Articles | Volume 10, issue 8
Geosci. Model Dev., 10, 2971–2999, 2017
https://doi.org/10.5194/gmd-10-2971-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue: Coupled chemistry–meteorology modelling: status and...
Geosci. Model Dev., 10, 2971–2999, 2017
https://doi.org/10.5194/gmd-10-2971-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
08 Aug 2017
Model description paper
| 08 Aug 2017
Enviro-HIRLAM online integrated meteorology–chemistry modelling system: strategy, methodology, developments and applications (v7.2)
Alexander Baklanov et al.
Related authors
Yuchen Wang, Xvli Guo, Yajie Huo, Mengying Li, Yuqing Pan, Shaocai Yu, Alexander Baklanov, Daniel Rosenfeld, John H. Seinfeld, and Pengfei Li
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-518, https://doi.org/10.5194/acp-2022-518, 2022
Preprint under review for ACP
Short summary
Short summary
Substantial advances have been made in recent years toward detecting and quantifying methane super-emitters from space. However, such advances have rarely been expanded to measure the global methane pledge because large-scale swath and high-resolution sampling have not been coordinated. Here we present a versatile spaceborne architecture that can juggle planet-scale and plant-level methane retrievals, challenge official emission reports, and versatile for upcoming stereoscopic measurements.
Ranjeet S. Sokhi, Nicolas Moussiopoulos, Alexander Baklanov, John Bartzis, Isabelle Coll, Sandro Finardi, Rainer Friedrich, Camilla Geels, Tiia Grönholm, Tomas Halenka, Matthias Ketzel, Androniki Maragkidou, Volker Matthias, Jana Moldanova, Leonidas Ntziachristos, Klaus Schäfer, Peter Suppan, George Tsegas, Greg Carmichael, Vicente Franco, Steve Hanna, Jukka-Pekka Jalkanen, Guus J. M. Velders, and Jaakko Kukkonen
Atmos. Chem. Phys., 22, 4615–4703, https://doi.org/10.5194/acp-22-4615-2022, https://doi.org/10.5194/acp-22-4615-2022, 2022
Short summary
Short summary
This review of air quality research focuses on developments over the past decade. The article considers current and future challenges that are important from air quality research and policy perspectives and highlights emerging prominent gaps of knowledge. The review also examines how air pollution management needs to adapt to new challenges and makes recommendations to guide the direction for future air quality research within the wider community and to provide support for policy.
Hanna K. Lappalainen, Tuukka Petäjä, Timo Vihma, Jouni Räisänen, Alexander Baklanov, Sergey Chalov, Igor Esau, Ekaterina Ezhova, Matti Leppäranta, Dmitry Pozdnyakov, Jukka Pumpanen, Meinrat O. Andreae, Mikhail Arshinov, Eija Asmi, Jianhui Bai, Igor Bashmachnikov, Boris Belan, Federico Bianchi, Boris Biskaborn, Michael Boy, Jaana Bäck, Bin Cheng, Natalia Chubarova, Jonathan Duplissy, Egor Dyukarev, Konstantinos Eleftheriadis, Martin Forsius, Martin Heimann, Sirkku Juhola, Vladimir Konovalov, Igor Konovalov, Pavel Konstantinov, Kajar Köster, Elena Lapshina, Anna Lintunen, Alexander Mahura, Risto Makkonen, Svetlana Malkhazova, Ivan Mammarella, Stefano Mammola, Stephany Buenrostro Mazon, Outi Meinander, Eugene Mikhailov, Victoria Miles, Stanislav Myslenkov, Dmitry Orlov, Jean-Daniel Paris, Roberta Pirazzini, Olga Popovicheva, Jouni Pulliainen, Kimmo Rautiainen, Torsten Sachs, Vladimir Shevchenko, Andrey Skorokhod, Andreas Stohl, Elli Suhonen, Erik S. Thomson, Marina Tsidilina, Veli-Pekka Tynkkynen, Petteri Uotila, Aki Virkkula, Nadezhda Voropay, Tobias Wolf, Sayaka Yasunaka, Jiahua Zhang, Yubao Qiu, Aijun Ding, Huadong Guo, Valery Bondur, Nikolay Kasimov, Sergej Zilitinkevich, Veli-Matti Kerminen, and Markku Kulmala
Atmos. Chem. Phys., 22, 4413–4469, https://doi.org/10.5194/acp-22-4413-2022, https://doi.org/10.5194/acp-22-4413-2022, 2022
Short summary
Short summary
We summarize results during the last 5 years in the northern Eurasian region, especially from Russia, and introduce recent observations of the air quality in the urban environments in China. Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis.
Roman Nuterman, Alexander Mahura, Alexander Baklanov, Bjarne Amstrup, and Ashraf Zakey
Atmos. Chem. Phys., 21, 11099–11112, https://doi.org/10.5194/acp-21-11099-2021, https://doi.org/10.5194/acp-21-11099-2021, 2021
Short summary
Short summary
The street air pollution is usually higher than the pollution at regional and urban scales. It mostly associated with both local emission sources and urban weather conditions. We present the downscaling system for regional, subregional-urban and street scales and evaluate it for acute air-pollution episode. Its evaluation showed a good prediction score across various spatiotemporal scales as well as feasibility of deterministic modelling approach for the operational street scale forecasting.
Alexander Kurganskiy, Carsten Ambelas Skjøth, Alexander Baklanov, Mikhail Sofiev, Annika Saarto, Elena Severova, Sergei Smyshlyaev, and Eigil Kaas
Atmos. Chem. Phys., 20, 2099–2121, https://doi.org/10.5194/acp-20-2099-2020, https://doi.org/10.5194/acp-20-2099-2020, 2020
Short summary
Short summary
The aim of the study was to evaluate three birch pollen source maps using a state-of-the-art atmospheric model Enviro-HIRLAM. Enviro-HIRLAM is a so-called online model where both weather and air pollution are calculated at all time steps.
The evaluation has been performed for 12 pollen observation sites located in Denmark, Finland, and Russia.
Mikhail Varentsov, Pavel Konstantinov, Alexander Baklanov, Igor Esau, Victoria Miles, and Richard Davy
Atmos. Chem. Phys., 18, 17573–17587, https://doi.org/10.5194/acp-18-17573-2018, https://doi.org/10.5194/acp-18-17573-2018, 2018
Short summary
Short summary
This study reports on the urban heat island (UHI) in a typical Arctic city in winter. Using in situ observations, remote sensing data and modeling, we show that the urban temperature anomaly reaches up to 11 K with a mean value of 1.9 K. At least 50 % of this anomaly is caused by the UHI effect, driven mostly by heating. The rest is created by natural microclimatic variability over the hilly terrain. This is a strong argument in support of energy efficiency measures in the Arctic cities.
Angela Benedetti, Jeffrey S. Reid, Peter Knippertz, John H. Marsham, Francesca Di Giuseppe, Samuel Rémy, Sara Basart, Olivier Boucher, Ian M. Brooks, Laurent Menut, Lucia Mona, Paolo Laj, Gelsomina Pappalardo, Alfred Wiedensohler, Alexander Baklanov, Malcolm Brooks, Peter R. Colarco, Emilio Cuevas, Arlindo da Silva, Jeronimo Escribano, Johannes Flemming, Nicolas Huneeus, Oriol Jorba, Stelios Kazadzis, Stefan Kinne, Thomas Popp, Patricia K. Quinn, Thomas T. Sekiyama, Taichu Tanaka, and Enric Terradellas
Atmos. Chem. Phys., 18, 10615–10643, https://doi.org/10.5194/acp-18-10615-2018, https://doi.org/10.5194/acp-18-10615-2018, 2018
Short summary
Short summary
Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centers. This development is due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation authorities, solar energy plant managers, climate service providers, and health professionals. This paper describes the advances in the field and sets out requirements for observations for the sustainability of these activities.
Laura Palacios-Peña, Rocío Baró, Alexander Baklanov, Alessandra Balzarini, Dominik Brunner, Renate Forkel, Marcus Hirtl, Luka Honzak, José María López-Romero, Juan Pedro Montávez, Juan Luis Pérez, Guido Pirovano, Roberto San José, Wolfram Schröder, Johannes Werhahn, Ralf Wolke, Rahela Žabkar, and Pedro Jiménez-Guerrero
Atmos. Chem. Phys., 18, 5021–5043, https://doi.org/10.5194/acp-18-5021-2018, https://doi.org/10.5194/acp-18-5021-2018, 2018
Short summary
Short summary
Atmospheric aerosols modify the radiative budget of the Earth, and it is therefore mandatory to have an accurate representation of their optical properties for understanding their climatic role. This work therefore evaluates the skill in the representation of optical properties by different remote-sensing sensors and regional online coupled chemistry–climate models over Europe.
Rocío Baró, Laura Palacios-Peña, Alexander Baklanov, Alessandra Balzarini, Dominik Brunner, Renate Forkel, Marcus Hirtl, Luka Honzak, Juan Luis Pérez, Guido Pirovano, Roberto San José, Wolfram Schröder, Johannes Werhahn, Ralf Wolke, Rahela Žabkar, and Pedro Jiménez-Guerrero
Atmos. Chem. Phys., 17, 9677–9696, https://doi.org/10.5194/acp-17-9677-2017, https://doi.org/10.5194/acp-17-9677-2017, 2017
Short summary
Short summary
The influence on modeled max., mean and min. temperature over Europe of including aerosol–radiation–cloud interactions has been assessed for two case studies in 2010. Data were taken from an ensemble of online regional chemistry–climate models from EuMetChem COST Action. The results indicate that including these interactions clearly improves the spatiotemporal variability in the temperature signal simulated by the models, with implications for reducing the uncertainty in climate projections.
Alexander Baklanov
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-1174, https://doi.org/10.5194/acp-2016-1174, 2017
Preprint retracted
Short summary
Short summary
The article provides an introduction to the COST Action ES1004 – European framework for online integrated air quality and meteorology modelling (EuMetChem) – goals and outcomes for this Special Issue "Coupled chemistry–meteorology modelling: status and relevance for numerical weather prediction, air quality and climate communities" which collects key scientific papers of EuMetChem and its collaborators from different continents.
Hanna K. Lappalainen, Veli-Matti Kerminen, Tuukka Petäjä, Theo Kurten, Aleksander Baklanov, Anatoly Shvidenko, Jaana Bäck, Timo Vihma, Pavel Alekseychik, Meinrat O. Andreae, Stephen R. Arnold, Mikhail Arshinov, Eija Asmi, Boris Belan, Leonid Bobylev, Sergey Chalov, Yafang Cheng, Natalia Chubarova, Gerrit de Leeuw, Aijun Ding, Sergey Dobrolyubov, Sergei Dubtsov, Egor Dyukarev, Nikolai Elansky, Kostas Eleftheriadis, Igor Esau, Nikolay Filatov, Mikhail Flint, Congbin Fu, Olga Glezer, Aleksander Gliko, Martin Heimann, Albert A. M. Holtslag, Urmas Hõrrak, Juha Janhunen, Sirkku Juhola, Leena Järvi, Heikki Järvinen, Anna Kanukhina, Pavel Konstantinov, Vladimir Kotlyakov, Antti-Jussi Kieloaho, Alexander S. Komarov, Joni Kujansuu, Ilmo Kukkonen, Ella-Maria Duplissy, Ari Laaksonen, Tuomas Laurila, Heikki Lihavainen, Alexander Lisitzin, Alexsander Mahura, Alexander Makshtas, Evgeny Mareev, Stephany Mazon, Dmitry Matishov, Vladimir Melnikov, Eugene Mikhailov, Dmitri Moisseev, Robert Nigmatulin, Steffen M. Noe, Anne Ojala, Mari Pihlatie, Olga Popovicheva, Jukka Pumpanen, Tatjana Regerand, Irina Repina, Aleksei Shcherbinin, Vladimir Shevchenko, Mikko Sipilä, Andrey Skorokhod, Dominick V. Spracklen, Hang Su, Dmitry A. Subetto, Junying Sun, Arkady Y. Terzhevik, Yuri Timofeyev, Yuliya Troitskaya, Veli-Pekka Tynkkynen, Viacheslav I. Kharuk, Nina Zaytseva, Jiahua Zhang, Yrjö Viisanen, Timo Vesala, Pertti Hari, Hans Christen Hansson, Gennady G. Matvienko, Nikolai S. Kasimov, Huadong Guo, Valery Bondur, Sergej Zilitinkevich, and Markku Kulmala
Atmos. Chem. Phys., 16, 14421–14461, https://doi.org/10.5194/acp-16-14421-2016, https://doi.org/10.5194/acp-16-14421-2016, 2016
Short summary
Short summary
After kick off in 2012, the Pan-Eurasian Experiment (PEEX) program has expanded fast and today the multi-disciplinary research community covers ca. 80 institutes and a network of ca. 500 scientists from Europe, Russia, and China. Here we introduce scientific topics relevant in this context. This is one of the first multi-disciplinary overviews crossing scientific boundaries, from atmospheric sciences to socio-economics and social sciences.
M. Kulmala, H. K. Lappalainen, T. Petäjä, T. Kurten, V.-M. Kerminen, Y. Viisanen, P. Hari, S. Sorvari, J. Bäck, V. Bondur, N. Kasimov, V. Kotlyakov, G. Matvienko, A. Baklanov, H. D. Guo, A. Ding, H.-C. Hansson, and S. Zilitinkevich
Atmos. Chem. Phys., 15, 13085–13096, https://doi.org/10.5194/acp-15-13085-2015, https://doi.org/10.5194/acp-15-13085-2015, 2015
Short summary
Short summary
The Pan-European Experiment (PEEX) is introduced. PEEX is a multidisciplinary, multiscale and multicomponent research, research infrastructure and capacity-building program. This paper outlines the mission, vision and objectives of PEEX and introduces its main components, including the research agenda, research infrastructure, knowledge transfer and potential impacts on society. The paper also summarizes the main scientific questions that PEEX is going to tackle in the future.
M. Beekmann, A. S. H. Prévôt, F. Drewnick, J. Sciare, S. N. Pandis, H. A. C. Denier van der Gon, M. Crippa, F. Freutel, L. Poulain, V. Ghersi, E. Rodriguez, S. Beirle, P. Zotter, S.-L. von der Weiden-Reinmüller, M. Bressi, C. Fountoukis, H. Petetin, S. Szidat, J. Schneider, A. Rosso, I. El Haddad, A. Megaritis, Q. J. Zhang, V. Michoud, J. G. Slowik, S. Moukhtar, P. Kolmonen, A. Stohl, S. Eckhardt, A. Borbon, V. Gros, N. Marchand, J. L. Jaffrezo, A. Schwarzenboeck, A. Colomb, A. Wiedensohler, S. Borrmann, M. Lawrence, A. Baklanov, and U. Baltensperger
Atmos. Chem. Phys., 15, 9577–9591, https://doi.org/10.5194/acp-15-9577-2015, https://doi.org/10.5194/acp-15-9577-2015, 2015
Short summary
Short summary
A detailed characterization of air quality in the Paris (France) agglomeration, a megacity, during two summer and winter intensive campaigns and from additional 1-year observations, revealed that about 70% of the fine particulate matter (PM) at urban background is transported into the megacity from upwind regions. Unexpectedly, a major part of organic PM is of modern origin (woodburning and cooking activities, secondary formation from biogenic VOC).
M. Bocquet, H. Elbern, H. Eskes, M. Hirtl, R. Žabkar, G. R. Carmichael, J. Flemming, A. Inness, M. Pagowski, J. L. Pérez Camaño, P. E. Saide, R. San Jose, M. Sofiev, J. Vira, A. Baklanov, C. Carnevale, G. Grell, and C. Seigneur
Atmos. Chem. Phys., 15, 5325–5358, https://doi.org/10.5194/acp-15-5325-2015, https://doi.org/10.5194/acp-15-5325-2015, 2015
Short summary
Short summary
Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of concentrations, and perform inverse modeling. Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. We review here the current status of data assimilation in atmospheric chemistry models, with a particular focus on future prospects for data assimilation in CCMM.
A. Baklanov, K. Schlünzen, P. Suppan, J. Baldasano, D. Brunner, S. Aksoyoglu, G. Carmichael, J. Douros, J. Flemming, R. Forkel, S. Galmarini, M. Gauss, G. Grell, M. Hirtl, S. Joffre, O. Jorba, E. Kaas, M. Kaasik, G. Kallos, X. Kong, U. Korsholm, A. Kurganskiy, J. Kushta, U. Lohmann, A. Mahura, A. Manders-Groot, A. Maurizi, N. Moussiopoulos, S. T. Rao, N. Savage, C. Seigneur, R. S. Sokhi, E. Solazzo, S. Solomos, B. Sørensen, G. Tsegas, E. Vignati, B. Vogel, and Y. Zhang
Atmos. Chem. Phys., 14, 317–398, https://doi.org/10.5194/acp-14-317-2014, https://doi.org/10.5194/acp-14-317-2014, 2014
Mykhailo Savenets, Larysa Pysarenko, Svitlana Krakovska, Alexander Mahura, and Tuukka Petäjä
Atmos. Chem. Phys., 22, 15777–15791, https://doi.org/10.5194/acp-22-15777-2022, https://doi.org/10.5194/acp-22-15777-2022, 2022
Short summary
Short summary
The paper explores the spatio-temporal variability of black carbon during a wildfire in August 2010, with a focus on Ukraine. As a research tool, the seamless Enviro-HIRLAM modelling system is used for investigating the atmospheric transport of aerosol particles emitted by wildfires from remote and local sources. The results of this study improve our understanding of the physical and chemical processes and the interactions of aerosols in the atmosphere.
Yuchen Wang, Xvli Guo, Yajie Huo, Mengying Li, Yuqing Pan, Shaocai Yu, Alexander Baklanov, Daniel Rosenfeld, John H. Seinfeld, and Pengfei Li
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-518, https://doi.org/10.5194/acp-2022-518, 2022
Preprint under review for ACP
Short summary
Short summary
Substantial advances have been made in recent years toward detecting and quantifying methane super-emitters from space. However, such advances have rarely been expanded to measure the global methane pledge because large-scale swath and high-resolution sampling have not been coordinated. Here we present a versatile spaceborne architecture that can juggle planet-scale and plant-level methane retrievals, challenge official emission reports, and versatile for upcoming stereoscopic measurements.
Jens Visbech, Tuhfe Göçmen, Charlotte Bay Hasager, Hristo Shkalov, Morten Handberg, and Kristian Pagh Nielsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-55, https://doi.org/10.5194/wes-2022-55, 2022
Revised manuscript accepted for WES
Short summary
Short summary
This paper presents a data-driven framework for modeling erosion damage based on real blade inspections and mesoscale weather data. The outcome of the framework is a machine-learning based model that can predict and/or forecast leading edge erosion damages based on weather data and user-specified wind turbine characteristics. The model output fits directly into the damage terminology used by industry and can therefore support site-specific maintenance planning and scheduling of repairs.
Ranjeet S. Sokhi, Nicolas Moussiopoulos, Alexander Baklanov, John Bartzis, Isabelle Coll, Sandro Finardi, Rainer Friedrich, Camilla Geels, Tiia Grönholm, Tomas Halenka, Matthias Ketzel, Androniki Maragkidou, Volker Matthias, Jana Moldanova, Leonidas Ntziachristos, Klaus Schäfer, Peter Suppan, George Tsegas, Greg Carmichael, Vicente Franco, Steve Hanna, Jukka-Pekka Jalkanen, Guus J. M. Velders, and Jaakko Kukkonen
Atmos. Chem. Phys., 22, 4615–4703, https://doi.org/10.5194/acp-22-4615-2022, https://doi.org/10.5194/acp-22-4615-2022, 2022
Short summary
Short summary
This review of air quality research focuses on developments over the past decade. The article considers current and future challenges that are important from air quality research and policy perspectives and highlights emerging prominent gaps of knowledge. The review also examines how air pollution management needs to adapt to new challenges and makes recommendations to guide the direction for future air quality research within the wider community and to provide support for policy.
Hanna K. Lappalainen, Tuukka Petäjä, Timo Vihma, Jouni Räisänen, Alexander Baklanov, Sergey Chalov, Igor Esau, Ekaterina Ezhova, Matti Leppäranta, Dmitry Pozdnyakov, Jukka Pumpanen, Meinrat O. Andreae, Mikhail Arshinov, Eija Asmi, Jianhui Bai, Igor Bashmachnikov, Boris Belan, Federico Bianchi, Boris Biskaborn, Michael Boy, Jaana Bäck, Bin Cheng, Natalia Chubarova, Jonathan Duplissy, Egor Dyukarev, Konstantinos Eleftheriadis, Martin Forsius, Martin Heimann, Sirkku Juhola, Vladimir Konovalov, Igor Konovalov, Pavel Konstantinov, Kajar Köster, Elena Lapshina, Anna Lintunen, Alexander Mahura, Risto Makkonen, Svetlana Malkhazova, Ivan Mammarella, Stefano Mammola, Stephany Buenrostro Mazon, Outi Meinander, Eugene Mikhailov, Victoria Miles, Stanislav Myslenkov, Dmitry Orlov, Jean-Daniel Paris, Roberta Pirazzini, Olga Popovicheva, Jouni Pulliainen, Kimmo Rautiainen, Torsten Sachs, Vladimir Shevchenko, Andrey Skorokhod, Andreas Stohl, Elli Suhonen, Erik S. Thomson, Marina Tsidilina, Veli-Pekka Tynkkynen, Petteri Uotila, Aki Virkkula, Nadezhda Voropay, Tobias Wolf, Sayaka Yasunaka, Jiahua Zhang, Yubao Qiu, Aijun Ding, Huadong Guo, Valery Bondur, Nikolay Kasimov, Sergej Zilitinkevich, Veli-Matti Kerminen, and Markku Kulmala
Atmos. Chem. Phys., 22, 4413–4469, https://doi.org/10.5194/acp-22-4413-2022, https://doi.org/10.5194/acp-22-4413-2022, 2022
Short summary
Short summary
We summarize results during the last 5 years in the northern Eurasian region, especially from Russia, and introduce recent observations of the air quality in the urban environments in China. Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis.
Sissal Vágsheyg Erenbjerg, Jon Albretsen, Knud Simonsen, Erna Lava Olsen, Eigil Kaas, and Bogi Hansen
Ocean Sci., 17, 1639–1655, https://doi.org/10.5194/os-17-1639-2021, https://doi.org/10.5194/os-17-1639-2021, 2021
Short summary
Short summary
Here, we describe a strait that has narrow and shallow sills in both ends and is close to an amphidromic region. This generates tidally driven flows into and out of the strait, but with very different exchange rates across the entrances in both ends so that it behaves like a mixture between a strait and a fjord. Using a numerical model, we find a fortnightly signal in the net transport through the strait, generated by long-period tides. Our findings are verified by observations.
Roman Nuterman, Alexander Mahura, Alexander Baklanov, Bjarne Amstrup, and Ashraf Zakey
Atmos. Chem. Phys., 21, 11099–11112, https://doi.org/10.5194/acp-21-11099-2021, https://doi.org/10.5194/acp-21-11099-2021, 2021
Short summary
Short summary
The street air pollution is usually higher than the pollution at regional and urban scales. It mostly associated with both local emission sources and urban weather conditions. We present the downscaling system for regional, subregional-urban and street scales and evaluate it for acute air-pollution episode. Its evaluation showed a good prediction score across various spatiotemporal scales as well as feasibility of deterministic modelling approach for the operational street scale forecasting.
Tuukka Petäjä, Ella-Maria Duplissy, Ksenia Tabakova, Julia Schmale, Barbara Altstädter, Gerard Ancellet, Mikhail Arshinov, Yurii Balin, Urs Baltensperger, Jens Bange, Alison Beamish, Boris Belan, Antoine Berchet, Rossana Bossi, Warren R. L. Cairns, Ralf Ebinghaus, Imad El Haddad, Beatriz Ferreira-Araujo, Anna Franck, Lin Huang, Antti Hyvärinen, Angelika Humbert, Athina-Cerise Kalogridis, Pavel Konstantinov, Astrid Lampert, Matthew MacLeod, Olivier Magand, Alexander Mahura, Louis Marelle, Vladimir Masloboev, Dmitri Moisseev, Vaios Moschos, Niklas Neckel, Tatsuo Onishi, Stefan Osterwalder, Aino Ovaska, Pauli Paasonen, Mikhail Panchenko, Fidel Pankratov, Jakob B. Pernov, Andreas Platis, Olga Popovicheva, Jean-Christophe Raut, Aurélie Riandet, Torsten Sachs, Rosamaria Salvatori, Roberto Salzano, Ludwig Schröder, Martin Schön, Vladimir Shevchenko, Henrik Skov, Jeroen E. Sonke, Andrea Spolaor, Vasileios K. Stathopoulos, Mikko Strahlendorff, Jennie L. Thomas, Vito Vitale, Sterios Vratolis, Carlo Barbante, Sabine Chabrillat, Aurélien Dommergue, Konstantinos Eleftheriadis, Jyri Heilimo, Kathy S. Law, Andreas Massling, Steffen M. Noe, Jean-Daniel Paris, André S. H. Prévôt, Ilona Riipinen, Birgit Wehner, Zhiyong Xie, and Hanna K. Lappalainen
Atmos. Chem. Phys., 20, 8551–8592, https://doi.org/10.5194/acp-20-8551-2020, https://doi.org/10.5194/acp-20-8551-2020, 2020
Short summary
Short summary
The role of polar regions is increasing in terms of megatrends such as globalization, new transport routes, demography, and the use of natural resources with consequent effects on regional and transported pollutant concentrations. Here we summarize initial results from our integrative project exploring the Arctic environment and pollution to deliver data products, metrics, and indicators for stakeholders.
Alexander Kurganskiy, Carsten Ambelas Skjøth, Alexander Baklanov, Mikhail Sofiev, Annika Saarto, Elena Severova, Sergei Smyshlyaev, and Eigil Kaas
Atmos. Chem. Phys., 20, 2099–2121, https://doi.org/10.5194/acp-20-2099-2020, https://doi.org/10.5194/acp-20-2099-2020, 2020
Short summary
Short summary
The aim of the study was to evaluate three birch pollen source maps using a state-of-the-art atmospheric model Enviro-HIRLAM. Enviro-HIRLAM is a so-called online model where both weather and air pollution are calculated at all time steps.
The evaluation has been performed for 12 pollen observation sites located in Denmark, Finland, and Russia.
Andreas Müller, Willem Deconinck, Christian Kühnlein, Gianmarco Mengaldo, Michael Lange, Nils Wedi, Peter Bauer, Piotr K. Smolarkiewicz, Michail Diamantakis, Sarah-Jane Lock, Mats Hamrud, Sami Saarinen, George Mozdzynski, Daniel Thiemert, Michael Glinton, Pierre Bénard, Fabrice Voitus, Charles Colavolpe, Philippe Marguinaud, Yongjun Zheng, Joris Van Bever, Daan Degrauwe, Geert Smet, Piet Termonia, Kristian P. Nielsen, Bent H. Sass, Jacob W. Poulsen, Per Berg, Carlos Osuna, Oliver Fuhrer, Valentin Clement, Michael Baldauf, Mike Gillard, Joanna Szmelter, Enda O'Brien, Alastair McKinstry, Oisín Robinson, Parijat Shukla, Michael Lysaght, Michał Kulczewski, Milosz Ciznicki, Wojciech Piątek, Sebastian Ciesielski, Marek Błażewicz, Krzysztof Kurowski, Marcin Procyk, Pawel Spychala, Bartosz Bosak, Zbigniew P. Piotrowski, Andrzej Wyszogrodzki, Erwan Raffin, Cyril Mazauric, David Guibert, Louis Douriez, Xavier Vigouroux, Alan Gray, Peter Messmer, Alexander J. Macfaden, and Nick New
Geosci. Model Dev., 12, 4425–4441, https://doi.org/10.5194/gmd-12-4425-2019, https://doi.org/10.5194/gmd-12-4425-2019, 2019
Short summary
Short summary
This paper presents an overview of the ESCAPE project. Dwarfs (key patterns in terms of computation and communication) are identified in weather prediction models. They are optimised for different hardware architectures. New algorithms are developed that are specifically designed for better energy efficiency and improved portability through domain-specific languages. Different numerical techniques are compared in terms of energy efficiency and performance for a variety of computing technologies.
Fidel Pankratov, Alexander Mahura, Tuukka Petäjä, Valentin Popov, and Vladimir Masloboev
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-1228, https://doi.org/10.5194/acp-2018-1228, 2018
Revised manuscript not accepted
Short summary
Short summary
Analysis of long-term observations for atmospheric mercury in the Russian Arctic, considering inter-annual, seasonal and monthly variabilities is in focus. Linkage of elevated concentrations with volcanic eruptions in Iceland is explored. Results showed that active volcanoes can play a role of sources for increased mercury levels in the Northern Hemisphere due to long-range atmospheric transport of volcanic clouds towards the Arctic.
Mikhail Varentsov, Pavel Konstantinov, Alexander Baklanov, Igor Esau, Victoria Miles, and Richard Davy
Atmos. Chem. Phys., 18, 17573–17587, https://doi.org/10.5194/acp-18-17573-2018, https://doi.org/10.5194/acp-18-17573-2018, 2018
Short summary
Short summary
This study reports on the urban heat island (UHI) in a typical Arctic city in winter. Using in situ observations, remote sensing data and modeling, we show that the urban temperature anomaly reaches up to 11 K with a mean value of 1.9 K. At least 50 % of this anomaly is caused by the UHI effect, driven mostly by heating. The rest is created by natural microclimatic variability over the hilly terrain. This is a strong argument in support of energy efficiency measures in the Arctic cities.
Dion Häfner, René Løwe Jacobsen, Carsten Eden, Mads R. B. Kristensen, Markus Jochum, Roman Nuterman, and Brian Vinter
Geosci. Model Dev., 11, 3299–3312, https://doi.org/10.5194/gmd-11-3299-2018, https://doi.org/10.5194/gmd-11-3299-2018, 2018
Short summary
Short summary
Well-performing, easy-to-use ocean models are a central ingredient to further the understanding of our Earth and climate. Veros, the versatile ocean simulator, is the first full-blown ocean model entirely written in the high-level programming language Python. It is considerably more approachable than traditional Fortran models and leverages modern best practices; at the same time, thanks to the Bohrium framework, Veros is about half as fast as a reference implementation in Fortran 90.
Angela Benedetti, Jeffrey S. Reid, Peter Knippertz, John H. Marsham, Francesca Di Giuseppe, Samuel Rémy, Sara Basart, Olivier Boucher, Ian M. Brooks, Laurent Menut, Lucia Mona, Paolo Laj, Gelsomina Pappalardo, Alfred Wiedensohler, Alexander Baklanov, Malcolm Brooks, Peter R. Colarco, Emilio Cuevas, Arlindo da Silva, Jeronimo Escribano, Johannes Flemming, Nicolas Huneeus, Oriol Jorba, Stelios Kazadzis, Stefan Kinne, Thomas Popp, Patricia K. Quinn, Thomas T. Sekiyama, Taichu Tanaka, and Enric Terradellas
Atmos. Chem. Phys., 18, 10615–10643, https://doi.org/10.5194/acp-18-10615-2018, https://doi.org/10.5194/acp-18-10615-2018, 2018
Short summary
Short summary
Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centers. This development is due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation authorities, solar energy plant managers, climate service providers, and health professionals. This paper describes the advances in the field and sets out requirements for observations for the sustainability of these activities.
Laura Palacios-Peña, Rocío Baró, Alexander Baklanov, Alessandra Balzarini, Dominik Brunner, Renate Forkel, Marcus Hirtl, Luka Honzak, José María López-Romero, Juan Pedro Montávez, Juan Luis Pérez, Guido Pirovano, Roberto San José, Wolfram Schröder, Johannes Werhahn, Ralf Wolke, Rahela Žabkar, and Pedro Jiménez-Guerrero
Atmos. Chem. Phys., 18, 5021–5043, https://doi.org/10.5194/acp-18-5021-2018, https://doi.org/10.5194/acp-18-5021-2018, 2018
Short summary
Short summary
Atmospheric aerosols modify the radiative budget of the Earth, and it is therefore mandatory to have an accurate representation of their optical properties for understanding their climatic role. This work therefore evaluates the skill in the representation of optical properties by different remote-sensing sensors and regional online coupled chemistry–climate models over Europe.
Ruth Mottram, Kristian Pagh Nielsen, Emily Gleeson, and Xiaohua Yang
Adv. Sci. Res., 14, 323–334, https://doi.org/10.5194/asr-14-323-2017, https://doi.org/10.5194/asr-14-323-2017, 2017
Short summary
Short summary
The HARMONIE weather forecasting model is used successfully in Greenland, but there are some problems over the ice sheet due to the lack of realistic glacier surface characteristics. By introducing a correction to the model, preventing glacier surface temperatures over 0 °C, we improve both 2 m air temperature and the surface winds (both strength and direction) forecast by the model.
We also identify other corrections needed before HARMONIE can be used for climate and ice sheet modelling.
Rocío Baró, Laura Palacios-Peña, Alexander Baklanov, Alessandra Balzarini, Dominik Brunner, Renate Forkel, Marcus Hirtl, Luka Honzak, Juan Luis Pérez, Guido Pirovano, Roberto San José, Wolfram Schröder, Johannes Werhahn, Ralf Wolke, Rahela Žabkar, and Pedro Jiménez-Guerrero
Atmos. Chem. Phys., 17, 9677–9696, https://doi.org/10.5194/acp-17-9677-2017, https://doi.org/10.5194/acp-17-9677-2017, 2017
Short summary
Short summary
The influence on modeled max., mean and min. temperature over Europe of including aerosol–radiation–cloud interactions has been assessed for two case studies in 2010. Data were taken from an ensemble of online regional chemistry–climate models from EuMetChem COST Action. The results indicate that including these interactions clearly improves the spatiotemporal variability in the temperature signal simulated by the models, with implications for reducing the uncertainty in climate projections.
Bo Christiansen, Nis Jepsen, Rigel Kivi, Georg Hansen, Niels Larsen, and Ulrik Smith Korsholm
Atmos. Chem. Phys., 17, 9347–9364, https://doi.org/10.5194/acp-17-9347-2017, https://doi.org/10.5194/acp-17-9347-2017, 2017
Short summary
Short summary
Ozone soundings in the troposphere from nine Arctic stations covering the period 1984–2014 have been analyzed. Stations with the best data coverage show a consistent and significant temporal variation with a maximum near 2005 followed by a decrease. Some significant changes are found in the annual cycle in agreement with the notion that the ozone summer maximum is appearing earlier in the year. Such changes in Arctic ozone in the free troposphere have not been reported before.
Laura Rontu, Emily Gleeson, Petri Räisänen, Kristian Pagh Nielsen, Hannu Savijärvi, and Bent Hansen Sass
Adv. Sci. Res., 14, 195–215, https://doi.org/10.5194/asr-14-195-2017, https://doi.org/10.5194/asr-14-195-2017, 2017
Short summary
Short summary
This paper provides an overview of the HLRADIA shortwave (SW) and longwave (LW) broadband radiation schemes used in the HIRLAM numerical weather prediction (NWP) model and available in the HARMONIE-AROME mesoscale NWP model. The advantage of broadband, over spectral, schemes is that they can be called more frequently within the NWP model, without compromising on computational efficiency. Fast physically based radiation parametrizations are also valuable for high-resolution ensemble forecasting.
Alexander Baklanov
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-1174, https://doi.org/10.5194/acp-2016-1174, 2017
Preprint retracted
Short summary
Short summary
The article provides an introduction to the COST Action ES1004 – European framework for online integrated air quality and meteorology modelling (EuMetChem) – goals and outcomes for this Special Issue "Coupled chemistry–meteorology modelling: status and relevance for numerical weather prediction, air quality and climate communities" which collects key scientific papers of EuMetChem and its collaborators from different continents.
Hanna K. Lappalainen, Veli-Matti Kerminen, Tuukka Petäjä, Theo Kurten, Aleksander Baklanov, Anatoly Shvidenko, Jaana Bäck, Timo Vihma, Pavel Alekseychik, Meinrat O. Andreae, Stephen R. Arnold, Mikhail Arshinov, Eija Asmi, Boris Belan, Leonid Bobylev, Sergey Chalov, Yafang Cheng, Natalia Chubarova, Gerrit de Leeuw, Aijun Ding, Sergey Dobrolyubov, Sergei Dubtsov, Egor Dyukarev, Nikolai Elansky, Kostas Eleftheriadis, Igor Esau, Nikolay Filatov, Mikhail Flint, Congbin Fu, Olga Glezer, Aleksander Gliko, Martin Heimann, Albert A. M. Holtslag, Urmas Hõrrak, Juha Janhunen, Sirkku Juhola, Leena Järvi, Heikki Järvinen, Anna Kanukhina, Pavel Konstantinov, Vladimir Kotlyakov, Antti-Jussi Kieloaho, Alexander S. Komarov, Joni Kujansuu, Ilmo Kukkonen, Ella-Maria Duplissy, Ari Laaksonen, Tuomas Laurila, Heikki Lihavainen, Alexander Lisitzin, Alexsander Mahura, Alexander Makshtas, Evgeny Mareev, Stephany Mazon, Dmitry Matishov, Vladimir Melnikov, Eugene Mikhailov, Dmitri Moisseev, Robert Nigmatulin, Steffen M. Noe, Anne Ojala, Mari Pihlatie, Olga Popovicheva, Jukka Pumpanen, Tatjana Regerand, Irina Repina, Aleksei Shcherbinin, Vladimir Shevchenko, Mikko Sipilä, Andrey Skorokhod, Dominick V. Spracklen, Hang Su, Dmitry A. Subetto, Junying Sun, Arkady Y. Terzhevik, Yuri Timofeyev, Yuliya Troitskaya, Veli-Pekka Tynkkynen, Viacheslav I. Kharuk, Nina Zaytseva, Jiahua Zhang, Yrjö Viisanen, Timo Vesala, Pertti Hari, Hans Christen Hansson, Gennady G. Matvienko, Nikolai S. Kasimov, Huadong Guo, Valery Bondur, Sergej Zilitinkevich, and Markku Kulmala
Atmos. Chem. Phys., 16, 14421–14461, https://doi.org/10.5194/acp-16-14421-2016, https://doi.org/10.5194/acp-16-14421-2016, 2016
Short summary
Short summary
After kick off in 2012, the Pan-Eurasian Experiment (PEEX) program has expanded fast and today the multi-disciplinary research community covers ca. 80 institutes and a network of ca. 500 scientists from Europe, Russia, and China. Here we introduce scientific topics relevant in this context. This is one of the first multi-disciplinary overviews crossing scientific boundaries, from atmospheric sciences to socio-economics and social sciences.
Emily Gleeson, Velle Toll, Kristian Pagh Nielsen, Laura Rontu, and Ján Mašek
Atmos. Chem. Phys., 16, 5933–5948, https://doi.org/10.5194/acp-16-5933-2016, https://doi.org/10.5194/acp-16-5933-2016, 2016
Short summary
Short summary
The direct shortwave (SW) radiative effect of aerosols under clear-sky conditions in the ALADIN-HIRLAM numerical weather prediction system was investigated using three SW radiation schemes in diagnostic single-column experiments. Each scheme accurately simulates the direct SW effect when observed aerosols are used, particularly for heavy pollution scenarios.
M. Kulmala, H. K. Lappalainen, T. Petäjä, T. Kurten, V.-M. Kerminen, Y. Viisanen, P. Hari, S. Sorvari, J. Bäck, V. Bondur, N. Kasimov, V. Kotlyakov, G. Matvienko, A. Baklanov, H. D. Guo, A. Ding, H.-C. Hansson, and S. Zilitinkevich
Atmos. Chem. Phys., 15, 13085–13096, https://doi.org/10.5194/acp-15-13085-2015, https://doi.org/10.5194/acp-15-13085-2015, 2015
Short summary
Short summary
The Pan-European Experiment (PEEX) is introduced. PEEX is a multidisciplinary, multiscale and multicomponent research, research infrastructure and capacity-building program. This paper outlines the mission, vision and objectives of PEEX and introduces its main components, including the research agenda, research infrastructure, knowledge transfer and potential impacts on society. The paper also summarizes the main scientific questions that PEEX is going to tackle in the future.
M. Beekmann, A. S. H. Prévôt, F. Drewnick, J. Sciare, S. N. Pandis, H. A. C. Denier van der Gon, M. Crippa, F. Freutel, L. Poulain, V. Ghersi, E. Rodriguez, S. Beirle, P. Zotter, S.-L. von der Weiden-Reinmüller, M. Bressi, C. Fountoukis, H. Petetin, S. Szidat, J. Schneider, A. Rosso, I. El Haddad, A. Megaritis, Q. J. Zhang, V. Michoud, J. G. Slowik, S. Moukhtar, P. Kolmonen, A. Stohl, S. Eckhardt, A. Borbon, V. Gros, N. Marchand, J. L. Jaffrezo, A. Schwarzenboeck, A. Colomb, A. Wiedensohler, S. Borrmann, M. Lawrence, A. Baklanov, and U. Baltensperger
Atmos. Chem. Phys., 15, 9577–9591, https://doi.org/10.5194/acp-15-9577-2015, https://doi.org/10.5194/acp-15-9577-2015, 2015
Short summary
Short summary
A detailed characterization of air quality in the Paris (France) agglomeration, a megacity, during two summer and winter intensive campaigns and from additional 1-year observations, revealed that about 70% of the fine particulate matter (PM) at urban background is transported into the megacity from upwind regions. Unexpectedly, a major part of organic PM is of modern origin (woodburning and cooking activities, secondary formation from biogenic VOC).
M. Bocquet, H. Elbern, H. Eskes, M. Hirtl, R. Žabkar, G. R. Carmichael, J. Flemming, A. Inness, M. Pagowski, J. L. Pérez Camaño, P. E. Saide, R. San Jose, M. Sofiev, J. Vira, A. Baklanov, C. Carnevale, G. Grell, and C. Seigneur
Atmos. Chem. Phys., 15, 5325–5358, https://doi.org/10.5194/acp-15-5325-2015, https://doi.org/10.5194/acp-15-5325-2015, 2015
Short summary
Short summary
Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of concentrations, and perform inverse modeling. Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. We review here the current status of data assimilation in atmospheric chemistry models, with a particular focus on future prospects for data assimilation in CCMM.
K. P. Nielsen, E. Gleeson, and L. Rontu
Geosci. Model Dev., 7, 1433–1449, https://doi.org/10.5194/gmd-7-1433-2014, https://doi.org/10.5194/gmd-7-1433-2014, 2014
P. H. Lauritzen, P. A. Ullrich, C. Jablonowski, P. A. Bosler, D. Calhoun, A. J. Conley, T. Enomoto, L. Dong, S. Dubey, O. Guba, A. B. Hansen, E. Kaas, J. Kent, J.-F. Lamarque, M. J. Prather, D. Reinert, V. V. Shashkin, W. C. Skamarock, B. Sørensen, M. A. Taylor, and M. A. Tolstykh
Geosci. Model Dev., 7, 105–145, https://doi.org/10.5194/gmd-7-105-2014, https://doi.org/10.5194/gmd-7-105-2014, 2014
A. Baklanov, K. Schlünzen, P. Suppan, J. Baldasano, D. Brunner, S. Aksoyoglu, G. Carmichael, J. Douros, J. Flemming, R. Forkel, S. Galmarini, M. Gauss, G. Grell, M. Hirtl, S. Joffre, O. Jorba, E. Kaas, M. Kaasik, G. Kallos, X. Kong, U. Korsholm, A. Kurganskiy, J. Kushta, U. Lohmann, A. Mahura, A. Manders-Groot, A. Maurizi, N. Moussiopoulos, S. T. Rao, N. Savage, C. Seigneur, R. S. Sokhi, E. Solazzo, S. Solomos, B. Sørensen, G. Tsegas, E. Vignati, B. Vogel, and Y. Zhang
Atmos. Chem. Phys., 14, 317–398, https://doi.org/10.5194/acp-14-317-2014, https://doi.org/10.5194/acp-14-317-2014, 2014
E. Kaas, B. Sørensen, P. H. Lauritzen, and A. B. Hansen
Geosci. Model Dev., 6, 2023–2047, https://doi.org/10.5194/gmd-6-2023-2013, https://doi.org/10.5194/gmd-6-2023-2013, 2013
B. Sørensen, E. Kaas, and U. S. Korsholm
Geosci. Model Dev., 6, 1029–1042, https://doi.org/10.5194/gmd-6-1029-2013, https://doi.org/10.5194/gmd-6-1029-2013, 2013
Related subject area
Atmospheric sciences
Adapting a deep convolutional RNN model with imbalanced regression loss for improved spatio-temporal forecasting of extreme wind speed events in the short to medium range
ICLASS 1.1, a variational Inverse modelling framework for the Chemistry Land-surface Atmosphere Soil Slab model: description, validation, and application
Towards an improved representation of carbonaceous aerosols over the Indian monsoon region in a regional climate model: RegCM
The E3SM Diagnostics Package (E3SM Diags v2.7): a Python-based diagnostics package for Earth system model evaluation
A method for transporting cloud-resolving model variance in a multiscale modeling framework
The Mission Support System (MSS v7.0.4) and its use in planning for the SouthTRAC aircraft campaign
GENerator of reduced Organic Aerosol mechanism (GENOA v1.0): an automatic generation tool of semi-explicit mechanisms
Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework
Evaluation of high-resolution predictions of fine particulate matter and its composition in an urban area using PMCAMx-v2.0
A local data assimilation method (Local DA v1.0) and its application in a simulated typhoon case
Improved advection, resolution, performance, and community access in the new generation (version 13) of the high-performance GEOS-Chem global atmospheric chemistry model (GCHP)
Lightning assimilation in the WRF model (Version 4.1.1): technique updates and assessment of the applications from regional to hemispheric scales
Optimization of snow-related parameters in the Noah land surface model (v3.4.1) using a micro-genetic algorithm (v1.7a)
Development of an LSTM broadcasting deep-learning framework for regional air pollution forecast improvement
A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF
A comprehensive evaluation of the use of Lagrangian particle dispersion models for inverse modeling of greenhouse gas emissions
Importance of different parameterization changes for the updated dust cycle modeling in the Community Atmosphere Model (version 6.1)
Evaluation of the NAQFC driven by the NOAA Global Forecast System (version 16): comparison with the WRF-CMAQ during the summer 2019 FIREX-AQ campaign
Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 1.0.0): EnVar implementation and evaluation
Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China
A lumped species approach for the simulation of secondary organic aerosol production from intermediate-volatility organic compounds (IVOCs): application to road transport in PMCAMx-iv (v1.0)
TrackMatcher – a tool for finding intercepts in tracks of geographical positions
Recovery of sparse urban greenhouse gas emissions
Tropospheric transport and unresolved convection: numerical experiments with CLaMS 2.0/MESSy
MUNICH v2.0: a street-network model coupled with SSH-aerosol (v1.2) for multi-pollutant modelling
A preliminary evaluation of FY-4A visible radiance data assimilation by the WRF (ARW v4.1.1)/DART (Manhattan release v9.8.0)-RTTOV (v12.3) system for a tropical storm case
Repeatable high-resolution statistical downscaling through deep learning
The second Met Office Unified Model/JULES Regional Atmosphere and Land configuration, RAL2
Atmospherically Relevant Chemistry and Aerosol box model – ARCA box (version 1.2)
MultilayerPy (v1.0): a Python-based framework for building, running and optimising kinetic multi-layer models of aerosols and films
Introduction of the DISAMAR radiative transfer model: determining instrument specifications and analysing methods for atmospheric retrieval (version 4.1.5)
Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of a convective storm case study
Cross-evaluating WRF-Chem v4.1.2, TROPOMI, APEX and in situ NO2 measurements over Antwerp, Belgium
Downscaling atmospheric chemistry simulations with physically consistent deep learning
Bayesian transdimensional inverse reconstruction of the 137Cs Fukushima-Daiichi release
A machine learning methodology for the generation of a parameterization of the hydroxyl radical
Large-eddy simulations with ClimateMachine v0.2.0: a new open-source code for atmospheric simulations on GPUs and CPUs
Hybrid ensemble-variational data assimilation in ABC-DA within a tropical framework
OpenIFS/AC: atmospheric chemistry and aerosol in OpenIFS 43r3
A modern-day Mars climate in the Met Office Unified Model: dry simulations
Simulations of aerosol pH in China using WRF-Chem (v4.0): sensitivities of aerosol pH and its temporal variations during haze episodes
A daily highest air temperature estimation method and spatial–temporal changes analysis of high temperature in China from 1979 to 2018
TransClim (v1.0): a chemistry–climate response model for assessing the effect of mitigation strategies for road traffic on ozone
A description of the first open-source community release of MISTRA-v9.0: a 0D/1D atmospheric boundary layer chemistry model
Integrated Methane Inversion (IMI 1.0): a user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations
Computationally efficient methods for large-scale atmospheric inverse modeling
Improving the joint estimation of CO2 and surface carbon fluxes using a constrained ensemble Kalman filter in COLA (v1.0)
Evaluation of a cloudy cold-air pool in the Columbia River Basin in different versions of the HRRR model
RAP-Net: Region Attention Predictive Network for precipitation nowcasting
Effects of point source emission heights in WRF–STILT: a step towards exploiting nocturnal observations in models
Daan R. Scheepens, Irene Schicker, Kateřina Hlaváčková-Schindler, and Claudia Plant
Geosci. Model Dev., 16, 251–270, https://doi.org/10.5194/gmd-16-251-2023, https://doi.org/10.5194/gmd-16-251-2023, 2023
Short summary
Short summary
The production of wind energy is increasing rapidly and relies heavily on atmospheric conditions. To ensure power grid stability, accurate predictions of wind speed are needed, especially in the short range and for extreme wind speed ranges. In this work, we demonstrate the forecasting skills of a data-driven deep learning model with model adaptations to suit higher wind speed ranges. The resulting model can be applied to other data and parameters, too, to improve nowcasting predictions.
Peter J. M. Bosman and Maarten C. Krol
Geosci. Model Dev., 16, 47–74, https://doi.org/10.5194/gmd-16-47-2023, https://doi.org/10.5194/gmd-16-47-2023, 2023
Short summary
Short summary
We describe an inverse modelling framework constructed around a simple model for the atmospheric boundary layer. This framework can be fed with various observation types to study the boundary layer and land–atmosphere exchange. With this framework, it is possible to estimate model parameters and the associated uncertainties. Some of these parameters are difficult to obtain directly by observations. An example application for a grassland in the Netherlands is included.
Sudipta Ghosh, Sagnik Dey, Sushant Das, Nicole Riemer, Graziano Giuliani, Dilip Ganguly, Chandra Venkataraman, Filippo Giorgi, Sachchida Nand Tripathi, Srikanthan Ramachandran, Thazhathakal Ayyappen Rajesh, Harish Gadhavi, and Atul Kumar Srivastava
Geosci. Model Dev., 16, 1–15, https://doi.org/10.5194/gmd-16-1-2023, https://doi.org/10.5194/gmd-16-1-2023, 2023
Short summary
Short summary
Accurate representation of aerosols in climate models is critical for minimizing the uncertainty in climate projections. Here, we implement region-specific emission fluxes and a more accurate scheme for carbonaceous aerosol ageing processes in a regional climate model (RegCM4) and show that it improves model performance significantly against in situ, reanalysis, and satellite data over the Indian subcontinent. We recommend improving the model performance before using them for climate studies.
Chengzhu Zhang, Jean-Christophe Golaz, Ryan Forsyth, Tom Vo, Shaocheng Xie, Zeshawn Shaheen, Gerald L. Potter, Xylar S. Asay-Davis, Charles S. Zender, Wuyin Lin, Chih-Chieh Chen, Chris R. Terai, Salil Mahajan, Tian Zhou, Karthik Balaguru, Qi Tang, Cheng Tao, Yuying Zhang, Todd Emmenegger, Susannah Burrows, and Paul A. Ullrich
Geosci. Model Dev., 15, 9031–9056, https://doi.org/10.5194/gmd-15-9031-2022, https://doi.org/10.5194/gmd-15-9031-2022, 2022
Short summary
Short summary
Earth system model (ESM) developers run automated analysis tools on data from candidate models to inform model development. This paper introduces a new Python package, E3SM Diags, that has been developed to support ESM development and use routinely in the development of DOE's Energy Exascale Earth System Model. This tool covers a set of essential diagnostics to evaluate the mean physical climate from simulations, as well as several process-oriented and phenomenon-based evaluation diagnostics.
Walter Hannah and Kyle Pressel
Geosci. Model Dev., 15, 8999–9013, https://doi.org/10.5194/gmd-15-8999-2022, https://doi.org/10.5194/gmd-15-8999-2022, 2022
Short summary
Short summary
A multiscale modeling framework couples two models of the atmosphere that each cover different scale ranges. Traditionally, fluctuations in the small-scale model are not transported by the flow on the large-scale model grid, but this is hypothesized to be responsible for a persistent, unphysical checkerboard pattern. A method is presented to facilitate the transport of these small-scale fluctuations, analogous to how small-scale clouds and turbulence are transported in the real atmosphere.
Reimar Bauer, Jens-Uwe Grooß, Jörn Ungermann, May Bär, Markus Geldenhuys, and Lars Hoffmann
Geosci. Model Dev., 15, 8983–8997, https://doi.org/10.5194/gmd-15-8983-2022, https://doi.org/10.5194/gmd-15-8983-2022, 2022
Short summary
Short summary
The Mission Support System (MSS) is an open source software package that has been used for planning flight tracks of scientific aircraft in multiple measurement campaigns during the last decade. Here, we describe the MSS software and its use during the SouthTRAC measurement campaign in 2019. As an example for how the MSS software is used in conjunction with many datasets, we describe the planning of a single flight probing orographic gravity waves propagating up into the lower mesosphere.
Zhizhao Wang, Florian Couvidat, and Karine Sartelet
Geosci. Model Dev., 15, 8957–8982, https://doi.org/10.5194/gmd-15-8957-2022, https://doi.org/10.5194/gmd-15-8957-2022, 2022
Short summary
Short summary
Air quality models need to reliably predict secondary organic aerosols (SOAs) at a reasonable computational cost. Thus, we developed GENOA v1.0, a mechanism reduction algorithm that preserves the accuracy of detailed gas-phase chemical mechanisms for SOA formation, thereby improving the practical use of actual chemistry in SOA models. With GENOA, a near-explicit chemical scheme was reduced to 2 % of its original size and computational time, with an average error of less than 3 %.
Felix Kleinert, Lukas H. Leufen, Aurelia Lupascu, Tim Butler, and Martin G. Schultz
Geosci. Model Dev., 15, 8913–8930, https://doi.org/10.5194/gmd-15-8913-2022, https://doi.org/10.5194/gmd-15-8913-2022, 2022
Short summary
Short summary
We examine the effects of spatially aggregated upstream information as input for a deep learning model forecasting near-surface ozone levels. Using aggregated data from one upstream sector (45°) improves the forecast by ~ 10 % for 4 prediction days. Three upstream sectors improve the forecasts by ~ 14 % on the first 2 d only. Our results serve as an orientation for other researchers or environmental agencies focusing on pointwise time-series predictions, for example, due to regulatory purposes.
Brian T. Dinkelacker, Pablo Garcia Rivera, Ioannis Kioutsioukis, Peter J. Adams, and Spyros N. Pandis
Geosci. Model Dev., 15, 8899–8912, https://doi.org/10.5194/gmd-15-8899-2022, https://doi.org/10.5194/gmd-15-8899-2022, 2022
Short summary
Short summary
The performance of a chemical transport model in reproducing PM2.5 concentrations and composition was evaluated at the finest scale using measurements from regulatory sites as well as a network of low-cost monitors. Total PM2.5 mass is reproduced well by the model during the winter when compared to regulatory measurements, but in the summer PM2.5 is underpredicted, mainly due to difficulties in reproducing regional secondary organic aerosol levels.
Shizhang Wang and Xiaoshi Qiao
Geosci. Model Dev., 15, 8869–8897, https://doi.org/10.5194/gmd-15-8869-2022, https://doi.org/10.5194/gmd-15-8869-2022, 2022
Short summary
Short summary
A local data assimilation scheme (Local DA v1.0) was proposed to leverage the advantage of hybrid covariance, multiscale localization, and parallel computation. The Local DA can perform covariance localization in model space, observation space, or both spaces. The Local DA that used the hybrid covariance and double-space localization produced the lowest analysis and forecast errors among all observing system simulation experiments.
Randall V. Martin, Sebastian D. Eastham, Liam Bindle, Elizabeth W. Lundgren, Thomas L. Clune, Christoph A. Keller, William Downs, Dandan Zhang, Robert A. Lucchesi, Melissa P. Sulprizio, Robert M. Yantosca, Yanshun Li, Lucas Estrada, William M. Putman, Benjamin M. Auer, Atanas L. Trayanov, Steven Pawson, and Daniel J. Jacob
Geosci. Model Dev., 15, 8731–8748, https://doi.org/10.5194/gmd-15-8731-2022, https://doi.org/10.5194/gmd-15-8731-2022, 2022
Short summary
Short summary
Atmospheric chemistry models must be able to operate both online as components of Earth system models and offline as standalone models. The widely used GEOS-Chem model operates both online and offline, but the classic offline version is not suitable for massively parallel simulations. We describe a new generation of the offline high-performance GEOS-Chem (GCHP) that enables high-resolution simulations on thousands of cores, including on the cloud, with improved access, performance, and accuracy.
Daiwen Kang, Nicholas K. Heath, Robert C. Gilliam, Tanya L. Spero, and Jonathan E. Pleim
Geosci. Model Dev., 15, 8561–8579, https://doi.org/10.5194/gmd-15-8561-2022, https://doi.org/10.5194/gmd-15-8561-2022, 2022
Short summary
Short summary
A lightning assimilation (LTA) technique implemented in the WRF model's Kain–Fritsch (KF) convective scheme is updated and applied to simulations from regional to hemispheric scales using observed lightning flashes from ground-based lightning detection networks. Different user-toggled options associated with the KF scheme on simulations with and without LTA are assessed. The model's performance is improved significantly by LTA, but it is sensitive to various factors.
Sujeong Lim, Hyeon-Ju Gim, Ebony Lee, Seungyeon Lee, Won Young Lee, Yong Hee Lee, Claudio Cassardo, and Seon Ki Park
Geosci. Model Dev., 15, 8541–8559, https://doi.org/10.5194/gmd-15-8541-2022, https://doi.org/10.5194/gmd-15-8541-2022, 2022
Short summary
Short summary
The land surface model (LSM) contains various uncertain parameters, which are obtained by the empirical relations reflecting the specific local region and can be a source of uncertainty. To seek the optimal parameter values in the snow-related processes of the Noah LSM over South Korea, we have implemented an optimization algorithm, a micro-genetic algorithm using the observations. As a result, the optimized snow parameters improve snowfall prediction.
Haochen Sun, Jimmy C. H. Fung, Yiang Chen, Zhenning Li, Dehao Yuan, Wanying Chen, and Xingcheng Lu
Geosci. Model Dev., 15, 8439–8452, https://doi.org/10.5194/gmd-15-8439-2022, https://doi.org/10.5194/gmd-15-8439-2022, 2022
Short summary
Short summary
This study developed a novel deep-learning layer, the broadcasting layer, to build an end-to-end LSTM-based deep-learning model for regional air pollution forecast. By combining the ground observation, WRF-CMAQ simulation, and the broadcasting LSTM deep-learning model, forecast accuracy has been significantly improved when compared to other methods. The broadcasting layer and its variants can also be applied in other research areas to supersede the traditional numerical interpolation methods.
Shunji Kotsuki, Takemasa Miyoshi, Keiichi Kondo, and Roland Potthast
Geosci. Model Dev., 15, 8325–8348, https://doi.org/10.5194/gmd-15-8325-2022, https://doi.org/10.5194/gmd-15-8325-2022, 2022
Short summary
Short summary
Data assimilation plays an important part in numerical weather prediction (NWP) in terms of combining forecasted states and observations. While data assimilation methods in NWP usually assume the Gaussian error distribution, some variables in the atmosphere, such as precipitation, are known to have non-Gaussian error statistics. This study extended a widely used ensemble data assimilation algorithm to enable the assimilation of more non-Gaussian observations.
Martin Vojta, Andreas Plach, Rona L. Thompson, and Andreas Stohl
Geosci. Model Dev., 15, 8295–8323, https://doi.org/10.5194/gmd-15-8295-2022, https://doi.org/10.5194/gmd-15-8295-2022, 2022
Short summary
Short summary
In light of recent global warming, we aim to improve methods for modeling greenhouse gas emissions in order to support the successful implementation of the Paris Agreement. In this study, we investigate certain aspects of a Bayesian inversion method that uses computer simulations and atmospheric observations to improve estimates of greenhouse gas emissions. We explore method limitations, discuss problems, and suggest improvements.
Longlei Li, Natalie M. Mahowald, Jasper F. Kok, Xiaohong Liu, Mingxuan Wu, Danny M. Leung, Douglas S. Hamilton, Louisa K. Emmons, Yue Huang, Neil Sexton, Jun Meng, and Jessica Wan
Geosci. Model Dev., 15, 8181–8219, https://doi.org/10.5194/gmd-15-8181-2022, https://doi.org/10.5194/gmd-15-8181-2022, 2022
Short summary
Short summary
This study advances mineral dust parameterizations in the Community Atmospheric Model (CAM; version 6.1). Efforts include 1) incorporating a more physically based dust emission scheme; 2) updating the dry deposition scheme; and 3) revising the gravitational settling velocity to account for dust asphericity. Substantial improvements achieved with these updates can help accurately quantify dust–climate interactions using CAM, such as the dust-radiation and dust–cloud interactions.
Youhua Tang, Patrick C. Campbell, Pius Lee, Rick Saylor, Fanglin Yang, Barry Baker, Daniel Tong, Ariel Stein, Jianping Huang, Ho-Chun Huang, Li Pan, Jeff McQueen, Ivanka Stajner, Jose Tirado-Delgado, Youngsun Jung, Melissa Yang, Ilann Bourgeois, Jeff Peischl, Tom Ryerson, Donald Blake, Joshua Schwarz, Jose-Luis Jimenez, James Crawford, Glenn Diskin, Richard Moore, Johnathan Hair, Greg Huey, Andrew Rollins, Jack Dibb, and Xiaoyang Zhang
Geosci. Model Dev., 15, 7977–7999, https://doi.org/10.5194/gmd-15-7977-2022, https://doi.org/10.5194/gmd-15-7977-2022, 2022
Short summary
Short summary
This paper compares two meteorological datasets for driving a regional air quality model: a regional meteorological model using WRF (WRF-CMAQ) and direct interpolation from an operational global model (GFS-CMAQ). In the comparison with surface measurements and aircraft data in summer 2019, these two methods show mixed performance depending on the corresponding meteorological settings. Direct interpolation is found to be a viable method to drive air quality models.
Zhiquan Liu, Chris Snyder, Jonathan J. Guerrette, Byoung-Joo Jung, Junmei Ban, Steven Vahl, Yali Wu, Yannick Trémolet, Thomas Auligné, Benjamin Ménétrier, Anna Shlyaeva, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
Geosci. Model Dev., 15, 7859–7878, https://doi.org/10.5194/gmd-15-7859-2022, https://doi.org/10.5194/gmd-15-7859-2022, 2022
Short summary
Short summary
JEDI-MPAS 1.0.0, a new data assimilation (DA) system for the MPAS model, was publicly released for community use. This article describes JEDI-MPAS's implementation of the ensemble–variational DA technique and demonstrates its robustness and credible performance by incrementally adding three types of microwave radiances (clear-sky AMSU-A, all-sky AMSU-A, clear-sky MHS) to a non-radiance DA experiment. We intend to periodically release new and improved versions of JEDI-MPAS in upcoming years.
Li Fang, Jianbing Jin, Arjo Segers, Hai Xiang Lin, Mijie Pang, Cong Xiao, Tuo Deng, and Hong Liao
Geosci. Model Dev., 15, 7791–7807, https://doi.org/10.5194/gmd-15-7791-2022, https://doi.org/10.5194/gmd-15-7791-2022, 2022
Short summary
Short summary
This study proposes a regional feature selection-based machine learning system to predict short-term air quality in China. The system has a tool that can figure out the importance of input data for better prediction. It provides large-scale air quality prediction that exhibits improved interpretability, fewer training costs, and higher accuracy compared with a standard machine learning system. It can act as an early warning for citizens and reduce exposure to PM2.5 and other air pollutants.
Stella E. I. Manavi and Spyros N. Pandis
Geosci. Model Dev., 15, 7731–7749, https://doi.org/10.5194/gmd-15-7731-2022, https://doi.org/10.5194/gmd-15-7731-2022, 2022
Short summary
Short summary
The paper describes the first step towards the development of a simulation framework for the chemistry and secondary organic aerosol production of intermediate-volatility organic compounds (IVOCs). These compounds can be a significant source of organic particulate matter. Our approach treats IVOCs as lumped compounds that retain their chemical characteristics. Estimated IVOC emissions from road transport were a factor of 8 higher than emissions used in previous applications.
Peter Bräuer and Matthias Tesche
Geosci. Model Dev., 15, 7557–7572, https://doi.org/10.5194/gmd-15-7557-2022, https://doi.org/10.5194/gmd-15-7557-2022, 2022
Short summary
Short summary
This paper presents a tool for (i) finding temporally and spatially resolved intersections between two- or three-dimensional geographical tracks (trajectories) and (ii) extracting of data in the vicinity of intersections to achieve the optimal combination of various data sets.
Benjamin Zanger, Jia Chen, Man Sun, and Florian Dietrich
Geosci. Model Dev., 15, 7533–7556, https://doi.org/10.5194/gmd-15-7533-2022, https://doi.org/10.5194/gmd-15-7533-2022, 2022
Short summary
Short summary
Gaussian priors (GPs) used in least squares inversion do not reflect the true distributions of greenhouse gas emissions well. A method that does not rely on GPs is sparse reconstruction (SR). We show that necessary conditions for SR are satisfied for cities and that the application of a wavelet transform can further enhance sparsity. We apply the theory of compressed sensing to SR. Our results show that SR needs fewer measurements and is superior for assessing unknown emitters compared to GPs.
Paul Konopka, Mengchu Tao, Marc von Hobe, Lars Hoffmann, Corinna Kloss, Fabrizio Ravegnani, C. Michael Volk, Valentin Lauther, Andreas Zahn, Peter Hoor, and Felix Ploeger
Geosci. Model Dev., 15, 7471–7487, https://doi.org/10.5194/gmd-15-7471-2022, https://doi.org/10.5194/gmd-15-7471-2022, 2022
Short summary
Short summary
Pure trajectory-based transport models driven by meteorology derived from reanalysis products (ERA5) take into account only the resolved, advective part of transport. That means neither mixing processes nor unresolved subgrid-scale advective processes like convection are included. The Chemical Lagrangian Model of the Stratosphere (CLaMS) includes these processes. We show that isentropic mixing dominates unresolved transport. The second most important transport process is unresolved convection.
Youngseob Kim, Lya Lugon, Alice Maison, Thibaud Sarica, Yelva Roustan, Myrto Valari, Yang Zhang, Michel André, and Karine Sartelet
Geosci. Model Dev., 15, 7371–7396, https://doi.org/10.5194/gmd-15-7371-2022, https://doi.org/10.5194/gmd-15-7371-2022, 2022
Short summary
Short summary
This paper presents the latest version of the street-network model MUNICH, v2.0. The description of MUNICH v1.0, which models gas-phase pollutants in a street network, was published in GMD in 2018. Since then, major modifications have been made to MUNICH. The comprehensive aerosol model SSH-aerosol is now coupled to MUNICH to simulate primary and secondary aerosol concentrations. New parameterisations have also been introduced. Test cases are defined to illustrate the new model functionalities.
Yongbo Zhou, Yubao Liu, Zhaoyang Huo, and Yang Li
Geosci. Model Dev., 15, 7397–7420, https://doi.org/10.5194/gmd-15-7397-2022, https://doi.org/10.5194/gmd-15-7397-2022, 2022
Short summary
Short summary
The study evaluates the performance of the Data Assimilation Research Testbed (DART), equipped with the recently added forward operator Radiative Transfer for TOVS (RTTOV), in assimilating FY-4A visible images into the Weather Research and Forecasting (WRF) model. The ability of the WRF-DART/RTTOV system to improve the forecasting skills for a tropical storm over East Asia and the Western Pacific is demonstrated in an Observing System Simulation Experiment framework.
Dánnell Quesada-Chacón, Klemens Barfus, and Christian Bernhofer
Geosci. Model Dev., 15, 7353–7370, https://doi.org/10.5194/gmd-15-7353-2022, https://doi.org/10.5194/gmd-15-7353-2022, 2022
Short summary
Short summary
We improved the performance of past perfect prognosis statistical downscaling methods while achieving full model repeatability with GPU-calculated deep learning models using the TensorFlow, climate4R, and VALUE frameworks. We employed the ERA5 reanalysis as predictors and ReKIS (eastern Ore Mountains, Germany, 1 km resolution) as precipitation predictand, while incorporating modern deep learning architectures. The achieved repeatability is key to accomplish further milestones with deep learning.
Mike Bush, Ian Boutle, John Edwards, Anke Finnenkoetter, Charmaine Franklin, Kirsty Hanley, Aravindakshan Jayakumar, Huw Lewis, Adrian Lock, Marion Mittermaier, Saji Mohandas, Rachel North, Aurore Porson, Belinda Roux, Stuart Webster, and Mark Weeks
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-209, https://doi.org/10.5194/gmd-2022-209, 2022
Preprint under review for GMD
Short summary
Short summary
Building on the baseline of RAL1, the RAL2 science configuration is used for regional modelling around the UM Partnership and in operations at the Met Office. RAL2 has been tested in different parts of the world including Australia, India and the U.K. RAL2 increases medium and low cloud amounts in the mid-latitudes compared to RAL1, leading to improved cloud forecasts and a reduced diurnal cycle of screen temperature. There is also a reduction in the frequency of heavier precipitation rates.
Petri Clusius, Carlton Xavier, Lukas Pichelstorfer, Putian Zhou, Tinja Olenius, Pontus Roldin, and Michael Boy
Geosci. Model Dev., 15, 7257–7286, https://doi.org/10.5194/gmd-15-7257-2022, https://doi.org/10.5194/gmd-15-7257-2022, 2022
Short summary
Short summary
Atmospheric chemistry and aerosol processes form a dynamic and sensitively balanced system, and solving problems regarding air quality or climate requires detailed modelling and coupling of the processes. The models involved are often very complex to use. We have addressed this problem with the new ARCA box model. It puts much of the current knowledge of the nano- and microscale aerosol dynamics and chemistry into usable software and has the potential to become a valuable tool in the community.
Adam Milsom, Amy Lees, Adam M. Squires, and Christian Pfrang
Geosci. Model Dev., 15, 7139–7151, https://doi.org/10.5194/gmd-15-7139-2022, https://doi.org/10.5194/gmd-15-7139-2022, 2022
Short summary
Short summary
MultilayerPy is a Python-based framework facilitating the creation, running and optimisation of state-of-the-art kinetic multi-layer models of aerosol and film processes. Models can be fit to data with local and global optimisation algorithms along with a statistical sampling algorithm, which quantifies the uncertainty in optimised model parameters. This “modelling study in a box” enables more reproducible and reliable results, with model code and outputs produced in a human-readable way.
Johan F. de Haan, Ping Wang, Maarten Sneep, J. Pepijn Veefkind, and Piet Stammes
Geosci. Model Dev., 15, 7031–7050, https://doi.org/10.5194/gmd-15-7031-2022, https://doi.org/10.5194/gmd-15-7031-2022, 2022
Short summary
Short summary
We present an overview of the DISAMAR radiative transfer code, highlighting the novel semi-analytical derivatives for the doubling–adding formulae and the new DISMAS technique for weak absorbers. DISAMAR includes forward simulations and retrievals for satellite spectral measurements from 270 to 2400 nm to determine instrument specifications for passive remote sensing. It has been used in various Sentinel-4/5P/5 projects and in the TROPOMI aerosol layer height and ozone profile products.
Ivette H. Banos, Will D. Mayfield, Guoqing Ge, Luiz F. Sapucci, Jacob R. Carley, and Louisa Nance
Geosci. Model Dev., 15, 6891–6917, https://doi.org/10.5194/gmd-15-6891-2022, https://doi.org/10.5194/gmd-15-6891-2022, 2022
Short summary
Short summary
A prototype data assimilation system for NOAA’s next-generation rapidly updated, convection-allowing forecast system, or Rapid Refresh Forecast System (RRFS) v0.1, is tested and evaluated. The impact of using data assimilation with a convective storm case study is examined. Although the convection in RRFS tends to be overestimated in intensity and underestimated in extent, the use of data assimilation proves to be crucial to improve short-term forecasts of storms and precipitation.
Catalina Poraicu, Jean-François Müller, Trissevgeni Stavrakou, Dominique Fonteyn, Frederik Tack, Felix Deutsch, Quentin Laffineur, Roeland Van Malderen, and Nele Veldeman
EGUsphere, https://doi.org/10.5194/egusphere-2022-882, https://doi.org/10.5194/egusphere-2022-882, 2022
Short summary
Short summary
High-resolution WRF-Chem simulations are conducted over Antwerp, Belgium in June 2019 and evaluated using meteorological data and in situ, airborne and spaceborne NO2 measurements. Intercomparison of model, aircraft and TROPOMI NO2 columns is conducted to characterize biases in versions v1.3.1 and 2.3.1 of the satellite product. A mass balance method is implemented to provide improved emissions for simulating NO2 distribution over the study area.
Andrew Geiss, Sam J. Silva, and Joseph C. Hardin
Geosci. Model Dev., 15, 6677–6694, https://doi.org/10.5194/gmd-15-6677-2022, https://doi.org/10.5194/gmd-15-6677-2022, 2022
Short summary
Short summary
This work demonstrates the use of modern machine learning techniques to enhance the resolution of atmospheric chemistry simulations. We evaluate the schemes for an 8 x 10 increase in resolution and find that they perform substantially better than conventional methods. Methods are introduced to target machine learning methods towards this type of problem, most notably by ensuring they do not break known physical constraints.
Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-168, https://doi.org/10.5194/gmd-2022-168, 2022
Revised manuscript accepted for GMD
Short summary
Short summary
When radionuclides are released into the atmosphere, the assessment of the consequences depends on the evaluation of the magnitude and temporal evolution of the release, which can be highly variable as in the case of Fukushima-Daiichi. In this paper, we propose Bayesian inverse modelling methods and the Reversible-Jump Markov Chain Monte Carlo technique, which allows to evaluate the temporal variability of the release and to integrate different types of information in the source reconstruction.
Daniel C. Anderson, Melanie B. Follette-Cook, Sarah A. Strode, Julie M. Nicely, Junhua Liu, Peter D. Ivatt, and Bryan N. Duncan
Geosci. Model Dev., 15, 6341–6358, https://doi.org/10.5194/gmd-15-6341-2022, https://doi.org/10.5194/gmd-15-6341-2022, 2022
Short summary
Short summary
The hydroxyl radical (OH) is the most important chemical in the atmosphere for removing certain pollutants, including methane, the second-most-important greenhouse gas. We present a methodology to create an easily modifiable parameterization that can calculate OH concentrations in a computationally efficient way. The parameterization, which predicts OH within 5 %, can be integrated into larger climate models to allow for calculation of the interactions between OH, methane, and other chemicals.
Akshay Sridhar, Yassine Tissaoui, Simone Marras, Zhaoyi Shen, Charles Kawczynski, Simon Byrne, Kiran Pamnany, Maciej Waruszewski, Thomas H. Gibson, Jeremy E. Kozdon, Valentin Churavy, Lucas C. Wilcox, Francis X. Giraldo, and Tapio Schneider
Geosci. Model Dev., 15, 6259–6284, https://doi.org/10.5194/gmd-15-6259-2022, https://doi.org/10.5194/gmd-15-6259-2022, 2022
Short summary
Short summary
ClimateMachine is a new open-source Julia-language atmospheric modeling code. We describe its limited-area configuration and the model equations, and we demonstrate applicability through benchmark problems, including atmospheric flow in the shallow cumulus regime. We show that the discontinuous Galerkin numerics and model equations allow global conservation of key variables (up to sources and sinks). We assess CPU strong scaling and GPU weak scaling to show its suitability for large simulations.
Joshua Chun Kwang Lee, Javier Amezcua, and Ross Noel Bannister
Geosci. Model Dev., 15, 6197–6219, https://doi.org/10.5194/gmd-15-6197-2022, https://doi.org/10.5194/gmd-15-6197-2022, 2022
Short summary
Short summary
In this article, we implement a novel data assimilation method for the ABC–DA system which combines traditional data assimilation approaches in a hybrid approach. We document the technical development and test the hybrid approach in idealised experiments within a tropical framework of the ABC–DA system. Our findings indicate that the hybrid approach outperforms individual traditional approaches. Its potential benefits have been highlighted and should be explored further within this framework.
Vincent Huijnen, Philippe Le Sager, Marcus O. Köhler, Glenn Carver, Samuel Rémy, Johannes Flemming, Simon Chabrillat, Quentin Errera, and Twan van Noije
Geosci. Model Dev., 15, 6221–6241, https://doi.org/10.5194/gmd-15-6221-2022, https://doi.org/10.5194/gmd-15-6221-2022, 2022
Short summary
Short summary
We report on the first implementation of atmospheric chemistry and aerosol as part of the OpenIFS model, based on the CAMS global model. We give an overview of the model and evaluate two reference model configurations, with and without the stratospheric chemistry extension, against a variety of observational datasets. This OpenIFS version with atmospheric composition components is open to the scientific user community under a standard OpenIFS license.
Danny McCulloch, Denis Sergeev, Nathan Mayne, Matthew Bate, James Manners, Ian Boutle, Benjamin Drummond, and Kristzian Kohary
EGUsphere, https://doi.org/10.5194/egusphere-2022-718, https://doi.org/10.5194/egusphere-2022-718, 2022
Short summary
Short summary
We present results from the Met Office Unified Model (UM) to study the dry Martian climate. We describe our model setup conditions and run two scenarios, one with dust that interacts with the environment and it does not. We compare both scenarios to results from an existing Mars climate model, the Planetary Climate Model. We find good agreement in winds and air temperatures, but dust amounts differ between models. This study highlights the importance of using the UM for future Mars research.
Xueyin Ruan, Chun Zhao, Rahul A. Zaveri, Pengzhen He, Xinming Wang, Jingyuan Shao, and Lei Geng
Geosci. Model Dev., 15, 6143–6164, https://doi.org/10.5194/gmd-15-6143-2022, https://doi.org/10.5194/gmd-15-6143-2022, 2022
Short summary
Short summary
Accurate prediction of aerosol pH in chemical transport models is essential to aerosol modeling. This study examines the performance of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) on aerosol pH predictions and the sensitivities to emissions of nonvolatile cations and NH3, aerosol-phase state assumption, and heterogeneous sulfate production. Temporal evolution of aerosol pH during haze cycles in Beijing and the driving factors are also presented and discussed.
Ping Wang, Kebiao Mao, Fei Meng, Zhihao Qin, Shu Fang, and Sayed M. Bateni
Geosci. Model Dev., 15, 6059–6083, https://doi.org/10.5194/gmd-15-6059-2022, https://doi.org/10.5194/gmd-15-6059-2022, 2022
Short summary
Short summary
In order to obtain the key parameters of high-temperature spatial–temporal variation analysis, this study proposed a daily highest air temperature (Tmax) estimation frame to build a Tmax dataset in China from 1979 to 2018. We found that the annual and seasonal mean Tmax in most areas of China showed an increasing trend. The abnormal temperature changes mainly occurred in El Nin~o years or La Nin~a years. IOBW had a stronger influence on China's warming events than other factors.
Vanessa Simone Rieger and Volker Grewe
Geosci. Model Dev., 15, 5883–5903, https://doi.org/10.5194/gmd-15-5883-2022, https://doi.org/10.5194/gmd-15-5883-2022, 2022
Short summary
Short summary
Road traffic emissions of nitrogen oxides, volatile organic compounds and carbon monoxide produce ozone in the troposphere and thus influence Earth's climate. To assess the ozone response to a broad range of mitigation strategies for road traffic, we developed a new chemistry–climate response model called TransClim. It is based on lookup tables containing climate–response relations and thus is able to quickly determine the climate response of a mitigation option.
Josué Bock, Jan Kaiser, Max Thomas, Andreas Bott, and Roland von Glasow
Geosci. Model Dev., 15, 5807–5828, https://doi.org/10.5194/gmd-15-5807-2022, https://doi.org/10.5194/gmd-15-5807-2022, 2022
Short summary
Short summary
MISTRA-v9.0 is an atmospheric boundary layer chemistry model. The model includes a detailed particle description with regards to the microphysics, gas–particle interactions, and liquid phase chemistry within particles. Version 9.0 is the first release of MISTRA as an open-source community model. This paper presents a thorough description of the model characteristics and components. We show some examples of simulations reproducing previous studies with MISTRA with good consistency.
Daniel J. Varon, Daniel J. Jacob, Melissa Sulprizio, Lucas A. Estrada, William B. Downs, Lu Shen, Sarah E. Hancock, Hannah Nesser, Zhen Qu, Elise Penn, Zichong Chen, Xiao Lu, Alba Lorente, Ashutosh Tewari, and Cynthia A. Randles
Geosci. Model Dev., 15, 5787–5805, https://doi.org/10.5194/gmd-15-5787-2022, https://doi.org/10.5194/gmd-15-5787-2022, 2022
Short summary
Short summary
Reducing atmospheric methane emissions is critical to slow near-term climate change. Globally surveying satellite instruments like the TROPOspheric Monitoring Instrument (TROPOMI) have unique capabilities for monitoring atmospheric methane around the world. Here we present a user-friendly cloud-computing tool that enables researchers and stakeholders to quantify methane emissions across user-selected regions of interest using TROPOMI satellite observations.
Taewon Cho, Julianne Chung, Scot M. Miller, and Arvind K. Saibaba
Geosci. Model Dev., 15, 5547–5565, https://doi.org/10.5194/gmd-15-5547-2022, https://doi.org/10.5194/gmd-15-5547-2022, 2022
Short summary
Short summary
Atmospheric inverse modeling describes the process of estimating greenhouse gas fluxes or air pollution emissions at the Earth's surface using observations of these gases collected in the atmosphere. The launch of new satellites, the expansion of surface observation networks, and a desire for more detailed maps of surface fluxes have yielded numerous computational and statistical challenges. This article describes computationally efficient methods for large-scale atmospheric inverse modeling.
Zhiqiang Liu, Ning Zeng, Yun Liu, Eugenia Kalnay, Ghassem Asrar, Bo Wu, Qixiang Cai, Di Liu, and Pengfei Han
Geosci. Model Dev., 15, 5511–5528, https://doi.org/10.5194/gmd-15-5511-2022, https://doi.org/10.5194/gmd-15-5511-2022, 2022
Short summary
Short summary
We described the application of a constrained ensemble Kalman filter (CEnKF) in a joint CO2 and surface carbon fluxes estimation study. By assimilating the pseudo-surface and OCO-2 observations, the annual global flux estimation is significantly biased without mass conservation. With the additional CEnKF process, the CO2 mass is strictly constrained, and the estimation of annual fluxes is significantly improved.
Bianca Adler, James M. Wilczak, Jaymes Kenyon, Laura Bianco, Irina V. Djalalova, Joseph B. Olson, and David D. Turner
EGUsphere, https://doi.org/10.5194/egusphere-2022-355, https://doi.org/10.5194/egusphere-2022-355, 2022
Short summary
Short summary
Rapid changes in wind speed make the integration of wind energy produced during persistent orographic cold pools difficult to integrate into the electrical grid. By evaluating three different versions of NOAA’s High-Resolution-Rapid Refresh model, we demonstrate how model developments targeted during the Second Wind Forecast Improvement Project improve the forecast of a persistent cold pool event.
Zheng Zhang, Chuyao Luo, Shanshan Feng, Rui Ye, Yunming Ye, and Xutao Li
Geosci. Model Dev., 15, 5407–5419, https://doi.org/10.5194/gmd-15-5407-2022, https://doi.org/10.5194/gmd-15-5407-2022, 2022
Short summary
Short summary
In this paper, we develop a model to predict radar echo sequences and apply it in the precipitation nowcasting field. Different from existing models, we propose two new attention modules. By introducing them, the performance of RAP-Net outperforms other models, especially in those regions with moderate and heavy rainfall. Considering that these regions cause more threats to human activities, the research in our work is significant for preventing natural disasters caused by heavy rainfall.
Fabian Maier, Christoph Gerbig, Ingeborg Levin, Ingrid Super, Julia Marshall, and Samuel Hammer
Geosci. Model Dev., 15, 5391–5406, https://doi.org/10.5194/gmd-15-5391-2022, https://doi.org/10.5194/gmd-15-5391-2022, 2022
Short summary
Short summary
We show that the default representation of point source emissions in WRF–STILT leads to large overestimations when modelling fossil fuel CO2 concentrations for a 30 m high observation site during stable atmospheric conditions. We therefore introduce a novel point source modelling approach in WRF-STILT that takes into account their effective emission heights and results in a much better agreement with observations.
Cited articles
Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation: 2. Multiple aerosol types, J. Geophys. Res.-Atmos., 105, 6837–6844, https://doi.org/10.1029/1999JD901161, 2000.
Abdul-Razzak, H., Ghan, S. J., and Rivera-Carpio, C.: A parameterization of aerosol activation: 1. Single aerosol type, J. Geophys. Res.-Atmos., 103, 6123–6131, https://doi.org/10.1029/97JD03735, 1998.
Allen, L., Beevers, S., Lindberg, F., Iamarino, M., Kitiwiroon, N., and Grimmond, C.: Global to City Scale Urban Anthropogenic Heat Flux: Model and Variability, MEGAPOLI Scientific Report, Tech. Rep. 10–01, King's College London, Environmental Monitoring and Modelling Group, London, 2010.
Anderson, G. P., Clough, S. A., Kneizys, F. X., Chetwynd, J. H., and Shettle, E. P.: AFGL Atmospheric Constituent Profiles (0–120 km), Tech. Rep. AFGL-TR-86-0110, Air Force Geophysics Lab Hanscom AFB, MA, USA, 1986.
Andreae, M. O. and Merlet, P.: Emission of trace gases and aerosols from biomass burning, Global Biogeochem. Cy., 15, 955–966, https://doi.org/10.1029/2000GB001382, 2001.
Austin, J. and Butchart, N.: Coupled chemistry-climate model simulations for the period 1980 to 2020: Ozone depletion and the start of ozone recovery, Q. J. Roy. Meteor. Soc., 129, 3225–3249, https://doi.org/10.1256/qj.02.203, 2003.
Baklanov, A.: Numerical Modelling in Mine Aerodynamics, USSR Academy of Science, Apatity, 200 pp., 1988 (in Russian).
Baklanov, A.: Modelling of formation and dynamics of radioactive aerosols in the atmosphere, in: Research on a Theory of Elementary Particles and Solid State, 4, 135–148, 2003.
Baklanov, A.: Integrated meteorological and atmospheric chemical transport modeling: perspectives and strategy for HIRLAM/HARMONIE, HIRLAM Newsletter, 53, 68–78, 2008.
Baklanov, A.: Chemical weather forecasting: a new concept of integrated modelling, Adv. Sci. Res., 4, 23–27, https://doi.org/10.5194/asr-4-23-2010, 2010.
Baklanov, A. and Korsholm, U.: On-line Integrated Meteorological and Chemical Transport Modelling: Advantages and Prospectives, in: Air Pollution Modeling and Its Application XIX, edited by: Borrego, C. and Miranda, A. I., Springer Netherlands, Dordrecht, 3–17, https://doi.org/10.1007/978-1-4020-8453-9_1, 2008.
Baklanov, A. A. and Nuterman, R. B.: Multi-scale atmospheric environment modelling for urban areas, Adv. Sci. Res., 3, 53–57, https://doi.org/10.5194/asr-3-53-2009, 2009.
Baklanov, A. and Sørensen, J.: Parameterisation of radionuclide deposition in atmospheric long-range transport modelling, Phys. Chem. the Earth Pt. B, 26, 787–799, https://doi.org/10.1016/S1464-1909(01)00087-9, 2001.
Baklanov, A., Mahura, A., Nielsen, N., and Petersen, C.: Approaches for urbanization of DMI-HIRLAM NWP model, HIRLAM Newsletter, 49, 61–75, December 2005.
Baklanov, A., Korsholm, U., Mahura, A., Petersen, C., and Gross, A.: ENVIRO-HIRLAM: on-line coupled modelling of urban meteorology and air pollution, Adv. Sci. Res., 2, 41–46, https://doi.org/10.5194/asr-2-41-2008, 2008a.
Baklanov, A., Mestayer, P. G., Clappier, A., Zilitinkevich, S., Joffre, S., Mahura, A., and Nielsen, N. W.: Towards improving the simulation of meteorological fields in urban areas through updated/advanced surface fluxes description, Atmos. Chem. Phys., 8, 523–543, https://doi.org/10.5194/acp-8-523-2008, 2008b.
Baklanov, A., Schlünzen, K., Suppan, P., Baldasano, J., Brunner, D., Aksoyoglu, S., Carmichael, G., Douros, J., Flemming, J., Forkel, R., Galmarini, S., Gauss, M., Grell, G., Hirtl, M., Joffre, S., Jorba, O., Kaas, E., Kaasik, M., Kallos, G., Kong, X., Korsholm, U., Kurganskiy, A., Kushta, J., Lohmann, U., Mahura, A., Manders-Groot, A., Maurizi, A., Moussiopoulos, N., Rao, S. T., Savage, N., Seigneur, C., Sokhi, R. S., Solazzo, E., Solomos, S., Sørensen, B., Tsegas, G., Vignati, E., Vogel, B., and Zhang, Y.: Online coupled regional meteorology chemistry models in Europe: current status and prospects, Atmos. Chem. Phys., 14, 317–398, https://doi.org/10.5194/acp-14-317-2014, 2014.
Baklanov, A., Bouchet, V., Vogel, B., Marécal, V., Benedetti, A., and Schlünzen, K. H.: Seamless Meteorology-Composition Models (SMCM): Challenges, gaps, needs and future directions, chap. 12 in: WWOSC Book: Seamless Prediction of the Earth System: from Minutes to Months, edited by: Brunet, G., Jones, S., and Ruti, P. M., WMO-No. 1156, Geneva, 213–233, 2015.
Baklanov, A., Brunner, D., Carmichael, G., Flemming, J., Freitas, S., Gauss, M., Hov, Ø., Mathur, R., Schlünzen, K. H., Seigneur, C., and Vogel, B.: Key issues for seamless integrated chemistry-meteorology modelling, B. Am. Meteorol. Soc., https://doi.org/10.1175/BAMS-D-15-00166.1, 2017.
Baklanov, A. A., Mahura, A., and Sokhi, R. (Eds.): Integrated Systems of Meso-Meteorological and Chemical Transport Models, Springer, 242 pp., https://doi.org/10.1007/978-3-642-13980-2_1, 2011a.
Baklanov, A. A., Korsholm, U. S., Mahura, A. G., Nuterman, R. B., Sass, B. H., and Zakey, A. S.: Physical and chemical weather forecasting as a joint problem: two-way interacting integrated modelling, in: American Meteorological Society 91st Annual Meeting, Paper 7.1, American Meteorological Society, Seattle, WA, USA, 2011b.
Bengtsson, L., Andrae, U., Aspelien, T., Batrak, Y., Calvo, J., de Rooy, W., Gleeson, E., Hansen-Sass, B., Homleid, M., Hortal, M., Ivarsson, K., Lenderink, G., Niemelä, S., Nielsen, K. P., Onvlee, J., Rontu, L., Samuelsson, P., Santos Muñoz, D., Subias, A., Tijm, S., Toll, V., Yang, X., and Ødegaard Køltzow, M.: The HARMONIE-AROME model configuration in the ALADIN-HIRLAM NWP system, Mon. Weather Rev., 145, 1919–1935, https://doi.org/10.1175/MWR-D-16-0417.1, 2017.
Berger, A., Leriche, M., Deguillaume, L., Mari, C., Tulet, P., Gazen, D., and Escobar, J.: Modeling Formation of SOA from Cloud Chemistry with the Meso-NH Model: Sensitivity Studies of Cloud Events Formed at the Puy de Dôme Station, in: Air Pollution Modeling and its Application XXIV, edited by: Steyn, D. and Chaumerliac, N., Springer Proceedings in Complexity, Springer, Cham., 2016.
Bott, A.: A Positive Definite Advection Scheme Obtained by Nonlinear Renormalization of the Advective Fluxes, Mon. Weather Rev., 117, 1006–1016, https://doi.org/10.1175/1520-0493(1989)117<1006:APDASO>2.0.CO;2, 1989.
Briegleb, B. P.: Longwave band model for thermal radiation in climate studies, J. Geophys. Res.-Atmos., 97, 11475–11485, https://doi.org/10.1029/92JD00806, 1992.
Chang, J., Chang, J. S., Brost, R. A., Isaksen, I. S. A., Madronich, S., Middleton, P., Stockwell, W. R., and Walcek, C.: A three-dimensional Eulerian and deposition model, Physical concepts and formulation, J. Geophys. Res., 92, 14681–14700, 1987.
Chenevez, J., Baklanov, A., and Sørensen, J. H.: Pollutant transport schemes integrated in a numerical weather prediction model: model description and verification results, Meteorol. Appl., 11, 265–275, https://doi.org/10.1017/S1350482704001343, 2004.
Cohard, J.-M., Pinty, J.-P., and Bedos, C.: Extending Twomey's Analytical Estimate of Nucleated Cloud Droplet Concentrations from CCN Spectra, J. Atmos. Sci., 55, 3348–3357, https://doi.org/10.1175/1520-0469(1998)055<3348:ETSAEO>2.0.CO;2, 1998.
Cohard, J.-M., Pinty, J.-P., and Suhre, K.: On the parameterization of activation spectra from cloud condensation nuclei microphysical properties, J. Geophys. Res.-Atmos., 105, 11753–11766, https://doi.org/10.1029/1999JD901195, 2000.
CORINE: Land Cover Database 2000, European Environmental Agency, available at: http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2000-clc2000-seamless-vector-database, last access: 14 June 2016.
Cuxart, J., Bougeault, P., and Redelsperger, J.-L.: A turbulence scheme allowing for mesoscale and large-eddy simulations, Q. J. Roy. Meteor. Soc., 126, 1–30, https://doi.org/10.1002/qj.49712656202, 2000.
de Grandpré, J., Beagley, S. R., Fomichev, V. I., Griffioen, E., McConnell, J. C., Medvedev, A. S., and Shepherd, T. G.: Ozone climatology using interactive chemistry: Results from the Canadian Middle Atmosphere Model, J. Geophys. Res.-Atmos., 105, 26475–26491, https://doi.org/10.1029/2000JD900427, 2000.
Dupont, S. and Mestayer, P. G.: Parameterization of the Urban Energy Budget with the Submesoscale Soil Model, J. Appl. Meteorol. Clim., 45, 1744–1765, https://doi.org/10.1175/JAM2417.1, 2006.
Dupont, S., Mestayer, P. G., Guilloteau, E., Berthier, E., and Andrieu, H.: Parameterization of the Urban Water Budget with the Submesoscale Soil Model, J. Appl. Meteorol. Clim., 45, 624–648, https://doi.org/10.1175/JAM2363.1, 2006.
Easter, R. C.: Two Modified Versions of Bott's Positive-Definite Numerical Advection Scheme, Mon. Weather Rev., 121, 297–304, https://doi.org/10.1175/1520-0493(1993)121<0297:TMVOBP>2.0.CO;2, 1993.
Ekman, A.: Implementation of an atmospheric sulfur scheme in the HIRLAM regional weather forecast model, Report, CM-96, International Meteorological Institute in Stockholm, Department of Meteorology, University of Stockholm, 2000.
Evans, M., Fiore, A., and Jacob, D.: The GEOS-CHEM chemical mechanism: Version 5-07-8, Tech. rep., University of Leeds, Leeds, UK, 2003.
Feichter, J., Kjellström, E., Rodhe, H., Dentener, F., Lelieveldi, J., and Roelofs, G.-J.: Simulation of the tropospheric sulfur cycle in a global climate model, Atmos. Environ., 30, 1693–1707, https://doi.org/10.1016/1352-2310(95)00394-0, 1996.
Feng, Y., Penner, J. E., Sillman, S., and Liu, X.: Effects of cloud overlap in photochemical models, J. Geophys. Res.-Atmos., 109, D04310, https://doi.org/10.1029/2003JD004040, 2004.
Ganzeveld, L. and Lelieveld, J.: Dry deposition parameterization in a chemistry general circulation model and its influence on the distribution of reactive trace gases, J. Geophys. Res.-Atmos., 100, 20999–21012, https://doi.org/10.1029/95JD02266, 1995.
Ganzeveld, L., Lelieveld, J., and Roelofs, G.-J.: A dry deposition parameterization for sulfur oxides in a chemistry and general circulation model, J. Geophys. Res.-Atmos., 103, 5679–5694, https://doi.org/10.1029/97JD03077, 1998.
Geoffroy, O., Brenguier, J.-L., and Burnet, F.: Parametric representation of the cloud droplet spectra for LES warm bulk microphysical schemes, Atmos. Chem. Phys., 10, 4835–4848, https://doi.org/10.5194/acp-10-4835-2010, 2010.
Gery, M. W., Whitten, G. Z., Killus, J. P., and Dodge, M. C.: A photochemical kinetics mechanism for urban and regional scale computer modeling, J. Geophys. Res.-Atmos., 94, 12925–12956, https://doi.org/10.1029/JD094iD10p12925, 1989.
Giglio, L.: Characterization of the tropical diurnal fire cycle using VIRS and MODIS observations, Remote Sens. Environ., 108, 407–421, https://doi.org/10.1016/j.rse.2006.11.018, 2007.
González-Aparicio, I., Nuterman, R., Korsholm, U., Mahura, A., Acero, J., Hidalgo, J., and Baklanov, A.: Land-Use Database Processing Approach for Meso-Scale Urban NWP Model Initialization, Tech. Rep. 10-02, Danish Meteorological Institute, Copenhagen, 2010.
González-Aparicio, I., Hidalgo, J., Baklanov, A., Korsholm, U., Nuterman, R., Mahura, A., and Santa-Coloma, O.: Urban boundary layer analysis in the complex coastal terrain of Bilbao using Enviro-HIRLAM, Theor. Appl. Climatol., 113, 511–527, https://doi.org/10.1007/s00704-012-0808-6, 2013.
González-Aparicio, I., Baklanov, A., Hidalgo, J., Korsholm, U., Nuterman, R., and Mahura, A.: Impact of city expansion and increased heat fluxes scenarios on the urban boundary layer of Bilbao using Enviro-HIRLAM, Urban Climate Journal, 10, 831–845, https://doi.org/10.1016/j.uclim.2014.07.010, 2014.
Grell, G. and Baklanov, A.: Integrated modeling for forecasting weather and air quality: A call for fully coupled approaches, Atmos. Environ., 45, 6845–6851, https://doi.org/10.1016/j.atmosenv.2011.01.017, 2011.
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF model, Atmos. Environ., 39, 6957–6975, https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005.
Gross, A. and Baklanov, A.: Modelling the influence of dimethyl sulphide on aerosol production in the marine boundary layer, Int. J. Environ. Pollut., 22, 51–71, https://doi.org/10.1504/IJEP.2004.005492, 2004.
Guerreiro, C., de Leeuw, F., Foltescu, V., and Horálek, J.: Air quality in Europe – 2014 report, Tech. Rep. 5, European Environment Agency, Copenhagen, https://doi.org/10.2800/22775, 2014.
Hairer, E. and Wanner, G.: Solving Ordinary Differential Equations II. Stiff and Differential-Algebraic Problems, Springer-Verlag, Berlin, 2nd Edn., 1996.
Holton, J. R.: An Introduction to Dynamic Meteorology, Elsevier academic press, Seattle, WA, USA, 4th Edn., 2004.
Horowitz, L. W., Walters, S., Mauzerall, D. L., Emmons, L. K., Rasch, P. J., Granier, C., Tie, X., Lamarque, J.-F., Schultz, M. G., Tyndall, G. S., Orlando, J. J., and Brasseur, G. P.: A global simulation of tropospheric ozone and related tracers: Description and evaluation of MOZART, version 2, J. Geophys. Res.-Atmos., 108, 4784, https://doi.org/10.1029/2002JD002853, 2003.
IFS-CY40r1: available at: http://www.ecmwf.int/search/site/cy40r1?retain-filters=1, last access: 13 June 2016.
Jacobson, M. Z.: Developing, coupling, and applying a gas, aerosol, transport, and radiation model to study urban and regional air pollution, PhD Dissertation, Dept. of Atmospheric Sciences, UCLA, 436 pp., 1994.
Jacobson, M. Z.: Studying the effects of calcium and magnesium on size-distributed nitrate and ammonium with EQUISOLV II, Atmos. Environ., 33, 3635–3649, https://doi.org/10.1016/S1352-2310(99)00105-3, 1999.
Jacobson, M. Z.: Control of fossil-fuel particulate black carbon plus organic matter, possibly the most effective method of slowing global warming, J. Geophys. Res., 107, 4410, https://doi.org/10.1029/2001JD001376, 2002.
Jantunen, J., Saarinen, K., and Rantio-Lehtimäki, A.: Allergy symptoms in relation to alder and birch pollen concentrations in Finland, Aerobiologia, 28, 169–176, https://doi.org/10.1007/s10453-011-9221-3, 2012.
Jöckel, P., von Kuhlmann, R., Lawrence, M. G., Steil, B., Brenninkmeijer, C. A. M., Crutzen, P. J., Rasch, P. J., and Eaton, B.: On a fundamental problem in implementing flux-form advection schemes for tracer transport in 3-dimensional general circulation and chemistry transport models, Q. J. Roy. Meteor. Soc., 127, 1035–1052, https://doi.org/10.1002/qj.49712757318, 2001.
Kaas, E.: A simple and efficient locally mass conserving semi-Lagrangian transport scheme, Tellus A, 60, 305–320, 2008.
Klein, T., Kukkonen, J., Dahl, Å., Bossioli, E., Baklanov, A., Vik, A. F., Agnew, P., Karatzas, K. D., and Sofiev, M.: Interactions of physical, chemical, and biological weather calling for an integrated approach to assessment, forecasting, and communication of air quality, Ambio, 41, 851–864, https://doi.org/10.1007/s13280-012-0288-z, 2012.
Köpke, P., Hess, M., Schult, I., and Shettle, E. P.: Global Aerosol Data Set (GADS), Tech. Rep. 243, Max-Planck-Institut für Meteorologie, Hamburg, Germany, 1997.
Korsholm, U. S.: Integrated modeling of aerosol indirect effects, Ph.D. thesis, University of Copenhagen, Niels Bohr Institute and Danish Meteorological Institute, Copenhagen, Denmark, 2009.
Korsholm, U. S., Baklanov, A., Gross, A., Mahura, A., Sass, B. H., and Kaas, E.: Online coupled chemical weather forecasting based on HIRLAM – overview and prospective of Enviro-HIRLAM, HIRLAM Newsletter, 54, 151–168, 2008.
Korsholm, U. S., Baklanov, A., Gross, A., and Sørensen, J. H.: On the importance of the meteorological coupling interval in dispersion modeling during ETEX-1, Atmos. Environ., 43, 4805–4810, https://doi.org/10.1016/j.atmosenv.2008.11.017, 2009.
Korsholm, U. S., Mahura, A., Baklanov, A., and Grell, G.: Interactions between Air Quality and Meteorology/Climate: Aerosol Feedbacks, in: FP7 MEGAPOLI Sci. Report, edited by: Baklanov, A. and Mahura, A., 10-10, 31–46, 2010.
Kuenen, J., Denier van der Gon, H., Visschedijk, A., van der Brugh, H., Finardi, S., Radice, P., d'Allura, A., Beevers, S., Theloke, J., Uzbasich, M., Honoré, C., and Perrussel, O.: MEGAPOLI European Gridded Emission Inventory (Final Version), MEGAPOLI Project Scientific Report, Tech. Rep. 10–17, TNO Built Environment and Geosciences, 2010.
Kuenen, J. J. P., Visschedijk, A. J. H., Jozwicka, M., and Denier van der Gon, H. A. C.: TNO-MACC_II emission inventory; a multi-year (2003–2009) consistent high-resolution European emission inventory for air quality modelling, Atmos. Chem. Phys., 14, 10963–10976, https://doi.org/10.5194/acp-14-10963-2014, 2014.
Kurganskiy, A., Mahura, A., Nuterman, R., Saarto, A., Rasmussen, A., Baklanov, A., Smyshlyaev, S., and Kaas, E.: Enviro-HIRLAM birch pollen modeling for Northern Europe, in: Report series in Aerosol Science, edited by: Kulmala, M., Zilitinkevich, S., Lappalainen, H., Kyrö, E.-M., and Kontkanen, J., 163, 229–234, 2015.
Kurganskiy, A., Nuterman, R., Mahura, A., Kaas, E., Baklanov, A., and Sass, B.: Modelling of black and organic carbon variability in the Northern Hemisphere, Geophys. Res. Abstr., 18, EGU2016-1404-1, EGU General Assembly 2016, Vienna, Austria, 2016.
Lawrence, M. G., Hov, Ø., Beekmann, M., Brandt, J., Elbern, H., Eskes, H., Feichter, H., and Takigawa, M.: The chemical weather, Environ. Chem., 2, 6–8, https://doi.org/10.1071/EN05014, 2005.
Linneberg, A.: The increase in allergy and extended challenges, Allergy, 66, 1–3, https://doi.org/10.1111/j.1398-9995.2011.02619.x, 2011.
Madronich, S. and Flocke, S.: The Role of Solar Radiation in Atmospheric Chemistry, in: Environmental Photochemistry, edited by: Boule, P., Springer Berlin Heidelberg, Berlin, Heidelberg, 1–26, 1999.
Mahura, A., Leroyer, S., Mestayer, P., Calmet, I., Dupont, S., Long, N., Baklanov, A., Petersen, C., Sattler, K., and Nielsen, N. W.: Large eddy simulation of urban features for Copenhagen metropolitan area, Atmos. Chem. Phys. Discuss., 5, 11183–11213, https://doi.org/10.5194/acpd-5-11183-2005, 2005a.
Mahura, A., Sattler, K., Petersen, C., Amstrup, B., and Baklanov, A.: DMI-HIRLAM Modelling with High Resolution Setup and Simulations for Areas of Denmark, DMI technical report, Tech. Rep. 05-12, Copenhagen, 2005b.
Mahura, A., Baklanov, A., Petersen, C., Sattler, K., Amstrup, B., and Nielsen, N. W.: ISBA Scheme Performance in High Resolution Modelling for Low Winds Conditions, HIRLAM Newsletter, 49, 22–35, 2006a.
Mahura, A., Baklanov, A., Rasmussen, A., Korsholm, U., and Petersen, C.: Birch pollen forecasting for Denmark, in: Abstracts of 6th Annual Meeting of European Meteorological Society (EMS), vol. 3, EMS2006-A-00495, 3–7 September, Ljubljana, Slovenia, 2006b.
Mahura, A., Baklanov, A., Hoe, S., Sorensen, J. H., Petersen, C., and Sattler, K.: Evaluation of land surface scheme modifications on atmospheric transport and deposition patterns in Copenhagen metropolitan area, in: Air Pollution Modeling and Its Application XVIII, edited by: Borrego, C. and Renner, E., vol. 6 of Developments in Environmental Science, Elsevier, 64–72, https://doi.org/10.1016/S1474-8177(07)06017-2, 2007a.
Mahura, A., Korsholm, U., Baklanov, A., and Rasmussen, A.: Elevated birch pollen episodes in Denmark: contributions from remote sources, Aerobiologia, 23, 171–179, https://doi.org/10.1007/s10453-007-9061-3, 2007b.
Mahura, A., Leroyer, S., Baklanov, A., Mestayer, P., Korsholm, U., and Calmet, I.: Temporal and Spatial Variability of Fluxes in Urbanized Areas, in: Urban Climate and Bioclimate, 219–232, 2008a.
Mahura, A., Petersen, C., Baklanov, A., and Amstrup, B.: Evaluation of Building Effect Parameterization Module for Urbanized Numerical Weather Prediction Modelling, in: Urban Climate and Bioclimate, 371–380, 2008b.
Mahura, A., Petersen, C., Baklanov, A., Amstrup, B., Korsholm, U. S., and Sattler, K.: Verification of Long-term DMI-HIRLAM NWP Model Runs Using Urbanisation and Building Effect Parameterization Modules, HIRLAM Newsletter, 53, 50–60, 2008c.
Mahura, A., Baklanov, A., and Korsholm, U.: Parameterization of the birch pollen diurnal cycle, Aerobiologia, 25, 203–208, https://doi.org/10.1007/s10453-009-9125-7, 2009.
Mahura, A., Korsholm, U., Baklanov, A., Petersen, C., and Rasmussen, A.: Birch Pollen: Modelling, Spatial and Temporal Variability, Elevated Episodes, Potential Source Regions, Emissions Parametrizations, and Future Research, in: Abstracts of International Conference on Environmental Observations, Modeling and Information Systems (ENVIROMIS-2010), 87–88, 5–11 July, Tomsk, Russia, 2010a.
Mahura, A., Nuterman, R., Gonzalez-Aparicio, I., Petersen, C., and Baklanov, A.: Environmental modelling in Metropolitan Areas, DMI Sci. Report, Tech. Rep. 10-06, 2010b.
Mahura, A., Nuterman, R., Gonzalez-Aparicio, I., Amstrup, B., Yang, X., and Baklanov, A.: Meteorological and Chemical Urban Scale Modelling for Shanghai Metropolitan Area, Geophys. Res. Abstr., 18, EGU2016-1394, EGU General Assembly 2016, Vienna, Austria, 2016.
Mahura A., Amstrup, B., Nuterman, R., Yang, X., and Baklanov, A.: Multi-Scale Enviro-HIRLAM Forecasting of Weather and Atmospheric Composition over China and its Megacities, Geophys. Res. Abstr., 19, EGU2017-9564, EGU General Assembly 2017, Vienna, Austria, 2017.
Marchuk, G. I.: Mathematical models in environmental problems, in: Studies in mathematics and its applications, 16, Elsevier Sci. Pub, Co. ISBN 044487965X, 217 pp., 1986.
Martilli, A., Clappier, A., and Rotach, M. W.: An Urban Surface Exchange Parameterisation for Mesoscale Models, Bound.-Lay. Meteorol., 104, 261–304, https://doi.org/10.1023/A:1016099921195, 2002.
Mayer, B. and Kylling, A.: Technical note: The libRadtran software package for radiative transfer calculations – description and examples of use, Atmos. Chem. Phys., 5, 1855–1877, https://doi.org/10.5194/acp-5-1855-2005, 2005.
Nightingale, P. D., Malin, G., Law, C. S., Watson, A. J., Liss, P. S., Liddicoat, M. I., Boutin, J., and Upstill-Goddard, R. C.: In situ evaluation of air-sea gas exchange parameterizations using novel conservative and volatile tracers, Global Biogeochem. Cy., 14, 373–387, https://doi.org/10.1029/1999GB900091, 2000.
Noilhan, J. and Planton, S.: A Simple Parameterization of Land Surface Processes for Meteorological Models, Mon. Weather Rev., 117, 536–549, https://doi.org/10.1175/1520-0493(1989)117<0536:ASPOLS>2.0.CO;2, 1989.
Nuterman, R., Korsholm, U., Zakey, A., Nielsen, K. P., Sørensen, B., Mahura, A., Rasmussen, A., Mažeikis, A., Gonzalez-Aparicio, I., Morozova, E., Sass, B. H., Kaas, E., and Baklanov, A.: New developments in Enviro-HIRLAM online integrated modeling system, Geophys. Res. Abstr., 15, EGU2013-12520-1, EGU General Assembly 2013, Vienna, Austria, 2013.
Nuterman, R., Mahura, A., Baklanov, A., Kurganskiy, A., Amstrup, B., and Kass, E.: Enviro-HIRLAM Applicability for Black Carbon Studies in Arctic, Geophys. Res. Abstr., 17, EGU2015-1571, EGU General Assembly 2015, Vienna, Austria, 2015.
Päivinen, R., Lehikoinen, M., Schuck, A., Häme, T., Väätäinen, S., Kennedy, P., and Folving, S.: Combining Earth Observation Data and Forest Statistics, Tech. Rep. 14, EFI, Joensuu and Joint Research Centre/European Commission, 2001.
Penenko, V. V. and Aloyan, A. E.: Models and methods for environment protection problems, Nauka, Novosibirsk, 1985 (in Russian).
Rasch, P. J. and Kristjansson, J. E.: A Comparison of the CCM3 Model Climate Using Diagnosed and Predicted Condensate Parameterizations, J. Climate, 11, 1587–1614, https://doi.org/10.1175/1520-0442(1998)011<1587:ACOTCM>2.0.CO;2, 1998.
Rasmussen, A.: The effects of climate change on the birch pollen season in Denmark, Aerobiologia, 18, 253–265, https://doi.org/10.1023/A:1021321615254, 2002.
Rasmussen, A., Mahura, A., Baklanov, A., and Sommer, J.: The Danish Operation Pollen Forecasting System, in: Abstracts of 8th International Congress on Aerobiology “Towards a comprehensive vision”, p. 179, Neuchâtel, Switzerland, 21–25 August 2006.
Robert, A.: A stable numerical integration scheme for the primitive meteorological equations, Atmos.-Ocean, 19, 35–46, https://doi.org/10.1080/07055900.1981.9649098, 1981.
Sander, S. P., Friedl, R. R., Golden, D. M., Kurylo, M. J., Huie, R. E., Orkin, V. L., Moortgaat, G. K., Ravishankara, A. R., Kolb, C. E., Molina, M. J., and Finlayson-Pitts, B. J.: Chemical kinetics and photochemical data for use in stratospheric modeling. Evaluation No. 14, JPL 02-25, 2003.
Sandu, A. and Sander, R.: Technical note: Simulating chemical systems in Fortran90 and Matlab with the Kinetic PreProcessor KPP-2.1, Atmos. Chem. Phys., 6, 187–195, https://doi.org/10.5194/acp-6-187-2006, 2006.
Sandu, A., Verwer, J. G., Blom, J. G., Spee, E. J., Carmichael, G. R., and Potra, F. A.: Benchmarking stiff ODE solvers for atmospheric chemistry problems II: Rosenbrock solvers, Atmos. Environ., 31, 3459–3472, 1997.
Sass, B. H.: A research version of the STRACO cloud scheme, Tech. Rep. 02-10, Danish Meteorological Institute, Copenhagen, 2002.
Savijärvi, H.: Fast Radiation Parameterization Schemes for Mesoscale and Short-Range Forecast Models, J. Appl. Meteorol., 29, 437–447, https://doi.org/10.1175/1520-0450(1990)029<0437:FRPSFM>2.0.CO;2, 1990.
Schlünzen, K. H. and Pahl, S.: Modification of dry deposition in a developing sea-breeze circulation – a numerical case study, Atmos. Environ., 26, 51–61, 1992.
Seifert, A. and Beheng, D. K.: A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description, Meteorol. Atmos. Phys., 92, 45–66, https://doi.org/10.1007/s00703-005-0112-4, 2006.
Seinfeld, J. and Pandis, S.: Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, A Wiley-Interscience publication, Wiley, 1st Edn., 1998.
Seinfeld, J. and Pandis, S.: Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, A Wiley-Interscience publication, Wiley, 2nd Edn., 2006.
Shalaby, A.: Coupling of Regional Climate Chem Aerosol Model, PhD thesis, Faculty of Science, Cairo University, Egypt, 2012.
Shalaby, A., Zakey, A. S., Tawfik, A. B., Solmon, F., Giorgi, F., Stordal, F., Sillman, S., Zaveri, R. A., and Steiner, A. L.: Implementation and evaluation of online gas-phase chemistry within a regional climate model (RegCM-CHEM4), Geosci. Model Dev., 5, 741–760, https://doi.org/10.5194/gmd-5-741-2012, 2012.
Siljamo, P., Sofiev, M., Filatova, E., Grewling, Ł., Jäger, S., Khoreva, E., Linkosalo, T., Ortega Jimenez, S., Ranta, H., Rantio-Lehtimäki, A., Svetlov, A., Veriankaite, L., Yakovleva, E., and Kukkonen, J.: A numerical model of birch pollen emission and dispersion in the atmosphere. Model evaluation and sensitivity analysis, Int. J. Biometeorol., 57, 125–136, https://doi.org/10.1007/s00484-012-0539-5, 2013.
Sillman, S.: A numerical solution for the equations of tropospheric chemistry based on an analysis of sources and sinks of odd hydrogen, J. Geophys. Res.-Atmos., 96, 20735–20744, https://doi.org/10.1029/91JD01967, 1991.
Skjøth, C. A., Geels, C., Hvidberg, M., Hertel, O., Brandt, J., Frohn, L. M., Hansen, K. M., Hedegaard, G. B., Christensen, J. H., and Moseholm, L.: An inventory of tree species in Europe – An essential data input for air pollution modelling, Ecol. Model., 217, 292–304, https://doi.org/10.1016/j.ecolmodel.2008.06.023, 2008.
Sofiev, M., Siljamo, P., Ranta, H., and Rantio-Lehtimäki, A.: Towards numerical forecasting of long-range air transport of birch pollen: theoretical considerations and a feasibility study, Int. J. Biometeorol., 50, 392–402, https://doi.org/10.1007/s00484-006-0027-x, 2006.
Sofiev, M., Siljamo, P., and Khvorostyanov, D.: Validation report of PBAP routine in SILAM and R-ENS models, Tech. Rep. D_R-ENS_1.7.4, 2011.
Sofiev, M., Ermakova, T., and Vankevich, R.: Evaluation of the smoke-injection height from wild-land fires using remote-sensing data, Atmos. Chem. Phys., 12, 1995–2006, https://doi.org/10.5194/acp-12-1995-2012, 2012.
Sofiev, M., Siljamo, P., Ranta, H., Linkosalo, T., Jaeger, S., Rasmussen, A., Rantio-Lehtimaki, A., Severova, E., and Kukkonen, J.: A numerical model of birch pollen emission and dispersion in the atmosphere. Description of the emission module, Int. J. Biometeorol., 57, 45–58, https://doi.org/10.1007/s00484-012-0532-z, 2013.
Sokhi, R., Baklanov, A., Schluenzen, H. (Eds.): Air Pollution and Meteorological Modelling for Atmospheric Research and Policy Applications, Anthem Press, in press, 260 pp., 2017.
Sørensen, B.: New mass conserving multi-tracer efficient transport schemes focusing on semi-Lagrangian and Lagrangian methods for online integration with chemistry, PhD thesis, University of Copenhagen, Niels Bohr Institute and Danish Meteorological Institute, Copenhagen, Denmark, 2012.
Sørensen, B., Kaas, E., and Korsholm, U. S.: A mass-conserving and multi-tracer efficient transport scheme in the online integrated Enviro-HIRLAM model, Geosci. Model Dev., 6, 1029–1042, https://doi.org/10.5194/gmd-6-1029-2013, 2013.
Stamnes, K., Tsay, S.-C., Wiscombe, W., and Jayaweera, K.: Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media, Appl. Optics, 27, 2502–2509, https://doi.org/10.1364/AO.27.002502, 1988.
Steil, B., Brühl, C., Manzini, E., Crutzen, P. J., Lelieveld, J., Rasch, P. J., Roeckner, E., and Krüger, K.: A new interactive chemistry-climate model: 1. Present-day climatology and interannual variability of the middle atmosphere using the model and 9 years of HALOE/UARS data, J. Geophys. Res.-Atmos., 108, 4290, https://doi.org/10.1029/2002JD002971, 2003.
Stier, P., Feichter, J., Kinne, S., Kloster, S., Vignati, E., Wilson, J., Ganzeveld, L., Tegen, I., Werner, M., Balkanski, Y., Schulz, M., Boucher, O., Minikin, A., and Petzold, A.: The aerosol-climate model ECHAM5-HAM, Atmos. Chem. Phys., 5, 1125–1156, https://doi.org/10.5194/acp-5-1125-2005, 2005.
Stockwell, W. R., Kirchner, F., Kuhn, M., and Seefeld, S.: A new mechanism for regional atmospheric chemistry modeling, J. Geophys. Res.-Atmos., 102, 25847–25879, https://doi.org/10.1029/97JD00849, 1997.
Thomas, G. E. and Stamnes, K.: Radiative Transfer in the Atmosphere and Ocean, Cambridge University Press, New York, NY, USA, 2002.
Twomey, S.: The nuclei of natural cloud formation part II: The supersaturation in natural clouds and the variation of cloud droplet concentration, Geophys. Pure Appl., 43, 243–249, https://doi.org/10.1007/BF01993560, 1959.
Undén, P., Rontu, L., Järvinen, H., Lynch, P., Calvo, J., Cats, G., Cuxart, J., Eerola, K., Fortelius, C., Garcia-Moya, J. A., Jones, C., Lender- link, G., McDonald, A., Mc-Grath, R., Navascues, B., Nielsen, N. W., Øidegaard, V., Rodriguez, E., Rummukainen, M., Rõõm, R., Sattler, K., Sass, B. H., Savijärvi, H., Schreur, B. W., Sigg, R., The, H., and Tijm, A.: HIRLAM-5 Scientific Documentation, Tech. rep., The HIRLAM project, Norrköping, Sweden, 2002.
Vignati, E., Wilson, J., and Stier, P.: M7: An efficient size-resolved aerosol microphysics module for large-scale aerosol transport models, J. Geophys. Res.-Atmos., 109, D22202, https://doi.org/10.1029/2003JD004485, 2004.
WHO: Phenology and human health: allergic disorders, Tech. rep., WHO Regional Office for Europe, Copenhagen, Denmark, 2003.
WMO: Coupled Chemistry-Meteorology/Climate Modelling (CCMM): status and relevance for numerical weather prediction, atmospheric pollution and climate research (Symposium materials). WMO GAW Report #226, WMO, Geneva, Switzerland, available at: https://www.wmo.int/pages/prog/arep/gaw/documents/Final_GAW_226_10_May.pdf (last access: 31 July 2017), 2016.
WMO-COST: Joint Report of COST Action 728 and GURME – Overview of Existing Integrated (off-line and on-line) Mesoscale Meteorological and Chemical Transport Modelling Systems in Europe (WMO TD No. 1427), GAW report 177, 106 pp., May 2008, available at: http://library.wmo.int/pmb_ged/wmo-td_1427.pdf (last access: 31 July 2017), 2008.
Wild, O., Zhu, X., and Prather, M. J.: Fast-J: Accurate Simulation of In- and Below-Cloud Photolysis in Tropospheric Chemical Models, J. Atmos. Chem., 37, 245–282, https://doi.org/10.1023/A:1006415919030, 2000.
Wyser, K., Rontu, L., and Savijärvi, H.: Introducing the effective radius into a fast radiation scheme of a mesoscale model, Contr. Atmos. Phys., 72, 205–218, 1999.
Zakey, A. S., Solmon, F., and Giorgi, F.: Implementation and testing of a desert dust module in a regional climate model, Atmos. Chem. Phys., 6, 4687–4704, https://doi.org/10.5194/acp-6-4687-2006, 2006.
Zakey, A. S., Giorgi, F., and Bi, X.: Modeling of sea salt in a regional climate model: Fluxes and radiative forcing, J. Geophys. Res.-Atmos., 113, D14221, https://doi.org/10.1029/2007JD009209, 2008.
Zaveri, R. A. and Peters, L. K.: A new lumped structure photochemical mechanism for large-scale applications, J. Geophys. Res.-Atmos., 104, 30387–30415, https://doi.org/10.1029/1999JD900876, 1999.
Zhang, Y.: Online-coupled meteorology and chemistry models: history, current status, and outlook, Atmos. Chem. Phys., 8, 2895–2932, https://doi.org/10.5194/acp-8-2895-2008, 2008.
Zilitinkevich, S. and Baklanov, A.: Calculation Of The Height Of The Stable Boundary Layer In Practical Applications, Bound.-Lay. Meteorol., 105, 389–409, https://doi.org/10.1023/A:1020376832738, 2002.
Zilitinkevich, S., Baklanov, A., Rost, J., Smedman, A.-S., Lykosov, V., and Calanca, P.: Diagnostic and prognostic equations for the depth of the stably stratified Ekman boundary layer, Q. J. Roy. Meteor. Soc., 128, 25–46, https://doi.org/10.1256/00359000260498770, 2002.
Short summary
The Environment – HIgh Resolution Limited Area Model (Enviro-HIRLAM) is developed as a fully online integrated numerical weather prediction and atmospheric chemical transport model for research and forecasting of joint meteorological, chemical and biological weather. Different aspects of online coupling methodology, research strategy and possible applications of the modelling system, and ''fit-for-purpose'' model configurations for the meteorological and air quality communities are discussed.
The Environment – HIgh Resolution Limited Area Model (Enviro-HIRLAM) is developed as a fully...
Special issue