Articles | Volume 17, issue 16
https://doi.org/10.5194/gmd-17-6319-2024
© Author(s) 2024. 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-17-6319-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
27 Aug 2024
Development and technical paper |
| 27 Aug 2024
| 27 Aug 2024
Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains
Lawrence Livermore National Laboratory, Livermore, CA, USA
Yunyan Zhang
Lawrence Livermore National Laboratory, Livermore, CA, USA
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Tianning Su and Yunyan Zhang
Atmos. Chem. Phys., 24, 6477–6493, https://doi.org/10.5194/acp-24-6477-2024, https://doi.org/10.5194/acp-24-6477-2024, 2024
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The planetary boundary layer is critical to our climate system. This study uses a deep learning approach to estimate the planetary boundary layer height (PBLH) from conventional meteorological measurements. By training data from comprehensive field observations, our model examines the influence of various meteorological factors on PBLH and demonstrates effectiveness across different scenarios, offering a reliable tool for understanding boundary layer dynamics.
Tianning Su, Youtong Zheng, and Zhanqing Li
Atmos. Chem. Phys., 22, 1453–1466, https://doi.org/10.5194/acp-22-1453-2022, https://doi.org/10.5194/acp-22-1453-2022, 2022
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To enrich our understanding of coupling of continental clouds, we developed a novel methodology to determine cloud coupling state from a lidar and a suite of surface meteorological instruments. This method is built upon advancement in our understanding of fundamental boundary layer processes and clouds. As the first remote sensing method for determining the coupling state of low clouds over land, this methodology paves a solid ground for further investigating the coupled land–atmosphere system.
Jianping Guo, Jian Zhang, Kun Yang, Hong Liao, Shaodong Zhang, Kaiming Huang, Yanmin Lv, Jia Shao, Tao Yu, Bing Tong, Jian Li, Tianning Su, Steve H. L. Yim, Ad Stoffelen, Panmao Zhai, and Xiaofeng Xu
Atmos. Chem. Phys., 21, 17079–17097, https://doi.org/10.5194/acp-21-17079-2021, https://doi.org/10.5194/acp-21-17079-2021, 2021
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The planetary boundary layer (PBL) is the lowest part of the troposphere, and boundary layer height (BLH) is the depth of the PBL and is of critical importance to the dispersion of air pollution. The study presents the first near-global BLH climatology by using high-resolution (5-10 m) radiosonde measurements. The variations in BLH exhibit large spatial and temporal dependence, with a peak at 17:00 local solar time. The most promising reanalysis product is ERA-5 in terms of modeling BLH.
Wenchao Han, Zhanqing Li, Fang Wu, Yuwei Zhang, Jianping Guo, Tianning Su, Maureen Cribb, Jiwen Fan, Tianmeng Chen, Jing Wei, and Seoung-Soo Lee
Atmos. Chem. Phys., 20, 6479–6493, https://doi.org/10.5194/acp-20-6479-2020, https://doi.org/10.5194/acp-20-6479-2020, 2020
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Observational data and model simulation were used to analyze the daytime urban heat island intensity (UHII) under polluted and clean conditions in China. We found that aerosols reduce the UHII in summer but increase the UHII in winter. Two mechanisms, the aerosol radiative effect (ARE) and the aerosol dynamic effect (ADE), behave differently in summer and winter. In summer, the UHII is mainly affected by the ARE, and the ADE is weak, and the opposite is the case in winter.
Tianning Su, Zhanqing Li, Chengcai Li, Jing Li, Wenchao Han, Chuanyang Shen, Wangshu Tan, Jing Wei, and Jianping Guo
Atmos. Chem. Phys., 20, 3713–3724, https://doi.org/10.5194/acp-20-3713-2020, https://doi.org/10.5194/acp-20-3713-2020, 2020
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We study the role of aerosol vertical distribution in thermodynamic stability and PBL development. Under different aerosol vertical structures, the diurnal cycles of PBLH and PM2.5 show distinct characteristics. Large differences in the heating rate affect atmospheric buoyancy and stability differently under different aerosol structures. As a result, the aerosol–PBL interaction can be strengthened by the inverse aerosol structure and potentially neutralized by the decreasing structure.
Tianning Su, Zhanqing Li, and Ralph Kahn
Atmos. Chem. Phys., 18, 15921–15935, https://doi.org/10.5194/acp-18-15921-2018, https://doi.org/10.5194/acp-18-15921-2018, 2018
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Surface particulate concentration has often been estimated from column-integrated aerosol optical depth (AOD). Their relationship is affected by various factors, such as the planetary layer height, meteorology (atmospheric stability, wind, relative humidity, etc.), and topography, which are investigated thoroughly using a combination of ~1500 surface station datasets, two ground-based lidars, and CALIPSO space-based lidar measurements made across China. Improved estimation of PM2.5 is achieved.
Tianning Su and Yunyan Zhang
Atmos. Chem. Phys., 24, 6477–6493, https://doi.org/10.5194/acp-24-6477-2024, https://doi.org/10.5194/acp-24-6477-2024, 2024
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The planetary boundary layer is critical to our climate system. This study uses a deep learning approach to estimate the planetary boundary layer height (PBLH) from conventional meteorological measurements. By training data from comprehensive field observations, our model examines the influence of various meteorological factors on PBLH and demonstrates effectiveness across different scenarios, offering a reliable tool for understanding boundary layer dynamics.
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.
Aurore Voldoire, Romain Roehrig, Hervé Giordani, Robin Waldman, Yunyan Zhang, Shaocheng Xie, and Marie-Nöelle Bouin
Geosci. Model Dev., 15, 3347–3370, https://doi.org/10.5194/gmd-15-3347-2022, https://doi.org/10.5194/gmd-15-3347-2022, 2022
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A single-column version of the global climate model CNRM-CM6-1 has been designed to ease development and validation of the model physics at the air–sea interface in a simplified environment. This model is then used to assess the ability to represent the sea surface temperature diurnal cycle. We conclude that the sea surface temperature diurnal variability is reasonably well represented in CNRM-CM6-1 with a 1 h coupling time step and the upper-ocean model resolution of 1 m.
Tianning Su, Youtong Zheng, and Zhanqing Li
Atmos. Chem. Phys., 22, 1453–1466, https://doi.org/10.5194/acp-22-1453-2022, https://doi.org/10.5194/acp-22-1453-2022, 2022
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To enrich our understanding of coupling of continental clouds, we developed a novel methodology to determine cloud coupling state from a lidar and a suite of surface meteorological instruments. This method is built upon advancement in our understanding of fundamental boundary layer processes and clouds. As the first remote sensing method for determining the coupling state of low clouds over land, this methodology paves a solid ground for further investigating the coupled land–atmosphere system.
Jianping Guo, Jian Zhang, Kun Yang, Hong Liao, Shaodong Zhang, Kaiming Huang, Yanmin Lv, Jia Shao, Tao Yu, Bing Tong, Jian Li, Tianning Su, Steve H. L. Yim, Ad Stoffelen, Panmao Zhai, and Xiaofeng Xu
Atmos. Chem. Phys., 21, 17079–17097, https://doi.org/10.5194/acp-21-17079-2021, https://doi.org/10.5194/acp-21-17079-2021, 2021
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The planetary boundary layer (PBL) is the lowest part of the troposphere, and boundary layer height (BLH) is the depth of the PBL and is of critical importance to the dispersion of air pollution. The study presents the first near-global BLH climatology by using high-resolution (5-10 m) radiosonde measurements. The variations in BLH exhibit large spatial and temporal dependence, with a peak at 17:00 local solar time. The most promising reanalysis product is ERA-5 in terms of modeling BLH.
Hsi-Yen Ma, Chen Zhou, Yunyan Zhang, Stephen A. Klein, Mark D. Zelinka, Xue Zheng, Shaocheng Xie, Wei-Ting Chen, and Chien-Ming Wu
Geosci. Model Dev., 14, 73–90, https://doi.org/10.5194/gmd-14-73-2021, https://doi.org/10.5194/gmd-14-73-2021, 2021
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We propose an experimental design of a suite of multi-year, short-term hindcasts and compare them with corresponding observations or measurements for periods based on different weather and climate phenomena. This atypical way of evaluating model performance is particularly useful and beneficial, as these hindcasts can give scientists a robust picture of modeled precipitation, and cloud and radiation processes from their diurnal variation to year-to-year variability.
Wenchao Han, Zhanqing Li, Fang Wu, Yuwei Zhang, Jianping Guo, Tianning Su, Maureen Cribb, Jiwen Fan, Tianmeng Chen, Jing Wei, and Seoung-Soo Lee
Atmos. Chem. Phys., 20, 6479–6493, https://doi.org/10.5194/acp-20-6479-2020, https://doi.org/10.5194/acp-20-6479-2020, 2020
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Observational data and model simulation were used to analyze the daytime urban heat island intensity (UHII) under polluted and clean conditions in China. We found that aerosols reduce the UHII in summer but increase the UHII in winter. Two mechanisms, the aerosol radiative effect (ARE) and the aerosol dynamic effect (ADE), behave differently in summer and winter. In summer, the UHII is mainly affected by the ARE, and the ADE is weak, and the opposite is the case in winter.
Tianning Su, Zhanqing Li, Chengcai Li, Jing Li, Wenchao Han, Chuanyang Shen, Wangshu Tan, Jing Wei, and Jianping Guo
Atmos. Chem. Phys., 20, 3713–3724, https://doi.org/10.5194/acp-20-3713-2020, https://doi.org/10.5194/acp-20-3713-2020, 2020
Short summary
Short summary
We study the role of aerosol vertical distribution in thermodynamic stability and PBL development. Under different aerosol vertical structures, the diurnal cycles of PBLH and PM2.5 show distinct characteristics. Large differences in the heating rate affect atmospheric buoyancy and stability differently under different aerosol structures. As a result, the aerosol–PBL interaction can be strengthened by the inverse aerosol structure and potentially neutralized by the decreasing structure.
Tianning Su, Zhanqing Li, and Ralph Kahn
Atmos. Chem. Phys., 18, 15921–15935, https://doi.org/10.5194/acp-18-15921-2018, https://doi.org/10.5194/acp-18-15921-2018, 2018
Short summary
Short summary
Surface particulate concentration has often been estimated from column-integrated aerosol optical depth (AOD). Their relationship is affected by various factors, such as the planetary layer height, meteorology (atmospheric stability, wind, relative humidity, etc.), and topography, which are investigated thoroughly using a combination of ~1500 surface station datasets, two ground-based lidars, and CALIPSO space-based lidar measurements made across China. Improved estimation of PM2.5 is achieved.
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Geosci. Model Dev., 17, 6489–6511, https://doi.org/10.5194/gmd-17-6489-2024, https://doi.org/10.5194/gmd-17-6489-2024, 2024
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The forecast error growth of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. With a negative result, we investigate in the extended Lorenz (2005) system whether omitting these phenomena will improve predictability. A theory explaining and describing this behavior is developed.
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Geosci. Model Dev., 17, 6465–6487, https://doi.org/10.5194/gmd-17-6465-2024, https://doi.org/10.5194/gmd-17-6465-2024, 2024
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Geosci. Model Dev., 17, 6379–6399, https://doi.org/10.5194/gmd-17-6379-2024, https://doi.org/10.5194/gmd-17-6379-2024, 2024
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Angeline G. Pendergrass, Michael P. Byrne, Oliver Watt-Meyer, Penelope Maher, and Mark J. Webb
Geosci. Model Dev., 17, 6365–6378, https://doi.org/10.5194/gmd-17-6365-2024, https://doi.org/10.5194/gmd-17-6365-2024, 2024
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Geosci. Model Dev., 17, 6301–6318, https://doi.org/10.5194/gmd-17-6301-2024, https://doi.org/10.5194/gmd-17-6301-2024, 2024
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Philip G. Sansom and Jennifer L. Catto
Geosci. Model Dev., 17, 6137–6151, https://doi.org/10.5194/gmd-17-6137-2024, https://doi.org/10.5194/gmd-17-6137-2024, 2024
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Kelly M. Núñez Ocasio and Zachary L. Moon
Geosci. Model Dev., 17, 6035–6049, https://doi.org/10.5194/gmd-17-6035-2024, https://doi.org/10.5194/gmd-17-6035-2024, 2024
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TAMS is an open-source Python-based package for tracking and classifying mesoscale convective systems that can be used to study observed and simulated systems. Each step of the algorithm is described in this paper with examples showing how to make use of visualization and post-processing tools within the package. A unique and valuable feature of this tracker is its support for unstructured grids in the identification stage and grid-independent tracking.
Irene C. Dedoussi, Daven K. Henze, Sebastian D. Eastham, Raymond L. Speth, and Steven R. H. Barrett
Geosci. Model Dev., 17, 5689–5703, https://doi.org/10.5194/gmd-17-5689-2024, https://doi.org/10.5194/gmd-17-5689-2024, 2024
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Atmospheric model gradients provide a meaningful tool for better understanding the underlying atmospheric processes. Adjoint modeling enables computationally efficient gradient calculations. We present the adjoint of the GEOS-Chem unified chemistry extension (UCX). With this development, the GEOS-Chem adjoint model can capture stratospheric ozone and other processes jointly with tropospheric processes. We apply it to characterize the Antarctic ozone depletion potential of active halogen species.
Sylvain Mailler, Sotirios Mallios, Arineh Cholakian, Vassilis Amiridis, Laurent Menut, and Romain Pennel
Geosci. Model Dev., 17, 5641–5655, https://doi.org/10.5194/gmd-17-5641-2024, https://doi.org/10.5194/gmd-17-5641-2024, 2024
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We propose two explicit expressions to calculate the settling speed of solid atmospheric particles with prolate spheroidal shapes. The first formulation is based on theoretical arguments only, while the second one is based on computational fluid dynamics calculations. We show that the first method is suitable for virtually all atmospheric aerosols, provided their shape can be adequately described as a prolate spheroid, and we provide an implementation of the first method in AerSett v2.0.2.
Hejun Xie, Lei Bi, and Wei Han
Geosci. Model Dev., 17, 5657–5688, https://doi.org/10.5194/gmd-17-5657-2024, https://doi.org/10.5194/gmd-17-5657-2024, 2024
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A radar operator plays a crucial role in utilizing radar observations to enhance numerical weather forecasts. However, developing an advanced radar operator is challenging due to various complexities associated with the wave scattering by non-spherical hydrometeors, radar beam propagation, and multiple platforms. In this study, we introduce a novel radar operator named the Accurate and Efficient Radar Operator developed by ZheJiang University (ZJU-AERO) which boasts several unique features.
Jonathan J. Day, Gunilla Svensson, Barbara Casati, Taneil Uttal, Siri-Jodha Khalsa, Eric Bazile, Elena Akish, Niramson Azouz, Lara Ferrighi, Helmut Frank, Michael Gallagher, Øystein Godøy, Leslie M. Hartten, Laura X. Huang, Jareth Holt, Massimo Di Stefano, Irene Suomi, Zen Mariani, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Teresa Remes, Rostislav Fadeev, Amy Solomon, Johanna Tjernström, and Mikhail Tolstykh
Geosci. Model Dev., 17, 5511–5543, https://doi.org/10.5194/gmd-17-5511-2024, https://doi.org/10.5194/gmd-17-5511-2024, 2024
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The YOPP site Model Intercomparison Project (YOPPsiteMIP), which was designed to facilitate enhanced weather forecast evaluation in polar regions, is discussed here, focussing on describing the archive of forecast data and presenting a multi-model evaluation at Arctic supersites during February and March 2018. The study highlights an underestimation in boundary layer temperature variance that is common across models and a related inability to forecast cold extremes at several of the sites.
Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Yanfeng He, and Md Firoz Khan
Geosci. Model Dev., 17, 5545–5571, https://doi.org/10.5194/gmd-17-5545-2024, https://doi.org/10.5194/gmd-17-5545-2024, 2024
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Using multi-platform observations, we validated global formaldehyde (HCHO) simulations from a chemistry transport model. HCHO is a crucial intermediate in the chemical catalytic cycle that governs the ozone formation in the troposphere. The model was capable of replicating the observed spatiotemporal variability in HCHO. In a few cases, the model's capability was limited. This is attributed to the uncertainties in the observations and the model parameters.
Zijun Liu, Li Dong, Zongxu Qiu, Xingrong Li, Huiling Yuan, Dongmei Meng, Xiaobin Qiu, Dingyuan Liang, and Yafei Wang
Geosci. Model Dev., 17, 5477–5496, https://doi.org/10.5194/gmd-17-5477-2024, https://doi.org/10.5194/gmd-17-5477-2024, 2024
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In this study, we completed a series of simulations with MPAS-Atmosphere (version 7.3) to study the extreme precipitation event of Henan, China, during 20–22 July 2021. We found the different performance of two built-in parameterization scheme suites (mesoscale and convection-permitting suites) with global quasi-uniform and variable-resolution meshes. This study holds significant implications for advancing the understanding of the scale-aware capability of MPAS-Atmosphere.
Laurent Menut, Arineh Cholakian, Romain Pennel, Guillaume Siour, Sylvain Mailler, Myrto Valari, Lya Lugon, and Yann Meurdesoif
Geosci. Model Dev., 17, 5431–5457, https://doi.org/10.5194/gmd-17-5431-2024, https://doi.org/10.5194/gmd-17-5431-2024, 2024
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A new version of the CHIMERE model is presented. This version contains both computational and physico-chemical changes. The computational changes make it easy to choose the variables to be extracted as a result, including values of maximum sub-hourly concentrations. Performance tests show that the model is 1.5 to 2 times faster than the previous version for the same setup. Processes such as turbulence, transport schemes and dry deposition have been modified and updated.
G. Alexander Sokolowsky, Sean W. Freeman, William K. Jones, Julia Kukulies, Fabian Senf, Peter J. Marinescu, Max Heikenfeld, Kelcy N. Brunner, Eric C. Bruning, Scott M. Collis, Robert C. Jackson, Gabrielle R. Leung, Nils Pfeifer, Bhupendra A. Raut, Stephen M. Saleeby, Philip Stier, and Susan C. van den Heever
Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024, https://doi.org/10.5194/gmd-17-5309-2024, 2024
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Building on previous analysis tools developed for atmospheric science, the original release of the Tracking and Object-Based Analysis (tobac) Python package, v1.2, was open-source, modular, and insensitive to the type of gridded input data. Here, we present the latest version of tobac, v1.5, which substantially improves scientific capabilities and computational efficiency from the previous version. These enhancements permit new uses for tobac in atmospheric science and potentially other fields.
Taneil Uttal, Leslie M. Hartten, Siri Jodha Khalsa, Barbara Casati, Gunilla Svensson, Jonathan Day, Jareth Holt, Elena Akish, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Laura X. Huang, Robert Crawford, Zen Mariani, Øystein Godøy, Johanna A. K. Tjernström, Giri Prakash, Nicki Hickmon, Marion Maturilli, and Christopher J. Cox
Geosci. Model Dev., 17, 5225–5247, https://doi.org/10.5194/gmd-17-5225-2024, https://doi.org/10.5194/gmd-17-5225-2024, 2024
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A Merged Observatory Data File (MODF) format to systematically collate complex atmosphere, ocean, and terrestrial data sets collected by multiple instruments during field campaigns is presented. The MODF format is also designed to be applied to model output data, yielding format-matching Merged Model Data Files (MMDFs). MODFs plus MMDFs will augment and accelerate the synergistic use of model results with observational data to increase understanding and predictive skill.
Chongzhi Yin, Shin-ichiro Shima, Lulin Xue, and Chunsong Lu
Geosci. Model Dev., 17, 5167–5189, https://doi.org/10.5194/gmd-17-5167-2024, https://doi.org/10.5194/gmd-17-5167-2024, 2024
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We investigate numerical convergence properties of a particle-based numerical cloud microphysics model (SDM) and a double-moment bulk scheme for simulating a marine stratocumulus case, compare their results with model intercomparison project results, and present possible explanations for the different results of the SDM and the bulk scheme. Aerosol processes can be accurately simulated using SDM, and this may be an important factor affecting the behavior and morphology of marine stratocumulus.
Alberto Martilli, Negin Nazarian, E. Scott Krayenhoff, Jacob Lachapelle, Jiachen Lu, Esther Rivas, Alejandro Rodriguez-Sanchez, Beatriz Sanchez, and José Luis Santiago
Geosci. Model Dev., 17, 5023–5039, https://doi.org/10.5194/gmd-17-5023-2024, https://doi.org/10.5194/gmd-17-5023-2024, 2024
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Here, we present a model that quantifies the thermal stress and its microscale variability at a city scale with a mesoscale model. This tool can have multiple applications, from early warnings of extreme heat to the vulnerable population to the evaluation of the effectiveness of heat mitigation strategies. It is the first model that includes information on microscale variability in a mesoscale model, something that is essential for fully evaluating heat stress.
Nathan P. Arnold
Geosci. Model Dev., 17, 5041–5056, https://doi.org/10.5194/gmd-17-5041-2024, https://doi.org/10.5194/gmd-17-5041-2024, 2024
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Earth system models often represent the land surface at smaller scales than the atmosphere, but surface–atmosphere coupling uses only aggregated surface properties. This study presents a method to allow heterogeneous surface properties to modify boundary layer updrafts. The method is tested in single column experiments. Updraft properties are found to reasonably covary with surface conditions, and simulated boundary layer variability is enhanced over more heterogeneous land surfaces.
Enrico Dammers, Janot Tokaya, Christian Mielke, Kevin Hausmann, Debora Griffin, Chris McLinden, Henk Eskes, and Renske Timmermans
Geosci. Model Dev., 17, 4983–5007, https://doi.org/10.5194/gmd-17-4983-2024, https://doi.org/10.5194/gmd-17-4983-2024, 2024
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Nitrogen dioxide (NOx) is produced by sources such as industry and traffic and is directly linked to negative impacts on health and the environment. The current construction of emission inventories to keep track of NOx emissions is slow and time-consuming. Satellite measurements provide a way to quickly and independently estimate emissions. In this study, we apply a consistent methodology to derive NOx emissions over Germany and illustrate the value of having such a method for fast projections.
Yuhan Xu, Sheng Fang, Xinwen Dong, and Shuhan Zhuang
Geosci. Model Dev., 17, 4961–4982, https://doi.org/10.5194/gmd-17-4961-2024, https://doi.org/10.5194/gmd-17-4961-2024, 2024
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Recent atmospheric radionuclide leakages from unknown sources have posed a new challenge in nuclear emergency assessment. Reconstruction via environmental observations is the only feasible way to identify sources, but simultaneous reconstruction of the source location and release rate yields high uncertainties. We propose a spatiotemporally separated reconstruction strategy that avoids these uncertainties and outperforms state-of-the-art methods with respect to accuracy and uncertainty ranges.
Shaokun Deng, Shengmu Yang, Shengli Chen, Daoyi Chen, Xuefeng Yang, and Shanshan Cui
Geosci. Model Dev., 17, 4891–4909, https://doi.org/10.5194/gmd-17-4891-2024, https://doi.org/10.5194/gmd-17-4891-2024, 2024
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Global offshore wind power development is moving from offshore to deeper waters, where floating offshore wind turbines have an advantage over bottom-fixed turbines. However, current wind farm parameterization schemes in mesoscale models are not applicable to floating turbines. We propose a floating wind farm parameterization scheme that accounts for the attenuation of the significant wave height by floating turbines. The results indicate that it has a significant effect on the power output.
Virve Eveliina Karsisto
Geosci. Model Dev., 17, 4837–4853, https://doi.org/10.5194/gmd-17-4837-2024, https://doi.org/10.5194/gmd-17-4837-2024, 2024
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RoadSurf is an open-source library that contains functions from the Finnish Meteorological Institute’s road weather model. The evaluation of the library shows that it is well suited for making road surface temperature forecasts. The evaluation was done by making forecasts for about 400 road weather stations in Finland with the library. Accurate forecasts help road authorities perform salting and plowing operations at the right time and keep roads safe for drivers.
Perrine Hamel, Martí Bosch, Léa Tardieu, Aude Lemonsu, Cécile de Munck, Chris Nootenboom, Vincent Viguié, Eric Lonsdorf, James A. Douglass, and Richard P. Sharp
Geosci. Model Dev., 17, 4755–4771, https://doi.org/10.5194/gmd-17-4755-2024, https://doi.org/10.5194/gmd-17-4755-2024, 2024
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The InVEST Urban Cooling model estimates the cooling effect of vegetation in cities. We further developed an algorithm to facilitate model calibration and evaluation. Applying the algorithm to case studies in France and in the United States, we found that nighttime air temperature estimates compare well with reference datasets. Estimated change in temperature from a land cover scenario compares well with an alternative model estimate, supporting the use of the model for urban planning decisions.
Gerrit Kuhlmann, Erik Koene, Sandro Meier, Diego Santaren, Grégoire Broquet, Frédéric Chevallier, Janne Hakkarainen, Janne Nurmela, Laia Amorós, Johanna Tamminen, and Dominik Brunner
Geosci. Model Dev., 17, 4773–4789, https://doi.org/10.5194/gmd-17-4773-2024, https://doi.org/10.5194/gmd-17-4773-2024, 2024
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We present a Python software library for data-driven emission quantification (ddeq). It can be used to determine the emissions of hot spots (cities, power plants and industry) from remote sensing images using different methods. ddeq can be extended for new datasets and methods, providing a powerful community tool for users and developers. The application of the methods is shown using Jupyter notebooks included in the library.
Wendell W. Walters, Masayuki Takeuchi, Nga L. Ng, and Meredith G. Hastings
Geosci. Model Dev., 17, 4673–4687, https://doi.org/10.5194/gmd-17-4673-2024, https://doi.org/10.5194/gmd-17-4673-2024, 2024
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The study introduces a novel chemical mechanism for explicitly tracking oxygen isotope transfer in oxidized reactive nitrogen and odd oxygen using the Regional Atmospheric Chemistry Mechanism, version 2. This model enhances our ability to simulate and compare oxygen isotope compositions of reactive nitrogen, revealing insights into oxidation chemistry. The approach shows promise for improving atmospheric chemistry models and tropospheric oxidation capacity predictions.
Bing Zhang, Mingjian Zeng, Anning Huang, Zhengkun Qin, Couhua Liu, Wenru Shi, Xin Li, Kefeng Zhu, Chunlei Gu, and Jialing Zhou
Geosci. Model Dev., 17, 4579–4601, https://doi.org/10.5194/gmd-17-4579-2024, https://doi.org/10.5194/gmd-17-4579-2024, 2024
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By directly analyzing the proximity of precipitation forecasts and observations, a precipitation accuracy score (PAS) method was constructed. This method does not utilize a traditional contingency-table-based classification verification; however, it can replace the threat score (TS), equitable threat score (ETS), and other skill score methods, and it can be used to calculate the accuracy of numerical models or quantitative precipitation forecasts.
Hai Bui, Mostafa Bakhoday-Paskyabi, and Mohammadreza Mohammadpour-Penchah
Geosci. Model Dev., 17, 4447–4465, https://doi.org/10.5194/gmd-17-4447-2024, https://doi.org/10.5194/gmd-17-4447-2024, 2024
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We developed a new wind turbine wake model, the Simple Actuator Disc for Large Eddy Simulation (SADLES), integrated with the widely used Weather Research and Forecasting (WRF) model. WRF-SADLES accurately simulates wind turbine wakes at resolutions of a few dozen meters, aligning well with idealized simulations and observational measurements. This makes WRF-SADLES a promising tool for wind energy research, offering a balance between accuracy, computational efficiency, and ease of implementation.
Changliang Shao and Lars Nerger
Geosci. Model Dev., 17, 4433–4445, https://doi.org/10.5194/gmd-17-4433-2024, https://doi.org/10.5194/gmd-17-4433-2024, 2024
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This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation (DA) system. A key advantage of the WRF-PDAF configuration is its ability to concurrently integrate all ensemble states, eliminating the need for time-consuming distribution and collection of ensembles during the coupling communication. The extra time required for DA amounts to only 20.6 % per cycle. Twin experiment results underscore the effectiveness of the WRF-PDAF system.
Jan Clemens, Lars Hoffmann, Bärbel Vogel, Sabine Grießbach, and Nicole Thomas
Geosci. Model Dev., 17, 4467–4493, https://doi.org/10.5194/gmd-17-4467-2024, https://doi.org/10.5194/gmd-17-4467-2024, 2024
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Lagrangian transport models simulate the transport of air masses in the atmosphere. For example, one model (CLaMS) is well suited to calculating transport as it uses a special coordinate system and special vertical wind. However, it only runs inefficiently on modern supercomputers. Hence, we have implemented the benefits of CLaMS into a new model (MPTRAC), which is already highly efficient on modern supercomputers. Finally, in extensive tests, we showed that CLaMS and MPTRAC agree very well.
Manuel López-Puertas, Federico Fabiano, Victor Fomichev, Bernd Funke, and Daniel R. Marsh
Geosci. Model Dev., 17, 4401–4432, https://doi.org/10.5194/gmd-17-4401-2024, https://doi.org/10.5194/gmd-17-4401-2024, 2024
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The radiative infrared cooling of CO2 in the middle atmosphere is crucial for computing its thermal structure. It requires one however to include non-local thermodynamic equilibrium processes which are computationally very expensive, which cannot be afforded by climate models. In this work, we present an updated, efficient, accurate and very fast (~50 µs) parameterization of that cooling able to cope with CO2 abundances from half the pre-industrial values to 10 times the current abundance.
Felix Wieser, Rolf Sander, Changmin Cho, Hendrik Fuchs, Thorsten Hohaus, Anna Novelli, Ralf Tillmann, and Domenico Taraborrelli
Geosci. Model Dev., 17, 4311–4330, https://doi.org/10.5194/gmd-17-4311-2024, https://doi.org/10.5194/gmd-17-4311-2024, 2024
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The chemistry scheme of the atmospheric box model CAABA/MECCA is expanded to achieve an improved aerosol formation from emitted organic compounds. In addition to newly added reactions, temperature-dependent partitioning of all new species between the gas and aqueous phases is estimated and included in the pre-existing scheme. Sensitivity runs show an overestimation of key compounds from isoprene, which can be explained by a lack of aqueous-phase degradation reactions and box model limitations.
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
Geosci. Model Dev., 17, 4383–4399, https://doi.org/10.5194/gmd-17-4383-2024, https://doi.org/10.5194/gmd-17-4383-2024, 2024
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There is relatively limited research on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPUs, have distinct advantages in energy efficiency and scalability. The air quality modeling system can run stably on the MIPS and LoongArch platforms, and the experiment results verify the stability of scientific computing on the platforms. The work provides a technical foundation for the scientific application based on MIPS and LoongArch.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev., 17, 4331–4353, https://doi.org/10.5194/gmd-17-4331-2024, https://doi.org/10.5194/gmd-17-4331-2024, 2024
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This research focuses on surface ozone (O3) pollution in Arizona, a historically air-quality-challenged arid and semi-arid region in the US. The unique characteristics of this kind of region, e.g., intense heat, minimal moisture, and persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model's skill in capturing diurnal and MDA8 O3 levels.
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 17, 4213–4228, https://doi.org/10.5194/gmd-17-4213-2024, https://doi.org/10.5194/gmd-17-4213-2024, 2024
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This study presents a method for identifying and tracking 3-D potential vorticity structures within African easterly waves (AEWs). Each identified structure is characterized by descriptors, including its 3-D position and orientation, which have been validated through composite comparisons. A trough-centric perspective on the descriptors reveals the evolution and distinct characteristics of AEWs. These descriptors serve as valuable statistical inputs for the study of AEW-related phenomena.
Sandro Vattioni, Andrea Stenke, Beiping Luo, Gabriel Chiodo, Timofei Sukhodolov, Elia Wunderlin, and Thomas Peter
Geosci. Model Dev., 17, 4181–4197, https://doi.org/10.5194/gmd-17-4181-2024, https://doi.org/10.5194/gmd-17-4181-2024, 2024
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We investigate the sensitivity of aerosol size distributions in the presence of strong SO2 injections for climate interventions or after volcanic eruptions to the call sequence and frequency of the routines for nucleation and condensation in sectional aerosol models with operator splitting. Using the aerosol–chemistry–climate model SOCOL-AERv2, we show that the radiative and chemical outputs are sensitive to these settings at high H2SO4 supersaturations and how to obtain reliable results.
Najmeh Kaffashzadeh and Abbas-Ali Aliakbari Bidokhti
Geosci. Model Dev., 17, 4155–4179, https://doi.org/10.5194/gmd-17-4155-2024, https://doi.org/10.5194/gmd-17-4155-2024, 2024
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This paper assesses the capability of two state-of-the-art global datasets in simulating surface ozone over Iran using a new methodology. It is found that the global model data need to be downscaled for regulatory purposes or policy applications at local scales. The method can be useful not only for the evaluation but also for the prediction of other chemical species, such as aerosols.
Franciscus Liqui Lung, Christian Jakob, A. Pier Siebesma, and Fredrik Jansson
Geosci. Model Dev., 17, 4053–4076, https://doi.org/10.5194/gmd-17-4053-2024, https://doi.org/10.5194/gmd-17-4053-2024, 2024
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Traditionally, high-resolution atmospheric models employ periodic boundary conditions, which limit simulations to domains without horizontal variations. In this research open boundary conditions are developed to replace the periodic boundary conditions. The implementation is tested in a controlled setup, and the results show minimal disturbances. Using these boundary conditions, high-resolution models can be forced by a coarser model to study atmospheric phenomena in realistic background states.
Caroline Arnold, Shivani Sharma, Tobias Weigel, and David S. Greenberg
Geosci. Model Dev., 17, 4017–4029, https://doi.org/10.5194/gmd-17-4017-2024, https://doi.org/10.5194/gmd-17-4017-2024, 2024
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In atmospheric models, rain formation is simplified to be computationally efficient. We trained a machine learning model, SuperdropNet, to emulate warm-rain formation based on super-droplet simulations. Here, we couple SuperdropNet with an atmospheric model in a warm-bubble experiment and find that the coupled simulation runs stable and produces reasonable results, making SuperdropNet a viable ML proxy for droplet simulations. We also present a comprehensive benchmark for coupling architectures.
Byoung-Joo Jung, Benjamin Ménétrier, Chris Snyder, Zhiquan Liu, Jonathan J. Guerrette, Junmei Ban, Ivette Hernández Baños, Yonggang G. Yu, and William C. Skamarock
Geosci. Model Dev., 17, 3879–3895, https://doi.org/10.5194/gmd-17-3879-2024, https://doi.org/10.5194/gmd-17-3879-2024, 2024
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We describe the multivariate static background error covariance (B) for the JEDI-MPAS 3D-Var data assimilation system. With tuned B parameters, the multivariate B gives physically balanced analysis increment fields in the single-observation test framework. In the month-long cycling experiment with a global 60 km mesh, 3D-Var with static B performs stably. Due to its simple workflow and minimal computational requirements, JEDI-MPAS 3D-Var can be useful for the research community.
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
Geosci. Model Dev., 17, 3867–3878, https://doi.org/10.5194/gmd-17-3867-2024, https://doi.org/10.5194/gmd-17-3867-2024, 2024
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For modeling atmospheric chemistry, it is necessary to provide data on emissions of pollutants. These can come from various sources and in various forms, and preprocessing of the data to be ingestible by chemistry models can be quite challenging. We developed the FUME processor to use a database layer that internally transforms all input data into a rigid structure, facilitating further processing to allow for emission processing from the continental to the street scale.
Bent Harnist, Seppo Pulkkinen, and Terhi Mäkinen
Geosci. Model Dev., 17, 3839–3866, https://doi.org/10.5194/gmd-17-3839-2024, https://doi.org/10.5194/gmd-17-3839-2024, 2024
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Probabilistic precipitation nowcasting (local forecasting for 0–6 h) is crucial for reducing damage from events like flash floods. For this goal, we propose the DEUCE neural-network-based model which uses data and model uncertainties to generate an ensemble of potential precipitation development scenarios for the next hour. Trained and evaluated with Finnish precipitation composites, DEUCE was found to produce more skillful and reliable nowcasts than established models.
Emma Howard, Steven Woolnough, Nicholas Klingaman, Daniel Shipley, Claudio Sanchez, Simon C. Peatman, Cathryn E. Birch, and Adrian J. Matthews
Geosci. Model Dev., 17, 3815–3837, https://doi.org/10.5194/gmd-17-3815-2024, https://doi.org/10.5194/gmd-17-3815-2024, 2024
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This paper describes a coupled atmosphere–mixed-layer ocean simulation setup that will be used to study weather processes in Southeast Asia. The set-up has been used to compare high-resolution simulations, which are able to partially resolve storms, to coarser simulations, which cannot. We compare the model performance at representing variability of rainfall and sea surface temperatures across length scales between the coarse and fine models.
Álvaro González-Cervera and Luis Durán
EGUsphere, https://doi.org/10.5194/egusphere-2024-958, https://doi.org/10.5194/egusphere-2024-958, 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 of broad scientific applications, from short-term forecasts to local-scale climate change scenarios.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-84, https://doi.org/10.5194/gmd-2024-84, 2024
Revised manuscript accepted for GMD
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We apply a comprehensive approach to select a subset of CMIP6 that is suitable for dynamical downscaling over Southeast Asia by considering model performance, model independence, data availability, and future climate change spread. The standardised benchmarking framework is applied to identify a subset of models through two stages of assessment: statistical-based and process-based metrics. We finalize a sub-set of two independent models for dynamical downscaling over Southeast Asia.
Andrés Yarce Botero, Michiel van Weele, Arjo Segers, Pier Siebesma, and Henk Eskes
Geosci. Model Dev., 17, 3765–3781, https://doi.org/10.5194/gmd-17-3765-2024, https://doi.org/10.5194/gmd-17-3765-2024, 2024
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HARMONIE WINS50 reanalysis data with 0.025° × 0.025° resolution from 2019 to 2021 were coupled with the LOTOS-EUROS Chemical Transport Model. HARMONIE and ECMWF meteorology configurations against Cabauw observations (52.0° N, 4.9° W) were evaluated as simulated NO2 concentrations with ground-level sensors. Differences in crucial meteorological input parameters (boundary layer height, vertical diffusion coefficient) between the hydrostatic and non-hydrostatic models were analysed.
Ulrich Voggenberger, Leopold Haimberger, Federico Ambrogi, and Paul Poli
Geosci. Model Dev., 17, 3783–3799, https://doi.org/10.5194/gmd-17-3783-2024, https://doi.org/10.5194/gmd-17-3783-2024, 2024
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This paper presents a method for calculating balloon drift from historical radiosonde ascent data. The drift can reach distances of several hundred kilometres and is often neglected. Verification shows the beneficial impact of the more accurate balloon position on model assimilation. The method is not limited to radiosondes but would also work for dropsondes, ozonesondes, or any other in situ sonde carried by the wind in the pre-GNSS era, provided the necessary information is available.
Cited articles
Altmann, A., Toloşi, L., Sander, O., and Lengauer, T.: Permutation importance: a corrected feature importance measure, Bioinformatics, 26, 1340–1347, 2010.
Atmospheric Radiation Measurement user facility (ARM): ARM Best Estimate Data Products (ARMBEATM). Southern Great Plains (SGP) Central Facility, Lamont, OK (C1), compiled by: Xiao, C. and Shaocheng, X., ARM Data Center [data set], https://doi.org/10.5439/1333748, 1994.
Atmospheric Radiation Measurement user facility (ARM): Active Remote Sensing of CLouds (ARSCL1CLOTH). 2024-02-05 to 2024-02-13, Southern Great Plains (SGP) Central Facility, Lamont, OK (C1), compiled by: Giangrande, S., Wang, D., Clothiaux, E., and Kollias, P., ARM Data Center [data set], https://doi.org/10.5439/1996113, 1996.
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., and Gulcehre, C.: Relational inductive biases, deep learning, and graph networks, arXiv [preprint], https://doi.org/10.48550/arXiv.1806.01261, 2018.
Berg, L. K. and Kassianov, E. I.: Temporal variability of fair-weather cumulus statistics at the ACRF SGP site, J. Climate, 21, 3344–3358, 2008.
Betts, A. K.: Land-surface-atmosphere coupling in observations and models, J. Adv. Model. Earth Sy., 1, 4, https://doi.org/10.3894/JAMES.2009.1.4, 2009.
Bogenschutz, P. A., Eldred, C., and Caldwell, P. M.: Horizontal resolution sensitivity of the Simple Convection-Permitting E3SM Atmosphere Model in a doubly-periodic configuration, J. Adv. Model. Earth Sy., 15, e2022MS003466, https://doi.org/10.1029/2022MS003466, 2023.
Bretherton, C. S., Blossey, P. N., and Uchida, J.: Cloud droplet sedimentation, entrainment efficiency, and subtropical stratocumulus albedo, Geophys. Res. Lett., 34, L03813, https://doi.org/10.1029/2006GL027648, 2007.
Cadeddu, M. P., Turner, D. D., and Liljegren, J. C.: A neural network for real-time retrievals of PWV and LWP from Arctic millimeter-wave ground-based observations, IEEE T. Geosci. Remote, 47, 1887–1900, 2009.
Caldwell, P. M., Terai, C. R., Hillman, B., Keen, N. D., Bogenschutz, P., Lin, W., Beydoun, H., Taylor, M., Bertagna, L., Bradley, A. M., and Clevenger, T. C.: Convection-permitting simulations with the E3SM global atmosphere model, J. Adv. Model. Earth Sy., 13, e2021MS002544, https://doi.org/10.1029/2021MS002544, 2021.
Chandra, A. S., Kollias, P., Giangrande, S. E., and Klein, S. A.: Long-term observations of the convective boundary layer using insect radar returns at the SGP ARM climate research facility, J. Climate, 23, 5699–5714, 2010.
Chu, Y., Li, J., Li, C., Tan, W., Su, T., and Li, J.: Seasonal and diurnal variability of planetary boundary layer height in Beijing: Intercomparison between MPL and WRF results, Atmos. Res., 227, 1–13, https://doi.org/10.1016/j.atmosres.2019.04.017, 2019.
Clayson, C. A. and Chen, A.: Sensitivity of a coupled single-column model in the tropics to treatment of the interfacial parameterizations, J. Climate, 15, 1805–1831, 2002.
Clothiaux, E. E., Ackerman, T. P., Mace, G. G., Moran, K. P., Marchand, R. T., Miller, M. A., and Martner, B. E.: Objective determination of cloud heights and radar reflectivities using a combination of active remote sensors at the ARM CART sites, J. Appl. Meteorol., 39, 645–665, 2000.
Clothiaux, E. E., Miller, M. A., Perez, R. C., Turner, D. D., Moran, K. P., Martner, B. E., Ackerman, T. P., Mace, G. G., Marchand, R. T., Widener, K. B., and Rodriguez, D. J.: The ARM millimeter wave cloud radars (MMCRs) and the active remote sensing of clouds (ARSCL) value added product (VAP) (No. DOE/SC-ARM/VAP-002.1), DOE Office of Science Atmospheric Radiation Measurement (ARM) Program (United States), https://doi.org/10.2172/1808567, 2001.
Cook, D. R.: Energy Balance Bowen Ratio (EBBR) instrument handbook, Technical Report Rep. DOE/SC-ARM/TR-037, U.S. Department of Energy, https://doi.org/10.2172/1020562, 2018.
Date, Y. and Kikuchi, J.: Application of a deep neural network to metabolomics studies and its performance in determining important variables, Anal. Chem., 90, 1805–1810, 2018.
De Roode, S. R., Siebesma, A. P., Dal Gesso, S., Jonker, H. J., Schalkwijk, J., and Sival, J.: A mixed-layer model study of the stratocumulus response to changes in large-scale conditions, J. Adv. Model. Earth Sy., 6, 1256–1270, 2014.
Fast, J. D., Berg, L. K., Alexander, L., Bell, D., D'Ambro, E., Hubbe, J., Kuang, C., Liu, J., Long, C., Matthews, A., and Mei, F.: Overview of the HI-SCALE field campaign: A new perspective on shallow convective clouds, B. Am. Meteorol. Soc., 100, 821–840, 2019.
Gagne II, D. J., Haupt, S. E., Nychka, D. W., and Thompson, G.: Interpretable deep learning for spatial analysis of severe hailstorms, Mon. Weather Rev., 147, 2827–2845, https://doi.org/10.1175/MWR-D-18-0316.1, 2019.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., and Wargan, K.: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), J. Climate, 30, 5419–5454, 2017.
Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., and Yacalis, G.: Could machine learning break the convection parameterization deadlock?, Geophys. Res. Lett., 45, 5742–5751, 2018.
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavg1_2d_rad_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Radiation Diagnostics V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC), Greenbelt, MD, USA [data set], https://doi.org/10.5067/Q9QMY5PBNV1T, 2015.
Golaz, J. C., Larson, V. E., and Cotton, W. R.: A PDF-based model for boundary layer clouds. Part I: Method and model description, J. Atmos. Sci., 59, 3540–3551, 2002.
Guo, J., Su, T., Li, Z., Miao, Y., Li, J., Liu, H., Xu, H., Cribb, M., and Zhai, P.: Declining frequency of summertime local-scale precipitation over eastern China from 1970 to 2010 and its potential link to aerosols, Geophys. Res. Lett., 44, 5700–5708, 2017.
Guo, J., Su, T., Chen, D., Wang, J., Li, Z., Lv, Y., Guo, X., Liu, H., Cribb, M., and Zhai, P.: Declining summertime local-scale precipitation frequency over China and the United States, 1981–2012: The disparate roles of aerosols, Geophys. Res. Lett., 46, 13281–13289, 2019.
Guo, J., Zhang, J., Shao, J., Chen, T., Bai, K., Sun, Y., Li, N., Wu, J., Li, R., Li, J., Guo, Q., Cohen, J. B., Zhai, P., Xu, X., and Hu, F.: A merged continental planetary boundary layer height dataset based on high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS, Earth Syst. Sci. Data, 16, 1–14, https://doi.org/10.5194/essd-16-1-2024, 2024.
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, 2020.
Haynes, J. M., Noh, Y. J., Miller, S. D., Haynes, K. D., Ebert-Uphoff, I., and Heidinger, A.: Low cloud detection in multilayer scenes using satellite imagery with machine learning methods, J. Atmos. Ocean. Tech., 39, 319–334, 2022.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., and Simmons, A.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023.
Holdridge, D., Ritsche, M., Prell, J., and Coulter, R.: Balloon-borne sounding system (SONDE) handbook, https://www.arm.gov/capabilities/instruments/sonde (last access: 3 May 2024), 2011.
Holzworth, G. C.: Estimates of mean maximum mixing depths in the contiguous United States, Mon. Weather Rev., 92, 235–242, https://doi.org/10.1175/1520-0493(1964)092<0235:EOMMMD>2.3.CO;2, 1964.
Klambauer, G., Unterthiner, T., Mayr, A., and Hochreiter, S.: Self-normalizing neural networks, arXiv [preprint], https://doi.org/10.48550/arXiv.1706.02515, 2017.
Kollias, P., Bharadwaj, N., Clothiaux, E. E., Lamer, K., Oue, M., Hardin, J., Isom, B., Lindenmaier, I., Matthews, A., Luke, E. P., and Giangrande, S. E.: The ARM radar network: At the leading edge of cloud and precipitation observations, B. Am. Meteorol. Soc., 101, E588–E607, 2020.
Kuma, P., McDonald, A. J., Morgenstern, O., Alexander, S. P., Cassano, J. J., Garrett, S., Halla, J., Hartery, S., Harvey, M. J., Parsons, S., Plank, G., Varma, V., and Williams, J.: Evaluation of Southern Ocean cloud in the HadGEM3 general circulation model and MERRA-2 reanalysis using ship-based observations, Atmos. Chem. Phys., 20, 6607–6630, https://doi.org/10.5194/acp-20-6607-2020, 2020.
Lareau, N. P., Zhang, Y., and Klein, S. A.: Observed boundary layer controls on shallow cumulus at the ARM Southern Great Plains site, J. Atmos. Sci., 75, 2235–2255, 2018.
Lee, J. M., Zhang, Y., and Klein, S. A.: The effect of land surface heterogeneity and background wind on shallow cumulus clouds and the transition to deeper convection, J. Atmos. Sci., 76, 401–419, 2019.
Lilly, D. K.: Models of cloud-topped mixed layers under a strong inversion. Q. J. Roy. Meteor. Soc., 94, 292–309, https://doi.org/10.1002/qj.49709440106, 1968.
Lu, C., Liu, Y., and Niu, S.: Examination of turbulent entrainment-mixing mechanisms using a combined approach, J. Geophys. Res.-Atmos., 116, D20207, https://doi.org/10.1029/2011JD015944, 2011.
Lu, C., Niu, S., Liu, Y., and Vogelmann, A. M.: Empirical relationship between entrainment rate and microphysics in cumulus clouds, Geophys. Res. Lett., 40, 2333–2338, 2013.
Luque, A., Carrasco, A., Martín, A., and de Las Heras, A.: The impact of class imbalance in classification performance metrics based on the binary confusion matrix, Pattern Recogn., 91, 216–231, 2019.
McGovern, A., Elmore, K. L., Gagne, D. J., Haupt, S. E., Karstens, C. D., Lagerquist, R., Smith, T., and Williams, J. K.: Using artificial intelligence to improve real-time decision-making for high-impact weather, B. Am. Meteorol. Soc., 98, 2073–2090, https://doi.org/10.1175/BAMS-D-16-0123.1, 2017.
Miao, H., Wang, X., Liu, Y., and Wu, G.: An evaluation of cloud vertical structure in three reanalyses against CloudSat/cloud-aerosol lidar and infrared pathfinder satellite observations, Atmos. Sci. Lett., 20, e906, https://doi.org/10.1002/asl.906, 2019.
Moeng, C. H., Cotton, W. R., Bretherton, C., Chlond, A., Khairoutdinov, M., Krueger, S., Lewellen, W. S., MacVean, M. K., Pasquier, J. R. M., Rand, H. A., and Siebesma, A. P.: Simulation of a stratocumulus-topped planetary boundary layer: Intercomparison among different numerical codes, B. Am. Meteorol. Soc., 77, 261–278, 1996.
Molero, F., Barragán, R., and Artíñano, B.: Estimation of the atmospheric boundary layer height by means of machine learning techniques using ground-level meteorological data, Atmos. Res., 279, 106401, https://doi.org/10.1016/j.atmosres.2022.106401, 2022.
Mooers, G., Pritchard, M., Beucler, T., Ott, J., Yacalis, G., Baldi, P., and Gentine, P.: Assessing the potential of deep learning for emulating cloud superparameterization in climate models with real-geography boundary conditions, J. Adv. Model. Earth Sy., 13, e2020MS002385, https://doi.org/10.1029/2020MS002385, 2021.
Morrison, H., van Lier-Walqui, M., Fridlind, A. M., Grabowski, W. W., Harrington, J. Y., Hoose, C., Korolev, A., Kumjian, M. R., Milbrandt, J. A., Pawlowska, H., and Posselt, D. J.: Confronting the challenge of modeling cloud and precipitation microphysics, J. Adv. Model. Earth Sy., 12, e2019MS001689, https://doi.org/10.1029/2019MS001689, 2020.
Nogherotto, R., Tompkins, A. M., Giuliani, G., Coppola, E., and Giorgi, F.: Numerical framework and performance of the new multiple-phase cloud microphysics scheme in RegCM4.5: precipitation, cloud microphysics, and cloud radiative effects, Geosci. Model Dev., 9, 2533–2547, https://doi.org/10.5194/gmd-9-2533-2016, 2016.
O'Gorman, P. A. and Dwyer, J. G.: Using machine learning to parameterize moist convection: Potential for modeling of climate, climate change, and extreme events, J. Adv. Model. Earth Sy., 10, 2548–2563, 2018.
Pal, S. R., Steinbrecht, W., and Carswell, A. I.: Automated method for lidar determination of cloud-base height and vertical extent, Appl. Optics, 31, 1488–1494, 1992.
Pelly, J. L. and Belcher, S. E.: A mixed-layer model of the well-mixed stratocumulus-topped boundary layer, Bound.-Lay. Meteorol., 100, 171–187, 2001.
Poll, S., Shrestha, P., and Simmer, C.: Grid resolution dependency of land surface heterogeneity effects on boundary-layer structure, Q. J. Roy. Meteor. Soc., 148, 141–158, 2022.
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K., Keller, M., Tölle, M., Gutjahr, O., Feser, F., and Brisson, E.: A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges, Rev. Geophys., 53, 323–361, 2015.
Qian, Y., Guo, Z., Larson, V. E., Leung, L. R., Lin, W., Ma, P. L., Wan, H., Wang, H., Xiao, H., Xie, S., and Yang, B.: Region and cloud regime dependence of parametric sensitivity in E3SM atmosphere model, Clim. Dynam., 62, 1517–1533, https://doi.org/10.1007/s00382-023-06977-3, 2024.
Raju, V. G., Lakshmi, K. P., Jain, V. M., Kalidindi, A., and Padma, V.: Study the influence of normalization/transformation process on the accuracy of supervised classification, in: 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2020, 729–735, https://doi.org/10.1109/ICSSIT48917.2020.9214160, 2020.
Randles, C. A., Da Silva, A. M., Buchard, V., Colarco, P. R., Darmenov, A., Govindaraju, R., Smirnov, A., Holben, B., Ferrare, R., Hair, J., and Shinozuka, Y.: The MERRA-2 aerosol reanalysis, 1980 onward. Part I: System description and data assimilation evaluation, J. Climate, 30, 6823–6850, 2017.
Randall, D. A., Khairoutdinov, M., Arakawa, A., and Grabowski, W.: Breaking the cloud parameterization deadlock, B. Am. Meteorol. Soc., 84, 1547–1564, https://doi.org/10.1175/BAMS-84-11-1547, 2003.
Rasp, S.: Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and Lorenz 96 case study (v1.0), Geosci. Model Dev., 13, 2185–2196, https://doi.org/10.5194/gmd-13-2185-2020, 2020.
Rieck, M., Hohenegger, C., and van Heerwaarden, C. C.: The influence of land surface heterogeneities on cloud size development, Mon. Weather Rev., 142, 3830–3846, 2014.
Ritsche, M.: Temperature Humidity Reference System Handbook, PNNL: Richland, WA, USA, https://doi.org/10.2172/948532, 2011.
Roldán-Henao, N., Su, T., and Li, Z.: Refining planetary boundary layer height retrievals from micropulse-lidar at multiple ARM sites around the world, J. Geophys. Res.-Atmos., 129, e2023JD040207, https://doi.org/10.1029/2023JD040207, 2024.
Romps, D. M.: Exact expression for the lifting condensation level, J. Atmos. Sci., 74, 3891–3900, 2017.
Sakaguchi, K., Berg, L. K., Chen, J., Fast, J., Newsom, R., Tai, S. L., Yang, Z., Gustafson Jr., W. I., Gaudet, B. J., Huang, M., and Pekour, M.: Determining spatial scales of soil moisture – Cloud coupling pathways using semi-idealized simulations, J. Geophys. Res.-Atmos., 127, e2021JD035282, https://doi.org/10.1029/2021JD035282, 2022.
Salimans, T. and Kingma, D. P.: Weight normalization: A simple reparameterization to accelerate training of deep neural networks, arXiv [preprint], https://doi.org/10.48550/arXiv.1602.07868, 2016.
Shamekh, S., Lamb, K. D., Huang, Y., and Gentine, P.: Implicit learning of convective organization explains precipitation stochasticity, P. Natl. Acad. Sci. USA, 120, e2216158120, https://doi.org/10.1073/pnas.2216158120, 2023.
Sisterson, D. L., Peppler, R. A., Cress, T. S., Lamb, P. J., and Turner, D. D.: The ARM Southern Great Plains (SGP) Site, Meteor. Mon., 57, 6.1–6.14, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0004.1, 2016.
Song, H., Lin, W., Lin, Y., Wolf, A. B., Donner, L. J., Del Genio, A. D., Neggers, R., Endo, S., and Liu, Y.: Evaluation of cloud fraction simulated by seven SCMs against the ARM observations at the SGP site, J. Climate, 27, 6698–6719, 2014.
Stull, R. B.: An Introduction to Boundary Layer Meteorology. Springer Netherlands, Dordrecht, https://doi.org/10.1007/978-94-009-3027-8, 1988.
Su, T.: Codes and Package of Deep Learning Driven Simulations of Boundary Layer Cloud over the US Southern Great Plains, Zenodo [code and data set], https://doi.org/10.5281/zenodo.10719342, 2024.
Su, T. and Zhang, Y.: Deep-learning-derived planetary boundary layer height from conventional meteorological measurements, Atmos. Chem. Phys., 24, 6477–6493, https://doi.org/10.5194/acp-24-6477-2024, 2024.
Su, T., Li, Z., and Kahn, R.: A new method to retrieve the diurnal variability of planetary boundary layer height from lidar under different thermodynamic stability conditions, Remote Sens. Environ., 237, 111519, https://doi.org/10.1016/j.rse.2019.111519, 2020.
Su, T., Zheng, Y., and Li, Z.: Methodology to determine the coupling of continental clouds with surface and boundary layer height under cloudy conditions from lidar and meteorological data, Atmos. Chem. Phys., 22, 1453–1466, https://doi.org/10.5194/acp-22-1453-2022, 2022.
Su, T., Li, Z., and Zheng, Y.: Cloud-Surface Coupling Alters the Morning Transition From Stable to Unstable Boundary Layer, Geophys. Res. Lett., 50, e2022GL102256, https://doi.org/10.1029/2022GL102256, 2023.
Su, T., Li, Z., Zhang, Y., Zheng, Y., and Zhang, H.: Observation and Reanalysis Derived Relationships Between Cloud and Land Surface Fluxes Across Cumulus and Stratiform Coupling Over the Southern Great Plains, Geophys. Res. Lett., 51, e2023GL108090, https://doi.org/10.1029/2023GL108090, 2024.
Tang, Q., Xie, S., Zhang, Y., Phillips, T. J., Santanello, J. A., Cook, D. R., Riihimaki, L. D., and Gaustad, K. L.: Heterogeneity in warm-season land-atmosphere coupling over the US Southern Great Plains, J. Geophys. Res.-Atmos., 123, 7867–7882, https://doi.org/10.1029/2018JD028463, 2018.
Tang, S., Xie, S., Zhang, M., Tang, Q., Zhang, Y., Klein, S. A., Cook, D. R., and Sullivan, R. C.: Differences in eddy-correlation and energy-balance surface turbulent heat flux measurements and their impacts on the large-scale forcing fields at the ARM SGP site, J. Geophys. Res.-Atmos., 124, 3301–3318, https://doi.org/10.1029/2018JD029689, 2019.
Tao, C., Zhang, Y., Tang, S., Tang, Q., Ma, H. Y., Xie, S., and Zhang, M.: Regional moisture budget and land-atmosphere coupling over the US Southern Great Plains inferred from the ARM long-term observations, J. Geophys. Res.-Atmos., 124, 10091–10108, 2019.
Tao, C., Zhang, Y., Tang, Q., Ma, H. Y., Ghate, V. P., Tang, S., Xie, S., and Santanello, J. A.: Land–Atmosphere coupling at the US Southern Great Plains: A comparison on local convective regimes between ARM observations, reanalysis, and climate model simulations, J. Hydrometeorol., 22, 463–481, 2021.
Teixeira, J. and Hogan, T. F.: Boundary layer clouds in a global atmospheric model: simple cloud cover parameterizations, J. Climate, 15, 1261–1276, 2002.
Tian, J., Zhang, Y., Klein, S. A., Öktem, R., and Wang, L.: How does land cover and its heterogeneity length scales affect the formation of summertime shallow cumulus clouds in observations from the US Southern Great Plains?, Geophys. Res. Lett., 49, e2021GL097070, https://doi.org/10.1029/2021GL097070, 2022.
Tiedtke, M.: Representation of clouds in large-scale models, Mon. Weather Rev., 121, 3040–3061, 1993.
Vassallo, D., Krishnamurthy, R., and Fernando, H. J. S.: Decreasing wind speed extrapolation error via domain-specific feature extraction and selection, Wind Energ. Sci., 5, 959–975, https://doi.org/10.5194/wes-5-959-2020, 2020.
Wang, C., Platnick, S., Meyer, K., Zhang, Z., and Zhou, Y.: A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations, Atmos. Meas. Tech., 13, 2257–2277, https://doi.org/10.5194/amt-13-2257-2020, 2020.
Wang, Y., Zheng, X., Dong, X., Xi, B., and Yung, Y. L.: Insights of warm-cloud biases in Community Atmospheric Model 5 and 6 from the single-column modeling framework and Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) observations, Atmos. Chem. Phys., 23, 8591–8605, https://doi.org/10.5194/acp-23-8591-2023, 2023.
Xiao, H., Berg, L. K., and Huang, M.: The impact of surface heterogeneities and land-atmosphere interactions on shallow clouds over ARM SGP site, J. Adv. Model. Earth Sy., 10, 1220–1244, 2018.
Xie, S., McCoy, R. B., Klein, S. A., Cederwall, R. T., Wiscombe, W. J., Jensen, M. P., Johnson, K. L., Clothiaux, E. E., Gaustad, K. L., Long, C. N., and Mather, J. H.: Clouds and more: ARM climate modeling best estimate data: a new data product for climate studies, B. Am. Meteorol. Soc., 91, 13–20, 2010.
Yang, Y., Zheng, X., Gao, Z., Wang, H., Wang, T., Li, Y., Lau, G. N., and Yim, S. H.: Long-term trends of persistent synoptic circulation events in planetary boundary layer and their relationships with haze pollution in winter half year over eastern China, J. Geophys. Res.-Atmos., 123, 10–991, 2018.
Yeo, H., Kim, M. H., Son, S. W., Jeong, J. H., Yoon, J. H., Kim, B. M., and Kim, S. W.: Arctic cloud properties and associated radiative effects in the three newer reanalysis datasets (ERA5, MERRA-2, JRA-55): Discrepancies and possible causes, Atmos. Res., 270, 106080, https://doi.org/10.1016/j.atmosres.2022.106080, 2022.
Zhang, L., Dong, X., Kennedy, A., Xi, B., and Li, Z.: Evaluation of NASA GISS post-CMIP5 single column model simulated clouds and precipitation using ARM Southern Great Plains observations, Adv. Atmos. Sci., 34, 306–320, 2017.
Zhang, T., Lin, W., Vogelmann, A. M., Zhang, M., Xie, S., Qin, Y., and Golaz, J. C.: Improving convection trigger functions in deep convective parameterization schemes using machine learning, J. Adv. Model. Earth Sy., 13, e2020MS002365, https://doi.org/10.1029/2020MS002365, 2021.
Zhang, Y. and Klein, S. A.: Mechanisms affecting the transition from shallow to deep convection over land: Inferences from observations of the diurnal cycle collected at the ARM Southern Great Plains site, J. Atmos. Sci., 67, 2943–2959, 2010.
Zhang, Y. and Klein, S. A.: Factors controlling the vertical extent of fair-weather shallow cumulus clouds over land: Investigation of diurnal-cycle observations collected at the ARM Southern Great Plains site, J. Atmos. Sci., 70, 1297–1315, 2013.
Zhang, Y., Stevens, B., Medeiros, B., and Ghil, M.: Low-Cloud Fraction, Lower-Tropospheric Stability, and Large-Scale Divergence, J. Climate, 22, 4827–4844, https://doi.org/10.1175/2009JCLI2891.1, 2009.
Zhang, Y., Klein, S. A., Fan, J., Chandra, A. S., Kollias, P., Xie, S., and Tang, S.: Large-eddy simulation of shallow cumulus over land: A composite case based on ARM long-term observations at its Southern Great Plains site, J. Atmos. Sci., 74, 3229–3251, 2017.
Zhang, Y., Stevens, B., and Ghil, M.: On the diurnal cycle and susceptibility to aerosol concentration in a stratocumulus-topped mixed layer, Q. J. Roy. Meteor. Soc., 131, 1567–1583, https://doi.org/10.1256/qj.04.103, 2005.
Zheng, X., Tao, C., Zhang, C., Xie, S., Zhang, Y., Xi, B., and Dong, X.: Assessment of CMIP5 and CMIP6 AMIP simulated clouds and surface shortwave radiation using ARM observations over different climate regions, J. Climate, 36, 8475–8495, 2023.
Zheng, Y., Zhang, H., Rosenfeld, D., Lee, S. S., Su, T., and Li, Z.: Idealized large-eddy simulations of stratocumulus advecting over cold water. Part I: Boundary layer decoupling, J. Atmos. Sci., 78, 4089–4102, 2021.
Short summary
Using 2 decades of field observations over the Southern Great Plains, this study developed a deep-learning model to simulate the complex dynamics of boundary layer clouds. The deep-learning model can serve as the cloud parameterization within reanalysis frameworks, offering insights into improving the simulation of low clouds. By quantifying biases due to various meteorological factors and parameterizations, this deep-learning-driven approach helps bridge the observation–modeling divide.
Using 2 decades of field observations over the Southern Great Plains, this study developed a...
