Articles | Volume 13, issue 3
https://doi.org/10.5194/gmd-13-1075-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-1075-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
10 Mar 2020
Model description paper |
| 10 Mar 2020
| 10 Mar 2020
EXPLUME v1.0: a model for personal exposure to ambient O3 and PM2.5
Myrto Valari
CORRESPONDING AUTHOR
LMD/IPSL, Laboratoire de Météorologie Dynamique, Sorbonne Université, Ecole Polytechnique, IPSL Research University, Ecole Normale Supérieure, CNRS, 75252 Paris, France
Konstandinos Markakis
LMD/IPSL, Laboratoire de Météorologie Dynamique, Sorbonne Université, Ecole Polytechnique, IPSL Research University, Ecole Normale Supérieure, CNRS, 75252 Paris, France
Emilie Powaga
Université Paris-Est, Centre scientifique et Technique du Bâtiment, Direction Santé Confort, Division Physico-chimie – Sources et Transferts de polluants, 38400 Saint-Martin-d'Hères, France
Bernard Collignan
Université Paris-Est, Centre scientifique et Technique du Bâtiment, Direction Santé Confort, Division Physico-chimie – Sources et Transferts de polluants, 38400 Saint-Martin-d'Hères, France
Olivier Perrussel
AIRPARIF, Association de surveillance de qualité de l'air en Île-de-France, 7 rue Crillon, 75004 Paris, France
Related authors
Sylvain Mailler, Laurent Menut, Dmitry Khvorostyanov, Myrto Valari, Florian Couvidat, Guillaume Siour, Solène Turquety, Régis Briant, Paolo Tuccella, Bertrand Bessagnet, Augustin Colette, Laurent Létinois, Kostantinos Markakis, and Frédérik Meleux
Geosci. Model Dev., 10, 2397–2423, https://doi.org/10.5194/gmd-10-2397-2017, https://doi.org/10.5194/gmd-10-2397-2017, 2017
Short summary
Short summary
CHIMERE is a chemistry-transport model initially designed for box-modelling of the regional atmospheric composition. In the recent years, CHIMERE has been extended to be able to model atmospheric composition at all scales from urban to hemispheric scale, which implied major changes on the coordinate systems as well as on physical processes. This study describes how and why these changes have been brought to the model, largely increasing the range of its possible use.
Konstantinos Markakis, Myrto Valari, Magnuz Engardt, Gwendoline Lacressonniere, Robert Vautard, and Camilla Andersson
Atmos. Chem. Phys., 16, 1877–1894, https://doi.org/10.5194/acp-16-1877-2016, https://doi.org/10.5194/acp-16-1877-2016, 2016
Short summary
Short summary
The overall climate benefit at both cities and pollutants is −2 to −10 % depending on metric. Over the city of Paris local mitigation of NOx emissions increases future ozone due to titration inhibition. Climate is the most influential factor for maximum ozone in Paris, which is particularly interesting from a health impact perspective. Over urban areas with major regional contribution (e.g. Stockholm) the bias due to coarse emission inventory may lead to policy misclassification.
K. Markakis, M. Valari, O. Perrussel, O. Sanchez, and C. Honore
Atmos. Chem. Phys., 15, 7703–7723, https://doi.org/10.5194/acp-15-7703-2015, https://doi.org/10.5194/acp-15-7703-2015, 2015
Short summary
Short summary
The efficacy of emission policies is explored by coarse resolution modeling applications. These were shown to be biased, overestimating that efficacy indicated in simulations with refined resolution. In order to improve our assessments, we need to quantify those biases. In this study we show that the ozone bias of the coarse run is reduced by 40% by adopting higher resolution emissions. For PM2.5, the coarse run cannot selectively incorporate local scale features in order to reduce model error.
K. Markakis, M. Valari, A. Colette, O. Sanchez, O. Perrussel, C. Honore, R. Vautard, Z. Klimont, and S. Rao
Atmos. Chem. Phys., 14, 7323–7340, https://doi.org/10.5194/acp-14-7323-2014, https://doi.org/10.5194/acp-14-7323-2014, 2014
L. Menut, B. Bessagnet, D. Khvorostyanov, M. Beekmann, N. Blond, A. Colette, I. Coll, G. Curci, G. Foret, A. Hodzic, S. Mailler, F. Meleux, J.-L. Monge, I. Pison, G. Siour, S. Turquety, M. Valari, R. Vautard, and M. G. Vivanco
Geosci. Model Dev., 6, 981–1028, https://doi.org/10.5194/gmd-6-981-2013, https://doi.org/10.5194/gmd-6-981-2013, 2013
Jinghui Lian, Thomas Lauvaux, Hervé Utard, François-Marie Bréon, Grégoire Broquet, Michel Ramonet, Olivier Laurent, Ivonne Albarus, Mali Chariot, Simone Kotthaus, Martial Haeffelin, Olivier Sanchez, Olivier Perrussel, Hugo Anne Denier van der Gon, Stijn Nicolaas Camiel Dellaert, and Philippe Ciais
Atmos. Chem. Phys., 23, 8823–8835, https://doi.org/10.5194/acp-23-8823-2023, https://doi.org/10.5194/acp-23-8823-2023, 2023
Short summary
Short summary
This study quantifies urban CO2 emissions via an atmospheric inversion for the Paris metropolitan area over a 6-year period from 2016 to 2021. Results show a long-term decreasing trend of about 2 % ± 0.6 % per year in the annual CO2 emissions over Paris. We conclude that our current capacity can deliver near-real-time CO2 emission estimates at the city scale in under a month, and the results agree within 10 % with independent estimates from multiple city-scale inventories.
Sylvain Mailler, Laurent Menut, Dmitry Khvorostyanov, Myrto Valari, Florian Couvidat, Guillaume Siour, Solène Turquety, Régis Briant, Paolo Tuccella, Bertrand Bessagnet, Augustin Colette, Laurent Létinois, Kostantinos Markakis, and Frédérik Meleux
Geosci. Model Dev., 10, 2397–2423, https://doi.org/10.5194/gmd-10-2397-2017, https://doi.org/10.5194/gmd-10-2397-2017, 2017
Short summary
Short summary
CHIMERE is a chemistry-transport model initially designed for box-modelling of the regional atmospheric composition. In the recent years, CHIMERE has been extended to be able to model atmospheric composition at all scales from urban to hemispheric scale, which implied major changes on the coordinate systems as well as on physical processes. This study describes how and why these changes have been brought to the model, largely increasing the range of its possible use.
Konstantinos Markakis, Myrto Valari, Magnuz Engardt, Gwendoline Lacressonniere, Robert Vautard, and Camilla Andersson
Atmos. Chem. Phys., 16, 1877–1894, https://doi.org/10.5194/acp-16-1877-2016, https://doi.org/10.5194/acp-16-1877-2016, 2016
Short summary
Short summary
The overall climate benefit at both cities and pollutants is −2 to −10 % depending on metric. Over the city of Paris local mitigation of NOx emissions increases future ozone due to titration inhibition. Climate is the most influential factor for maximum ozone in Paris, which is particularly interesting from a health impact perspective. Over urban areas with major regional contribution (e.g. Stockholm) the bias due to coarse emission inventory may lead to policy misclassification.
K. Markakis, M. Valari, O. Perrussel, O. Sanchez, and C. Honore
Atmos. Chem. Phys., 15, 7703–7723, https://doi.org/10.5194/acp-15-7703-2015, https://doi.org/10.5194/acp-15-7703-2015, 2015
Short summary
Short summary
The efficacy of emission policies is explored by coarse resolution modeling applications. These were shown to be biased, overestimating that efficacy indicated in simulations with refined resolution. In order to improve our assessments, we need to quantify those biases. In this study we show that the ozone bias of the coarse run is reduced by 40% by adopting higher resolution emissions. For PM2.5, the coarse run cannot selectively incorporate local scale features in order to reduce model error.
K. Markakis, M. Valari, A. Colette, O. Sanchez, O. Perrussel, C. Honore, R. Vautard, Z. Klimont, and S. Rao
Atmos. Chem. Phys., 14, 7323–7340, https://doi.org/10.5194/acp-14-7323-2014, https://doi.org/10.5194/acp-14-7323-2014, 2014
L. Menut, B. Bessagnet, D. Khvorostyanov, M. Beekmann, N. Blond, A. Colette, I. Coll, G. Curci, G. Foret, A. Hodzic, S. Mailler, F. Meleux, J.-L. Monge, I. Pison, G. Siour, S. Turquety, M. Valari, R. Vautard, and M. G. Vivanco
Geosci. Model Dev., 6, 981–1028, https://doi.org/10.5194/gmd-6-981-2013, https://doi.org/10.5194/gmd-6-981-2013, 2013
Related subject area
Atmospheric sciences
A general comprehensive evaluation method for cross-scale precipitation forecasts
Implementation of a Simple Actuator Disk for Large-Eddy Simulation in the Weather Research and Forecasting Model (WRF-SADLES v1.2) for wind turbine wake simulation
WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework
Implementation and evaluation of diabatic advection in the Lagrangian transport model MPTRAC 2.6
An improved and extended parameterization of the CO2 15 µm cooling in the middle and upper atmosphere (CO2_cool_fort-1.0)
Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)
Application of regional meteorology and air quality models based on the microprocessor without interlocked piped stages (MIPS) and LoongArch CPU platforms
Investigating ground-level ozone pollution in semi-arid and arid regions of Arizona using WRF-Chem v4.4 modeling
An objective identification technique for potential vorticity structures associated with African easterly waves
Importance of microphysical settings for climate forcing by stratospheric SO2 injections as modeled by SOCOL-AERv2
Assessment of surface ozone products from downscaled CAMS reanalysis and CAMS daily forecast using urban air quality monitoring stations in Iran
Open boundary conditions for atmospheric large-eddy simulations and their implementation in DALES4.4
Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5)
Three-dimensional variational assimilation with a multivariate background error covariance for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta)
FUME 2.0 – Flexible Universal processor for Modeling Emissions
DEUCE v1.0: a neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties
Evaluation of multi-season convection-permitting atmosphere – mixed-layer ocean simulations of the Maritime Continent
Investigating the impact of coupling HARMONIE-WINS50 (cy43) meteorology to LOTOS-EUROS (v2.2.002) on a simulation of NO2 concentrations over the Netherlands
Balloon drift estimation and improved position estimates for radiosondes
Emission ensemble approach to improve the development of multi-scale emission inventories
What is the relative impact of nudging and online coupling on meteorological variables, pollutant concentrations and aerosol optical properties?
Diagnosing drivers of PM2.5 simulation biases in China from meteorology, chemical composition, and emission sources using an efficient machine learning method
Validation and analysis of the Polair3D v1.11 chemical transport model over Quebec
Assimilation of GNSS tropospheric gradients into the Weather Research and Forecasting (WRF) model version 4.4.1
Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1
Assessing acetone for the GISS ModelE2.1 Earth system model
Bergen metrics: composite error metrics for assessing performance of climate models using EURO-CORDEX simulations
A dynamic approach to three-dimensional radiative transfer in subkilometer-scale numerical weather prediction models: the dynamic TenStream solver v1.0
Evaluation and development of surface layer scheme representation of temperature inversions over boreal forests in Arctic wintertime conditions
Modelling wind farm effects in HARMONIE–AROME (cycle 43.2.2) – Part 1: Implementation and evaluation
Analytical and adaptable initial conditions for dry and moist baroclinic waves in the global hydrostatic model OpenIFS (CY43R3)
Challenges of constructing and selecting the “perfect” boundary conditions for the large-eddy simulation model PALM
A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model
Decision Support System version 1.0 (DSS v1.0) for air quality management in Delhi, India
How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe–AERCHEM, v1.0) sub-submodel of MESSy
A grid model for vertical correction of precipitable water vapor over the Chinese mainland and surrounding areas using random forest
MEXPLORER 1.0.0 – a mechanism explorer for analysis and visualization of chemical reaction pathways based on graph theory
Evaluating CHASER V4.0 global formaldehyde (HCHO) simulations using satellite, aircraft, and ground-based remote sensing observations
Advances and prospects of deep learning for medium-range extreme weather forecasting
An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)
cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands
PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations
Deep learning applied to CO2 power plant emissions quantification using simulated satellite images
Sensitivity of the WRF-Chem v4.4 simulations of ozone and formaldehyde and their precursors to multiple bottom-up emission inventories over East Asia during the KORUS-AQ 2016 field campaign
Optimising urban measurement networks for CO2 flux estimation: a high-resolution observing system simulation experiment using GRAMM/GRAL
Assessment of climate biases in OpenIFS version 43r3 across model horizontal resolutions and time steps
High-resolution multi-scaling of outdoor human thermal comfort and its intra-urban variability based on machine learning
Effects of vertical grid spacing on the climate simulated in the ICON-Sapphire global storm-resolving model
Development of the tangent linear and adjoint models of the global online chemical transport model MPAS-CO2 v7.3
Impacts of updated reaction kinetics on the global GEOS-Chem simulation of atmospheric chemistry
Bing Zhang, Mingjian Zeng, Anning Huang, Zhengkun Qin, Couhua Liu, Wenru Shi, Xin Li, Kefeng Zhu, Chunlei Gu, and Jialing Zhou
Geosci. Model Dev., 17, 4579–4601, https://doi.org/10.5194/gmd-17-4579-2024, https://doi.org/10.5194/gmd-17-4579-2024, 2024
Short summary
Short summary
By directly analyzing the proximity of precipitation forecasts and observations, a precipitation accuracy score (PAS) method was constructed. This method does not utilize a traditional contingency-table-based classification verification; however, it can replace the threat score (TS), equitable threat score (ETS), and other skill score methods, and it can be used to calculate the accuracy of numerical models or quantitative precipitation forecasts.
Hai Bui, Mostafa Bakhoday-Paskyabi, and Mohammadreza Mohammadpour-Penchah
Geosci. Model Dev., 17, 4447–4465, https://doi.org/10.5194/gmd-17-4447-2024, https://doi.org/10.5194/gmd-17-4447-2024, 2024
Short summary
Short summary
We developed a new wind turbine wake model, the Simple Actuator Disc for Large Eddy Simulation (SADLES), integrated with the widely used Weather Research and Forecasting (WRF) model. WRF-SADLES accurately simulates wind turbine wakes at resolutions of a few dozen meters, aligning well with idealized simulations and observational measurements. This makes WRF-SADLES a promising tool for wind energy research, offering a balance between accuracy, computational efficiency, and ease of implementation.
Changliang Shao and Lars Nerger
Geosci. Model Dev., 17, 4433–4445, https://doi.org/10.5194/gmd-17-4433-2024, https://doi.org/10.5194/gmd-17-4433-2024, 2024
Short summary
Short summary
This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation (DA) system. A key advantage of the WRF-PDAF configuration is its ability to concurrently integrate all ensemble states, eliminating the need for time-consuming distribution and collection of ensembles during the coupling communication. The extra time required for DA amounts to only 20.6 % per cycle. Twin experiment results underscore the effectiveness of the WRF-PDAF system.
Jan Clemens, Lars Hoffmann, Bärbel Vogel, Sabine Grießbach, and Nicole Thomas
Geosci. Model Dev., 17, 4467–4493, https://doi.org/10.5194/gmd-17-4467-2024, https://doi.org/10.5194/gmd-17-4467-2024, 2024
Short summary
Short summary
Lagrangian transport models simulate the transport of air masses in the atmosphere. For example, one model (CLaMS) is well suited to calculating transport as it uses a special coordinate system and special vertical wind. However, it only runs inefficiently on modern supercomputers. Hence, we have implemented the benefits of CLaMS into a new model (MPTRAC), which is already highly efficient on modern supercomputers. Finally, in extensive tests, we showed that CLaMS and MPTRAC agree very well.
Manuel López-Puertas, Federico Fabiano, Victor Fomichev, Bernd Funke, and Daniel R. Marsh
Geosci. Model Dev., 17, 4401–4432, https://doi.org/10.5194/gmd-17-4401-2024, https://doi.org/10.5194/gmd-17-4401-2024, 2024
Short summary
Short summary
The radiative infrared cooling of CO2 in the middle atmosphere is crucial for computing its thermal structure. It requires one however to include non-local thermodynamic equilibrium processes which are computationally very expensive, which cannot be afforded by climate models. In this work, we present an updated, efficient, accurate and very fast (~50 µs) parameterization of that cooling able to cope with CO2 abundances from half the pre-industrial values to 10 times the current abundance.
Felix Wieser, Rolf Sander, Changmin Cho, Hendrik Fuchs, Thorsten Hohaus, Anna Novelli, Ralf Tillmann, and Domenico Taraborrelli
Geosci. Model Dev., 17, 4311–4330, https://doi.org/10.5194/gmd-17-4311-2024, https://doi.org/10.5194/gmd-17-4311-2024, 2024
Short summary
Short summary
The chemistry scheme of the atmospheric box model CAABA/MECCA is expanded to achieve an improved aerosol formation from emitted organic compounds. In addition to newly added reactions, temperature-dependent partitioning of all new species between the gas and aqueous phases is estimated and included in the pre-existing scheme. Sensitivity runs show an overestimation of key compounds from isoprene, which can be explained by a lack of aqueous-phase degradation reactions and box model limitations.
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
Geosci. Model Dev., 17, 4383–4399, https://doi.org/10.5194/gmd-17-4383-2024, https://doi.org/10.5194/gmd-17-4383-2024, 2024
Short summary
Short summary
There is relatively limited research on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPUs, have distinct advantages in energy efficiency and scalability. The air quality modeling system can run stably on the MIPS and LoongArch platforms, and the experiment results verify the stability of scientific computing on the platforms. The work provides a technical foundation for the scientific application based on MIPS and LoongArch.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev., 17, 4331–4353, https://doi.org/10.5194/gmd-17-4331-2024, https://doi.org/10.5194/gmd-17-4331-2024, 2024
Short summary
Short summary
This research focuses on surface ozone (O3) pollution in Arizona, a historically air-quality-challenged arid and semi-arid region in the US. The unique characteristics of this kind of region, e.g., intense heat, minimal moisture, and persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model's skill in capturing diurnal and MDA8 O3 levels.
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 17, 4213–4228, https://doi.org/10.5194/gmd-17-4213-2024, https://doi.org/10.5194/gmd-17-4213-2024, 2024
Short summary
Short summary
This study presents a method for identifying and tracking 3-D potential vorticity structures within African easterly waves (AEWs). Each identified structure is characterized by descriptors, including its 3-D position and orientation, which have been validated through composite comparisons. A trough-centric perspective on the descriptors reveals the evolution and distinct characteristics of AEWs. These descriptors serve as valuable statistical inputs for the study of AEW-related phenomena.
Sandro Vattioni, Andrea Stenke, Beiping Luo, Gabriel Chiodo, Timofei Sukhodolov, Elia Wunderlin, and Thomas Peter
Geosci. Model Dev., 17, 4181–4197, https://doi.org/10.5194/gmd-17-4181-2024, https://doi.org/10.5194/gmd-17-4181-2024, 2024
Short summary
Short summary
We investigate the sensitivity of aerosol size distributions in the presence of strong SO2 injections for climate interventions or after volcanic eruptions to the call sequence and frequency of the routines for nucleation and condensation in sectional aerosol models with operator splitting. Using the aerosol–chemistry–climate model SOCOL-AERv2, we show that the radiative and chemical outputs are sensitive to these settings at high H2SO4 supersaturations and how to obtain reliable results.
Najmeh Kaffashzadeh and Abbas-Ali Aliakbari Bidokhti
Geosci. Model Dev., 17, 4155–4179, https://doi.org/10.5194/gmd-17-4155-2024, https://doi.org/10.5194/gmd-17-4155-2024, 2024
Short summary
Short summary
This paper assesses the capability of two state-of-the-art global datasets in simulating surface ozone over Iran using a new methodology. It is found that the global model data need to be downscaled for regulatory purposes or policy applications at local scales. The method can be useful not only for the evaluation but also for the prediction of other chemical species, such as aerosols.
Franciscus Liqui Lung, Christian Jakob, A. Pier Siebesma, and Fredrik Jansson
Geosci. Model Dev., 17, 4053–4076, https://doi.org/10.5194/gmd-17-4053-2024, https://doi.org/10.5194/gmd-17-4053-2024, 2024
Short summary
Short summary
Traditionally, high-resolution atmospheric models employ periodic boundary conditions, which limit simulations to domains without horizontal variations. In this research open boundary conditions are developed to replace the periodic boundary conditions. The implementation is tested in a controlled setup, and the results show minimal disturbances. Using these boundary conditions, high-resolution models can be forced by a coarser model to study atmospheric phenomena in realistic background states.
Caroline Arnold, Shivani Sharma, Tobias Weigel, and David S. Greenberg
Geosci. Model Dev., 17, 4017–4029, https://doi.org/10.5194/gmd-17-4017-2024, https://doi.org/10.5194/gmd-17-4017-2024, 2024
Short summary
Short summary
In atmospheric models, rain formation is simplified to be computationally efficient. We trained a machine learning model, SuperdropNet, to emulate warm-rain formation based on super-droplet simulations. Here, we couple SuperdropNet with an atmospheric model in a warm-bubble experiment and find that the coupled simulation runs stable and produces reasonable results, making SuperdropNet a viable ML proxy for droplet simulations. We also present a comprehensive benchmark for coupling architectures.
Byoung-Joo Jung, Benjamin Ménétrier, Chris Snyder, Zhiquan Liu, Jonathan J. Guerrette, Junmei Ban, Ivette Hernández Baños, Yonggang G. Yu, and William C. Skamarock
Geosci. Model Dev., 17, 3879–3895, https://doi.org/10.5194/gmd-17-3879-2024, https://doi.org/10.5194/gmd-17-3879-2024, 2024
Short summary
Short summary
We describe the multivariate static background error covariance (B) for the JEDI-MPAS 3D-Var data assimilation system. With tuned B parameters, the multivariate B gives physically balanced analysis increment fields in the single-observation test framework. In the month-long cycling experiment with a global 60 km mesh, 3D-Var with static B performs stably. Due to its simple workflow and minimal computational requirements, JEDI-MPAS 3D-Var can be useful for the research community.
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
Geosci. Model Dev., 17, 3867–3878, https://doi.org/10.5194/gmd-17-3867-2024, https://doi.org/10.5194/gmd-17-3867-2024, 2024
Short summary
Short summary
For modeling atmospheric chemistry, it is necessary to provide data on emissions of pollutants. These can come from various sources and in various forms, and preprocessing of the data to be ingestible by chemistry models can be quite challenging. We developed the FUME processor to use a database layer that internally transforms all input data into a rigid structure, facilitating further processing to allow for emission processing from the continental to the street scale.
Bent Harnist, Seppo Pulkkinen, and Terhi Mäkinen
Geosci. Model Dev., 17, 3839–3866, https://doi.org/10.5194/gmd-17-3839-2024, https://doi.org/10.5194/gmd-17-3839-2024, 2024
Short summary
Short summary
Probabilistic precipitation nowcasting (local forecasting for 0–6 h) is crucial for reducing damage from events like flash floods. For this goal, we propose the DEUCE neural-network-based model which uses data and model uncertainties to generate an ensemble of potential precipitation development scenarios for the next hour. Trained and evaluated with Finnish precipitation composites, DEUCE was found to produce more skillful and reliable nowcasts than established models.
Emma Howard, Steven Woolnough, Nicholas Klingaman, Daniel Shipley, Claudio Sanchez, Simon C. Peatman, Cathryn E. Birch, and Adrian J. Matthews
Geosci. Model Dev., 17, 3815–3837, https://doi.org/10.5194/gmd-17-3815-2024, https://doi.org/10.5194/gmd-17-3815-2024, 2024
Short summary
Short summary
This paper describes a coupled atmosphere–mixed-layer ocean simulation setup that will be used to study weather processes in Southeast Asia. The set-up has been used to compare high-resolution simulations, which are able to partially resolve storms, to coarser simulations, which cannot. We compare the model performance at representing variability of rainfall and sea surface temperatures across length scales between the coarse and fine models.
Andrés Yarce Botero, Michiel van Weele, Arjo Segers, Pier Siebesma, and Henk Eskes
Geosci. Model Dev., 17, 3765–3781, https://doi.org/10.5194/gmd-17-3765-2024, https://doi.org/10.5194/gmd-17-3765-2024, 2024
Short summary
Short summary
HARMONIE WINS50 reanalysis data with 0.025° × 0.025° resolution from 2019 to 2021 were coupled with the LOTOS-EUROS Chemical Transport Model. HARMONIE and ECMWF meteorology configurations against Cabauw observations (52.0° N, 4.9° W) were evaluated as simulated NO2 concentrations with ground-level sensors. Differences in crucial meteorological input parameters (boundary layer height, vertical diffusion coefficient) between the hydrostatic and non-hydrostatic models were analysed.
Ulrich Voggenberger, Leopold Haimberger, Federico Ambrogi, and Paul Poli
Geosci. Model Dev., 17, 3783–3799, https://doi.org/10.5194/gmd-17-3783-2024, https://doi.org/10.5194/gmd-17-3783-2024, 2024
Short summary
Short summary
This paper presents a method for calculating balloon drift from historical radiosonde ascent data. The drift can reach distances of several hundred kilometres and is often neglected. Verification shows the beneficial impact of the more accurate balloon position on model assimilation. The method is not limited to radiosondes but would also work for dropsondes, ozonesondes, or any other in situ sonde carried by the wind in the pre-GNSS era, provided the necessary information is available.
Philippe Thunis, Jeroen Kuenen, Enrico Pisoni, Bertrand Bessagnet, Manjola Banja, Lech Gawuc, Karol Szymankiewicz, Diego Guizardi, Monica Crippa, Susana Lopez-Aparicio, Marc Guevara, Alexander De Meij, Sabine Schindlbacher, and Alain Clappier
Geosci. Model Dev., 17, 3631–3643, https://doi.org/10.5194/gmd-17-3631-2024, https://doi.org/10.5194/gmd-17-3631-2024, 2024
Short summary
Short summary
An ensemble emission inventory is created with the aim of monitoring the status and progress made with the development of EU-wide inventories. This emission ensemble serves as a common benchmark for the screening and allows for the comparison of more than two inventories at a time. Because the emission “truth” is unknown, the approach does not tell which inventory is the closest to reality, but it identifies inconsistencies that require special attention.
Laurent Menut, Bertrand Bessagnet, Arineh Cholakian, Guillaume Siour, Sylvain Mailler, and Romain Pennel
Geosci. Model Dev., 17, 3645–3665, https://doi.org/10.5194/gmd-17-3645-2024, https://doi.org/10.5194/gmd-17-3645-2024, 2024
Short summary
Short summary
This study is about the modelling of the atmospheric composition in Europe during the summer of 2022, when massive wildfires were observed. It is a sensitivity study dedicated to the relative impacts of two modelling processes that are able to modify the meteorology used for the calculation of the atmospheric chemistry and transport of pollutants.
Shuai Wang, Mengyuan Zhang, Yueqi Gao, Peng Wang, Qingyan Fu, and Hongliang Zhang
Geosci. Model Dev., 17, 3617–3629, https://doi.org/10.5194/gmd-17-3617-2024, https://doi.org/10.5194/gmd-17-3617-2024, 2024
Short summary
Short summary
Numerical models are widely used in air pollution modeling but suffer from significant biases. The machine learning model designed in this study shows high efficiency in identifying such biases. Meteorology (relative humidity and cloud cover), chemical composition (secondary organic components and dust aerosols), and emission sources (residential activities) are diagnosed as the main drivers of bias in modeling PM2.5, a typical air pollutant. The results will help to improve numerical models.
Shoma Yamanouchi, Shayamilla Mahagammulla Gamage, Sara Torbatian, Jad Zalzal, Laura Minet, Audrey Smargiassi, Ying Liu, Ling Liu, Forood Azargoshasbi, Jinwoong Kim, Youngseob Kim, Daniel Yazgi, and Marianne Hatzopoulou
Geosci. Model Dev., 17, 3579–3597, https://doi.org/10.5194/gmd-17-3579-2024, https://doi.org/10.5194/gmd-17-3579-2024, 2024
Short summary
Short summary
Air pollution is a major health hazard, and chemical transport models (CTMs) are valuable tools that aid in our understanding of the risks of air pollution at both local and regional scales. In this study, the Polair3D CTM of the Polyphemus air quality modeling platform was set up over Quebec, Canada, to assess the model’s capability in predicting key air pollutant species over the region, at seasonal temporal scales and at regional spatial scales.
Rohith Thundathil, Florian Zus, Galina Dick, and Jens Wickert
Geosci. Model Dev., 17, 3599–3616, https://doi.org/10.5194/gmd-17-3599-2024, https://doi.org/10.5194/gmd-17-3599-2024, 2024
Short summary
Short summary
Global Navigation Satellite Systems (GNSS) provides moisture observations through its densely distributed ground station network. In this research, we assimilate a new type of observation called tropospheric gradient observations, which has never been incorporated into a weather model. We develop a forward operator for gradient-based observations and conduct an assimilation impact study. The study shows significant improvements in the model's humidity fields.
Ankur Mahesh, Travis A. O'Brien, Burlen Loring, Abdelrahman Elbashandy, William Boos, and William D. Collins
Geosci. Model Dev., 17, 3533–3557, https://doi.org/10.5194/gmd-17-3533-2024, https://doi.org/10.5194/gmd-17-3533-2024, 2024
Short summary
Short summary
Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. We train convolutional neural networks (CNNs) to detect ARs with an estimate of the uncertainty. We present a framework to generalize these CNNs to a variety of datasets of past, present, and future climate. Using a simplified simulation of the Earth's atmosphere, we validate the CNNs. We explore the role of ARs in maintaining energy balance in the Earth system.
Alexandra Rivera, Kostas Tsigaridis, Gregory Faluvegi, and Drew Shindell
Geosci. Model Dev., 17, 3487–3505, https://doi.org/10.5194/gmd-17-3487-2024, https://doi.org/10.5194/gmd-17-3487-2024, 2024
Short summary
Short summary
This paper describes and evaluates an improvement to the representation of acetone in the GISS ModelE2.1 Earth system model. We simulate acetone's concentration and transport across the atmosphere as well as its dependence on chemistry, the ocean, and various global emissions. Comparisons of our model’s estimates to past modeling studies and field measurements have shown encouraging results. Ultimately, this paper contributes to a broader understanding of acetone's role in the atmosphere.
Alok K. Samantaray, Priscilla A. Mooney, and Carla A. Vivacqua
Geosci. Model Dev., 17, 3321–3339, https://doi.org/10.5194/gmd-17-3321-2024, https://doi.org/10.5194/gmd-17-3321-2024, 2024
Short summary
Short summary
Any interpretation of climate model data requires a comprehensive evaluation of the model performance. Numerous error metrics exist for this purpose, and each focuses on a specific aspect of the relationship between reference and model data. Thus, a comprehensive evaluation demands the use of multiple error metrics. However, this can lead to confusion. We propose a clustering technique to reduce the number of error metrics needed and a composite error metric to simplify the interpretation.
Richard Maier, Fabian Jakub, Claudia Emde, Mihail Manev, Aiko Voigt, and Bernhard Mayer
Geosci. Model Dev., 17, 3357–3383, https://doi.org/10.5194/gmd-17-3357-2024, https://doi.org/10.5194/gmd-17-3357-2024, 2024
Short summary
Short summary
Based on the TenStream solver, we present a new method to accelerate 3D radiative transfer towards the speed of currently used 1D solvers. Using a shallow-cumulus-cloud time series, we evaluate the performance of this new solver in terms of both speed and accuracy. Compared to a 3D benchmark simulation, we show that our new solver is able to determine much more accurate irradiances and heating rates than a 1D δ-Eddington solver, even when operated with a similar computational demand.
Julia Maillard, Jean-Christophe Raut, and François Ravetta
Geosci. Model Dev., 17, 3303–3320, https://doi.org/10.5194/gmd-17-3303-2024, https://doi.org/10.5194/gmd-17-3303-2024, 2024
Short summary
Short summary
Atmospheric models struggle to reproduce the strong temperature inversions in the vicinity of the surface over forested areas in the Arctic winter. In this paper, we develop modified simplified versions of surface layer schemes widely used by the community. Our modifications are used to correct the fact that original schemes place strong limits on the turbulent collapse, leading to a lower surface temperature gradient at low wind speeds. Modified versions show a better performance.
Jana Fischereit, Henrik Vedel, Xiaoli Guo Larsén, Natalie E. Theeuwes, Gregor Giebel, and Eigil Kaas
Geosci. Model Dev., 17, 2855–2875, https://doi.org/10.5194/gmd-17-2855-2024, https://doi.org/10.5194/gmd-17-2855-2024, 2024
Short summary
Short summary
Wind farms impact local wind and turbulence. To incorporate these effects in weather forecasting, the explicit wake parameterization (EWP) is added to the forecasting model HARMONIE–AROME. We evaluate EWP using flight data above and downstream of wind farms, comparing it with an alternative wind farm parameterization and another weather model. Results affirm the correct implementation of EWP, emphasizing the necessity of accounting for wind farm effects in accurate weather forecasting.
Clément Bouvier, Daan van den Broek, Madeleine Ekblom, and Victoria A. Sinclair
Geosci. Model Dev., 17, 2961–2986, https://doi.org/10.5194/gmd-17-2961-2024, https://doi.org/10.5194/gmd-17-2961-2024, 2024
Short summary
Short summary
An analytical initial background state has been developed for moist baroclinic wave simulation on an aquaplanet and implemented into OpenIFS. Seven parameters can be controlled, which are used to generate the background states and the development of baroclinic waves. The meteorological and numerical stability has been assessed. Resulting baroclinic waves have proven to be realistic and sensitive to the jet's width.
Jelena Radović, Michal Belda, Jaroslav Resler, Kryštof Eben, Martin Bureš, Jan Geletič, Pavel Krč, Hynek Řezníček, and Vladimír Fuka
Geosci. Model Dev., 17, 2901–2927, https://doi.org/10.5194/gmd-17-2901-2024, https://doi.org/10.5194/gmd-17-2901-2024, 2024
Short summary
Short summary
Boundary conditions are of crucial importance for numerical model (e.g., PALM) validation studies and have a large influence on the model results, especially when studying the atmosphere of real, complex, and densely built urban environments. Our experiments with different driving conditions for the large-eddy simulation model PALM show its strong dependency on boundary conditions, which is important for the proper separation of errors coming from the boundary conditions and the model itself.
Sonya L. Fiddes, Marc D. Mallet, Alain Protat, Matthew T. Woodhouse, Simon P. Alexander, and Kalli Furtado
Geosci. Model Dev., 17, 2641–2662, https://doi.org/10.5194/gmd-17-2641-2024, https://doi.org/10.5194/gmd-17-2641-2024, 2024
Short summary
Short summary
In this study we present an evaluation that considers complex, non-linear systems in a holistic manner. This study uses XGBoost, a machine learning algorithm, to predict the simulated Southern Ocean shortwave radiation bias in the ACCESS model using cloud property biases as predictors. We then used a novel feature importance analysis to quantify the role that each cloud bias plays in predicting the radiative bias, laying the foundation for advanced Earth system model evaluation and development.
Gaurav Govardhan, Sachin D. Ghude, Rajesh Kumar, Sumit Sharma, Preeti Gunwani, Chinmay Jena, Prafull Yadav, Shubhangi Ingle, Sreyashi Debnath, Pooja Pawar, Prodip Acharja, Rajmal Jat, Gayatry Kalita, Rupal Ambulkar, Santosh Kulkarni, Akshara Kaginalkar, Vijay K. Soni, Ravi S. Nanjundiah, and Madhavan Rajeevan
Geosci. Model Dev., 17, 2617–2640, https://doi.org/10.5194/gmd-17-2617-2024, https://doi.org/10.5194/gmd-17-2617-2024, 2024
Short summary
Short summary
A newly developed air quality forecasting framework, Decision Support System (DSS), for air quality management in Delhi, India, provides source attribution with numerous emission reduction scenarios besides forecasts. DSS shows that during post-monsoon and winter seasons, Delhi and its neighboring districts contribute to 30 %–40 % each to pollution in Delhi. On average, a 40 % reduction in the emissions in Delhi and the surrounding districts would result in a 24 % reduction in Delhi's pollution.
Simon Rosanka, Holger Tost, Rolf Sander, Patrick Jöckel, Astrid Kerkweg, and Domenico Taraborrelli
Geosci. Model Dev., 17, 2597–2615, https://doi.org/10.5194/gmd-17-2597-2024, https://doi.org/10.5194/gmd-17-2597-2024, 2024
Short summary
Short summary
The capabilities of the Modular Earth Submodel System (MESSy) are extended to account for non-equilibrium aqueous-phase chemistry in the representation of deliquescent aerosols. When applying the new development in a global simulation, we find that MESSy's bias in modelling routinely observed reduced inorganic aerosol mass concentrations, especially in the United States. Furthermore, the representation of fine-aerosol pH is particularly improved in the marine boundary layer.
Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Huang, and Feijuan Li
Geosci. Model Dev., 17, 2569–2581, https://doi.org/10.5194/gmd-17-2569-2024, https://doi.org/10.5194/gmd-17-2569-2024, 2024
Short summary
Short summary
In this study, we have developed a model (RF-PWV) to characterize precipitable water vapor (PWV) variation with altitude in the study area. RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.
Rolf Sander
Geosci. Model Dev., 17, 2419–2425, https://doi.org/10.5194/gmd-17-2419-2024, https://doi.org/10.5194/gmd-17-2419-2024, 2024
Short summary
Short summary
The open-source software MEXPLORER 1.0.0 is presented here. The program can be used to analyze, reduce, and visualize complex chemical reaction mechanisms. The mathematics behind the tool is based on graph theory: chemical species are represented as vertices, and reactions as edges. MEXPLORER is a community model published under the GNU General Public License.
Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Yanfeng He, and Md Firoz Khan
EGUsphere, https://doi.org/10.22541/essoar.169903618.82717612/v2, https://doi.org/10.22541/essoar.169903618.82717612/v2, 2024
Short summary
Short summary
Using multi-platform observations, we validated global formaldehyde (HCHO) simulations from a chemistry transport model. HCHO is a crucial intermediate of the chemical catalytic cycle that governs the ozone formation in the troposphere. The model was capable of replicating the observed spatiotemporal variability in HCHO. In a few cases, the model capability was limited. This is attributed to the uncertainties in the observations and the model parameters.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024, https://doi.org/10.5194/gmd-17-2347-2024, 2024
Short summary
Short summary
In the last decades, weather forecasting up to 15 d into the future has been dominated by physics-based numerical models. Recently, deep learning models have challenged this paradigm. However, the latter models may struggle when forecasting weather extremes. In this article, we argue for deep learning models specifically designed to handle extreme events, and we propose a foundational framework to develop such models.
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024, https://doi.org/10.5194/gmd-17-2265-2024, 2024
Short summary
Short summary
Here, we project future climate across the western United States through the end of the 21st century using a regional climate model, embedded within 16 latest-generation global climate models, to provide the community with a high-resolution physically based ensemble of climate data for use at local scales. Strengths and weaknesses of the data are frankly discussed as we overview the downscaled dataset.
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024, https://doi.org/10.5194/gmd-17-2247-2024, 2024
Short summary
Short summary
This paper introduces a new method for detecting atmospheric cloud bands to identify long convective cloud bands that extend from the tropics to the midlatitudes. The algorithm allows for easy use and enables researchers to study the life cycle and climatology of cloud bands and associated rainfall. This method provides insights into the large-scale processes involved in cloud band formation and their connections between different regions, as well as differences across ocean basins.
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024, https://doi.org/10.5194/gmd-17-2053-2024, 2024
Short summary
Short summary
PyRTlib is an attractive educational tool because it provides a flexible and user-friendly way to broadly simulate how electromagnetic radiation travels through the atmosphere as it interacts with atmospheric constituents (such as gases, aerosols, and hydrometeors). PyRTlib is a so-called radiative transfer model; these are commonly used to simulate and understand remote sensing observations from ground-based, airborne, or satellite instruments.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 17, 1995–2014, https://doi.org/10.5194/gmd-17-1995-2024, https://doi.org/10.5194/gmd-17-1995-2024, 2024
Short summary
Short summary
Our research presents an innovative approach to estimating power plant CO2 emissions from satellite images of the corresponding plumes such as those from the forthcoming CO2M satellite constellation. The exploitation of these images is challenging due to noise and meteorological uncertainties. To overcome these obstacles, we use a deep learning neural network trained on simulated CO2 images. Our method outperforms alternatives, providing a positive perspective for the analysis of CO2M images.
Kyoung-Min Kim, Si-Wan Kim, Seunghwan Seo, Donald R. Blake, Seogju Cho, James H. Crawford, Louisa K. Emmons, Alan Fried, Jay R. Herman, Jinkyu Hong, Jinsang Jung, Gabriele G. Pfister, Andrew J. Weinheimer, Jung-Hun Woo, and Qiang Zhang
Geosci. Model Dev., 17, 1931–1955, https://doi.org/10.5194/gmd-17-1931-2024, https://doi.org/10.5194/gmd-17-1931-2024, 2024
Short summary
Short summary
Three emission inventories were evaluated for East Asia using data acquired during a field campaign in 2016. The inventories successfully reproduced the daily variations of ozone and nitrogen dioxide. However, the spatial distributions of model ozone did not fully agree with the observations. Additionally, all simulations underestimated carbon monoxide and volatile organic compound (VOC) levels. Increasing VOC emissions over South Korea resulted in improved ozone simulations.
Sanam Noreen Vardag and Robert Maiwald
Geosci. Model Dev., 17, 1885–1902, https://doi.org/10.5194/gmd-17-1885-2024, https://doi.org/10.5194/gmd-17-1885-2024, 2024
Short summary
Short summary
We use the atmospheric transport model GRAMM/GRAL in a Bayesian inversion to estimate urban CO2 emissions on a neighbourhood scale. We analyse the effect of varying number, precision and location of CO2 sensors for CO2 flux estimation. We further test the inclusion of co-emitted species and correlation in the inversion. The study showcases the general usefulness of GRAMM/GRAL in measurement network design.
Abhishek Savita, Joakim Kjellsson, Robin Pilch Kedzierski, Mojib Latif, Tabea Rahm, Sebastian Wahl, and Wonsun Park
Geosci. Model Dev., 17, 1813–1829, https://doi.org/10.5194/gmd-17-1813-2024, https://doi.org/10.5194/gmd-17-1813-2024, 2024
Short summary
Short summary
The OpenIFS model is used to examine the impact of horizontal resolutions (HR) and model time steps. We find that the surface wind biases over the oceans, in particular the Southern Ocean, are sensitive to the model time step and HR, with the HR having the smallest biases. When using a coarse-resolution model with a shorter time step, a similar improvement is also found. Climate biases can be reduced in the OpenIFS model at a cheaper cost by reducing the time step rather than increasing the HR.
Ferdinand Briegel, Jonas Wehrle, Dirk Schindler, and Andreas Christen
Geosci. Model Dev., 17, 1667–1688, https://doi.org/10.5194/gmd-17-1667-2024, https://doi.org/10.5194/gmd-17-1667-2024, 2024
Short summary
Short summary
We present a new approach to model heat stress in cities using artificial intelligence (AI). We show that the AI model is fast in terms of prediction but accurate when evaluated with measurements. The fast-predictive AI model enables several new potential applications, including heat stress prediction and warning; downscaling of potential future climates; evaluation of adaptation effectiveness; and, more fundamentally, development of guidelines to support urban planning and policymaking.
Hauke Schmidt, Sebastian Rast, Jiawei Bao, Amrit Cassim, Shih-Wei Fang, Diego Jimenez-de la Cuesta, Paul Keil, Lukas Kluft, Clarissa Kroll, Theresa Lang, Ulrike Niemeier, Andrea Schneidereit, Andrew I. L. Williams, and Bjorn Stevens
Geosci. Model Dev., 17, 1563–1584, https://doi.org/10.5194/gmd-17-1563-2024, https://doi.org/10.5194/gmd-17-1563-2024, 2024
Short summary
Short summary
A recent development in numerical simulations of the global atmosphere is the increase in horizontal resolution to grid spacings of a few kilometers. However, the vertical grid spacing of these models has not been reduced at the same rate as the horizontal grid spacing. Here, we assess the effects of much finer vertical grid spacings, in particular the impacts on cloud quantities and the atmospheric energy balance.
Tao Zheng, Sha Feng, Jeffrey Steward, Xiaoxu Tian, David Baker, and Martin Baxter
Geosci. Model Dev., 17, 1543–1562, https://doi.org/10.5194/gmd-17-1543-2024, https://doi.org/10.5194/gmd-17-1543-2024, 2024
Short summary
Short summary
The tangent linear and adjoint models have been successfully implemented in the MPAS-CO2 system, which has undergone rigorous accuracy testing. This development lays the groundwork for a global carbon flux data assimilation system, which offers the flexibility of high-resolution focus on specific areas, while maintaining a coarser resolution elsewhere. This approach significantly reduces computational costs and is thus perfectly suited for future CO2 geostationery and imager satellites.
Kelvin H. Bates, Mathew J. Evans, Barron H. Henderson, and Daniel J. Jacob
Geosci. Model Dev., 17, 1511–1524, https://doi.org/10.5194/gmd-17-1511-2024, https://doi.org/10.5194/gmd-17-1511-2024, 2024
Short summary
Short summary
Accurate representation of rates and products of chemical reactions in atmospheric models is crucial for simulating concentrations of pollutants and climate forcers. We update the widely used GEOS-Chem atmospheric chemistry model with reaction parameters from recent compilations of experimental data and demonstrate the implications for key atmospheric chemical species. The updates decrease tropospheric CO mixing ratios and increase stratospheric nitrogen oxide mixing ratios, among other changes.
Cited articles
AIRPARIF: Quelle qualité de l'air en voiture pendant les trajets
quotidiens domicile-travail, Tech. rep., AIRPARIF,
available at:
https://www.airparif.asso.fr/_pdf/publications/synthese_expovoituredomtra.pdf (last access: 29 July 2019),
2009. a
Anderson, H. R., Atkinson, R. W., Peacock, J., Marston, L., and Konstantinou,
K.: Meta-analysis of time-series studies and panel studies of particulate
matter (PM) and ozone (O3): report of a WHO task group,
available at: https://apps.who.int/iris/handle/10665/107557 (last access: 28 July 2019), 2004. a
Appel, K. W., Gilliam, R. C., Pleim, J. E., Pouliot, G. A.,
Wong, D. C., Hogrefe, C., Roselle, S. J., and Mathur, R.:
Improvements to the WRF-CMAQ modeling system for fine-scale air quality
simulations, EM: Air And Waste Management Association's Magazine For
Environmental Managers, 16–21,
available at: https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=NERL&dirEntryId=288280 (last access: 28 July 2019),
2014. a
Atkinson, R. W., Mills, I. C., Walton, H. A., and Anderson, H. R.: Fine
particle components and health – a systematic review and meta-analysis of
epidemiological time series studies of daily mortality and hospital
admissions, J. Expo. Sci. Env. Epid., 25,
208–214, https://doi.org/10.1038/jes.2014.63,
2015. a
Batterman, S., Ganguly, R., Isakov, V., Burke, J., Arunachalam, S., Snyder, M.,
Robins, T., and Lewis, T.: Dispersion Modeling of Traffic-Related Air
Pollutant Exposures and Health Effects Among Children with
Asthma in Detroit, Michigan, Transp. Res. Record, 2452,
105–112, https://doi.org/10.3141/2452-13, 2014. a
Beauchamp, M., Malherbe, L., and de Fouquet, C.: A pragmatic approach to
estimate the number of days in exceedance of PM10 limit value, Atmos.
Environ., 111, 79–93, https://doi.org/10.1016/j.atmosenv.2015.03.062,
2015. a
Beelen, R., Hoek, G., Vienneau, D., Eeftens, M., Dimakopoulou, K., Pedeli, X.,
Tsai, M.-Y., Künzli, N., Schikowski, T., Marcon, A., Eriksen, K. T.,
Raaschou-Nielsen, O., Stephanou, E., Patelarou, E., Lanki, T., Yli-Tuomi, T.,
Declercq, C., Falq, G., Stempfelet, M., Birk, M., Cyrys, J., von Klot, S.,
Nádor, G., Varró, M. J., Dėdelė, A., Gražulevičienė, R., Mölter, A.,
Lindley, S., Madsen, C., Cesaroni, G., Ranzi, A., Badaloni, C., Hoffmann, B.,
Nonnemacher, M., Krämer, U., Kuhlbusch, T., Cirach, M., de Nazelle, A.,
Nieuwenhuijsen, M., Bellander, T., Korek, M., Olsson, D., Strömgren, M.,
Dons, E., Jerrett, M., Fischer, P., Wang, M., Brunekreef, B., and de Hoogh,
K.: Development of NO2 and NOx land use regression models for estimating
air pollution exposure in 36 study areas in Europe – The ESCAPE
project, Atmos. Environ., 72, 10–23,
https://doi.org/10.1016/j.atmosenv.2013.02.037,
2013. a
Beevers, S. D., Kitwiroon, N., Williams, M. L., Kelly, F. J., Ross Anderson,
H., and Carslaw, D. C.: Air pollution dispersion models for human exposure
predictions in London, J. Expo. Sci. Env.
Epid., 23, 647–653, https://doi.org/10.1038/jes.2013.6, 2013. a
Bell, M. L., Dominici, F., and Samet, J. M.: A meta-analysis of time-series
studies of ozone and mortality with comparison to the national morbidity,
mortality, and air pollution study, Epidemiology, 16,
436–445, 2005. a
Berchet, A., Zink, K., Muller, C., Oettl, D., Brunner, J., Emmenegger, L., and
Brunner, D.: A cost-effective method for simulating city-wide air flow and
pollutant dispersion at building resolving scale, Atmos. Environ.,
158, 181–196, https://doi.org/10.1016/j.atmosenv.2017.03.030,
2017. a
Blair, A., Stewart, P., Lubin, J. H., and Forastiere, F.: Methodological issues
regarding confounding and exposure misclassification in epidemiological
studies of occupational exposures, Am. J. Ind. Med., 50, 199–207, https://doi.org/10.1002/ajim.20281,
2007. a
Cassee, F. R., Héroux, M.-E., Gerlofs-Nijland, M. E., and Kelly, F. J.:
Particulate matter beyond mass: recent health evidence on the role of
fractions, chemical constituents and sources of emission, Inhal.
Toxicol., 25, 802–812, https://doi.org/10.3109/08958378.2013.850127,
013. a
Cattani, G., Gaeta, A., Di Menno di Bucchianico, A., De Santis, A., Gaddi, R.,
Cusano, M., Ancona, C., Badaloni, C., Forastiere, F., Gariazzo, C., Sozzi,
R., Inglessis, M., Silibello, C., Salvatori, E., Manes, F., and Cesaroni, G.:
Development of land-use regression models for exposure assessment to
ultrafine particles in Rome, Italy, Atmos. Environ., 156, 52–60,
https://doi.org/10.1016/j.atmosenv.2017.02.028,
2017. a
Chen, C. and Zhao, B.: Review of relationship between indoor and outdoor
particles: I/O ratio, infiltration factor and penetration factor,
Atmos. Environ., 45, 275–288, https://doi.org/10.1016/j.atmosenv.2010.09.048,
2011. a
Collignan, B., Lorkowski, C., and Améon, R.: Development of a methodology to
characterize radon entry in dwellings, Build. Environ., 57,
176–183, https://doi.org/10.1016/j.buildenv.2012.05.002,
2012. a, b, c
Cyrys, J., Pitz, M., Bischof, W., Wichmann, H.-E., and Heinrich, J.:
Relationship between indoor and outdoor levels of fine particle mass,
particle number concentrations and black smoke under different ventilation
conditions, J. Expo. Sci. Env. Epid., 14, 275–283,
https://doi.org/10.1038/sj.jea.7500317,
2004. a
Di, Q., Wang, Y., Zanobetti, A., Wang, Y., Koutrakis, P., Choirat, C.,
Dominici, F., and Schwartz, J. D.: Air Pollution and Mortality in the
Medicare Population, New Engl. J. Med., 376, 2513–2522,
https://doi.org/10.1056/NEJMoa1702747,
2017. a
Dias, D. and Tchepel, O.: Spatial and Temporal Dynamics in Air
Pollution Exposure Assessment, Int. J. Env.
Res. Pub. He., 15, 558, https://doi.org/10.3390/ijerph15030558,
2018. a
Edwards, J. K. and Keil, A. P.: Measurement Error and Environmental
Epidemiology: A Policy Perspective, Current environmental health
reports, 4, 79–88, https://doi.org/10.1007/s40572-017-0125-4, 2017. a
EEA: Air quality in Europe – 2019, Tech. Rep. 10/2019,
available at: https://www.eea.europa.eu//publications/air-quality-in-europe-2019, last access: 4 November
2019. a
Franklin, M., Vora, H., Avol, E., McConnell, R., Lurmann, F., Liu, F., Penfold,
B., Berhane, K., Gilliland, F., and Gauderman, W. J.: Predictors of
intra-community variation in air quality, J. Expo. Sci.
Env. Epid., 22, 135–147, https://doi.org/10.1038/jes.2011.45,
2012. a
Georgopoulos, P. G., Wang, S.-W., Vyas, V. M., Sun, Q., Burke, J., Vedantham,
R., McCurdy, T., and Ozkaynak, H.: A source-to-dose assessment of population
exposures to fine PM and ozone in Philadelphia, PA, during a summer
1999 episode, J. Expo. Anal. Env. Epid., 15, 439–457,
https://doi.org/10.1038/sj.jea.7500422, 2005. a
HEI: Traffic-Related Air Pollution: A Critical Review of the
Literature on Emissions, Exposure, and Health Effects, Tech. Rep.
Special Report 17, Health Effects Institute, Boston, MA,
available at: https://www.healtheffects.org/publication/traffic-related-air-pollution-critical-review-literature-emissions-exposure-and-health ()last access: 28 July 2019,
2010. a
Hodas, N., Meng, Q., Lunden, M. M., Rich, D. Q., Özkaynak, H., Baxter, L. K.,
Zhang, Q., and Turpin, B. J.: Variability in the fraction of ambient fine
particulate matter found indoors and observed heterogeneity in health effect
estimates, J. Expo. Sci. Env. Epid., 22,
448–454, https://doi.org/10.1038/jes.2012.34,
2012. a
Hwang, Y. and Lee, K.: Contribution of microenvironments to personal exposures
to PM10 and PM2.5 in summer and winter, Atmos. Environ., 175,
192–198, https://doi.org/10.1016/j.atmosenv.2017.12.009,
2018. a
Korek, M. J., Bellander, T. D., Lind, T., Bottai, M., Eneroth, K. M.,
Caracciolo, B., Faire, U. H. d., Fratiglioni, L., Hilding, A., Leander, K.,
Magnusson, P. K. E., Pedersen, N. L., Östenson, C.-G., Pershagen, G., and
Penell, J. C.: Traffic-related air pollution exposure and incidence of stroke
in four cohorts from Stockholm, J. Expo. Sci. Env. Epid., 25, 517–523, https://doi.org/10.1038/jes.2015.22,
2015. a
Morawska, L. and He, C.: Relationship between indoor/outdoor concentrations of
particles: a critical review, in: Proceedings of the 7th International
Conference Healthy Buildings, National University of
Singapore, Singapore, 7–11, 2003. a
Lim, S., Kim, J., Kim, T., Lee, K., Yang, W., Jun, S., and Yu, S.: Personal
exposures to PM2.5 and their relationships with microenvironmental
concentrations, Atmos. Environ., 47, 407–412,
https://doi.org/10.1016/j.atmosenv.2011.10.043,
2012. a, b
Lipfert, F. W. and Wyzga, R. E.: On exposure and response relationships for
health effects associated with exposure to vehicular traffic, J.
Expo. Sci. Env. Epid., 18, 588–599,
https://doi.org/10.1038/jes.2008.4,
2008. a
Mailler, S., Menut, L., Khvorostyanov, D., Valari, M., Couvidat, F., Siour, G., Turquety, S., Briant, R., Tuccella, P., Bessagnet, B., Colette, A., Létinois, L., Markakis, K., and Meleux, F.: CHIMERE-2017: from urban to hemispheric chemistry-transport modeling, Geosci. Model Dev., 10, 2397–2423, https://doi.org/10.5194/gmd-10-2397-2017, 2017. a, b
Matson, U.: Indoor and outdoor concentrations of ultrafine particles in some
Scandinavian rural and urban areas, Sci. Total Environ., 343,
169–176, https://doi.org/10.1016/j.scitotenv.2004.10.002,
2005. a
McBride, S. J., Williams, R. W., and Creason, J.: Bayesian hierarchical
modeling of personal exposure to particulate matter, Atmos. Environ.,
41, 6143–6155, https://doi.org/10.1016/j.atmosenv.2007.04.005,
2007. a
Miranda, M. L., Edwards, S. E., Chang, H. H., and Auten, R. L.: Proximity to
roadways and pregnancy outcomes, J. Expo. Sci. Env.
Epid., 23, 32–38, https://doi.org/10.1038/jes.2012.78, 2013. a
Monn, C.: Exposure assessment of air pollutants: a review on spatial
heterogeneity and indoor/outdoor/personal exposure to suspended particulate
matter, nitrogen dioxide and ozone, Atmos. Environ., 35, 1–32,
https://doi.org/10.1016/S1352-2310(00)00330-7,
2001. a, b
Morales Betancourt, R., Galvis, B., Rincón-Riveros, J. M., Rincón-Caro,
M. A., Rodriguez-Valencia, A., and Sarmiento, O. L.: Personal exposure to air
pollutants in a Bus Rapid Transit System: Impact of fleet age and
emission standard, Atmos. Environ., 202, 117–127,
https://doi.org/10.1016/j.atmosenv.2019.01.026,
2019. a
Olsson, D., Bråbäck, L., and Forsberg, B.: Air pollution exposure during
pregnancy and infancy and childhood asthma, Eur. Respir. J., 44, p. 4237,
2014. a
Olstrup, H., Johansson, C., Forsberg, B., Tornevi, A., Ekebom, A., and Meister,
K.: A Multi-Pollutant Air Quality Health Index (AQHI) Based
on Short-Term Respiratory Effects in Stockholm, Sweden,
Int. J. Env. Res. Pub. He., 16, 105,
https://doi.org/10.3390/ijerph16010105, 2019a. a
Olstrup, H., Johansson, C., Forsberg, B., and Åström, C.: Association between
Mortality and Short-Term Exposure to Particles, Ozone and
Nitrogen Dioxide in Stockholm, Sweden, Int. J. Env. Res. Pub. He., 16, 1028, https://doi.org/10.3390/ijerph16061028,
2019b. a
OQAI: Campagne nationale Logements Etat de la qualité de l'air dans les
gogements francais Rapport final, Tech. Rep. DDD/SB – 2006-57,
Observatoire de la qualité de l'air interieur, edited by: Kirchner, S., Arenes, J.-F.,
Cochet, C., Derbez, M., Duboudin, C., Elias, P., Gregoire, A., Jédor, B., Lucas, J.-P.,
Pasquier, N., Pigneret, M., and Ramalho, O., 2006. a
Orru, H., Lövenheim, B., Johansson, C., and Forsberg, B.: Potential health impacts of changes in air pollution exposure associated with moving traffic into a road tunnel, J. Expo. Sci. Environ. Epidemiol., 25, 524–531, https://doi.org/10.1038/jes.2015.24, 2015. a
Pascal, M., Corso, M., Chanel, O., Declercq, C., Badaloni, C., Cesaroni, G.,
Henschel, S., Meister, K., Haluza, D., Martin-Olmedo, P., and Medina, S.:
Assessing the public health impacts of urban air pollution in 25 European
cities: Results of the Aphekom project, Sci. Total Environ.,
449, 390–400, https://doi.org/10.1016/j.scitotenv.2013.01.077,
2013. a
Pascal, M., de Crouy Chanel, P., Wagner, V., Corso, M., Tillier, C., Bentayeb,
M., Blanchard, M., Cochet, A., Pascal, L., Host, S., Goria, S., Le Tertre,
A., Chatignoux, E., Ung, A., Beaudeau, P., and Medina, S.: The mortality
impacts of fine particles in France, Sci. Total Environ., 571,
416–425, https://doi.org/10.1016/j.scitotenv.2016.06.213,
2016. a
Ryan, P. H. and LeMasters, G. K.: A review of land-use regression models for
characterizing intraurban air pollution exposure, Inhal. Toxicol., 19, 127–133, https://doi.org/10.1080/08958370701495998, 2007. a
Sarnat, J. A., Wilson, W. E., Strand, M., Brook, J., Wyzga, R., and Lumley, T.:
Panel discussion review: session 1–exposure assessment and related errors in
air pollution epidemiologic studies, J. Expo. Sci. Env. Epid., 17, S75–82,
https://doi.org/10.1038/sj.jes.7500621,
2007. a
Shekarrizfard, M., Faghih-Imani, A., and Hatzopoulou, M.: An examination of
population exposure to traffic related air pollution: Comparing spatially
and temporally resolved estimates against long-term average exposures at the
home location, Environ. Res., 147, 435–444,
https://doi.org/10.1016/j.envres.2016.02.039,
2016. a, b, c
Skamarock, C., Klemp, B., Dudhia, J., Gill, O., Barker, D., Duda, G., Huang,
X.-y., Wang, W., and Powers, G.: A Description of the Advanced Research
WRF Version 3, https://doi.org/10.5065/D68S4MVH,
2008. a
Smith, J. D., Mitsakou, C., Kitwiroon, N., Barratt, B. M., Walton, H. A.,
Taylor, J. G., Anderson, H. R., Kelly, F. J., and Beevers, S. D.: London
Hybrid Exposure Model: Improving Human Exposure Estimates to
NO2 and PM2.5 in an Urban Setting, Environ. Sci.
Technol., 50, 11760–11768, https://doi.org/10.1021/acs.est.6b01817,
2016. a, b
Soares, J., Kousa, A., Kukkonen, J., Matilainen, L., Kangas, L., Kauhaniemi, M., Riikonen, K., Jalkanen, J.-P., Rasila, T., Hänninen, O., Koskentalo, T., Aarnio, M., Hendriks, C., and Karppinen, A.: Refinement of a model for evaluating the population exposure in an urban area, Geosci. Model Dev., 7, 1855–1872, https://doi.org/10.5194/gmd-7-1855-2014, 2014. a
Stephens, B., Gall, E. T., and Siegel, J. A.: Measuring the penetration of
ambient ozone into residential buildings, Environ. Sci.
Technol., 46, 929–936, https://doi.org/10.1021/es2028795, 2012. a
Sun, Q., Wang, W., Chen, C., Ban, J., Xu, D., Zhu, P., He, M. Z., and Li, T.:
Acute effect of multiple ozone metrics on mortality by season in 34 Chinese
counties in 2013–2015, J. Intern. Med., 283, 481–488,
https://doi.org/10.1111/joim.12724,
2018. a
Thatcher, T. L., Lunden, M. M., Revzan, K. L., Sextro, R. G., and Brown, N. J.:
A Concentration Rebound Method for Measuring Particle Penetration
and Deposition in the Indoor Environment, Aerosol Sci.
Tech., 37, 847–864, https://doi.org/10.1080/02786820300940,
2003. a
Valari, M. and Menut, L.: Transferring the heterogeneity of surface emissions
to variability in pollutant concentrations over urban areas through a
chemistry-transport model, Atmos. Environ., 44, 3229–3238,
https://doi.org/10.1016/j.atmosenv.2010.06.001,
2010. a
Valari, M. and Markakis, K.: mvalari/EXPLUME: First release of EXPLUME (Version v1.0.0), Zenodo, https://doi.org/10.5281/zenodo.3352714, 2019. a
Valari, M., Menut, L., and Chatignoux, E.: Using a chemistry transport model to
account for the spatial variability of exposure concentrations in
epidemiologic air pollution studies, J. Air Waste Manage., 61, 164–179, 2011. a
Vizcaino, P. and Lavalle, C.: Development of European NO2 Land Use
Regression Model for present and future exposure assessment:
Implications for policy analysis, Environ. Pollut., 240, 140–154,
https://doi.org/10.1016/j.envpol.2018.03.075,
2018.
a
Walker, I., Sherman, M., and Nazaroff, W.: Ozone Reductions Using
Residential Building Envelopes, Tech. Rep. LBNL-1563E, Lawrence
Berkeley National Laboratory, california Institute for Energy and
Environment, 2009. a
Walker, I. S. and Sherman, M. H.: Effect of ventilation strategies on
residential ozone levels, Build. Environ., 59, 456–465,
https://doi.org/10.1016/j.buildenv.2012.09.013,
2013. a
Weschler, C. J.: Ozone in Indoor Environments: Concentration and Chemistry, Indoor Air, 10, 269–288, https://doi.org/10.1034/j.1600-0668.2000.010004269.x, 2000. a, b
WHO: Review of evidence on health aspects of air pollution – REVIHAAP project: final technical report, available at:
http://www.euro.who.int/en/health-topics/environment-and-health/air-quality/publications/2013/review-of-evidence-on-health-aspects-of-air-pollution-revihaap-project-final-technical-report (last access: 28 February 2020), 2013. a
Willers, S. M., Eriksson, C., Gidhagen, L., Nilsson, M. E., Pershagen, G., and
Bellander, T.: Fine and coarse particulate air pollution in relation to
respiratory health in Sweden, Eur. Respir. J., 42, 924–934,
https://doi.org/10.1183/09031936.00088212,
2013. a
Williams, R. D. and Knibbs, L. D.: Daily personal exposure to black carbon: A
pilot study, Atmos. Environ., 132, 296–299,
https://doi.org/10.1016/j.atmosenv.2016.03.023,
2016. a
Xie, X., Semanjski, I., Gautama, S., Tsiligianni, E., Deligiannis, N., Rajan,
R. T., Pasveer, F., and Philips, W.: A Review of Urban Air Pollution
Monitoring and Exposure Assessment Methods, ISPRS Int.
Geo-Inf., 6, 389, https://doi.org/10.3390/ijgi6120389,
2017. a
Xu, H., Bechle, M. J., Wang, M., Szpiro, A. A., Vedal, S., Bai, Y., and
Marshall, J. D.: National PM2.5 and NO2 exposure models for China based
on land use regression, satellite measurements, and universal kriging,
Sci. Total Environ., 655, 423–433,
https://doi.org/10.1016/j.scitotenv.2018.11.125,
2019a. a
Xu, H., Léon, J.-F., Liousse, C., Guinot, B., Yoboué, V., Akpo, A. B., Adon, J., Ho, K. F., Ho, S. S. H., Li, L., Gardrat, E., Shen, Z., and Cao, J.: Personal exposure to PM2.5 emitted from typical anthropogenic sources in southern West Africa: chemical characteristics and associated health risks, Atmos. Chem. Phys., 19, 6637–6657, https://doi.org/10.5194/acp-19-6637-2019, 2019b. a
Yu, X., Stuart, A. L., Liu, Y., Ivey, C. E., Russell, A. G., Kan, H., Henneman,
L. R. F., Sarnat, S. E., Hasan, S., Sadmani, A., Yang, X., and Yu, H.: On the
accuracy and potential of Google Maps location history data to
characterize individual mobility for air pollution health studies,
Environ. Pollut., 252, 924–930, https://doi.org/10.1016/j.envpol.2019.05.081,
2019. a
Short summary
To understand how atmospheric pollution affects human health, we need to know the inhaled dose of pollutants. We develop a model that follows the individuals of a population during their daily activities and estimates pollutant concentration levels in the ambient air. We show that certain practices, such as biking in the city, expose people to PM2.5 concentration levels higher than the WHO recommendations. We also show that living in green buildings will significantly decrease exposure to ozone.
To understand how atmospheric pollution affects human health, we need to know the inhaled dose...
