Articles | Volume 12, issue 1
https://doi.org/10.5194/gmd-12-131-2019
© Author(s) 2019. 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-12-131-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
07 Jan 2019
Development and technical paper |
| 07 Jan 2019
| 07 Jan 2019
The AFWA dust emission scheme for the GOCART aerosol model in WRF-Chem v3.8.1
Sandra L. LeGrand
CORRESPONDING AUTHOR
U.S. Army Engineer Research and Development Center, Hanover, NH, USA
Chris Polashenski
Alaska Projects Office, U.S. Army Cold Regions Research and Engineering Laboratory, Fairbanks, AK, USA
Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
Theodore W. Letcher
U.S. Army Engineer Research and Development Center, Hanover, NH, USA
Glenn A. Creighton
U.S. Air Force 557th Weather Wing, 16th Weather Squadron, Offutt Air Force Base, NE, USA
Steven E. Peckham
U.S. Army Engineer Research and Development Center, Hanover, NH, USA
Jeffrey D. Cetola
U.S. Air Force, Joint Base Langley–Eustis, VA, USA
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Sandra L. LeGrand, Theodore W. Letcher, Gregory S. Okin, Nicholas P. Webb, Alex R. Gallagher, Saroj Dhital, Taylor S. Hodgdon, Nancy P. Ziegler, and Michelle L. Michaels
Geosci. Model Dev., 16, 1009–1038, https://doi.org/10.5194/gmd-16-1009-2023, https://doi.org/10.5194/gmd-16-1009-2023, 2023
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Ground cover affects dust emissions by reducing wind flow over the immediate soil surface. This study reviews a method for estimating ground cover effects on wind erosion from satellite-detected terrain shadows. We conducted a case study for a US dust event using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Adding the shadow-based method for ground cover effects markedly improved simulated results and may lead to better dust modeling outcomes in vegetated drylands.
Ian A. Raphael, Donald K. Perovich, Christopher M. Polashenski, and Robert L. Hawley
EGUsphere, https://doi.org/10.5194/egusphere-2025-187, https://doi.org/10.5194/egusphere-2025-187, 2025
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Snow plays competing roles in the sea ice cycle by reflecting sunlight during summer (reducing melt) and insulating the ice from the cold atmosphere during winter (reducing growth). Observing where, when, and how much snow accumulates on sea ice is thus central to understanding the Arctic. Here, we describe a new snow depth observation system that is substantially cheaper and lighter than existing tools, and present a study demonstrating its potential to improve snow measurements on sea ice.
Sandra L. LeGrand, Theodore W. Letcher, Gregory S. Okin, Nicholas P. Webb, Alex R. Gallagher, Saroj Dhital, Taylor S. Hodgdon, Nancy P. Ziegler, and Michelle L. Michaels
Geosci. Model Dev., 16, 1009–1038, https://doi.org/10.5194/gmd-16-1009-2023, https://doi.org/10.5194/gmd-16-1009-2023, 2023
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Ground cover affects dust emissions by reducing wind flow over the immediate soil surface. This study reviews a method for estimating ground cover effects on wind erosion from satellite-detected terrain shadows. We conducted a case study for a US dust event using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Adding the shadow-based method for ground cover effects markedly improved simulated results and may lead to better dust modeling outcomes in vegetated drylands.
Theodore Letcher, Julie Parno, Zoe Courville, Lauren Farnsworth, and Jason Olivier
The Cryosphere, 16, 4343–4361, https://doi.org/10.5194/tc-16-4343-2022, https://doi.org/10.5194/tc-16-4343-2022, 2022
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We present a radiative transfer model that uses ray tracing to determine optical properties from computer-generated 3D renderings of snow resolved at the microscale and to simulate snow spectral reflection and transmission for visible and near-infrared light. We expand ray-tracing techniques applied to sub-1 cm3 snow samples to model an entire snowpack column. The model is able to reproduce known snow surface optical properties, and simulations compare well against field observations.
Océane Hames, Mahdi Jafari, David Nicholas Wagner, Ian Raphael, David Clemens-Sewall, Chris Polashenski, Matthew D. Shupe, Martin Schneebeli, and Michael Lehning
Geosci. Model Dev., 15, 6429–6449, https://doi.org/10.5194/gmd-15-6429-2022, https://doi.org/10.5194/gmd-15-6429-2022, 2022
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This paper presents an Eulerian–Lagrangian snow transport model implemented in the fluid dynamics software OpenFOAM, which we call snowBedFoam 1.0. We apply this model to reproduce snow deposition on a piece of ridged Arctic sea ice, which was produced during the MOSAiC expedition through scan measurements. The model appears to successfully reproduce the enhanced snow accumulation and deposition patterns, although some quantitative uncertainties were shown.
Marika M. Holland, David Clemens-Sewall, Laura Landrum, Bonnie Light, Donald Perovich, Chris Polashenski, Madison Smith, and Melinda Webster
The Cryosphere, 15, 4981–4998, https://doi.org/10.5194/tc-15-4981-2021, https://doi.org/10.5194/tc-15-4981-2021, 2021
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As the most reflective and most insulative natural material, snow has important climate effects. For snow on sea ice, its high reflectivity reduces ice melt. However, its high insulating capacity limits ice growth. These counteracting effects make its net influence on sea ice uncertain. We find that with increasing snow, sea ice in both hemispheres is thicker and more extensive. However, the drivers of this response are different in the two hemispheres due to different climate conditions.
Nicholas C. Wright, Chris M. Polashenski, Scott T. McMichael, and Ross A. Beyer
The Cryosphere, 14, 3523–3536, https://doi.org/10.5194/tc-14-3523-2020, https://doi.org/10.5194/tc-14-3523-2020, 2020
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This work presents a new dataset of sea ice surface fractions along NASA Operation IceBridge flight tracks created by processing hundreds of thousands of aerial images. These results are then analyzed to investigate the behavior of meltwater on first-year ice in comparison to multiyear ice. We find preliminary evidence that first-year ice frequently has a lower melt pond fraction than adjacent multiyear ice, contrary to established knowledge in the sea ice community.
Aleksey Malinka, Eleonora Zege, Larysa Istomina, Georg Heygster, Gunnar Spreen, Donald Perovich, and Chris Polashenski
The Cryosphere, 12, 1921–1937, https://doi.org/10.5194/tc-12-1921-2018, https://doi.org/10.5194/tc-12-1921-2018, 2018
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Melt ponds occupy a large part of the Arctic sea ice in summer and strongly affect the radiative budget of the atmosphere–ice–ocean system. The melt pond reflectance is modeled in the framework of the radiative transfer theory and validated with field observations. It improves understanding of melting sea ice and enables better parameterization of the surface in Arctic atmospheric remote sensing (clouds, aerosols, trace gases) and re-evaluating Arctic climatic feedbacks at a new accuracy level.
Nicholas C. Wright and Chris M. Polashenski
The Cryosphere, 12, 1307–1329, https://doi.org/10.5194/tc-12-1307-2018, https://doi.org/10.5194/tc-12-1307-2018, 2018
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Satellites, planes, and drones capture thousands of images of the Arctic sea ice cover each year. However, few methods exist to reliably and automatically process these images for scientifically usable information. In this paper, we take the next step towards a community standard for analyzing these images by presenting an open-source platform able to accurately classify sea ice imagery into several important surface types.
Kimberly A. Casey, Chris M. Polashenski, Justin Chen, and Marco Tedesco
The Cryosphere, 11, 1781–1795, https://doi.org/10.5194/tc-11-1781-2017, https://doi.org/10.5194/tc-11-1781-2017, 2017
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We analyzed Greenland Ice Sheet (GrIS) average summer surface reflectance and albedo (2001–2016). MODIS Collection 6 data show a decreased magnitude of change over time due to sensor calibration corrections. Spectral band maps provide insight into GrIS surface processes likely occurring. Correctly measuring albedo and surface reflectance changes over time is crucial to monitoring atmosphere–ice interactions and ice mass balance. The results are applicable to many long-term MODIS studies.
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The sensitivity of aerosol data assimilation to vertical profiles: case study of dust storm assimilation with LOTOS-EUROS v2.2
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a neural network
Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching
A novel method for quantifying the contribution of regional transport to PM2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Diagnosis of winter precipitation types using the spectral bin model (version 1DSBM-19M): comparison of five methods using ICE-POP 2018 field experiment data
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UA-ICON with the NWP physics package (version ua-icon-2.1): mean state and variability of the middle atmosphere
Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations
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Matthias Kohl, Christoph Brühl, Jennifer Schallock, Holger Tost, Patrick Jöckel, Adrian Jost, Steffen Beirle, Michael Höpfner, and Andrea Pozzer
Geosci. Model Dev., 18, 3985–4007, https://doi.org/10.5194/gmd-18-3985-2025, https://doi.org/10.5194/gmd-18-3985-2025, 2025
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Gunho Loren Oh and Philip H. Austin
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It is difficult to study the behaviour of a cloud field due to internal fluctuations and observational noise. We perform a high-resolution simulation of the boundary-layer cloud field and introduce statistical and numerical techniques, including machine-learning models, to study the evolution of the cloud field, which shows a periodic behaviour. We aim to use the numerical techniques to identify the underlying behaviour within noisy observations.
Oscar Jacquot and Karine Sartelet
Geosci. Model Dev., 18, 3965–3984, https://doi.org/10.5194/gmd-18-3965-2025, https://doi.org/10.5194/gmd-18-3965-2025, 2025
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Modelling the size distribution and the number concentration is important to represent ultrafine particles. A new analytic formulation is presented to compute coagulation partition coefficients, allowing us to lower the numerical diffusion associated with the resolution of aerosol dynamics. The significance of this effect is assessed in a 0D box model and over greater Paris with a chemistry transport model, using different size resolutions of the particle distribution.
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
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RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre- and sub-kilometre-scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and an improved representation of clouds and visibility.
Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Batjargal Buyantogtokh, Yixuan Gu, Jiandong Li, Hai Xiang Lin, Hong Liao, and Wei Han
Geosci. Model Dev., 18, 3781–3798, https://doi.org/10.5194/gmd-18-3781-2025, https://doi.org/10.5194/gmd-18-3781-2025, 2025
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Aerosol data assimilation has gained popularity as it combines the advantages of modelling and observation. However, few studies have addressed the challenges in the prior vertical structure. Different observations are assimilated to examine the sensitivity of assimilation to vertical structure. Results show that assimilation can optimize the dust field in general. However, if the prior introduces an incorrect structure, the assimilation can significantly deteriorate the integrity of the aerosol profile.
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
Geosci. Model Dev., 18, 3707–3733, https://doi.org/10.5194/gmd-18-3707-2025, https://doi.org/10.5194/gmd-18-3707-2025, 2025
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This work focuses on the prediction of aerosol concentration values at the ground level, which are a strong indicator of air quality, using artificial neural networks. A study of different variables and their efficiency as inputs for these models is also proposed and reveals that the best results are obtained when using all of them. Comparison between network architectures and information fusion methods allows for the extraction of knowledge on the most efficient methods in the context of this study.
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025, https://doi.org/10.5194/gmd-18-3681-2025, 2025
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Tuning a climate model means adjusting uncertain parameters in the model to best match observations like the global radiation balance and cloud cover. This is usually done by running many simulations of the model with different settings, which can be time-consuming and relies heavily on expert knowledge. To make this process faster and more objective, we developed a machine learning emulator to create a large ensemble and apply a method called history matching to find the best settings.
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev., 18, 3623–3634, https://doi.org/10.5194/gmd-18-3623-2025, https://doi.org/10.5194/gmd-18-3623-2025, 2025
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This study combines machine learning with concentration-weighted trajectory analysis to quantify regional transport PM2.5. From 2013–2020, local emissions dominated Beijing's pollution events. The Air Pollution Prevention and Control Action Plan reduced regional transport pollution, but the eastern region showed the smallest decrease. Beijing should prioritize local emission reduction while considering the east region's contributions in future strategies.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025, https://doi.org/10.5194/gmd-18-3607-2025, 2025
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We developed a deep learning method to estimate CO2 emissions from power plants using satellite images. Trained and validated on simulated data, our model accurately predicts emissions despite challenges like cloud cover. When applied to real OCO3 satellite images, the results closely match reported emissions. This study shows that neural networks trained on simulations can effectively analyse real satellite data, offering a new way to monitor CO2 emissions from space.
Wonbae Bang, Jacob T. Carlin, Kwonil Kim, Alexander V. Ryzhkov, Guosheng Liu, and GyuWon Lee
Geosci. Model Dev., 18, 3559–3581, https://doi.org/10.5194/gmd-18-3559-2025, https://doi.org/10.5194/gmd-18-3559-2025, 2025
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Microphysics model-based diagnosis, such as the spectral bin model (SBM), has recently been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM has a relatively higher accuracy for dry-snow and wet-snow events, whereas it has lower accuracy for rain events. When the microphysics scheme in the SBM was optimized for the corresponding region, the accuracy for rain events improved.
Gabriel Colas, Valéry Masson, François Bouttier, Ludovic Bouilloud, Laura Pavan, and Virve Karsisto
Geosci. Model Dev., 18, 3453–3472, https://doi.org/10.5194/gmd-18-3453-2025, https://doi.org/10.5194/gmd-18-3453-2025, 2025
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In winter, snow- and ice-covered artificial surfaces are important aspects of the urban climate. They may influence the magnitude of the urban heat island effect, but this is still unclear. In this study, we improved the representation of the snow and ice cover in the Town Energy Balance (TEB) urban climate model. Evaluations have shown that the results are promising for using TEB to study the climate of cold cities.
Markus Kunze, Christoph Zülicke, Tarique A. Siddiqui, Claudia C. Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev., 18, 3359–3385, https://doi.org/10.5194/gmd-18-3359-2025, https://doi.org/10.5194/gmd-18-3359-2025, 2025
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We present the Icosahedral Nonhydrostatic (ICON) general circulation model with an upper-atmospheric extension with the physics package for numerical weather prediction (UA-ICON(NWP)). We optimized the parameters for the gravity wave parameterizations and achieved realistic modeling of the thermal and dynamic states of the mesopause regions. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
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Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025, https://doi.org/10.5194/gmd-18-3265-2025, 2025
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The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model setup, are discussed, and the official recommendations for the project are presented.
Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut
Geosci. Model Dev., 18, 3211–3239, https://doi.org/10.5194/gmd-18-3211-2025, https://doi.org/10.5194/gmd-18-3211-2025, 2025
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This study proposes using a statistical model to freeze errors due to differences in environmental forcing when evaluating the surface turbulent heat fluxes from numerical simulations with observations. The statistical model is first built with observations and then applied to the simulated environment to generate possibly observed fluxes. This novel method provides insight into differently evaluating the numerical formulation of turbulent heat fluxes with a long period of observational data.
Oxana Drofa
Geosci. Model Dev., 18, 3175–3209, https://doi.org/10.5194/gmd-18-3175-2025, https://doi.org/10.5194/gmd-18-3175-2025, 2025
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This paper presents the result of many years of effort of the author, who developed an original mathematical numerical model of heat and moisture exchange processes in soil, vegetation, and snow. The author relied on her 30 years of research experience in atmospheric numerical modelling. The presented model is the fruit of the author's research on physical processes at the surface–atmosphere interface and their numerical approximation and aims at improving numerical weather forecasting and climate simulations.
Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani
Geosci. Model Dev., 18, 3065–3079, https://doi.org/10.5194/gmd-18-3065-2025, https://doi.org/10.5194/gmd-18-3065-2025, 2025
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We developed ClimKern, a Python package and radiative kernel repository, to simplify calculating radiative feedbacks and make climate sensitivity studies more reproducible. Testing of ClimKern with sample climate model data reveals that radiative kernel choice may be more important than previously thought, especially in polar regions. Our work highlights the need for kernel sensitivity analyses to be included in future studies.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025, https://doi.org/10.5194/gmd-18-2983-2025, 2025
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Particle size is a key factor determining the properties of aerosol particles which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis
Geosci. Model Dev., 18, 2861–2890, https://doi.org/10.5194/gmd-18-2861-2025, https://doi.org/10.5194/gmd-18-2861-2025, 2025
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We estimate carbon monoxide emissions through inverse modeling, an approach where measurements of tracers in the atmosphere are fed to a model to calculate backwards in time (inverse) where the tracers came from. We introduce measurements from a new satellite instrument and show that, in most places globally, these on their own sufficiently constrain the emissions. This alleviates the need for additional datasets, which could shorten the delay for future carbon monoxide source estimates.
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025, https://doi.org/10.5194/gmd-18-2747-2025, 2025
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This paper introduces the Multi-Compartment Mercury (Hg) Modeling and Analysis Project (MCHgMAP) aimed at informing the effectiveness evaluations of two multilateral environmental agreements: the Minamata Convention on Mercury and the Convention on Long-Range Transboundary Air Pollution. The experimental design exploits a variety of models (atmospheric, land, oceanic ,and multimedia mass balance models) to assess the short- and long-term influences of anthropogenic Hg releases into the environment.
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025, https://doi.org/10.5194/gmd-18-2701-2025, 2025
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Machine learning has the potential to aid the identification of organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning models in atmospheric sciences.
Juan Escobar, Philippe Wautelet, Joris Pianezze, Florian Pantillon, Thibaut Dauhut, Christelle Barthe, and Jean-Pierre Chaboureau
Geosci. Model Dev., 18, 2679–2700, https://doi.org/10.5194/gmd-18-2679-2025, https://doi.org/10.5194/gmd-18-2679-2025, 2025
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The Meso-NH weather research code is adapted for GPUs using OpenACC, leading to significant performance and energy efficiency improvements. Called MESONH-v55-OpenACC, it includes enhanced memory management, communication optimizations and a new solver. On the AMD MI250X Adastra platform, it achieved up to 6× speedup and 2.3× energy efficiency gain compared to CPUs. Storm simulations at 100 m resolution show positive results, positioning the code for future use on exascale supercomputers.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
Geosci. Model Dev., 18, 2569–2586, https://doi.org/10.5194/gmd-18-2569-2025, https://doi.org/10.5194/gmd-18-2569-2025, 2025
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The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate this effect, the radiative kernel is constructed and applied. The results show that the kernels can reproduce aerosol radiative effects and are expected to simulate stratospheric aerosol radiative effects. This approach reduces computational expense, is consistent with radiative model calculations, and can be applied to atmospheric models with speed requirements.
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
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This study evaluates the Weather Research and Forecasting Model (WRF) coupled with Chemistry (WRF-Chem) to predict a mega Asian dust storm (ADS) over South Korea on 28–29 March 2021. We assessed combinations of five dust emission and four land surface schemes by analyzing meteorological and air quality variables. The best scheme combination reduced the root mean square error (RMSE) for particulate matter 10 (PM10) by up to 29.6 %, demonstrating the highest performance.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025, https://doi.org/10.5194/gmd-18-2231-2025, 2025
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The effectiveness of this assimilation system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. This study advances our understanding of the selection of basic parameters in the four-dimensional local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate-matter-polluted environment.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
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To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
anomalous event.
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, https://doi.org/10.5194/gmd-18-1965-2025, 2025
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The MAR (Modèle Régional Atmosphérique) is a regional climate model used for weather forecasting and studying the climate over various regions. This paper presents an update of MAR thanks to which it can precisely decompose solar radiation, in particular in the UV (ultraviolet) and photosynthesis ranges, both being critical to human health and ecosystems. As a first application of this new capability, this paper presents a method for predicting UV indices with MAR.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
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Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025, https://doi.org/10.5194/gmd-18-1879-2025, 2025
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The High-Resolution Global Forecast Model (HGFM) is an advanced iteration of the operational Global Forecast System (GFS) model. HGFM can produce forecasts at a spatial scale of ~6 km in tropics. It demonstrates improved accuracy in short- to medium-range weather prediction over the Indian region, with notable success in predicting extreme events. Further, the model will be entrusted to operational forecasting agencies after validation and testing.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
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Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025, https://doi.org/10.5194/gmd-18-1809-2025, 2025
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Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.
Andrin Jörimann, Timofei Sukhodolov, Beiping Luo, Gabriel Chiodo, Graham Mann, and Thomas Peter
EGUsphere, https://doi.org/10.5194/egusphere-2025-145, https://doi.org/10.5194/egusphere-2025-145, 2025
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Aerosol particles in the stratosphere affect our climate. Climate models therefore need an accurate description of their properties and evolution. Satellites measure how strongly aerosol particles extinguish light passing through the stratosphere. We describe a method to use such aerosol extinction data to retrieve the number and sizes of the aerosol particles and calculate their optical effects. The resulting data sets for models are validated against ground-based and balloon observations.
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025, https://doi.org/10.5194/gmd-18-1769-2025, 2025
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This study evaluates the performance of four planetary boundary layer (PBL) schemes in near-surface wind fields over the Sichuan Basin, China. Using 112 sensitivity experiments with the Weather Research and Forecasting (WRF) model and focusing on 28 wind events, it is found that wind direction was less sensitive to the PBL schemes. The quasi-normal scale elimination (QNSE) scheme captured temporal variations best, while the Mellor–Yamada–Janjić (MYJ) scheme had the least error in wind speed.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
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It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, and Raffaele Montuoro
Geosci. Model Dev., 18, 1635–1660, https://doi.org/10.5194/gmd-18-1635-2025, https://doi.org/10.5194/gmd-18-1635-2025, 2025
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The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
Yanwei Zhu, Aitor Atencia, Markus Dabernig, and Yong Wang
Geosci. Model Dev., 18, 1545–1559, https://doi.org/10.5194/gmd-18-1545-2025, https://doi.org/10.5194/gmd-18-1545-2025, 2025
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Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
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The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
Astrid Kerkweg, Timo Kirfel, Duong H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev., 18, 1265–1286, https://doi.org/10.5194/gmd-18-1265-2025, https://doi.org/10.5194/gmd-18-1265-2025, 2025
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Normally, the Modular Earth Submodel System (MESSy) is linked to complete dynamic models to create chemical climate models. However, the modular concept of MESSy and the newly developed DWARF component presented here make it possible to create simplified models that contain only one or a few process descriptions. This is very useful for technical optimisation, such as porting to GPUs, and can be used to create less complex models, such as a chemical box model.
Peter Wind and Willem van Caspel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3571, https://doi.org/10.5194/egusphere-2024-3571, 2025
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This paper presents a numerical method to assess the origin of air pollution. Combined with a numerical air pollution transport and chemistry model, it can follow the contributions from a large number of emission sources. The result is a series of maps that give the relative contributions from for example all European countries at each point.
Julian Vogel, Sebastian Stadler, Ganesh Chockalingam, Afshin Afshari, Johanna Henning, and Matthias Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2025-144, https://doi.org/10.5194/egusphere-2025-144, 2025
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This study presents a toolkit to simplify input data creation for the urban microclimate model PALM-4U. It introduces novel methods to automate the use of open data sources. Our analysis of four test cases created from different geographic data sources shows variations in temperature, humidity, and wind speed, influenced by data quality. Validation indicates that the automated methods yield results comparable to expert-driven approaches, facilitating user-friendly urban climate modeling.
Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
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An enhanced emission module has been developed for the PALM model system, improving flexibility and scalability of emission source representation across different sectors. A model for parametrized domestic emissions has also been included, for which an idealized model run is conducted for particulate matter (PM10). The results show that, in addition to individual sources and diurnal variations in energy consumption, vertical transport and urban topology play a role in concentration distribution.
Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, and Tobias Hell
Geosci. Model Dev., 18, 1141–1153, https://doi.org/10.5194/gmd-18-1141-2025, https://doi.org/10.5194/gmd-18-1141-2025, 2025
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As lightning is a brief and localized event, it is not explicitly resolved in atmospheric models. Instead, expert-based auxiliary descriptions are used to assess it. This study explores how AI can improve our understanding of lightning without relying on traditional expert knowledge. We reveal that AI independently identified the key factors known to experts as essential for lightning in the Alps region. This shows how knowledge discovery could be sped up in areas with limited expert knowledge.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev., 18, 1103–1118, https://doi.org/10.5194/gmd-18-1103-2025, https://doi.org/10.5194/gmd-18-1103-2025, 2025
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The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements at 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev., 18, 1017–1039, https://doi.org/10.5194/gmd-18-1017-2025, https://doi.org/10.5194/gmd-18-1017-2025, 2025
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In our study, we built upon previous work to investigate the patterns artificial intelligence (AI) learns to detect atmospheric features like tropical cyclones (TCs) and atmospheric rivers (ARs). As primary objective, we adopt a method to explain the AI used and investigate the plausibility of learned patterns. We find that plausible patterns are learned for both TCs and ARs. Hence, the chosen method is very useful for gaining confidence in the AI-based detection of atmospheric features.
Leon Geers, Ruud Janssen, Gudrun Thorkelsdottir, Jordi Vilà-Guerau de Arellano, and Martijn Schaap
EGUsphere, https://doi.org/10.5194/egusphere-2025-426, https://doi.org/10.5194/egusphere-2025-426, 2025
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High-resolution data on reactive nitrogen deposition are needed to inform cost-effective policies. Here, we describe the implementation of a dry deposition module into a large eddy simulation code. With this model, we are able to represent the turbulent exchange of tracers at the hectometer resolution. The model calculates the dispersion and deposition of NOx and NH3 in great spatial detail, clearly showing the influence of local land use patterns.
Raphaël Périllat, Sylvain Girard, and Irène Korsakissok
EGUsphere, https://doi.org/10.5194/egusphere-2024-3838, https://doi.org/10.5194/egusphere-2024-3838, 2025
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We developed a method to improve decision-making during nuclear crises by predicting the spread of radiation more efficiently. Existing approaches are often too slow, especially when analyzing complex data like radiation maps. Our method combines techniques to simplify these maps and predict them quickly using statistical tools. This approach could help authorities respond faster and more accurately in emergencies, reducing risks to the population and the environment.
Shaofeng Hua, Gang Chen, Baojun Chen, Mingshan Li, and Xin Xu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3834, https://doi.org/10.5194/egusphere-2024-3834, 2025
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Hail forecasting using numerical models remains a challenge. In this study, we found that the commonly used graupel-to-hail conversion parameterization method led to hail overforecasting in heavy rainfall cases where no hail was observed. By incorporating the spongy wet growth process, we successfully mitigated hail overforecasting. The modified scheme also produced hail in real hail events. This research contributes to a better understanding of hail formation.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
EGUsphere, https://doi.org/10.5194/egusphere-2024-3512, https://doi.org/10.5194/egusphere-2024-3512, 2025
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Non-thermal emission from chemical reactions in the Earth's middle und upper atmosphere strongly contributes to the brightness of the night sky below about 2.3 µm. The new Paranal Airglow Line and Continuum Emission model calculates the emission spectrum and its variability with an unprecedented accuracy. Relying on a large spectroscopic data set from astronomical spectrographs and theoretical molecular/atomic data, it is valuable for airglow research and astronomical observatories.
Felipe Cifuentes, Henk Eskes, Enrico Dammers, Charlotte Bryan, and Folkert Boersma
Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025, https://doi.org/10.5194/gmd-18-621-2025, 2025
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We tested the capability of the flux divergence approach (FDA) to reproduce known NOx emissions using synthetic NO2 satellite column retrievals from high-resolution model simulations. The FDA accurately reproduced NOx emissions when column observations were limited to the boundary layer and when the variability of the NO2 lifetime, the NOx : NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces strong model dependency, reducing the simplicity of the original FDA formulation.
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025, https://doi.org/10.5194/gmd-18-529-2025, 2025
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We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
Cited articles
Alfaro, S. C., Gaudichet, A., Gomes, L., and Maillé, M.: Mineral aerosol
production by wind erosion: aerosol particle sizes and binding energies,
Geophys. Res. Lett., 25, 991–994, https://doi.org/10.1029/98GL00502,
1998. a
Al-Hemoud, A., Al-Sudairawi, M., Neelamanai, S., Naseeb, A., and Behbehani,
W.: Socioeconomic effect of dust storms in Kuwait, Arab. J. Geosci., 10,
18, doi:10.1007/s12517-016-2816-9, 2017. a
Alizadeh Choobari, O., Zawar-Reza, P., and Sturman, A.: Low level jet
intensification by mineral dust aerosols, Ann. Geophys., 31, 625–632,
https://doi.org/10.5194/angeo-31-625-2013, 2013. a
Barnard, J. C., Fast, J. D., Paredes-Miranda, G., Arnott, W. P., and Laskin,
A.: Technical Note: Evaluation of the WRF-Chem “Aerosol Chemical to Aerosol
Optical Properties” Module using data from the MILAGRO campaign, Atmos. Chem.
Phys., 10, 7325–7340, https://doi.org/10.5194/acp-10-7325-2010, 2010. a
Barnum, B. H., Winstead, N. S., Wesely, J., Hakola, A., Colarco, P. R., Toon,
O. B., Ginoux, P., Brooks, G., Hasselbarth, L., and Toth, B.: Forecasting
dust storms using the CARMA-Dust Model and MM5 weather data, Environ. Model.
Softw., 19, 129–140, https://doi.org/10.1016/S1364-8152(03)00115-4, 2004. a, b
Beljaars, A. C. M.: The parameterization of surface fluxes in large-scale
models under free convection, Q. J. Roy. Meteor. Soc., 121, 255–270,
https://doi.org/10.1002/qj.49712152203, 1994. a
Bian, H., Tie, X., Cao, J., Ying, Z., Han, S., and Xue, Y.: Analysis of a
severe dust storm event over China: Application of the WRF-Dust model,
Aerosol Air Qual. Res., 11, 419–428,
https://doi.org/10.4209/aaqr.2011.04.0053, 2011. a
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster,
P., Kerminen, V. M., Kondo, Y., Liao, H., Lohmann, U., and Rasch, P.: Clouds
and aerosols, in: Climate Change 2013: The Physical Science Basis.
Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin,
D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia,
Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge,
United Kingdom and New York, NY, USA, 571–657, 2013. a
Bullard, J. E., McTainsh, G. H., and Pudmenzky, C.: Factors affecting the
nature and rate of dust production from natural dune sands, Sedimentology,
54, 169–182, https://doi.org/10.1111/j.1365-3091.2006.00827.x, 2007. a
Chappell, A., Warren, A., O'Donoghue, A., Robinson, A., Thomas, A., and
Bristow, C.: The implications for dust emission modeling of spatial and
vertical variations in horizontal dust flux and particle size in the
Bodélé Depression, Northern Chad, J. Geophys. Res.-Atmos., 113,
D04214, https://doi.org/10.1029/2007JD009032, 2008. a
Chen, S., Zhao, C., Qian, Y., Leung, L. R., Huang, J., Huang, Z., Bi, J.,
Zhang, W., Shi, J., Yang, L., and Li, D.: Regional modeling of dust mass
balance and radiative forcing over East Asia using WRF-Chem, Aeolian Res.,
15, 15–30, https://doi.org/10.1016/j.aeolia.2014.02.001, 2014. a
Chepil, W. S.: Dynamics of wind erosion: I. Nature of movement of soil by
wind, Soil Sci., 60, 305–320, 1945. a
Chin, M., Savoie, D. L., Huebert, B. J., Bandy, A. R., Thornton, D. C.,
Bates, T. S., Quinn, P. K., Saltzman, E. S., and De Bruyn, W. J.: Atmospheric
sulfur cycle simulated in the global model GOCART: Comparison with field
observations and regional budgets, J. Geophys. Res.-Atmos., 105,
24689–24712, https://doi.org/10.1029/2000JD900385, 2000. a, b
Colarco, P., da Silva, A., Chin, M., and Diehl, T.: Online simulations of
global aerosol distributions in the NASA GEOS-4 model and comparisons to
satellite and ground-based aerosol optical depth, J. Geophys. Res.-Atmos.
115, D14207, https://doi.org/10.1029/2009JD012820, 2010. a
Colarco, P. R., Toon, O. B., and Holben, B. N.: Saharan dust transport to the
Caribbean during PRIDE: 1. Influence of dust sources and removal mechanisms
on the timing and magnitude of downwind aerosol optical depth events from
simulations of in situ and remote sensing observations, J. Geophys.
Res.-Atmos., 108, 8589,
https://doi.org/10.1029/2002JD002658, 2003a. a, b, c, d, e
Colarco, P. R., Toon, O. B., Reid, J. S., Livingston, J. M., Russell, P. B.,
Redemann, J., Schmid, B., Maring, H. B., Savoie, D., Welton, E. J., and
Campbell, J. R.: Saharan dust transport to the Caribbean during PRIDE: 2.
Transport, vertical profiles, and deposition in simulations of in situ and
remote sensing observations, J. Geophys. Res.-Atmos., 108,
8590, https://doi.org/10.1029/2002JD002659, 2003b. a
Cremades, P. G., Fernández, R. P., Alllende, D. G., Mulena, G. C., and
Puliafito, S. E.: High resolution satellite derived erodibility factors for
WRF/Chem windblown dust simulations in Argentina, Atmósfera, 30, 11–25,
https://doi.org/10.20937/atm.2017.30.01.02, 2017. a
Darmenova, K., Sokolik, I. N., Shao, Y., Marticorena, B., and Bergametti, G.:
Development of a physically based dust emission module within the Weather
Research and Forecasting (WRF) model: Assessment of dust emission
parameterizations and input parameters for source regions in Central and East
Asia, J. Geophys. Res.-Atmos., 114, D14201, https://doi.org/10.1029/2008JD011236, 2009. a, b, c, d, e
Defries, R. S. and Townshend, J. R. G.: Global land cover characterization
from satellite data: from research to operational implementation?, Global
Ecol. Biogeogr., 8, 367–379,
https://doi.org/10.1046/j.1365-2699.1999.00139.x, 1999. a
De Longueville, F., Hountondji, Y.-C., Henry, S., and Ozer, P.: What do we
know about effects of desert dust on air quality and human health in West
Africa compared to other regions?, Sci. Total Environ., 409, 1–8,
https://doi.org/10.1016/j.scitotenv.2010.09.025, 2010. a
DeMott, P. J., Prenni, A. J., Liu, X., Kreidenweis, S. M., Petters, M. D.,
Twohy, C. H., Richardson, M. S., Eidhammer, T., and Rogers, D. C.: Predicting
global atmospheric ice nuclei distributions and their impacts on climate,
Proc. Natl. Acad. Sci. USA, 107, 11217–11222,
https://doi.org/10.1073/pnas.0910818107, 2010. a
Dipu, S., Prabha, T. V., Pandithurai, G., Dudhia, J., Pfister, G., Rajesh,
K., and Goswami, B. N.: Impact of elevated aerosol layer on the cloud
macrophysical properties prior to monsoon onset, Atmos. Environ., 70,
454–467, https://doi.org/10.1016/j.atmosenv.2012.12.036, 2013. a
Fast, J. D., Gustafson Jr., W. I., Easter, R. C., Zaveri, R. A., Barnard, J.
C., Chapman, E. G., Grell, G. A., and Peckham, S. E.: Evolution of ozone,
particulates, and aerosol direct forcing in an urban area using a new
fully-coupled meteorology, chemistry, and aerosol model, J. Geophys. Res.,
111, D21305, https://doi.org/10.1029/2005JD006721, 2006. a, b
Flaounas, E., Kotroni, V., Lagouvardos, K., Klose, M., Flamant, C., and
Giannaros, T. M.: Assessing atmospheric dust modelling performance of
WRF-Chem over the semi-arid and arid regions around the Mediterranean, Atmos.
Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-307, 2016. a
Fountoukis, C., Ackermann, L., Ayoub, M. A., Gladich, I., Hoehn, R. D., and
Skillern, A.: Impact of atmospheric dust emission schemes on dust production
and concentration over the Arabian Peninsula, Model. Earth Syst. Environ.,
2, 115, https://doi.org/10.1007/s40808-016-0181-z, 2016. a
Freitas, S. R., Longo, K. M., Alonso, M. F., Pirre, M., Marecal, V., Grell,
G., Stockler, R., Mello, R. F., and Sánchez Gácita, M.: PREP-CHEM-SRC
– 1.0: a preprocessor of trace gas and aerosol emission fields for
regional and global atmospheric chemistry models, Geosci. Model Dev., 4,
419–433, https://doi.org/10.5194/gmd-4-419-2011, 2011. a
Gillette, D. A.: Production of dust that may be carried great distances, in:
Desert Dust: Origin, Characteristics, and Effect on Man, edited by:
Pewe, T. L., Spec. Pap. Geol. Soc. Am., 86, 11–26, 1981. a
Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B., Dubovik, O.,
and Lin, S. J.: Sources and distributions of dust aerosols simulated with the
GOCART model. J. Geophys. Res.-Atmos., 106, 20255–20273,
https://doi.org/10.1029/2000JD000053, 2001. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s
Ginoux, P., Prospero, J. M., Torres, O., and Chin, M.: Long-term simulation
of global dust distribution with the GOCART model: correlation with North
Atlantic Oscillation, Environ. Modell. Softw., 19, 113–128,
https://doi.org/10.1016/S1364-8152(03)00114-2, 2004. a
Gong, S. L., Zhang, X. Y., Zhao, T. L., McKendry, I. G., Jaffe, D. A., and
Lu, N. M.: Characterization of soil dust aerosol in China and its transport
and distribution during 2001 ACE-Asia: 2. Model simulation and validation, J.
Geophys. Res.-Atmos., 108, 4262,
https://doi.org/10.1029/2002JD002633, 2003.
Goudie, A. S. and Middleton, N. J.: Desert Dust in the Global System,
Springer, Berlin, 2006. a
Huang, J., Wang, T., Wang, W., Li, Z., and Yan, H.: Climate effects of dust
aerosols over East Asian arid and semiarid regions, J. Geophys. Res.-Atmos.,
119, 11398–11416, https://doi.org/10.1002/2014JD021796, 2014. a
Hunt, E., Adams-Selin, R. Jones, S. L., and Creighton G.: Using a modified
Fécan soil moisture calculation to predict dust emissions over semi-arid
and arid regions, paper presented at the 15th Annual WRF Users Workshop,
National Center for Atmospheric Research, Boulder, CO, June 2014,
available at: http://www2.mmm.ucar.edu/wrf/users/workshops/WS2014/ppts/5B.4.pdf
(last access: May 2018), 2014. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S.
A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos.,
113, D13103, https://doi.org/10.1029/2008JD009944, 2008. a
In, H. J. and Park, S. U.: A simulation of long-range transport of Yellow
Sand observed in April 1998 in Korea, Atmos. Environ., 36, 4173–4187,
https://doi.org/10.1016/S1352-2310(02)00361-8, 2002. a
Iversen, J. D. and White, B. R.: Saltation threshold on Earth, Mars and
Venus, Sedimentology, 29, 111–119,
https://doi.org/10.1111/j.1365-3091.1982.tb01713.x, 1982. a
Jish Prakash, P., Stenchikov, G., Kalenderski, S., Osipov, S., and Bangalath,
H.: The impact of dust storms on the Arabian Peninsula and the Red Sea,
Atmos. Chem. Phys., 15, 199–222, https://doi.org/10.5194/acp-15-199-2015,
2015. a
Kain, J. S.: The Kain-Fritsch convective parameterization: an update, J.
Appl. Meteorol., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004. a
Kalenderski, S., Stenchikov, G. L., and Zhao, C.: Modeling a typical
winter-time dust event over the Arabian Peninsula and the Red Sea, Atmos.
Chem. Phys., 13, 1999–2013, https://doi.org/10.5194/acp-13-1999-2013,
2013. a
Kalenderski, S. and Stenchikov, G.: High-resolution regional modeling of
summertime transport and impact of African dust over the Red Sea and Arabian
Peninsula, J. Geophys. Res.-Atmos., 121, 6435–6458,
https://doi.org/10.1002/2015JD024480, 2016. a
Klose, M. and Shao, Y.: Stochastic parameterization of dust emission and
application to convective atmospheric conditions, Atmos. Chem. Phys., 12,
7309–7320, https://doi.org/10.5194/acp-12-7309-2012, 2012. a
Klose, M. and Shao, Y.: Large-eddy simulation of turbulent dust emission,
Aeolian Res., 8, 49–58, https://doi.org/10.1016/j.aeolia.2012.10.010, 2013. a
Klose, M., Shao, Y., Li, X., Zhang, H., Ishizuka, M., Mikami, M., and Leys,
J. F.: Further development of a parameterization for convective turbulent dust
emission and evaluation based on field observations, J. Geophys. Res.-Atmos.,
119, 10441–10457, https://doi.org/10.1002/2014JD021688, 2014. a
Kok, J. F., Parteli, E. J., Michaels, T. I., and Karam, D. B.: The physics of
wind-blown sand and dust, Rep. Prog. Phys., 75, 106901,
https://doi.org/10.1088/0034-4885/75/10/106901, 2012. a
Kumar, R., Barth, M. C., Pfister, G. G., Naja, M., and Brasseur, G. P.:
WRF-Chem simulations of a typical pre-monsoon dust storm in northern India:
influences on aerosol optical properties and radiation budget, Atmos. Chem.
Phys., 14, 2431–2446, https://doi.org/10.5194/acp-14-2431-2014, 2014. a
Letcher, T. W. and LeGrand, S. L.: A comparison of simulated dust produced by
three dust-emission schemes in WRF-Chem, ERDC/CRREL TR-18-13, U.S. Army
Engineer Research and Development Center, Hanover, New Hampshire, USA, 2018. a
Liu, M., Westphal, D. L., Wang, S., Shimizu, A., Sugimoto, N., Zhou, J., and
Chen, Y.: A high-resolution numerical study of the Asian dust storms of April
2001, J. Geophys. Res.-Atmos., 108, 8653,
https://doi.org/10.1029/2002JD003178, 2003. a
Liu, M., Westphal, D. L., Walker, A. L., Holt, T. R., Richardson, K. A., and
Miller, S. D.: COAMPS real-time dust storm forecasting during Operation Iraqi
Freedom, Weather Forecast., 22, 192–206, https://doi.org/10.1175/WAF971.1,
2007. a
Liu, Z., Liu, Q., Lin, H.-C., Schwartz, C. S., Lee, Y.-H., and Wang, T.:
Three?dimensional variational assimilation of MODIS aerosol optical depth:
Implementation and application to a dust storm over East Asia, J. Geophys.
Res.-Atmos., 116, D23206,
https://doi.org/10.1029/2011JD016159, 2011. a
Lu, H. and Shao, Y.: A new model for dust emission by saltation bombardment,
J. Geophys. Res.-Atmos., 104, 16827–16842,
https://doi.org/10.1029/1999JD900169, 1999. a
Lu, S., da Silva, A. M., Chin, M., Wang, J., Moorthi, S., Juang, H., Chuang,
H.-Y., Tang, Y., Jones, L., Iredell, M., and McQueen, J. T.: The NEMS GFS
aerosol component; NCEP's global aerosol forecast system, NCEP Office Note
472, 2013. a
Lyapustin, A. and Wang, Y.: MCD19A2 MODIS/Terra+Aqua Land Aerosol Optical
Depth Daily L2G Global 1km SIN Grid V006, distributed by NASA EOSDIS Land
Processes DAAC, https://doi.org/10.5067/MODIS/MCD19A2.006, 2018. a
Mahowald, N., Albani, S., Kok, J. F., Engelstaeder, S., Scanza, R., Ward, D.
S., and Flanner M. G.: The size distribution of desert dust aerosols and its
impact on the Earth system, Aeolian Res., 15, 53–71,
https://doi.org/10.1016/j.aeolia.2013.09.002, 2014. a
Mahowald, N. M., Baker, A. R., Bergametti, G., Brooks, N., Duce, R. A.,
Jickells, T. D., Kubilay, N., Prospero, J. M., and Tegen, I.: Atmospheric
global dust cycle and iron inputs to the ocean, Global Biogeochem. Cy.,
19, GB4025, https://doi.org/10.1029/2004GB002402, 2005. a
Mahowald, N. M., Kloster, S., Engelstaedter, S., Moore, J. K., Mukhopadhyay,
S., McConnell, J. R., Albani, S., Doney, S. C., Bhattacharya, A., Curran, M.
A. J., Flanner, M. G., Hoffman, F. M., Lawrence, D. M., Lindsay, K.,
Mayewski, P. A., Neff, J., Rothenberg, D., Thomas, E., Thornton, P. E., and
Zender, C. S.: Observed 20th century desert dust variability: impact on
climate and biogeochemistry, Atmos. Chem. Phys., 10, 10875–10893,
https://doi.org/10.5194/acp-10-10875-2010, 2010. a
Marticorena, B. and Bergametti, G.: Modeling the atmospheric dust cycle: 1.
Design of a soil-derived dust emission scheme, J. Geophys. Res.-Atmos.,
100, 16415–16430, https://doi.org/10.1029/95JD00690, 1995. a
McDonald, E. V. and Caldwell, T. G.: Geochemical characteristics of Iraqi dust
and soil samples and related impacts to weapon malfunctions, in: Military
Geography and Geology: History and Technology, edited by: Nathanail, C. P.,
Abrahart, R. J., and Bradshaw, R. P., Land Quality Press, Nottingham, 258–265,
2008. a
Middleton, N. J.: Desert dust hazards: A global review, Aeolian Res., 24,
53–63, https://doi.org/10.1016/j.aeolia.2016.12.001, 2017. a
Mitchell, K.: The community Noah land surface model (LSM), User's Guide,
available at:
ftp://ftp.emc.ncep.noaa.gov/mmb/gcp/ldas/noahlsm/ver_2.7.1
(last access: May 2018), 2005. a
Nabavi, S. O., Haimberger, L., and Samimi, C.: Sensitivity of WRF-chem
predictions to dust source function specification in West Asia, Aeolian Res.,
24, 115–131, https://doi.org/10.1016/j.aeolia.2016.12.005, 2017. a
Nakanishi, M. and Niino, H.: An improved Mellor-Yamada level-3 model: Its
numerical stability and application to a regional prediction of advection
fog, Bound.-Layer Meteor., 119, 397–407,
https://doi.org/10.1007/s10546-005-9030-8, 2006. a
Nickovic, S., Kallos, G., Papadopoulos, A., and Kakaliagou, O.: A model for
prediction of desert dust cycle in the atmosphere, J. Geophys. Res.-Atmos.,
106, 18113–18129, https://doi.org/10.1029/2000JD900794, 2001. a
NOAA/NCEP (National Centers for Environmental Prediction/National Weather
Service/NOAA/U.S. Department of Commerce): NCEP FNL Operational Model Global
Tropospheric Analyses, continuing from July 1999, Research data archive at
the National Center for Atmospheric Research, Computational and Information
Systems Laboratory, Boulder, CO,
https://doi.org/10.5065/D6M043C6 (last access: July 2017), 2000. a
Okin, G. S., Bullard, J. E., Reynolds, R. L., Ballantine, J. A. C.,
Schepanski, K., Todd, M. C., Belnap, J., Baddock, M. C., Gill, T. E., and
Miller, M. E.: Dust: Small-scale processes with global consequences. Eos,
Trans. Amer. Geophys. Union, 92, 241–242,
https://doi.org/10.1029/2011EO290001, 2011. a
Owen, P. R.: Saltation of uniform grains in air, J. Fluid Mech., 20,
225–242, 1964. a
Park, S. H., Gong, S. L., Zhao, T. L., Vet, R. J., Bouchet, V. S., Gong, W.,
Makar, P. A., Moran, M. D., Stroud, C., and Zhang, J.: Simulation of
entrainment and transport of dust particles within North America in April
2001 (“Red Dust Episode”), J. Geophys. Res., 112, D20209,
https://doi.org/10.1029/2007JD008443, 2007. a
Peters-Lidard, C. D., Kemp, E. M., Matsui, T., Santanello Jr., J. A., Kumar,
S. V., Jacob, J. P., Clune, T., Tao, W.-K., Chin, M., Hou, A., Case, J. L.,
Kim, D., Kim, K.-M., Lau, W., Liu, Y., Shi, J., Starr, D., Tan, Q., Tao, Z.,
Zaitchik, B. F., Zavodsky, B., Zhang, S. Q., and Zupanski, M.: Integrated
modeling of aerosol, cloud, precipitation and land processes at
satellite-resolved scales, Environ. Model. Softw., 67, 149–159,
https://doi.org/10.1016/j.envsoft.2015.01.007, 2015. a
Raupach, M.: Drag and drag partition on rough surfaces, Bound.-Lay. Meteor.,
60, 375–395, https://doi.org/10.1007/BF00155203, 1992. a
Ravi, S., D'odorico, P., Breshears, D. D., Field, J. P., Goudie, A. S.,
Huxman, T. E., Li, J., Okin, G. S., Swap, R. J., Thomas, A. D., Van Pelt, S.,
Whicker, J. J., and Zobeck, T.: Aeolian processes and the biosphere, Rev.
Geophys., 49, RG3001, https://doi.org/10.1029/2010RG000328, 2011. a
Reynolds, C. A., Jackson, T. J., and Rawls, W. J.: Estimating soil
water-holding capacities by linking the Food and Agriculture Organization
soil map of the world with global pedon databases and continuous pedotransfer
functions, Water Resour. Res., 36, 3653–3662,
https://doi.org/10.1029/2000WR900130, 2000. a, b, c
Rizza, U., Anabor, V., Mangia, C., Miglietta, M. M., Degrazia, G. A., and
Passerini, G.: WRF-Chem simulation of a saharan dust outbreak over the
mediterranean regions, Ciência e Natura, 38, 330–336,
https://doi.org/10.5902/2179460X20249, 2016. a
Rushing, J. F., Harrison, J. A., and Tingle, J., S.: Evaluation of
application methods and products for mitigating dust for
lines-of-communication and base camp operations, ERDC/GSL TR-05-9, U.S. Army
Engineer Research and Development Center, Waterways Experiment Station,
Vicksburg, Mississippi, USA, 2005. a
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P.,
Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R.,
Gayno, G., Wang, J., Hou, Y.-T., Chuang, H., Juang, H.-M. H., Sela, J.,
Iredell, M., Treadon, R., Kleist, D., Van Delst., P., Keyser, D., Derber, J.,
Ek., M., Meng, J., Wei., H., Yang, R., Lord, S., van den Dool, H., Kumar, A.,
Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.-K.,
Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C.-Z.,
Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds., R. W., Rutledge, G., and
Goldberg, M.: The NCEP climate forecast system reanalysis, B. Am.
Meteorol. Soc., 91, 1015–1058, https://doi.org/10.1175/2010BAMS3001.1, 2010. a
Shao, Y. P.: Physics and Modelling of Wind Erosion, Springer, Heidelberg,
2008. a
Shepherd, G., Terradellas, E., Baklanov, A., Kang, U., Sprigg, W., Nickovic,
S., Boloorani, A. D., Al-Dousari, A., Basart, S., Benedetti, A., and Sealy,
A.: Global assessment of sand and dust storms, United Nations Environment
Programme, Nairobi, 2016. a
Shinn, E. A., Smith, G. W., Prospero, J. M., Betzer, P., Hayes, M. L.,
Garrison, V., and Barber, R. T.: African dust and the demise of Caribbean
coral reefs, Geophys. Res. Lett., 27, 3029–3032,
https://doi.org/10.1029/2000GL011599, 2000. a
Sinclair, S. N. and Jones, S. L.: Subjective mapping of dust emission sources
by using MODIS imagery: Reproducibility assessment, ERDC/CRREL TR-17-8, U.S.
Army Engineer Research and Development Center, Hanover, New Hampshire, USA,
2017. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda,
M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A description of the
Advanced Research WRF Version 3, NCAR technical note, Mesoscale and
Microscale Meteorology Division, National Center for Atmospheric Research,
Boulder, Colorado, USA, 2008. a
Skiles, S. M., Painter, T. H., Belnap, J., Holland, L., Reynolds, R. L.,
Goldstein, H. L., and Lin, J.: Regional variability in dust-on-snow processes
and impacts in the Upper Colorado River Basin, Hydrol. Process., 29,
5397–5413, https://doi.org/10.1002/hyp.10569, 2015. a
Sprigg, W. A., Nickovic, S., Galgiani, J. N., Pejanovic, G., Petkovic, S.,
Vujadinovic, M., Vukovic, A., Dacic, M., DiBiase, S., Prasad, A., and
El-Askary, H.: Regional dust storm modeling for health services: the case of
valley fever, Aeolian Res., 14, 53–73,
https://doi.org/10.1016/j.aeolia.2014.03.001, 2014. a, b
Su, L. and Fung, J. C. H.: Sensitivities of WRF-Chem to dust emission schemes
and land surface properties in simulating dust cycles during springtime over
East Asia, J. Geophys. Res.-Atmos., 120, 11215–11230,
https://doi.org/10.1002/2015JD023446, 2015. a
Tanaka, T. Y. and Chiba, M.: Global simulation of dust aerosol with a
chemical transport model, MASINGAR, J. Meteor. Soc. Japan, 83, 255–278,
https://doi.org/10.2151/jmsj.83A.255, 2005. a
Teixeira, J. C., Carvalho, A. C., Tuccella, P., Curci, G., and Rocha, A.:
WRF-chem sensitivity to vertical resolution during a saharan dust event,
Phys. Chem. Earth, Parts A/B/C, 94, 188–195,
https://doi.org/10.1016/j.pce.2015.04.002, 2016. a
Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M. A., Mitchell, K., Ek,
M., Gayno, G., Wegiel, J., and Cuenca, R. H.: Implementation and verification
of the unified NOAH land surface model, 20th Conference on Weather Analysis
and Forecasting/16th Conference on Numerical Weather Prediction, Seattle, WA,
American Meteorological Society, 10–15 January, 2004. a
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit
forecasts of winter precipitation using an improved bulk microphysics scheme.
Part II: Implementation of a new snow parameterization, Mon. Weather Rev.,
136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1, 2008. a
Uzan, L., Egert, S., and Alpert, P.: Ceilometer evaluation of the eastern
Mediterranean summer boundary layer height – first study of two Israeli
sites, Atmos. Meas. Tech., 9, 4387–4398,
https://doi.org/10.5194/amt-9-4387-2016, 2016. a
Wang, F., Zhao, X., Gerlein-Safdi, C., Mu, Y., Wang, D., and Lu, Q.: Global
sources, emissions, transport and deposition of dust and sand and their
effects on the climate and environment: a review, Front. Environ. Sci. Eng.,
11, 13,
https://doi.org/10.1007/s11783-017-0904-z, 2017. a
Wang, K., Zhang, Y., Yahya, K., Wu, S. Y., and Grell, G.: Implementation and
initial application of new chemistry-aerosol options in WRF/Chem for
simulating secondary organic aerosols and aerosol indirect effects for
regional air quality, Atmos. Environ., 115, 716–732,
https://doi.org/10.1016/j.atmosenv.2014.12.007, 2015. a
Wang, W., Bruyère, C., Duda, M., Dudhia, J., Gill, D., Michael, K., Keene,
K., Chen, M., Lin, H.-C., Michalakes, J., Rizvi, S., Zhang, X., Berner, J.,
Soyoung, H., and Fossell, K.: Guide for the Advanced Research WRF (ARW)
Modeling System Version 3.9, NCAR technical note, Mesoscale and Microscale
Meteorology Division, National Center for Atmospheric Research, Boulder,
Colorado, USA, 2017. a
Wang, Z., Ueda, H., and Huang, M., Y.: A deflation module for use in modeling
long-range transport of yellow sand over East Asia, J. Geophys. Res., 105,
26947–26959, https://doi.org/10.1029/2000JD900370, 2000. a
Webb, N. P., Chappell, A., Strong, C. L., Marx, S. K., and McTainsh, G. H.:
The significance of carbon-enriched dust for global carbon accounting, Global
Change Biol., 18, 3275–3278,
https://doi.org/10.1111/j.1365-2486.2012.02780.x, 2012. a
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z.,
Hunt, W. H., and Young, S. A.: Overview of the CALIPSO mission and CALIOP
data processing algorithms, J. Atmos. Ocean. Technol., 26, 2310–2323,
https://doi.org/10.1175/2009JTECHA1281.1, 2009. a
Woodward, S.: Modeling the atmospheric life cycle and radiative impact of
mineral dust in the Hadley Centre climate model, J. Geophys. Res., 106,
18155–18166, https://doi.org/10.1029/2000JD900795, 2001. a
Young, S. A. and Vaughan, M. A.: The retrieval of profiles of particulate
extinction from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite
Observations (CALIPSO) data: Algorithm Description. J. Atmos. Ocean.
Technol., 26, 1105–1119, https://doi.org/10.1175/2008JTECHA1221.1, 2009. a
Zender, C. S.: Mineral Dust Entrainment and Deposition (DEAD) Model:
Description and 1990s dust climatology, J. Geophys. Res., 108, 4416,
https://doi.org/10.1029/2002JD002775, 2003. a
Zhang, Y., Liu, Y., Kucera, P. A., Alharbi, B. H., Pan, L., and Ghulam, A.:
Dust modeling over Saudi Arabia using WRF-Chem: March 2009 severe dust case,
Atmos. Environ., 119, 118–130,
https://doi.org/10.1016/j.atmosenv.2015.08.032, 2015. a
Zhao, C., Liu, X., Leung, L. R., Johnson, B., McFarlane, S. A., Gustafson
Jr., W. I., Fast, J. D., and Easter, R.: The spatial distribution of mineral
dust and its shortwave radiative forcing over North Africa: modeling
sensitivities to dust emissions and aerosol size treatments, Atmos. Chem.
Phys., 10, 8821–8838, https://doi.org/10.5194/acp-10-8821-2010, 2010. a
Zhao, C., Liu, X., Ruby Leung, L., and Hagos, S.: Radiative impact of mineral
dust on monsoon precipitation variability over West Africa, Atmos. Chem.
Phys., 11, 1879-1893, https://doi.org/10.5194/acp-11-1879-2011, 2011. a
Zhao, C., Chen, S., Leung, L. R., Qian, Y., Kok, J. F., Zaveri, R. A., and
Huang, J.: Uncertainty in modeling dust mass balance and radiative forcing
from size parameterization, Atmos. Chem. Phys., 13, 10733–10753,
https://doi.org/10.5194/acp-13-10733-2013, 2013. a
Zhao, T. L., Gong, S. L., Zhang, X. Y., Abdel-Mawgoud, A., and Shao, Y. P.:
An assessment of dust emission schemes in modeling east Asian dust storms, J.
Geophys. Res., 111, D05S90, https://doi.org/10.1029/2004JD005746, 2006. a
Short summary
This paper reviews the history, code, and performance of the three dust emission schemes embedded in the WRF-Chem model, including the GOCART, AFWA, and UoC dust emission schemes, and provides the first full documentation of the AFWA scheme. A simulation case study is provided to explore differences in model output. Results highlight the relative strengths of each scheme, indicate reasons for disagreement, and demonstrate the need for improved terrain characterization in dust emission models.
This paper reviews the history, code, and performance of the three dust emission schemes...
