Articles | Volume 7, issue 4
https://doi.org/10.5194/gmd-7-1451-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-7-1451-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Methods for assessment of models
| 
16 Jul 2014
Methods for assessment of models |
| 16 Jul 2014
Comparison of the ensemble Kalman filter and 4D-Var assimilation methods using a stratospheric tracer transport model
S. Skachko
Belgian Institute for Space Aeronomy, BIRA-IASB, Brussels, 1180, Belgium
Q. Errera
Belgian Institute for Space Aeronomy, BIRA-IASB, Brussels, 1180, Belgium
R. Ménard
Air Quality Research Division, Environment Canada, Dorval, Canada
Y. Christophe
Belgian Institute for Space Aeronomy, BIRA-IASB, Brussels, 1180, Belgium
S. Chabrillat
Belgian Institute for Space Aeronomy, BIRA-IASB, Brussels, 1180, Belgium
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The study presents a weakly coupled atmosphere–ocean data assimilation system that uses coupled atmosphere–ocean–ice short-term forecasts as background states for atmospheric and ocean systems that independently compute atmospheric and ocean analyses. This system leads to better agreement between the coupled atmosphere–ocean analyses and coupled forecasts that have been used operationally for the last year.
Quentin Errera, Simon Chabrillat, Yves Christophe, Jonas Debosscher, Daan Hubert, William Lahoz, Michelle L. Santee, Masato Shiotani, Sergey Skachko, Thomas von Clarmann, and Kaley Walker
Atmos. Chem. Phys., 19, 13647–13679, https://doi.org/10.5194/acp-19-13647-2019, https://doi.org/10.5194/acp-19-13647-2019, 2019
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BRAM2 is a 13-year reanalysis of the chemical composition from the upper troposphere to the lower mesosphere based on the assimilation of the Microwave Limb Sounder observations where eight species are assimilated: O3, H2O, N2O, HNO3, HCl, ClO, CH3Cl and CO. BRAM2 agrees generally well with independent observations in the middle stratosphere, the polar vortex and the upper troposphere–lower stratosphere but also shows several issues in the model and in the observations.
Quentin Errera, Simone Ceccherini, Yves Christophe, Simon Chabrillat, Michaela I. Hegglin, Alyn Lambert, Richard Ménard, Piera Raspollini, Sergey Skachko, Michiel van Weele, and Kaley A. Walker
Atmos. Meas. Tech., 9, 5895–5909, https://doi.org/10.5194/amt-9-5895-2016, https://doi.org/10.5194/amt-9-5895-2016, 2016
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When this study started, its goal was to provide a reanalysis of the stratospheric composition of methane and nitrous oxide, two important sources of hydrogen and nitrogen species in the stratosphere that influence the ozone abundance. However, the goal changed when several issues in the assimilated observations were discovered. Finally, this study illustrates how data assimilation methods can be used to add value to the observations as well as to diagnose their limitations.
Sergey Skachko, Richard Ménard, Quentin Errera, Yves Christophe, and Simon Chabrillat
Geosci. Model Dev., 9, 2893–2908, https://doi.org/10.5194/gmd-9-2893-2016, https://doi.org/10.5194/gmd-9-2893-2016, 2016
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In the present work, we performed a comparison of two broadly used data assimilation algorithms, 4D-Var and EnKF, applied to a state-of-the-art atmospheric chemistry transport model. The comparison is carried out using carefully calibrated error statistics. The paper discusses the advantages and disadvantages of each method applied to real-life conditions of a numerical atmospheric chemistry data assimilation.
Annika Vogel and Richard Ménard
Nonlin. Processes Geophys., 30, 375–398, https://doi.org/10.5194/npg-30-375-2023, https://doi.org/10.5194/npg-30-375-2023, 2023
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Accurate estimation of the error statistics required for data assimilation remains an ongoing challenge, as statistical assumptions are required to solve the estimation problem. This work provides a conceptual view of the statistical error estimation problem in light of the increasing number of available datasets. We found that the total number of required assumptions increases with the number of overlapping datasets, but the relative number of error statistics that can be estimated increases.
Eric Sauvageat, Klemens Hocke, Eliane Maillard Barras, Shengyi Hou, Quentin Errera, Alexander Haefele, and Axel Murk
Atmos. Chem. Phys., 23, 7321–7345, https://doi.org/10.5194/acp-23-7321-2023, https://doi.org/10.5194/acp-23-7321-2023, 2023
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Klaus-Peter Heue, Diego Loyola, Fabian Romahn, Walter Zimmer, Simon Chabrillat, Quentin Errera, Jerry Ziemke, and Natalya Kramarova
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Geosci. Model Dev., 15, 6221–6241, https://doi.org/10.5194/gmd-15-6221-2022, https://doi.org/10.5194/gmd-15-6221-2022, 2022
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Atmos. Meas. Tech., 15, 2407–2416, https://doi.org/10.5194/amt-15-2407-2022, https://doi.org/10.5194/amt-15-2407-2022, 2022
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Atmos. Meas. Tech., 14, 4737–4753, https://doi.org/10.5194/amt-14-4737-2021, https://doi.org/10.5194/amt-14-4737-2021, 2021
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ALTIUS is a micro-satellite which will measure the distribution of the ozone layer. Micro-satellites are intended to be cost-effective, but does this make the ALTIUS measurements any less valuable? To answer this, we simulated ALTIUS data and measured how it could constrain a model of the ozone layer; we then compared these results with those obtained from the state-of-the-art NASA Aura MLS satellite ozone measurements. The outcome shows us that the ALTIUS
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Atmos. Chem. Phys., 20, 12609–12631, https://doi.org/10.5194/acp-20-12609-2020, https://doi.org/10.5194/acp-20-12609-2020, 2020
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The climatology of the N2O transport budget in the stratosphere is studied in the transformed Eulerian mean framework across a variety of datasets: a chemistry climate model, a chemistry transport model driven by four reanalyses and a chemical reanalysis. The impact of vertical advection on N2O agrees well in the datasets, but horizontal mixing presents large differences above the Antarctic and in the whole Northern Hemisphere.
Sergey Skachko, Mark Buehner, Stéphane Laroche, Ervig Lapalme, Gregory Smith, François Roy, Dorina Surcel-Colan, Jean-Marc Bélanger, and Louis Garand
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Quentin Errera, Simon Chabrillat, Yves Christophe, Jonas Debosscher, Daan Hubert, William Lahoz, Michelle L. Santee, Masato Shiotani, Sergey Skachko, Thomas von Clarmann, and Kaley Walker
Atmos. Chem. Phys., 19, 13647–13679, https://doi.org/10.5194/acp-19-13647-2019, https://doi.org/10.5194/acp-19-13647-2019, 2019
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Maxime Prignon, Simon Chabrillat, Daniele Minganti, Simon O'Doherty, Christian Servais, Gabriele Stiller, Geoffrey C. Toon, Martin K. Vollmer, and Emmanuel Mahieu
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Vincent Huijnen, Andrea Pozzer, Joaquim Arteta, Guy Brasseur, Idir Bouarar, Simon Chabrillat, Yves Christophe, Thierno Doumbia, Johannes Flemming, Jonathan Guth, Béatrice Josse, Vlassis A. Karydis, Virginie Marécal, and Sophie Pelletier
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Mean age of stratospheric air is computed for the period 1989–2015 with a kinematic transport model which uses surface pressure and wind fields from five reanalyses: ERA-I, MERRA-2, MERRA, CFSR, JRA-55. The spread between the resulting datasets is as large as in climate model intercomparisons; the age trends have large disagreement and depend strongly on the considered period. We highlight the need for similar studies using diabatic transport models which also use temperature and heating rates.
Masatomo Fujiwara, Jonathon S. Wright, Gloria L. Manney, Lesley J. Gray, James Anstey, Thomas Birner, Sean Davis, Edwin P. Gerber, V. Lynn Harvey, Michaela I. Hegglin, Cameron R. Homeyer, John A. Knox, Kirstin Krüger, Alyn Lambert, Craig S. Long, Patrick Martineau, Andrea Molod, Beatriz M. Monge-Sanz, Michelle L. Santee, Susann Tegtmeier, Simon Chabrillat, David G. H. Tan, David R. Jackson, Saroja Polavarapu, Gilbert P. Compo, Rossana Dragani, Wesley Ebisuzaki, Yayoi Harada, Chiaki Kobayashi, Will McCarty, Kazutoshi Onogi, Steven Pawson, Adrian Simmons, Krzysztof Wargan, Jeffrey S. Whitaker, and Cheng-Zhi Zou
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Quentin Errera, Simone Ceccherini, Yves Christophe, Simon Chabrillat, Michaela I. Hegglin, Alyn Lambert, Richard Ménard, Piera Raspollini, Sergey Skachko, Michiel van Weele, and Kaley A. Walker
Atmos. Meas. Tech., 9, 5895–5909, https://doi.org/10.5194/amt-9-5895-2016, https://doi.org/10.5194/amt-9-5895-2016, 2016
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When this study started, its goal was to provide a reanalysis of the stratospheric composition of methane and nitrous oxide, two important sources of hydrogen and nitrogen species in the stratosphere that influence the ozone abundance. However, the goal changed when several issues in the assimilated observations were discovered. Finally, this study illustrates how data assimilation methods can be used to add value to the observations as well as to diagnose their limitations.
Vincent Huijnen, Johannes Flemming, Simon Chabrillat, Quentin Errera, Yves Christophe, Anne-Marlene Blechschmidt, Andreas Richter, and Henk Eskes
Geosci. Model Dev., 9, 3071–3091, https://doi.org/10.5194/gmd-9-3071-2016, https://doi.org/10.5194/gmd-9-3071-2016, 2016
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We present a model description and benchmark evaluation of an extension of the tropospheric chemistry module in the ECMWF Integrated Forecasting System (IFS) with stratospheric chemistry. The stratospheric chemistry originates from the one used in the Belgian Assimilation System for Chemical ObsErvations (BASCOE), and is here combined with the modified CB05 chemistry module for the troposphere as currently used operationally in the Copernicus Atmosphere Monitoring Service (CAMS).
Sergey Skachko, Richard Ménard, Quentin Errera, Yves Christophe, and Simon Chabrillat
Geosci. Model Dev., 9, 2893–2908, https://doi.org/10.5194/gmd-9-2893-2016, https://doi.org/10.5194/gmd-9-2893-2016, 2016
Short summary
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In the present work, we performed a comparison of two broadly used data assimilation algorithms, 4D-Var and EnKF, applied to a state-of-the-art atmospheric chemistry transport model. The comparison is carried out using carefully calibrated error statistics. The paper discusses the advantages and disadvantages of each method applied to real-life conditions of a numerical atmospheric chemistry data assimilation.
H. Eskes, V. Huijnen, A. Arola, A. Benedictow, A.-M. Blechschmidt, E. Botek, O. Boucher, I. Bouarar, S. Chabrillat, E. Cuevas, R. Engelen, H. Flentje, A. Gaudel, J. Griesfeller, L. Jones, J. Kapsomenakis, E. Katragkou, S. Kinne, B. Langerock, M. Razinger, A. Richter, M. Schultz, M. Schulz, N. Sudarchikova, V. Thouret, M. Vrekoussis, A. Wagner, and C. Zerefos
Geosci. Model Dev., 8, 3523–3543, https://doi.org/10.5194/gmd-8-3523-2015, https://doi.org/10.5194/gmd-8-3523-2015, 2015
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The MACC project is preparing the operational atmosphere service of the European Copernicus Programme, and uses data assimilation to combine atmospheric models with available observations. Our paper provides an overview of the aerosol and trace gas validation activity of MACC. Topics are the validation requirements, the measurement data, the assimilation systems, the upgrade procedure, operational aspects and the scoring methods. A summary is provided of recent results, including special events.
A. Inness, A.-M. Blechschmidt, I. Bouarar, S. Chabrillat, M. Crepulja, R. J. Engelen, H. Eskes, J. Flemming, A. Gaudel, F. Hendrick, V. Huijnen, L. Jones, J. Kapsomenakis, E. Katragkou, A. Keppens, B. Langerock, M. de Mazière, D. Melas, M. Parrington, V. H. Peuch, M. Razinger, A. Richter, M. G. Schultz, M. Suttie, V. Thouret, M. Vrekoussis, A. Wagner, and C. Zerefos
Atmos. Chem. Phys., 15, 5275–5303, https://doi.org/10.5194/acp-15-5275-2015, https://doi.org/10.5194/acp-15-5275-2015, 2015
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The paper presents results from data assimilation studies with the new Composition-IFS model developed in the MACC project. This system was used in MACC to produce daily analyses and 5-day forecasts of atmospheric composition and is now run daily in the EU’s Copernicus Atmosphere Monitoring Service. The paper looks at the quality of the CO, O3 and NO2 analysis fields obtained with this system, comparing them against observations, a control run and an older version of the model.
K. Lefever, R. van der A, F. Baier, Y. Christophe, Q. Errera, H. Eskes, J. Flemming, A. Inness, L. Jones, J.-C. Lambert, B. Langerock, M. G. Schultz, O. Stein, A. Wagner, and S. Chabrillat
Atmos. Chem. Phys., 15, 2269–2293, https://doi.org/10.5194/acp-15-2269-2015, https://doi.org/10.5194/acp-15-2269-2015, 2015
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We validate and discuss the analyses of stratospheric ozone delivered in near-real time between 2009 and 2012 by four different data assimilation systems: IFS-MOZART, BASCOE, SACADA and TM3DAM. It is shown that the characteristics of the assimilation systems are much less important than those of the assimilated data sets. A correct representation of the vertical distribution of ozone requires satellite observations which are well resolved vertically and extend into the lowermost stratosphere.
A. Schanz, K. Hocke, N. Kämpfer, S. Chabrillat, A. Inness, M. Palm, J. Notholt, I. Boyd, A. Parrish, and Y. Kasai
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-14-32667-2014, https://doi.org/10.5194/acpd-14-32667-2014, 2014
Revised manuscript not accepted
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The manuscript describes novel findings in the diurnal variation of stratospheric ozone by means of the MACC reanalysis, the ERA-Interim reanalysis and the WACCM model. The diurnal variation in ozone has dynamical and photochemical origins which lead to substantial amplitudes especially in the polar, stratospheric regions. The unprecedented, global view on diurnal ozone variation strengthens the implication to correct diurnally sampled satellite observations used for ozone trend estimates.
A. Robichaud and R. Ménard
Atmos. Chem. Phys., 14, 1769–1800, https://doi.org/10.5194/acp-14-1769-2014, https://doi.org/10.5194/acp-14-1769-2014, 2014
A. Inness, F. Baier, A. Benedetti, I. Bouarar, S. Chabrillat, H. Clark, C. Clerbaux, P. Coheur, R. J. Engelen, Q. Errera, J. Flemming, M. George, C. Granier, J. Hadji-Lazaro, V. Huijnen, D. Hurtmans, L. Jones, J. W. Kaiser, J. Kapsomenakis, K. Lefever, J. Leitão, M. Razinger, A. Richter, M. G. Schultz, A. J. Simmons, M. Suttie, O. Stein, J.-N. Thépaut, V. Thouret, M. Vrekoussis, C. Zerefos, and the MACC team
Atmos. Chem. Phys., 13, 4073–4109, https://doi.org/10.5194/acp-13-4073-2013, https://doi.org/10.5194/acp-13-4073-2013, 2013
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A new version of the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) is developed to represent heterogeneities of concentrations in streets. The street volume is discretized vertically and horizontally to limit the artificial dilution of emissions and concentrations. This new version is applied to street networks in Copenhagen and Paris. The comparisons to observations are improved, with higher concentrations of pollutants emitted by traffic at the bottom of the street.
Junsu Gil, Meehye Lee, Jeonghwan Kim, Gangwoong Lee, Joonyoung Ahn, and Cheol-Hee Kim
Geosci. Model Dev., 16, 5251–5263, https://doi.org/10.5194/gmd-16-5251-2023, https://doi.org/10.5194/gmd-16-5251-2023, 2023
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In this study, the framework for calculating reactive nitrogen species using a deep neural network (RND) was developed. It works through simple Python codes and provides high-accuracy reactive nitrogen oxide data. In the first version (RNDv1.0), the model calculates the nitrous acid (HONO) in urban areas, which has an important role in producing O3 and fine aerosol.
Daniel Yazgi and Tinja Olenius
Geosci. Model Dev., 16, 5237–5249, https://doi.org/10.5194/gmd-16-5237-2023, https://doi.org/10.5194/gmd-16-5237-2023, 2023
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We present flexible tools to implement aerosol formation rate predictions in climate and chemical transport models. New-particle formation is a significant but uncertain factor affecting aerosol numbers and an active field within molecular modeling which provides data for assessing formation rates for different chemical species. We introduce tools to generate and interpolate formation rate lookup tables for user-defined data, thus enabling the easy inclusion and testing of formation schemes.
Vineet Yadav, Subhomoy Ghosh, and Charles E. Miller
Geosci. Model Dev., 16, 5219–5236, https://doi.org/10.5194/gmd-16-5219-2023, https://doi.org/10.5194/gmd-16-5219-2023, 2023
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Measuring the performance of inversions in linear Bayesian problems is crucial in real-life applications. In this work, we provide analytical forms of the local and global sensitivities of the estimated fluxes with respect to various inputs. We provide methods to uniquely map the observational signal to spatiotemporal domains. Utilizing this, we also show techniques to assess correlations between the Jacobians that naturally translate to nonstationary covariance matrix components.
Mingzhao Liu, Lars Hoffmann, Sabine Griessbach, Zhongyin Cai, Yi Heng, and Xue Wu
Geosci. Model Dev., 16, 5197–5217, https://doi.org/10.5194/gmd-16-5197-2023, https://doi.org/10.5194/gmd-16-5197-2023, 2023
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We introduce new and revised chemistry and physics modules in the Massive-Parallel Trajectory Calculations (MPTRAC) Lagrangian transport model aiming to improve the representation of volcanic SO2 transport and depletion. We test these modules in a case study of the Ambae eruption in July 2018 in which the SO2 plume underwent wet removal and convection. The lifetime of SO2 shows highly variable and complex dependencies on the atmospheric conditions at different release heights.
Bernhard M. Enz, Jan P. Engelmann, and Ulrike Lohmann
Geosci. Model Dev., 16, 5093–5112, https://doi.org/10.5194/gmd-16-5093-2023, https://doi.org/10.5194/gmd-16-5093-2023, 2023
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An algorithm to track tropical cyclones in model simulation data has been developed. The algorithm uses many combinations of varying parameter thresholds to detect weaker phases of tropical cyclones while still being resilient to false positives. It is shown that the algorithm performs well and adequately represents the tropical cyclone activity of the underlying simulation data. The impact of false positives on overall tropical cyclone activity is shown to be insignificant.
Sepehr Fathi, Mark Gordon, and Yongsheng Chen
Geosci. Model Dev., 16, 5069–5091, https://doi.org/10.5194/gmd-16-5069-2023, https://doi.org/10.5194/gmd-16-5069-2023, 2023
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We have combined various capabilities within a WRF model to generate simulations of atmospheric pollutant dispersion at 50 m resolution. The study objective was to resolve transport processes at the scale of measurements to assess and optimize aircraft-based emission rate retrievals. Model performance evaluation resulted in agreement within 5 % of observed meteorological and within 1–2 standard deviations of observed wind fields. Mass was conserved in the model within 5 % of input emissions.
Dylan Reynolds, Ethan Gutmann, Bert Kruyt, Michael Haugeneder, Tobias Jonas, Franziska Gerber, Michael Lehning, and Rebecca Mott
Geosci. Model Dev., 16, 5049–5068, https://doi.org/10.5194/gmd-16-5049-2023, https://doi.org/10.5194/gmd-16-5049-2023, 2023
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The challenge of running geophysical models is often compounded by the question of where to obtain appropriate data to give as input to a model. Here we present the HICAR model, a simplified atmospheric model capable of running at spatial resolutions of hectometers for long time series or over large domains. This makes physically consistent atmospheric data available at the spatial and temporal scales needed for some terrestrial modeling applications, for example seasonal snow forecasting.
Li Fang, Jianbing Jin, Arjo Segers, Hong Liao, Ke Li, Bufan Xu, Wei Han, Mijie Pang, and Hai Xiang Lin
Geosci. Model Dev., 16, 4867–4882, https://doi.org/10.5194/gmd-16-4867-2023, https://doi.org/10.5194/gmd-16-4867-2023, 2023
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Machine learning models have gained great popularity in air quality prediction. However, they are only available at air quality monitoring stations. In contrast, chemical transport models (CTM) provide predictions that are continuous in the 3D field. Owing to complex error sources, they are typically biased. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our regional feature selection machine learning prediction (RFSML v1.0) and a CTM prediction.
Manu Goudar, Juliëtte C. S. Anema, Rajesh Kumar, Tobias Borsdorff, and Jochen Landgraf
Geosci. Model Dev., 16, 4835–4852, https://doi.org/10.5194/gmd-16-4835-2023, https://doi.org/10.5194/gmd-16-4835-2023, 2023
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A framework was developed to automatically detect plumes and compute emission estimates with cross-sectional flux method (CFM) for biomass burning events in TROPOMI CO datasets using Visible Infrared Imaging Radiometer Suite active fire data. The emissions were more reliable when changing plume height in downwind direction was used instead of constant injection height. The CFM had uncertainty even when the meteorological conditions were accurate; thus there is a need for better inversion models.
Drew C. Pendergrass, Daniel J. Jacob, Hannah Nesser, Daniel J. Varon, Melissa Sulprizio, Kazuyuki Miyazaki, and Kevin W. Bowman
Geosci. Model Dev., 16, 4793–4810, https://doi.org/10.5194/gmd-16-4793-2023, https://doi.org/10.5194/gmd-16-4793-2023, 2023
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We have built a tool called CHEEREIO that allows scientists to use observations of pollutants or gases in the atmosphere, such as from satellites or surface stations, to update supercomputer models that simulate the Earth. CHEEREIO uses the difference between the model simulations of the atmosphere and real-world observations to come up with a good guess for the actual composition of our atmosphere, the true emissions of various pollutants, and whatever else they may want to study.
Yosuke Yamazaki
Geosci. Model Dev., 16, 4749–4766, https://doi.org/10.5194/gmd-16-4749-2023, https://doi.org/10.5194/gmd-16-4749-2023, 2023
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The Earth's atmosphere can support various types of global-scale waves. Some waves propagate eastward and others westward, and they can have different zonal wavenumbers. The Fourier–wavelet analysis is a useful technique for identifying different components of global-scale waves and their temporal variability. This paper introduces an easy-to-implement method to derive Fourier–wavelet spectra from 2-D space–time data. Application examples are presented using atmospheric models.
Bok H. Baek, Carlie Coats, Siqi Ma, Chi-Tsan Wang, Yunyao Li, Jia Xing, Daniel Tong, Soontae Kim, and Jung-Hun Woo
Geosci. Model Dev., 16, 4659–4676, https://doi.org/10.5194/gmd-16-4659-2023, https://doi.org/10.5194/gmd-16-4659-2023, 2023
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To enable the direct feedback effects of aerosols and local meteorology in an air quality modeling system without any computational bottleneck, we have developed an inline meteorology-induced emissions coupler module within the U.S. Environmental Protection Agency’s Community Multiscale Air Quality modeling system to dynamically model the complex MOtor Vehicle Emission Simulator (MOVES) on-road mobile emissions inline without a separate dedicated emissions processing model like SMOKE.
Christoph Neuhauser, Maicon Hieronymus, Michael Kern, Marc Rautenhaus, Annika Oertel, and Rüdiger Westermann
Geosci. Model Dev., 16, 4617–4638, https://doi.org/10.5194/gmd-16-4617-2023, https://doi.org/10.5194/gmd-16-4617-2023, 2023
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Numerical weather prediction models rely on parameterizations for sub-grid-scale processes, which are a source of uncertainty. We present novel visual analytics solutions to analyze interactively the sensitivities of a selected prognostic variable to multiple model parameters along trajectories regarding similarities in temporal development and spatiotemporal relationships. The proposed workflow is applied to cloud microphysical sensitivities along coherent strongly ascending trajectories.
Yingqi Zheng, Minttu Havu, Huizhi Liu, Xueling Cheng, Yifan Wen, Hei Shing Lee, Joyson Ahongshangbam, and Leena Järvi
Geosci. Model Dev., 16, 4551–4579, https://doi.org/10.5194/gmd-16-4551-2023, https://doi.org/10.5194/gmd-16-4551-2023, 2023
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The performance of the Surface Urban Energy and Water Balance Scheme (SUEWS) is evaluated against the observed surface exchanges (fluxes) of heat and carbon dioxide in a densely built neighborhood in Beijing. The heat flux modeling is noticeably improved by using the observed maximum conductance and by optimizing the vegetation phenology modeling. SUEWS also performs well in simulating carbon dioxide flux.
Simone Dietmüller, Sigrun Matthes, Katrin Dahlmann, Hiroshi Yamashita, Abolfazl Simorgh, Manuel Soler, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Christian Weder, Volker Grewe, Feijia Yin, and Federica Castino
Geosci. Model Dev., 16, 4405–4425, https://doi.org/10.5194/gmd-16-4405-2023, https://doi.org/10.5194/gmd-16-4405-2023, 2023
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Climate-optimized aircraft trajectories avoid atmospheric regions with a large climate impact due to aviation emissions. This requires spatially and temporally resolved information on aviation's climate impact. We propose using algorithmic climate change functions (aCCFs) for CO2 and non-CO2 effects (ozone, methane, water vapor, contrail cirrus). Merged aCCFs combine individual aCCFs by assuming aircraft-specific parameters and climate metrics. Technically this is done with a Python library.
Andreas A. Beckert, Lea Eisenstein, Annika Oertel, Tim Hewson, George C. Craig, and Marc Rautenhaus
Geosci. Model Dev., 16, 4427–4450, https://doi.org/10.5194/gmd-16-4427-2023, https://doi.org/10.5194/gmd-16-4427-2023, 2023
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We investigate the benefit of objective 3-D front detection with modern interactive visual analysis techniques for case studies of extra-tropical cyclones and comparisons of frontal structures between different numerical weather prediction models. The 3-D frontal structures show agreement with 2-D fronts from surface analysis charts and augment them in the vertical dimension. We see great potential for more complex studies of atmospheric dynamics and for operational weather forecasting.
Zhenxin Liu, Yuanhao Chen, Yuhang Wang, Cheng Liu, Shuhua Liu, and Hong Liao
Geosci. Model Dev., 16, 4385–4403, https://doi.org/10.5194/gmd-16-4385-2023, https://doi.org/10.5194/gmd-16-4385-2023, 2023
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The heterogeneous layout of urban buildings leads to the complex wind field in and over the urban canopy. Large discrepancies between the observations and the current simulations result from misunderstanding the character of the wind field. The Inhomogeneous Wind Scheme in Urban Street (IWSUS) was developed to simulate the heterogeneity of the wind speed in a typical street and then improve the simulated energy budget in the lower atmospheric layer over the urban canopy.
Kai Cao, Qizhong Wu, Lingling Wang, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongqing Li, and Lanning Wang
Geosci. Model Dev., 16, 4367–4383, https://doi.org/10.5194/gmd-16-4367-2023, https://doi.org/10.5194/gmd-16-4367-2023, 2023
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Offline performance experiment results show that the GPU-HADVPPM on a V100 GPU can achieve up to 1113.6 × speedups to its original version on an E5-2682 v4 CPU. A series of optimization measures are taken, and the CAMx-CUDA model improves the computing efficiency by 128.4 × on a single V100 GPU card. A parallel architecture with an MPI plus CUDA hybrid paradigm is presented, and it can achieve up to 4.5 × speedup when launching eight CPU cores and eight GPU cards.
Laurent Menut
Geosci. Model Dev., 16, 4265–4281, https://doi.org/10.5194/gmd-16-4265-2023, https://doi.org/10.5194/gmd-16-4265-2023, 2023
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This study analyzes forecasts that were made in 2021 to help trigger measurements during the CADDIWA experiment. The WRF and CHIMERE models were run each day, and the first goal is to quantify the variability of the forecast as a function of forecast leads and forecast location. The possibility of using the different leads as an ensemble is also tested. For some locations, the correlation scores are better with this approach. This could be tested on operational forecast chains in the future.
Emily de Jong, John Ben Mackay, Oleksii Bulenok, Anna Jaruga, and Sylwester Arabas
Geosci. Model Dev., 16, 4193–4211, https://doi.org/10.5194/gmd-16-4193-2023, https://doi.org/10.5194/gmd-16-4193-2023, 2023
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In clouds, collisional breakup occurs when two colliding droplets splinter into new, smaller fragments. Particle-based modeling approaches often do not represent breakup because of the computational demands of creating new droplets. We present a particle-based breakup method that preserves the computational efficiency of these methods. In a series of simple demonstrations, we show that this representation alters cloud processes in reasonable and expected ways.
Caiyi Jin, Qiangqiang Yuan, Tongwen Li, Yuan Wang, and Liangpei Zhang
Geosci. Model Dev., 16, 4137–4154, https://doi.org/10.5194/gmd-16-4137-2023, https://doi.org/10.5194/gmd-16-4137-2023, 2023
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The semi-empirical physical approach derives PM2.5 with strong physical significance. However, due to the complex optical characteristic, the physical parameters are difficult to express accurately. Thus, combining the atmospheric physical mechanism and machine learning, we propose an optimized model. It creatively embeds the random forest model into the physical PM2.5 remote sensing approach to simulate a physical parameter. Our method shows great optimized performance in the validations.
Cyril Caram, Sophie Szopa, Anne Cozic, Slimane Bekki, Carlos A. Cuevas, and Alfonso Saiz-Lopez
Geosci. Model Dev., 16, 4041–4062, https://doi.org/10.5194/gmd-16-4041-2023, https://doi.org/10.5194/gmd-16-4041-2023, 2023
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We studied the role of halogenated compounds (containing chlorine, bromine and iodine), emitted by natural processes (mainly above the oceans), in the chemistry of the lower layers of the atmosphere. We introduced this relatively new chemistry in a three-dimensional climate–chemistry model and looked at how this chemistry will disrupt the ozone. We showed that the concentration of ozone decreases by 22 % worldwide and that of the atmospheric detergent, OH, by 8 %.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Marc Bocquet, Jinghui Lian, Grégoire Broquet, Gerrit Kuhlmann, Alexandre Danjou, and Thomas Lauvaux
Geosci. Model Dev., 16, 3997–4016, https://doi.org/10.5194/gmd-16-3997-2023, https://doi.org/10.5194/gmd-16-3997-2023, 2023
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Monitoring of CO2 emissions is key to the development of reduction policies. Local emissions, from cities or power plants, may be estimated from CO2 plumes detected in satellite images. CO2 plumes generally have a weak signal and are partially concealed by highly variable background concentrations and instrument errors, which hampers their detection. To address this problem, we propose and apply deep learning methods to detect the contour of a plume in simulated CO2 satellite images.
Min-Seop Ahn, Paul A. Ullrich, Peter J. Gleckler, Jiwoo Lee, Ana C. Ordonez, and Angeline G. Pendergrass
Geosci. Model Dev., 16, 3927–3951, https://doi.org/10.5194/gmd-16-3927-2023, https://doi.org/10.5194/gmd-16-3927-2023, 2023
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We introduce a framework for regional-scale evaluation of simulated precipitation distributions with 62 climate reference regions and 10 metrics and apply it to evaluate CMIP5 and CMIP6 models against multiple satellite-based precipitation products. The common model biases identified in this study are mainly associated with the overestimated light precipitation and underestimated heavy precipitation. These biases persist from earlier-generation models and have been slightly improved in CMIP6.
Christine Wiedinmyer, Yosuke Kimura, Elena C. McDonald-Buller, Louisa K. Emmons, Rebecca R. Buchholz, Wenfu Tang, Keenan Seto, Maxwell B. Joseph, Kelley C. Barsanti, Annmarie G. Carlton, and Robert Yokelson
Geosci. Model Dev., 16, 3873–3891, https://doi.org/10.5194/gmd-16-3873-2023, https://doi.org/10.5194/gmd-16-3873-2023, 2023
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The Fire INventory from NCAR (FINN) provides daily global estimates of emissions from open fires based on satellite detections of hot spots. This version has been updated to apply MODIS and VIIRS satellite fire detection and better represents both large and small fires. FINNv2.5 generates more emissions than FINNv1 and is in general agreement with other fire emissions inventories. The new estimates are consistent with satellite observations, but uncertainties remain regionally and by pollutant.
Lichao Yang, Wansuo Duan, and Zifa Wang
Geosci. Model Dev., 16, 3827–3848, https://doi.org/10.5194/gmd-16-3827-2023, https://doi.org/10.5194/gmd-16-3827-2023, 2023
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An approach is proposed to refine a ground meteorological observation network to improve the PM2.5 forecasts in the Beijing–Tianjin–Hebei region. A cost-effective observation network is obtained and makes the relevant PM2.5 forecasts assimilate fewer observations but achieve the forecasting skill comparable to or higher than that obtained by assimilating all ground station observations, suggesting that many of the current ground stations can be greatly scattered to avoid much unnecessary work.
Abhishekh Kumar Srivastava, Paul Aaron Ullrich, Deeksha Rastogi, Pouya Vahmani, Andrew Jones, and Richard Grotjahn
Geosci. Model Dev., 16, 3699–3722, https://doi.org/10.5194/gmd-16-3699-2023, https://doi.org/10.5194/gmd-16-3699-2023, 2023
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Stakeholders need high-resolution regional climate data for applications such as assessing water availability and mountain snowpack. This study examines 3 h and 24 h historical precipitation over the contiguous United States in the 12 km WRF version 4.2.1-based dynamical downscaling of the ERA5 reanalysis. WRF improves precipitation characteristics such as the annual cycle and distribution of the precipitation maxima, but it also displays regionally and seasonally varying precipitation biases.
Haixia Xiao, Yaqiang Wang, Yu Zheng, Yuanyuan Zheng, Xiaoran Zhuang, Hongyan Wang, and Mei Gao
Geosci. Model Dev., 16, 3611–3628, https://doi.org/10.5194/gmd-16-3611-2023, https://doi.org/10.5194/gmd-16-3611-2023, 2023
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Due to the small-scale and nonstationary nature of convective wind gusts (CGs), reliable CG nowcasting has remained unattainable. Here, we developed a deep learning model — namely CGsNet — for 0—2 h of quantitative CG nowcasting, first achieving minute—kilometer-level forecasts. Based on the CGsNet model, the average surface wind speed (ASWS) and peak wind gust speed (PWGS) predictions are obtained. Experiments indicate that CGsNet exhibits higher accuracy than the traditional method.
Maria Krutova, Mostafa Bakhoday-Paskyabi, Joachim Reuder, and Finn Gunnar Nielsen
Geosci. Model Dev., 16, 3553–3564, https://doi.org/10.5194/gmd-16-3553-2023, https://doi.org/10.5194/gmd-16-3553-2023, 2023
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Local refinement of the grid is a powerful method allowing us to reduce the computational time while preserving the accuracy in the area of interest. Depending on the implementation, the local refinement may introduce unwanted numerical effects into the results. We study the wind speed common to the wind turbine operational speeds and confirm strong alteration of the result when the heat fluxes are present, except for the specific refinement scheme used.
Sylvia Sullivan, Behrooz Keshtgar, Nicole Albern, Elzina Bala, Christoph Braun, Anubhav Choudhary, Johannes Hörner, Hilke Lentink, Georgios Papavasileiou, and Aiko Voigt
Geosci. Model Dev., 16, 3535–3551, https://doi.org/10.5194/gmd-16-3535-2023, https://doi.org/10.5194/gmd-16-3535-2023, 2023
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Clouds absorb and re-emit infrared radiation from Earth's surface and absorb and reflect incoming solar radiation. As a result, they change atmospheric temperature gradients that drive large-scale circulation. To better simulate this circulation, we study how the radiative heating and cooling from clouds depends on model settings like grid spacing; whether we describe convection approximately or exactly; and the level of detail used to describe small-scale processes, or microphysics, in clouds.
Denis E. Sergeev, Nathan J. Mayne, Thomas Bendall, Ian A. Boutle, Alex Brown, Iva Kavcic, James Kent, Krisztian Kohary, James Manners, Thomas Melvin, Enrico Olivier, Lokesh K. Ragta, Ben J. Shipway, Jon Wakelin, Nigel Wood, and Mohamed Zerroukat
EGUsphere, https://doi.org/10.5194/egusphere-2023-647, https://doi.org/10.5194/egusphere-2023-647, 2023
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3D climate models are one of the best tools we have to study planetary atmospheres. Here, we apply LFRic-Atmosphere, a new model developed by the Met Office, to seven different scenarios for terrestrial planetary climates, including four for the exoplanet TRAPPIST-1e, a primary target for future observations. LFRic-Atmosphere reproduces these scenarios within the spread of the existing models across a range of key climatic variables, justifying its use in future exoplanet studies.
James Weber, James A. King, Katerina Sindelarova, and Maria Val Martin
Geosci. Model Dev., 16, 3083–3101, https://doi.org/10.5194/gmd-16-3083-2023, https://doi.org/10.5194/gmd-16-3083-2023, 2023
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The emissions of volatile organic compounds from vegetation (BVOCs) influence atmospheric composition and contribute to certain gases and aerosols (tiny airborne particles) which play a role in climate change. BVOC emissions are likely to change in the future due to changes in climate and land use. Therefore, accurate simulation of BVOC emission is important, and this study describes an update to the simulation of BVOC emissions in the United Kingdom Earth System Model (UKESM).
Koichi Sakaguchi, L. Ruby Leung, Colin M. Zarzycki, Jihyeon Jang, Seth McGinnis, Bryce E. Harrop, William C. Skamarock, Andrew Gettelman, Chun Zhao, William J. Gutowski, Stephen Leak, and Linda Mearns
Geosci. Model Dev., 16, 3029–3081, https://doi.org/10.5194/gmd-16-3029-2023, https://doi.org/10.5194/gmd-16-3029-2023, 2023
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We document details of the regional climate downscaling dataset produced by a global variable-resolution model. The experiment is unique in that it follows a standard protocol designed for coordinated experiments of regional models. We found negligible influence of post-processing on statistical analysis, importance of simulation quality outside of the target region, and computational challenges that our model code faced due to rapidly changing super computer systems.
Xueying Liu, Yuxuan Wang, Shailaja Wasti, Wei Li, Ehsan Soleimanian, James Flynn, Travis Griggs, Sergio Alvarez, John T. Sullivan, Maurice Roots, Laurence Twigg, Guillaume Gronoff, Timothy Berkoff, Paul Walter, Mark Estes, Johnathan W. Hair, Taylor Shingler, Amy Jo Scarino, Marta Fenn, and Laura Judd
EGUsphere, https://doi.org/10.5194/egusphere-2023-892, https://doi.org/10.5194/egusphere-2023-892, 2023
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With a comprehensive suite of ground-based and airborne remote sensing measurements during the 2021 Tracking Aerosol Convection Experiment Air Quality (TRACER-AQ) campaign in Houston, this study evaluates the simulation of the planetary boundary layer (PBL) height and the ozone vertical profile by a high-resolution (1.33 km) 3-D photochemical model Weather Research and Forecasting-driven GEOS-Chem (WRF-GC).
Xiaohan Li, Yi Zhang, Xindong Peng, Baiquan Zhou, Jian Li, and Yiming Wang
Geosci. Model Dev., 16, 2975–2993, https://doi.org/10.5194/gmd-16-2975-2023, https://doi.org/10.5194/gmd-16-2975-2023, 2023
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The weather and climate physics suites used in GRIST-A22.7.28 are compared using single-column modeling. The source of their discrepancies in terms of modeling cloud and precipitation is explored. Convective parameterization is found to be a key factor responsible for the differences. The two suites also have intrinsic differences in the interaction between microphysics and other processes, resulting in different cloud features and time step sensitivities.
Owen Kenneth Hughes and Christiane Jablonowski
EGUsphere, https://doi.org/10.5194/egusphere-2023-376, https://doi.org/10.5194/egusphere-2023-376, 2023
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Atmospheric models benefit from idealized tests that assess their accuracy in a simpler simulation. A new test with artificial mountains is developed for models on a spherical earth. The mountains trigger the development of both planetary-scale and small-scale waves. These can be analyzed in dry or moist environments with a simple rainfall mechanism. Four atmospheric models are intercompared. This sheds light on the pros and cons of the model designs and the impact of mountains on the flow.
Shaohui Zhou, Yuchao Gao, Zexia Duan, Xingya Xi, and Yubin Li
EGUsphere, https://doi.org/10.5194/egusphere-2023-945, https://doi.org/10.5194/egusphere-2023-945, 2023
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The proposed wind speed correction model (VMD-PCA-RF) demonstrates the highest prediction accuracy and stability in the five southern provinces in nearly a year and at different heights. VMD-PCA-RF evaluation indexes for 10 months remain relatively stable: accuracy rate FA is above 85 %. In future research, the proposed VMD-PCA-RF algorithm can be extrapolated to the 3 km grid points of the five southern provinces to generate a 3 km grid-corrected wind speed product.
Virginie Marécal, Ronan Voisin-Plessis, Tjarda Jane Roberts, Alessandro Aiuppa, Herizo Narivelo, Paul David Hamer, Béatrice Josse, Jonathan Guth, Luke Surl, and Lisa Grellier
Geosci. Model Dev., 16, 2873–2898, https://doi.org/10.5194/gmd-16-2873-2023, https://doi.org/10.5194/gmd-16-2873-2023, 2023
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We implemented a halogen volcanic chemistry scheme in a one-dimensional modelling framework preparing for further use in a three-dimensional global chemistry-transport model. The results of the simulations for an eruption of Mt Etna in 2008, including various sensitivity tests, show a good consistency with previous modelling studies.
Zhe Feng, Joseph Hardin, Hannah C. Barnes, Jianfeng Li, L. Ruby Leung, Adam Varble, and Zhixiao Zhang
Geosci. Model Dev., 16, 2753–2776, https://doi.org/10.5194/gmd-16-2753-2023, https://doi.org/10.5194/gmd-16-2753-2023, 2023
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PyFLEXTRKR is a flexible atmospheric feature tracking framework with specific capabilities to track convective clouds from a variety of observations and model simulations. The package has a collection of multi-object identification algorithms and has been optimized for large datasets. This paper describes the algorithms and demonstrates applications for tracking deep convective cells and mesoscale convective systems from observations and model simulations at a wide range of scales.
Yan Ji, Bing Gong, Michael Langguth, Amirpasha Mozaffari, and Xiefei Zhi
Geosci. Model Dev., 16, 2737–2752, https://doi.org/10.5194/gmd-16-2737-2023, https://doi.org/10.5194/gmd-16-2737-2023, 2023
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Formulating short-term precipitation forecasting as a video prediction task, a novel deep learning architecture (convolutional long short-term memory generative adversarial network, CLGAN) is proposed. A benchmark dataset is built on minute-level precipitation measurements. Results show that with the GAN component the model generates predictions sharing statistical properties with observations, resulting in it outperforming the baseline in dichotomous and spatial scores for heavy precipitation.
Aleksander Lacima, Hervé Petetin, Albert Soret, Dene Bowdalo, Oriol Jorba, Zhaoyue Chen, Raúl F. Méndez Turrubiates, Hicham Achebak, Joan Ballester, and Carlos Pérez García-Pando
Geosci. Model Dev., 16, 2689–2718, https://doi.org/10.5194/gmd-16-2689-2023, https://doi.org/10.5194/gmd-16-2689-2023, 2023
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Understanding how air pollution varies across space and time is of key importance for the safeguarding of human health. This work arose in the context of the project EARLY-ADAPT, for which the Barcelona Supercomputing Center developed an air pollution database covering all of Europe. Through different statistical methods, we compared two global pollution models against measurements from ground stations and found significant discrepancies between the observed and the modeled surface pollution.
Dien Wu, Joshua L. Laughner, Junjie Liu, Paul I. Palmer, John C. Lin, and Paul O. Wennberg
EGUsphere, https://doi.org/10.5194/egusphere-2023-876, https://doi.org/10.5194/egusphere-2023-876, 2023
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To balance computational expenses and chemical complexity in extracting emission signals from tropospheric NO2 columns, we propose a simplified non-linear Lagrangian chemistry transport model and evaluate modeled results against TROPOMI v2 over multiple power plants and cities. Using this model, we then discuss how NOx chemistry affects the relationship between NOx and CO2 emissions and how studying NO2 columns helps quantify modeled biases in wind direction and prior emissions.
William Rudisill, Alejandro Flores, and Rosemary Carroll
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-69, https://doi.org/10.5194/gmd-2023-69, 2023
Revised manuscript accepted for GMD
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It's important to know how well atmospheric models do in the mountains, but there aren't very many weather stations. We evaluate rain and snow from a model from 1987–2020 in the Upper Colorado river basin against the data that's available. The model works pretty well but, there are still some uncertainties in remote locations. We then use snow maps collected by aircraft, streamflow measurements, and some advanced statistics to help identify how well the model works in ways we couldn't before.
Andrew Geiss, Po-Lun Ma, Balwinder Singh, and Joseph C. Hardin
Geosci. Model Dev., 16, 2355–2370, https://doi.org/10.5194/gmd-16-2355-2023, https://doi.org/10.5194/gmd-16-2355-2023, 2023
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Atmospheric aerosols play a critical role in Earth's climate, but it is too computationally expensive to directly model their interaction with radiation in climate simulations. This work develops a new neural-network-based parameterization of aerosol optical properties for use in the Energy Exascale Earth System Model that is much more accurate than the current one; it also introduces a unique model optimization method that involves randomly generating neural network architectures.
Joey C. Y. Lam, Amos P. K. Tai, Jason A. Ducker, and Christopher D. Holmes
Geosci. Model Dev., 16, 2323–2342, https://doi.org/10.5194/gmd-16-2323-2023, https://doi.org/10.5194/gmd-16-2323-2023, 2023
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We developed a new component within an atmospheric chemistry model to better simulate plant ecophysiological processes relevant for ozone air quality. We showed that it reduces simulated biases in plant uptake of ozone in prior models. The new model enables us to explore how future climatic changes affect air quality via affecting plants, examine ozone–vegetation interactions and feedbacks, and evaluate the impacts of changing atmospheric chemistry and climate on vegetation productivity.
Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce Daube, Jeff Peischl, Chelsea Thompson, and Pieternel Levelt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-50, https://doi.org/10.5194/gmd-2023-50, 2023
Revised manuscript accepted for GMD
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The new MUSICAv0 model enables the study of atmospheric chemistry across all relevant scales. We develop a MUSICAv0 grid for Africa. We evaluate MUSICAv0 with observations, and compare it with a previously used model – WRF-Chem. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Based on model-satellite discrepancies, we find that future field campaigns in an East African region (30° E – 45° E, 5° S – 5° N) could substantially improve the predictive skill of air quality models.
Qian Shu, Sergey L. Napelenok, William T. Hutzell, Kirk R. Baker, Barron H. Henderson, Benjamin N. Murphy, and Christian Hogrefe
Geosci. Model Dev., 16, 2303–2322, https://doi.org/10.5194/gmd-16-2303-2023, https://doi.org/10.5194/gmd-16-2303-2023, 2023
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Source attribution methods are generally used to determine culpability of precursor emission sources to ambient pollutant concentrations. However, source attribution of secondarily formed pollutants such as ozone and its precursors cannot be explicitly measured, making evaluation of source apportionment methods challenging. In this study, multiple apportionment approach comparisons show common features but still reveal wide variations in predicted sector contribution and species dependency.
Simone Tilmes, Michael J. Mills, Yunqian Zhu, Charles G. Bardeen, Francis Vitt, Pengfei Yu, David Fillmore, Xiaohong Liu, Brian Toon, and Terry Deshler
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-79, https://doi.org/10.5194/gmd-2023-79, 2023
Revised manuscript accepted for GMD
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We implemented an alternative aerosol scheme in the high and low-top model versions of the Community Earth System Model Version 2 (CESM2) with a more detailed description of tropospheric and stratospheric aerosol size distributions than the existing aerosol model. The development enables the comparison of different aerosol schemes with different complexity in the same model framework and identifies improvements in comparison to a range of observations in both the troposphere and stratosphere.
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