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GMD | Articles | Volume 12, issue 10
Geosci. Model Dev., 12, 4245–4259, 2019
https://doi.org/10.5194/gmd-12-4245-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-4245-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue: The Lagrangian particle dispersion model FLEXPART
Geosci. Model Dev., 12, 4245–4259, 2019
https://doi.org/10.5194/gmd-12-4245-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-4245-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Model description paper 09 Oct 2019
Model description paper | 09 Oct 2019
Development of turbulent scheme in the FLEXPART-AROME v1.2.1 Lagrangian particle dispersion model
Bert Verreyken et al.
Related authors
Bert Verreyken, Crist Amelynck, Jérôme Brioude, Jean-François Müller, Niels Schoon, Nicolas Kumps, Aurélie Colomb, Jean-Marc Metzger, Christopher F. Lee, Theodore K. Koenig, Rainer Volkamer, and Trissevgeni Stavrakou
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-637, https://doi.org/10.5194/acp-2020-637, 2020
Preprint under review for ACP
Short summary
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Biomass burning (BB) plumes arriving at the Maïdo observatory located in the South-West Indian Ocean during August 2018 and August 2019 are studied using trace gas measurements, Lagrangian transport models and the CAMS near-real-time atmospheric composition service. We investigate (i) secondary production of volatile organic compounds during transport, (ii) efficacy of the CAMS model to reproduce the chemical makeup of BB plumes and (iii) the impact of BB on the remote marine boundary layer.
Stephanie Evan, Jerome Brioude, Karen Rosenlof, Sean M. Davis, Holger Vömel, Damien Héron, Françoise Posny, Jean-Marc Metzger, Valentin Duflot, Guillaume Payen, Hélène Vérèmes, Philippe Keckhut, and Jean-Pierre Cammas
Atmos. Chem. Phys., 20, 10565–10586, https://doi.org/10.5194/acp-20-10565-2020, https://doi.org/10.5194/acp-20-10565-2020, 2020
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The role of deep convection in the southwest Indian Ocean (the 3rd most active tropical cyclone basin) on the composition of the tropical tropopause layer (TTL) and the climate system is less understood due to scarce observations. Balloon-borne lidar and satellite measurements in the southwest Indian Ocean were used to study tropical cyclones' influence on TTL composition. This study compares the impact of a tropical storm and cyclone on the humidification of the TTL over the SW Indian Ocean.
Clémence Rose, Matti P. Rissanen, Siddharth Iyer, Jonathan Duplissy, Chao Yan, John B. Nowak, Aurélie Colomb, Régis Dupuy, Xu-Cheng He, Janne Lampilahti, Yee Jun Tham, Daniela Wimmer, Jean-Marc Metzger, Pierre Tulet, Jérôme Brioude, Céline Planche, Markku Kulmala, and Karine Sellegri
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-636, https://doi.org/10.5194/acp-2020-636, 2020
Preprint under review for ACP
Short summary
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Sulfuric acid (H2SO4) is commonly accepted as a key precursor for atmospheric new particle formation. However, direct measurements of [H2SO4] remain challenging, thus motivating the development of proxies. Using data collected in two different volcanic plumes, we show, in these specific conditions, the good performance of a proxy from the literature and highlight as well the benefit of the newly developed proxies for the prediction of the highest [H2SO4] concentrations.
Bert Verreyken, Crist Amelynck, Jérôme Brioude, Jean-François Müller, Niels Schoon, Nicolas Kumps, Aurélie Colomb, Jean-Marc Metzger, Christopher F. Lee, Theodore K. Koenig, Rainer Volkamer, and Trissevgeni Stavrakou
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-637, https://doi.org/10.5194/acp-2020-637, 2020
Preprint under review for ACP
Short summary
Short summary
Biomass burning (BB) plumes arriving at the Maïdo observatory located in the South-West Indian Ocean during August 2018 and August 2019 are studied using trace gas measurements, Lagrangian transport models and the CAMS near-real-time atmospheric composition service. We investigate (i) secondary production of volatile organic compounds during transport, (ii) efficacy of the CAMS model to reproduce the chemical makeup of BB plumes and (iii) the impact of BB on the remote marine boundary layer.
Damien Héron, Stéphanie Evan, Jérôme Brioude, Karen Rosenlof, Françoise Posny, Jean-Marc Metzger, and Jean-Pierre Cammas
Atmos. Chem. Phys., 20, 8611–8626, https://doi.org/10.5194/acp-20-8611-2020, https://doi.org/10.5194/acp-20-8611-2020, 2020
Short summary
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Using a statistical method, summer variations (between 2013 and 2016) of ozone and water vapor are characterized in the upper troposphere above Réunion island (21° S, 55° E). It suggests a convective influence between 9 and 13 km. As deep convection is rarely observed near Réunion island, this study provides new insights on the long-range impact of deep convective outflow from the Intertropical Convergence Zone (ITCZ) on the upper troposphere over a subtropical site.
Baye T. P. Thera, Pamela Dominutti, Fatma Öztürk, Thérèse Salameh, Stéphane Sauvage, Charbel Afif, Banu Çetin, Cécile Gaimoz, Melek Keleş, Stéphanie Evan, and Agnès Borbon
Atmos. Chem. Phys., 19, 15131–15156, https://doi.org/10.5194/acp-19-15131-2019, https://doi.org/10.5194/acp-19-15131-2019, 2019
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A large set of volatile organic compound observations was collected for the first time in Istanbul in September 2014. A source–receptor approach has been carried out to apportion emission sources, estimate anthropogenic emissions, and evaluate emission inventories. Unexpectedly, transport was not the most dominant source in this study. Our work stresses the need to better represent VOC emissions in the eastern Mediterranean region with an effort on non-traffic sources and oxygenated VOCs.
Ignacio Pisso, Espen Sollum, Henrik Grythe, Nina I. Kristiansen, Massimo Cassiani, Sabine Eckhardt, Delia Arnold, Don Morton, Rona L. Thompson, Christine D. Groot Zwaaftink, Nikolaos Evangeliou, Harald Sodemann, Leopold Haimberger, Stephan Henne, Dominik Brunner, John F. Burkhart, Anne Fouilloux, Jerome Brioude, Anne Philipp, Petra Seibert, and Andreas Stohl
Geosci. Model Dev., 12, 4955–4997, https://doi.org/10.5194/gmd-12-4955-2019, https://doi.org/10.5194/gmd-12-4955-2019, 2019
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We present the latest release of the Lagrangian transport model FLEXPART, which simulates the transport, diffusion, dry and wet deposition, radioactive decay, and 1st-order chemical reactions of atmospheric tracers. The model has been recently updated both technically and in the representation of physicochemical processes. We describe the changes, document the most recent input and output files, provide working examples, and introduce testing capabilities.
Pierre Tulet, Andréa Di Muro, Aurélie Colomb, Cyrielle Denjean, Valentin Duflot, Santiago Arellano, Brice Foucart, Jérome Brioude, Karine Sellegri, Aline Peltier, Alessandro Aiuppa, Christelle Barthe, Chatrapatty Bhugwant, Soline Bielli, Patrice Boissier, Guillaume Boudoire, Thierry Bourrianne, Christophe Brunet, Fréderic Burnet, Jean-Pierre Cammas, Franck Gabarrot, Bo Galle, Gaetano Giudice, Christian Guadagno, Fréderic Jeamblu, Philippe Kowalski, Jimmy Leclair de Bellevue, Nicolas Marquestaut, Dominique Mékies, Jean-Marc Metzger, Joris Pianezze, Thierry Portafaix, Jean Sciare, Arnaud Tournigand, and Nicolas Villeneuve
Atmos. Chem. Phys., 17, 5355–5378, https://doi.org/10.5194/acp-17-5355-2017, https://doi.org/10.5194/acp-17-5355-2017, 2017
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The STRAP campaign was conducted in 2015 to investigate the volcanic plumes of Piton de La Fournaise (La Réunion, France). For the first time, measurements were conducted at the local (near the vent) and regional scales around the island. The STRAP 2015 campaign gave a unique set of multi-disciplinary data that can now be used by modellers to improve the numerical parameterisations of the physical and chemical evolution of the volcanic plumes.
Hélène Vérèmes, Guillaume Payen, Philippe Keckhut, Valentin Duflot, Jean-Luc Baray, Jean-Pierre Cammas, Jimmy Leclair De Bellevue, Stéphanie Evan, Françoise Posny, Franck Gabarrot, Jean-Marc Metzger, Nicolas Marquestaut, Susanne Meier, Holger Vömel, and Ruud Dirksen
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2017-32, https://doi.org/10.5194/amt-2017-32, 2017
Preprint withdrawn
Elton Chan, Douglas Chan, Misa Ishizawa, Felix Vogel, Jerome Brioude, Andy Delcloo, Yuehua Wu, and Baisuo Jin
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-213, https://doi.org/10.5194/gmd-2016-213, 2016
Revised manuscript not accepted
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The main objective of this study is to examine the impacts of errors introduced by different components in our newly developed inversion system on flux estimates with a series of controlled experiments. It is very critical for any inversion system to be fully evaluated prior to applying to real observations. As demonstrated, the results can be very sensitive to the model setup and region. It is not reasonable to expect realistic results can always be obtained using the same approach.
Carsten Warneke, Michael Trainer, Joost A. de Gouw, David D. Parrish, David W. Fahey, A. R. Ravishankara, Ann M. Middlebrook, Charles A. Brock, James M. Roberts, Steven S. Brown, Jonathan A. Neuman, Brian M. Lerner, Daniel Lack, Daniel Law, Gerhard Hübler, Iliana Pollack, Steven Sjostedt, Thomas B. Ryerson, Jessica B. Gilman, Jin Liao, John Holloway, Jeff Peischl, John B. Nowak, Kenneth C. Aikin, Kyung-Eun Min, Rebecca A. Washenfelder, Martin G. Graus, Mathew Richardson, Milos Z. Markovic, Nick L. Wagner, André Welti, Patrick R. Veres, Peter Edwards, Joshua P. Schwarz, Timothy Gordon, William P. Dube, Stuart A. McKeen, Jerome Brioude, Ravan Ahmadov, Aikaterini Bougiatioti, Jack J. Lin, Athanasios Nenes, Glenn M. Wolfe, Thomas F. Hanisco, Ben H. Lee, Felipe D. Lopez-Hilfiker, Joel A. Thornton, Frank N. Keutsch, Jennifer Kaiser, Jingqiu Mao, and Courtney D. Hatch
Atmos. Meas. Tech., 9, 3063–3093, https://doi.org/10.5194/amt-9-3063-2016, https://doi.org/10.5194/amt-9-3063-2016, 2016
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In this paper we describe the experimental approach, the science goals and early results of the NOAA SENEX campaign, which was focused on studying the interactions between biogenic and anthropogenic emissions to form secondary pollutants.
During SENEX, the NOAA WP-3D aircraft conducted 20 research flights between 27 May and 10 July 2013 based out of Smyrna, TN. The SENEX flights included day- and nighttime flights in the Southeast as well as flights over areas with intense shale gas extraction.
E. Chan, D. Chan, M. Ishizawa, F. Vogel, J. Brioude, A. Delcloo, Y. Wu, and B. Jin
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-15-22715-2015, https://doi.org/10.5194/acpd-15-22715-2015, 2015
Revised manuscript not accepted
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This paper uses synthetic data experiments to investigate the impacts of different error sources associated with prior flux, transport model and optimisation method on the atmospheric greenhouse gas inverse estimates. Results indicate that estimation errors are dominated by the transport model error and can propagate to the flux estimates non-linearly. It is necessary to obtain stable and realistic results in synthetic data experiments before a real observation-based inversion is performed.
W. M. Angevine, J. Brioude, S. McKeen, and J. S. Holloway
Geosci. Model Dev., 7, 2817–2829, https://doi.org/10.5194/gmd-7-2817-2014, https://doi.org/10.5194/gmd-7-2817-2014, 2014
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Uncertainty in Lagrangian particle dispersion model simulations was evaluated using an ensemble of WRF meteorological model runs. Uncertainty of tracer concentrations due solely to meteorological uncertainty is 30-40%. Spatial and temporal averaging reduces the uncertainty marginally. Tracer age uncertainty due solely to meteorological uncertainty is 15-20%. These are lower bounds on the uncertainty, because a number of processes are not accounted for in the analysis.
Y. Y. Cui, A. Hodzic, J. N. Smith, J. Ortega, J. Brioude, H. Matsui, E. J. T. Levin, A. Turnipseed, P. Winkler, and B. de Foy
Atmos. Chem. Phys., 14, 11011–11029, https://doi.org/10.5194/acp-14-11011-2014, https://doi.org/10.5194/acp-14-11011-2014, 2014
C. Knote, A. Hodzic, J. L. Jimenez, R. Volkamer, J. J. Orlando, S. Baidar, J. Brioude, J. Fast, D. R. Gentner, A. H. Goldstein, P. L. Hayes, W. B. Knighton, H. Oetjen, A. Setyan, H. Stark, R. Thalman, G. Tyndall, R. Washenfelder, E. Waxman, and Q. Zhang
Atmos. Chem. Phys., 14, 6213–6239, https://doi.org/10.5194/acp-14-6213-2014, https://doi.org/10.5194/acp-14-6213-2014, 2014
D. R. Gentner, E. Ormeño, S. Fares, T. B. Ford, R. Weber, J.-H. Park, J. Brioude, W. M. Angevine, J. F. Karlik, and A. H. Goldstein
Atmos. Chem. Phys., 14, 5393–5413, https://doi.org/10.5194/acp-14-5393-2014, https://doi.org/10.5194/acp-14-5393-2014, 2014
D. R. Gentner, T. B. Ford, A. Guha, K. Boulanger, J. Brioude, W. M. Angevine, J. A. de Gouw, C. Warneke, J. B. Gilman, T. B. Ryerson, J. Peischl, S. Meinardi, D. R. Blake, E. Atlas, W. A. Lonneman, T. E. Kleindienst, M. R. Beaver, J. M. St. Clair, P. O. Wennberg, T. C. VandenBoer, M. Z. Markovic, J. G. Murphy, R. A. Harley, and A. H. Goldstein
Atmos. Chem. Phys., 14, 4955–4978, https://doi.org/10.5194/acp-14-4955-2014, https://doi.org/10.5194/acp-14-4955-2014, 2014
J. Brioude, D. Arnold, A. Stohl, M. Cassiani, D. Morton, P. Seibert, W. Angevine, S. Evan, A. Dingwell, J. D. Fast, R. C. Easter, I. Pisso, J. Burkhart, and G. Wotawa
Geosci. Model Dev., 6, 1889–1904, https://doi.org/10.5194/gmd-6-1889-2013, https://doi.org/10.5194/gmd-6-1889-2013, 2013
J. Brioude, W. M. Angevine, R. Ahmadov, S.-W. Kim, S. Evan, S. A. McKeen, E.-Y. Hsie, G. J. Frost, J. A. Neuman, I. B. Pollack, J. Peischl, T. B. Ryerson, J. Holloway, S. S. Brown, J. B. Nowak, J. M. Roberts, S. C. Wofsy, G. W. Santoni, T. Oda, and M. Trainer
Atmos. Chem. Phys., 13, 3661–3677, https://doi.org/10.5194/acp-13-3661-2013, https://doi.org/10.5194/acp-13-3661-2013, 2013
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Large air quality and climate models calculate different physical and chemical phenomena in separate operators within the overall model, some of which are computationally intensive. Machine learning tools can memorize the behavior of these operators and replace them, but the replacements must still obey physical laws, like conservation principles. This work derives a mathematical framework for machine learning replacements that conserves properties, such as mass or energy, to machine precision.
Paul D. Hamer, Sam-Erik Walker, Gabriela Sousa-Santos, Matthias Vogt, Dam Vo-Thanh, Susana Lopez-Aparicio, Philipp Schneider, Martin O. P. Ramacher, and Matthias Karl
Geosci. Model Dev., 13, 4323–4353, https://doi.org/10.5194/gmd-13-4323-2020, https://doi.org/10.5194/gmd-13-4323-2020, 2020
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EPISODE is an air quality model designed to give information on air pollution in cities down to distances measured in metres from the roadside and other pollution sources. We demonstrate that EPISODE can adequately describe nitrogen dioxide air pollution in a case study in six Norwegian cities. From this, we conclude that EPISODE can be used to provide air quality information to public bodies and society in order to help in the understanding and management of air pollution in urban environments.
Nicola Bodini, Julie K. Lundquist, and Mike Optis
Geosci. Model Dev., 13, 4271–4285, https://doi.org/10.5194/gmd-13-4271-2020, https://doi.org/10.5194/gmd-13-4271-2020, 2020
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While turbulence dissipation rate (ε) is an essential parameter for the prediction of wind speed, its current representation in weather prediction models is inaccurate, especially in complex terrain. In this study, we leverage the potential of machine-learning techniques to provide a more accurate representation of turbulence dissipation rate. Our results show a 30 % reduction in the average error compared to the current model representation of ε and a total elimination of its average bias.
Mario Mech, Maximilian Maahn, Stefan Kneifel, Davide Ori, Emiliano Orlandi, Pavlos Kollias, Vera Schemann, and Susanne Crewell
Geosci. Model Dev., 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, https://doi.org/10.5194/gmd-13-4229-2020, 2020
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The Passive and Active Microwave TRAnsfer tool (PAMTRA) is a public domain software package written in Python and Fortran for the simulation of microwave remote sensing observations. PAMTRA models the interaction of radiation with gases, clouds, precipitation, and the surface using either in situ observations or model output as input parameters. The wide range of applications is demonstrated for passive (radiometer) and active (radar) instruments on ground, airborne, and satellite platforms.
Shin-ichiro Shima, Yousuke Sato, Akihiro Hashimoto, and Ryohei Misumi
Geosci. Model Dev., 13, 4107–4157, https://doi.org/10.5194/gmd-13-4107-2020, https://doi.org/10.5194/gmd-13-4107-2020, 2020
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Using the super-droplet method, we constructed a detailed numerical model of mixed-phase clouds based on kinetic description and subsequently demonstrated that a large-eddy simulation of a cumulonimbus which predicts ice particle morphology without assuming ice categories or mass–dimension relationships is possible. Our results strongly support the particle-based modeling methodology’s efficacy for simulating mixed-phase clouds.
Swen Brands, Guillermo Fernández-García, Marta García Vivanco, Marcos Tesouro Montecelo, Nuria Gallego Fernández, Anthony David Saunders Estévez, Pablo Enrique Carracedo García, Anabela Neto Venâncio, Pedro Melo Da Costa, Paula Costa Tomé, Cristina Otero, María Luz Macho, and Juan Taboada
Geosci. Model Dev., 13, 3947–3973, https://doi.org/10.5194/gmd-13-3947-2020, https://doi.org/10.5194/gmd-13-3947-2020, 2020
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The capability of numerical models to predict air quality depends on many factors. Here, the role of the applied model resolution, emission configuration and model chemistry is assessed for the CHIMERE model and the northwestern Iberian Peninsula. Although heterogeneous results are obtained, the forecasts can be systematically improved by increasing the vertical resolution in the lower and middle troposphere. This finding might also apply to other regions with similar characteristics.
Xiao Lu, Lin Zhang, Tongwen Wu, Michael S. Long, Jun Wang, Daniel J. Jacob, Fang Zhang, Jie Zhang, Sebastian D. Eastham, Lu Hu, Lei Zhu, Xiong Liu, and Min Wei
Geosci. Model Dev., 13, 3817–3838, https://doi.org/10.5194/gmd-13-3817-2020, https://doi.org/10.5194/gmd-13-3817-2020, 2020
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This study presents the development and evaluation of a new climate chemistry model, BCC-GEOS-Chem v1.0, which couples the GEOS-Chem chemical transport model as an atmospheric chemistry component in the Beijing Climate Center atmospheric general circulation model. A 3-year (2012–2014) simulation of BCC-GEOS-Chem v1.0 shows that the model captures well the spatiotemporal distributions of tropospheric ozone, other gaseous pollutants, and aerosols.
Ilya Stanevich, Dylan B. A. Jones, Kimberly Strong, Robert J. Parker, Hartmut Boesch, Debra Wunch, Justus Notholt, Christof Petri, Thorsten Warneke, Ralf Sussmann, Matthias Schneider, Frank Hase, Rigel Kivi, Nicholas M. Deutscher, Voltaire A. Velazco, Kaley A. Walker, and Feng Deng
Geosci. Model Dev., 13, 3839–3862, https://doi.org/10.5194/gmd-13-3839-2020, https://doi.org/10.5194/gmd-13-3839-2020, 2020
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Systematic errors in atmospheric models pose a challenge for inverse modeling studies of methane (CH4) emissions. We evaluated the CH4 simulation in the GEOS-Chem model at the horizontal resolutions of 4° × 5° and 2° × 2.5°. Our analysis identified resolution-dependent biases in the model, which we attributed to discrepancies between the two model resolutions in vertical transport in the troposphere and in stratosphere–troposphere exchange.
Ross Noel Bannister
Geosci. Model Dev., 13, 3789–3816, https://doi.org/10.5194/gmd-13-3789-2020, https://doi.org/10.5194/gmd-13-3789-2020, 2020
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Forecasting models start from initial conditions, and data assimilation (DA) is the way that initial conditions are found from a combination of previous model data and latest observations. The ABC model is a simplified convective-scale model developed previously, and ABC-DA is the version of this system that includes the DA capability. This system is described in the present paper, and its performance is demonstrated with a range of options that control how the data assimilation is done.
Daisuke Goto, Yousuke Sato, Hisashi Yashiro, Kentaroh Suzuki, Eiji Oikawa, Rei Kudo, Takashi M. Nagao, and Teruyuki Nakajima
Geosci. Model Dev., 13, 3731–3768, https://doi.org/10.5194/gmd-13-3731-2020, https://doi.org/10.5194/gmd-13-3731-2020, 2020
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We executed a global aerosol model over 3 years with the finest grid size in the world. The results elucidated that global annual averages of parameters associated with the aerosols were generally comparable to those obtained from a low-resolution model (LRM), but spatiotemporal variabilities of the aerosol components and their associated parameters provided better results closer to the observations than those from the LRM. This study clarified the advantages of the high-resolution model.
Arman Pouyaei, Yunsoo Choi, Jia Jung, Bavand Sadeghi, and Chul Han Song
Geosci. Model Dev., 13, 3489–3505, https://doi.org/10.5194/gmd-13-3489-2020, https://doi.org/10.5194/gmd-13-3489-2020, 2020
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This paper introduces a novel Lagrangian model (Concentration Trajectory of Air pollution with an Integrated Lagrangian model, C-TRAIL) for showing the source and receptor areas by following polluted air masses. To investigate the concentrations and trajectories of air masses simultaneously, we use the trajectory-grid (TG) Lagrangian advection model. The TG model follows the concentrations of representative air
packetsof species along trajectories determined by the wind field.
Arianna Valmassoi, Jimy Dudhia, Silvana Di Sabatino, and Francesco Pilla
Geosci. Model Dev., 13, 3179–3201, https://doi.org/10.5194/gmd-13-3179-2020, https://doi.org/10.5194/gmd-13-3179-2020, 2020
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Irrigation affects the atmosphere and models are required to understand its full impact. However, there is no agreed procedure to describe irrigation within regional models. The present study introduces three new methods to integrate this process into the models and validates it for the Po Valley in northern Italy. All the tests done show that the results are improved with the new irrigation techniques when compared against some measures (e.g., temperature, potential evapotranspiration).
Dai Koshin, Kaoru Sato, Kazuyuki Miyazaki, and Shingo Watanabe
Geosci. Model Dev., 13, 3145–3177, https://doi.org/10.5194/gmd-13-3145-2020, https://doi.org/10.5194/gmd-13-3145-2020, 2020
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A new data assimilation system with a 4D local ensemble transform Kalman filter for the whole neutral atmosphere is developed using a T42L124 general circulation model. A conventional observation dataset and bias-corrected satellite temperature data are assimilated. After the improvements of the forecast model, the assimilation parameters are optimized. The minimum optimal number of ensembles is also examined. Results are evaluated using the reanalysis data and independent radar observations.
Dominik Fröhlich and Andreas Matzarakis
Geosci. Model Dev., 13, 3055–3065, https://doi.org/10.5194/gmd-13-3055-2020, https://doi.org/10.5194/gmd-13-3055-2020, 2020
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In the frame of the project "MOSAIK - Model-based city planning and application in climate change", a biometeorology module was implemented into the Parallelized Large-Eddy Simulation Model (PALM). It is comprised of methods for the estimation of three popular thermal indices for the assessment of human thermal comfort or thermal stress. Results of the new module are compared to those by the SkyHelios model as well as the version of the indices published in VDI guideline 3787, part 2.
Solène Turquety, Laurent Menut, Guillaume Siour, Sylvain Mailler, Juliette Hadji-Lazaro, Maya George, Cathy Clerbaux, Daniel Hurtmans, and Pierre-François Coheur
Geosci. Model Dev., 13, 2981–3009, https://doi.org/10.5194/gmd-13-2981-2020, https://doi.org/10.5194/gmd-13-2981-2020, 2020
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Biomass burning emissions are a major source of trace gases and aerosols that need to be accounted for in air quality assessment and forecasting. The APIFLAME model presented in this paper allows the calculation of these emissions based on merged satellite observations at hourly time steps and kilometer scales. Implementing emissions in a chemistry transport model allows realistic simulations of fire plumes as illustrated for wildfires in Portugal in August 2016 using the CHIMERE model.
Shunliu Zhao, Matthew G. Russell, Amir Hakami, Shannon L. Capps, Matthew D. Turner, Daven K. Henze, Peter B. Percell, Jaroslav Resler, Huizhong Shen, Armistead G. Russell, Athanasios Nenes, Amanda J. Pappin, Sergey L. Napelenok, Jesse O. Bash, Kathleen M. Fahey, Gregory R. Carmichael, Charles O. Stanier, and Tianfeng Chai
Geosci. Model Dev., 13, 2925–2944, https://doi.org/10.5194/gmd-13-2925-2020, https://doi.org/10.5194/gmd-13-2925-2020, 2020
Gan Luo, Fangqun Yu, and Jonathan M. Moch
Geosci. Model Dev., 13, 2879–2903, https://doi.org/10.5194/gmd-13-2879-2020, https://doi.org/10.5194/gmd-13-2879-2020, 2020
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This work improved pH calculation for cloud, rain, and wet surfaces, fraction of cloud available for aqueous-phase chemistry, rainout efficiencies for various types of cloud, empirical washout by rain and snow, and wet surface uptake in GEOS-Chem v12.6.0. We compared simulated mass concentrations of aerosol precursors and aerosols with surface monitoring networks, Arctic sites, and ATom observations, and showed that the model results with the updated wet processes agree better for most species.
Laura D. Fowler, Mary C. Barth, and Kiran Alapaty
Geosci. Model Dev., 13, 2851–2877, https://doi.org/10.5194/gmd-13-2851-2020, https://doi.org/10.5194/gmd-13-2851-2020, 2020
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The cloud liquid and ice water path and precipitation simulated with the Model for Prediction Across Scales are compared against satellite data over the tropical Pacific Ocean. Uniform and variable-resolution experiments using scale-aware convection schemes produce strong biases between simulated and observed diagnostics. Results underscore the importance of evaluating clouds, their optical properties, and radiation budget in addition to precipitation in mesh refinement global simulations.
Alessandro Cotronei and Thomas Slawig
Geosci. Model Dev., 13, 2783–2804, https://doi.org/10.5194/gmd-13-2783-2020, https://doi.org/10.5194/gmd-13-2783-2020, 2020
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We converted the radiation part of the atmospheric model ECHAM to single-precision arithmetic, using a step-by-step change in all modules. A small code portion still requires higher precision. The generated code can be easily changed from double to single precision and vice versa. The quality of the output of the single-precision version is comparable to observational data and the one of the original code. The runtime was reduced by 40 %, and the energy consumption could also be decreased.
Carlos A. F. Marques, Martinho Marta-Almeida, and José M. Castanheira
Geosci. Model Dev., 13, 2763–2781, https://doi.org/10.5194/gmd-13-2763-2020, https://doi.org/10.5194/gmd-13-2763-2020, 2020
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A free software package for the computation of the 3-D normal modes of an hydrostatic atmosphere is presented. This software performs the computations in isobaric coordinates and was developed for two user-friendly languages: MATLAB and Python. The software can be used to expand the global atmospheric circulation onto the 3-D normal modes.
Ingrid Super, Hugo A. C. Denier van der Gon, Michiel K. van der Molen, Stijn N. C. Dellaert, and Wouter Peters
Geosci. Model Dev., 13, 2695–2721, https://doi.org/10.5194/gmd-13-2695-2020, https://doi.org/10.5194/gmd-13-2695-2020, 2020
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Understanding urban CO2 fluxes is increasingly important to support emission reduction policies. In this work we extended an existing framework for emission verification to increase its suitability for urban areas and source-sector-based decision-making by using a dynamic emission model. We find that the dynamic emission model provides a better understanding of emissions at small scales and their uncertainties. By including co-emitted species, emission can be related to specific source sectors.
Fangqun Yu, Alexey B. Nadykto, Gan Luo, and Jason Herb
Geosci. Model Dev., 13, 2663–2670, https://doi.org/10.5194/gmd-13-2663-2020, https://doi.org/10.5194/gmd-13-2663-2020, 2020
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Secondary particles formed via nucleation have important implications for air quality and climate. Here we describe nucleation rate lookup tables for four different nucleation mechanisms that can be readily used in chemistry transport and climate models. The nucleation rates predicted have been assessed against state-of-the-art laboratory measurements. The lookup tables cover a wide range of key parameters controlling binary, ternary, and ion-mediated nucleation in the Earth's atmosphere.
Jessica M. Tomaszewski and Julie K. Lundquist
Geosci. Model Dev., 13, 2645–2662, https://doi.org/10.5194/gmd-13-2645-2020, https://doi.org/10.5194/gmd-13-2645-2020, 2020
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Wind farms can briefly impact the nearby environment by reducing wind speeds and mixing warmer air down to the surface. The wind farm parameterization (WFP) in the Weather Research and Forecasting (WRF) model is a tool that numerically simulates wind farms and these meteorological impacts. We highlight the importance of choice in model settings and find that sufficiently fine vertical and horizontal grids with turbine turbulence are needed to accurately simulate wind farm meteorological impacts.
Georgy Ayzel, Tobias Scheffer, and Maik Heistermann
Geosci. Model Dev., 13, 2631–2644, https://doi.org/10.5194/gmd-13-2631-2020, https://doi.org/10.5194/gmd-13-2631-2020, 2020
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In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting, which was trained to predict continuous precipitation intensities at a lead time of 5 min. RainNet significantly outperformed the benchmark models at all lead times up to 60 min. Yet, an undesirable property of RainNet predictions is the level of spatial smoothing. Obviously, RainNet learned an optimal level of smoothing to produce a nowcast at 5 min lead time.
Erik H. Hoffmann, Roland Schrödner, Andreas Tilgner, Ralf Wolke, and Hartmut Herrmann
Geosci. Model Dev., 13, 2587–2609, https://doi.org/10.5194/gmd-13-2587-2020, https://doi.org/10.5194/gmd-13-2587-2020, 2020
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A condensed multiphase halogen and DMS chemistry mechanism for application in chemical transport models has been developed and applied by 2D simulations to explore multiphase marine chemistry above the pristine open ocean. The model simulations have demonstrated the ability of the mechanism in studying aerosol cloud processing effects in the marine atmosphere. First 2D simulations have shown significant differences in the DMS processing under convective and stratiform cloud conditions.
Sam J. Silva, Colette L. Heald, and Alex B. Guenther
Geosci. Model Dev., 13, 2569–2585, https://doi.org/10.5194/gmd-13-2569-2020, https://doi.org/10.5194/gmd-13-2569-2020, 2020
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Simulating the influence of the biosphere on atmospheric chemistry has traditionally been computationally intensive. We describe a surrogate canopy physics model parameterized using a statistical learning technique and specifically designed for use in large-scale chemical transport models. Our surrogate model reproduces a more detailed model to within 10 % without a large computational demand, improving the process representation of biosphere–atmosphere exchange.
Guangzhi Xu, Xiaohui Ma, Ping Chang, and Lin Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-135, https://doi.org/10.5194/gmd-2020-135, 2020
Revised manuscript accepted for GMD
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We observed considerable limitations in existing atmospheric river (AR) detection methods, and looked into other disciplines for inspirations of tackling the AR detection problem. A new method is derived from an image-processing technique, and encodes the spatio-temporal scale information of AR systems, which is a key physical ingredient of ARs that is more stable than the vapor flux intensities, making it more suitable for climate scale studies when models often have different biases.
Paul-Arthur Monerie, Amulya Chevuturi, Peter Cook, Nick Klingaman, and Christopher E. Holloway
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-125, https://doi.org/10.5194/gmd-2020-125, 2020
Revised manuscript accepted for GMD
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In this study, we assess how increasing the horizontal resolution of HadGEM3-GC31 can allow simulating better tropical and subtropical South American precipitation. We compare simulations of HadGEM3-GC3.1, performed at three different horizontal resolutions. We show that increasing resolution allows decreasing precipitation biases over the Andes and north-east Brazil, and, improves the simulation of daily precipitation distribution.
Lu Shen, Daniel J. Jacob, Mauricio Santillana, Xuan Wang, and Wei Chen
Geosci. Model Dev., 13, 2475–2486, https://doi.org/10.5194/gmd-13-2475-2020, https://doi.org/10.5194/gmd-13-2475-2020, 2020
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Chemical mechanisms in air quality models tend to get more complicated with time, reflecting both increasing knowledge and the need for greater scope. This objectively improves the models but increases the computational burden. In this work, we present an approach that can reduce the computational cost of chemical integration by 30–40 % while maintaining an accuracy better than 1 %. It retains the complexity of the full mechanism where it is needed and preserves full diagnostic information.
Shelley C. van der Graaf, Richard Kranenburg, Arjo J. Segers, Martijn Schaap, and Jan Willem Erisman
Geosci. Model Dev., 13, 2451–2474, https://doi.org/10.5194/gmd-13-2451-2020, https://doi.org/10.5194/gmd-13-2451-2020, 2020
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Chemical transport models (CTMs) are important tools to determine the fate of reactive nitrogen (Nr) emissions. The parameterization of the surface–atmosphere exchange in CTMs is often only linked to fixed, land-use-dependent values. In this paper, we present an approach to derive more realistic, dynamic leaf area index (LAI) and roughness length (z0) input maps using multiple satellite products. We evaluate the effect on Nr concentration and deposition fields modelled in the LOTOS-EUROS CTM.
Michael Jähn, Gerrit Kuhlmann, Qing Mu, Jean-Matthieu Haussaire, David Ochsner, Katherine Osterried, Valentin Clément, and Dominik Brunner
Geosci. Model Dev., 13, 2379–2392, https://doi.org/10.5194/gmd-13-2379-2020, https://doi.org/10.5194/gmd-13-2379-2020, 2020
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Emission inventories of air pollutants and greenhouse gases are widely used as input for atmospheric chemistry transport models. However, the pre-processing of these data is both time-consuming and requires a large amount of disk storage. To overcome this issue, a Python package has been developed and tested for two different models. There, the inventory is projected to the model grid and scaling factors are provided. This approach saves computational time while remaining numerically equivalent.
Joe R. McNorton, Nicolas Bousserez, Anna Agustí-Panareda, Gianpaolo Balsamo, Margarita Choulga, Andrew Dawson, Richard Engelen, Zak Kipling, and Simon Lang
Geosci. Model Dev., 13, 2297–2313, https://doi.org/10.5194/gmd-13-2297-2020, https://doi.org/10.5194/gmd-13-2297-2020, 2020
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To infer carbon emissions from observations using atmospheric models, detailed knowledge of uncertainty is required. The uncertainties associated with models are often estimated because they are difficult to attribute. Here we use a state-of-the-art weather model to assess the impact of uncertainty in the wind fields on atmospheric concentrations of carbon dioxide. These results can be used to help quantify the uncertainty in estimated carbon emissions from atmospheric observations.
Axel Kleidon and Lee M. Miller
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-77, https://doi.org/10.5194/gmd-2020-77, 2020
Revised manuscript accepted for GMD
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When winds are used as renewable energy by more and more wind turbines, one needs to account for the effect of wind turbines on the atmospheric flow. The Kinetic Energy Budget of the Atmosphere (KEBA) model provides a simple, physics-based approach to account for this effect very well when compared to much more detailed numerical simulations with an atmospheric model. KEBA should be useful to derive lower, more realistic wind energy resource potentials of different regions.
Stephan Rasp
Geosci. Model Dev., 13, 2185–2196, https://doi.org/10.5194/gmd-13-2185-2020, https://doi.org/10.5194/gmd-13-2185-2020, 2020
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Subgrid parameterizations are largely responsible for uncertainties in climate models. Recently, several studies tried to improve the representation of subgrid processes by learning parameterization directly from high-resolution modeling data. In this paper, the current state of the art of this research direction is summarized, and an algorithm is proposed to combat major problems with existing approaches, namely instabilities and biases.
Li Pan, HyunCheol Kim, Pius Lee, Rick Saylor, YouHua Tang, Daniel Tong, Barry Baker, Shobha Kondragunta, Chuanyu Xu, Mark G. Ruminski, Weiwei Chen, Jeff Mcqueen, and Ivanka Stajner
Geosci. Model Dev., 13, 2169–2184, https://doi.org/10.5194/gmd-13-2169-2020, https://doi.org/10.5194/gmd-13-2169-2020, 2020
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Compared to anthropogenic emissions, emissions from wildfires are largely uncontrolled and unpredictable. Quantitatively describing wildfire emissions and their contributions to air pollution remains a substantial challenge for air quality forecasting efforts. In this study, we test the wildfire calculation algorithm used by the National Air Quality Forecasting Capability (NAQFC) by comparison with ground, satellite and flight measurements during the Southeast Nexus (SENEX) field experiment.
Isabella Capel-Timms, Stefán Thor Smith, Ting Sun, and Sue Grimmond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-52, https://doi.org/10.5194/gmd-2020-52, 2020
Revised manuscript accepted for GMD
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Thermal emissions or anthropogenic heat fluxes (QF) from human activities impact the local and larger scales urban climate. DASH considers both urban form and function in simulating QF by use of an agent-based structure that includes behavioural characteristics of city populations. This allows social practices to drive the calculation of QF as occupants move, varying by day type, demographic, location, activity, socio-economic factors and in response to environmental conditions.
Yi Zeng, Minghuai Wang, Chun Zhao, Siyu Chen, Zhoukun Liu, Xin Huang, and Yang Gao
Geosci. Model Dev., 13, 2125–2147, https://doi.org/10.5194/gmd-13-2125-2020, https://doi.org/10.5194/gmd-13-2125-2020, 2020
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Dust aerosol can impact many processes of the Earth system, but large uncertainties still remain in dust simulations. In this study, we investigated dust simulation sensitivity to two dust emission schemes and three dry deposition schemes using WRF-Chem. An optimal combination of dry deposition scheme and dust emission scheme has been identified to best simulate the dust storm in comparison with observation. Our results highlight the importance of dry deposition schemes for dust simulation.
Luke M. Western, Zhe Sha, Matthew Rigby, Anita L. Ganesan, Alistair J. Manning, Kieran M. Stanley, Simon J. O'Doherty, Dickon Young, and Jonathan Rougier
Geosci. Model Dev., 13, 2095–2107, https://doi.org/10.5194/gmd-13-2095-2020, https://doi.org/10.5194/gmd-13-2095-2020, 2020
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Assessments of greenhouse gas emissions using atmospheric measurements and meteorological models, or
top-downmethods, are important to verify national inventories or produce a stand-alone estimate where no inventory exists. We present a novel top-down method to estimate emissions. This approach uses a fast method called an integrated nested Laplacian approximation to estimate how these emissions are correlated with other emissions in different locations and at different times.
Jorge Baño-Medina, Rodrigo Manzanas, and José Manuel Gutiérrez
Geosci. Model Dev., 13, 2109–2124, https://doi.org/10.5194/gmd-13-2109-2020, https://doi.org/10.5194/gmd-13-2109-2020, 2020
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In this study we intercompare different deep learning topologies for statistical downscaling purposes. As compared to the top-ranked methods in the largest-to-date downscaling intercomparison study, our results better predict the local climate variability. Moreover, deep learning approaches can be suitably applied to large regions (e.g., continents), which can therefore foster the use of statistical downscaling in flagship initiatives such as CORDEX.
Laurent Menut, Guillaume Siour, Bertrand Bessagnet, Florian Couvidat, Emilie Journet, Yves Balkanski, and Karine Desboeufs
Geosci. Model Dev., 13, 2051–2071, https://doi.org/10.5194/gmd-13-2051-2020, https://doi.org/10.5194/gmd-13-2051-2020, 2020
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Modelling of mineral dust is often done using one single mean species. In this study, differentiated mineral species with their chemical composition are implemented in the CHIMERE regional chemistry-transport model by using global databases. Simulations are carried out to quantify the realism and gain of such mineralogy.
Stelios Myriokefalitakis, Nikos Daskalakis, Angelos Gkouvousis, Andreas Hilboll, Twan van Noije, Jason E. Williams, Philippe Le Sager, Vincent Huijnen, Sander Houweling, Tommi Bergman, Johann Rasmus Nüß, Mihalis Vrekoussis, Maria Kanakidou, and Maarten C. Krol
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-110, https://doi.org/10.5194/gmd-2020-110, 2020
Revised manuscript accepted for GMD
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This work documents and evaluates the detailed tropospheric gas-phase chemical mechanism MOGUNTIA in the three-dimensional chemistry transport model (CTM) TM5-MP. Moreover, the Rosenbrock solver, as generated by the KPP software, is implemented in the chemistry code, which can replace the classical Euler Backward Integration method. Overall, compared to other chemistry schemes in use in CTMs, the MOGUNTIA simulates a large suite of oxygenated VOCs, expanding the applications of the model.
Mike Bush, Tom Allen, Caroline Bain, Ian Boutle, John Edwards, Anke Finnenkoetter, Charmaine Franklin, Kirsty Hanley, Humphrey Lean, Adrian Lock, James Manners, Marion Mittermaier, Cyril Morcrette, Rachel North, Jon Petch, Chris Short, Simon Vosper, David Walters, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Nigel Wood, and Mohamed Zerroukat
Geosci. Model Dev., 13, 1999–2029, https://doi.org/10.5194/gmd-13-1999-2020, https://doi.org/10.5194/gmd-13-1999-2020, 2020
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In this paper we define the first Regional Atmosphere and Land (RAL) science configuration for kilometre-scale modelling using the Unified Model (UM) as the basis for the atmosphere and the Joint UK Land Environment Simulator (JULES) for the land. RAL1 defines the science configuration of the dynamics and physics schemes of the atmosphere and land. This configuration will provide a model baseline for any future weather or climate model developments to be described against.
Mariko Oue, Aleksandra Tatarevic, Pavlos Kollias, Dié Wang, Kwangmin Yu, and Andrew M. Vogelmann
Geosci. Model Dev., 13, 1975–1998, https://doi.org/10.5194/gmd-13-1975-2020, https://doi.org/10.5194/gmd-13-1975-2020, 2020
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We developed the Cloud-resolving model Radar SIMulator (CR-SIM) capable of apples-to-apples comparisons between the multiwavelength, zenith-pointing, and scanning radar and multi-remote-sensing (radar and lidar) observations and the high-resolution atmospheric model output. Applications of CR-SIM as a virtual observatory operator aid interpretation of the differences and improve understanding of the representativeness errors due to the sampling limitations of the ground-based measurements.
Thomas Schwitalla, Kirsten Warrach-Sagi, Volker Wulfmeyer, and Michael Resch
Geosci. Model Dev., 13, 1959–1974, https://doi.org/10.5194/gmd-13-1959-2020, https://doi.org/10.5194/gmd-13-1959-2020, 2020
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Performing seasonal simulations on horizontal grid resolutions of a few kilometres over the entire globe remains challenging. We demonstrate the added value of simulating large-scale patterns and feedbacks at 3 km resolution compared to a coarser-resolution forecast using the WRF numerical weather model on a latitude-belt domain. Results show an improvement of cloud coverage in the tropics, better representation of teleconnection, and improvements of precipitation patterns in different regions.
Mathieu Lachatre, Sylvain Mailler, Laurent Menut, Solène Turquety, Pasquale Sellitto, Henda Guermazi, Giuseppe Salerno, Tommaso Caltabiano, and Elisa Carboni
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-62, https://doi.org/10.5194/gmd-2020-62, 2020
Revised manuscript accepted for GMD
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Excessive numerical diffusion is a major limitations in the representation of long-range transport atmospheric models. In the present study, we focus on excessive diffusion in the vertical direction. We explore three possible ways to address this problem: increase vertical resolution, use an advection scheme with antidiffusive properties, and represent more accurately the vertical wind. This study focused on a particular volcanic eruption event to improve atmospheric transport modelling.
Anna-Leah Nickl, Mariano Mertens, Anke Roiger, Andreas Fix, Axel Amediek, Alina Fiehn, Christoph Gerbig, Michal Galkowski, Astrid Kerkweg, Theresa Klausner, Maximilian Eckl, and Patrick Jöckel
Geosci. Model Dev., 13, 1925–1943, https://doi.org/10.5194/gmd-13-1925-2020, https://doi.org/10.5194/gmd-13-1925-2020, 2020
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Based on the global and regional chemistry–climate model system MECO(n), we implemented a forecast system to support the planning of measurement campaign research flights with chemical weather forecasts. We applied this system for the first time to provide 6 d forecasts in support of the CoMet 1.0
campaign targeting methane emitted from coal mining ventilation shafts in the Upper Silesian Coal Basin in Poland. We describe the new forecast system and evaluate its forecast skill.
Rolf Zentek and Günther Heinemann
Geosci. Model Dev., 13, 1809–1825, https://doi.org/10.5194/gmd-13-1809-2020, https://doi.org/10.5194/gmd-13-1809-2020, 2020
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We used the climate model CCLM to simulate the Weddell Sea region with a resolutions of 15 and 5 km. By adjusting the turbulence parametrization a warm bias over the Antarctic Plateau was removed. For sea ice we found a temperature bias around +/-1 K and a wind speed bias of 1 m s-1. Comparisons of radio soundings showed a bias around zero and a RMSE of 1–2 K for temperature and 3–4 m s-1 for wind speed. Comparison with wind Doppler lidar yielded almost no bias and a RMSE of ca. 2 m s-1.
Matthieu Pommier, Hilde Fagerli, Michael Schulz, Alvaro Valdebenito, Richard Kranenburg, and Martijn Schaap
Geosci. Model Dev., 13, 1787–1807, https://doi.org/10.5194/gmd-13-1787-2020, https://doi.org/10.5194/gmd-13-1787-2020, 2020
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The EMEP and LOTOS-EUROS models comprise the operational source contribution prediction system for the European cities within the Copernicus Atmosphere Monitoring Service (CAMS). This study presents a first evaluation of this system, with hourly resolution, by focusing on one PM10 episode in December 2016, dominated by the influence of domestic emissions. It shows that the system provides valuable information on the composition and contributions of different countries to PM10.
Martin Dörenkämper, Bjarke T. Olsen, Björn Witha, Andrea N. Hahmann, Neil N. Davis, Jordi Barcons, Yasemin Ezber, Elena García-Bustamante, J. Fidel González-Rouco, Jorge Navarro, Mariano Sastre-Marugán, Tija Sīle, Wilke Trei, Mark Žagar, Jake Badger, Julia Gottschall, Javier Sanz Rodrigo, and Jakob Mann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-23, https://doi.org/10.5194/gmd-2020-23, 2020
Revised manuscript accepted for GMD
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This is the second of two papers that documents the creation of the New European Wind Atlas (NEWA). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulations and the microscale downscaling for generating the climatology. A comprehensive evaluation of each component of the NEWA model chain is presented using observations from a large set of tall masts located all over Europe.
Scot M. Miller, Arvind K. Saibaba, Michael E. Trudeau, Marikate E. Mountain, and Arlyn E. Andrews
Geosci. Model Dev., 13, 1771–1785, https://doi.org/10.5194/gmd-13-1771-2020, https://doi.org/10.5194/gmd-13-1771-2020, 2020
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New observations of greenhouse gases from satellites and aircraft provide an unprecedented window into global carbon sources and sinks. However, these new datasets also present enormous computational challenges due to the sheer number of observations. In this article, we discuss the challenges of estimating greenhouse gas source and sinks using very large atmospheric datasets and evaluate several strategies for overcoming these challenges.
Cited articles
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The Lagrangian particle dispersion model FLEXPART-AROME was built to study air mass transport around La Réunion, a volcanic island in the southwest Indian Ocean. To harmonize turbulent transport between the numerical weather prediction model and the transport model, turbulent kinetic energy from AROME is directly used in FLEXPART-AROME using discrete interfaces between different turbulent regions. An adaptive time step was implemented to satisfy physical constraints on turbulent transport.
The Lagrangian particle dispersion model FLEXPART-AROME was built to study air mass transport...
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