Articles | Volume 10, issue 1
https://doi.org/10.5194/gmd-10-189-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/gmd-10-189-2017
© 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
| 
13 Jan 2017
Model description paper |
| 13 Jan 2017
The Brazilian developments on the Regional Atmospheric Modeling System (BRAMS 5.2): an integrated environmental model tuned for tropical areas
Saulo R. Freitas
CORRESPONDING AUTHOR
Centro de Previsão de Tempo e Estudos Climáticos, Instituto
Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, Brazil
now at: Universities Space
Research Association/Goddard Earth Sciences Technology and Research at the
Global Modeling and Assimilation Office, NASA Goddard Space Flight Center,
Greenbelt, MD, USA
Jairo Panetta
Divisão de Ciência da Computação, Instituto
Tecnológico de Aeronáutica, São José dos Campos, SP, Brazil
Karla M. Longo
Centro de Previsão de Tempo e Estudos Climáticos, Instituto
Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, Brazil
now at: Universities Space
Research Association/Goddard Earth Sciences Technology and Research at the
Global Modeling and Assimilation Office, NASA Goddard Space Flight Center,
Greenbelt, MD, USA
Luiz F. Rodrigues
Centro de Previsão de Tempo e Estudos Climáticos, Instituto
Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, Brazil
Demerval S. Moreira
Universidade Estadual Paulista (Unesp), Faculdade de Ciências,
Bauru, SP, Brazil
Centro de Meteorologia de Bauru (IPMet), Bauru,
SP, Brazil
Nilton E. Rosário
Departamento de Ciências Ambientais, Universidade
Federal de São Paulo, Diadema, SP, Brazil
Pedro L. Silva Dias
Instituto de
Astronomia, Geofísica e Ciências Atmosféricas, Universidade de
São Paulo, São Paulo, SP, Brazil
Maria A. F. Silva Dias
Instituto de
Astronomia, Geofísica e Ciências Atmosféricas, Universidade de
São Paulo, São Paulo, SP, Brazil
Enio P. Souza
Departamento de
Ciências Atmosféricas, Universidade Federal de Campina Grande,
Campina Grande, PB, Brazil
Edmilson D. Freitas
Instituto de
Astronomia, Geofísica e Ciências Atmosféricas, Universidade de
São Paulo, São Paulo, SP, Brazil
Marcos Longo
Embrapa Informática
Agropecuária, Campinas, SP, Brazil
Ariane Frassoni
Centro de Previsão de Tempo e Estudos Climáticos, Instituto
Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, Brazil
Alvaro L. Fazenda
Instituto de Ciência e
Tecnologia, Universidade Federal de São Paulo, São José dos
Campos, SP, Brazil
Cláudio M. Santos e Silva
Departamento de Ciências Atmosféricas
e Climáticas/Programa de Pós graduação em ciências
Climáticas, Universidade Federal do Rio Grande do Norte,
Natal, RN, Brazil
Cláudio A. B. Pavani
Centro de Previsão de Tempo e Estudos Climáticos, Instituto
Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, Brazil
Denis Eiras
Centro de Previsão de Tempo e Estudos Climáticos, Instituto
Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, Brazil
Daniela A. França
Centro de Previsão de Tempo e Estudos Climáticos, Instituto
Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, Brazil
Daniel Massaru
Centro de Previsão de Tempo e Estudos Climáticos, Instituto
Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, Brazil
Fernanda B. Silva
Centro de Previsão de Tempo e Estudos Climáticos, Instituto
Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, Brazil
Fernando C. Santos
Centro de Ciências do Sistema Terrestre,
Instituto Nacional de Pesquisas Espaciais, São Jose dos Campos, SP,
Brazil
Gabriel Pereira
Departamento de Geociências, Universidade Federal de
São João del-Rei, MG, Brazil
Gláuber Camponogara
Instituto de
Astronomia, Geofísica e Ciências Atmosféricas, Universidade de
São Paulo, São Paulo, SP, Brazil
Gonzalo A. Ferrada
Centro de Previsão de Tempo e Estudos Climáticos, Instituto
Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, Brazil
Haroldo F. Campos Velho
Laboratório Associado de
Computação e Matemática Aplicada, Instituto Nacional de Pesquisas
Espaciais, São José dos Campos, SP, Brazil
Isilda Menezes
Instituto de Ciências Agrárias e Ambientais
Mediterrânicas, Universidade de Évora, Évora, Portugal
Centro Interdisciplinar de Desenvolvimento em Ambiente, Gestão
Aplicada e Espaço, Universidade Lusófona de
Humanidades e Tecnologia, Campo Grande, Lisbon, Portugal
Julliana L. Freire
Centro de Previsão de Tempo e Estudos Climáticos, Instituto
Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, Brazil
Marcelo F. Alonso
Faculdade de Meteorologia, Universidade Federal de Pelotas,
Pelotas, RS, Brazil
Madeleine S. Gácita
Centro de Previsão de Tempo e Estudos Climáticos, Instituto
Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, Brazil
Maurício Zarzur
Laboratório Associado de
Computação e Matemática Aplicada, Instituto Nacional de Pesquisas
Espaciais, São José dos Campos, SP, Brazil
Rafael M. Fonseca
Centro de Previsão de Tempo e Estudos Climáticos, Instituto
Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, Brazil
Rafael S. Lima
Centro de Previsão de Tempo e Estudos Climáticos, Instituto
Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, Brazil
Ricardo A. Siqueira
Centro de Previsão de Tempo e Estudos Climáticos, Instituto
Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, Brazil
Rodrigo Braz
Centro de Previsão de Tempo e Estudos Climáticos, Instituto
Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, Brazil
Simone Tomita
Centro de Previsão de Tempo e Estudos Climáticos, Instituto
Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, Brazil
Valter Oliveira
Centro de Previsão de Tempo e Estudos Climáticos, Instituto
Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, Brazil
Leila D. Martins
Universidade Tecnológica Federal do
Paraná, Londrina, PR, Brazil
Related authors
Isilda Cunha Menezes, Luiz Flávio Rodrigues, Karla M. Longo, Mateus Ferreira e Freitas, Saulo R. Freitas, Rodrigo Braz, Valter Ferreira de Oliveira, Sílvia Coelho, and Ana Isabel Miranda
EGUsphere, https://doi.org/10.5194/egusphere-2025-2495, https://doi.org/10.5194/egusphere-2025-2495, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
BRAMS v6.0 was enhanced by integrating crown fire spread into its coupled fire module, SFIRE, and dynamic smoke emissions. The model was applied to the 2017 Sertã wildfire in mountainous central Portugal. Simulations were validated against MERRA-2, accurately reproducing the smoke optical properties. Results show the model's ability to simulate radiative impacts, including CAPE and CIN displacement and inversion layer modifications.
Nilton Évora do Rosário, Karla M. Longo, Pedro H. Toso, Saulo R. Freitas, Marcia A. Yamasoe, Luiz Flávio Rodrigues, Otavio Medeiros, Haroldo Campos Velho, Isilda da Cunha Menezes, and Ana Isabel Miranda
EGUsphere, https://doi.org/10.5194/egusphere-2025-454, https://doi.org/10.5194/egusphere-2025-454, 2025
Short summary
Short summary
The present article focuses on the topic of observations to constrain aerosol optical properties in climate models . We combine a machine learning approach (based on clustering), used to identify and characterize aerosol optical regimes, with another machine learning technique (Random Forest), used to train the prescription of the identified optical regimes from a mixture of columnar mass density of different aerosol-types.
Yunyao Li, Daniel Tong, Siqi Ma, Saulo R. Freitas, Ravan Ahmadov, Mikhail Sofiev, Xiaoyang Zhang, Shobha Kondragunta, Ralph Kahn, Youhua Tang, Barry Baker, Patrick Campbell, Rick Saylor, Georg Grell, and Fangjun Li
Atmos. Chem. Phys., 23, 3083–3101, https://doi.org/10.5194/acp-23-3083-2023, https://doi.org/10.5194/acp-23-3083-2023, 2023
Short summary
Short summary
Plume height is important in wildfire smoke dispersion and affects air quality and human health. We assess the impact of plume height on wildfire smoke dispersion and the exceedances of the National Ambient Air Quality Standards. A higher plume height predicts lower pollution near the source region, but higher pollution in downwind regions, due to the faster spread of the smoke once ejected, affects pollution exceedance forecasts and the early warning of extreme air pollution events.
Gonzalo A. Ferrada, Meng Zhou, Jun Wang, Alexei Lyapustin, Yujie Wang, Saulo R. Freitas, and Gregory R. Carmichael
Geosci. Model Dev., 15, 8085–8109, https://doi.org/10.5194/gmd-15-8085-2022, https://doi.org/10.5194/gmd-15-8085-2022, 2022
Short summary
Short summary
The smoke from fires is composed of different compounds that interact with the atmosphere and can create poor air-quality episodes. Here, we present a new fire inventory based on satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS). We named this inventory the VIIRS-based Fire Emission Inventory (VFEI). Advantages of VFEI are its high resolution (~500 m) and that it provides information for many species. VFEI is publicly available and has provided data since 2012.
Aditya Kumar, R. Bradley Pierce, Ravan Ahmadov, Gabriel Pereira, Saulo Freitas, Georg Grell, Chris Schmidt, Allen Lenzen, Joshua P. Schwarz, Anne E. Perring, Joseph M. Katich, John Hair, Jose L. Jimenez, Pedro Campuzano-Jost, and Hongyu Guo
Atmos. Chem. Phys., 22, 10195–10219, https://doi.org/10.5194/acp-22-10195-2022, https://doi.org/10.5194/acp-22-10195-2022, 2022
Short summary
Short summary
We use the WRF-Chem model with new implementations of GOES-16 wildfire emissions and plume rise based on fire radiative power (FRP) to interpret aerosol observations during the 2019 NASA–NOAA FIREX-AQ field campaign and perform model evaluations. The model shows significant improvements in simulating the variety of aerosol loading environments sampled during FIREX-AQ. Our results also highlight the importance of accurate wildfire diurnal cycle and aerosol chemical mechanisms in models.
Saulo R. Freitas, Georg A. Grell, and Haiqin Li
Geosci. Model Dev., 14, 5393–5411, https://doi.org/10.5194/gmd-14-5393-2021, https://doi.org/10.5194/gmd-14-5393-2021, 2021
Short summary
Short summary
Convection parameterization (CP) is a component of atmospheric models aiming to represent the statistical effects of subgrid-scale convective clouds. Because the atmosphere contains circulations with a broad spectrum of scales, the truncation needed to run models in computers requires the introduction of parameterizations to account for processes that are not explicitly resolved. We detail recent developments in the Grell–Freitas CP, which has been applied in several regional and global models.
Fernando Santos, Karla Longo, Alex Guenther, Saewung Kim, Dasa Gu, Dave Oram, Grant Forster, James Lee, James Hopkins, Joel Brito, and Saulo Freitas
Atmos. Chem. Phys., 18, 12715–12734, https://doi.org/10.5194/acp-18-12715-2018, https://doi.org/10.5194/acp-18-12715-2018, 2018
Short summary
Short summary
We investigated the impact of biomass burning on the chemical composition of trace gases in the Amazon. The findings corroborate the influence of biomass burning activity not only on direct emissions of particulate matter but also on the oxidative capacity to produce secondary organic aerosol. The scientists plan to use this information to improve the numerical model simulation with a better representativeness of the chemical processes, which can impact on global climate prediction.
Paulo R. Teixeira, Saulo R. de Freitas, Francis W. Correia, and Antonio O. Manzi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-81, https://doi.org/10.5194/gmd-2018-81, 2018
Publication in GMD not foreseen
Short summary
Short summary
Emissions of gases and particulates in urban areas are associated with a mixture of various sources, both natural and anthropogenic. Understanding and quantifying these emissions is necessary in studies of climate change, local air pollution issues, and weather modification. This work will also contribute to improved air quality numerical simulations, provide more accurate scenarios for policymakers and regulatory agencies to develop strategies for controlling the vehicular emissions.
Demerval S. Moreira, Karla M. Longo, Saulo R. Freitas, Marcia A. Yamasoe, Lina M. Mercado, Nilton E. Rosário, Emauel Gloor, Rosane S. M. Viana, John B. Miller, Luciana V. Gatti, Kenia T. Wiedemann, Lucas K. G. Domingues, and Caio C. S. Correia
Atmos. Chem. Phys., 17, 14785–14810, https://doi.org/10.5194/acp-17-14785-2017, https://doi.org/10.5194/acp-17-14785-2017, 2017
Short summary
Short summary
Fire in the Amazon forest produces a large amount of smoke that is released into the atmosphere and covers a large portion of South America for about 3 months each year. The smoke affects the energy and CO2 budgets. Using a numerical atmospheric model, we demonstrated that the smoke changes the forest from a source to a sink of CO2 to the atmosphere. The smoke ultimately acts to at least partially compensate for the forest carbon lost due to fire emissions.
Madeleine Sánchez Gácita, Karla M. Longo, Julliana L. M. Freire, Saulo R. Freitas, and Scot T. Martin
Atmos. Chem. Phys., 17, 2373–2392, https://doi.org/10.5194/acp-17-2373-2017, https://doi.org/10.5194/acp-17-2373-2017, 2017
Short summary
Short summary
This study uses an adiabatic cloud model to simulate the activation of smoke aerosol particles in the Amazon region as cloud condensation nuclei (CCN). The relative importance of variability in hygroscopicity, mixing state, and activation kinetics for the activated fraction and maximum supersaturation is assessed. Our findings on uncertainties and sensitivities provide guidance on appropriate simplifications that can be used for modeling of smoke aerosols within general circulation models.
Carolin Walter, Saulo R. Freitas, Christoph Kottmeier, Isabel Kraut, Daniel Rieger, Heike Vogel, and Bernhard Vogel
Atmos. Chem. Phys., 16, 9201–9219, https://doi.org/10.5194/acp-16-9201-2016, https://doi.org/10.5194/acp-16-9201-2016, 2016
Short summary
Short summary
Buoyancy produced by vegetation fires can lead to substantial plume rise with consequences for the dispersion of aerosol emitted by the fires. To study this effect a 1-D plume rise model was included into the regional online integrated model system COSMO-ART. Comparing model results and satellite data for a case study of 2010 Canadian wildfires shows, that the plume rise model outperforms prescribed emission height. The radiative impact of the aerosol leads to a pronounced temperature change.
Gabriel Pereira, Ricardo Siqueira, Nilton E. Rosário, Karla L. Longo, Saulo R. Freitas, Francielle S. Cardozo, Johannes W. Kaiser, and Martin J. Wooster
Atmos. Chem. Phys., 16, 6961–6975, https://doi.org/10.5194/acp-16-6961-2016, https://doi.org/10.5194/acp-16-6961-2016, 2016
Short summary
Short summary
Fires associated with land use and land cover changes release large amounts of aerosols and trace gases into the atmosphere. Although several inventories of biomass burning emissions cover Brazil, there are still considerable uncertainties and differences among them. However, results indicate that emission derived via similar methods tend to agree with one other, but aerosol emissions from fires with particularly high biomass consumption still lead to an underestimation.
R. Paugam, M. Wooster, S. Freitas, and M. Val Martin
Atmos. Chem. Phys., 16, 907–925, https://doi.org/10.5194/acp-16-907-2016, https://doi.org/10.5194/acp-16-907-2016, 2016
Short summary
Short summary
Landscape fire plume height controls fire emissions release in the atmosphere, in particular their transport that may also affect the longevity, chemical conversion, and fate of the plumes chemical constituents. Here, we review how such landscape-scale fire smoke plume injection heights are represented in large-scale atmospheric transport models aiming to represent the impacts of wildfire emissions on component of the Earth system.
R. Paugam, M. Wooster, J. Atherton, S. R. Freitas, M. G. Schultz, and J. W. Kaiser
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-15-9815-2015, https://doi.org/10.5194/acpd-15-9815-2015, 2015
Revised manuscript not accepted
Short summary
Short summary
The transport of Biomass Burning emissions in Chemical Transport Model rely on parametrization of plumes injection height. Using fire observation selected to ensure match-up of fire-atmosphere-plume dynamics; a popular plume rise model was improved and optimized. The resulting model shows response to the effect of atmospheric stability consistent with previous findings and is able to predict higher injection height than any other tested parametrizations, giving a closer match with observation.
S. Archer-Nicholls, D. Lowe, E. Darbyshire, W. T. Morgan, M. M. Bela, G. Pereira, J. Trembath, J. W. Kaiser, K. M. Longo, S. R. Freitas, H. Coe, and G. McFiggans
Geosci. Model Dev., 8, 549–577, https://doi.org/10.5194/gmd-8-549-2015, https://doi.org/10.5194/gmd-8-549-2015, 2015
Short summary
Short summary
The regional WRF-Chem model was used to study aerosol particles from biomass burning in South America. The modelled estimates of fire plume injection heights were found to be too high, with serious implications for modelled aerosol vertical distribution, transport and impacts on local climate. A modified emission scenario was developed which improved the predicted injection height. Model results were compared and evaluated against in situ measurements from the 2012 SAMBBA flight campaign.
M. M. Bela, K. M. Longo, S. R. Freitas, D. S. Moreira, V. Beck, S. C. Wofsy, C. Gerbig, K. Wiedemann, M. O. Andreae, and P. Artaxo
Atmos. Chem. Phys., 15, 757–782, https://doi.org/10.5194/acp-15-757-2015, https://doi.org/10.5194/acp-15-757-2015, 2015
Short summary
Short summary
In the Amazon Basin, gases that lead to the formation of ozone (O3), an air pollutant and greenhouse gas, are emitted from fire, urban and biogenic sources. This study presents the first basin wide aircraft measurements of O3 during the dry-to-wet and wet-to-dry transition seasons, which show extremely low values above undisturbed forest and increases from fires. This work also demonstrates the capabilities and limitations of regional atmospheric chemistry models in representing O3 in Amazonia.
J. Brito, L. V. Rizzo, W. T. Morgan, H. Coe, B. Johnson, J. Haywood, K. Longo, S. Freitas, M. O. Andreae, and P. Artaxo
Atmos. Chem. Phys., 14, 12069–12083, https://doi.org/10.5194/acp-14-12069-2014, https://doi.org/10.5194/acp-14-12069-2014, 2014
Short summary
Short summary
This paper details the physical--chemical characteristics of aerosols in a region strongly impacted by biomass burning in the western part of the Brazilian Amazon region. For such, a large suite of state-of-the-art instruments for realtime analysis was deployed at a ground site. Among the key findings, we observe the strong prevalence of organic aerosols associated to fire emissions, with important climate effects, and indications of its very fast processing in the atmosphere.
G. A. Grell and S. R. Freitas
Atmos. Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014, https://doi.org/10.5194/acp-14-5233-2014, 2014
A. F. dos Santos, S. R. Freitas, J. G. Z. de Mattos, H. F. de Campos Velho, M. A. Gan, E. F. P. da Luz, and G. A. Grell
Adv. Geosci., 35, 123–136, https://doi.org/10.5194/adgeo-35-123-2013, https://doi.org/10.5194/adgeo-35-123-2013, 2013
K. M. Longo, S. R. Freitas, M. Pirre, V. Marécal, L. F. Rodrigues, J. Panetta, M. F. Alonso, N. E. Rosário, D. S. Moreira, M. S. Gácita, J. Arteta, R. Fonseca, R. Stockler, D. M. Katsurayama, A. Fazenda, and M. Bela
Geosci. Model Dev., 6, 1389–1405, https://doi.org/10.5194/gmd-6-1389-2013, https://doi.org/10.5194/gmd-6-1389-2013, 2013
D. S. Moreira, S. R. Freitas, J. P. Bonatti, L. M. Mercado, N. M. É. Rosário, K. M. Longo, J. B. Miller, M. Gloor, and L. V. Gatti
Geosci. Model Dev., 6, 1243–1259, https://doi.org/10.5194/gmd-6-1243-2013, https://doi.org/10.5194/gmd-6-1243-2013, 2013
V. Beck, C. Gerbig, T. Koch, M. M. Bela, K. M. Longo, S. R. Freitas, J. O. Kaplan, C. Prigent, P. Bergamaschi, and M. Heimann
Atmos. Chem. Phys., 13, 7961–7982, https://doi.org/10.5194/acp-13-7961-2013, https://doi.org/10.5194/acp-13-7961-2013, 2013
M. Stuefer, S. R. Freitas, G. Grell, P. Webley, S. Peckham, S. A. McKeen, and S. D. Egan
Geosci. Model Dev., 6, 457–468, https://doi.org/10.5194/gmd-6-457-2013, https://doi.org/10.5194/gmd-6-457-2013, 2013
N. E. Rosário, K. M. Longo, S. R. Freitas, M. A. Yamasoe, and R. M. Fonseca
Atmos. Chem. Phys., 13, 2923–2938, https://doi.org/10.5194/acp-13-2923-2013, https://doi.org/10.5194/acp-13-2923-2013, 2013
S. Strada, S. R. Freitas, C. Mari, K. M. Longo, and R. Paugam
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-6-721-2013, https://doi.org/10.5194/gmdd-6-721-2013, 2013
Preprint withdrawn
Isilda Cunha Menezes, Luiz Flávio Rodrigues, Karla M. Longo, Mateus Ferreira e Freitas, Saulo R. Freitas, Rodrigo Braz, Valter Ferreira de Oliveira, Sílvia Coelho, and Ana Isabel Miranda
EGUsphere, https://doi.org/10.5194/egusphere-2025-2495, https://doi.org/10.5194/egusphere-2025-2495, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
BRAMS v6.0 was enhanced by integrating crown fire spread into its coupled fire module, SFIRE, and dynamic smoke emissions. The model was applied to the 2017 Sertã wildfire in mountainous central Portugal. Simulations were validated against MERRA-2, accurately reproducing the smoke optical properties. Results show the model's ability to simulate radiative impacts, including CAPE and CIN displacement and inversion layer modifications.
Amauri C. Prudente Junior, Luiz A. T. Machado, Felipe S. Silva, Tercio Ambrizzi, Paulo Artaxo, Santiago Botia, Luan P. Cordeiro, Cleo Q. Dias Junior, Edmilson Freitas, Demerval S. Moreira, Christopher Pöhlker, Ivan M. C. Toro, Xiyan Xu, and Luciana V. Rizzo
EGUsphere, https://doi.org/10.5194/egusphere-2025-2869, https://doi.org/10.5194/egusphere-2025-2869, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
This study propoes a new method of spatialization to estimate carbon fluxes in the Brazilian Amazon biome. To do so, was used a land surface model (JULES) and two vegetation properties. The results of this spatialization resulted in a carbon fluxes of -1.34 Pg C during the year of 2021 in the entire Brazilian Amazon biome being the states of Amapa and Acre main relevant regions of carbon source.
Konstantin Gregor, Benjamin F. Meyer, Tillmann Gaida, Victor Justo Vasquez, Karina Bett-Williams, Matthew Forrest, João P. Darela-Filho, Sam Rabin, Marcos Longo, Joe R. Melton, Johan Nord, Peter Anthoni, Vladislav Bastrikov, Thomas Colligan, Christine Delire, Michael C. Dietze, George Hurtt, Akihiko Ito, Lasse T. Keetz, Jürgen Knauer, Johannes Köster, Tzu-Shun Lin, Lei Ma, Marie Minvielle, Stefan Olin, Sebastian Ostberg, Hao Shi, Reiner Schnur, Urs Schönenberger, Qing Sun, Peter E. Thornton, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2025-1733, https://doi.org/10.5194/egusphere-2025-1733, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Geoscientific models are crucial for understanding Earth’s processes. However, they sometimes do not adhere to highest software quality standards, and scientific results are often hard to reproduce due to the complexity of the workflows. Here we gather the expertise of 20 modeling groups and software engineers to define best practices for making geoscientific models maintainable, usable, and reproducible. We conclude with an open-source example serving as a reference for modeling communities.
Guilherme Martins Pereira, Leonardo Yoshiaki Kamigauti, Rubens Fabio Pereira, Djacinto Monteiro dos Santos, Thayná da Silva Santos, José Vinicius Martins, Célia Alves, Cátia Gonçalves, Ismael Casotti Rienda, Nora Kováts, Thiago Nogueira, Luciana Rizzo, Paulo Artaxo, Regina Maura de Miranda, Marcia Akemi Yamasoe, Edmilson Dias de Freitas, Pérola de Castro Vasconcellos, and Maria de Fatima Andrade
Atmos. Chem. Phys., 25, 4587–4616, https://doi.org/10.5194/acp-25-4587-2025, https://doi.org/10.5194/acp-25-4587-2025, 2025
Short summary
Short summary
The chemical composition of fine particulate matter was studied in the megacity of São Paulo (Brazil) during a polluted period. Vehicular-related sources remain relevant; however, a high contribution of biomass burning was observed and correlated with sample ecotoxicity. Emerging biomass burning sources, such as forest fires and sugarcane-bagasse-based power plants, highlight the need for additional control measures alongside stricter rules concerning vehicular emissions.
Nilton Évora do Rosário, Karla M. Longo, Pedro H. Toso, Saulo R. Freitas, Marcia A. Yamasoe, Luiz Flávio Rodrigues, Otavio Medeiros, Haroldo Campos Velho, Isilda da Cunha Menezes, and Ana Isabel Miranda
EGUsphere, https://doi.org/10.5194/egusphere-2025-454, https://doi.org/10.5194/egusphere-2025-454, 2025
Short summary
Short summary
The present article focuses on the topic of observations to constrain aerosol optical properties in climate models . We combine a machine learning approach (based on clustering), used to identify and characterize aerosol optical regimes, with another machine learning technique (Random Forest), used to train the prescription of the identified optical regimes from a mixture of columnar mass density of different aerosol-types.
Jorge Rosas Santana, Gabriela Lima da Silva, Marcia Akemi Yamasoe, and Nilton Èvora do Rosario
EGUsphere, https://doi.org/10.5194/egusphere-2025-9, https://doi.org/10.5194/egusphere-2025-9, 2025
Short summary
Short summary
This study examines a rare event in São Paulo, Brazil, where wildfire smoke from South America mixed with clouds, causing midday darkness on 19 August 2019. Satellite data, surface measurements, and air mass modeling tracked the smoke from fires in Brazil, Bolivia, and Paraguay, transported to São Paulo within two days. The smoke-cloud interaction reduced surface irradiance to zero for 40 minutes and increased radiative efficiency by 7 %, highlighting impacts on air quality, energy, and climate.
Abdulamid A. Fakoya, Jens Redemann, Pablo E. Saide, Lan Gao, Logan T. Mitchell, Calvin Howes, Amie Dobracki, Ian Chang, Gonzalo A. Ferrada, Kristina Pistone, Samuel E. Leblanc, Michal Segal-Rozenhaimer, Arthur J. Sedlacek III, Thomas Eck, Brent Holben, Pawan Gupta, Elena Lind, Paquita Zuidema, Gregory Carmichael, and Connor J. Flynn
EGUsphere, https://doi.org/10.5194/egusphere-2024-3197, https://doi.org/10.5194/egusphere-2024-3197, 2025
Short summary
Short summary
Tiny atmospheric particles from wildfire smoke impact climate by interacting with sunlight and clouds, the extent of which is uncertain due to gaps in understanding how smoke changes over time. We developed a new method using remote sensing instruments to track how these particles evolve during atmospheric transport. Our results show that the ability of these particles to absorb sunlight increased as they travel. This discovery could help improve predictions of future climate scenarios.
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
Short summary
Short summary
Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Ian Chang, Lan Gao, Connor J. Flynn, Yohei Shinozuka, Sarah J. Doherty, Michael S. Diamond, Karla M. Longo, Gonzalo A. Ferrada, Gregory R. Carmichael, Patricia Castellanos, Arlindo M. da Silva, Pablo E. Saide, Calvin Howes, Zhixin Xue, Marc Mallet, Ravi Govindaraju, Qiaoqiao Wang, Yafang Cheng, Yan Feng, Sharon P. Burton, Richard A. Ferrare, Samuel E. LeBlanc, Meloë S. Kacenelenbogen, Kristina Pistone, Michal Segal-Rozenhaimer, Kerry G. Meyer, Ju-Mee Ryoo, Leonhard Pfister, Adeyemi A. Adebiyi, Robert Wood, Paquita Zuidema, Sundar A. Christopher, and Jens Redemann
Atmos. Chem. Phys., 23, 4283–4309, https://doi.org/10.5194/acp-23-4283-2023, https://doi.org/10.5194/acp-23-4283-2023, 2023
Short summary
Short summary
Abundant aerosols are present above low-level liquid clouds over the southeastern Atlantic during late austral spring. The model simulation differences in the proportion of aerosol residing in the planetary boundary layer and in the free troposphere can greatly affect the regional aerosol radiative effects. This study examines the aerosol loading and fractional aerosol loading in the free troposphere among various models and evaluates them against measurements from the NASA ORACLES campaign.
Yunyao Li, Daniel Tong, Siqi Ma, Saulo R. Freitas, Ravan Ahmadov, Mikhail Sofiev, Xiaoyang Zhang, Shobha Kondragunta, Ralph Kahn, Youhua Tang, Barry Baker, Patrick Campbell, Rick Saylor, Georg Grell, and Fangjun Li
Atmos. Chem. Phys., 23, 3083–3101, https://doi.org/10.5194/acp-23-3083-2023, https://doi.org/10.5194/acp-23-3083-2023, 2023
Short summary
Short summary
Plume height is important in wildfire smoke dispersion and affects air quality and human health. We assess the impact of plume height on wildfire smoke dispersion and the exceedances of the National Ambient Air Quality Standards. A higher plume height predicts lower pollution near the source region, but higher pollution in downwind regions, due to the faster spread of the smoke once ejected, affects pollution exceedance forecasts and the early warning of extreme air pollution events.
Gonzalo A. Ferrada, Meng Zhou, Jun Wang, Alexei Lyapustin, Yujie Wang, Saulo R. Freitas, and Gregory R. Carmichael
Geosci. Model Dev., 15, 8085–8109, https://doi.org/10.5194/gmd-15-8085-2022, https://doi.org/10.5194/gmd-15-8085-2022, 2022
Short summary
Short summary
The smoke from fires is composed of different compounds that interact with the atmosphere and can create poor air-quality episodes. Here, we present a new fire inventory based on satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS). We named this inventory the VIIRS-based Fire Emission Inventory (VFEI). Advantages of VFEI are its high resolution (~500 m) and that it provides information for many species. VFEI is publicly available and has provided data since 2012.
Aditya Kumar, R. Bradley Pierce, Ravan Ahmadov, Gabriel Pereira, Saulo Freitas, Georg Grell, Chris Schmidt, Allen Lenzen, Joshua P. Schwarz, Anne E. Perring, Joseph M. Katich, John Hair, Jose L. Jimenez, Pedro Campuzano-Jost, and Hongyu Guo
Atmos. Chem. Phys., 22, 10195–10219, https://doi.org/10.5194/acp-22-10195-2022, https://doi.org/10.5194/acp-22-10195-2022, 2022
Short summary
Short summary
We use the WRF-Chem model with new implementations of GOES-16 wildfire emissions and plume rise based on fire radiative power (FRP) to interpret aerosol observations during the 2019 NASA–NOAA FIREX-AQ field campaign and perform model evaluations. The model shows significant improvements in simulating the variety of aerosol loading environments sampled during FIREX-AQ. Our results also highlight the importance of accurate wildfire diurnal cycle and aerosol chemical mechanisms in models.
Jiaying Zhang, Rafael L. Bras, Marcos Longo, and Tamara Heartsill Scalley
Geosci. Model Dev., 15, 5107–5126, https://doi.org/10.5194/gmd-15-5107-2022, https://doi.org/10.5194/gmd-15-5107-2022, 2022
Short summary
Short summary
We implemented hurricane disturbance in a vegetation dynamics model and calibrated the model with observations of a tropical forest. We used the model to study forest recovery from hurricane disturbance and found that a single hurricane disturbance enhances AGB and BA in the long term compared with a no-hurricane situation. The model developed and results presented in this study can be utilized to understand the impact of hurricane disturbances on forest recovery under the changing climate.
Elias C. Massoud, A. Anthony Bloom, Marcos Longo, John T. Reager, Paul A. Levine, and John R. Worden
Hydrol. Earth Syst. Sci., 26, 1407–1423, https://doi.org/10.5194/hess-26-1407-2022, https://doi.org/10.5194/hess-26-1407-2022, 2022
Short summary
Short summary
The water balance on river basin scales depends on a number of soil physical processes. Gaining information on these quantities using observations is a key step toward improving the skill of land surface hydrology models. In this study, we use data from the Gravity Recovery and Climate Experiment (NASA-GRACE) to inform and constrain these hydrologic processes. We show that our model is able to simulate the land hydrologic cycle for a watershed in the Amazon from January 2003 to December 2012.
Sarah J. Doherty, Pablo E. Saide, Paquita Zuidema, Yohei Shinozuka, Gonzalo A. Ferrada, Hamish Gordon, Marc Mallet, Kerry Meyer, David Painemal, Steven G. Howell, Steffen Freitag, Amie Dobracki, James R. Podolske, Sharon P. Burton, Richard A. Ferrare, Calvin Howes, Pierre Nabat, Gregory R. Carmichael, Arlindo da Silva, Kristina Pistone, Ian Chang, Lan Gao, Robert Wood, and Jens Redemann
Atmos. Chem. Phys., 22, 1–46, https://doi.org/10.5194/acp-22-1-2022, https://doi.org/10.5194/acp-22-1-2022, 2022
Short summary
Short summary
Between July and October, biomass burning smoke is advected over the southeastern Atlantic Ocean, leading to climate forcing. Model calculations of forcing by this plume vary significantly in both magnitude and sign. This paper compares aerosol and cloud properties observed during three NASA ORACLES field campaigns to the same in four models. It quantifies modeled biases in properties key to aerosol direct radiative forcing and evaluates how these biases propagate to biases in forcing.
Xinxin Ye, Pargoal Arab, Ravan Ahmadov, Eric James, Georg A. Grell, Bradley Pierce, Aditya Kumar, Paul Makar, Jack Chen, Didier Davignon, Greg R. Carmichael, Gonzalo Ferrada, Jeff McQueen, Jianping Huang, Rajesh Kumar, Louisa Emmons, Farren L. Herron-Thorpe, Mark Parrington, Richard Engelen, Vincent-Henri Peuch, Arlindo da Silva, Amber Soja, Emily Gargulinski, Elizabeth Wiggins, Johnathan W. Hair, Marta Fenn, Taylor Shingler, Shobha Kondragunta, Alexei Lyapustin, Yujie Wang, Brent Holben, David M. Giles, and Pablo E. Saide
Atmos. Chem. Phys., 21, 14427–14469, https://doi.org/10.5194/acp-21-14427-2021, https://doi.org/10.5194/acp-21-14427-2021, 2021
Short summary
Short summary
Wildfire smoke has crucial impacts on air quality, while uncertainties in the numerical forecasts remain significant. We present an evaluation of 12 real-time forecasting systems. Comparison of predicted smoke emissions suggests a large spread in magnitudes, with temporal patterns deviating from satellite detections. The performance for AOD and surface PM2.5 and their discrepancies highlighted the role of accurately represented spatiotemporal emission profiles in improving smoke forecasts.
Saulo R. Freitas, Georg A. Grell, and Haiqin Li
Geosci. Model Dev., 14, 5393–5411, https://doi.org/10.5194/gmd-14-5393-2021, https://doi.org/10.5194/gmd-14-5393-2021, 2021
Short summary
Short summary
Convection parameterization (CP) is a component of atmospheric models aiming to represent the statistical effects of subgrid-scale convective clouds. Because the atmosphere contains circulations with a broad spectrum of scales, the truncation needed to run models in computers requires the introduction of parameterizations to account for processes that are not explicitly resolved. We detail recent developments in the Grell–Freitas CP, which has been applied in several regional and global models.
Kristina Pistone, Paquita Zuidema, Robert Wood, Michael Diamond, Arlindo M. da Silva, Gonzalo Ferrada, Pablo E. Saide, Rei Ueyama, Ju-Mee Ryoo, Leonhard Pfister, James Podolske, David Noone, Ryan Bennett, Eric Stith, Gregory Carmichael, Jens Redemann, Connor Flynn, Samuel LeBlanc, Michal Segal-Rozenhaimer, and Yohei Shinozuka
Atmos. Chem. Phys., 21, 9643–9668, https://doi.org/10.5194/acp-21-9643-2021, https://doi.org/10.5194/acp-21-9643-2021, 2021
Short summary
Short summary
Using aircraft-based measurements off the Atlantic coast of Africa, we found the springtime smoke plume was strongly correlated with the amount of water vapor in the atmosphere (more smoke indicated more humidity). We see the same general feature in satellite-assimilated and free-running models. Our analysis suggests this relationship is not caused by the burning but originates due to coincident continental meteorology plus fires. This air is transported over the ocean without further mixing.
Marcia Akemi Yamasoe, Nilton Manuel Évora Rosário, Samantha Novaes Santos Martins Almeida, and Martin Wild
Atmos. Chem. Phys., 21, 6593–6603, https://doi.org/10.5194/acp-21-6593-2021, https://doi.org/10.5194/acp-21-6593-2021, 2021
Short summary
Short summary
Spatio-temporal disparity to assess global dimming and brightening phenomena has been a critical topic. For instance, few studies addressed surface solar irradiation (SSR) long-term trend in South America. In this study, SSR, sunshine duration (SD) and the diurnal temperature range (DTR) are analysed for São Paulo, Brazil. We found a dimming phase, identified by SSR, SD and DTR, extending till 1983. Then, while SSR is still declining, consistent with cloud increasing, SD and DTR are increasing.
Jens Redemann, Robert Wood, Paquita Zuidema, Sarah J. Doherty, Bernadette Luna, Samuel E. LeBlanc, Michael S. Diamond, Yohei Shinozuka, Ian Y. Chang, Rei Ueyama, Leonhard Pfister, Ju-Mee Ryoo, Amie N. Dobracki, Arlindo M. da Silva, Karla M. Longo, Meloë S. Kacenelenbogen, Connor J. Flynn, Kristina Pistone, Nichola M. Knox, Stuart J. Piketh, James M. Haywood, Paola Formenti, Marc Mallet, Philip Stier, Andrew S. Ackerman, Susanne E. Bauer, Ann M. Fridlind, Gregory R. Carmichael, Pablo E. Saide, Gonzalo A. Ferrada, Steven G. Howell, Steffen Freitag, Brian Cairns, Brent N. Holben, Kirk D. Knobelspiesse, Simone Tanelli, Tristan S. L'Ecuyer, Andrew M. Dzambo, Ousmane O. Sy, Greg M. McFarquhar, Michael R. Poellot, Siddhant Gupta, Joseph R. O'Brien, Athanasios Nenes, Mary Kacarab, Jenny P. S. Wong, Jennifer D. Small-Griswold, Kenneth L. Thornhill, David Noone, James R. Podolske, K. Sebastian Schmidt, Peter Pilewskie, Hong Chen, Sabrina P. Cochrane, Arthur J. Sedlacek, Timothy J. Lang, Eric Stith, Michal Segal-Rozenhaimer, Richard A. Ferrare, Sharon P. Burton, Chris A. Hostetler, David J. Diner, Felix C. Seidel, Steven E. Platnick, Jeffrey S. Myers, Kerry G. Meyer, Douglas A. Spangenberg, Hal Maring, and Lan Gao
Atmos. Chem. Phys., 21, 1507–1563, https://doi.org/10.5194/acp-21-1507-2021, https://doi.org/10.5194/acp-21-1507-2021, 2021
Short summary
Short summary
Southern Africa produces significant biomass burning emissions whose impacts on regional and global climate are poorly understood. ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) is a 5-year NASA investigation designed to study the key processes that determine these climate impacts. The main purpose of this paper is to familiarize the broader scientific community with the ORACLES project, the dataset it produced, and the most important initial findings.
Breno Raphaldini, André S. W. Teruya, Pedro Leite da Silva Dias, Lucas Massaroppe, and Daniel Yasumasa Takahashi
Earth Syst. Dynam., 12, 83–101, https://doi.org/10.5194/esd-12-83-2021, https://doi.org/10.5194/esd-12-83-2021, 2021
Short summary
Short summary
Several recent studies suggest a modulation of the Madden–Julian oscillation (MJO) by the quasi-biennial oscillation (QBO). The physics behind this interaction, however, remain poorly understood. In this study, we investigated the QBO–MJO interaction and the role of stratospheric ozone as a forcing mechanism. A normal-mode decomposition procedure combined with causality analysis reveals significant interactions between MJO-related modes and QBO-related modes.
Maoyi Huang, Yi Xu, Marcos Longo, Michael Keller, Ryan G. Knox, Charles D. Koven, and Rosie A. Fisher
Biogeosciences, 17, 4999–5023, https://doi.org/10.5194/bg-17-4999-2020, https://doi.org/10.5194/bg-17-4999-2020, 2020
Short summary
Short summary
The Functionally Assembled Terrestrial Ecosystem Simulator (FATES) is enhanced to mimic the ecological, biophysical, and biogeochemical processes following a logging event. The model can specify the timing and aerial extent of logging events; determine the survivorship of cohorts in the disturbed forest; and modifying the biomass, coarse woody debris, and litter pools. This study lays the foundation to simulate land use change and forest degradation in FATES as part of an Earth system model.
Yohei Shinozuka, Pablo E. Saide, Gonzalo A. Ferrada, Sharon P. Burton, Richard Ferrare, Sarah J. Doherty, Hamish Gordon, Karla Longo, Marc Mallet, Yan Feng, Qiaoqiao Wang, Yafang Cheng, Amie Dobracki, Steffen Freitag, Steven G. Howell, Samuel LeBlanc, Connor Flynn, Michal Segal-Rosenhaimer, Kristina Pistone, James R. Podolske, Eric J. Stith, Joseph Ryan Bennett, Gregory R. Carmichael, Arlindo da Silva, Ravi Govindaraju, Ruby Leung, Yang Zhang, Leonhard Pfister, Ju-Mee Ryoo, Jens Redemann, Robert Wood, and Paquita Zuidema
Atmos. Chem. Phys., 20, 11491–11526, https://doi.org/10.5194/acp-20-11491-2020, https://doi.org/10.5194/acp-20-11491-2020, 2020
Short summary
Short summary
In the southeast Atlantic, well-defined smoke plumes from Africa advect over marine boundary layer cloud decks; both are most extensive around September, when most of the smoke resides in the free troposphere. A framework is put forth for evaluating the performance of a range of global and regional atmospheric composition models against observations made during the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) airborne mission in September 2016.
Kirk Knobelspiesse, Henrique M. J. Barbosa, Christine Bradley, Carol Bruegge, Brian Cairns, Gao Chen, Jacek Chowdhary, Anthony Cook, Antonio Di Noia, Bastiaan van Diedenhoven, David J. Diner, Richard Ferrare, Guangliang Fu, Meng Gao, Michael Garay, Johnathan Hair, David Harper, Gerard van Harten, Otto Hasekamp, Mark Helmlinger, Chris Hostetler, Olga Kalashnikova, Andrew Kupchock, Karla Longo De Freitas, Hal Maring, J. Vanderlei Martins, Brent McBride, Matthew McGill, Ken Norlin, Anin Puthukkudy, Brian Rheingans, Jeroen Rietjens, Felix C. Seidel, Arlindo da Silva, Martijn Smit, Snorre Stamnes, Qian Tan, Sebastian Val, Andrzej Wasilewski, Feng Xu, Xiaoguang Xu, and John Yorks
Earth Syst. Sci. Data, 12, 2183–2208, https://doi.org/10.5194/essd-12-2183-2020, https://doi.org/10.5194/essd-12-2183-2020, 2020
Short summary
Short summary
The Aerosol Characterization from Polarimeter and Lidar (ACEPOL) field campaign is a resource for the next generation of spaceborne multi-angle polarimeter (MAP) and lidar missions. Conducted in the fall of 2017 from the Armstrong Flight Research Center in Palmdale, California, four MAP instruments and two lidars were flown on the high-altitude ER-2 aircraft over a variety of scene types and ground assets. Data are freely available to the public and useful for algorithm development and testing.
Renato Kerches Braghiere, Marcia Akemi Yamasoe, Nilton Manuel Évora do Rosário, Humberto Ribeiro da Rocha, José de Souza Nogueira, and Alessandro Carioca de Araújo
Atmos. Chem. Phys., 20, 3439–3458, https://doi.org/10.5194/acp-20-3439-2020, https://doi.org/10.5194/acp-20-3439-2020, 2020
Short summary
Short summary
We evaluate how the interaction of smoke with sun light impacts the exchange of energy and mass between vegetation and the atmosphere using a machine learning technique. We found an effect of the smoke on CO2, energy, and water fluxes, linking the effects of smoke with temperature, humidity, and winds. CO2 exchange increased by up to 55 % in the presence of smoke. A decrease of 12 % was observed for a site with simpler vegetation. Energy fluxes were negatively impacted for all study sites.
Marcos Longo, Ryan G. Knox, David M. Medvigy, Naomi M. Levine, Michael C. Dietze, Yeonjoo Kim, Abigail L. S. Swann, Ke Zhang, Christine R. Rollinson, Rafael L. Bras, Steven C. Wofsy, and Paul R. Moorcroft
Geosci. Model Dev., 12, 4309–4346, https://doi.org/10.5194/gmd-12-4309-2019, https://doi.org/10.5194/gmd-12-4309-2019, 2019
Short summary
Short summary
Our paper describes the Ecosystem Demography model. This computer program calculates how plants and ground exchange heat, water, and carbon with the air, and how plants grow, reproduce and die in different climates. Most models simplify forests to an average big tree. We consider that tall, deep-rooted trees get more light and water than small plants, and that some plants can with shade and drought. This diversity helps us to better explain how plants live and interact with the atmosphere.
Marcos Longo, Ryan G. Knox, Naomi M. Levine, Abigail L. S. Swann, David M. Medvigy, Michael C. Dietze, Yeonjoo Kim, Ke Zhang, Damien Bonal, Benoit Burban, Plínio B. Camargo, Matthew N. Hayek, Scott R. Saleska, Rodrigo da Silva, Rafael L. Bras, Steven C. Wofsy, and Paul R. Moorcroft
Geosci. Model Dev., 12, 4347–4374, https://doi.org/10.5194/gmd-12-4347-2019, https://doi.org/10.5194/gmd-12-4347-2019, 2019
Short summary
Short summary
The Ecosystem Demography model calculates the fluxes of heat, water, and carbon between plants and ground and the air, and the life cycle of plants in different climates. To test if our calculations were reasonable, we compared our results with field and satellite measurements. Our model predicts well the extent of the Amazon forest, how much light forests absorb, and how much water forests release to the air. However, it must improve the tree growth rates and how fast dead plants decompose.
Marcos A. S. Scaranello, Michael Keller, Marcos Longo, Maiza N. dos-Santos, Veronika Leitold, Douglas C. Morton, Ekena R. Pinagé, and Fernando Del Bon Espírito-Santo
Biogeosciences, 16, 3457–3474, https://doi.org/10.5194/bg-16-3457-2019, https://doi.org/10.5194/bg-16-3457-2019, 2019
Short summary
Short summary
The coarse dead wood component of the tropical forest carbon pool is rarely measured. For the first time, we developed models for predicting coarse dead wood in Amazonian forests by using airborne laser scanning data. Our models produced site-based estimates similar to independent field estimates found in the literature. Our study provides an approach for estimating coarse dead wood pools from remotely sensed data and mapping those pools over large scales in intact and degraded forests.
Lianet Hernández Pardo, Luiz Augusto Toledo Machado, Micael Amore Cecchini, and Madeleine Sánchez Gácita
Atmos. Chem. Phys., 19, 7839–7857, https://doi.org/10.5194/acp-19-7839-2019, https://doi.org/10.5194/acp-19-7839-2019, 2019
Short summary
Short summary
This work analyzes the effects of changes in the environmental aerosol population on Amazonian clouds. The results confirm that the clouds can be very sensitive to changes in the aerosol properties, but the relative importance of each property is variable and depends on the values of the other aerosol characteristics, especially aerosol size. This is controlled by the degree to which the cloud mixes with the surrounding air and by the efficiency with which aerosols are consumed by droplets.
Fernando Santos, Karla Longo, Alex Guenther, Saewung Kim, Dasa Gu, Dave Oram, Grant Forster, James Lee, James Hopkins, Joel Brito, and Saulo Freitas
Atmos. Chem. Phys., 18, 12715–12734, https://doi.org/10.5194/acp-18-12715-2018, https://doi.org/10.5194/acp-18-12715-2018, 2018
Short summary
Short summary
We investigated the impact of biomass burning on the chemical composition of trace gases in the Amazon. The findings corroborate the influence of biomass burning activity not only on direct emissions of particulate matter but also on the oxidative capacity to produce secondary organic aerosol. The scientists plan to use this information to improve the numerical model simulation with a better representativeness of the chemical processes, which can impact on global climate prediction.
Matthew N. Hayek, Marcos Longo, Jin Wu, Marielle N. Smith, Natalia Restrepo-Coupe, Raphael Tapajós, Rodrigo da Silva, David R. Fitzjarrald, Plinio B. Camargo, Lucy R. Hutyra, Luciana F. Alves, Bruce Daube, J. William Munger, Kenia T. Wiedemann, Scott R. Saleska, and Steven C. Wofsy
Biogeosciences, 15, 4833–4848, https://doi.org/10.5194/bg-15-4833-2018, https://doi.org/10.5194/bg-15-4833-2018, 2018
Short summary
Short summary
We investigated the roles that weather and forest disturbances like drought play in shaping changes in ecosystem photosynthesis and carbon exchange in an Amazon forest. We discovered that weather largely influenced differences between years, but a prior drought, which occurred 3 years before measurements started, likely hampered photosynthesis in the first year. This is the first atmospheric evidence that drought can have legacy impacts on Amazon forest photosynthesis.
Paulo R. Teixeira, Saulo R. de Freitas, Francis W. Correia, and Antonio O. Manzi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-81, https://doi.org/10.5194/gmd-2018-81, 2018
Publication in GMD not foreseen
Short summary
Short summary
Emissions of gases and particulates in urban areas are associated with a mixture of various sources, both natural and anthropogenic. Understanding and quantifying these emissions is necessary in studies of climate change, local air pollution issues, and weather modification. This work will also contribute to improved air quality numerical simulations, provide more accurate scenarios for policymakers and regulatory agencies to develop strategies for controlling the vehicular emissions.
Luiz A. T. Machado, Alan J. P. Calheiros, Thiago Biscaro, Scott Giangrande, Maria A. F. Silva Dias, Micael A. Cecchini, Rachel Albrecht, Meinrat O. Andreae, Wagner F. Araujo, Paulo Artaxo, Stephan Borrmann, Ramon Braga, Casey Burleyson, Cristiano W. Eichholz, Jiwen Fan, Zhe Feng, Gilberto F. Fisch, Michael P. Jensen, Scot T. Martin, Ulrich Pöschl, Christopher Pöhlker, Mira L. Pöhlker, Jean-François Ribaud, Daniel Rosenfeld, Jaci M. B. Saraiva, Courtney Schumacher, Ryan Thalman, David Walter, and Manfred Wendisch
Atmos. Chem. Phys., 18, 6461–6482, https://doi.org/10.5194/acp-18-6461-2018, https://doi.org/10.5194/acp-18-6461-2018, 2018
Short summary
Short summary
This overview discuss the main precipitation processes and their sensitivities to environmental conditions in the Central Amazon Basin. It presents a review of the knowledge acquired about cloud processes and rainfall formation in Amazonas. In addition, this study provides a characterization of the seasonal variation and rainfall sensitivities to topography, surface cover, and aerosol concentration. Airplane measurements were evaluated to characterize and contrast cloud microphysical properties.
Amy K. Hodgson, William T. Morgan, Sebastian O'Shea, Stéphane Bauguitte, James D. Allan, Eoghan Darbyshire, Michael J. Flynn, Dantong Liu, James Lee, Ben Johnson, Jim M. Haywood, Karla M. Longo, Paulo E. Artaxo, and Hugh Coe
Atmos. Chem. Phys., 18, 5619–5638, https://doi.org/10.5194/acp-18-5619-2018, https://doi.org/10.5194/acp-18-5619-2018, 2018
Short summary
Short summary
We flew a large atmospheric research aircraft across a number of different biomass burning environments in the Amazon Basin in September and October 2012. In this paper, we focus on smoke sampled very close to fresh fires (only 600–900 m above the fires and smoke that was 4–6 min old) to examine the chemical components that make up the smoke and their abundance. We found substantial differences in the emitted smoke that are due to the fuel type and combustion processes driving the fires.
Gláuber Camponogara, Maria Assunção Faus da Silva Dias, and Gustavo G. Carrió
Atmos. Chem. Phys., 18, 2081–2096, https://doi.org/10.5194/acp-18-2081-2018, https://doi.org/10.5194/acp-18-2081-2018, 2018
Short summary
Short summary
This study is one of the first that seeks to understand the microphysical effects of biomass burning aerosols from the Amazon Basin on mesoscale convective systems over the La Plata Basin (and neighboring regions). We observed an important link between biomass burning aerosols from the Amazon Basin on mesoscale convective systems over the La Plata Basin throughout numerical simulations. This result is an important step toward the understanding of biomass burning impacts in the Amazon.
Guilherme Augusto Verola Mataveli, Maria Elisa Siqueira Silva, Gabriel Pereira, Francielle da Silva Cardozo, Fernando Shinji Kawakubo, Gabriel Bertani, Julio Cezar Costa, Raquel de Cássia Ramos, and Viviane Valéria da Silva
Nat. Hazards Earth Syst. Sci., 18, 125–144, https://doi.org/10.5194/nhess-18-125-2018, https://doi.org/10.5194/nhess-18-125-2018, 2018
Short summary
Short summary
Orbital remote sensing showed that the Cerrado was the second/first biome for the occurrence of hotspots and burned area (BA), which were higher in the dry season and in the savanna land use and are tending to decrease. Spatial analysis showed that values for the entire biome can hide patterns and that there is a 2- to 3-month lag between precipitation and hotspots/BA, while minimum VCI and maximum hotspots/BA occur in the same month. VCI indicates the susceptibility of vegetation to ignition.
Demerval S. Moreira, Karla M. Longo, Saulo R. Freitas, Marcia A. Yamasoe, Lina M. Mercado, Nilton E. Rosário, Emauel Gloor, Rosane S. M. Viana, John B. Miller, Luciana V. Gatti, Kenia T. Wiedemann, Lucas K. G. Domingues, and Caio C. S. Correia
Atmos. Chem. Phys., 17, 14785–14810, https://doi.org/10.5194/acp-17-14785-2017, https://doi.org/10.5194/acp-17-14785-2017, 2017
Short summary
Short summary
Fire in the Amazon forest produces a large amount of smoke that is released into the atmosphere and covers a large portion of South America for about 3 months each year. The smoke affects the energy and CO2 budgets. Using a numerical atmospheric model, we demonstrated that the smoke changes the forest from a source to a sink of CO2 to the atmosphere. The smoke ultimately acts to at least partially compensate for the forest carbon lost due to fire emissions.
Scott E. Giangrande, Zhe Feng, Michael P. Jensen, Jennifer M. Comstock, Karen L. Johnson, Tami Toto, Meng Wang, Casey Burleyson, Nitin Bharadwaj, Fan Mei, Luiz A. T. Machado, Antonio O. Manzi, Shaocheng Xie, Shuaiqi Tang, Maria Assuncao F. Silva Dias, Rodrigo A. F de Souza, Courtney Schumacher, and Scot T. Martin
Atmos. Chem. Phys., 17, 14519–14541, https://doi.org/10.5194/acp-17-14519-2017, https://doi.org/10.5194/acp-17-14519-2017, 2017
Short summary
Short summary
The Amazon forest is the largest tropical rain forest on the planet, featuring
prolific and diverse cloud conditions. The Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) experiment was motivated by demands to gain a better understanding of aerosol and cloud interactions on climate and the global circulation. The routine DOE ARM observations from this 2-year campaign are summarized to help quantify controls on clouds and precipitation over this undersampled region.
Bruce K. N. Silva, Paulo S. Lucio, Cláudio M. S. Silva, Maria H. C. Spyrides, Madson T. Silva, and Lara M. B. Andradre
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2017-377, https://doi.org/10.5194/nhess-2017-377, 2017
Manuscript not accepted for further review
Sameh A. Abou Rafee, Leila D. Martins, Ana B. Kawashima, Daniela S. Almeida, Marcos V. B. Morais, Rita V. A. Souza, Maria B. L. Oliveira, Rodrigo A. F. Souza, Adan S. S. Medeiros, Viviana Urbina, Edmilson D. Freitas, Scot T. Martin, and Jorge A. Martins
Atmos. Chem. Phys., 17, 7977–7995, https://doi.org/10.5194/acp-17-7977-2017, https://doi.org/10.5194/acp-17-7977-2017, 2017
Short summary
Short summary
This paper evaluates the impact of the emissions from mobile and stationary sources in the Amazon rainforest by using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Results show that stationary sources have an important role in the contribution of human activity in Manaus; a future scenario of the expansion in the urban area demonstrates that it could increase air pollution; and the pollutant urban plume of Manaus has an impact over hundreds of kilometers in length.
Joana A. Rizzolo, Cybelli G. G. Barbosa, Guilherme C. Borillo, Ana F. L. Godoi, Rodrigo A. F. Souza, Rita V. Andreoli, Antônio O. Manzi, Marta O. Sá, Eliane G. Alves, Christopher Pöhlker, Isabella H. Angelis, Florian Ditas, Jorge Saturno, Daniel Moran-Zuloaga, Luciana V. Rizzo, Nilton E. Rosário, Theotonio Pauliquevis, Rosa M. N. Santos, Carlos I. Yamamoto, Meinrat O. Andreae, Paulo Artaxo, Philip E. Taylor, and Ricardo H. M. Godoi
Atmos. Chem. Phys., 17, 2673–2687, https://doi.org/10.5194/acp-17-2673-2017, https://doi.org/10.5194/acp-17-2673-2017, 2017
Short summary
Short summary
Particles collected from the air above the Amazon Basin during the wet season were identified as Saharan dust. Soluble minerals were analysed to assess the bioavailability of iron. Dust deposited onto the canopy and topsoil can likely benefit organisms such as fungi and lichens. The ongoing deposition of Saharan dust across the Amazon rainforest provides an iron-rich source of essential macronutrients and micronutrients to plant roots, and also directly to plant leaves during the wet season.
Madeleine Sánchez Gácita, Karla M. Longo, Julliana L. M. Freire, Saulo R. Freitas, and Scot T. Martin
Atmos. Chem. Phys., 17, 2373–2392, https://doi.org/10.5194/acp-17-2373-2017, https://doi.org/10.5194/acp-17-2373-2017, 2017
Short summary
Short summary
This study uses an adiabatic cloud model to simulate the activation of smoke aerosol particles in the Amazon region as cloud condensation nuclei (CCN). The relative importance of variability in hygroscopicity, mixing state, and activation kinetics for the activated fraction and maximum supersaturation is assessed. Our findings on uncertainties and sensitivities provide guidance on appropriate simplifications that can be used for modeling of smoke aerosols within general circulation models.
Carolin Walter, Saulo R. Freitas, Christoph Kottmeier, Isabel Kraut, Daniel Rieger, Heike Vogel, and Bernhard Vogel
Atmos. Chem. Phys., 16, 9201–9219, https://doi.org/10.5194/acp-16-9201-2016, https://doi.org/10.5194/acp-16-9201-2016, 2016
Short summary
Short summary
Buoyancy produced by vegetation fires can lead to substantial plume rise with consequences for the dispersion of aerosol emitted by the fires. To study this effect a 1-D plume rise model was included into the regional online integrated model system COSMO-ART. Comparing model results and satellite data for a case study of 2010 Canadian wildfires shows, that the plume rise model outperforms prescribed emission height. The radiative impact of the aerosol leads to a pronounced temperature change.
Gabriel Pereira, Ricardo Siqueira, Nilton E. Rosário, Karla L. Longo, Saulo R. Freitas, Francielle S. Cardozo, Johannes W. Kaiser, and Martin J. Wooster
Atmos. Chem. Phys., 16, 6961–6975, https://doi.org/10.5194/acp-16-6961-2016, https://doi.org/10.5194/acp-16-6961-2016, 2016
Short summary
Short summary
Fires associated with land use and land cover changes release large amounts of aerosols and trace gases into the atmosphere. Although several inventories of biomass burning emissions cover Brazil, there are still considerable uncertainties and differences among them. However, results indicate that emission derived via similar methods tend to agree with one other, but aerosol emissions from fires with particularly high biomass consumption still lead to an underestimation.
S. T. Martin, P. Artaxo, L. A. T. Machado, A. O. Manzi, R. A. F. Souza, C. Schumacher, J. Wang, M. O. Andreae, H. M. J. Barbosa, J. Fan, G. Fisch, A. H. Goldstein, A. Guenther, J. L. Jimenez, U. Pöschl, M. A. Silva Dias, J. N. Smith, and M. Wendisch
Atmos. Chem. Phys., 16, 4785–4797, https://doi.org/10.5194/acp-16-4785-2016, https://doi.org/10.5194/acp-16-4785-2016, 2016
Short summary
Short summary
The Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) Experiment took place in central Amazonia throughout 2014 and 2015. The experiment focused on the complex links among vegetation, atmospheric chemistry, and aerosol production on the one hand and their connections to aerosols, clouds, and precipitation on the other, especially when altered by urban pollution. This article serves as an introduction to the special issue of publications presenting findings of this experiment.
Douglas C. Morton, Jérémy Rubio, Bruce D. Cook, Jean-Philippe Gastellu-Etchegorry, Marcos Longo, Hyeungu Choi, Maria Hunter, and Michael Keller
Biogeosciences, 13, 2195–2206, https://doi.org/10.5194/bg-13-2195-2016, https://doi.org/10.5194/bg-13-2195-2016, 2016
Short summary
Short summary
Seasonal dynamics of tropical forest productivity remain an important source of uncertainty in assessments of the land carbon sink. This study confirms the potential for canopy structure and illumination geometry to alter the seasonal availability of light for canopy photosynthesis without changes in canopy composition. Our results point to the need for 3-D forest structure in ecosystem models to account the impact of changing illumination geometry on tropical forest productivity.
R. Paugam, M. Wooster, S. Freitas, and M. Val Martin
Atmos. Chem. Phys., 16, 907–925, https://doi.org/10.5194/acp-16-907-2016, https://doi.org/10.5194/acp-16-907-2016, 2016
Short summary
Short summary
Landscape fire plume height controls fire emissions release in the atmosphere, in particular their transport that may also affect the longevity, chemical conversion, and fate of the plumes chemical constituents. Here, we review how such landscape-scale fire smoke plume injection heights are represented in large-scale atmospheric transport models aiming to represent the impacts of wildfire emissions on component of the Earth system.
R. Paugam, M. Wooster, J. Atherton, S. R. Freitas, M. G. Schultz, and J. W. Kaiser
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-15-9815-2015, https://doi.org/10.5194/acpd-15-9815-2015, 2015
Revised manuscript not accepted
Short summary
Short summary
The transport of Biomass Burning emissions in Chemical Transport Model rely on parametrization of plumes injection height. Using fire observation selected to ensure match-up of fire-atmosphere-plume dynamics; a popular plume rise model was improved and optimized. The resulting model shows response to the effect of atmospheric stability consistent with previous findings and is able to predict higher injection height than any other tested parametrizations, giving a closer match with observation.
S. Archer-Nicholls, D. Lowe, E. Darbyshire, W. T. Morgan, M. M. Bela, G. Pereira, J. Trembath, J. W. Kaiser, K. M. Longo, S. R. Freitas, H. Coe, and G. McFiggans
Geosci. Model Dev., 8, 549–577, https://doi.org/10.5194/gmd-8-549-2015, https://doi.org/10.5194/gmd-8-549-2015, 2015
Short summary
Short summary
The regional WRF-Chem model was used to study aerosol particles from biomass burning in South America. The modelled estimates of fire plume injection heights were found to be too high, with serious implications for modelled aerosol vertical distribution, transport and impacts on local climate. A modified emission scenario was developed which improved the predicted injection height. Model results were compared and evaluated against in situ measurements from the 2012 SAMBBA flight campaign.
M. M. Bela, K. M. Longo, S. R. Freitas, D. S. Moreira, V. Beck, S. C. Wofsy, C. Gerbig, K. Wiedemann, M. O. Andreae, and P. Artaxo
Atmos. Chem. Phys., 15, 757–782, https://doi.org/10.5194/acp-15-757-2015, https://doi.org/10.5194/acp-15-757-2015, 2015
Short summary
Short summary
In the Amazon Basin, gases that lead to the formation of ozone (O3), an air pollutant and greenhouse gas, are emitted from fire, urban and biogenic sources. This study presents the first basin wide aircraft measurements of O3 during the dry-to-wet and wet-to-dry transition seasons, which show extremely low values above undisturbed forest and increases from fires. This work also demonstrates the capabilities and limitations of regional atmospheric chemistry models in representing O3 in Amazonia.
R. G. Knox, M. Longo, A. L. S. Swann, K. Zhang, N. M. Levine, P. R. Moorcroft, and R. L. Bras
Hydrol. Earth Syst. Sci., 19, 241–273, https://doi.org/10.5194/hess-19-241-2015, https://doi.org/10.5194/hess-19-241-2015, 2015
J. Brito, L. V. Rizzo, W. T. Morgan, H. Coe, B. Johnson, J. Haywood, K. Longo, S. Freitas, M. O. Andreae, and P. Artaxo
Atmos. Chem. Phys., 14, 12069–12083, https://doi.org/10.5194/acp-14-12069-2014, https://doi.org/10.5194/acp-14-12069-2014, 2014
Short summary
Short summary
This paper details the physical--chemical characteristics of aerosols in a region strongly impacted by biomass burning in the western part of the Brazilian Amazon region. For such, a large suite of state-of-the-art instruments for realtime analysis was deployed at a ground site. Among the key findings, we observe the strong prevalence of organic aerosols associated to fire emissions, with important climate effects, and indications of its very fast processing in the atmosphere.
J. R. Rozante, D. S. Moreira, R. C. M. Godoy, and A. A. Fernandes
Geosci. Model Dev., 7, 2333–2343, https://doi.org/10.5194/gmd-7-2333-2014, https://doi.org/10.5194/gmd-7-2333-2014, 2014
G. A. Grell and S. R. Freitas
Atmos. Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014, https://doi.org/10.5194/acp-14-5233-2014, 2014
G. Camponogara, M. A. F. Silva Dias, and G. G. Carrió
Atmos. Chem. Phys., 14, 4397–4407, https://doi.org/10.5194/acp-14-4397-2014, https://doi.org/10.5194/acp-14-4397-2014, 2014
R. G. Knox, M. Longo, A. L. S. Swann, K. Zhang, N. M. Levine, P. R. Moorcroft, and R. L. Bras
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-15295-2013, https://doi.org/10.5194/hessd-10-15295-2013, 2013
Preprint withdrawn
A. F. dos Santos, S. R. Freitas, J. G. Z. de Mattos, H. F. de Campos Velho, M. A. Gan, E. F. P. da Luz, and G. A. Grell
Adv. Geosci., 35, 123–136, https://doi.org/10.5194/adgeo-35-123-2013, https://doi.org/10.5194/adgeo-35-123-2013, 2013
K. M. Longo, S. R. Freitas, M. Pirre, V. Marécal, L. F. Rodrigues, J. Panetta, M. F. Alonso, N. E. Rosário, D. S. Moreira, M. S. Gácita, J. Arteta, R. Fonseca, R. Stockler, D. M. Katsurayama, A. Fazenda, and M. Bela
Geosci. Model Dev., 6, 1389–1405, https://doi.org/10.5194/gmd-6-1389-2013, https://doi.org/10.5194/gmd-6-1389-2013, 2013
D. S. Moreira, S. R. Freitas, J. P. Bonatti, L. M. Mercado, N. M. É. Rosário, K. M. Longo, J. B. Miller, M. Gloor, and L. V. Gatti
Geosci. Model Dev., 6, 1243–1259, https://doi.org/10.5194/gmd-6-1243-2013, https://doi.org/10.5194/gmd-6-1243-2013, 2013
V. Beck, C. Gerbig, T. Koch, M. M. Bela, K. M. Longo, S. R. Freitas, J. O. Kaplan, C. Prigent, P. Bergamaschi, and M. Heimann
Atmos. Chem. Phys., 13, 7961–7982, https://doi.org/10.5194/acp-13-7961-2013, https://doi.org/10.5194/acp-13-7961-2013, 2013
M. Stuefer, S. R. Freitas, G. Grell, P. Webley, S. Peckham, S. A. McKeen, and S. D. Egan
Geosci. Model Dev., 6, 457–468, https://doi.org/10.5194/gmd-6-457-2013, https://doi.org/10.5194/gmd-6-457-2013, 2013
N. E. Rosário, K. M. Longo, S. R. Freitas, M. A. Yamasoe, and R. M. Fonseca
Atmos. Chem. Phys., 13, 2923–2938, https://doi.org/10.5194/acp-13-2923-2013, https://doi.org/10.5194/acp-13-2923-2013, 2013
S. Strada, S. R. Freitas, C. Mari, K. M. Longo, and R. Paugam
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-6-721-2013, https://doi.org/10.5194/gmdd-6-721-2013, 2013
Preprint withdrawn
Related subject area
Atmospheric sciences
A new set of indicators for model evaluation complementing FAIRMODE's modelling quality objective (MQO)
Impact of multiple radar wind profiler data assimilation on convective-scale short-term rainfall forecasts: OSSE studies over the Beijing–Tianjin–Hebei region
New submodel for emissions from Explosive Volcanic ERuptions (EVER v1.1) within the Modular Earth Submodel System (MESSy, version 2.55.1)
Quantifying the oscillatory evolution of simulated boundary-layer cloud fields using Gaussian process regression
Numerical investigations on the modelling of ultrafine particles in SSH-aerosol-v1.3a: size resolution and redistribution
The third Met Office Unified Model–JULES Regional Atmosphere and Land Configuration, RAL3
The sensitivity of aerosol data assimilation to vertical profiles: case study of dust storm assimilation with LOTOS-EUROS v2.2
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a neural network
Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching
A novel method for quantifying the contribution of regional transport to PM2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Diagnosis of winter precipitation types using the spectral bin model (version 1DSBM-19M): comparison of five methods using ICE-POP 2018 field experiment data
Improving winter condition simulations in SURFEX-TEB v9.0 with a multi-layer snow model and ice
UA-ICON with the NWP physics package (version ua-icon-2.1): mean state and variability of the middle atmosphere
Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study
Pochva: a new hydro-thermal process model in soil, snow, and vegetation for application in atmosphere numerical models
ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks
Accounting for effects of coagulation and model uncertainties in particle number concentration estimates based on measurements from sampling lines – a Bayesian inversion approach with SLIC v1.0
Top-down CO emission estimates using TROPOMI CO data in the TM5-4DVAR (r1258) inverse modeling suit
The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
Similarity-based analysis of atmospheric organic compounds for machine learning applications
Porting the Meso-NH atmospheric model on different GPU architectures for the next generation of supercomputers (version MESONH-v55-OpenACC)
Estimation of aerosol and cloud radiative heating rate in the tropical stratosphere using a radiative kernel method
Evaluation of dust emission and land surface schemes in predicting a mega Asian dust storm over South Korea using WRF-Chem
Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy
A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks
Inclusion of the ECMWF ecRad radiation scheme (v1.5.0) in the MAR (v3.14), regional evaluation for Belgium, and assessment of surface shortwave spectral fluxes at Uccle
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
NeuralMie (v1.0): an aerosol optics emulator
A REtrieval Method for optical and physical Aerosol Properties in the stratosphere (REMAPv1)
Simulation performance of planetary boundary layer schemes in WRF v4.3.1 for near-surface wind over the western Sichuan Basin: a single-site assessment
FootNet v1.0: development of a machine learning emulator of atmospheric transport
Updates and evaluation of NOAA's online-coupled air quality model version 7 (AQMv7) within the Unified Forecast System
Quantifying the analysis uncertainty for nowcasting application
Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01
The MESSy DWARF (based on MESSy v2.55.2)
Generalized local fractions – a method for the calculation of sensitivities to emissions from multiple sources for chemically active species, illustrated using the EMEP MSC-W model (rv5.5)
SanDyPALM v1.0: Static and Dynamic Drivers for the PALM-4U Model to Facilitate Realistic Urban Microclimate Simulations
An enhanced emission module for the PALM model system 23.10 with application for PM10 emission from urban domestic heating
Identifying lightning processes in ERA5 soundings with deep learning
Sensitivity of predicted ultrafine particle size distributions in Europe to different nucleation rate parameterizations using PMCAMx-UF v2.2
Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data
Implementation of a dry deposition module (DEPAC v3.11) in a large eddy simulation code (DALES v4.4)
Accurate and fast prediction of radioactive pollution by Kriging coupled with Auto-Associative Models
Mitigating Hail Overforecasting in the 2-Moment Milbrandt-Yau Microphysics Scheme (v2.25.2_beta_04) in WRF (v4.5.1) by Incorporating the Graupel Spongy Wet Growth Process (MY2_GSWG v1.0)
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
Geosci. Model Dev., 18, 4231–4245, https://doi.org/10.5194/gmd-18-4231-2025, https://doi.org/10.5194/gmd-18-4231-2025, 2025
Short summary
Short summary
We assess relevance and utility indicators by evaluating nine Copernicus Atmospheric Monitoring Service models in calculated air pollutant values. For NO2, the results highlight difficulties at traffic stations. For PM2.5 and PM10 the bias and winter–summer gradients reveal issues. O3 evaluation shows that seasonal gradients are useful. Overall, the indicators reveal model limitations, yet there is a need to reconsider the strictness of some indicators for certain pollutants.
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev., 18, 4075–4101, https://doi.org/10.5194/gmd-18-4075-2025, https://doi.org/10.5194/gmd-18-4075-2025, 2025
Short summary
Short summary
A series of observing system simulation experiments are conducted to assess the impact of multiple radar wind profiler (RWP) networks on convective-scale numerical weather prediction. Results from three southwest-type heavy rainfall cases in the Beijing–Tianjin–Hebei region suggest the added forecast skill of ridge and foothill networks associated with the Taihang Mountains over the existing RWP network. This research provides valuable guidance for designing optimal RWP networks in the region.
Matthias Kohl, Christoph Brühl, Jennifer Schallock, Holger Tost, Patrick Jöckel, Adrian Jost, Steffen Beirle, Michael Höpfner, and Andrea Pozzer
Geosci. Model Dev., 18, 3985–4007, https://doi.org/10.5194/gmd-18-3985-2025, https://doi.org/10.5194/gmd-18-3985-2025, 2025
Short summary
Short summary
SO2 from explosive volcanic eruptions reaching the stratosphere can oxidize and form sulfur aerosols, potentially persisting for several years. We developed a new submodel, Explosive Volcanic ERuptions (EVER), that seamlessly includes stratospheric volcanic SO2 emissions in global numerical simulations based on a novel standard historical model setup, successfully evaluated with satellite observations. Sensitivity studies on the Nabro eruption in 2011 evaluate different emission methods.
Gunho Loren Oh and Philip H. Austin
Geosci. Model Dev., 18, 3921–3940, https://doi.org/10.5194/gmd-18-3921-2025, https://doi.org/10.5194/gmd-18-3921-2025, 2025
Short summary
Short summary
It is difficult to study the behaviour of a cloud field due to internal fluctuations and observational noise. We perform a high-resolution simulation of the boundary-layer cloud field and introduce statistical and numerical techniques, including machine-learning models, to study the evolution of the cloud field, which shows a periodic behaviour. We aim to use the numerical techniques to identify the underlying behaviour within noisy observations.
Oscar Jacquot and Karine Sartelet
Geosci. Model Dev., 18, 3965–3984, https://doi.org/10.5194/gmd-18-3965-2025, https://doi.org/10.5194/gmd-18-3965-2025, 2025
Short summary
Short summary
Modelling the size distribution and the number concentration is important to represent ultrafine particles. A new analytic formulation is presented to compute coagulation partition coefficients, allowing us to lower the numerical diffusion associated with the resolution of aerosol dynamics. The significance of this effect is assessed in a 0D box model and over greater Paris with a chemistry transport model, using different size resolutions of the particle distribution.
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev., 18, 3819–3855, https://doi.org/10.5194/gmd-18-3819-2025, https://doi.org/10.5194/gmd-18-3819-2025, 2025
Short summary
Short summary
RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre- and sub-kilometre-scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and an improved representation of clouds and visibility.
Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Batjargal Buyantogtokh, Yixuan Gu, Jiandong Li, Hai Xiang Lin, Hong Liao, and Wei Han
Geosci. Model Dev., 18, 3781–3798, https://doi.org/10.5194/gmd-18-3781-2025, https://doi.org/10.5194/gmd-18-3781-2025, 2025
Short summary
Short summary
Aerosol data assimilation has gained popularity as it combines the advantages of modelling and observation. However, few studies have addressed the challenges in the prior vertical structure. Different observations are assimilated to examine the sensitivity of assimilation to vertical structure. Results show that assimilation can optimize the dust field in general. However, if the prior introduces an incorrect structure, the assimilation can significantly deteriorate the integrity of the aerosol profile.
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
Geosci. Model Dev., 18, 3707–3733, https://doi.org/10.5194/gmd-18-3707-2025, https://doi.org/10.5194/gmd-18-3707-2025, 2025
Short summary
Short summary
This work focuses on the prediction of aerosol concentration values at the ground level, which are a strong indicator of air quality, using artificial neural networks. A study of different variables and their efficiency as inputs for these models is also proposed and reveals that the best results are obtained when using all of them. Comparison between network architectures and information fusion methods allows for the extraction of knowledge on the most efficient methods in the context of this study.
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025, https://doi.org/10.5194/gmd-18-3681-2025, 2025
Short summary
Short summary
Tuning a climate model means adjusting uncertain parameters in the model to best match observations like the global radiation balance and cloud cover. This is usually done by running many simulations of the model with different settings, which can be time-consuming and relies heavily on expert knowledge. To make this process faster and more objective, we developed a machine learning emulator to create a large ensemble and apply a method called history matching to find the best settings.
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev., 18, 3623–3634, https://doi.org/10.5194/gmd-18-3623-2025, https://doi.org/10.5194/gmd-18-3623-2025, 2025
Short summary
Short summary
This study combines machine learning with concentration-weighted trajectory analysis to quantify regional transport PM2.5. From 2013–2020, local emissions dominated Beijing's pollution events. The Air Pollution Prevention and Control Action Plan reduced regional transport pollution, but the eastern region showed the smallest decrease. Beijing should prioritize local emission reduction while considering the east region's contributions in future strategies.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025, https://doi.org/10.5194/gmd-18-3607-2025, 2025
Short summary
Short summary
We developed a deep learning method to estimate CO2 emissions from power plants using satellite images. Trained and validated on simulated data, our model accurately predicts emissions despite challenges like cloud cover. When applied to real OCO3 satellite images, the results closely match reported emissions. This study shows that neural networks trained on simulations can effectively analyse real satellite data, offering a new way to monitor CO2 emissions from space.
Wonbae Bang, Jacob T. Carlin, Kwonil Kim, Alexander V. Ryzhkov, Guosheng Liu, and GyuWon Lee
Geosci. Model Dev., 18, 3559–3581, https://doi.org/10.5194/gmd-18-3559-2025, https://doi.org/10.5194/gmd-18-3559-2025, 2025
Short summary
Short summary
Microphysics model-based diagnosis, such as the spectral bin model (SBM), has recently been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM has a relatively higher accuracy for dry-snow and wet-snow events, whereas it has lower accuracy for rain events. When the microphysics scheme in the SBM was optimized for the corresponding region, the accuracy for rain events improved.
Gabriel Colas, Valéry Masson, François Bouttier, Ludovic Bouilloud, Laura Pavan, and Virve Karsisto
Geosci. Model Dev., 18, 3453–3472, https://doi.org/10.5194/gmd-18-3453-2025, https://doi.org/10.5194/gmd-18-3453-2025, 2025
Short summary
Short summary
In winter, snow- and ice-covered artificial surfaces are important aspects of the urban climate. They may influence the magnitude of the urban heat island effect, but this is still unclear. In this study, we improved the representation of the snow and ice cover in the Town Energy Balance (TEB) urban climate model. Evaluations have shown that the results are promising for using TEB to study the climate of cold cities.
Markus Kunze, Christoph Zülicke, Tarique A. Siddiqui, Claudia C. Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev., 18, 3359–3385, https://doi.org/10.5194/gmd-18-3359-2025, https://doi.org/10.5194/gmd-18-3359-2025, 2025
Short summary
Short summary
We present the Icosahedral Nonhydrostatic (ICON) general circulation model with an upper-atmospheric extension with the physics package for numerical weather prediction (UA-ICON(NWP)). We optimized the parameters for the gravity wave parameterizations and achieved realistic modeling of the thermal and dynamic states of the mesopause regions. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
Short summary
Short summary
Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025, https://doi.org/10.5194/gmd-18-3265-2025, 2025
Short summary
Short summary
The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model setup, are discussed, and the official recommendations for the project are presented.
Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut
Geosci. Model Dev., 18, 3211–3239, https://doi.org/10.5194/gmd-18-3211-2025, https://doi.org/10.5194/gmd-18-3211-2025, 2025
Short summary
Short summary
This study proposes using a statistical model to freeze errors due to differences in environmental forcing when evaluating the surface turbulent heat fluxes from numerical simulations with observations. The statistical model is first built with observations and then applied to the simulated environment to generate possibly observed fluxes. This novel method provides insight into differently evaluating the numerical formulation of turbulent heat fluxes with a long period of observational data.
Oxana Drofa
Geosci. Model Dev., 18, 3175–3209, https://doi.org/10.5194/gmd-18-3175-2025, https://doi.org/10.5194/gmd-18-3175-2025, 2025
Short summary
Short summary
This paper presents the result of many years of effort of the author, who developed an original mathematical numerical model of heat and moisture exchange processes in soil, vegetation, and snow. The author relied on her 30 years of research experience in atmospheric numerical modelling. The presented model is the fruit of the author's research on physical processes at the surface–atmosphere interface and their numerical approximation and aims at improving numerical weather forecasting and climate simulations.
Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani
Geosci. Model Dev., 18, 3065–3079, https://doi.org/10.5194/gmd-18-3065-2025, https://doi.org/10.5194/gmd-18-3065-2025, 2025
Short summary
Short summary
We developed ClimKern, a Python package and radiative kernel repository, to simplify calculating radiative feedbacks and make climate sensitivity studies more reproducible. Testing of ClimKern with sample climate model data reveals that radiative kernel choice may be more important than previously thought, especially in polar regions. Our work highlights the need for kernel sensitivity analyses to be included in future studies.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025, https://doi.org/10.5194/gmd-18-2983-2025, 2025
Short summary
Short summary
Particle size is a key factor determining the properties of aerosol particles which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis
Geosci. Model Dev., 18, 2861–2890, https://doi.org/10.5194/gmd-18-2861-2025, https://doi.org/10.5194/gmd-18-2861-2025, 2025
Short summary
Short summary
We estimate carbon monoxide emissions through inverse modeling, an approach where measurements of tracers in the atmosphere are fed to a model to calculate backwards in time (inverse) where the tracers came from. We introduce measurements from a new satellite instrument and show that, in most places globally, these on their own sufficiently constrain the emissions. This alleviates the need for additional datasets, which could shorten the delay for future carbon monoxide source estimates.
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025, https://doi.org/10.5194/gmd-18-2747-2025, 2025
Short summary
Short summary
This paper introduces the Multi-Compartment Mercury (Hg) Modeling and Analysis Project (MCHgMAP) aimed at informing the effectiveness evaluations of two multilateral environmental agreements: the Minamata Convention on Mercury and the Convention on Long-Range Transboundary Air Pollution. The experimental design exploits a variety of models (atmospheric, land, oceanic ,and multimedia mass balance models) to assess the short- and long-term influences of anthropogenic Hg releases into the environment.
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025, https://doi.org/10.5194/gmd-18-2701-2025, 2025
Short summary
Short summary
Machine learning has the potential to aid the identification of organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning models in atmospheric sciences.
Juan Escobar, Philippe Wautelet, Joris Pianezze, Florian Pantillon, Thibaut Dauhut, Christelle Barthe, and Jean-Pierre Chaboureau
Geosci. Model Dev., 18, 2679–2700, https://doi.org/10.5194/gmd-18-2679-2025, https://doi.org/10.5194/gmd-18-2679-2025, 2025
Short summary
Short summary
The Meso-NH weather research code is adapted for GPUs using OpenACC, leading to significant performance and energy efficiency improvements. Called MESONH-v55-OpenACC, it includes enhanced memory management, communication optimizations and a new solver. On the AMD MI250X Adastra platform, it achieved up to 6× speedup and 2.3× energy efficiency gain compared to CPUs. Storm simulations at 100 m resolution show positive results, positioning the code for future use on exascale supercomputers.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
Geosci. Model Dev., 18, 2569–2586, https://doi.org/10.5194/gmd-18-2569-2025, https://doi.org/10.5194/gmd-18-2569-2025, 2025
Short summary
Short summary
The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate this effect, the radiative kernel is constructed and applied. The results show that the kernels can reproduce aerosol radiative effects and are expected to simulate stratospheric aerosol radiative effects. This approach reduces computational expense, is consistent with radiative model calculations, and can be applied to atmospheric models with speed requirements.
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
Short summary
Short summary
This study evaluates the Weather Research and Forecasting Model (WRF) coupled with Chemistry (WRF-Chem) to predict a mega Asian dust storm (ADS) over South Korea on 28–29 March 2021. We assessed combinations of five dust emission and four land surface schemes by analyzing meteorological and air quality variables. The best scheme combination reduced the root mean square error (RMSE) for particulate matter 10 (PM10) by up to 29.6 %, demonstrating the highest performance.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025, https://doi.org/10.5194/gmd-18-2231-2025, 2025
Short summary
Short summary
The effectiveness of this assimilation system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. This study advances our understanding of the selection of basic parameters in the four-dimensional local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate-matter-polluted environment.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
Short summary
Short summary
To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
anomalous event.
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, https://doi.org/10.5194/gmd-18-1965-2025, 2025
Short summary
Short summary
The MAR (Modèle Régional Atmosphérique) is a regional climate model used for weather forecasting and studying the climate over various regions. This paper presents an update of MAR thanks to which it can precisely decompose solar radiation, in particular in the UV (ultraviolet) and photosynthesis ranges, both being critical to human health and ecosystems. As a first application of this new capability, this paper presents a method for predicting UV indices with MAR.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
Short summary
Short summary
Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025, https://doi.org/10.5194/gmd-18-1879-2025, 2025
Short summary
Short summary
The High-Resolution Global Forecast Model (HGFM) is an advanced iteration of the operational Global Forecast System (GFS) model. HGFM can produce forecasts at a spatial scale of ~6 km in tropics. It demonstrates improved accuracy in short- to medium-range weather prediction over the Indian region, with notable success in predicting extreme events. Further, the model will be entrusted to operational forecasting agencies after validation and testing.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
Short summary
Short summary
Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025, https://doi.org/10.5194/gmd-18-1809-2025, 2025
Short summary
Short summary
Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.
Andrin Jörimann, Timofei Sukhodolov, Beiping Luo, Gabriel Chiodo, Graham Mann, and Thomas Peter
EGUsphere, https://doi.org/10.5194/egusphere-2025-145, https://doi.org/10.5194/egusphere-2025-145, 2025
Short summary
Short summary
Aerosol particles in the stratosphere affect our climate. Climate models therefore need an accurate description of their properties and evolution. Satellites measure how strongly aerosol particles extinguish light passing through the stratosphere. We describe a method to use such aerosol extinction data to retrieve the number and sizes of the aerosol particles and calculate their optical effects. The resulting data sets for models are validated against ground-based and balloon observations.
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025, https://doi.org/10.5194/gmd-18-1769-2025, 2025
Short summary
Short summary
This study evaluates the performance of four planetary boundary layer (PBL) schemes in near-surface wind fields over the Sichuan Basin, China. Using 112 sensitivity experiments with the Weather Research and Forecasting (WRF) model and focusing on 28 wind events, it is found that wind direction was less sensitive to the PBL schemes. The quasi-normal scale elimination (QNSE) scheme captured temporal variations best, while the Mellor–Yamada–Janjić (MYJ) scheme had the least error in wind speed.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
Short summary
Short summary
It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, and Raffaele Montuoro
Geosci. Model Dev., 18, 1635–1660, https://doi.org/10.5194/gmd-18-1635-2025, https://doi.org/10.5194/gmd-18-1635-2025, 2025
Short summary
Short summary
The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
Yanwei Zhu, Aitor Atencia, Markus Dabernig, and Yong Wang
Geosci. Model Dev., 18, 1545–1559, https://doi.org/10.5194/gmd-18-1545-2025, https://doi.org/10.5194/gmd-18-1545-2025, 2025
Short summary
Short summary
Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
Short summary
Short summary
The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
Astrid Kerkweg, Timo Kirfel, Duong H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev., 18, 1265–1286, https://doi.org/10.5194/gmd-18-1265-2025, https://doi.org/10.5194/gmd-18-1265-2025, 2025
Short summary
Short summary
Normally, the Modular Earth Submodel System (MESSy) is linked to complete dynamic models to create chemical climate models. However, the modular concept of MESSy and the newly developed DWARF component presented here make it possible to create simplified models that contain only one or a few process descriptions. This is very useful for technical optimisation, such as porting to GPUs, and can be used to create less complex models, such as a chemical box model.
Peter Wind and Willem van Caspel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3571, https://doi.org/10.5194/egusphere-2024-3571, 2025
Short summary
Short summary
This paper presents a numerical method to assess the origin of air pollution. Combined with a numerical air pollution transport and chemistry model, it can follow the contributions from a large number of emission sources. The result is a series of maps that give the relative contributions from for example all European countries at each point.
Julian Vogel, Sebastian Stadler, Ganesh Chockalingam, Afshin Afshari, Johanna Henning, and Matthias Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2025-144, https://doi.org/10.5194/egusphere-2025-144, 2025
Short summary
Short summary
This study presents a toolkit to simplify input data creation for the urban microclimate model PALM-4U. It introduces novel methods to automate the use of open data sources. Our analysis of four test cases created from different geographic data sources shows variations in temperature, humidity, and wind speed, influenced by data quality. Validation indicates that the automated methods yield results comparable to expert-driven approaches, facilitating user-friendly urban climate modeling.
Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
Short summary
Short summary
An enhanced emission module has been developed for the PALM model system, improving flexibility and scalability of emission source representation across different sectors. A model for parametrized domestic emissions has also been included, for which an idealized model run is conducted for particulate matter (PM10). The results show that, in addition to individual sources and diurnal variations in energy consumption, vertical transport and urban topology play a role in concentration distribution.
Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, and Tobias Hell
Geosci. Model Dev., 18, 1141–1153, https://doi.org/10.5194/gmd-18-1141-2025, https://doi.org/10.5194/gmd-18-1141-2025, 2025
Short summary
Short summary
As lightning is a brief and localized event, it is not explicitly resolved in atmospheric models. Instead, expert-based auxiliary descriptions are used to assess it. This study explores how AI can improve our understanding of lightning without relying on traditional expert knowledge. We reveal that AI independently identified the key factors known to experts as essential for lightning in the Alps region. This shows how knowledge discovery could be sped up in areas with limited expert knowledge.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev., 18, 1103–1118, https://doi.org/10.5194/gmd-18-1103-2025, https://doi.org/10.5194/gmd-18-1103-2025, 2025
Short summary
Short summary
The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements at 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev., 18, 1017–1039, https://doi.org/10.5194/gmd-18-1017-2025, https://doi.org/10.5194/gmd-18-1017-2025, 2025
Short summary
Short summary
In our study, we built upon previous work to investigate the patterns artificial intelligence (AI) learns to detect atmospheric features like tropical cyclones (TCs) and atmospheric rivers (ARs). As primary objective, we adopt a method to explain the AI used and investigate the plausibility of learned patterns. We find that plausible patterns are learned for both TCs and ARs. Hence, the chosen method is very useful for gaining confidence in the AI-based detection of atmospheric features.
Leon Geers, Ruud Janssen, Gudrun Thorkelsdottir, Jordi Vilà-Guerau de Arellano, and Martijn Schaap
EGUsphere, https://doi.org/10.5194/egusphere-2025-426, https://doi.org/10.5194/egusphere-2025-426, 2025
Short summary
Short summary
High-resolution data on reactive nitrogen deposition are needed to inform cost-effective policies. Here, we describe the implementation of a dry deposition module into a large eddy simulation code. With this model, we are able to represent the turbulent exchange of tracers at the hectometer resolution. The model calculates the dispersion and deposition of NOx and NH3 in great spatial detail, clearly showing the influence of local land use patterns.
Raphaël Périllat, Sylvain Girard, and Irène Korsakissok
EGUsphere, https://doi.org/10.5194/egusphere-2024-3838, https://doi.org/10.5194/egusphere-2024-3838, 2025
Short summary
Short summary
We developed a method to improve decision-making during nuclear crises by predicting the spread of radiation more efficiently. Existing approaches are often too slow, especially when analyzing complex data like radiation maps. Our method combines techniques to simplify these maps and predict them quickly using statistical tools. This approach could help authorities respond faster and more accurately in emergencies, reducing risks to the population and the environment.
Shaofeng Hua, Gang Chen, Baojun Chen, Mingshan Li, and Xin Xu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3834, https://doi.org/10.5194/egusphere-2024-3834, 2025
Short summary
Short summary
Hail forecasting using numerical models remains a challenge. In this study, we found that the commonly used graupel-to-hail conversion parameterization method led to hail overforecasting in heavy rainfall cases where no hail was observed. By incorporating the spongy wet growth process, we successfully mitigated hail overforecasting. The modified scheme also produced hail in real hail events. This research contributes to a better understanding of hail formation.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
EGUsphere, https://doi.org/10.5194/egusphere-2024-3512, https://doi.org/10.5194/egusphere-2024-3512, 2025
Short summary
Short summary
Non-thermal emission from chemical reactions in the Earth's middle und upper atmosphere strongly contributes to the brightness of the night sky below about 2.3 µm. The new Paranal Airglow Line and Continuum Emission model calculates the emission spectrum and its variability with an unprecedented accuracy. Relying on a large spectroscopic data set from astronomical spectrographs and theoretical molecular/atomic data, it is valuable for airglow research and astronomical observatories.
Cited articles
Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation 2. Multiple aerosol types, J. Geophys. Res., 105, 6837–6844, https://doi.org/10.1029/1999JD901161, 2000.
Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation 3. Sectional representation, J. Geophys. Res., 107, 4026, https://doi.org/10.1029/2001JD000483, 2002.
Albini, F. A.: PROGRAM BURNUP: A simulation model of the burning of large woody natural fuels, final Report on Research Grant INT-92754-GR by U.S.F.S. to Montana State Univ., Mechanical Engineering Dept., 1994.
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989.
Albrecht, B. A., Ramanathan, V., and Boville, B. A.: The effects of cumulus moisture transports on the simulation of climate with a general circulation model, J. Atmos. Sci., 43, 2443–2462, 1986.
Alonso, M. F., Longo, K., Freitas, S., Fonseca, R., Marécal,V., Pirre, M., and Klenner, L.: An urban emissions inventory for South America and its application in numerical modeling of atmospheric chemical composition at local and regional scales, Atmos. Environ., 44, 5072–5083, https://doi.org/10.1016/j.atmosenv.2010.09.013, 2010.
Anderson, H. E.: Aids to determining fuel models for estimating fire behavior, USDA Forest Service, Intermountain Forest and Range Experiment Station, Research Report INT-122, 1982.
Andrade, M. F., Ynoue, R. Y., Harley, R., and Miguel, A. H.: Air quality model simulating photochemical formation of pollutants: the São Paulo Metropolitan Area, Brazil, Int. J. Environ. Pollut., 22, 460–475, 2004.
Arakawa, A. and Schubert, W. H.: Interaction of a cumulus cloud ensemble with the large-scale environment. Part I, J. Atmos. Sci., 31, 674–701, 1974.
Arakawa, A., Jung, J.-H., and Wu, C.-M.: Toward unification of the multiscale modeling of the atmosphere, Atmos. Chem. Phys., 11, 3731–3742, https://doi.org/10.5194/acp-11-3731-2011, 2011.
Asselin, R.: Frequency filter for time integrations, Mon. Weather Rev., 100, 487–490, 1972.
Baba, Y. and Takahashi, K.: Weighted essentially non-oscillatory scheme for cloud edge problem, Q. J. Roy. Meteor. Soc., 139, 1374–1388, https://doi.org/10.1002/qj.2030, 2013.
Barker, H. W., Cole, J. N. S., Morcrette, J.-J., Pincus, R., Räisänen, P., von Salzen, K., and Vaillancourt, P.: The Monte Carlo Independent Column Approximation: An assessment using several global atmospheric models, Q. J. Roy. Meteor. Soc., 134, 1463–1478, https://doi.org/10.1002/qj.303, 2008.
Baldauf, M.: Stability analysis for linear discretisations of the advection equation with Runge-Kutta time integration, J. Comput. Phys., 227, 6638–6659, 2008.
Baldauf, M.: Linear stability analysis of Runge-Kutta based partial time-splitting schemes for the Euler equations, Mon. Weather Rev., 138, 4475–4496, 2010.
Bauer, S. E., Wright, D. L., Koch, D., Lewis, E. R., McGraw, R., Chang, L.-S., Schwartz, S. E., and Ruedy, R.: MATRIX (Multiconfiguration Aerosol TRacker of mIXing state): an aerosol microphysical module for global atmospheric models, Atmos. Chem. Phys., 8, 6003–6035, https://doi.org/10.5194/acp-8-6003-2008, 2008.
Bechtold, P., Chaboureau, J. P., Beljaars, A., Betts, A. K., Köhler, M., Miller, M., and Redelsperger, J. L.: The simulation of the diurnal cycle of convection precipitations over land in a global model, Q. J. Roy. Meteor. Soc., 130, 3119–3137, 2004.
Bechtold, P., Köhler, M., Jung, T., Doblas-Reyes, F., Leutbecher, M., Rodwell, M. J., Vitart, F., and Balsamo, G.: Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time-scales, Q. J. Roy. Meteor. Soc., 134, 1337–1351, 2008.
Bechtold, P., Semane, N., Lopez, P., Chaboureau, J.-P., Beljaars, A., and Bormann, N.: Representing equilibrium and nonequilibrium convection in large-scale models, J. Atmos. Sci., 71, 734–753, https://doi.org/10.1175/JAS-D-13-0163.1, 2014.
Bela, M. M., Longo, K. M., Freitas, S. R., Moreira, D. S., Beck, V., Wofsy, S. C., Gerbig, C., Wiedemann, K., Andreae, M. O., and Artaxo, P.: Ozone production and transport over the Amazon Basin during the dry-to-wet and wet-to-dry transition seasons, Atmos. Chem. Phys., 15, 757–782, https://doi.org/10.5194/acp-15-757-2015, 2015.
Beltran-Przekurat, A., Pielke, R. A., Eastman, J. L., and Coughenour, M. B.: Modeling the effects of land-use/land-cover changes on the near-surface atmosphere in southern South America, Int. J. Climatol., 32, 1206–1225, https://doi.org/10.1002/joc.2346, 2011.
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011.
Brian, H. and Prather, M. J.: Fast-J2: Accurate simulation of stratospheric photolysis in global chemistry models, J. Atmos. Chem., 41, 281–296, 2002.
Carvalho, V. S. B.: O impacto das megacidades sobre a qualidade do ar:os casos das regiões metropolitanas de São Paulo e: do Rio de Janeiro. 234 f. Tese de Doutorado – Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, São Paulo, 2010.
Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M. J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O., Harding, R. J., Huntingford, C., and Cox, P. M.: The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics, Geosci. Model Dev., 4, 701–722, https://doi.org/10.5194/gmd-4-701-2011, 2011.
Clark, T. L., Coen, J. L., and Latham, D.: Description of a coupled atmosphere-fire model, Int. J. Wildland Fire, 13, 49–64, 2004.
Coen, J. L.: Simulation of the Big Elk Fire using coupled atmosphere-fire modeling, Int. J. Wildland Fire, 14, 49–59, 2005.
Costa, S. M. S., Lima, W. F. A., Freitas, S. R., Ceballos, J. C., and Rodrigues, J. V.: Monitoramento dos Traços de Cinzas do Vulcão Chileno Puyehue-Cordón Caulle, in: Congresso Brasileiro De Meteorologia, 17. (CBMET), 2012, Gramado Annals, 1–5, 2012.
Cotton, W. R., Pielke Sr., R. A., Walko, R. L., Liston, G. E., Tremback, C. J., Jiang, H., McAnelly, R. L., Harrington, J. Y., Nicholls, M. E., Carrio, G. G., and McFadden, J. P.: RAMS 2001: Current status and future directions, Meteorol. Atmos. Phys., 82, 5–29, 2003.
Crassier, V., Suhre, K., Tulet, P., and Rosset, R.: Development of a reduced chemical scheme for use in mesoscale meteorological models, Atmos. Environ., 34, 2633–2644, 2000.
Djouad, R., Sportisse, B., and Audiffren, N.: Numerical simulation of aqueous-phase atmospheric models: use of a non-autonomous Rosenbrock method, Atmos. Environ., 36, 873–879, 2002.
Damian, V., Sandu, A., Damian, M., Carmichael, G. R., and Potra, F. A.: KPP – A symbolic preprocessor for chemistry kinetics – User's guide, Technical report, The University of Iowa, IowaCity, IA52246, 1995.
Davies, H. C.: Limitations of some common lateral boundary schemes used in regional NWP models, Mon. Weather Rev., 111, 1002–1012, 1983.
Deardorff, J. W.: Stratocumulus-capped mixed layers derived from a three-dimensional model, Bound.-Lay. Meteorol., 18, 495–527, 1980.
Degrazia, G. A., Anfossi, D., de Campos Velho, H. F., and Ferrero, E.: A Lagrangian Decorrelation Time Scale for Nonhomogeneous Turbulence, Bound.-Lay. Meteorol., 86, 525–534, 1998.
Dos Santos, A. F., Freitas, S. R., de Mattos, J. G. Z., Campos Velho H. F., Gan, M. A., Luz, E. F. P., and Grell, G.: Using the Firefly optimization method to weight the ensemble of rainfall forecasts of the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS), Adv. Geosci., 35, 123–136, https://doi.org/10.5194/adgeo-35-123-2013, 2013.
Eastman, J. L., Coughenour, M. B., and Pielke, R. A.: The effects of CO2 and landscape change using a coupled plant and meteorological model, Glob. Change Biol., 7, 797–815, 2001a.
Eastman, J. L., Coughenour, M. B., and Pielke, R. A.: Does grazing affect regional climate?, J. Hydrometeorol., 2, 243–253, 2001b.
Ebert, E. E. and Curry, J. A.: A parameterization of ice cloud optical properties for climate models, J. Geophys. Res., 97, 3831–3836, 1992.
Eidhammer, T., DeMott, P. J., and Kreidenweis, S. M.: A comparison of heterogeneous ice nucleation parameterizations using a parcel model framework, J. Geophys. Res., 114, D06202, https://doi.org/10.1029/2008JD011095, 2009.
Fazenda, A. L., Panetta, J., Katsurayama, D. M., Rodrigues, L. F., Motta, L. G., and Navaux, P. O. A.: Challenges and solutions to improve the scalability of an operational regional meteorological forecasting model, Int. J. High Perform. S., 3, p. 87, 2011.
Fazenda, A. L., Rodrigues, E. R., Tomita, S. S., Panetta, J., and Mendes, C. L.: Improving the scalability of an operational scientific application on a large multi-core cluster, WSCAD-SSC, 2012.
Feingold, G. and Heymsfield, A. J.: Parameterizations of condensational growth of droplets for use in general circulation models, J. Atmos. Sci., 49, 2325–2342, 1992.
Frank, W. M. and Cohen, C.: Simulation of tropical convective systems, Part I: A cumulus parameterization, J. Atmos. Sci., 44, 3787–3799, 1987.
Freitas, E. D., Martins, L. D., Dias, P. L. D., and Andrade, M. D.: A simple photochemical module implemented in RAMS for tropospheric ozone concentration forecast in the metropolitan area of Sao Paulo, Brazil: Coupling and validation, Atmos. Environ., 39, 6352–6361, 2005.
Freitas, E. D., Rozoff, C. M., Cotton, W. R., and Silva Dias, P. L.: Interactions of an urban heat island and sea breeze circulations during winter over the Metropolitan Area of São Paulo – Brazil, Bound.-Lay. Meteorol., 122, 43–65, 2007.
Freitas, S. R., Longo, K. M., Silva Dias, M. A. F., and Artaxo, P.: Numerical modeling of air mass trajectories from biomass burning areas of the Amazon basin, Anais da Academia Brasileira de Ciências, Brasil, 68, 193–296, 1996.
Freitas, S. R., Dias, M. A. F. S., Dias, P. L. S., Longo, K. M., Artaxo, P., Andreae, M. O., and Fischer, H.: A convective kinematic trajectory technique for low-resolution atmospheric models, J. Geophys. Res., 105, 24375–24386, 2000.
Freitas, S. R., Longo, K. M., Silva Dias, M., Silva Dias, P., Chatfield, R., Prins, E., Artaxo, P., Grell, G., and Recuero, F.: Monitoring the transport of biomass burning emissions in South America, Environ. Fluid Mech., 5, 135–167, https://doi.org/10.1007/s10652-005-0243-7, 2005.
Freitas, S. R., Longo, K. M., and Andreae, M. O.: Impact of including the plume rise of vegetation fires in numerical simulations of associated atmospheric pollutants, Geophys. Res. Lett., 33, L17808, https://doi.org/10.1029/2006GL026608, 2006.
Freitas, S. R., Longo, K. M., Silva Dias, M. A. F., Chatfield, R., Silva Dias, P., Artaxo, P., Andreae, M. O., Grell, G., Rodrigues, L. F., Fazenda, A., and Panetta, J.: The Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS) – Part 1: Model description and evaluation, Atmos. Chem. Phys., 9, 2843–2861, https://doi.org/10.5194/acp-9-2843-2009, 2009.
Freitas, S. R., Longo, K. M., Trentmann, J., and Latham, D.: Technical Note: Sensitivity of 1-D smoke plume rise models to the inclusion of environmental wind drag, Atmos. Chem. Phys., 10, 585–594, https://doi.org/10.5194/acp-10-585-2010, 2010.
Freitas, S. R., Longo, K. M., Alonso, M. F., Pirre, M., Marecal, V., Grell, G., Stockler, R., Mello, R. F., and Sánchez Gácita, M.: PREP-CHEM-SRC – 1.0: a preprocessor of trace gas and aerosol emission fields for regional and global atmospheric chemistry models, Geosci. Model Dev., 4, 419–433, https://doi.org/10.5194/gmd-4-419-2011, 2011.
Freitas, S. R., Rodrigues, L. F., Longo, K. M., and Panetta, J.: Impact of a monotonic advection scheme with low numerical diffusion on transport modeling of emissions from biomass burning, J. Adv. Model. Earth Syst., 4, M01001, https://doi.org/10.1029/2011MS000084, 2012.
Gerbig, C., Lin, J. C., Wofsy, S. C., Daube, B. C., Andrews, A. E., Stephens, B. B., Bakwin, P. S., and Grainger, C. A.: Toward constraining regional-scale fluxes of CO2 with atmospheric observations over a continent: 1. Observed spatial variability from airborne platforms, J. Geophys. Res.-Atmos., 108, 4756, https://doi.org/10.1029/2002JD003018, 2003.
Gevaerd, R. and Freitas, S. R.: Estimativa operacional da umidade do solo para inicialização de modelos de previsão numérica da atmosfera. Parte I: Descrição da metodologia e validação, Rev. Bras. Meteorol., 21, 1–15, 2006.
Grell, G. A.: Prognostic evaluation of assumptions used by cumulus parameterizations within a generalized framework, Mon. Weather Rev., 121, 764–787, 1993.
Grell, G. A. and Devenyi, D.: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques, Geophys. Res. Lett., 29, 38-1, https://doi.org/10.1029/2002GL015311, 2002.
Grell, G. A. and Freitas, S. R.: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling, Atmos. Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014, 2014.
Grell, G. A., Peckham, S., McKeen, S., Schmitz, R., Frost, G., Skamarock, W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF model, Atmos. Environ., 39, 6957–6975, 2005.
Hack, J. J., Boville, B. A., Briegleb, B. P., Kiehl, J. T., Rasch, P. J., and Williamson, D. L.: Description of the NCAR Community Climate Model (CCM2), NCAR Technical Note, NCAR/TN-382+STR, 1993.
Hamill, T. M.: Hypothesis Tests for Evaluating Numerical Precipitation Forecasts, Weather Forecast., 14, 155–167, 1999.
Hanna, S.: Applications in Air Pollution Modeling, in: Atmospheric Turbulence and Air Pollution Modelling, edited by: Nieuwstadt, F. and van Dop, H., vol. 1 of Atmospheric Sciences Library, chap. 7 275–310, Springer Netherlands, https://doi.org/10.1007/978-94-010-9112-1_7, 1982.
Helfand, H. M. and Labraga, J. C.: Design of a Nonsingular Level 2.5 Second-Order Closure Model for the Prediction of Atmospheric Turbulence, J. Atmos. Sci., 45, 113–132, 1988.
Hill, G. E.: Factors Controlling the Size and Spacing of Cumulus Clouds as Revealed by Numerical Experiments, J. Atmos. Sci., 31, 646–673, 1974.
Holben, B. N., Eck, T. F., Slutsker, I., Tanreé, D., Buis, J. P., Setzer, A., Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., Lavenu, F., Jankowiak, I., and Smirnov, A.: AERONET – a federated instrument network and data archive for aerosol characterization, Remote Sens. Environ., 66, 1–16, https://doi.org/10.1016/s0034-4257(98)00031-5, 1998.
Hu, Y. X. and Stamnes, K.: An accurate parameterization of the radiative properties of water clouds suitable for use in climate models, J. Climate, 6, 728–742, 1993.
Huffman, G. J., Adler, R. F., Bolvin, D. T., Gu, G. J., Nelkin, E. J., Bowman, K. P., Hong, Y., Stocker, E. F., and Wolff, D. B.: The TRMM multi-satellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales, J. Hydrometeorol., 8, 38–55, 2007.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008.
Jakob, C. and Siebesma, A. P.: A new subcloud model for mass-flux convection schemes: influence on triggering, updrafts properties, and model climate, Mon. Weather Rev., 131, 2765–2778, 2003.
Janjić, Z.: Nonsingular implementation of the Mellor-Yamada Level 2.5 Scheme in the NCEP Meso model, Office note 437, National Center for Environmental Prediction, Boulder, CO, available at: http://www.lib.ncep.noaa.gov/ncepofficenotes/2000s/ (last access: 10 January 2017), 2001.
Johansson, E., Spangenberg, J., Gouvêa, M. L., and Freitas, E. D.: Scale-integrated atmospheric simulations to assess thermal comfort in different urban tissues in the warm humid summer of São Paulo, Brazil, Urban Climate, 6, 24–43, 2013.
Kain, J. S. and Fritsch, J. M.: The role of the convective “trigger function” in numerical forecasts of mesoscale convective systems, Meteorol. Atmos. Phys., 49, 93–106, 1992.
Khan, S. and Simpson, R.: Effect of a heat island on the meteorology of a complex urban airshed, Bound.-Lay. Meteorol., 100, 487–506, 2001.
Kiehl, J. T., Hack, J. J., Bonan, G. B., Boville, B. A., Williamson, D. L., and Rasch, P. L.: The National Center for Atmospheric Research Community Climate Model: CCM3, J. Climate, 11, 1131–1149, 1998.
Klein, S. A. and Hartmann, D. L.: The seasonal cycle of low stratiform clouds, J. Climate, 6, 1588–1606, 1993.
Klemp, J. B. and Wilhelmson, R. B.: The Simulation of Three-Dimensional Convective Storm Dynamics, J. Atmos. Sci., 35, 1070–1096, 1978.
Krishnamurti, T. N., Low-Nam, S., and Pasch, R.: Cumulus parameterizations and rainfall rates II, Mon. Weather Rev., 111, 815–828, 1983.
Krol, M., Houweling, S., Bregman, B., van den Broek, M., Segers, A., van Velthoven, P., Peters, W., Dentener, F., and Bergamaschi, P.: The two-way nested global chemistry-transport zoom model TM5: algorithm and applications, Atmos. Chem. Phys., 5, 417–432, https://doi.org/10.5194/acp-5-417-2005, 2005.
Lin, J. C., Gerbig, C., Wofsy, S. C., Andrews, A. E., Daube, B. C., Davis, K. J., and Grainger, C. A.: A near-field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time-Inverted Lagrangian Transport (STILT) model, J. Geophys. Res.-Atmos., 108, 4493, https://doi.org/10.1029/2002JD003161, 2003.
Liu, Y., Daum, P. H., Guo, H., and Peng, Y.: Dispersion bias, dispersion effect, and the aerosol-cloud conundrum. Environ. Res. Lett., 3, 045021, https://doi.org/10.1088/1748-9326/3/4/045021, 2008.
Lilly, D. K.: On the numerical simulation of buoyant convection, Tellus, 14, 148–172, https://doi.org/10.1111/j.2153-3490.1962.tb00128.x, 1962.
Longo, K. M., Freitas, S. R., Andreae, M. O., Setzer, A., Prins, E., and Artaxo, P.: The Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS) – Part 2: Model sensitivity to the biomass burning inventories, Atmos. Chem. Phys., 10, 5785–5795, https://doi.org/10.5194/acp-10-5785-2010, 2010.
Longo, K. M., Freitas, S. R., Pirre, M., Marécal, V., Rodrigues, L. F., Panetta, J., Alonso, M. F., Rosário, N. E., Moreira, D. S., Gácita, M. S., Arteta, J., Fonseca, R., Stockler, R., Katsurayama, D. M., Fazenda, A., and Bela, M.: The Chemistry CATT-BRAMS model (CCATT-BRAMS 4.5): a regional atmospheric model system for integrated air quality and weather forecasting and research, Geosci. Model Dev., 6, 1389–1405, https://doi.org/10.5194/gmd-6-1389-2013, 2013.
Lu, L., Pielke, R. A., Liston, G. E., Parton, W. J., Ojima, D., and Hartman, M.: Implementation of a two-way interactive atmospheric and ecological model and its application to the central United States, J. Climate, 14, 900–919, 2001.
Lyons, W. A., Pielke, R. A., Tremback, C. J., Walko, R. L., Moon, D. A., and Keen, C. S.: Modeling the impacts of mesoscale vertical motions upon coastal zone air pollution dispersion, Atmos. Environ., 29, 283–301, 1995.
Madronich, S.: Photodissociation in the atmosphere: 1. Actinic flux and the effect of ground reflections and clouds, J. Geophys. Res., 92, 9740–9752, https://doi.org/10.1029/JD092iD08p09740, 1989.
Mandel, J., Beezley, J. D., Coen, J. L., and Kim, M.: Data assimilation for wildland fires: Ensemble Kalman filters in coupled atmosphere-surface models, IEEE Contr. Syst. Mag., 29, 47–65, https://doi.org/10.1109/MCS.2009.932224, 2009.
Mandel, J., Beezley, J. D., and Kochanski, A. K.: Coupled atmosphere-wildland fire modeling with WRF 3.3 and SFIRE 2011, Geosci. Model Dev., 4, 591–610, https://doi.org/10.5194/gmd-4-591-2011, 2011.
Masson, V.: A physically-based scheme for the urban energy budget in atmospheric models, Bound.-Lay. Meteorol., 94, 357–397, 2000.
Mastin, L., Guffanti, M., Servranckx, R., Webley, P., Barsotti, S., Dean, K., Durant, A., Ewert, J., Neri, A., and Rose, W.: A multidisciplinary effort to assign realistic source parameters to models of volcanic ash-cloud transport and dispersion during eruptions, J. Volcanol. Geoth. Res., 186, 10–21, 2009.
McKain, K., Down, A., Raciti, S. M., Budney, J., Hutyra, L. R., Floerchinger, C., Herndon, S. C., Nehrkorn, T., Zahniser, M. S., Jackson, R. B., Phillips, N., and Wofsy, S. C.: Methane emissions from natural gas infrastructure and use in the urban region of Boston, Massachusetts, P. Natl. Acad. Sci. USA, 112, 1941–1946, https://doi.org/10.1073/pnas.1416261112, 2015.
Medvigy, D., Moorcroft, P. R., Avissar, R., and Walko, R. L.: Mass conservation and atmospheric dynamics in the Regional Atmospheric Modeling System (RAMS), Environ. Fluid Mech., 5, 109–134, https://doi.org/10.1007/s10652-005-5275-5, 2005.
Mellor, G. L. and Yamada, T.: Development of a turbulence closure model for geophysical fluid problems, Rev. Geophys. Space Phys., 20, 851–875, https://doi.org/10.1029/RG020i004p00851, 1982.
Menezes, I. C.: Construção de um modelo de interacção atmosfera/fogo aplicado à gestão florestal e avaliação de risco de fogos florestais no Alentejo, PhD thesis, University of Évora, Portugal, 2015.
Meyers, M. P., Walko, R. L., Harrington, J. Y., and Cotton, W. R.: New RAMS cloud microphysics parameterization: Part II. The two-moment scheme, Atmos. Res., 45, 3–39, 1997.
Miller, S. M., Matross, D. M., Andrews, A. E., Millet, D. B., Longo, M., Gottlieb, E. W., Hirsch, A. I., Gerbig, C., Lin, J. C., Daube, B. C., Hudman, R. C., Dias, P. L. S., Chow, V. Y., and Wofsy, S. C.: Sources of carbon monoxide and formaldehyde in North America determined from high-resolution atmospheric data, Atmos. Chem. Phys., 8, 7673–7696, https://doi.org/10.5194/acp-8-7673-2008, 2008.
Miller, S. M., Wofsy, S. C., Michalak, A. M., Kort, E. A., Andrews, A. E., Biraud, S. C., Dlugokencky, E. J., Eluszkiewicz, J., Fischer, M. L., Janssens-Maenhout, G., Miller, B. R., Miller, J. B., Montzka, S. A., Nehrkorn, T., and Sweeney, C.: Anthropogenic emissions of methane in the United States, P. Natl. Acad. Sci. USA, 110, 20018–20022, https://doi.org/10.1073/pnas.1314392110, 2013.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmosphere: RRTM a validated correlated-k model for the longwave, J. Geophys. Res., 102, 16663–16682, 1997.
Moreira, D. S., Freitas, S. R., Bonatti, J. P., Mercado, L. M., Rosário, N. M. É., Longo, K. M., Miller, J. B., Gloor, M., and Gatti, L. V.: Coupling between the JULES land-surface scheme and the CCATT-BRAMS atmospheric chemistry model (JULES-CCATT-BRAMS1.0): applications to numerical weather forecasting and the CO2 budget in South America, Geosci. Model Dev., 6, 1243–1259, https://doi.org/10.5194/gmd-6-1243-2013, 2013.
Nair, K. N., Freitas, E. D., Sánchez-Ccoyllo, O. R., Silva Dias M. A. F., Silva Dias, P. L., Andrade, M. F., and Massambani, O.: Dynamics of urban boundary layer over Sao Paulo associated with mesoscale processes, Meteorol. Atmos. Phys., 86, 87–98, 2004.
Nakanishi, M. and Niino, H.: An Improved Mellor–Yamada Level-3 Model with Condensation Physics: Its Design and Verification, Bound.-Lay. Meteorol., 112, 1–31, https://doi.org/10.1023/B:BOUN.0000020164.04146.98, 2004.
Nakanishi, M. and Niino, H.: An Improved Mellor–Yamada Level-3 Model: Its Numerical Stability and Application to a Regional Prediction of Advection Fog, Bound.-Lay. Meteorol., 119, 397–407, https://doi.org/10.1007/s10546-005-9030-8, 2006.
Nehrkorn, T., Eluszkiewicz, J., Wofsy, S. C., Lin, J. C., Gerbig, C., Longo, M., and Freitas, S.: Coupled weather research and forecasting–stochastic time-inverted lagrangian transport (WRF–STILT) model, Meteorol. Atmos. Phys., 107, 51–64, https://doi.org/10.1007/s00703-010-0068-x, 2010.
Pavani, C. A. B.: Modelagem numérica do transporte de emissões vulcânicas: caso do vulcão Puyehue, 184 pp., (sid.inpe.br/mtc-m18/2014/01.20.11.25-TDI), Dissertation (Master in Meteorology) – Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, 2014, available at: http://urlib.net/8JMKD3MGP8W/3FJUGQ8, last access: 27 May 2014.
Pavanni, C., Freitas, S. R., Lima, W. F. A., Costa, S. M. S., Rosario, N. M., Moreira, D. S., and Yoshida, M. C.: Incluindo funcionalidades no modelo BRAMS para simular o transporte de cinzas vulcânicas: descrição e análise de sensibilidade aplicada ao evento eruptivo do Puyehue em 2011, Revista Brasileira de Meteorologia, 31(4), 377–393, https://doi.org/10.1590/0102-778631231420150035, 2016.
Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of hygroscopic growth and cloud condensation nucleus activity, Atmos. Chem. Phys., 7, 1961–1971, https://doi.org/10.5194/acp-7-1961-2007, 2007.
Pielke, R. A.: Mesoscale meteorological modeling, 3rd Edn., Academic Press, San Diego, CA, 2013.
Pielke, R. A. and Uliasz, M.: Use of meteorological models as input to regional and mesoscale air quality models – Limitations and strengths. Atmos. Environ., 32, 1455–1466, 1998.
Pielke, R. A., Cotton, W. R., Walko, R. L., Tremback, C. J., Lyons, W. A., Grasso, L. D., Nicholls, M. E., Moran, M. D., Wesley, D. A., Lee, T. J., and Copeland, J. H.: A comprehensive meteorological modeling system – RAMS, Meteorol. Atmos. Phys., 49, 69–91, 1992.
Pincus, R. and Baker, M. B.: Effect of precipitation on the albedo susceptibility of clouds in the marine boundary layer, Nature, 372, 250–252, 1994.
Pincus, R., Barker, H. R., and Morcrette, J.-J.: A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields, J. Geophys. Res., 108, 4376, https://doi.org/10.1029/2002JD003322, 2003.
Procopio, A. S., Remer, L. A., Artaxo, P., Kaufman, Y. J., and Holben, B. N.: Modeled spectral optical properties for smoke aerosols in Amazonia, Geophys. Res. Lett., 30, 2265, https://doi.org/10.1029/2003gl018063, 2003.
RAMS: The Regional Atmospheric Modeling System: Technical Description (Draft), Technical report, ATMET, Fort Collins, CO, USA, available at: http://www.atmet.com/html/docs/ documentation.shtml (last access: 10 January 2017), 2003.
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, 1998.
Rennó, N. O. and Ingersoll, A. P.: Natural convection as a heat engine: A theory for CAPE, J. Atmos. Sci., 53, 572–585, 1996.
Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C., and Wang, W.: An Improved In Situ and Satellite SST Analysis for Climate, J. Climate, 15, 1609–1625, 2002.
Rosário, N. E., Longo, K. M., Freitas, S. R., Yamasoe, M. A., and Fonseca, R. M.: Modeling the South American regional smoke plume: aerosol optical depth variability and surface shortwave flux perturbation, Atmos. Chem. Phys., 13, 2923–2938, https://doi.org/10.5194/acp-13-2923-2013, 2013.
Saleeby, S. M. and Cotton, W. R.: A large-droplet mode and prognostic number concentration of cloud droplets in the Colorado State University Regional Atmospheric Modeling System (RAMS). Part I: Module descriptions and supercell test simulations, J. Appl. Meteorol., 43, 182–195, 2004.
Saleeby, S. M. and Cotton, W. R.: A Binned Approach to Cloud-Droplet Riming Implemented in a Bulk Microphysics Model, J. Appl. Meteorol., 47, 694–703, 2008.
Sánchez Gácita, M., Longo, K. M., Freire, J. L. M., Freitas, S. R., and Martin, S. T.: Impact of mixing state and hygroscopicity on CCN activity of biomass burning aerosol in Amazonia, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-248, in review, 2016.
Santos, A. F.: Inverse problems using the optimization method firefly applied in the precipitation parameterization of the model brams over South America. PhD thesis– National Institute for Space Research (INPE), São José dos Campos, 2014 (in Portuguese).
Santos e Silva, C. M., Gielow, R., and Freitas, S. R.: Diurnal and semidiurnal rainfall cycles during the rain season in SW Amazonia, observed via rain gauges and estimated using S-band radar, Atmos. Sci. Lett., 10, 87–93, https://doi.org/10.1002/asl.214, 2009.
Santos e Silva, C. M., Freitas, S. R., and Gielow, R.: Numerical simulation of the diurnal cycle of rainfall in SW Amazon basin during the 1999 rainy season: the role of convective trigger function, Theor. Appl. Climatol., 109, 473–483, 2012.
Savijärvi, H.: Shortwave optical properties of rain, Tellus, 49a, 177–181, 1997.
Savijärvi, H. and Raisanen, P.: Long-wave optical properties of water clouds and rain, Tellus, 50A, 1–11, 1998.
Savijärvi, H., Arola, A., and Räisänen, P.: Short-wave optical properties of precipitating water clouds, Q. J. Roy. Meteor. Soc., 123, 883–899, https://doi.org/10.1002/qj.49712354005, 1997.
Sestini, M. F., Reimer, E. S., Valeriano, D. M., Alvalá, R. C. S., Mello, E. M. K., Chan, C. S., and Nobre, C. A.: Mapa de cobertura da terra da Amazônia legal para uso em modelos meteorológicos, in: Anais do Simpósio Brasileiro de Sensoriamento Remoto, 11, Belo Horizonte, 2901–2906, 2003 (in Portuguese).
Skamarock, W. C.: Positive-definite and monotonic limiters for unrestricted-time-step transport schemes, Mon. Weather Rev., 134, 2241–2250, 2006.
Skamarock, W. C. and Klemp, J. B.: A time-split non-hydrostatic atmospheric model for weather research and forecasting applications, J. Comput. Phys., 227, 3465–3485, https://doi.org/10.1016/j.jcp.2007.01.037, 2008.
Skamarock, W. C., Klemp, J. B., Duda, M. G., Fowler, L. D., Park, S. H., and Ringler, T. D.: A multi-scale non-hydrostatic atmospheric model using centroidal Voronoi tesselations and C-grid staggering, Mon. Weather Rev., 240, 3090–3105, 2012.
Slingo, J. M.: The development and verifcation of a cloud prediction scheme for the ECMWF model, Q. J. Roy. Meteor. Soc., 113, 899–927, 1987.
Smagorinsky, J.: General circulation experiments with the primitive equations, Mon. Weather Rev., 91, 99–164, https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO,2, 1963.
Souto, R. P., Silva Dias, P. L., and Vigilant, F.: Parallel Performance Analysis of a Regional Numerical Weather Prediction Model in a Petascale Machine, in: High Performance Computing, Communications in Computer and Information Science, 565, 146–150, 2015.
Souza, E. P.: Theoretical and numerical study of the relationship between convection and heterogeneous surfaces in the Amazon region, 121 pp., PhD Dissertation – University of São Paulo, São Paulo, 1999 (in Portuguese).
Stockwell, W. R., Kirchner, F., and Kuhn, M.: A new mechanism for regional chemistry modeling, J. Geophys. Res., 102, 25847–25879, 1997.
Stuefer, M., Freitas, S. R., Grell, G., Webley, P., Peckham, S., McKeen, S. A., and Egan, S. D.: Inclusion of ash and SO2 emissions from volcanic eruptions in WRF-Chem: development and some applications, Geosci. Model Dev., 6, 457–468, https://doi.org/10.5194/gmd-6-457-2013, 2013.
Stull, R. B.: An introduction to boundary layer meteorology, Kluwer Academic Publishers, Dordrecht, Netherlands, 1988.
Sun, Z. and Shine, K. P.: Studies of the radiative properties of ice and mixed-phase clouds, Q. J. Roy. Meteor. Soc., 120, 111–137, https://doi.org/10.1002/qj.49712051508, 1994.
Thompson, G. and Eidhammer, T.: A study of aerosol impacts on clouds and precipitation development in a large winter cyclone, J. Atmos. Sci., 71, 3636–3658, https://doi.org/10.1175/JAS-D-13-0305, 2014.
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization, Mon. Weather Rev., 136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1, 2008.
Tie, X., Madronich, S., Walters, S., Zhang, R., Rasch, P., and Collins, W.: Effects of clouds on photolysis and oxydants in the troposphere, J. Geophys. Res., 108, 1–25, 2003.
Toon, O. B., McKay, C. P., Ackerman, T. P., and Santhanam, K.: Rapid Calculation of Radiative Heating Rates and Photodissociation Rates in Inhomogeneous Multiple Scattering Atmospheres, J. Geophys. Res., 94, 16287–16301, https://doi.org/10.1029/JD094iD13p16287, 1989.
Tremback, C., Powell, J., Cotton, W., and Pielke, R.: The forward-in-time upstream advection scheme: Extension to higher orders, Mon. Weather Rev., 115, 540–555, 1987.
Tremback, C. J.: Numerical simulation of a mesoscale convective complex: model development and numerical results. Ph.D. dissertation, Atmos. Sci. Paper No. 465, Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, 247 pp., 1990.
Tripoli, G. J. and Cotton, W. R.: The Colorado State University three-dimensional cloud/mesoscale model. Part I: General theoretical framework and sensitivity experiments, J. Rech. Atmos., 16, 185–220, 1982.
Twomey, S.: Pollution and the planetary albedo, Atmos. Environ., 8, 1251–1256, https://doi.org/10.1016/0004-6981(74)90004-3, 1974.
Vogel, B., Vogel, H., Bäumer, D., Bangert, M., Lundgren, K., Rinke, R., and Stanelle, T.: The comprehensive model system COSMO-ART – Radiative impact of aerosol on the state of the atmosphere on the regional scale, Atmos. Chem. Phys., 9, 8661–8680, https://doi.org/10.5194/acp-9-8661-2009, 2009.
Vogelezang, D. and Holtslag, A.: Evaluation and model impacts of alternative boundary-layer height formulations, Bound.-Lay. Meteorol., 81, 245–269, https://doi.org/10.1007/BF02430331, 1996.
Walcek, C. J.: Minor flux adjustment near mixing ratio extremes for simplified yet highly accurate monotonic calculation of tracer advection, J. Geophys. Res., 105, 9335–9348, https://doi.org/10.1029/1999JD901142, 2000.
Walko, R. L., Cotton, W. R., Harrington, J. L., and Meyers, M. P.: New RAMS cloud microphysics parameterization. Part I: The single-moment scheme, Atmos. Res., 38, 29–62, 1995a.
Walko, R. L., Tremback, C. J., Pielke, R. A., and Cotton, W. R.: An interactive nesting algorithm for stretched grids and variable nesting ratios, J. Appl. Meteor., 34, 994–999, 1995b.
Walko, R., Band, L., Baron, J., Kittel, F., Lammers, R., Lee, T., Ojima, D., Pielke, R., Taylor, C., Tague, C., Tremback, C., and Vidale, P.: Coupled atmosphere-biophysics- hydrology models for environmental modeling, J. Appl. Meteorol., 39, 931–944, 2000.
Wicker, L. J. and Skamarock, W. C.: A time-splitting scheme for the elastic equations incorporating second-order Runge-Kutta time differencing, Mon. Weather Rev., 126, 1992–1999, 1998.
Wicker, L. J.: A two-step Adams-Bashforth-Moulton split-explicit integrator for compressible atmospheric models, Mon. Weather Rev., 137, 3588–3595, 2009.
Wicker, L. J. and Skamarock, W. C.: Time-splitting methods for elastic models using forward time schemes, Mon. Weather Rev., 130, 2088–2097, 2002.
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, 2000.
Wilde, N. P., Stull, R. B., and Eloranta, E. W.: The LCL zone and cumulus onset, J. Clim. Appl. Meteor., 24, 640–657, 1985.
Williams, P. D.: A proposed modification to the Robert-Asselin time filter, Mon. Weather Rev., 137, 2538–2546, 2009.
Wyser, K. and Yang, P.: Average ice crystal size and bulk single-scattering properties of cirrus clouds, Atmos. Res., 49, 315–335, 1989.
Xiang, B., Miller, S. M., Kort, E. A., Santoni, G. W., Daube, B. C., Commane, R., Angevine, W. M., Ryerson, T. B., Trainer, M. K., Andrews, A. E., Nehrkorn, T., Tian, H., and Wofsy, S. C.: Nitrous oxide (N2O) emissions from California based on 2010 CalNex airborne measurements, J. Geophys. Res.-Atmos., 118, 2809–2820, https://doi.org/10.1002/jgrd.50189, 2013.
Xu, K.-M. and Krueger, S. K.: Evaluation of cloudiness parameterizations using a cumulus ensemble model, Mon. Weather Rev., 119, 342–367, 1991.
Xu, K.-M. and Randall, D.A.: A semiempirical cloudiness parameterization for use in climate models, J. Atmos. Sci., 53, 3084–3102, 1996.
Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms, Luviner Press, 2008.
Yarwood, G., Rao, S., Yocke, M., and Whitten, G. Z.: Updates to the Carbon Bond chemical mechanism: CB05, Final Report to the US EPA, RT-0400675, Novato, CA, available at: http://www.camx.com/publ/pdfs/cb05_final_report_120805.pdf (last access: 10 January 2017), 2005.
Short summary
We present a new version of the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS) where different previous versions for weather, chemistry, and the carbon cycle were unified in a single harmonized software system. This version also has a new set of state-of-the-art physical parametrizations and higher computational parallel and memory usage efficiency. BRAMS has been applied for research and operational weather and air quality forecasting, largely in South America.
We present a new version of the Brazilian developments on the Regional Atmospheric Modeling...
Special issue