rain is recognized for its impacts on human health and ecosystems, and programs to mitigate these effects have had implications for atmospheric acidity. Historical measurements indicate that cloud and fog droplet acidity has changed in recent decades in response to controls on emissions from human activity, while the limited trend data for suspended particles indicate acidity may be relatively constant. This review synthesizes knowledge on the acidity of atmospheric particles and clouds.
Rebecca H. Schwantes, Louisa K. Emmons, John J. Orlando, Mary C. Barth, Geoffrey S. Tyndall, Samuel R. Hall, Kirk Ullmann, Jason M. St. Clair, Donald R. Blake, Armin Wisthaler, and Thao Paul V. Bui
Atmos. Chem. Phys., 20, 3739–3776, https://doi.org/10.5194/acp-20-3739-2020, https://doi.org/10.5194/acp-20-3739-2020, 2020
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Ozone is a greenhouse gas and air pollutant that is harmful to human health and plants. During the summer in the southeastern US, many regional and global models are biased high for surface ozone compared to observations. Here adding more complex and updated chemistry for isoprene and terpenes, which are biogenic hydrocarbons emitted from trees and vegetation, into an earth system model greatly reduces the simulated surface ozone bias compared to aircraft and monitoring station data.
R. Kumar, M. C. Barth, V. S. Nair, G. G. Pfister, S. Suresh Babu, S. K. Satheesh, K. Krishna Moorthy, G. R. Carmichael, Z. Lu, and D. G. Streets
Atmos. Chem. Phys., 15, 5415–5428, https://doi.org/10.5194/acp-15-5415-2015, https://doi.org/10.5194/acp-15-5415-2015, 2015
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We examine differences in the surface BC between the Bay of Bengal (BoB) and the Arabian Sea (AS) and identify dominant sources of BC in South Asia during ICARB. Anthropogenic emissions were the main source of BC during ICARB and had about 5 times stronger influence on the BoB compared to the AS. Regional-scale transport contributes up to 25% of BC mass concentrations in western and eastern India, suggesting that surface BC mass concentrations cannot be linked directly to the local emissions.
M. S. Mallard, C. G. Nolte, T. L. Spero, O. R. Bullock, K. Alapaty, J. A. Herwehe, J. Gula, and J. H. Bowden
Geosci. Model Dev., 8, 1085–1096, https://doi.org/10.5194/gmd-8-1085-2015, https://doi.org/10.5194/gmd-8-1085-2015, 2015
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Because global climate models (GCMs) are typically run at coarse spatial resolution, lakes are often poorly resolved in their global fields. When downscaling such GCMs using the Weather Research & Forecasting (WRF) model, use of WRF’s default interpolation methods can result in unrealistic lake temperatures and ice cover, which can impact simulated air temperatures and precipitation. Here, alternative methods for setting lake variables in WRF downscaling applications are presented and compared.
T. Amnuaylojaroen, M. C. Barth, L. K. Emmons, G. R. Carmichael, J. Kreasuwun, S. Prasitwattanaseree, and S. Chantara
Atmos. Chem. Phys., 14, 12983–13012, https://doi.org/10.5194/acp-14-12983-2014, https://doi.org/10.5194/acp-14-12983-2014, 2014
S. Yu, R. Mathur, J. Pleim, D. Wong, R. Gilliam, K. Alapaty, C. Zhao, and X. Liu
Atmos. Chem. Phys., 14, 11247–11285, https://doi.org/10.5194/acp-14-11247-2014, https://doi.org/10.5194/acp-14-11247-2014, 2014
R. Kumar, M. C. Barth, S. Madronich, M. Naja, G. R. Carmichael, G. G. Pfister, C. Knote, G. P. Brasseur, N. Ojha, and T. Sarangi
Atmos. Chem. Phys., 14, 6813–6834, https://doi.org/10.5194/acp-14-6813-2014, https://doi.org/10.5194/acp-14-6813-2014, 2014
J. Ortega, A. Turnipseed, A. B. Guenther, T. G. Karl, D. A. Day, D. Gochis, J. A. Huffman, A. J. Prenni, E. J. T. Levin, S. M. Kreidenweis, P. J. DeMott, Y. Tobo, E. G. Patton, A. Hodzic, Y. Y. Cui, P. C. Harley, R. S. Hornbrook, E. C. Apel, R. K. Monson, A. S. D. Eller, J. P. Greenberg, M. C. Barth, P. Campuzano-Jost, B. B. Palm, J. L. Jimenez, A. C. Aiken, M. K. Dubey, C. Geron, J. Offenberg, M. G. Ryan, P. J. Fornwalt, S. C. Pryor, F. N. Keutsch, J. P. DiGangi, A. W. H. Chan, A. H. Goldstein, G. M. Wolfe, S. Kim, L. Kaser, R. Schnitzhofer, A. Hansel, C. A. Cantrell, R. L. Mauldin, and J. N. Smith
Atmos. Chem. Phys., 14, 6345–6367, https://doi.org/10.5194/acp-14-6345-2014, https://doi.org/10.5194/acp-14-6345-2014, 2014
R. Kumar, M. C. Barth, G. G. Pfister, M. Naja, and G. P. Brasseur
Atmos. Chem. Phys., 14, 2431–2446, https://doi.org/10.5194/acp-14-2431-2014, https://doi.org/10.5194/acp-14-2431-2014, 2014
X. Jiang, M. C. Barth, C. Wiedinmyer, and S. T. Massie
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-13-21383-2013, https://doi.org/10.5194/acpd-13-21383-2013, 2013
Revised manuscript not accepted
J. Wong, M. C. Barth, and D. Noone
Geosci. Model Dev., 6, 429–443, https://doi.org/10.5194/gmd-6-429-2013, https://doi.org/10.5194/gmd-6-429-2013, 2013
K. A. Cummings, T. L. Huntemann, K. E. Pickering, M. C. Barth, W. C. Skamarock, H. Höller, H.-D. Betz, A. Volz-Thomas, and H. Schlager
Atmos. Chem. Phys., 13, 2757–2777, https://doi.org/10.5194/acp-13-2757-2013, https://doi.org/10.5194/acp-13-2757-2013, 2013
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Geosci. Model Dev., 18, 1545–1559, https://doi.org/10.5194/gmd-18-1545-2025, https://doi.org/10.5194/gmd-18-1545-2025, 2025
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Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
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Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
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Geosci. Model Dev., 18, 1265–1286, https://doi.org/10.5194/gmd-18-1265-2025, https://doi.org/10.5194/gmd-18-1265-2025, 2025
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Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
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Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, and Tobias Hell
Geosci. Model Dev., 18, 1141–1153, https://doi.org/10.5194/gmd-18-1141-2025, https://doi.org/10.5194/gmd-18-1141-2025, 2025
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David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev., 18, 1103–1118, https://doi.org/10.5194/gmd-18-1103-2025, https://doi.org/10.5194/gmd-18-1103-2025, 2025
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Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev., 18, 1017–1039, https://doi.org/10.5194/gmd-18-1017-2025, https://doi.org/10.5194/gmd-18-1017-2025, 2025
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Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025, https://doi.org/10.5194/gmd-18-621-2025, 2025
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Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025, https://doi.org/10.5194/gmd-18-529-2025, 2025
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We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025, https://doi.org/10.5194/gmd-18-483-2025, 2025
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Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025, https://doi.org/10.5194/gmd-18-433-2025, 2025
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Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025, https://doi.org/10.5194/gmd-18-405-2025, 2025
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Radiation is relevant to the atmospheric impact on people and infrastructure in cities as it can influence the urban heat island, building energy consumption, and human thermal comfort. A new urban radiation model, assuming a more realistic form of urban morphology, is coupled to the urban climate model Town Energy Balance (TEB). The new TEB is evaluated with a reference radiation model for a variety of urban morphologies, and an improvement in the simulated radiative observables is found.
Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro
Geosci. Model Dev., 18, 253–286, https://doi.org/10.5194/gmd-18-253-2025, https://doi.org/10.5194/gmd-18-253-2025, 2025
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Contrails forming in some atmospheric conditions may persist and become strongly warming cirrus, while in other conditions may be neutral or cooling. We develop a contrail forecast model to predict contrail climate forcing for any arbitrary point in space and time and explore integration into flight planning and air traffic management. This approach enables contrail interventions to target high-probability high-climate-impact regions and reduce unintended consequences of contrail management.
Nils Eingrüber, Alina Domm, Wolfgang Korres, and Karl Schneider
Geosci. Model Dev., 18, 141–160, https://doi.org/10.5194/gmd-18-141-2025, https://doi.org/10.5194/gmd-18-141-2025, 2025
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Climate change adaptation measures like unsealings can reduce urban heat stress. As grass grid pavers have never been parameterized for microclimate model simulations with ENVI-met, a new parameterization was developed based on field measurements. To analyse the cooling potential, scenario analyses were performed for a densely developed area in Cologne. Statistically significant average cooling effects of up to −11.1 K were found for surface temperature and up to −2.9 K for 1 m air temperature.
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
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The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) was developed to improve the estimation of the aerosol direct radiation effect and was coupled online with a chemical weather model. The AI-NAOS scheme considers black carbon as fractal aggregates and soil dust as super-spheroids, encapsulated with hygroscopic aerosols. Real-case simulations emphasize the necessity of accurately representing nonspherical and inhomogeneous aerosols in chemical weather models.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025, https://doi.org/10.5194/gmd-18-101-2025, 2025
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The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Geosci. Model Dev., 18, 1–18, https://doi.org/10.5194/gmd-18-1-2025, https://doi.org/10.5194/gmd-18-1-2025, 2025
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The Modular and Integrated Data Assimilation System (MIDAS) software is described. The flexible design of MIDAS enables both deterministic and ensemble prediction applications for the atmosphere and several other Earth system components. It is currently used for all main operational weather prediction systems in Canada and also for sea ice and sea surface temperature analysis. The use of MIDAS for multiple Earth system components will facilitate future research on coupled data assimilation.
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024, https://doi.org/10.5194/gmd-17-8885-2024, 2024
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We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can reproduce PAH distribution well. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change in BaP is lower than that in PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although the Action Plan has been implemented.
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024, https://doi.org/10.5194/gmd-17-8773-2024, 2024
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We have developed a complete two-moment version of the LIMA (Liquid Ice Multiple Aerosols) microphysics scheme. We have focused on collection processes, where the hydrometeor number transfer is often estimated in proportion to the mass transfer. The impact of these parameterizations on a convective system and the prospects for more realistic estimates of secondary parameters (reflectivity, hydrometeor size) are shown in a first test on an idealized case.
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
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A new parameterisation for dynamic anthropogenic heat and electricity consumption is described. The model reproduced the temporal variation in and spatial distributions of electricity consumption and temperature well in summer and winter. The partial air conditioning was the most critical factor, significantly affecting the value of anthropogenic heat emission.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
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To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
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Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
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Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
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The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
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Inadequate representation of surface–atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
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Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
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This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
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Marine cloud brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed. However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume-spreading model.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
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Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
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This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
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In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
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The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
EGUsphere, https://doi.org/10.5194/egusphere-2024-3321, https://doi.org/10.5194/egusphere-2024-3321, 2024
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The effectiveness of assimilation system and its sensitivity to ensemble member size and length of assimilation window have been investigated. This study advances our understanding about the selection of basic parameters in the four-dimension local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate matter polluted environment.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
EGUsphere, https://doi.org/10.5194/egusphere-2024-2884, https://doi.org/10.5194/egusphere-2024-2884, 2024
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Assimilating Ground-based microwave radiometers' observations into numerical weather prediction models holds significant promise for enhancing forecast accuracy. Radiative transfer models (RTM) are crucial for direct data assimilation. We propose a new RTM capable of simulating brightness temperatures observed by GMRs and their Jacobians. Several improvements are introduced to achieve higher accuracy.The RTM align with RTTOV-gb well and can achieve smaller STD in water vapor absorption channels.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
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The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
Geosci. Model Dev., 17, 7595–7627, https://doi.org/10.5194/gmd-17-7595-2024, https://doi.org/10.5194/gmd-17-7595-2024, 2024
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Computer models are essential for improving our understanding of how gases and particles move in the atmosphere. We present an update of the atmospheric transport model FLEXPART. FLEXPART 11 is more accurate due to a reduced number of interpolations and a new scheme for wet deposition. It can simulate non-spherical aerosols and includes linear chemical reactions. It is parallelised using OpenMP and includes new user options. A new user manual details how to use FLEXPART 11.
Bjarke Tobias Eisensøe Olsen, Andrea Noemi Hahmann, Nicolás González Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
EGUsphere, https://doi.org/10.5194/egusphere-2024-3123, https://doi.org/10.5194/egusphere-2024-3123, 2024
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Low-level jets (LLJs) are strong winds in the lower atmosphere, important for wind energy as turbines get taller. This study compares a weather model (WRF) with real data across five North and Baltic Sea sites. Adjusting the model improved accuracy over the widely-used ERA5. In key offshore regions, LLJs occur 10–15 % of the time and significantly boost wind power, especially in spring and summer, contributing up to 30 % of total capacity in some areas.
Jaroslav Resler, Petra Bauerová, Michal Belda, Martin Bureš, Kryštof Eben, Vladimír Fuka, Jan Geletič, Radek Jareš, Jan Karel, Josef Keder, Pavel Krč, William Patiño, Jelena Radović, Hynek Řezníček, Matthias Sühring, Adriana Šindelářová, and Ondřej Vlček
Geosci. Model Dev., 17, 7513–7537, https://doi.org/10.5194/gmd-17-7513-2024, https://doi.org/10.5194/gmd-17-7513-2024, 2024
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Detailed modeling of urban air quality in stable conditions is a challenge. We show the unprecedented sensitivity of a large eddy simulation (LES) model to meteorological boundary conditions and model parameters in an urban environment under stable conditions. We demonstrate the crucial role of boundary conditions for the comparability of results with observations. The study reveals a strong sensitivity of the results to model parameters and model numerical instabilities during such conditions.
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
EGUsphere, https://doi.org/10.5194/egusphere-2024-2676, https://doi.org/10.5194/egusphere-2024-2676, 2024
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This work focuses on the prediction of aerosol concentration values at ground level, which are a strong indicator of air quality, using Artificial Neural Networks. A study of different variables and their efficiency as inputs for these models is also proposed, and reveals that the best results are obtained when using all of them. Comparison of networks architectures and information fusion methods allows the extraction of knowledge on the most efficient methods in the context of this study.
Jorge E. Pachón, Mariel A. Opazo, Pablo Lichtig, Nicolas Huneeus, Idir Bouarar, Guy Brasseur, Cathy W. Y. Li, Johannes Flemming, Laurent Menut, Camilo Menares, Laura Gallardo, Michael Gauss, Mikhail Sofiev, Rostislav Kouznetsov, Julia Palamarchuk, Andreas Uppstu, Laura Dawidowski, Nestor Y. Rojas, María de Fátima Andrade, Mario E. Gavidia-Calderón, Alejandro H. Delgado Peralta, and Daniel Schuch
Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024, https://doi.org/10.5194/gmd-17-7467-2024, 2024
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Latin America (LAC) has some of the most populated urban areas in the world, with high levels of air pollution. Air quality management in LAC has been traditionally focused on surveillance and building emission inventories. This study performed the first intercomparison and model evaluation in LAC, with interesting and insightful findings for the region. A multiscale modeling ensemble chain was assembled as a first step towards an air quality forecasting system.
David Ho, Michał Gałkowski, Friedemann Reum, Santiago Botía, Julia Marshall, Kai Uwe Totsche, and Christoph Gerbig
Geosci. Model Dev., 17, 7401–7422, https://doi.org/10.5194/gmd-17-7401-2024, https://doi.org/10.5194/gmd-17-7401-2024, 2024
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Atmospheric model users often overlook the impact of the land–atmosphere interaction. This study accessed various setups of WRF-GHG simulations that ensure consistency between the model and driving reanalysis fields. We found that a combination of nudging and frequent re-initialization allows certain improvement by constraining the soil moisture fields and, through its impact on atmospheric mixing, improves atmospheric transport.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
EGUsphere, https://doi.org/10.5194/egusphere-2024-2815, https://doi.org/10.5194/egusphere-2024-2815, 2024
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The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate that effect, the radiative kernel is constructed and applied. The results show that the kernels can reproduce aerosol radiative effects and are expected to simulate stratospheric aerosol radiative effects. This approach reduces computational expense and consists well with radiative model calculations and can be applied to atmospheric models with speed requirements.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, https://doi.org/10.5194/gmd-17-7285-2024, 2024
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We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer
Geosci. Model Dev., 17, 7263–7284, https://doi.org/10.5194/gmd-17-7263-2024, https://doi.org/10.5194/gmd-17-7263-2024, 2024
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Monitoring greenhouse gas emission reductions requires a combination of models and observations, as well as an initial emission estimate. Each component provides information with a certain level of certainty and is weighted to yield the most reliable estimate of actual emissions. We describe efforts for estimating the uncertainty in the initial emission estimate, which significantly impacts the outcome. Hence, a good uncertainty estimate is key for obtaining reliable information on emissions.
Álvaro González-Cervera and Luis Durán
Geosci. Model Dev., 17, 7245–7261, https://doi.org/10.5194/gmd-17-7245-2024, https://doi.org/10.5194/gmd-17-7245-2024, 2024
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RASCAL is an open-source Python tool designed for reconstructing daily climate observations, especially in regions with complex local phenomena. It merges large-scale weather patterns with local weather using the analog method. Evaluations in central Spain show that RASCAL outperforms ERA20C reanalysis in reconstructing precipitation and temperature. RASCAL offers opportunities for broad scientific applications, from short-term forecasts to local-scale climate change scenarios.
Juan Escobar, Philippe Wautelet, Joris Pianezze, Florian Pantillon, Thibaut Dauhut, Christelle Barthe, and Jean-Pierre Chaboureau
EGUsphere, https://doi.org/10.5194/egusphere-2024-2879, https://doi.org/10.5194/egusphere-2024-2879, 2024
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The Meso-NH weather research code is adapted for GPUs using OpenACC, leading to significant performance and energy efficiency improvements. Called MESONH-v55-OpenACC, it includes enhanced memory management, communication optimizations, and a new solver. On the AMD MI250X Adastra platform, it achieved up to 6× speedup and 2.3× energy efficiency gain compared to CPUs. Storm simulations at 100 m resolution show successful results , positioning the code for future use on exascale supercomputers.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1898, https://doi.org/10.5194/egusphere-2024-1898, 2024
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Particle size is a key factor determining the properties of aerosol particles, which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt
Geosci. Model Dev., 17, 7199–7218, https://doi.org/10.5194/gmd-17-7199-2024, https://doi.org/10.5194/gmd-17-7199-2024, 2024
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We enhance the WDM6 scheme by incorporating predicted graupel density. The modification affects graupel characteristics, including fall velocity–diameter and mass–diameter relationships. Simulations highlight changes in graupel distribution and precipitation patterns, potentially influencing surface snow amounts. The study underscores the significance of integrating predicted graupel density for a more realistic portrayal of microphysical properties in weather models.
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam
Geosci. Model Dev., 17, 7001–7027, https://doi.org/10.5194/gmd-17-7001-2024, https://doi.org/10.5194/gmd-17-7001-2024, 2024
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We present a summary of enabling high-performance computing of the Community Multiscale Air Quality Model (CMAQ) – a state-of-the-science community multiscale air quality model – on two cloud computing platforms through documenting the technologies, model performance, scaling and relative merits. This may be a new paradigm for computationally intense future model applications. We initiated this work due to a need to leverage cloud computing advances and to ease the learning curve for new users.
Peter A. Bogenschutz, Jishi Zhang, Qi Tang, and Philip Cameron-Smith
Geosci. Model Dev., 17, 7029–7050, https://doi.org/10.5194/gmd-17-7029-2024, https://doi.org/10.5194/gmd-17-7029-2024, 2024
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Using high-resolution and state-of-the-art modeling techniques we simulate five atmospheric river events for California to test the capability to represent precipitation for these events. We find that our model is able to capture the distribution of precipitation very well but suffers from overestimating the precipitation amounts over high elevation. Increasing the resolution further has no impact on reducing this bias, while increasing the domain size does have modest impacts.
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.
Bechtold, P., Köhler, M., Jung, T., Doblas-Reyes, F., Leutbecher, M.,
Rodwell, M. J., Vitart, F., and Balsamo, G.: Advances in simulating
atmospheric variability with the ECMWF model: From synoptic to decadal
time-scales, Q. J. Roy. Meteor. Soc., 134, 1337–1351, https://doi.org/10.1002/qj.289, 2008.
Brown, J. M.: Mesoscale unsaturated downdrafts driven by rainfall
evaporation: A numerical study, J. Atmos. Sci., 36, 313–338, https://doi.org/10.1175/1520-0469(1979)036<0313:MUDDBR>2.0.CO;2, 1979.
Chen, F. and Dudhia, J.: Coupling an advanced land surface-hydrology model
with the Penn State-NCAR MM5 modeling system. Part I: Model implementation
and sensitivity, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, I., Biblot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Greer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Kohler, M.,
Matricardi, M., McNally, A. P., Mong-Sanz, B. M., Morcette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart,
F.: The ERA-Interim reanalysis: Configuration and performance of the data
assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Dolinar, E. K., Dong, X., Xi, B., Jiang, J. H., and Su, H.: Evaluation of
CMIP5 simulated clouds and TOA radiation budgets using NASA satellite
observations, Clim. Dynam., 44, 2229-2247, https://doi.org/10.1007/s00382-014-2158-9, 2015.
Fowler, L. D.: experimentsMPAS-v5.2, Zenodo, https://doi.org/10.5281/zenodo.3515440, 2019.
Fowler, L. D., Skamarock, W. C., Grell, G. A., Freitas, S. R., and Duda, M. G.:
Analyzing the Grell-Freitas convection scheme from hydrostatic to
nonhydrostatic scales within a global model, Mon. Weather Rev., 144,
2285–2306, https://doi.org/10.1175/MWR-D-15-0311.1, 2016.
Frank, W. M., and Cohen, C.: Simulation of tropical convective systems. Part
I: A cumulus parameterization, J. Atmos. Sci., 44, 3787–3799, https://doi.org/10.1175/1520-0469(1987)044<3787:SOTCSP>2.0.CO;2, 1987.
Fritsch, J. M. and Chappell, C. F.: Numerical prediction of convectively
driven mesoscale pressure systems. Part I: Convective parameterization, J.
Atmos. Sci., 37, 1722–1733, https://doi.org/10.1175/1520-0469(1980)037<1722:NPOCDM>2.0.CO;2, 1980.
Geier, E. B., Green, R. N., Kratz, D. P., Minnis, P., Miller, W. F., Nolan,
S. K., and Franklin, C. B.: Clouds and the Earth's Radiant Energy System
(CERES) data management system, Single Satellite Footprint TOA/Surface
Fluxes and Clouds (SSF) collection document, Release 2, Version 1, 243 pp.,
2003.
Giorgetta, M. A., Brokopf, R., Crueger, T., Esch, M., Fiedler, S., Helmert,
J., Hohenegger, C., Kornblueh, L., Köhler, M., Manzini, E., Mauritsen,
T., Nam, C., Raddatz, T., Rast, S., Reinert, D., Sakradzija, M., Schmidt,
H., Schneck, R., Schnur, R., Silvers, L., Wan, H., Zängl, G., and
Stevens, B: ICON-A, the atmosphere component of the ICON Earth System Model:
I. Model description, J. Adv. Model. Earth Sy., 10, 1613–1637, https://doi.org/10.1029/2017MS001242, 2018.
Glotfelty, T., Alapaty, K., He, J., Hawbecker, P., Song, X., and Zhang, G.:
The Weather Research and Forecasting Model with aerosol cloud-interactions
(WRF-ACI): Development, evaluation, and initial applications, Mon. Wea.
Rev., 147, 1491–1511, https://doi.org/10.1175/MWR-D-18-0267.1,
2019.
Greenwald, T. J., Stephens, G. L., Vonder Haar, T. H., and Jackson, D. L.: A
physical retrieval of cloud liquid water over global oceans using special
sensor microwave/imager (SSM/I) observations, J. Geophys. Res., 98,
18471–18488, https://doi.org/10.1029/93JD00339, 1993.
Grell, G. A.: Prognostic evaluation of assumptions uses by cumulus
parameterizations, Mon. Weather Rev., 121, 764–787, https://doi.org/10.1175/1520-0493(1993)121<0764:PEOAUB>2.0.CO;2, 1993.
Grell, G. A. and Dévényi, D.: A generalized approach to
parameterizing convection combining ensemble and data assimilation
techniques, Geophys. Res. Lett., 29, 38-1–38-4, https://doi.org/10.1029/2002GL015311, 2002.
Grell, G. A. and Freitas, S. R.: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling, Atmos. Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014, 2014.
Guo, H., Golaz, J.-C., Donner, L., Wyman, B., Zhao, M., and Ginoux, P.:
CLUBB as a unified cloud parameterization: opportunities and challenges,
Geophys. Res. Lett., 42, 4540–4547, https://doi.org/10.1002/2015GL063672, 2015.
Hagos, S., Leung, L. R., Rauscher, S. A., and Ringler, T.: Error
characteristics of two grid refinement approaches in aquaplanet simulations:
MPAS-A and WRF, Mon. Wea. Rev., 141, 3022–3036, https://doi.org/10.1175/MWR-D-12-00338.1, 2013.
He, J. and Alapaty, K.: Precipitation partitioning in multiscale
atmospheric simulations: Impacts of stability restoration methods, J.
Geophys. Res., 123, 10185–10201, https://doi.org/10.1029/2018JD028710, 2018.
Herwehe, J. A., Alapaty, K., and Bullock Jr., O. R: Evaluation of developments
toward a multi-scale Kain-Fritsch parameterization in WRF. 2014 Community
Modeling and Analysis System Conference, Chapel Hill, NC, EPA, 2014.
Hong, S.-Y. and Lim, J.-O: The WRF single moment 6-class microphysics
scheme (WSM6), J. Korean Meteor. Soc., 42, 129–151, 2006.
Hong, S.-Y., Choi, J., Chang, E.-C., Park, H., and Kim Y.-J.:
Lower-tropospheric enhancement of gravity wave drag in a global spectral
atmospheric forecast model, Weather Forecast., 23, 523–531, https://doi.org/10.1175/2007WAF2007030.1, 2008.
Huffman, G. J., Balvin, D. T., Nelkin, E. J., and Wolff, D. B.: The TRMM
Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear,
combined-sensor precipitation at fine scales, J. Hydrometeorol., 8, 38–55,
https://doi.org/10.1175/JHM560.1, 2007.
Iacono, M. J., Mlawer, E. J., Clough, S. A., and Morcrette, J.-J.: Impact of
an improved longwave radiation model, RRTM, on the energy budget and
thermodynamic properties of the NCAR Community Climate Model, CCM3, J.
Geophys. Res., 105, 14873–14890, https://doi.org/10.1029/2000JD900091, 2000.
Jiang, J. H., Su, H., Zhai, C., Perun, V. S, Del Genio, A., Nazarenko, L. S.,
Donner, L. J., Horowitz, L., Seman, C., Cole, J., Gettelman, A., Ringer,
M. A., Rotstayn, L., Jeffrey, S., Wu, T., Brient, F., Dufresne, J.-L., Kawai,
H., Koshiro, T., Watanabe, M., L'Ecuyer, T. S., Volodin, E. M., Iversen, T.,
Drange, H., Mesquita, M. D. S., Read, W. G., Waters, J. W., Tian, B., Teixeira,
J., and Stephens, G. L.: Evaluation of cloud and water vapor simulations in
CMIP5 climate models using NASA “A-Train” satellite observations, J.
Geophys. Res., 117, D14105, https://doi.org/10.1029/2011JD017237, 2012.
Ju, L., Ringler, T., and Gunzburger, M.: Voronoi tessellations and their
applications to climate and global modeling, in: Numerical Techniques for Global
Atmospheric Models, edited by: Lauritzen, P., Jablonowski, C., Taylor, M., and Nair R., Springer, 313–342, 2011.
Judt, F.: Atmospheric predictability of the tropics, middle latitudes, and
polar regions explored through global storm-resolving simulations, J. Atmos.
Sci., 77, 257–276, https://doi.org/10.1175/JAS-D-19-0116.1, 2020.
Kain, J. S.: The Kain-Fritsch parameterization: An update, J. Appl.
Meteorol., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004.
Kain, J. S and Fritsch, J. M.: A one-dimensional entraining/detraining plume
model and its application in convective parameterization, J. Atmos. Sci.,
47, 2784–2802, https://doi.org/10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2, 1990.
Kain, J. S. and Fritsch, J. M.: The role of convective “trigger function”
in numerical forecasts of mesoscale convective systems, Meteorol. Atmos.
Phys., 49, 93–106, https://doi.org/10.1007/BF01025402, 1992.
Kain, J. S. and Fritsch, J. M.: Convective parameterization for mesoscale
models: The Kain-Fritsch scheme, The Representation of Cumulus Convection in
Numerical Models, Meteor. Mon., No. 24, American Meteorological Society,
Boston, MA, 165–170, https://doi.org/10.1007/978-1-935704-13-3_16, 1993.
Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannary, C., Strand, G.,
Arblaster, J. M., Bates, S. C., Danabasoglu, G., Edwards, J., Holland, M.,
Kushner, P., Lamarque, J.-F., Lawrence, D., Lindsay, K., Middleton, A.,
Munoz, E., Neale, R., Oleson, K., Polvani, L., and Vertenstein, M.: The
Community Earth System Model (CESM) large ensemble project, B. Am. Meteorol.
Soc., 96, 1333–1349, https://doi.org/10.1175/BAMS-D-13-00255.1,
2015.
Kessler, E.: On the distribution and continuity of water substances in
atmospheric circulation, Meteor. Mon., No. 10, American Meteorological
Society, Boston, MA, 1–84, https://doi.org/10.1007/978-1-935704-36-2_1, 1969.
Klemp, J. B.: A terrain-following coordinate with smoothed coordinate
surfaces, Mon. Weather Rev., 139, 2163–2169, https://doi.org/10.1175/MWR-D-10-05046.1, 2011
Klemp, J. B., Skamarock, W. C., and Dudhia, J.: Conservative split-explicit
time integration methods for the compressible nonhydrostatic equations, Mon.
Weather Rev., 135, 2897–2913, https://doi.org/10.1175/MWR3440.1, 2007.
Krishnamurti, T. N., Low-Nam, S., and Pasch, R.: Cumulus parameterization and
rainfall rates II, Mon. Weather Rev., 111, 815–828, https://doi.org/10.1175/1520-0493(1983)111<0815:CPARRI>2.0.CO;2, 1983.
Li, J.-L., Waliser, D., Woods, C., Teixeira, J., Bacmeister, J., Chern, J.,
Shen, B.-W., Tompkins, A., Tao, W.-K., and Köhler, M.: Comparisons of
satellites liquid water estimates to ECMWF and GMAO analyses, 20th
century IPCC AR4 climate simulations and GCM simulations, Geophys. Res.
Lett., 35, L9710, https://doi.org/10.1029/2008GL035427, 2008.
Li, J.-L., Waliser, D.E., Chen, W.-T., Guan, B., Kubar, T., Stephens, G.,
Ma, H.-Y., Deng, M., Donner, L., Seman, C., and Horowitz, L.: An
observational based evaluation of cloud ice water in CMIP3 and CMIP5 GCMs
and contemporary reanalyses using contemporary satellite data, J. Geophys.
Res., 117, D16105, https://doi.org/10.1029/2012JD017640, 2012.
Li, J.-L., Lee, S., Ma, H.-Y, Stephens, G., and Guan, B.: Assessment of the
cloud liquid water from climate models and reanalysis using satellite
observations, Terr. Atmos. Ocean. Sci., 29, 653–678, https://doi.org/10.3319/TAO.2018.07.04.01, 2018.
Mahoney, K. M.: The representation of cumulus convection in high-resolution
simulations of the 2013 Colorado front range flood, Mon. Weather Rev., 144,
4265–4278, https://doi.org/10.1175/MWR-D-16-0211.1, 2016.
Meehl, G. A., Delworth, T. L., Latiff, M., McAveney, B., Mitchell, J. F. B.,
Stouffer, R. J., and Taylor, K. E.: The WCRP CMIP3 multimodel dataset: A new
era in climate change research, B. Am. Meteorol. Soc., 88, 1383–1394,
https://doi.org/10.1175/BAMS-88-9-1383, 2007.
Minnis, P., Sun-Mack, S., Young, D. F., Heck, P. W., Garber, D. P., Chen, Y., Spangenberg, D. A., Arduini,
R. F., Trepte, Q. Z., Smith, W. L., Ayers, J. K., Gibson, S. C., Miller, W. F., Hong, G., Chakrapani, V.,
Takano, Y., Liou, K.-N., Xie, Y., and Yang, P.: CERES Edition-2 cloud property retrievals using
TRMM VIRS and Terra and Aqua MODIS data-Part I: Algorithms, IEEE T. Geosci.
Remote., 49, 4374–4400, https://doi.org/10.1109/TGRS.2011.2144601, 2011.
Minnis, P., Kratz, D. P, Coakley, J. J. A., King, M. D., Garber, D., Heck, P.,
Mayor, S., Young, D. F., and Arduini, R.: Cloud optical property retrieval
(Subsystem 4.3), in: Clouds and the Earth's Radiant Energy System (CERES)
Algorithm Theoretical Basis Document, Vol. III, Clouds and Radiance
Inversions (Subsystem 4), NASA RP 1376, edited by: Science Team CERES,
NASA, Washington DC, 135–176, 1995.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S.
A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated
correlated-k model for the longwave, J. Geophys. Res., 102, 16663–16682,
https://doi.org/10.1029/97JD00237, 1997.
Molod, A., Takacs, L., Suarez, M., Bacmeister, J., Song, I.-S., and Eichman,
A.: The GEOS-5 atmospheric general circulation model: Mean climate from
MERRA to Fortuna, Technical Report Series on Global Modeling and
Assimilations, Vol. 28, 124 pp., 2012.
Moorthi, S. and Suarez, M. J.: Relaxed Arakawa-Schubert: a parameterization
of moist convection for general circulation models, Mon. Weather Rev., 210,
978–1002, https://doi.org/10.1175/1520-0493(1992)120<0978:RASAPO>2.0.CO;2, 1992.
Nakanishi, M. and Niino, H.: Development of an improved turbulence closure
model for the atmospheric boundary layer, J. Meteor. Soc. Jpn., 87,
895–912, https://doi.org/10.2151/jmsj.87.895, 2009.
NCAR: Command Language, Version 6.3.2, software, UCAR/NCAR/CISL/TDD, Boulder, CO, https://doi.org/10.5065/D6WD3XH5, 2019.
Ogura, Y. and Cho, H.-R.: Diagnostic determination of cumulus cloud
populations from observed large-scale variables, J. Atmos. Sci., 30,
1276–1286, https://doi.org/10.1175/1520-0469(1973)030<1276:DDOCCP>2.0.CO;2, 1973.
Olson, J. B., Kenyon, J. S., Angevine, W. M., Brown, J. M., Pagowski, M., and
Suselj, K.: A description of the MYNN-EDMF scheme and the coupling to other
components in WRF-ARW, NOAA Technical Memorandum OAR GSD, 61, 37 pp., 2019.
Platnick, S., King, M. D., Ackerman, S. A., Wenzel, W. P., Baum, B. A., Riedl,
J. C., and Frey, R. A.: The MODIS cloud products: Algorithms and examples from
Terra, IEEE T. Geosci. Remote, 41, 459–473, https://doi.org/10.1109/TGRS.2002.808301, 2003.
Qiao, F. and Liang, X.-Z: Effects of cumulus parameterization closures on
the simulations of summer precipitation over the United States coastal
oceans, J. Adv. Model. Earth Sy., 8, 764–785, https://doi.org/10.1002/2015MS000621, 2015.
Raymond, D. J.: Regulation of moist convection over the west Pacific warm
pool, J. Atmos. Sci., 52, 3945–3959, https://doi.org/10.1175/1520-0469(1995)052<3945:ROMCOT>2.0.CO;2, 1995.
Sakaguchi, K., Leung, L.R., Zhao, C., Yang, Q., Lu, J., and Hagos, S.:
Exploring a multiresolution approach using AMIP simulations, J. Clim., 28,
5549–5574, https://doi.org/10.1175/JCLI-D-14-00729.1, 2015.
Schwarz, C. S.: Medium-range convection-allowing ensemble forecasts with a
variable-resolution global model, Mon. Weather Rev., 147, 2997–3023,
https://doi.org/10.1175/MWR-D-18-0452.1, 2019.
Simpson, J. and Wiggert, V.: Models of precipitating cumulus towers, Mon.
Weather Rev., 97, 471–489, https://doi.org/10.1175/1520-0493(1969)097<0471:MOPCT>2.3.CO;2, 1969.
Skamarock, W. C. and Gassmann, A.: Conservative transport schemes for
spherical geodesic grids: High-order flux operators for ODE-based time
integration, Mon. Weather Rev., 139, 2962–2975, https://doi.org/10.1175/MWR-D-10-05056.1, 2011.
Skamarock, W. C., Klemp, J. B., Duda, M. G., Fowler, L. D., Park, S.-H., and
Ringler, T. D.: A multiscale nonhydrostatic atmospheric model using
Centroidal Voronoi tessellations and C-grid staggering, Mon. Weather Rev.,
140, 3090–3105, https://doi.org/10.1175/MWR-D-11-00215.1, 2012.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X.-Y., Wang,
W., and Powers, J. G.: A description of the Advanced Research WRF
version 3, NCAR Tech. Note NCAR/TN-475+STR, 113 pp., 2008.
Smagorinsky, J.: General circulation experiments with the primitive
equations. I. The basic experiment, Mon. Weather Rev., 91, 99–164,
https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2, 1963.
Stanfield, R.E., Dong, X., Xi, B., Del Genio, A.D., Minnis, P., Doelling,
D., and Loeb, N.: Assessment of NASA GISS CMIP5 and Post-CMIP5 simulated
clouds and TOA radiation budgets using satellite observations. Part II: TOA
radiation budget and CREs, J. Climate, 28, 1842–1863, https://doi.org/10.1175/JCLI-D-14-00249.1, 2015.
Stephens, G. L. and Kummerow, C. D.: The remote sensing of clouds and
precipitation from space: A review, J. Atmos. Sci., 64, 3742–3765,
https://doi.org/10.1175/2006JAS2375.1, 2007.
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z.,
Illingworth, A. J., O'Connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D.,
Austin, R. T., Benedetti, A., Mitrescu, C., and the CloudSat Science Team:
The CloudSat mission and the A-Train: A new dimension and space-based
observations of clouds and precipitation, B. Am. Meteorol. Soc., 83,
1771–1790, https://doi.org/10.1175/BAMS-83-12-1771, 2002.
Storer, R. L., Griffin, B. M., Höft, J., Weber, J. K., Raut, E., Larson, V. E., Wang, M., and Rasch, P. J.: Parameterizing deep convection using the assumed probability density function method, Geosci. Model Dev., 8, 1–19, https://doi.org/10.5194/gmd-8-1-2015, 2015.
Strauss, D. and Paolino, D.: Intermediate time error growth and
predictability: tropics versus mid-latitudes, Tellus A, 61, 579–586, https://doi.org/10.1111/j.1600-0870.2009.00411.x, 2008.
Suhas, E. and Zhang, Q. J.: Evaluation of trigger functions for convective
parameterization schemes using observations, J. Climate, 27, 7647–7666,
https://doi.org/10.1175/JCLI-D-13-00718.1, 2014.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the
experiment design, B. Am. Meteorol. Soc., 93, 485–4398, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012.
Thayer-Calder, K., Gettelman, A., Craig, C., Goldhaber, S., Bogenschutz, P. A., Chen, C.-C., Morrison, H., Höft, J., Raut, E., Griffin, B. M., Weber, J. K., Larson, V. E., Wyant, M. C., Wang, M., Guo, Z., and Ghan, S. J.: A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model, Geosci. Model Dev., 8, 3801–3821, https://doi.org/10.5194/gmd-8-3801-2015, 2015.
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit
forecasts of winter precipitation using an improved bulk cloud microphysics
scheme. Part II: Implementation of a new snow parameterization, Mon. Weather
Rev., 136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1,
2008.
Thompson, G., Rasmussen, R. M., and Manning, K.: Explicit forecasts of winter
precipitation using an improved bulk cloud microphysics scheme. Part I:
Description and sensitivity analysis, Mon. Weather Rev., 132, 519–542,
https://doi.org/10.1175/1520-0493(2004)132<0519:EFOWPU>2.0.CO;2, 2004.
Thompson, G., Tewari, M., Ikeda, K., Tessendorf, S., Weeks, C., Otkin, J.,
and Kong, F.: Explicitly-coupled cloud physics and radiation
parameterizations and subsequent evaluation in WRF high-resolution
convective forecasts, Atmos. Res., 168, 92–104, https://doi.org/10.1016/j.atmosres.2015.09.005, 2016.
Tokioka, T., Yamazaki, K., Kotoh, A., and Ose, T.: The equatorial 30-60 day
oscillation and the Arakawa–Schubert penetrative cumulus parameterization,
J. Meteor. Soc. Jpn., 66, 883–900, https://doi.org/10.2151/jmsj1965.66.6_883, 1988.
Waliser, D. E., Li, J.-L., Woods, C. P., Austin, R. T., Bacmeister, J., Chern,
J., Del Genio, A., Jiang, J. H., Juang, Z., Meng, H., Minnis, P., Platnick,
S., Rossow, W. B., Stephens, G. L., Sun-Mack, S., Tao, W.-K., Tompkins, A. M.,
Vane, D. G., Walker, C., and Wu, D.: Cloud ice: A climate model challenge
with signs and expectations of progress, J. Geophys. Res., 114, D00A21,
https://doi.org/10.1029/2008JD010015, 2009.
Wicker, L. J. and Skamarock, W. C.: Time-splitting methods for elastic models
using forward time schemes, Mon. Weather Rev., 130, 2088–2097, https://doi.org/10.1175/1520-0493(2002)130<2088:TSMFEM>2.0.CO;2, 2002.
Wielicki, B. A., Barkstrom, B. R., Harrison, E. F., Lee III, R. B., Smith, G. L.,
and Cooper, J. E.: Clouds and the Earth's Radiation Energy System (CERES): An
Earth Observing System experiment, B. Am. Meteorol. Soc., 77, 853–868,
https://doi.org/10.1175/1520-0477(1996)077<0853:CATERE>2.0.CO;2, 1996.
Williamson, D.: The effect of time step and time-scales on parameterization
suites, Q. J. Roy. Meteor. Soc., 139, 548–560, https://doi.org/10.1002/qj.1992, 2013.
Wong, M. and Skamarock, W. C.: Spectral characteristics of convective-scale
precipitation observations and forecasts, Mon. Weather Rev., 144, 4183–4195,
https://doi.org/10.1175/MWR-D-16-0183.1, 2016.
Xu, K.-M. and Krueger, S. K.: Evaluation of cloudiness parameterizations
using a cumulus ensemble model, Mon. Weather Rev., 119, 342–367, https://doi.org/10.1175/1520-0493(1991)119<0342:EOCPUA>2.0.CO;2, 1991.
Xu, K.-M. and Randall, D. A.: A semi-empirical cloudiness parameterization
for use in climate models, J. Atmos. Sci., 53, 3084–3102, https://doi.org/10.1175/1520-0469(1996)053<3084:ASCPFU>2.0.CO;2, 1996.
Zheng, Y., Alapaty, K., Herwehe, J. A., Del Genio, A. D., and Niyogi, D.:
Improving high-resolution weather forecasts using the Weather Research and
Forecasting (WRF) model with an updated Kain-Fritsch scheme, Mon. Weather
Rev., 144, 833–860, https://doi.org/10.1175/MWR-D-15-0005.1,
2016.
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.
The cloud liquid and ice water path and precipitation simulated with the Model for Prediction...
