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Geoscientific Model Development
An interactive open-access journal of the European Geosciences Union
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GMD | Articles | Volume 11, issue 3
Geosci. Model Dev., 11, 1093–1113, 2018
https://doi.org/10.5194/gmd-11-1093-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-1093-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 11, 1093–1113, 2018
https://doi.org/10.5194/gmd-11-1093-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-1093-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Development and technical paper 27 Mar 2018
Development and technical paper | 27 Mar 2018
Optimizing UV Index determination from broadband irradiances
Keith A. Tereszchuk et al.
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Atmos. Chem. Phys., 20, 5591–5607, https://doi.org/10.5194/acp-20-5591-2020, https://doi.org/10.5194/acp-20-5591-2020, 2020
Xiaoyi Zhao, Debora Griffin, Vitali Fioletov, Chris McLinden, Alexander Cede, Martin Tiefengraber, Moritz Müller, Kristof Bognar, Kimberly Strong, Folkert Boersma, Henk Eskes, Jonathan Davies, Akira Ogyu, and Sum Chi Lee
Atmos. Meas. Tech., 13, 2131–2159, https://doi.org/10.5194/amt-13-2131-2020, https://doi.org/10.5194/amt-13-2131-2020, 2020
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Pandora NO2 measurements made at three sites located in the Toronto area are used to evaluate the TROPOspheric Monitoring Instrument (TROPOMI) NO2 data products, including standard NO2 and research data developed using a high-resolution regional air quality forecast model. TROPOMI pixels located upwind and downwind from the Pandora sites were analyzed by a new wind-based validation method, which revealed the spatial patterns of local and transported emissions and regional air quality changes.
Debora Griffin, Christopher Sioris, Jack Chen, Nolan Dickson, Andrew Kovachik, Martin de Graaf, Swadhin Nanda, Pepijn Veefkind, Enrico Dammers, Chris A. McLinden, Paul Makar, and Ayodeji Akingunola
Atmos. Meas. Tech., 13, 1427–1445, https://doi.org/10.5194/amt-13-1427-2020, https://doi.org/10.5194/amt-13-1427-2020, 2020
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Zoë Y. W. Davis, Udo Frieß, Kevin B. Strawbridge, Monika Aggarwaal, Sabour Baray, Elijah G. Schnitzler, Akshay Lobo, Vitali E. Fioletov, Ihab Abboud, Chris A. McLinden, Jim Whiteway, Megan D. Willis, Alex K. Y. Lee, Jeff Brook, Jason Olfert, Jason O'Brien, Ralf Staebler, Hans D. Osthoff, Cristian Mihele, and Robert McLaren
Atmos. Meas. Tech., 13, 1129–1155, https://doi.org/10.5194/amt-13-1129-2020, https://doi.org/10.5194/amt-13-1129-2020, 2020
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Mark W. Shephard, Enrico Dammers, Karen E. Cady-Pereira, Shailesh K. Kharol, Jesse Thompson, Yonatan Gainariu-Matz, Junhua Zhang, Chris A. McLinden, Andrew Kovachik, Michael Moran, Shabtai Bittman, Christopher E. Sioris, Debora Griffin, Matthew J. Alvarado, Chantelle Lonsdale, Verica Savic-Jovcic, and Qiong Zheng
Atmos. Chem. Phys., 20, 2277–2302, https://doi.org/10.5194/acp-20-2277-2020, https://doi.org/10.5194/acp-20-2277-2020, 2020
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Atmos. Chem. Phys., 20, 99–116, https://doi.org/10.5194/acp-20-99-2020, https://doi.org/10.5194/acp-20-99-2020, 2020
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Atmos. Chem. Phys., 19, 12261–12293, https://doi.org/10.5194/acp-19-12261-2019, https://doi.org/10.5194/acp-19-12261-2019, 2019
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Yves J. Rochon, Michael Sitwell, and Young-Min Cho
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Cristen Adams, Chris A. McLinden, Mark W. Shephard, Nolan Dickson, Enrico Dammers, Jack Chen, Paul Makar, Karen E. Cady-Pereira, Naomi Tam, Shailesh K. Kharol, Lok N. Lamsal, and Nickolay A. Krotkov
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Fei Liu, Sungyeon Choi, Can Li, Vitali E. Fioletov, Chris A. McLinden, Joanna Joiner, Nickolay A. Krotkov, Huisheng Bian, Greet Janssens-Maenhout, Anton S. Darmenov, and Arlindo M. da Silva
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Sulfur dioxide measurements from space have been used to detect emissions from large sources. We developed a new emission inventory by combining the satellite-based emission estimates and the conventional bottom-up inventory for smaller sources. The new inventory improves the model agreement with in situ observations and offers the possibility of rapid updates to emissions.
Jeffrey A. Geddes, Randall V. Martin, Eric J. Bucsela, Chris A. McLinden, and Daniel J. M. Cunningham
Atmos. Meas. Tech., 11, 6271–6287, https://doi.org/10.5194/amt-11-6271-2018, https://doi.org/10.5194/amt-11-6271-2018, 2018
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Matthew J. Cooper, Randall V. Martin, Alexei I. Lyapustin, and Chris A. McLinden
Atmos. Meas. Tech., 11, 2983–2994, https://doi.org/10.5194/amt-11-2983-2018, https://doi.org/10.5194/amt-11-2983-2018, 2018
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To accurately infer air pollutant concentrations from satellite observations, we must first know the reflectivity of the Earth’s surface. Using a model, we show that satellite observations are better able to observe NO2 near the surface if snow is present. However, knowing when snow is present is difficult due to its variability. We test seven existing snow cover data sets to assess their ability to inform future satellite observations and find that the IMS data set is best suited for this task.
Pieternel F. Levelt, Joanna Joiner, Johanna Tamminen, J. Pepijn Veefkind, Pawan K. Bhartia, Deborah C. Stein Zweers, Bryan N. Duncan, David G. Streets, Henk Eskes, Ronald van der A, Chris McLinden, Vitali Fioletov, Simon Carn, Jos de Laat, Matthew DeLand, Sergey Marchenko, Richard McPeters, Jerald Ziemke, Dejian Fu, Xiong Liu, Kenneth Pickering, Arnoud Apituley, Gonzalo González Abad, Antti Arola, Folkert Boersma, Christopher Chan Miller, Kelly Chance, Martin de Graaf, Janne Hakkarainen, Seppo Hassinen, Iolanda Ialongo, Quintus Kleipool, Nickolay Krotkov, Can Li, Lok Lamsal, Paul Newman, Caroline Nowlan, Raid Suleiman, Lieuwe Gijsbert Tilstra, Omar Torres, Huiqun Wang, and Krzysztof Wargan
Atmos. Chem. Phys., 18, 5699–5745, https://doi.org/10.5194/acp-18-5699-2018, https://doi.org/10.5194/acp-18-5699-2018, 2018
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The aim of this paper is to highlight the many successes of the Ozone Monitoring Instrument (OMI) spanning more than 13 years. Data from OMI have been used in a wide range of applications. Due to its unprecedented spatial resolution, in combination with daily global coverage, OMI plays a unique role in measuring trace gases important for the ozone layer, air quality, and climate change. OMI data continue to be used for new research and applications.
Adam E. Bourassa, Chris Z. Roth, Daniel J. Zawada, Landon A. Rieger, Chris A. McLinden, and Douglas A. Degenstein
Atmos. Meas. Tech., 11, 489–498, https://doi.org/10.5194/amt-11-489-2018, https://doi.org/10.5194/amt-11-489-2018, 2018
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OSIRIS satellite measurements of ozone in the stratosphere are corrected for slowly varying errors. These changes make the OSIRIS data compare better with other satellite measurements over the long term and make an impact on our understanding of the recovery of the ozone layer.
Cristen Adams, Adam E. Bourassa, Chris A. McLinden, Chris E. Sioris, Thomas von Clarmann, Bernd Funke, Landon A. Rieger, and Douglas A. Degenstein
Atmos. Chem. Phys., 17, 8063–8080, https://doi.org/10.5194/acp-17-8063-2017, https://doi.org/10.5194/acp-17-8063-2017, 2017
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We measured the relationship between volcanic aerosol and trace gases in the stratosphere using the OSIRIS and MIPAS satellite instruments between 2002 and 2014. We found that levels of stratospheric NO2 and N2O5 both decreased significantly in the presence of volcanic aerosol. These decreases were consistent with the modeling results.
Shailesh K. Kharol, Chris A. McLinden, Christopher E. Sioris, Mark W. Shephard, Vitali Fioletov, Aaron van Donkelaar, Sajeev Philip, and Randall V. Martin
Atmos. Chem. Phys., 17, 5921–5929, https://doi.org/10.5194/acp-17-5921-2017, https://doi.org/10.5194/acp-17-5921-2017, 2017
Yan Zhang, Can Li, Nickolay A. Krotkov, Joanna Joiner, Vitali Fioletov, and Chris McLinden
Atmos. Meas. Tech., 10, 1495–1509, https://doi.org/10.5194/amt-10-1495-2017, https://doi.org/10.5194/amt-10-1495-2017, 2017
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In this study, we demonstrate a very good consistency of the SO2 retrievals from OMI and OMPS using our state-of-the-art principal component analysis technique. Four full years of OMI and OMPS SO2 retrievals, during 2012–2015 have been analyzed over some of the world’s most polluted regions: eastern China, Mexico, and South Africa. The consistency of retrievals between OMI and OMPS make it possible to continue the long-term global SO2 pollution monitoring.
Christopher E. Sioris, Landon A. Rieger, Nicholas D. Lloyd, Adam E. Bourassa, Chris Z. Roth, Douglas A. Degenstein, Claude Camy-Peyret, Klaus Pfeilsticker, Gwenaël Berthet, Valéry Catoire, Florence Goutail, Jean-Pierre Pommereau, and Chris A. McLinden
Atmos. Meas. Tech., 10, 1155–1168, https://doi.org/10.5194/amt-10-1155-2017, https://doi.org/10.5194/amt-10-1155-2017, 2017
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A new OSIRIS NO2 retrieval algorithm is described and validated using > 40 balloon-based profile measurements. The validation results indicate a slight improvement relative to the existing operational algorithm in terms of the bias versus the balloon data, particularly in the lower stratosphere. The implication is that this new algorithm should replace the operational one. The motivation was to combine spectral fitting and the SaskTRAN radiative transfer model to achieve an improved product.
Christopher E. Sioris, Chris A. McLinden, Mark W. Shephard, Vitali E. Fioletov, and Ihab Abboud
Atmos. Chem. Phys., 17, 1931–1943, https://doi.org/10.5194/acp-17-1931-2017, https://doi.org/10.5194/acp-17-1931-2017, 2017
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The contribution of the oil sands region to the local aerosol optical depth (AOD) is sought. Satellite data are used since they provide spatial coverage over many years. Satellites measure AOD with high correlation and small biases relative to coincident AERONET AODs. Trends are determined using annual mean AODs, and an increasing trend is found near the Shell mines. Spatially variable and high surface albedo is challenging for some sensors. Measuring polarization appears to be an asset.
Patrick E. Sheese, Kaley A. Walker, Chris D. Boone, Chris A. McLinden, Peter F. Bernath, Adam E. Bourassa, John P. Burrows, Doug A. Degenstein, Bernd Funke, Didier Fussen, Gloria L. Manney, C. Thomas McElroy, Donal Murtagh, Cora E. Randall, Piera Raspollini, Alexei Rozanov, James M. Russell III, Makoto Suzuki, Masato Shiotani, Joachim Urban, Thomas von Clarmann, and Joseph M. Zawodny
Atmos. Meas. Tech., 9, 5781–5810, https://doi.org/10.5194/amt-9-5781-2016, https://doi.org/10.5194/amt-9-5781-2016, 2016
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This study validates version 3.5 of the ACE-FTS NOy species data sets by comparing diurnally scaled ACE-FTS data to correlative data from 11 other satellite limb sounders. For all five species examined (NO, NO2, HNO3, N2O5, and ClONO2), there is good agreement between ACE-FTS and the other data sets in various regions of the atmosphere. In these validated regions, these NOy data products can be used for further investigation into the composition, dynamics, and climate of the stratosphere.
Vitali E. Fioletov, Chris A. McLinden, Nickolay Krotkov, Can Li, Joanna Joiner, Nicolas Theys, Simon Carn, and Mike D. Moran
Atmos. Chem. Phys., 16, 11497–11519, https://doi.org/10.5194/acp-16-11497-2016, https://doi.org/10.5194/acp-16-11497-2016, 2016
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We introduce the first space-based catalogue of SO2 emission sources seen by OMI. The inventory contains about 500 sources. They account for about a half of all SO2 emissions; the remaining half is likely related to sources emitting less than 30 kt yr−1 and not detected by OMI. The sources are grouped by type (volcanoes, power plants, oil- and gas-related sources, and smelters) and country. The catalogue presented herein can be used for verification of available SO2 emission inventories.
Cristen Adams, Elise N. Normand, Chris A. McLinden, Adam E. Bourassa, Nicholas D. Lloyd, Douglas A. Degenstein, Nickolay A. Krotkov, Maria Belmonte Rivas, K. Folkert Boersma, and Henk Eskes
Atmos. Meas. Tech., 9, 4103–4122, https://doi.org/10.5194/amt-9-4103-2016, https://doi.org/10.5194/amt-9-4103-2016, 2016
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A new "OMI-minus-OSIRIS" (OmO) prototype dataset for tropospheric NO2 was created by combining information from the OMI satellite instrument, which is sensitive to NO2 in both the troposphere and stratosphere, with information from the OSIRIS satellite instrument, which measures NO2 in the stratosphere. This paper demonstrates that this approach is feasible and could be applied to future geostationary missions.
Vitali E. Fioletov, Chris A. McLinden, Alexander Cede, Jonathan Davies, Cristian Mihele, Stoyka Netcheva, Shao-Meng Li, and Jason O'Brien
Atmos. Meas. Tech., 9, 2961–2976, https://doi.org/10.5194/amt-9-2961-2016, https://doi.org/10.5194/amt-9-2961-2016, 2016
Nickolay A. Krotkov, Chris A. McLinden, Can Li, Lok N. Lamsal, Edward A. Celarier, Sergey V. Marchenko, William H. Swartz, Eric J. Bucsela, Joanna Joiner, Bryan N. Duncan, K. Folkert Boersma, J. Pepijn Veefkind, Pieternel F. Levelt, Vitali E. Fioletov, Russell R. Dickerson, Hao He, Zifeng Lu, and David G. Streets
Atmos. Chem. Phys., 16, 4605–4629, https://doi.org/10.5194/acp-16-4605-2016, https://doi.org/10.5194/acp-16-4605-2016, 2016
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We examine changes in SO2 and NO2 over the world's most polluted regions during the first decade of Aura OMI observations. Over the eastern US, both NO2 and SO2 levels decreased by 40 % and 80 %, respectively. OMI confirmed large reductions in SO2 over eastern Europe's largest coal power plants. The North China Plain has the world's most severe SO2 pollution, but a decreasing trend been observed since 2011, with a 50 % reduction in 2012–2014. India's SO2 and NO2 levels are growing at a fast pace.
M. W. Shephard, C. A. McLinden, K. E. Cady-Pereira, M. Luo, S. G. Moussa, A. Leithead, J. Liggio, R. M. Staebler, A. Akingunola, P. Makar, P. Lehr, J. Zhang, D. K. Henze, D. B. Millet, J. O. Bash, L. Zhu, K. C. Wells, S. L. Capps, S. Chaliyakunnel, M. Gordon, K. Hayden, J. R. Brook, M. Wolde, and S.-M. Li
Atmos. Meas. Tech., 8, 5189–5211, https://doi.org/10.5194/amt-8-5189-2015, https://doi.org/10.5194/amt-8-5189-2015, 2015
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This study provides direct validations of Tropospheric Emission Spectrometer (TES) satellite retrieved profiles against coincident aircraft profiles of carbon monoxide, ammonia, methanol, and formic acid, all of which are of interest for air quality. The comparisons are performed over the Canadian oil sands region during an intensive field campaign in support of the Joint Canada-Alberta Implementation Plan for the Oil Sands Monitoring (JOSM). Initial model evaluations are also provided.
V. Proschek, G. Kirchengast, S. Schweitzer, J. S. A. Brooke, P. F. Bernath, C. B. Thomas, J.-G. Wang, K. A. Tereszchuk, G. González Abad, R. J. Hargreaves, C. A. Beale, J. J. Harrison, P. A. Martin, V. L. Kasyutich, C. Gerbig, O. Kolle, and A. Loescher
Atmos. Meas. Tech., 8, 3315–3336, https://doi.org/10.5194/amt-8-3315-2015, https://doi.org/10.5194/amt-8-3315-2015, 2015
A. E. Bourassa, D. A. Degenstein, W. J. Randel, J. M. Zawodny, E. Kyrölä, C. A. McLinden, C. E. Sioris, and C. Z. Roth
Atmos. Chem. Phys., 14, 6983–6994, https://doi.org/10.5194/acp-14-6983-2014, https://doi.org/10.5194/acp-14-6983-2014, 2014
C. A. McLinden, V. Fioletov, K. F. Boersma, S. K. Kharol, N. Krotkov, L. Lamsal, P. A. Makar, R. V. Martin, J. P. Veefkind, and K. Yang
Atmos. Chem. Phys., 14, 3637–3656, https://doi.org/10.5194/acp-14-3637-2014, https://doi.org/10.5194/acp-14-3637-2014, 2014
C. E. Sioris, C. A. McLinden, V. E. Fioletov, C. Adams, J. M. Zawodny, A. E. Bourassa, C. Z. Roth, and D. A. Degenstein
Atmos. Chem. Phys., 14, 3479–3496, https://doi.org/10.5194/acp-14-3479-2014, https://doi.org/10.5194/acp-14-3479-2014, 2014
C. Adams, A. E. Bourassa, V. Sofieva, L. Froidevaux, C. A. McLinden, D. Hubert, J.-C. Lambert, C. E. Sioris, and D. A. Degenstein
Atmos. Meas. Tech., 7, 49–64, https://doi.org/10.5194/amt-7-49-2014, https://doi.org/10.5194/amt-7-49-2014, 2014
J. Liu, D. W. Tarasick, V. E. Fioletov, C. McLinden, T. Zhao, S. Gong, C. Sioris, J. J. Jin, G. Liu, and O. Moeini
Atmos. Chem. Phys., 13, 11441–11464, https://doi.org/10.5194/acp-13-11441-2013, https://doi.org/10.5194/acp-13-11441-2013, 2013
D. Griffin, K. A. Walker, J. E. Franklin, M. Parrington, C. Whaley, J. Hopper, J. R. Drummond, P. I. Palmer, K. Strong, T. J. Duck, I. Abboud, P. F. Bernath, C. Clerbaux, P.-F. Coheur, K. R. Curry, L. Dan, E. Hyer, J. Kliever, G. Lesins, M. Maurice, A. Saha, K. Tereszchuk, and D. Weaver
Atmos. Chem. Phys., 13, 10227–10241, https://doi.org/10.5194/acp-13-10227-2013, https://doi.org/10.5194/acp-13-10227-2013, 2013
P. I. Palmer, M. Parrington, J. D. Lee, A. C. Lewis, A. R. Rickard, P. F. Bernath, T. J. Duck, D. L. Waugh, D. W. Tarasick, S. Andrews, E. Aruffo, L. J. Bailey, E. Barrett, S. J.-B. Bauguitte, K. R. Curry, P. Di Carlo, L. Chisholm, L. Dan, G. Forster, J. E. Franklin, M. D. Gibson, D. Griffin, D. Helmig, J. R. Hopkins, J. T. Hopper, M. E. Jenkin, D. Kindred, J. Kliever, M. Le Breton, S. Matthiesen, M. Maurice, S. Moller, D. P. Moore, D. E. Oram, S. J. O'Shea, R. C. Owen, C. M. L. S. Pagniello, S. Pawson, C. J. Percival, J. R. Pierce, S. Punjabi, R. M. Purvis, J. J. Remedios, K. M. Rotermund, K. M. Sakamoto, A. M. da Silva, K. B. Strawbridge, K. Strong, J. Taylor, R. Trigwell, K. A. Tereszchuk, K. A. Walker, D. Weaver, C. Whaley, and J. C. Young
Atmos. Chem. Phys., 13, 6239–6261, https://doi.org/10.5194/acp-13-6239-2013, https://doi.org/10.5194/acp-13-6239-2013, 2013
K. A. Tereszchuk, D. P. Moore, J. J. Harrison, C. D. Boone, M. Park, J. J. Remedios, W. J. Randel, and P. F. Bernath
Atmos. Chem. Phys., 13, 5601–5613, https://doi.org/10.5194/acp-13-5601-2013, https://doi.org/10.5194/acp-13-5601-2013, 2013
C. Adams, A. E. Bourassa, A. F. Bathgate, C. A. McLinden, N. D. Lloyd, C. Z. Roth, E. J. Llewellyn, J. M. Zawodny, D. E. Flittner, G. L. Manney, W. H. Daffer, and D. A. Degenstein
Atmos. Meas. Tech., 6, 1447–1459, https://doi.org/10.5194/amt-6-1447-2013, https://doi.org/10.5194/amt-6-1447-2013, 2013
K. A. Tereszchuk, G. González Abad, C. Clerbaux, J. Hadji-Lazaro, D. Hurtmans, P.-F. Coheur, and P. F. Bernath
Atmos. Chem. Phys., 13, 4529–4541, https://doi.org/10.5194/acp-13-4529-2013, https://doi.org/10.5194/acp-13-4529-2013, 2013
R. A. Stachnik, L. Millán, R. Jarnot, R. Monroe, C. McLinden, S. Kühl, J. Puķīte, M. Shiotani, M. Suzuki, Y. Kasai, F. Goutail, J. P. Pommereau, M. Dorf, and K. Pfeilsticker
Atmos. Chem. Phys., 13, 3307–3319, https://doi.org/10.5194/acp-13-3307-2013, https://doi.org/10.5194/acp-13-3307-2013, 2013
C. Adams, K. Strong, X. Zhao, A. E. Bourassa, W. H. Daffer, D. Degenstein, J. R. Drummond, E. E. Farahani, A. Fraser, N. D. Lloyd, G. L. Manney, C. A. McLinden, M. Rex, C. Roth, S. E. Strahan, K. A. Walker, and I. Wohltmann
Atmos. Chem. Phys., 13, 611–624, https://doi.org/10.5194/acp-13-611-2013, https://doi.org/10.5194/acp-13-611-2013, 2013
Related subject area
Atmospheric Sciences
Dynamic Anthropogenic activitieS impacting Heat emissions (DASH v1.0): development and evaluation
Role of atmospheric horizontal resolution in simulating tropical and subtropical South American precipitation in HadGEM3-GC31
Image-processing-based atmospheric river tracking method version 1 (IPART-1)
Simulating the forest fire plume dispersion, chemistry, and aerosol formation using SAM-ASP version 1.0
A mass- and energy-conserving framework for using machine learning to speed computations: a photochemistry example
The urban dispersion model EPISODE v10.0 – Part 1: An Eulerian and sub-grid-scale air quality model and its application in Nordic winter conditions
Can machine learning improve the model representation of turbulent kinetic energy dissipation rate in the boundary layer for complex terrain?
PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere
Predicting the morphology of ice particles in deep convection using the super-droplet method: development and evaluation of SCALE-SDM 0.2.5-2.2.0, -2.2.1, and -2.2.2
An exploratory performance assessment of the CHIMERE model (version 2017r4) for the northwestern Iberian Peninsula and the summer season
Development of the global atmospheric chemistry general circulation model BCC-GEOS-Chem v1.0: model description and evaluation
Characterizing model errors in chemical transport modeling of methane: impact of model resolution in versions v9-02 of GEOS-Chem and v35j of its adjoint model
The ABC-DA system (v1.4): a variational data assimilation system for convective-scale assimilation research with a study of the impact of a balance constraint
Global aerosol simulations using NICAM.16 on a 14 km grid spacing for a climate study: improved and remaining issues relative to a lower-resolution model
Concentration Trajectory Route of Air pollution with an Integrated Lagrangian model (C-TRAIL Model v1.0) derived from the Community Multiscale Air Quality Model (CMAQ Model v5.2)
Evaluation of three new surface irrigation parameterizations in the WRF-ARW v3.8.1 model: the Po Valley (Italy) case study
An ensemble Kalman filter data assimilation system for the whole neutral atmosphere
Calculating human thermal comfort and thermal stress in the PALM model system 6.0
APIFLAME v2.0 biomass burning emissions model: impact of refined input parameters on atmospheric concentration in Portugal in summer 2016
Multi-layer coupling between SURFEX-TEB-V9.0 and Meso-NH-v5.3 for modelling the urban climate of high-rise cities
A multiphase CMAQ version 5.0 adjoint
Further improvement of wet process treatments in GEOS-Chem v12.6.0: impact on global distributions of aerosols and aerosol precursors
Impact of scale-aware deep convection on the cloud liquid and ice water paths and precipitation using the Model for Prediction Across Scales (MPAS-v5.2)
Single-precision arithmetic in ECHAM radiation reduces runtime and energy consumption
TITAM (v1.0): Time Independent Tracking Algorithm for Medicanes
Modeling lightning observations from space-based platforms (CloudScat.jl 1.0)
Three-dimensional normal mode functions: open-access tools for their computation in isobaric coordinates (p-3DNMF.v1)
Optimizing a dynamic fossil fuel CO2 emission model with CTDAS (CarbonTracker Data Assimilation Shell, v1.0) for an urban area using atmospheric observations of CO2, CO, NOx, and SO2
H2SO4–H2O binary and H2SO4–H2O–NH3 ternary homogeneous and ion-mediated nucleation: lookup tables version 1.0 for 3-D modeling application
Simulated wind farm wake sensitivity to configuration choices in the Weather Research and Forecasting model version 3.8.1
RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting
Sensitivity of spatial aerosol particle distributions to the boundary conditions in the PALM model system 6.0
CAPRAM reduction towards an operational multiphase halogen and dimethyl sulfide chemistry treatment in the chemistry transport model COSMO-MUSCAT(5.04e)
Development of a reduced-complexity plant canopy physics surrogate model for use in chemical transport models: a case study with GEOS-Chem v12.3.0
An adaptive method for speeding up the numerical integration of chemical mechanisms in atmospheric chemistry models: application to GEOS-Chem version 12.0.0
Silicone v1.0.0: an open-source Python package for inferring missing emissions data for climate change research
Satellite-derived leaf area index and roughness length information for surface–atmosphere exchange modelling: a case study for reactive nitrogen deposition in north-western Europe using LOTOS-EUROS v2.0
An online emission module for atmospheric chemistry transport models: implementation in COSMO-GHG v5.6a and COSMO-ART v5.1-3.1
Prioritising the sources of pollution in European cities: do air quality modelling applications provide consistent responses?
Representing model uncertainty for global atmospheric CO2 flux inversions using ECMWF-IFS-46R1
The Kinetic Energy Budget of the Atmosphere (KEBA) model 1.0: A simple yet physical approach for estimating regional wind energy resource potentials that includes the kinetic energy removal effect by wind turbines
Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and Lorenz 96 case study (v1.0)
Effects of horizontal resolution and air-sea coupling on simulated moisture sources for regional East Asian precipitation
Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign
WRF-Chem v3.9 simulations of the East Asian dust storm in May 2017: modeling sensitivities to dust emission and dry deposition schemes
Bayesian spatio-temporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields
Configuration and intercomparison of deep learning neural models for statistical downscaling
Modelling the mineralogical composition and solubility of mineral dust in the Mediterranean area with CHIMERE 2017r4
Description and evaluation of a detailed gas-phase chemistry scheme in the TM5-MP global chemistry transport model (r112)
The first Met Office Unified Model–JULES Regional Atmosphere and Land configuration, RAL1
Isabella Capel-Timms, Stefán Thor Smith, Ting Sun, and Sue Grimmond
Geosci. Model Dev., 13, 4891–4924, https://doi.org/10.5194/gmd-13-4891-2020, https://doi.org/10.5194/gmd-13-4891-2020, 2020
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Thermal emissions or anthropogenic heat fluxes (QF) from human activities impact the local- and larger-scale 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, and socio-economic factors and in response to environmental conditions.
Paul-Arthur Monerie, Amulya Chevuturi, Peter Cook, Nicholas P. Klingaman, and Christopher E. Holloway
Geosci. Model Dev., 13, 4749–4771, https://doi.org/10.5194/gmd-13-4749-2020, https://doi.org/10.5194/gmd-13-4749-2020, 2020
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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 northeast Brazil and improves the simulation of daily precipitation distribution.
Guangzhi Xu, Xiaohui Ma, Ping Chang, and Lin Wang
Geosci. Model Dev., 13, 4639–4662, https://doi.org/10.5194/gmd-13-4639-2020, https://doi.org/10.5194/gmd-13-4639-2020, 2020
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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 spatiotemporal-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.
Chantelle R. Lonsdale, Matthew J. Alvarado, Anna L. Hodshire, Emily Ramnarine, and Jeffrey R. Pierce
Geosci. Model Dev., 13, 4579–4593, https://doi.org/10.5194/gmd-13-4579-2020, https://doi.org/10.5194/gmd-13-4579-2020, 2020
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The System for Atmospheric Modelling (SAM) has been coupled with the detailed gas/aerosol chemistry model, the Aerosol Simulation Program (ASP), to capture cross-plume concentration gradients as fire plumes evolve downwind. SAM-ASP v1.0 will lead to the development of parameterizations of near-source biomass burning chemistry that can be used to more accurately simulate biomass burning chemical and physical transformations of trace gases and aerosols within coarser chemical transport models.
Patrick Obin Sturm and Anthony S. Wexler
Geosci. Model Dev., 13, 4435–4442, https://doi.org/10.5194/gmd-13-4435-2020, https://doi.org/10.5194/gmd-13-4435-2020, 2020
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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.
Robert Schoetter, Yu Ting Kwok, Cécile de Munck, Kevin Ka Lun Lau, Wai Kin Wong, and Valéry Masson
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-190, https://doi.org/10.5194/gmd-2020-190, 2020
Revised manuscript accepted for GMD
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Cities change the meteorological conditions for example by increasing air temperature, which can negatively impact their inhabitants during heat waves. TEB was developed to calculate the meteorological conditions in low- and mid-rise cities since it interacts only with the lowest level of an atmospheric model. Here, a multi-layer coupling of TEB is introduced to enable modelling the urban climate of cities with many skyscrapers; the new version is tested for the high-rise city of Hong Kong.
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.
Enrique Pravia-Sarabia, Juan José Gómez-Navarro, Pedro Jiménez-Guerrero, and Juan Pedro Montávez
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-149, https://doi.org/10.5194/gmd-2020-149, 2020
Revised manuscript accepted for GMD
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This work shows TITAM, a time independent tracking algorithm specifically suited for Mediterranean tropical-like cyclones, often referred to as Medicanes. The new methodology developed has the capacity to track multiple simultaneous cyclones, the ability to track a Medicane in the presence of intense extratropical lows, the potential to separate the Medicane from other similar structures handling the intermittent loss of structure and managing the tilting of the axis.
Alejandro Luque, Francisco José Gordillo-Vázquez, Dongshuai Li, Alejandro Malagón-Romero, Francisco Javier Pérez-Invernón, Anthony Schmalzried, Sergio Soler, Olivier Chanrion, Matthias Heumesser, Torsten Neubert, Víctor Reglero, and Nikolai Østgaard
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-161, https://doi.org/10.5194/gmd-2020-161, 2020
Revised manuscript accepted for GMD
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Lightning flashes are often recorded from space-based platforms. Besides being valuable inputs to weather forecasting, these observations also enable research into fundamental questions in lightning physics. To exploit them it is essential to understand how light propagates from a lightning flash to a space-based observing instrument. Here we present an open-source software tool to model this process that extends previous works and overcomes some of their limitations.
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.
Mona Kurppa, Pontus Roldin, Jani Strömberg, Anna Balling, Sasu Karttunen, Heino Kuuluvainen, Jarkko V. Niemi, Liisa Pirjola, Topi Rönkkö, Hilkka Timonen, Antti Hellsten, and Leena Järvi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-163, https://doi.org/10.5194/gmd-2020-163, 2020
Revised manuscript accepted for GMD
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High-resolution modelling is needed to solve the aerosol concentrations in a complex urban area. Here the performance of an aerosol module within the PALM model to simulate the detailed horizontal and vertical distribution of aerosol particles is studies. Further, sensitivity to the meteorological and aerosol boundary conditions is assessed using both model and observation data. The horizontal distribution is sensitive to the wind speed and stability, and vertical to the wind direction.
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.
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.
Robin D. Lamboll, Zebedee R. J. Nicholls, Jarmo S. Kikstra, Malte Meinshausen, and Joeri Rogelj
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-138, https://doi.org/10.5194/gmd-2020-138, 2020
Revised manuscript accepted for GMD
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Many models project how human activity can lead to more or less climate change, but most of these models do not project all climate-relevant emissions, potentially biasing climate projections. This paper outlines a Python package called Silicone which can add missing emissions in a flexible yet high-throughput manner. It does this 'infilling' based on more complete literature projections. It facilitates a more complete understanding of the climate impact of alternative emission pathways.
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.
Bart Degraeuwe, Enrico Pisoni, and Philippe Thunis
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-90, https://doi.org/10.5194/gmd-2020-90, 2020
Revised manuscript accepted for GMD
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To take decisions on how to improve air quality, it is useful to identify the main sources of pollution for an area of interest. Often these sources of pollution are identified with complex models that, even if accurate, are time consuming and complex. In this work we use another approach, simplified models, to accomplish the same task. The results, computed with two different set of simplified models, show the main sources of pollution for selected cities, and the associated uncertainties.
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.
Liang Guo, Ruud J. van der Ent, Nicholas P. Klingaman, Marie-Estelle Demory, Pier Luigi Vidale, Andrew G. Turner, Claudia C. Stephan, and Amulya Chevuturi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-104, https://doi.org/10.5194/gmd-2020-104, 2020
Revised manuscript accepted for GMD
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Precipitation over East Asia simulated in Met Office Unified Model is compared with observations. Moisture sources of EA precipitation are traced using a moisture tracking model. Biases in moisture sources are linked to biases in precipitation. By using the tracking model, changes in moisture sources can be attributed to changes in SST, circulation and associated evaporation. This proves that the method used in this study is useful to identify biases that cause biases in regional precipitation.
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.
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.
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To reduce computational costs, ECCC's new method to calculate the UV Index involves scaling and weighting the irradiance contribution of four low-res UV broadbands currently produced by the GEM forecast model. A high-res irradiance spectrum was produced using Cloud-J to create simulated GEM broadbands to calibrate the original GEM broadbands. The scaled GEM broadbands are then weighted accordingly so that the resultant UV Index field emulates the high-res UV Index field calculated from Cloud-J.
To reduce computational costs, ECCC's new method to calculate the UV Index involves scaling and...
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