Articles | Volume 16, issue 20
https://doi.org/10.5194/gmd-16-6029-2023
© Author(s) 2023. 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-16-6029-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
27 Oct 2023
Methods for assessment of models |
| 27 Oct 2023
| 27 Oct 2023
A standardized methodology for the validation of air quality forecast applications (F-MQO): lessons learnt from its application across Europe
Lina Vitali
National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Department for Sustainability, Bologna, Italy
Kees Cuvelier
European Commission – Joint Research Centre (JRC), Ispra, Italy
retired
Antonio Piersanti
National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Department for Sustainability, Bologna, Italy
Alexandra Monteiro
CESAM, Department of Environment, University of Aveiro, Aveiro, Portugal
Mario Adani
National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Department for Sustainability, Bologna, Italy
Roberta Amorati
Regional Agency for Prevention, Environment and Energy (ARPAE) of the Emilia-Romagna region, Bologna, Italy
Agnieszka Bartocha
ATMOTERM, Opole, Poland
Alessandro D'Ausilio
Flemish Institute for Technological Research (VITO), Mol, Belgium
Paweł Durka
Institute of Environmental Protection (IEP) – National Research Institute, Warsaw, Poland
Carla Gama
CESAM, Department of Environment, University of Aveiro, Aveiro, Portugal
Giulia Giovannini
Regional Agency for Prevention, Environment and Energy (ARPAE) of the Emilia-Romagna region, Bologna, Italy
Stijn Janssen
Flemish Institute for Technological Research (VITO), Mol, Belgium
Tomasz Przybyła
ATMOTERM, Opole, Poland
Michele Stortini
Regional Agency for Prevention, Environment and Energy (ARPAE) of the Emilia-Romagna region, Bologna, Italy
Stijn Vranckx
Flemish Institute for Technological Research (VITO), Mol, Belgium
Philippe Thunis
CORRESPONDING AUTHOR
European Commission – Joint Research Centre (JRC), Ispra, Italy
Related authors
Ilaria D'Elia, Gino Briganti, Lina Vitali, Antonio Piersanti, Gaia Righini, Massimo D'Isidoro, Andrea Cappelletti, Mihaela Mircea, Mario Adani, Gabriele Zanini, and Luisella Ciancarella
Atmos. Chem. Phys., 21, 10825–10849, https://doi.org/10.5194/acp-21-10825-2021, https://doi.org/10.5194/acp-21-10825-2021, 2021
Short summary
Short summary
We present an analysis of modelled trends of PM10, NO2, and O3 airborne concentrations over the Italian territory in 2003–2010. Our analysis shows a general downward simulated trend for all pollutants, with good agreement between observed and modelled values and the model widening both coverage and significance of air concentration trends. Due to the complex atmospheric dynamics, emission reductions do not always result in decreasing concentrations, especially for secondary pollutants.
Mario Adani, Guido Guarnieri, Lina Vitali, Luisella Ciancarella, Ilaria D'Elia, Mihaela Mircea, Maurizio Gualtieri, Andrea Cappelletti, Massimo D'Isidoro, Gino Briganti, Antonio Piersanti, Milena Stracquadanio, Gaia Righini, Felicita Russo, Giuseppe Cremona, Maria Gabriella Villani, and Gabriele Zanini
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-54, https://doi.org/10.5194/gmd-2020-54, 2020
Publication in GMD not foreseen
Short summary
Short summary
The National Air Quality forecasting system FORAIR_IT may be considered a state of the art model, and as far as we know it is the first forecasting system at high spatial resolution proposed at Italian National level. FORAIR_IT may be a useful tool that the policy makers might use in order to apply extraordinary procedure to prevent/mitigate high levels of air pollution. Moreover general population might take advantage of FORAIR_IT to get used to the complexity of air quality issues.
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.
Eduardo Torre-Pascual, Gotzon Gangoiti, Ana Rodríguez-García, Estibaliz Sáez de Cámara, Joana Ferreira, Carla Gama, María Carmen Gómez, Iñaki Zuazo, Jose Antonio García, and Maite de Blas
Atmos. Chem. Phys., 24, 4305–4329, https://doi.org/10.5194/acp-24-4305-2024, https://doi.org/10.5194/acp-24-4305-2024, 2024
Short summary
Short summary
We present an analysis of an intense air pollution episode of tropospheric ozone (O3) along the Atlantic coast of the Iberian Peninsula, incorporating both measured and simulated parameters. Our study extends beyond surface-level factors to include altitude-related parameters. These episodes stem from upper-atmosphere O3 accumulation in preceding days, transported to surface layers, causing rapid O3 concentration increase.
Giorgio Veratti, Alessandro Bigi, Michele Stortini, Sergio Teggi, and Grazia Ghermandi
EGUsphere, https://doi.org/10.5194/egusphere-2023-2641, https://doi.org/10.5194/egusphere-2023-2641, 2024
Short summary
Short summary
In a study of two consecutive winter seasons, we used measurements and modeling tools to identify the levels and sources of black carbon pollution in a medium-sized urban area of the Po valley, Italy. Our findings show that biomass burning and traffic-related emissions (especially from Euro 4 diesel cars), significantly contribute to BC concentrations. This research offers crucial insights for policymakers and urban planners aiming to improve air quality in cities.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, Enrico Pisoni, and Bertrand Bessagnet
Geosci. Model Dev., 17, 587–606, https://doi.org/10.5194/gmd-17-587-2024, https://doi.org/10.5194/gmd-17-587-2024, 2024
Short summary
Short summary
In our study the robustness of the model responses to emission reductions in the EU is assessed when the emission data are changed. Our findings are particularly important to better understand the uncertainties associated to the emission inventories and how these uncertainties impact the level of accuracy of the resulting air quality modelling, which is a key for designing air quality plans. Also crucial is the choice of indicator to avoid misleading interpretations of the results.
Philippe Thunis, Alain Clappier, Enrico Pisoni, Bertrand Bessagnet, Jeroen Kuenen, Marc Guevara, and Susana Lopez-Aparicio
Geosci. Model Dev., 15, 5271–5286, https://doi.org/10.5194/gmd-15-5271-2022, https://doi.org/10.5194/gmd-15-5271-2022, 2022
Short summary
Short summary
In this work, we propose a screening method to improve the quality of emission inventories, which are responsible for large uncertainties in air-quality modeling. The first step of screening consists of keeping only emission contributions that are relevant enough. In a second step, the method identifies large differences that provide evidence of methodological divergence or errors. We used the approach to compare two versions of the CAMS-REG European-scale inventory over 150 European cities.
Svetlana Tsyro, Wenche Aas, Augustin Colette, Camilla Andersson, Bertrand Bessagnet, Giancarlo Ciarelli, Florian Couvidat, Kees Cuvelier, Astrid Manders, Kathleen Mar, Mihaela Mircea, Noelia Otero, Maria-Teresa Pay, Valentin Raffort, Yelva Roustan, Mark R. Theobald, Marta G. Vivanco, Hilde Fagerli, Peter Wind, Gino Briganti, Andrea Cappelletti, Massimo D'Isidoro, and Mario Adani
Atmos. Chem. Phys., 22, 7207–7257, https://doi.org/10.5194/acp-22-7207-2022, https://doi.org/10.5194/acp-22-7207-2022, 2022
Short summary
Short summary
Particulate matter (PM) air pollution causes adverse health effects. In Europe, the emissions caused by anthropogenic activities have been reduced in the last decades. To assess the efficiency of emission reductions in improving air quality, we have studied the evolution of PM pollution in Europe. Simulations with six air quality models and observational data indicate a decrease in PM concentrations by 10 % to 30 % across Europe from 2000 to 2010, which is mainly a result of emission reductions.
Philippe Thunis, Alain Clappier, Alexander de Meij, Enrico Pisoni, Bertrand Bessagnet, and Leonor Tarrason
Atmos. Chem. Phys., 21, 18195–18212, https://doi.org/10.5194/acp-21-18195-2021, https://doi.org/10.5194/acp-21-18195-2021, 2021
Short summary
Short summary
Air pollution's origin in cities is still a point of discussion, and approaches to assess the city's responsibility for its pollution are not harmonized and thus not comparable, resulting in sometimes contradicting interpretations. We show that methodological choices can easily lead to differences of a factor of 2 in terms of responsibility outcome and stress that methodological choices and assumptions most often lead to a systematic and important underestimation of the city's responsibility.
Ilaria D'Elia, Gino Briganti, Lina Vitali, Antonio Piersanti, Gaia Righini, Massimo D'Isidoro, Andrea Cappelletti, Mihaela Mircea, Mario Adani, Gabriele Zanini, and Luisella Ciancarella
Atmos. Chem. Phys., 21, 10825–10849, https://doi.org/10.5194/acp-21-10825-2021, https://doi.org/10.5194/acp-21-10825-2021, 2021
Short summary
Short summary
We present an analysis of modelled trends of PM10, NO2, and O3 airborne concentrations over the Italian territory in 2003–2010. Our analysis shows a general downward simulated trend for all pollutants, with good agreement between observed and modelled values and the model widening both coverage and significance of air concentration trends. Due to the complex atmospheric dynamics, emission reductions do not always result in decreasing concentrations, especially for secondary pollutants.
Philippe Thunis, Alain Clappier, Matthias Beekmann, Jean Philippe Putaud, Cornelis Cuvelier, Jessie Madrazo, and Alexander de Meij
Atmos. Chem. Phys., 21, 9309–9327, https://doi.org/10.5194/acp-21-9309-2021, https://doi.org/10.5194/acp-21-9309-2021, 2021
Short summary
Short summary
Modelling simulations are used to identify the most efficient emission reduction strategies to reduce PM2.5 concentration levels in northern Italy. Results show contrasting chemical regimes and important non-linearities during wintertime, with the striking result that PM2.5 levels may increase when NOx reductions are applied in NOx-rich areas – a process that may have contributed to the absence of significant PM2.5 decrease during the COVID-19 lockdowns in many European cities.
Jérôme Barré, Hervé Petetin, Augustin Colette, Marc Guevara, Vincent-Henri Peuch, Laurence Rouil, Richard Engelen, Antje Inness, Johannes Flemming, Carlos Pérez García-Pando, Dene Bowdalo, Frederik Meleux, Camilla Geels, Jesper H. Christensen, Michael Gauss, Anna Benedictow, Svetlana Tsyro, Elmar Friese, Joanna Struzewska, Jacek W. Kaminski, John Douros, Renske Timmermans, Lennart Robertson, Mario Adani, Oriol Jorba, Mathieu Joly, and Rostislav Kouznetsov
Atmos. Chem. Phys., 21, 7373–7394, https://doi.org/10.5194/acp-21-7373-2021, https://doi.org/10.5194/acp-21-7373-2021, 2021
Short summary
Short summary
This study provides a comprehensive assessment of air quality changes across the main European urban areas induced by the COVID-19 lockdown using satellite observations, surface site measurements, and the forecasting system from the Copernicus Atmospheric Monitoring Service (CAMS). We demonstrate the importance of accounting for weather and seasonal variability when calculating such estimates.
Bart Degraeuwe, Enrico Pisoni, and Philippe Thunis
Geosci. Model Dev., 13, 5725–5736, https://doi.org/10.5194/gmd-13-5725-2020, https://doi.org/10.5194/gmd-13-5725-2020, 2020
Short summary
Short summary
To make decisions on how to improve air quality, it is useful to identify the main sources of pollution for an area of interest. Often these sources of pollution are identified with complex models that, even if accurate, are time consuming and complex. In this work we use another approach, simplified models, to accomplish the same task. The results, computed with two different set of simplified models, show the main sources of pollution for selected cities, and the associated uncertainties.
Philippe Thunis, Monica Crippa, Cornelis Cuvelier, Diego Guizzardi, Alexander De Meij, Gabriel Oreggioni, and Enrico Pisoni
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2020-144, https://doi.org/10.5194/essd-2020-144, 2020
Preprint withdrawn
Short summary
Short summary
A comparison of emissions inventories for air quality modelling, in Europe, is presented. Among these inventories, EDGAR v5.0 for air pollutants is introduced and validated, through a simulation with the EMEP model.
Mario Adani, Guido Guarnieri, Lina Vitali, Luisella Ciancarella, Ilaria D'Elia, Mihaela Mircea, Maurizio Gualtieri, Andrea Cappelletti, Massimo D'Isidoro, Gino Briganti, Antonio Piersanti, Milena Stracquadanio, Gaia Righini, Felicita Russo, Giuseppe Cremona, Maria Gabriella Villani, and Gabriele Zanini
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-54, https://doi.org/10.5194/gmd-2020-54, 2020
Publication in GMD not foreseen
Short summary
Short summary
The National Air Quality forecasting system FORAIR_IT may be considered a state of the art model, and as far as we know it is the first forecasting system at high spatial resolution proposed at Italian National level. FORAIR_IT may be a useful tool that the policy makers might use in order to apply extraordinary procedure to prevent/mitigate high levels of air pollution. Moreover general population might take advantage of FORAIR_IT to get used to the complexity of air quality issues.
Giancarlo Ciarelli, Mark R. Theobald, Marta G. Vivanco, Matthias Beekmann, Wenche Aas, Camilla Andersson, Robert Bergström, Astrid Manders-Groot, Florian Couvidat, Mihaela Mircea, Svetlana Tsyro, Hilde Fagerli, Kathleen Mar, Valentin Raffort, Yelva Roustan, Maria-Teresa Pay, Martijn Schaap, Richard Kranenburg, Mario Adani, Gino Briganti, Andrea Cappelletti, Massimo D'Isidoro, Cornelis Cuvelier, Arineh Cholakian, Bertrand Bessagnet, Peter Wind, and Augustin Colette
Geosci. Model Dev., 12, 4923–4954, https://doi.org/10.5194/gmd-12-4923-2019, https://doi.org/10.5194/gmd-12-4923-2019, 2019
Short summary
Short summary
The novel multi-model EURODELTA-Trends exercise provided 21 years of continuous PM components and their gas-phase precursor concentrations over Europe from the year 1990. The models’ capabilities to reproduce PM components and gas-phase PM precursor trends over the 1990–2010 period is the key focus of this study. The models were able to reproduce the observed trends relatively well, indicating a possible shift in the thermodynamic equilibrium between gas and particle phases.
Mark R. Theobald, Marta G. Vivanco, Wenche Aas, Camilla Andersson, Giancarlo Ciarelli, Florian Couvidat, Kees Cuvelier, Astrid Manders, Mihaela Mircea, Maria-Teresa Pay, Svetlana Tsyro, Mario Adani, Robert Bergström, Bertrand Bessagnet, Gino Briganti, Andrea Cappelletti, Massimo D'Isidoro, Hilde Fagerli, Kathleen Mar, Noelia Otero, Valentin Raffort, Yelva Roustan, Martijn Schaap, Peter Wind, and Augustin Colette
Atmos. Chem. Phys., 19, 379–405, https://doi.org/10.5194/acp-19-379-2019, https://doi.org/10.5194/acp-19-379-2019, 2019
Short summary
Short summary
Model estimates of the mean European wet deposition of nitrogen and sulfur for 1990 to 2010 were within 40 % of the observed values. As a result of systematic biases, the models were better at estimating relative trends for the periods 1990–2000 and 2000–2010 than the absolute trends. Although the predominantly decreasing trends were mostly due to emission reductions, they were partially offset by other factors (e.g. changes in precipitation) during the first period, but not the second.
Noelia Otero, Jana Sillmann, Kathleen A. Mar, Henning W. Rust, Sverre Solberg, Camilla Andersson, Magnuz Engardt, Robert Bergström, Bertrand Bessagnet, Augustin Colette, Florian Couvidat, Cournelius Cuvelier, Svetlana Tsyro, Hilde Fagerli, Martijn Schaap, Astrid Manders, Mihaela Mircea, Gino Briganti, Andrea Cappelletti, Mario Adani, Massimo D'Isidoro, María-Teresa Pay, Mark Theobald, Marta G. Vivanco, Peter Wind, Narendra Ojha, Valentin Raffort, and Tim Butler
Atmos. Chem. Phys., 18, 12269–12288, https://doi.org/10.5194/acp-18-12269-2018, https://doi.org/10.5194/acp-18-12269-2018, 2018
Short summary
Short summary
This paper evaluates the capability of air-quality models to capture the observed relationship between surface ozone concentrations and meteorology over Europe. The air-quality models tended to overestimate the influence of maximum temperature and surface solar radiation. None of the air-quality models captured the strength of the observed relationship between ozone and relative humidity appropriately, underestimating the effect of relative humidity, a key factor in the ozone removal processes.
Marta G. Vivanco, Mark R. Theobald, Héctor García-Gómez, Juan Luis Garrido, Marje Prank, Wenche Aas, Mario Adani, Ummugulsum Alyuz, Camilla Andersson, Roberto Bellasio, Bertrand Bessagnet, Roberto Bianconi, Johannes Bieser, Jørgen Brandt, Gino Briganti, Andrea Cappelletti, Gabriele Curci, Jesper H. Christensen, Augustin Colette, Florian Couvidat, Cornelis Cuvelier, Massimo D'Isidoro, Johannes Flemming, Andrea Fraser, Camilla Geels, Kaj M. Hansen, Christian Hogrefe, Ulas Im, Oriol Jorba, Nutthida Kitwiroon, Astrid Manders, Mihaela Mircea, Noelia Otero, Maria-Teresa Pay, Luca Pozzoli, Efisio Solazzo, Svetlana Tsyro, Alper Unal, Peter Wind, and Stefano Galmarini
Atmos. Chem. Phys., 18, 10199–10218, https://doi.org/10.5194/acp-18-10199-2018, https://doi.org/10.5194/acp-18-10199-2018, 2018
Short summary
Short summary
European wet and dry atmospheric deposition of N and S estimated by 14 air quality models was found to vary substantially. An ensemble of models meeting acceptability criteria was used to estimate the exceedances of the critical loads for N in habitats within the Natura 2000 network, as well as their lower and upper limits. Scenarios with 20 % emission reductions in different regions of the world showed that European emissions are responsible for most of the N and S deposition in Europe.
Alain Clappier, Claudio A. Belis, Denise Pernigotti, and Philippe Thunis
Geosci. Model Dev., 10, 4245–4256, https://doi.org/10.5194/gmd-10-4245-2017, https://doi.org/10.5194/gmd-10-4245-2017, 2017
Short summary
Short summary
This work demonstrates that when the relationship between emissions and concentrations is nonlinear, sensitivity approaches, generally used for air quality planning, are not suitable to retrieve source contributions and source apportionment methods are not appropriate to evaluate the impact of abatement strategies on air quality. A simple theoretical example is used highlighting differences and potential implications for policy.
Augustin Colette, Camilla Andersson, Astrid Manders, Kathleen Mar, Mihaela Mircea, Maria-Teresa Pay, Valentin Raffort, Svetlana Tsyro, Cornelius Cuvelier, Mario Adani, Bertrand Bessagnet, Robert Bergström, Gino Briganti, Tim Butler, Andrea Cappelletti, Florian Couvidat, Massimo D'Isidoro, Thierno Doumbia, Hilde Fagerli, Claire Granier, Chris Heyes, Zig Klimont, Narendra Ojha, Noelia Otero, Martijn Schaap, Katarina Sindelarova, Annemiek I. Stegehuis, Yelva Roustan, Robert Vautard, Erik van Meijgaard, Marta Garcia Vivanco, and Peter Wind
Geosci. Model Dev., 10, 3255–3276, https://doi.org/10.5194/gmd-10-3255-2017, https://doi.org/10.5194/gmd-10-3255-2017, 2017
Short summary
Short summary
The EURODELTA-Trends numerical experiment has been designed to assess the capability of chemistry-transport models to capture the evolution of surface air quality over the 1990–2010 period in Europe. It also includes sensitivity experiments in order to analyse the relative contribution of (i) emission changes, (ii) meteorological variability, and (iii) boundary conditions to air quality trends. The article is a detailed presentation of the experiment design and participating models.
Bertrand Bessagnet, Guido Pirovano, Mihaela Mircea, Cornelius Cuvelier, Armin Aulinger, Giuseppe Calori, Giancarlo Ciarelli, Astrid Manders, Rainer Stern, Svetlana Tsyro, Marta García Vivanco, Philippe Thunis, Maria-Teresa Pay, Augustin Colette, Florian Couvidat, Frédérik Meleux, Laurence Rouïl, Anthony Ung, Sebnem Aksoyoglu, José María Baldasano, Johannes Bieser, Gino Briganti, Andrea Cappelletti, Massimo D'Isidoro, Sandro Finardi, Richard Kranenburg, Camillo Silibello, Claudio Carnevale, Wenche Aas, Jean-Charles Dupont, Hilde Fagerli, Lucia Gonzalez, Laurent Menut, André S. H. Prévôt, Pete Roberts, and Les White
Atmos. Chem. Phys., 16, 12667–12701, https://doi.org/10.5194/acp-16-12667-2016, https://doi.org/10.5194/acp-16-12667-2016, 2016
Short summary
Short summary
The EURODELTA III exercise allows a very comprehensive intercomparison and evaluation of air quality models' performance. On average, the models provide a rather good picture of the particulate matter (PM) concentrations over Europe even if the highest concentrations are underestimated. The meteorology is responsible for model discrepancies, while the lack of emissions, particularly in winter, is mentioned as the main reason for the underestimations of PM.
G. Kiesewetter, J. Borken-Kleefeld, W. Schöpp, C. Heyes, P. Thunis, B. Bessagnet, E. Terrenoire, H. Fagerli, A. Nyiri, and M. Amann
Atmos. Chem. Phys., 15, 1539–1553, https://doi.org/10.5194/acp-15-1539-2015, https://doi.org/10.5194/acp-15-1539-2015, 2015
Short summary
Short summary
We describe the multi-stage approach applied in the GAINS model to assess compliance with PM10 limit values at more than 1850 individual air quality monitoring stations in Europe. We analyse source contributions to ambient concentrations and the implications of future policy choices on air quality for 2030. While current legislation does not solve compliance issues, problems are largely eliminated by EU-wide adoption of the best available emission control technology.
E. Terrenoire, B. Bessagnet, L. Rouïl, F. Tognet, G. Pirovano, L. Létinois, M. Beauchamp, A. Colette, P. Thunis, M. Amann, and L. Menut
Geosci. Model Dev., 8, 21–42, https://doi.org/10.5194/gmd-8-21-2015, https://doi.org/10.5194/gmd-8-21-2015, 2015
Short summary
Short summary
The model reproduces the temporal variability of NO2, O3, PM10, PM2.5 better at rural than urban background stations.
The fractional biases show that the model performs slightly better at RB sites than at UB sites for NO2, O3 and PM10.
At UB sites, CHIMERE reproduces PM2.5 better than PM10.
This is primarily the result of an underestimation of coarse particulate matter (PM) associated with uncertainties on SOA chemistry and their precursor emissions, dust and sea salt.
D. Lauwaet, P. Viaene, E. Brisson, N.P.M. van Lipzig, T. van Noije, A. Strunk, S. Van Looy, N. Veldeman, L. Blyth, K. De Ridder, and S. Janssen
Atmos. Chem. Phys., 14, 5893–5904, https://doi.org/10.5194/acp-14-5893-2014, https://doi.org/10.5194/acp-14-5893-2014, 2014
G. Kiesewetter, J. Borken-Kleefeld, W. Schöpp, C. Heyes, P. Thunis, B. Bessagnet, E. Terrenoire, A. Gsella, and M. Amann
Atmos. Chem. Phys., 14, 813–829, https://doi.org/10.5194/acp-14-813-2014, https://doi.org/10.5194/acp-14-813-2014, 2014
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
Adani, M., Piersanti, A., Ciancarella, L., D'Isidoro, M., Villani, M. G., and Vitali, L.: Preliminary Tests on the Sensitivity of the FORAIR_IT Air Quality Forecasting System to Different Meteorological Drivers, Atmosphere, 11, 574, https://doi.org/10.3390/atmos11060574, 2020.
Adani, M., D'Isidoro, M., Mircea, M., Guarnieri, G., Vitali, L., D'Elia, I., Ciancarella, L., Gualtieri, M., Briganti, G., Cappelletti, A., Piersanti, A., Stracquadanio, M., Righini, G., Russo, F., Cremona, G., Villani, M. G., and Zanini, G.: Evaluation of air quality forecasting system FORAIR-IT over Europe and Italy at high resolution for year 2017, Atmos. Pollut. Res., 13, 101456, https://doi.org/10.1016/j.apr.2022.101456, 2022.
Agarwal, S., Sharma, S., R., S., Rahman, M. H., Vranckx, S., Maiheu, B., Blyth, L., Janssen, S., Gargava, P., Shukla, V. K., and Batra, S.: Air quality forecasting using artificial neural networks with real time dynamic error correction in highly polluted regions, Sci. Total Environ., 735, 139454, https://doi.org/10.1016/j.scitotenv.2020.139454, 2020.
Alfaro, S. C. and Gomes, L.: Modeling mineral aerosol production by wind erosion: Emission intensities and aerosol size distributions in source areas, J. Geophys. Res.-Atmos., 106, 18075–18084, https://doi.org/10.1029/2000JD900339, 2001.
Bai, L., Wang, J., Ma, X., and Lu, H.: Air Pollution Forecasts: An Overview, Int. J. Environ. Res. Publ. He., 15, 780, https://doi.org/10.3390/ijerph15040780, 2018.
Baklanov, A. and Zhang, Y.: Advances in air quality modeling and forecasting, Glob. Transit., 2, 261–270, https://doi.org/10.1016/j.glt.2020.11.001, 2020.
Baklanov, A., Schlünzen, K., Suppan, P., Baldasano, J., Brunner, D., Aksoyoglu, S., Carmichael, G., Douros, J., Flemming, J., Forkel, R., Galmarini, S., Gauss, M., Grell, G., Hirtl, M., Joffre, S., Jorba, O., Kaas, E., Kaasik, M., Kallos, G., Kong, X., Korsholm, U., Kurganskiy, A., Kushta, J., Lohmann, U., Mahura, A., Manders-Groot, A., Maurizi, A., Moussiopoulos, N., Rao, S. T., Savage, N., Seigneur, C., Sokhi, R. S., Solazzo, E., Solomos, S., Sørensen, B., Tsegas, G., Vignati, E., Vogel, B., and Zhang, Y.: Online coupled regional meteorology chemistry models in Europe: current status and prospects, Atmos. Chem. Phys., 14, 317–398, https://doi.org/10.5194/acp-14-317-2014, 2014.
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities, Mon. Weather Rev., 139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011.
Borrego, C., Monteiro, A., Ferreira, J., Miranda, A. I., Costa, A. M., Carvalho, A. C., and Lopes, M.: Procedures for estimation of modelling uncertainty in air quality assessment, Environ. Int., 34, 613–620, https://doi.org/10.1016/j.envint.2007.12.005, 2008.
Boylan, J. W. and Russell, A. G.: PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models, Atmos. Environ., 40, 4946–4959, https://doi.org/10.1016/j.atmosenv.2005.09.087, 2006.
Cabaneros, S. M., Calautit, J. K., and Hughes, B. R.: A review of artificial neural network models for ambient air pollution prediction, Environ. Model. Softw., 119, 285–304, https://doi.org/10.1016/j.envsoft.2019.06.014, 2019.
Carnevale, C., Finzi, G., Pederzoli, A., Pisoni, E., Thunis, P., Turrini, E., and Volta, M.: A methodology for the evaluation of re-analyzed PM10 concentration fields: a case study over the PO Valley, Air Qual. Atmos. Health, 8, 533–544, https://doi.org/10.1007/s11869-014-0307-2, 2015.
Chang, J. C. and Hanna, S. R.: Air quality model performance evaluation, Meteorol. Atmos. Phys., 87, 167–196, https://doi.org/10.1007/s00703-003-0070-7, 2004.
Chemel, C., Sokhi, R. S., Yu, Y., Hayman, G. D., Vincent, K. J., Dore, A. J., Tang, Y. S., Prain, H. D., and Fisher, B. E. A.: Evaluation of a CMAQ simulation at high resolution over the UK for the calendar year 2003, Atmos. Environ., 44, 2927–2939, https://doi.org/10.1016/j.atmosenv.2010.03.029, 2010.
D'Elia, I., Briganti, G., Vitali, L., Piersanti, A., Righini, G., D'Isidoro, M., Cappelletti, A., Mircea, M., Adani, M., Zanini, G., and Ciancarella, L.: Measured and modelled air quality trends in Italy over the period 2003–2010, Atmos. Chem. Phys., 21, 10825–10849, https://doi.org/10.5194/acp-21-10825-2021, 2021.
Dennis, R., Fox, T., Fuentes, M., Gilliland, A., Hanna, S., Hogrefe, C., Irwin, J., Rao, S. T., Scheffe, R., Schere, K., Steyn, D., and Venkatram, A.: A framework for evaluating regional-scale numerical photochemical modeling systems, Environ. Fluid Mech., 10, 471–489, https://doi.org/10.1007/s10652-009-9163-2, 2010.
Doms, G. and Baldauf, M.: A Description of the Non Hydrostatic Regional COSMO-Model. Part I: Dynamics and Numeric. User Guide Documentation, http://www.cosmo-model.org (last access: 20 October 2023), 2018.
Eder, B., Kang, D., Rao, S. T., Mathur, R., Yu, S., Otte, T., Schere, K., Wayland, R., Jackson, S., Davidson, P., McQueen, J., and Bridgers, G.: Using National Air Quality Forecast Guidance to Develop Local Air Quality Index Forecasts, B. Am. Meteorol. Soc., 91, 313–326, https://doi.org/10.1175/2009BAMS2734.1, 2010.
Emery, C., Liu, Z., Russell, A. G., Odman, M. T., Yarwood, G., and Kumar, N.: Recommendations on statistics and benchmarks to assess photochemical model performance, J. Air Waste Manag. Assoc., 67, 582–598, https://doi.org/10.1080/10962247.2016.1265027, 2017.
EPA: Guidelines for Developing an Air Quality (Ozone and PM2.5) Forecasting Program, EPA-456/R-03-002 June 2003, 2003.
European Union: Directive 2004/107/EC of the European Parliament and of the Council of 15 December 2004 relating to arsenic, cadmium, mercury, nickel and polycyclic aromatic hydrocarbons in ambient air, 2004.
European Union: Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe, OJ L, 152, 2008.
European Union: Proposal for a DIRECTIVE OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on ambient air quality and cleaner air for Europe (recast), 2022.
Georgieva, E., Syrakov, D., Prodanova, M., Etropolska, I., and Slavov, K.: Evaluating the performance of WRF-CMAQ air quality modelling system in Bulgaria by means of the DELTA tool, Int. J. Environ. Pollut., 57, 272–284, https://doi.org/10.1504/IJEP.2015.074512, 2015.
Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B., Dubovik, O., and Lin, S.-J.: Sources and distributions of dust aerosols simulated with the GOCART model, J. Geophys. Res.-Atmos., 106, 20255–20273, https://doi.org/10.1029/2000JD000053, 2001.
Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and Geron, C.: Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys., 6, 3181–3210, https://doi.org/10.5194/acp-6-3181-2006, 2006.
Hanna, S. R. and Chang, J.: Setting Acceptance Criteria for Air Quality Models, in: Air Pollution Modeling and its Application XXI, Dordrecht, 479–484, https://doi.org/10.1007/978-94-007-1359-8_80, 2012.
Hintze, J. L. and Nelson, R. D.: Violin Plots: A Box Plot-Density Trace Synergism, Am. Stat., 52, 181–184, https://doi.org/10.1080/00031305.1998.10480559, 1998.
Hooyberghs, J., Mensink, C., Dumont, G., Fierens, F., and Brasseur, O.: A neural network forecast for daily average PM10 concentrations in Belgium, Atmos. Environ., 39, 3279–3289, https://doi.org/10.1016/j.atmosenv.2005.01.050, 2005.
Janssen, S. and Thunis, P.: FAIRMODE Guidance Document on Modelling Quality Objectives and Benchmarking (version 3.3), EUR 31068 EN, Publications Office of the European Union, Luxembourg, 2022, JRC129254, ISBN 978-92-76-52425-0, https://doi.org/10.2760/41988, 2022.
Janssen, S., Dumont, G., Fierens, F., and Mensink, C.: Spatial interpolation of air pollution measurements using CORINE land cover data, Atmos. Environ., 42, 4884–4903, https://doi.org/10.1016/j.atmosenv.2008.02.043, 2008.
Kang, D., Eder, B. K., Stein, A. F., Grell, G. A., Peckham, S. E., and McHenry, J.: The New England Air Quality Forecasting Pilot Program: Development of an Evaluation Protocol and Performance Benchmark, J. Air Waste Manag. Assoc., 55, 1782–1796, https://doi.org/10.1080/10473289.2005.10464775, 2005.
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization, arXiv preprint arXiv:1412.6980, https://doi.org/10.48550/arXiv.1412.6980, 2014.
Knaff, J. A. and Landsea, C. W.: An El Niño–Southern Oscillation Climatology and Persistence (CLIPER) Forecasting Scheme, Weather Forecast., 12, 633–652, https://doi.org/10.1175/1520-0434(1997)012<0633:AENOSO>2.0.CO;2, 1997.
Kukkonen, J., Olsson, T., Schultz, D. M., Baklanov, A., Klein, T., Miranda, A. I., Monteiro, A., Hirtl, M., Tarvainen, V., Boy, M., Peuch, V.-H., Poupkou, A., Kioutsioukis, I., Finardi, S., Sofiev, M., Sokhi, R., Lehtinen, K. E. J., Karatzas, K., San José, R., Astitha, M., Kallos, G., Schaap, M., Reimer, E., Jakobs, H., and Eben, K.: A review of operational, regional-scale, chemical weather forecasting models in Europe, Atmos. Chem. Phys., 12, 1–87, https://doi.org/10.5194/acp-12-1-2012, 2012.
Kushta, J., Georgiou, G. K., Proestos, Y., Christoudias, T., Thunis, P., Savvides, C., Papadopoulos, C., and Lelieveld, J.: Evaluation of EU air quality standards through modeling and the FAIRMODE benchmarking methodology, Air Qual. Atmos. Health, 12, 73–86, https://doi.org/10.1007/s11869-018-0631-z, 2019.
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.
Marécal, V., Peuch, V.-H., Andersson, C., Andersson, S., Arteta, J., Beekmann, M., Benedictow, A., Bergström, R., Bessagnet, B., Cansado, A., Chéroux, F., Colette, A., Coman, A., Curier, R. L., Denier van der Gon, H. A. C., Drouin, A., Elbern, H., Emili, E., Engelen, R. J., Eskes, H. J., Foret, G., Friese, E., Gauss, M., Giannaros, C., Guth, J., Joly, M., Jaumouillé, E., Josse, B., Kadygrov, N., Kaiser, J. W., Krajsek, K., Kuenen, J., Kumar, U., Liora, N., Lopez, E., Malherbe, L., Martinez, I., Melas, D., Meleux, F., Menut, L., Moinat, P., Morales, T., Parmentier, J., Piacentini, A., Plu, M., Poupkou, A., Queguiner, S., Robertson, L., Rouïl, L., Schaap, M., Segers, A., Sofiev, M., Tarasson, L., Thomas, M., Timmermans, R., Valdebenito, Á., van Velthoven, P., van Versendaal, R., Vira, J., and Ung, A.: A regional air quality forecasting system over Europe: the MACC-II daily ensemble production, Geosci. Model Dev., 8, 2777–2813, https://doi.org/10.5194/gmd-8-2777-2015, 2015.
Marongiu, A., Angelino, E., Moretti, M., Malvestiti, G., and Fossati, G.: Atmospheric Emission Sources in the Po-Basin from the LIFE-IP PREPAIR Project, Open J. Air Pollut., 11, 70–83, https://doi.org/10.4236/ojap.2022.113006, 2022.
Masood, A. and Ahmad, K.: A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance, J. Clean. Prod., 322, 129072, https://doi.org/10.1016/j.jclepro.2021.129072, 2021.
Menut, L., Bessagnet, B., Khvorostyanov, D., Beekmann, M., Blond, N., Colette, A., Coll, I., Curci, G., Foret, G., Hodzic, A., Mailler, S., Meleux, F., Monge, J.-L., Pison, I., Siour, G., Turquety, S., Valari, M., Vautard, R., and Vivanco, M. G.: CHIMERE 2013: a model for regional atmospheric composition modelling, Geosci. Model Dev., 6, 981–1028, https://doi.org/10.5194/gmd-6-981-2013, 2013.
Mircea, M., Ciancarella, L., Briganti, G., Calori, G., Cappelletti, A., Cionni, I., Costa, M., Cremona, G., D'Isidoro, M., Finardi, S., Pace, G., Piersanti, A., Righini, G., Silibello, C., Vitali, L., and Zanini, G.: Assessment of the AMS-MINNI system capabilities to simulate air quality over Italy for the calendar year 2005, Atmos. Environ., 84, 178–188, https://doi.org/10.1016/j.atmosenv.2013.11.006, 2014.
Mittermaier, M. P.: The Potential Impact of Using Persistence as a Reference Forecast on Perceived Forecast Skill, Weather Forecast., 23, 1022–1031, https://doi.org/10.1175/2008WAF2007037.1, 2008.
Monteiro, A., Lopes, M., Miranda, A. I., Borrego, C., and Robert Vautard: Air pollution forecast in Portugal: a demand from the new air quality framework directive, Int. J. Environ. Pollut., 25, 4–15, https://doi.org/10.1504/IJEP.2005.007650, 2005.
Monteiro, A., Miranda, A. I., Borrego, C., and Vautard, R.: Air quality assessment for Portugal, Sci. Total Environ., 373, 22–31, https://doi.org/10.1016/j.scitotenv.2006.10.014, 2007a.
Monteiro, A., Miranda, A. I., Borrego, C., Vautard, R., Ferreira, J., and Perez, A. T.: Long-term assessment of particulate matter using CHIMERE model, Atmos. Environ., 41, 7726–7738, https://doi.org/10.1016/j.atmosenv.2007.06.008, 2007b.
Monteiro, A., Durka, P., Flandorfer, C., Georgieva, E., Guerreiro, C., Kushta, J., Malherbe, L., Maiheu, B., Miranda, A. I., Santos, G., Stocker, J., Trimpeneers, E., Tognet, F., Stortini, M., Wesseling, J., Janssen, S., and Thunis, P.: Strengths and weaknesses of the FAIRMODE benchmarking methodology for the evaluation of air quality models, Air Qual. Atmos. Health, 11, 373–383, https://doi.org/10.1007/s11869-018-0554-8, 2018.
Olesen, H. R.: Toward the Establishment of a Common Framework for Model Evaluation, in: Air Pollution Modeling and Its Application XI, edited by: Gryning, S.-E. and Schiermeier, F. A., Springer US, Boston, MA, 519–528, https://doi.org/10.1007/978-1-4615-5841-5_54, 1996.
Pernigotti, D., Gerboles, M., Belis, C. A., and Thunis, P.: Model quality objectives based on measurement uncertainty. Part II: NO2 and PM10, Atmos. Environ., 79, 869–878, https://doi.org/10.1016/j.atmosenv.2013.07.045, 2013.
Raffaelli, K., Deserti, M., Stortini, M., Amorati, R., Vasconi, M., and Giovannini, G.: Improving Air Quality in the Po Valley, Italy: Some Results by the LIFE-IP-PREPAIR Project, Atmosphere, 11, 429, https://doi.org/10.3390/atmos11040429, 2020.
Rahman, M. H., Agarwal, S., Sharma, S., Suresh, R., Kundu, S., Vranckx, S., Maiheu, B., Blyth, L., Janssen, S., Jorge, S., Gargava, P., Shukla, V. K., and Batra, S.: High-Resolution Mapping of Air Pollution in Delhi Using Detrended Kriging Model, Environ. Model. Assess., 28, 39–54, https://doi.org/10.1007/s10666-022-09842-5, 2023.
Russell, A. and Dennis, R.: NARSTO critical review of photochemical models and modeling, Atmos. Environ., 34, 2283–2324, https://doi.org/10.1016/S1352-2310(99)00468-9, 2000.
Ryan, W. F.: The air quality forecast rote: Recent changes and future challenges, J. Air Waste Manag. Assoc., 66, 576–596, https://doi.org/10.1080/10962247.2016.1151469, 2016.
Seigneur, C., Pun, B., Pai, P., Louis, J.-F., Solomon, P., Emery, C., Morris, R., Zahniser, M., Worsnop, D., Koutrakis, P., White, W., and Tombach, I.: Guidance for the Performance Evaluation of Three-Dimensional Air Quality Modeling Systems for Particulate Matter and Visibility, J. Air Waste Manag. Assoc., 50, 588–599, https://doi.org/10.1080/10473289.2000.10464036, 2000.
Skamarock, C., Klemp, B., Dudhia, J., Gill, O., Barker, D., Duda, G., Huang, X., Wang, W., and Powers, G.: A Description of the Advanced Research WRF Version 3. NCAR Tech. Note NCAR/TN-475+STR, https://doi.org/10.5065/D68S4MVH, 2008.
Sokhi, R. S., Moussiopoulos, N., Baklanov, A., Bartzis, J., Coll, I., Finardi, S., Friedrich, R., Geels, C., Grönholm, T., Halenka, T., Ketzel, M., Maragkidou, A., Matthias, V., Moldanova, J., Ntziachristos, L., Schäfer, K., Suppan, P., Tsegas, G., Carmichael, G., Franco, V., Hanna, S., Jalkanen, J.-P., Velders, G. J. M., and Kukkonen, J.: Advances in air quality research – current and emerging challenges, Atmos. Chem. Phys., 22, 4615–4703, https://doi.org/10.5194/acp-22-4615-2022, 2022.
Stortini, M., Arvani, B., and Deserti, M.: Operational Forecast and Daily Assessment of the Air Quality in Italy: A Copernicus-CAMS Downstream Service, Atmosphere, 11, 447, https://doi.org/10.3390/atmos11050447, 2020.
Szopa, S., Foret, G., Menut, L., and Cozic, A.: Impact of large scale circulation on European summer surface ozone and consequences for modelling forecast, Atmos. Environ., 43, 1189–1195, https://doi.org/10.1016/j.atmosenv.2008.10.039, 2009.
Tesche, T. W., Lurmann, F. R., Roth, P. M., Georgopoulos, P., and Seinfeld, J. H.: Improvement of procedures for evaluating photochemical models, Final report, Radian Corp., Sacramento, CA (USA), 1990.
Thunis, P. and Vitali, L.: Supporting data and tool, for the paper “A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe” (Version v2), Zenodo [data set], https://doi.org/10.5281/zenodo.7949868, 2023.
Thunis, P., Georgieva, E., and Pederzoli, A.: A tool to evaluate air quality model performances in regulatory applications, Environ. Model. Softw., 38, 220–230, https://doi.org/10.1016/j.envsoft.2012.06.005, 2012a.
Thunis, P., Pederzoli, A., and Pernigotti, D.: Performance criteria to evaluate air quality modeling applications, Atmos. Environ., 59, 476–482, https://doi.org/10.1016/j.atmosenv.2012.05.043, 2012b.
Thunis, P., Pernigotti, D., and Gerboles, M.: Model quality objectives based on measurement uncertainty. Part I: Ozone, Atmos. Environ., 79, 861–868, https://doi.org/10.1016/j.atmosenv.2013.05.018, 2013.
Zhang, B., Rong, Y., Yong, R., Qin, D., Li, M., Zou, G., and Pan, J.: Deep learning for air pollutant concentration prediction: A review, Atmos. Environ., 290, 119347, https://doi.org/10.1016/j.atmosenv.2022.119347, 2022.
Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C., and Baklanov, A.: Real-time air quality forecasting, part I: History, techniques, and current status, Atmos. Environ., 60, 632–655, https://doi.org/10.1016/j.atmosenv.2012.06.031, 2012.
Short summary
Air quality forecasting models play a key role in fostering short-term measures aimed at reducing human exposure to air pollution. Together with this role comes the need for a thorough assessment of the model performances to build confidence in models’ capabilities, in particular when model applications support policymaking. In this paper, we propose an evaluation methodology and test it on several domains across Europe, highlighting its strengths and room for improvement.
Air quality forecasting models play a key role in fostering short-term measures aimed at...
