Articles | Volume 16, issue 10
https://doi.org/10.5194/gmd-16-2753-2023
© Author(s) 2023. 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-16-2753-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
23 May 2023
Methods for assessment of models |
| 23 May 2023
| 23 May 2023
PyFLEXTRKR: a flexible feature tracking Python software for convective cloud analysis
Atmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, Washington, USA
Joseph Hardin
Atmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, Washington, USA
Hannah C. Barnes
Atmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, Washington, USA
Cooperative Institute for Research in Environmental Sciences at the
University of Colorado, Boulder, Colorado, USA
NOAA Global Systems Laboratory, Boulder, Colorado, USA
Jianfeng Li
Atmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, Washington, USA
L. Ruby Leung
Atmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, Washington, USA
Adam Varble
Atmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, Washington, USA
Zhixiao Zhang
Department of Atmospheric Sciences, University of Utah, Salt Lake
City, Utah, USA
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Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
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Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
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Nat. Hazards Earth Syst. Sci., 23, 3823–3838, https://doi.org/10.5194/nhess-23-3823-2023, https://doi.org/10.5194/nhess-23-3823-2023, 2023
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Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, https://doi.org/10.5194/gmd-16-6355-2023, 2023
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Calvin Howes, Pablo E. Saide, Hugh Coe, Amie Dobracki, Steffen Freitag, Jim M. Haywood, Steven G. Howell, Siddhant Gupta, Janek Uin, Mary Kacarab, Chongai Kuang, L. Ruby Leung, Athanasios Nenes, Greg M. McFarquhar, James Podolske, Jens Redemann, Arthur J. Sedlacek, Kenneth L. Thornhill, Jenny P. S. Wong, Robert Wood, Huihui Wu, Yang Zhang, Jianhao Zhang, and Paquita Zuidema
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Adam C. Varble, Adele L. Igel, Hugh Morrison, Wojciech W. Grabowski, and Zachary J. Lebo
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As atmospheric particles called aerosols increase in number, the number of droplets in clouds tends to increase, which has been theorized to increase storm intensity. We critically evaluate the evidence for this theory, showing that flaws and limitations of previous studies coupled with unaddressed cloud process complexities draw it into question. We provide recommendations for future observations and modeling to overcome current uncertainties.
Adam C. Varble, Po-Lun Ma, Matthew W. Christensen, Johannes Mülmenstädt, Shuaiqi Tang, and Jerome Fast
Atmos. Chem. Phys., 23, 13523–13553, https://doi.org/10.5194/acp-23-13523-2023, https://doi.org/10.5194/acp-23-13523-2023, 2023
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We evaluate how clouds change in response to changing atmospheric particle (aerosol) concentrations in a climate model and find that the model-predicted cloud brightness increases too much as aerosols increase because the cloud drop number increases too much. Excessive drizzle in the model mutes this difference. Many differences between observational and model estimates are explained by varying assumptions of how much liquid has been lost in clouds, which impacts the estimated cloud drop number.
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
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Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
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High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Koichi Sakaguchi, L. Ruby Leung, Colin M. Zarzycki, Jihyeon Jang, Seth McGinnis, Bryce E. Harrop, William C. Skamarock, Andrew Gettelman, Chun Zhao, William J. Gutowski, Stephen Leak, and Linda Mearns
Geosci. Model Dev., 16, 3029–3081, https://doi.org/10.5194/gmd-16-3029-2023, https://doi.org/10.5194/gmd-16-3029-2023, 2023
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We document details of the regional climate downscaling dataset produced by a global variable-resolution model. The experiment is unique in that it follows a standard protocol designed for coordinated experiments of regional models. We found negligible influence of post-processing on statistical analysis, importance of simulation quality outside of the target region, and computational challenges that our model code faced due to rapidly changing super computer systems.
Zeyu Xue, Paul Ullrich, and Lai-Yung Ruby Leung
Hydrol. Earth Syst. Sci., 27, 1909–1927, https://doi.org/10.5194/hess-27-1909-2023, https://doi.org/10.5194/hess-27-1909-2023, 2023
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We examine the sensitivity and robustness of conclusions drawn from the PGW method over the NEUS by conducting multiple PGW experiments and varying the perturbation spatial scales and choice of perturbed meteorological variables to provide a guideline for this increasingly popular regional modeling method. Overall, we recommend PGW experiments be performed with perturbations to temperature or the combination of temperature and wind at the gridpoint scale, depending on the research question.
Andrew Geiss, Po-Lun Ma, Balwinder Singh, and Joseph C. Hardin
Geosci. Model Dev., 16, 2355–2370, https://doi.org/10.5194/gmd-16-2355-2023, https://doi.org/10.5194/gmd-16-2355-2023, 2023
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Atmospheric aerosols play a critical role in Earth's climate, but it is too computationally expensive to directly model their interaction with radiation in climate simulations. This work develops a new neural-network-based parameterization of aerosol optical properties for use in the Energy Exascale Earth System Model that is much more accurate than the current one; it also introduces a unique model optimization method that involves randomly generating neural network architectures.
Matthew W. Christensen, Po-Lun Ma, Peng Wu, Adam C. Varble, Johannes Mülmenstädt, and Jerome D. Fast
Atmos. Chem. Phys., 23, 2789–2812, https://doi.org/10.5194/acp-23-2789-2023, https://doi.org/10.5194/acp-23-2789-2023, 2023
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An increase in aerosol concentration (tiny airborne particles) is shown to suppress rainfall and increase the abundance of droplets in clouds passing over Graciosa Island in the Azores. Cloud drops remain affected by aerosol for several days across thousands of kilometers in satellite data. Simulations from an Earth system model show good agreement, but differences in the amount of cloud water and its extent remain despite modifications to model parameters that control the warm-rain process.
Dalei Hao, Gautam Bisht, Karl Rittger, Timbo Stillinger, Edward Bair, Yu Gu, and L. Ruby Leung
The Cryosphere, 17, 673–697, https://doi.org/10.5194/tc-17-673-2023, https://doi.org/10.5194/tc-17-673-2023, 2023
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We comprehensively evaluated the snow simulations in E3SM land model over the western United States in terms of spatial patterns, temporal correlations, interannual variabilities, elevation gradients, and change with forest cover of snow properties and snow phenology. Our study underscores the need for diagnosing model biases and improving the model representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.
Chandan Sarangi, Yun Qian, L. Ruby Leung, Yang Zhang, Yufei Zou, and Yuhang Wang
Atmos. Chem. Phys., 23, 1769–1783, https://doi.org/10.5194/acp-23-1769-2023, https://doi.org/10.5194/acp-23-1769-2023, 2023
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We show that for air quality, the densely populated eastern US may see even larger impacts of wildfires due to long-distance smoke transport and associated positive climatic impacts, partially compensating the improvements from regulations on anthropogenic emissions. This study highlights the tension between natural and anthropogenic contributions and the non-local nature of air pollution that complicate regulatory strategies for improving future regional air quality for human health.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
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Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Dongyu Feng, Zeli Tan, Darren Engwirda, Chang Liao, Donghui Xu, Gautam Bisht, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 5473–5491, https://doi.org/10.5194/hess-26-5473-2022, https://doi.org/10.5194/hess-26-5473-2022, 2022
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Sea level rise, storm surge and river discharge can cause coastal backwater effects in downstream sections of rivers, creating critical flood risks. This study simulates the backwater effects using a large-scale river model on a coastal-refined computational mesh. By decomposing the backwater drivers, we revealed their relative importance and long-term variations. Our analysis highlights the increasing strength of backwater effects due to sea level rise and more frequent storm surge.
Yilin Fang, L. Ruby Leung, Charles D. Koven, Gautam Bisht, Matteo Detto, Yanyan Cheng, Nate McDowell, Helene Muller-Landau, S. Joseph Wright, and Jeffrey Q. Chambers
Geosci. Model Dev., 15, 7879–7901, https://doi.org/10.5194/gmd-15-7879-2022, https://doi.org/10.5194/gmd-15-7879-2022, 2022
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We develop a model that integrates an Earth system model with a three-dimensional hydrology model to explicitly resolve hillslope topography and water flow underneath the land surface to understand how local-scale hydrologic processes modulate vegetation along water availability gradients. Our coupled model can be used to improve the understanding of the diverse impact of local heterogeneity and water flux on nutrient availability and plant communities.
Andrew Geiss, Sam J. Silva, and Joseph C. Hardin
Geosci. Model Dev., 15, 6677–6694, https://doi.org/10.5194/gmd-15-6677-2022, https://doi.org/10.5194/gmd-15-6677-2022, 2022
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This work demonstrates the use of modern machine learning techniques to enhance the resolution of atmospheric chemistry simulations. We evaluate the schemes for an 8 x 10 increase in resolution and find that they perform substantially better than conventional methods. Methods are introduced to target machine learning methods towards this type of problem, most notably by ensuring they do not break known physical constraints.
Meng Huang, Po-Lun Ma, Nathaniel W. Chaney, Dalei Hao, Gautam Bisht, Megan D. Fowler, Vincent E. Larson, and L. Ruby Leung
Geosci. Model Dev., 15, 6371–6384, https://doi.org/10.5194/gmd-15-6371-2022, https://doi.org/10.5194/gmd-15-6371-2022, 2022
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The land surface in one grid cell may be diverse in character. This study uses an explicit way to account for that subgrid diversity in a state-of-the-art Earth system model (ESM) and explores its implications for the overlying atmosphere. We find that the shallow clouds are increased significantly with the land surface diversity. Our work highlights the importance of accurately representing the land surface and its interaction with the atmosphere in next-generation ESMs.
Yilin Fang, L. Ruby Leung, Ryan Knox, Charlie Koven, and Ben Bond-Lamberty
Geosci. Model Dev., 15, 6385–6398, https://doi.org/10.5194/gmd-15-6385-2022, https://doi.org/10.5194/gmd-15-6385-2022, 2022
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Accounting for water movement in the soil and water transport within the plant is important for plant growth in Earth system modeling. We implemented different numerical approaches for a plant hydrodynamic model and compared their impacts on the simulated aboveground biomass (AGB) at single points and globally. We found care should be taken when discretizing the number of soil layers for numerical simulations as it can significantly affect AGB if accuracy and computational costs are of concern.
Sol Kim, L. Ruby Leung, Bin Guan, and John C. H. Chiang
Geosci. Model Dev., 15, 5461–5480, https://doi.org/10.5194/gmd-15-5461-2022, https://doi.org/10.5194/gmd-15-5461-2022, 2022
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The Energy Exascale Earth System Model (E3SM) project is a state-of-the-science Earth system model developed by the US Department of Energy (DOE). Understanding how the water cycle behaves in this model is of particular importance to the DOE’s mission. Atmospheric rivers (ARs) – which are crucial to the global water cycle – move vast amounts of water vapor through the sky and produce rain and snow. We find that this model reliably represents atmospheric rivers around the world.
Lingcheng Li, Gautam Bisht, and L. Ruby Leung
Geosci. Model Dev., 15, 5489–5510, https://doi.org/10.5194/gmd-15-5489-2022, https://doi.org/10.5194/gmd-15-5489-2022, 2022
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Land surface heterogeneity plays a critical role in the terrestrial water, energy, and biogeochemical cycles. Our study systematically quantified the effects of four dominant heterogeneity sources on water and energy partitioning via Sobol' indices. We found that atmospheric forcing and land use land cover are the most dominant heterogeneity sources in determining spatial variability of water and energy partitioning. Our findings can help prioritize the future development of land surface models.
Kai Zhang, Wentao Zhang, Hui Wan, Philip J. Rasch, Steven J. Ghan, Richard C. Easter, Xiangjun Shi, Yong Wang, Hailong Wang, Po-Lun Ma, Shixuan Zhang, Jian Sun, Susannah M. Burrows, Manish Shrivastava, Balwinder Singh, Yun Qian, Xiaohong Liu, Jean-Christophe Golaz, Qi Tang, Xue Zheng, Shaocheng Xie, Wuyin Lin, Yan Feng, Minghuai Wang, Jin-Ho Yoon, and L. Ruby Leung
Atmos. Chem. Phys., 22, 9129–9160, https://doi.org/10.5194/acp-22-9129-2022, https://doi.org/10.5194/acp-22-9129-2022, 2022
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Here we analyze the effective aerosol forcing simulated by E3SM version 1 using both century-long free-running and short nudged simulations. The aerosol forcing in E3SMv1 is relatively large compared to other models, mainly due to the large indirect aerosol effect. Aerosol-induced changes in liquid and ice cloud properties in E3SMv1 have a strong correlation. The aerosol forcing estimates in E3SMv1 are sensitive to the parameterization changes in both liquid and ice cloud processes.
Donghui Xu, Gautam Bisht, Khachik Sargsyan, Chang Liao, and L. Ruby Leung
Geosci. Model Dev., 15, 5021–5043, https://doi.org/10.5194/gmd-15-5021-2022, https://doi.org/10.5194/gmd-15-5021-2022, 2022
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The runoff outputs in Earth system model simulations involve high uncertainty, which needs to be constrained by parameter calibration. In this work, we used a surrogate-assisted Bayesian framework to efficiently calibrate the runoff-generation processes in the Energy Exascale Earth System Model v1 at a global scale. The model performance was improved compared to the default parameter after calibration, and the associated parametric uncertainty was significantly constrained.
Shuaiqi Tang, Jerome D. Fast, Kai Zhang, Joseph C. Hardin, Adam C. Varble, John E. Shilling, Fan Mei, Maria A. Zawadowicz, and Po-Lun Ma
Geosci. Model Dev., 15, 4055–4076, https://doi.org/10.5194/gmd-15-4055-2022, https://doi.org/10.5194/gmd-15-4055-2022, 2022
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We developed an Earth system model (ESM) diagnostics package to compare various types of aerosol properties simulated in ESMs with aircraft, ship, and surface measurements from six field campaigns across spatial scales. The diagnostics package is coded and organized to be flexible and modular for future extension to other field campaign datasets and adapted to higher-resolution model simulations. Future releases will include comprehensive cloud and aerosol–cloud interaction diagnostics.
Yun Lin, Jiwen Fan, Pengfei Li, Lai-yung Ruby Leung, Paul J. DeMott, Lexie Goldberger, Jennifer Comstock, Ying Liu, Jong-Hoon Jeong, and Jason Tomlinson
Atmos. Chem. Phys., 22, 6749–6771, https://doi.org/10.5194/acp-22-6749-2022, https://doi.org/10.5194/acp-22-6749-2022, 2022
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How sea spray aerosols may affect cloud and precipitation over the region by acting as ice-nucleating particles (INPs) is unknown. We explored the effects of INPs from marine aerosols on orographic cloud and precipitation for an atmospheric river event observed during the 2015 ACAPEX field campaign. The marine INPs enhance the formation of ice and snow, leading to less shallow warm clouds but more mixed-phase and deep clouds. This work suggests models need to consider the impacts of marine INPs.
Pinya Wang, Yang Yang, Huimin Li, Lei Chen, Ruijun Dang, Daokai Xue, Baojie Li, Jianping Tang, L. Ruby Leung, and Hong Liao
Atmos. Chem. Phys., 22, 4705–4719, https://doi.org/10.5194/acp-22-4705-2022, https://doi.org/10.5194/acp-22-4705-2022, 2022
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China is now suffering from both severe ozone (O3) pollution and heat events. We highlight that North China Plain is the hot spot of the co-occurrences of extremes in O3 and high temperatures in China. Such coupled extremes exhibit an increasing trend during 2014–2019 and will continue to increase until the middle of this century. And the coupled extremes impose more severe health impacts to human than O3 pollution occurring alone because of elevated O3 levels and temperatures.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
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An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Sally S.-C. Wang, Yun Qian, L. Ruby Leung, and Yang Zhang
Atmos. Chem. Phys., 22, 3445–3468, https://doi.org/10.5194/acp-22-3445-2022, https://doi.org/10.5194/acp-22-3445-2022, 2022
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This study develops an interpretable machine learning (ML) model predicting monthly PM2.5 fire emission over the contiguous US at 0.25° resolution and compares the prediction skills of the ML and process-based models. The comparison facilitates attributions of model biases and better understanding of the strengths and uncertainties in the two types of models at regional scales, for informing future model development and their applications in fire emission projection.
Guta Wakbulcho Abeshu, Hong-Yi Li, Zhenduo Zhu, Zeli Tan, and L. Ruby Leung
Earth Syst. Sci. Data, 14, 929–942, https://doi.org/10.5194/essd-14-929-2022, https://doi.org/10.5194/essd-14-929-2022, 2022
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Existing riverbed sediment particle size data are sparsely available at individual sites. We develop a continuous map of median riverbed sediment particle size over the contiguous US corresponding to millions of river segments based on the existing observations and machine learning methods. This map is useful for research in large-scale river sediment using model- and data-driven approaches, teaching environmental and earth system sciences, planning and managing floodplain zones, etc.
Hong-Yi Li, Zeli Tan, Hongbo Ma, Zhenduo Zhu, Guta Wakbulcho Abeshu, Senlin Zhu, Sagy Cohen, Tian Zhou, Donghui Xu, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 665–688, https://doi.org/10.5194/hess-26-665-2022, https://doi.org/10.5194/hess-26-665-2022, 2022
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We introduce a new multi-process river sediment module for Earth system models. Application and validation over the contiguous US indicate a satisfactory model performance over large river systems, including those heavily regulated by reservoirs. This new sediment module enables future modeling of the transportation and transformation of carbon and nutrients carried by the fine sediment along the river–ocean continuum to close the global carbon and nutrient cycles.
Andrew Geiss and Joseph C. Hardin
Atmos. Meas. Tech., 14, 7729–7747, https://doi.org/10.5194/amt-14-7729-2021, https://doi.org/10.5194/amt-14-7729-2021, 2021
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Radars can suffer from missing or poor-quality data regions for several reasons: beam blockage, instrument failure, and near-ground blind zones, etc. Here, we demonstrate how deep convolutional neural networks can be used for filling in radar-missing data regions and that they can significantly outperform conventional approaches in terms of realism and accuracy.
Claudia Tebaldi, Kalyn Dorheim, Michael Wehner, and Ruby Leung
Earth Syst. Dynam., 12, 1427–1501, https://doi.org/10.5194/esd-12-1427-2021, https://doi.org/10.5194/esd-12-1427-2021, 2021
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We address the question of how large an initial condition ensemble of climate model simulations should be if we are concerned with accurately projecting future changes in temperature and precipitation extremes. We find that for most cases (and both models considered), an ensemble of 20–25 members is sufficient for many extreme metrics, spatial scales and time horizons. This may leave computational resources to tackle other uncertainties in climate model simulations with our ensembles.
Dalei Hao, Gautam Bisht, Yu Gu, Wei-Liang Lee, Kuo-Nan Liou, and L. Ruby Leung
Geosci. Model Dev., 14, 6273–6289, https://doi.org/10.5194/gmd-14-6273-2021, https://doi.org/10.5194/gmd-14-6273-2021, 2021
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Topography exerts significant influence on the incoming solar radiation at the land surface. This study incorporated a well-validated sub-grid topographic parameterization in E3SM land model (ELM) version 1.0. The results demonstrate that sub-grid topography has non-negligible effects on surface energy budget, snow cover, and surface temperature over the Tibetan Plateau and that the ELM simulations are sensitive to season, elevation, and spatial scale.
Jianfeng Li, Yuhang Wang, Ruixiong Zhang, Charles Smeltzer, Andrew Weinheimer, Jay Herman, K. Folkert Boersma, Edward A. Celarier, Russell W. Long, James J. Szykman, Ruben Delgado, Anne M. Thompson, Travis N. Knepp, Lok N. Lamsal, Scott J. Janz, Matthew G. Kowalewski, Xiong Liu, and Caroline R. Nowlan
Atmos. Chem. Phys., 21, 11133–11160, https://doi.org/10.5194/acp-21-11133-2021, https://doi.org/10.5194/acp-21-11133-2021, 2021
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Comprehensive evaluations of simulated diurnal cycles of NO2 and NOy concentrations, vertical profiles, and tropospheric vertical column densities at two different resolutions with various measurements during the DISCOVER-AQ 2011 campaign show potential distribution biases of NOx emissions in the National Emissions Inventory 2011 at both 36 and 4 km resolutions, providing another possible explanation for the overestimation of model results.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
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The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Christopher R. Williams, Karen L. Johnson, Scott E. Giangrande, Joseph C. Hardin, Ruşen Öktem, and David M. Romps
Atmos. Meas. Tech., 14, 4425–4444, https://doi.org/10.5194/amt-14-4425-2021, https://doi.org/10.5194/amt-14-4425-2021, 2021
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In addition to detecting clouds, vertically pointing cloud radars detect individual insects passing over head. If these insects are not identified and removed from raw observations, then radar-derived cloud properties will be contaminated. This work identifies clouds in radar observations due to their continuous and smooth structure in time, height, and velocity. Cloud masks are produced that identify cloud vertical structure that are free of insect contamination.
Sam J. Silva, Po-Lun Ma, Joseph C. Hardin, and Daniel Rothenberg
Geosci. Model Dev., 14, 3067–3077, https://doi.org/10.5194/gmd-14-3067-2021, https://doi.org/10.5194/gmd-14-3067-2021, 2021
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The activation of aerosol into cloud droplets is an important but uncertain process in the Earth system. The physical and chemical interactions that govern this process are too computationally expensive to explicitly resolve in modern Earth system models. Here, we demonstrate how hybrid machine learning approaches can provide a potential path forward, enabling the representation of the more detailed physics and chemistry at a reduced computational cost while still retaining physical information.
Jianfeng Li, Zhe Feng, Yun Qian, and L. Ruby Leung
Earth Syst. Sci. Data, 13, 827–856, https://doi.org/10.5194/essd-13-827-2021, https://doi.org/10.5194/essd-13-827-2021, 2021
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Deep convection has different properties at different scales. We develop a 4 km h−1 observational data product of mesoscale convective systems and isolated deep convection in the United States from 2004–2017. We find that both types of convective systems contribute significantly to precipitation east of the Rocky Mountains but with distinct spatiotemporal characteristics. The data product will be useful for observational analyses and model evaluations of convection events at different scales.
Cited articles
Ashley, W. S., Mote, T. L., Dixon, P. G., Trotter, S. L., Powell, E. J.,
Durkee, J. D., and Grundstein, A. J.: Distribution of Mesoscale Convective
Complex Rainfall in the United States, Mon. Weather Rev., 131,
3003–3017, https://doi.org/10.1175/1520-0493(2003)131<3003:DOMCCR>2.0.CO;2, 2003.
Barber, K. A., Burleyson, C. D., Feng, Z., and Hagos, S. M.: The influence
of shallow cloud populations on transitions to deep convection in the
Amazon, J. Atmos. Sci., 79, 723–743, https://doi.org/10.1175/jas-d-21-0141.1, 2021.
Caldwell, P. M., Mametjanov, A., Tang, Q., Van Roekel, L. P., Golaz, J.-C.,
Lin, W., Bader, D. C., Keen, N. D., Feng, Y., Jacob, R., Maltrud, M. E.,
Roberts, A. F., Taylor, M. A., Veneziani, M., Wang, H., Wolfe, J. D.,
Balaguru, K., Cameron-Smith, P., Dong, L., Klein, S. A., Leung, L. R., Li,
H.-Y., Li, Q., Liu, X., Neale, R. B., Pinheiro, M., Qian, Y., Ullrich, P.
A., Xie, S., Yang, Y., Zhang, Y., Zhang, K., and Zhou, T.: The DOE E3SM
Coupled Model Version 1: Description and Results at High Resolution, J. Adv.
Model. Earth Sy., 11, 4095–4146, https://doi.org/10.1029/2019ms001870, 2019.
Catto, J. L., Jakob, C., and Nicholls, N.: Can the CMIP5 models represent
winter frontal precipitation?, Geophys. Res. Lett., 42, 8596–8604,
https://doi.org/10.1002/2015GL066015, 2015.
Chen, J., Hagos, S., Feng, Z., Fast, J. D., and Xiao, H.: The Role of
Cloud-Cloud Interactions in the Organization of Shallow Cumulus Clouds,
J. Atmos. Sci., 80, 671–686, https://doi.org/10.1175/jas-d-22-0004.1, 2022.
Chen, X., Leung, L. R., Feng, Z., and Song, F.: Crucial Role of Mesoscale
Convective Systems in the Vertical Mass, Water and Energy Transports of the
South Asian Summer Monsoon, J. Climate, 35, 1–46,
https://doi.org/10.1175/jcli-d-21-0124.1, 2021.
Clark, A. J., Bullock, R. G., Jensen, T. L., Xue, M., and Kong, F. Y.:
Application of Object-Based Time-Domain Diagnostics for Tracking
Precipitation Systems in Convection-Allowing Models, Weather
Forecast., 29, 517–542, https://doi.org/10.1175/Waf-D-13-00098.1, 2014.
Cui, W., Dong, X., Xi, B., Feng, Z., and Fan, J.: Can the GPM IMERG Final
Product Accurately Represent MCSs' Precipitation Characteristics over the
Central and Eastern United States?, J. Hydrometeorol., 21, 39–57,
https://doi.org/10.1175/jhm-d-19-0123.1, 2020.
Dixon, M. and Wiener, G.: TITAN: Thunderstorm Identification, Tracking,
Analysis, and Nowcasting?A radar-based methodology, J. Atmos. Ocean.
Tech., 10, 785–797, 1993.
Fast, J. D., Berg, L. K., Feng, Z., Mei, F., Newsom, R., Sakaguchi, K., and
Xiao, H.: The Impact of Variable Land-Atmosphere Coupling on Convective
Cloud Populations Observed During the 2016 HI-SCALE Field Campaign, J. Adv.
Model. Earth Sy., 11, 2629–2654, https://doi.org/10.1029/2019ms001727, 2019.
Feng, Z.: C-SAPR2 Convective Cell Tracking Database during CACTI (V1),
Atmospheric Radiation Measurement Program [data set], https://doi.org/10.5439/1844991, 2022a.
Feng, Z.: PyFLEXTRKR Initial Public Release Codes (2022.11.0), Zenodo [code], https://doi.org/10.5281/zenodo.7429446, 2022b.
Feng, Z.: Dataset used in GMD journal manuscript (egusphere-2022-1136) (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.7236445, 2022c.
Feng, Z., Dong, X. Q., Xi, B. K., McFarlane, S. A., Kennedy, A., Lin, B.,
and Minnis, P.: Life cycle of midlatitude deep convective systems in a
Lagrangian framework, J. Geophys. Res.-Atmos., 117, https://doi.org/10.1029/2012jd018362, 2012.
Feng, Z., Hagos, S., Rowe, A. K., Burleyson, C. D., Martini, M. N., and de
Szoeke, S. P.: Mechanisms of convective cloud organization by cold pools
over tropical warm ocean during the AMIE/DYNAMO field campaign, J. Adv. Model.
Earth Sy., 7, 357–381, https://doi.org/10.1002/2014MS000384, 2015.
Feng, Z., Leung, L. R., Hagos, S., Houze, R. A., Burleyson, C. D., and
Balaguru, K.: More frequent intense and long-lived storms dominate the
springtime trend in central US rainfall, Nat. Commun., 7, 13429,
https://doi.org/10.1038/ncomms13429, 2016.
Feng, Z., Leung, L. R., Houze, R. A., Hagos, S., Hardin, J., Yang, Q., Han,
B., and Fan, J.: Structure and Evolution of Mesoscale Convective Systems:
Sensitivity to Cloud Microphysics in Convection-Permitting Simulations Over
the United States, J. Adv. Model. Earth Sy., 10, 1470–1494,
https://doi.org/10.1029/2018ms001305, 2018.
Feng, Z., Houze, R. A., Leung, L. R., Song, F., Hardin, J. C., Wang, J.,
Gustafson, W. I., and Homeyer, C. R.: Spatiotemporal Characteristics and
Large-scale Environments of Mesoscale Convective Systems East of the Rocky
Mountains, J. Climate, 32, 7303–7328, https://doi.org/10.1175/jcli-d-19-0137.1,
2019.
Feng, Z., Leung, L. R., Liu, N., Wang, J., Houze, R. A., Li, J., Hardin, J.
C., Chen, D., and Guo, J.: A Global High-Resolution Mesoscale Convective
System Database Using Satellite-Derived Cloud Tops, Surface Precipitation,
and Tracking, J. Geophys. Res.-Atmos., 126, e2020JD034202,
https://doi.org/10.1029/2020jd034202, 2021a.
Feng, Z., Song, F., Sakaguchi, K., and Leung, L. R.: Evaluation of Mesoscale
Convective Systems in Climate Simulations: Methodological Development and
Results from MPAS-CAM over the United States, J. Climate, 34,
2611–2633, https://doi.org/10.1175/jcli-d-20-0136.1, 2021b.
Feng, Z., Varble, A., Hardin, J., Marquis, J., Hunzinger, A., Zhang, Z., and
Thieman, M.: Deep Convection Initiation, Growth, and Environments in the
Complex Terrain of Central Argentina during CACTI, Mon. Weather Rev.,
150, 1135–1155, https://doi.org/10.1175/mwr-d-21-0237.1, 2022.
Feng, Z., Leung, L. R., Hardin, J., Terai, C. R., Song, F., and Caldwell,
P.: Mesoscale Convective Systems in DYAMOND Global Convection-Permitting
Simulations, Geophys. Res. Lett., 50, e2022GL102603, https://doi.org/10.1029/2022GL102603, 2023.
Fiolleau, T. and Roca, R.: An Algorithm for the Detection and Tracking of
Tropical Mesoscale Convective Systems Using Infrared Images From
Geostationary Satellite, IEEE T. Geosci. Remote,
51, 4302–4315, https://doi.org/10.1109/TGRS.2012.2227762, 2013.
Fritsch, J. M., Kane, R. J., and Chelius, C. R.: The Contribution of
Mesoscale Convective Weather Systems to the Warm-Season Precipitation in the
United States, J. Climate Appl. Meteorol., 25, 1333–1345,
https://doi.org/10.1175/1520-0450(1986)025<1333:TCOMCW>2.0.CO;2, 1986.
Futyan, J. M. and Del Genio, A. D.: Deep Convective System Evolution over
Africa and the Tropical Atlantic, J. Climate, 20, 5041–5060,
https://doi.org/10.1175/JCLI4297.1, 2007.
Gropp, M. E. and Davenport, C. E.: Python-Based Supercell Tracking for
Coarse Temporal and Spatial Resolution Numerical Model Simulations, J. Atmos. Ocean. Tech., 38, 1551–1559,
https://doi.org/10.1175/jtech-d-20-0122.1, 2021.
Gustafson, W. I., Vogelmann, A. M., Li, Z., Cheng, X., Dumas, K. K., Endo,
S., Johnson, K. L., Krishna, B., Fairless, T., and Xiao, H.: The Large-Eddy
Simulation (LES) Atmospheric Radiation Measurement (ARM) Symbiotic
Simulation and Observation (LASSO) Activity for Continental Shallow
Convection, B. Am. Meteorol. Soc., 101, E462–E479,
https://doi.org/10.1175/bams-d-19-0065.1, 2020.
Haberlie, A. M. and Ashley, W. S.: A Method for Identifying Midlatitude
Mesoscale Convective Systems in Radar Mosaics. Part I: Segmentation and
Classification, J. Appl. Meteorol. Climatol., 57,
1575–1598, https://doi.org/10.1175/jamc-d-17-0293.1, 2018a.
Haberlie, A. M. and Ashley, W. S.: A Method for Identifying Midlatitude
Mesoscale Convective Systems in Radar Mosaics. Part II: Tracking, J. Appl. Meteorol. Climatol., 57, 1599–1621,
https://doi.org/10.1175/jamc-d-17-0294.1, 2018b.
Hagos, S., Feng, Z., McFarlane, S., and Leung, L. R.: Environment and the
Lifetime of Tropical Deep Convection in a Cloud-Permitting Regional Model
Simulation, J. Atmos. Sci., 70, 2409–2425,
https://doi.org/10.1175/JAS-D-12-0260.1, 2013.
Hardin, J., Hunzinger, A., Schuman, E., Matthews, A., Bharadwaj, N., Varble,
A., Johnson, K., and Giangrande, S.: C-band Scanning ARM Precipitation
Radar, CF-Radial, Quality-Controlled Hemispheric Range-Height Indicator
Scans (CSAPR2CFRHSRHIQC), ARM Data Center [data set], https://doi.org/10.5439/1615607, 2018a.
Hardin, J., Hunzinger, A., Schuman, E., Matthews, A., Bharadwaj, N., Varble,
A., Johnson, K., and Giangrande, S.: C-band Scanning ARM Precipitation
Radar, CF-Radial, Quality-Controlled Plan Position IndicatorScans
(CSAPR2CFRPPIQC), ARM Data Center [data set], https://doi.org/10.5439/1615604, 2018b.
Hayden, L., Liu, C., and Liu, N.: Properties of Mesoscale Convective Systems
Throughout Their Lifetimes Using IMERG, GPM, WWLLN, and a Simplified
Tracking Algorithm, J. Geophys. Res.-Atmos., 126,
e2021JD035264, https://doi.org/10.1029/2021JD035264, 2021.
Heikenfeld, M., Marinescu, P. J., Christensen, M., Watson-Parris, D., Senf, F., van den Heever, S. C., and Stier, P.: tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets, Geosci. Model Dev., 12, 4551–4570, https://doi.org/10.5194/gmd-12-4551-2019, 2019.
Helmus, J. J. and Collis, S. M.: The Python ARM Radar Toolkit (Py-ART), a
Library for Working with Weather Radar Data in the Python Programming
Language, J. Open Res. Softw., 4, e25, https://doi.org/10.5334/jors.119, 2016.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay,
P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5
global reanalysis, Q. J. Roy. Meteor. Soc.,
146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hewson, T. D. and Titley, H. A.: Objective identification, typing and
tracking of the complete life-cycles of cyclonic features at high spatial
resolution, Meteorol. Appl., 17, 355–381, https://doi.org/10.1002/met.204, 2010.
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C.
J.: The International Best Track Archive for Climate Stewardship (IBTrACS):
Unifying Tropical Cyclone Data, B. Am. Meteorol. Soc., 91, 363–376, https://doi.org/10.1175/2009bams2755.1, 2010.
Laing, A. G. and Fritsch, J. M.: The global population of mesoscale
convective complexes, Q. J. Roy. Meteor. Soc.,
123, 389–405, https://doi.org/10.1002/qj.49712353807, 1997.
Li, J., Feng, Z., Qian, Y., and Leung, L. R.: A high-resolution unified observational data product of mesoscale convective systems and isolated deep convection in the United States for 2004–2017, Earth Syst. Sci. Data, 13, 827–856, https://doi.org/10.5194/essd-13-827-2021, 2021.
Li, R., Qi, D., Zhang, Y., and Wang, K.: A new pixel-to-object method for
evaluating the capability of the GPM IMERG product to quantify precipitation
systems, J. Hydrol., 128476, https://doi.org/10.1016/j.jhydrol.2022.128476, 2022.
Lin, G., Jones, C. R., Leung, L. R., Feng, Z., and Ovchinnikov, M.:
Mesoscale Convective Systems in a Superparameterized E3SM Simulation at High
Resolution, J. Adv. Model. Earth Sy., 14, e2021MS002660, https://doi.org/10.1029/2021MS002660, 2022.
Liu, C., Ikeda, K., Rasmussen, R., Barlage, M., Newman, A. J., Prein, A. F.,
Chen, F., Chen, L., Clark, M., Dai, A., Dudhia, J., Eidhammer, T., Gochis,
D., Gutmann, E., Kurkute, S., Li, Y., Thompson, G., and Yates, D.:
Continental-scale convection-permitting modeling of the current and future
climate of North America, Clim. Dynam., 49, 71–95, https://doi.org/10.1007/s00382-016-3327-9,
2017.
Liu, C., Ikeda, K., Prein, A., Rasmussen, R., Dominguez, F., and Dudhia, J.:
Convection-Permitting Climate Simulations over South America:
Experimentation during Different Phases of ENSO, in preparation, 2023.
Machado, L. A. T., Rossow, W. B., Guedes, R. L., and Walker, A. W.: Life
cycle variations of mesoscale convective systems over the Americas, Mon. Weather Rev., 126, 1630–1654, https://doi.org/10.1175/1520-0493(1998)126<1630:Lcvomc>2.0.Co;2, 1998.
Núñez Ocasio, K. M., Evans, J. L., and Young, G. S.: Tracking
Mesoscale Convective Systems that are Potential Candidates for Tropical
Cyclogenesis, Mon. Weather Rev., 148, 655–669, https://doi.org/10.1175/mwr-d-19-0070.1,
2020.
Padfield, D.: Masked Object Registration in the Fourier Domain, IEEE
T. Image Proc., 21, 2706–2718, https://doi.org/10.1109/TIP.2011.2181402,
2012.
Prein, A., Liu, C., Ikeda, K., Bullock, R., Rasmussen, R., Holland, G., and
Clark, M.: Simulating North American mesoscale convective systems with a
convection-permitting climate model, Clim. Dynam., 55, 95–110, https://doi.org/10.1007/s00382-017-3993-2,
2017.
Raut, B. A., Jackson, R., Picel, M., Collis, S. M., Bergemann, M., and
Jakob, C.: An Adaptive Tracking Algorithm for Convection in Simulated and
Remote Sensing Data, J. Appl. Meteorol. Climatol., 60,
513–526, https://doi.org/10.1175/jamc-d-20-0119.1, 2021.
Roca, R., Aublanc, J., Chambon, P., Fiolleau, T., and Viltard, N.: Robust
Observational Quantification of the Contribution of Mesoscale Convective
Systems to Rainfall in the Tropics, J. Climate, 27, 4952–4958,
https://doi.org/10.1175/JCLI-D-13-00628.1, 2014.
Roca, R., Fiolleau, T., and Bouniol, D.: A Simple Model of the Life Cycle of
Mesoscale Convective Systems Cloud Shield in the Tropics, J. Climate, 30, 4283–4298, https://doi.org/10.1175/jcli-d-16-0556.1, 2017.
Rutz, J. J., Shields, C. A., Lora, J. M., Payne, A. E., Guan, B., Ullrich,
P., O'Brien, T., Leung, L. R., Ralph, F. M., Wehner, M., Brands, S., Collow,
A., Goldenson, N., Gorodetskaya, I., Griffith, H., Kashinath, K., Kawzenuk,
B., Krishnan, H., Kurlin, V., Lavers, D., Magnusdottir, G., Mahoney, K.,
McClenny, E., Muszynski, G., Nguyen, P. D., Prabhat, M., Qian, Y., Ramos, A.
M., Sarangi, C., Sellars, S., Shulgina, T., Tome, R., Waliser, D., Walton,
D., Wick, G., Wilson, A. M., and Viale, M.: The Atmospheric River Tracking
Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in
Atmospheric River Climatology, J. Geophys. Res.-Atmos.,
124, 13777–13802, https://doi.org/10.1029/2019jd030936, 2019.
Satoh, M., Stevens, B., Judt, F., Khairoutdinov, M., Lin, S.-J., Putman, W.
M., and Düben, P.: Global Cloud-Resolving Models, Current Climate Change
Reports, 5, 172–184, https://doi.org/10.1007/s40641-019-00131-0, 2019.
Sinclair, M. R.: Objective Identification of Cyclones and Their Circulation
Intensity, and Climatology, Weather Forecast., 12, 595–612,
https://doi.org/10.1175/1520-0434(1997)012<0595:Oiocat>2.0.Co;2, 1997.
Steiner, M., Houze, R. A., and Yuter, S. E.: Climatological Characterization
of Three-Dimensional Storm Structure from Operational Radar and Rain Gauge
Data, J. Appl. Meteorol., 34, 1978–2007,
https://doi.org/10.1175/1520-0450(1995)034<1978:CCOTDS>2.0.CO;2, 1995.
Stevens, B., Satoh, M., Auger, L., Biercamp, J., Bretherton, C. S., Chen,
X., Düben, P., Judt, F., Khairoutdinov, M., Klocke, D., Kodama, C.,
Kornblueh, L., Lin, S.-J., Neumann, P., Putman, W. M., Röber, N.,
Shibuya, R., Vanniere, B., Vidale, P. L., Wedi, N., and Zhou, L.: DYAMOND:
the DYnamics of the Atmospheric general circulation Modeled On
Non-hydrostatic Domains, Prog. Earth Planet. Sc., 6, 61,
https://doi.org/10.1186/s40645-019-0304-z, 2019.
Tamarin-Brodsky, T., Hodges, K., Hoskins, B. J., and Shepherd, T. G.:
Changes in Northern Hemisphere temperature variability shaped by regional
warming patterns, Nat. Geosci., 13, 414–421, https://doi.org/10.1038/s41561-020-0576-3,
2020.
Ullrich, P. A. and Zarzycki, C. M.: TempestExtremes: a framework for scale-insensitive pointwise feature tracking on unstructured grids, Geosci. Model Dev., 10, 1069–1090, https://doi.org/10.5194/gmd-10-1069-2017, 2017.
Ullrich, P. A., Zarzycki, C. M., McClenny, E. E., Pinheiro, M. C., Stansfield, A. M., and Reed, K. A.: TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets, Geosci. Model Dev., 14, 5023–5048, https://doi.org/10.5194/gmd-14-5023-2021, 2021.
Varble, A. C., Nesbitt, S. W., Salio, P., Hardin, J. C., Bharadwaj, N.,
Borque, P., DeMott, P. J., Feng, Z., Hill, T. C. J., Marquis, J. N.,
Matthews, A., Mei, F., Öktem, R., Castro, V., Goldberger, L., Hunzinger,
A., Barry, K. R., Kreidenweis, S. M., McFarquhar, G. M., McMurdie, L. A.,
Pekour, M., Powers, H., Romps, D. M., Saulo, C., Schmid, B., Tomlinson, J.
M., van den Heever, S. C., Zelenyuk, A., Zhang, Z., and Zipser, E. J.:
Utilizing a Storm-Generating Hotspot to Study Convective Cloud Transitions:
The CACTI Experiment, B. Am. Meteorol. Soc., 102,
1–67, https://doi.org/10.1175/bams-d-20-0030.1, 2021.
Velasco, I. and Fritsch, J. M.: Mesoscale convective complexes in the
Americas, J. Geophys. Res.-Atmos., 92, 9591–9613,
https://doi.org/10.1029/JD092iD08p09591, 1987.
Vishnu, S., Boos, W. R., Ullrich, P. A., and O'Brien, T. A.: Assessing
Historical Variability of South Asian Monsoon Lows and Depressions With an
Optimized Tracking Algorithm, J. Geophys. Res.-Atmos.,
125, e2020JD032977, https://doi.org/10.1029/2020jd032977, 2020.
Walsh, K. J. E., Fiorino, M., Landsea, C. W., and McInnes, K. L.:
Objectively Determined Resolution-Dependent Threshold Criteria for the
Detection of Tropical Cyclones in Climate Models and Reanalyses, J. Climate, 20, 2307–2314, https://doi.org/10.1175/jcli4074.1, 2007.
Whitehall, K., Mattmann, C. A., Jenkins, G., Rwebangira, M., Demoz, B.,
Waliser, D., Kim, J., Goodale, C., Hart, A., Ramirez, P., Joyce, M. J.,
Boustani, M., Zimdars, P., Loikith, P., and Lee, H.: Exploring a graph
theory based algorithm for automated identification and characterization of
large mesoscale convective systems in satellite datasets, Earth Sci.
Inform., 8, 663–675, https://doi.org/10.1007/s12145-014-0181-3, 2015.
Williams, M. and Houze, R. A.: Satellite-Observed Characteristics of Winter
Monsoon Cloud Clusters, Mon. Weather Rev., 115, 505–519,
https://doi.org/10.1175/1520-0493(1987)115<0505:socowm>2.0.co;2, 1987.
Yang, G. Y. and Slingo, J.: The diurnal cycle in the Tropics, Mon. Weather Rev., 129, 784–801, https://doi.org/10.1175/1520-0493(2001)129<0784:Tdcitt>2.0.Co;2, 2001.
Zhang, Z., Varble, A., Feng, Z., Hardin, J., and Zipser, E.: Growth of
Mesoscale Convective Systems in Observations and a Seasonal
Convection-Permitting Simulation over Argentina, Mon. Weather Rev.,
149, 3469–3490, https://doi.org/10.1175/mwr-d-20-0411.1, 2021.
Short summary
PyFLEXTRKR is a flexible atmospheric feature tracking framework with specific capabilities to track convective clouds from a variety of observations and model simulations. The package has a collection of multi-object identification algorithms and has been optimized for large datasets. This paper describes the algorithms and demonstrates applications for tracking deep convective cells and mesoscale convective systems from observations and model simulations at a wide range of scales.
PyFLEXTRKR is a flexible atmospheric feature tracking framework with specific capabilities to...
