Articles | Volume 14, issue 3
https://doi.org/10.5194/gmd-14-1553-2021
© Author(s) 2021. 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-14-1553-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Mar 2021
Model description paper |
| 17 Mar 2021
| 17 Mar 2021
MLAir (v1.0) – a tool to enable fast and flexible machine learning on air data time series
Lukas Hubert Leufen
CORRESPONDING AUTHOR
Jülich Supercomputing Centre, Research Centre Jülich, Jülich, Germany
Institute of Geosciences, Rhenish Friedrich Wilhelm University of Bonn, Bonn, Germany
Felix Kleinert
Jülich Supercomputing Centre, Research Centre Jülich, Jülich, Germany
Institute of Geosciences, Rhenish Friedrich Wilhelm University of Bonn, Bonn, Germany
Martin G. Schultz
Jülich Supercomputing Centre, Research Centre Jülich, Jülich, Germany
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Earth system models for producing climate projections under given forcings include additional processes and feedbacks that traditional physical climate models do not consider. We present an overview of climate feedbacks for key Earth system components and discuss the evaluation of these feedbacks. The target group for this article includes generalists with a background in natural sciences and an interest in climate change as well as experts working in interdisciplinary climate research.
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Ina Tegen, David Neubauer, Sylvaine Ferrachat, Colombe Siegenthaler-Le Drian, Isabelle Bey, Nick Schutgens, Philip Stier, Duncan Watson-Parris, Tanja Stanelle, Hauke Schmidt, Sebastian Rast, Harri Kokkola, Martin Schultz, Sabine Schroeder, Nikos Daskalakis, Stefan Barthel, Bernd Heinold, and Ulrike Lohmann
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Rolf Sander, Andreas Baumgaertner, David Cabrera-Perez, Franziska Frank, Sergey Gromov, Jens-Uwe Grooß, Hartwig Harder, Vincent Huijnen, Patrick Jöckel, Vlassis A. Karydis, Kyle E. Niemeyer, Andrea Pozzer, Hella Riede, Martin G. Schultz, Domenico Taraborrelli, and Sebastian Tauer
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We present the atmospheric chemistry box model CAABA/MECCA which
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Chemical Mechanism (MCM) and other chemical mechanisms, updated
isotope tagging, improved and new photolysis modules, and the new
feature of coexisting multiple chemistry mechanisms.
CAABA/MECCA is a community model published under the GPL.
Kai-Lan Chang, Owen R. Cooper, J. Jason West, Marc L. Serre, Martin G. Schultz, Meiyun Lin, Virginie Marécal, Béatrice Josse, Makoto Deushi, Kengo Sudo, Junhua Liu, and Christoph A. Keller
Geosci. Model Dev., 12, 955–978, https://doi.org/10.5194/gmd-12-955-2019, https://doi.org/10.5194/gmd-12-955-2019, 2019
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Arlene M. Fiore, Emily V. Fischer, George P. Milly, Shubha Pandey Deolal, Oliver Wild, Daniel A. Jaffe, Johannes Staehelin, Olivia E. Clifton, Dan Bergmann, William Collins, Frank Dentener, Ruth M. Doherty, Bryan N. Duncan, Bernd Fischer, Stefan Gilge, Peter G. Hess, Larry W. Horowitz, Alexandru Lupu, Ian A. MacKenzie, Rokjin Park, Ludwig Ries, Michael G. Sanderson, Martin G. Schultz, Drew T. Shindell, Martin Steinbacher, David S. Stevenson, Sophie Szopa, Christoph Zellweger, and Guang Zeng
Atmos. Chem. Phys., 18, 15345–15361, https://doi.org/10.5194/acp-18-15345-2018, https://doi.org/10.5194/acp-18-15345-2018, 2018
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Harri Kokkola, Thomas Kühn, Anton Laakso, Tommi Bergman, Kari E. J. Lehtinen, Tero Mielonen, Antti Arola, Scarlet Stadtler, Hannele Korhonen, Sylvaine Ferrachat, Ulrike Lohmann, David Neubauer, Ina Tegen, Colombe Siegenthaler-Le Drian, Martin G. Schultz, Isabelle Bey, Philip Stier, Nikos Daskalakis, Colette L. Heald, and Sami Romakkaniemi
Geosci. Model Dev., 11, 3833–3863, https://doi.org/10.5194/gmd-11-3833-2018, https://doi.org/10.5194/gmd-11-3833-2018, 2018
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In this paper we present a global aerosol–chemistry–climate model with the focus on its representation for atmospheric aerosol particles. In the model, aerosols are simulated using the aerosol module SALSA2.0, which in this paper is compared to satellite, ground, and aircraft-based observations of the properties of atmospheric aerosol. Based on this study, the model simulated aerosol properties compare well with the observations.
Scarlet Stadtler, Thomas Kühn, Sabine Schröder, Domenico Taraborrelli, Martin G. Schultz, and Harri Kokkola
Geosci. Model Dev., 11, 3235–3260, https://doi.org/10.5194/gmd-11-3235-2018, https://doi.org/10.5194/gmd-11-3235-2018, 2018
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Atmospheric aerosols interact with our climate system and have adverse health effects. Nevertheless, these particles are a source of uncertainty in climate projections and the formation process of secondary aerosols formed by organic gas-phase precursors is particularly not fully understood. In order to gain a deeper understanding of secondary organic aerosol formation, this model system explicitly represents gas-phase and aerosol formation processes. Finally, this allows for process discussion.
Martin G. Schultz, Scarlet Stadtler, Sabine Schröder, Domenico Taraborrelli, Bruno Franco, Jonathan Krefting, Alexandra Henrot, Sylvaine Ferrachat, Ulrike Lohmann, David Neubauer, Colombe Siegenthaler-Le Drian, Sebastian Wahl, Harri Kokkola, Thomas Kühn, Sebastian Rast, Hauke Schmidt, Philip Stier, Doug Kinnison, Geoffrey S. Tyndall, John J. Orlando, and Catherine Wespes
Geosci. Model Dev., 11, 1695–1723, https://doi.org/10.5194/gmd-11-1695-2018, https://doi.org/10.5194/gmd-11-1695-2018, 2018
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The chemistry–climate model ECHAM-HAMMOZ contains a detailed representation of tropospheric and stratospheric reactive chemistry and state-of-the-art parameterizations of aerosols. It thus allows for detailed investigations of chemical processes in the climate system. Evaluation of the model with various observational data yields good results, but the model has a tendency to produce too much OH in the tropics. This highlights the important interplay between atmospheric chemistry and dynamics.
Scarlet Stadtler, David Simpson, Sabine Schröder, Domenico Taraborrelli, Andreas Bott, and Martin Schultz
Atmos. Chem. Phys., 18, 3147–3171, https://doi.org/10.5194/acp-18-3147-2018, https://doi.org/10.5194/acp-18-3147-2018, 2018
Florian Berkes, Patrick Neis, Martin G. Schultz, Ulrich Bundke, Susanne Rohs, Herman G. J. Smit, Andreas Wahner, Paul Konopka, Damien Boulanger, Philippe Nédélec, Valerie Thouret, and Andreas Petzold
Atmos. Chem. Phys., 17, 12495–12508, https://doi.org/10.5194/acp-17-12495-2017, https://doi.org/10.5194/acp-17-12495-2017, 2017
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This study highlights the importance of independent global measurements with high and long-term accuracy to quantify long-term changes, especially in the UTLS region, and to help identify inconsistencies between different data sets of observations and models. Here we investigated temperature trends over different regions within a climate-sensitive area of the atmosphere and demonstrated the value of the IAGOS temperature observations as an anchor point for the evaluation of reanalyses.
Alexandra-Jane Henrot, Tanja Stanelle, Sabine Schröder, Colombe Siegenthaler, Domenico Taraborrelli, and Martin G. Schultz
Geosci. Model Dev., 10, 903–926, https://doi.org/10.5194/gmd-10-903-2017, https://doi.org/10.5194/gmd-10-903-2017, 2017
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This paper describes the basic results of the biogenic emission scheme, based on MEGAN, integrated into the ECHAM6-HAMMOZ chemistry climate model. Sensitivity to vegetation and climate-dependent parameters is also analysed. This version of the model is now suitable for many tropospheric investigations concerning the impact of biogenic volatile organic compound emissions on the ozone budget, secondary aerosol formation, and atmospheric chemistry.
Olga Lyapina, Martin G. Schultz, and Andreas Hense
Atmos. Chem. Phys., 16, 6863–6881, https://doi.org/10.5194/acp-16-6863-2016, https://doi.org/10.5194/acp-16-6863-2016, 2016
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This study applies numerical clustering for the classification of about 1500 ozone data sets in Europe. We show the usefulness of cluster analysis (CA) for the quantitative evaluation of a global model: pre-selection of stations and validation of a global model in a phase-space produce clearer and more interpretable results. CA can be easily updated for new stations, different length of data, and other type of input properties, as well as other type of data (for example, meteorological).
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.
S. Fadnavis, K. Semeniuk, M. G. Schultz, M. Kiefer, A. Mahajan, L. Pozzoli, and S. Sonbawane
Atmos. Chem. Phys., 15, 11477–11499, https://doi.org/10.5194/acp-15-11477-2015, https://doi.org/10.5194/acp-15-11477-2015, 2015
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The model and MIPAS satellite data show that there are three regions which contribute substantial pollution to the south Asian UTLS: the Asian summer monsoon (ASM), the North American monsoon (NAM) and the West African monsoon (WAM). However, penetration due to ASM convection reaches deeper into the UTLS compared to NAM and WAM outflow. Simulations show that westerly winds drive North American and European pollutants eastward where they can become part of the ASM and lifted to LS.
E. Katragkou, P. Zanis, A. Tsikerdekis, J. Kapsomenakis, D. Melas, H. Eskes, J. Flemming, V. Huijnen, A. Inness, M. G. Schultz, O. Stein, and C. S. Zerefos
Geosci. Model Dev., 8, 2299–2314, https://doi.org/10.5194/gmd-8-2299-2015, https://doi.org/10.5194/gmd-8-2299-2015, 2015
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This work is an extended evaluation of near-surface ozone as part of the global reanalysis of atmospheric composition, produced within the European-funded project MACC (Monitoring Atmospheric Composition and Climate). It includes an evaluation over the period 2003-2012 and provides an overall assessment of the modelling system performance with respect to near surface ozone for specific European subregions.
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.
J. Flemming, V. Huijnen, J. Arteta, P. Bechtold, A. Beljaars, A.-M. Blechschmidt, M. Diamantakis, R. J. Engelen, A. Gaudel, A. Inness, L. Jones, B. Josse, E. Katragkou, V. Marecal, V.-H. Peuch, A. Richter, M. G. Schultz, O. Stein, and A. Tsikerdekis
Geosci. Model Dev., 8, 975–1003, https://doi.org/10.5194/gmd-8-975-2015, https://doi.org/10.5194/gmd-8-975-2015, 2015
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We describe modules for atmospheric chemistry, wet and dry deposition and lightning NO production, which have been newly introduced in ECMWF's weather forecasting model. With that model, we want to forecast global air pollution as part of the European Copernicus Atmosphere Monitoring Service. We show that the new model results compare as well or better with in situ and satellite observations of ozone, CO, NO2, SO2 and formaldehyde as the previous model.
R. Paugam, M. Wooster, J. Atherton, S. R. Freitas, M. G. Schultz, and J. W. Kaiser
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-15-9815-2015, https://doi.org/10.5194/acpd-15-9815-2015, 2015
Revised manuscript not accepted
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The transport of Biomass Burning emissions in Chemical Transport Model rely on parametrization of plumes injection height. Using fire observation selected to ensure match-up of fire-atmosphere-plume dynamics; a popular plume rise model was improved and optimized. The resulting model shows response to the effect of atmospheric stability consistent with previous findings and is able to predict higher injection height than any other tested parametrizations, giving a closer match with observation.
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.
S. Fadnavis, M. G. Schultz, K. Semeniuk, A. S. Mahajan, L. Pozzoli, S. Sonbawne, S. D. Ghude, M. Kiefer, and E. Eckert
Atmos. Chem. Phys., 14, 12725–12743, https://doi.org/10.5194/acp-14-12725-2014, https://doi.org/10.5194/acp-14-12725-2014, 2014
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The Asian summer monsoon transports pollutants from local emission sources to the upper troposphere and lower stratosphere (UTLS). The increasing trend of these pollutants may have climatic impact. This study addresses the impact of convectively lifted Indian and Chinese emissions on the ULTS. Sensitivity experiments with emission changes in particular regions show that Chinese emissions have a greater impact on the concentrations of NOY species than Indian emissions.
O. Stein, M. G. Schultz, I. Bouarar, H. Clark, V. Huijnen, A. Gaudel, M. George, and C. Clerbaux
Atmos. Chem. Phys., 14, 9295–9316, https://doi.org/10.5194/acp-14-9295-2014, https://doi.org/10.5194/acp-14-9295-2014, 2014
S. Fadnavis, K. Semeniuk, M. G. Schultz, A. Mahajan, L. Pozzoli, S. Sonbawane, and M. Kiefer
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-14-20159-2014, https://doi.org/10.5194/acpd-14-20159-2014, 2014
Revised manuscript not accepted
A. Basu, M. G. Schultz, S. Schröder, L. Francois, X. Zhang, G. Lohmann, and T. Laepple
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-14-3193-2014, https://doi.org/10.5194/acpd-14-3193-2014, 2014
Revised manuscript not accepted
S. Fadnavis, K. Semeniuk, L. Pozzoli, M. G. Schultz, S. D. Ghude, S. Das, and R. Kakatkar
Atmos. Chem. Phys., 13, 8771–8786, https://doi.org/10.5194/acp-13-8771-2013, https://doi.org/10.5194/acp-13-8771-2013, 2013
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
Related subject area
Atmospheric sciences
Evaluation of dust emission and land surface schemes in predicting a mega Asian dust storm over South Korea using WRF-Chem
Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy
A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks
Inclusion of the ECMWF ecRad radiation scheme (v1.5.0) in the MAR (v3.14), regional evaluation for Belgium, and assessment of surface shortwave spectral fluxes at Uccle
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
NeuralMie (v1.0): an aerosol optics emulator
Simulation performance of planetary boundary layer schemes in WRF v4.3.1 for near-surface wind over the western Sichuan Basin: a single-site assessment
FootNet v1.0: development of a machine learning emulator of atmospheric transport
Updates and evaluation of NOAA's online-coupled air quality model version 7 (AQMv7) within the Unified Forecast System
Quantifying the analysis uncertainty for nowcasting application
Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01
The MESSy DWARF (based on MESSy v2.55.2)
An enhanced emission module for the PALM model system 23.10 with application for PM10 emission from urban domestic heating
Identifying lightning processes in ERA5 soundings with deep learning
Sensitivity of predicted ultrafine particle size distributions in Europe to different nucleation rate parameterizations using PMCAMx-UF v2.2
Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes
Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation
ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool
Coupling the urban canopy model TEB (SURFEXv9.0) with the radiation model SPARTACUS-Urbanv0.6.1 for more realistic urban radiative exchange calculation
Forecasting contrail climate forcing for flight planning and air traffic management applications: the CocipGrid model in pycontrails 0.51.0
Simulation of the heat mitigation potential of unsealing measures in cities by parameterizing grass grid pavers for urban microclimate modelling with ENVI-met (V5)
AI-NAOS: an AI-based nonspherical aerosol optical scheme for the chemical weather model GRAPES_Meso5.1/CUACE
Orbital-Radar v1.0.0: a tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data
The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1)
A new set of indicators for model evaluation complementing to FAIRMODE’s MQO
Modeling of polycyclic aromatic hydrocarbons (PAHs) from global to regional scales: model development (IAP-AACM_PAH v1.0) and investigation of health risks in 2013 and 2018 in China
LIMA (v2.0): A full two-moment cloud microphysical scheme for the mesoscale non-hydrostatic model Meso-NH v5-6
SLUCM+BEM (v1.0): a simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban (v4.3.2)
NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components
Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ
Observational operator for fair model evaluation with ground NO2 measurements
Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting
The third Met Office Unified Model-JULES Regional Atmosphere and Land Configuration, RAL3
An updated parameterization of the unstable atmospheric surface layer in the Weather Research and Forecasting (WRF) modeling system
The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators
An updated aerosol simulation in the Community Earth System Model (v2.1.3): dust and marine aerosol emissions and secondary organic aerosol formation
Exploring ship track spreading rates with a physics-informed Langevin particle parameterization
Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast
Development of the MPAS-CMAQ coupled system (V1.0) for multiscale global air quality modeling
UA-ICON with NWP physics package (version: ua-icon-2.1): mean state and variability of the middle atmosphere
Assessment of object-based indices to identify convective organization
Diagnosis of winter precipitation types using Spectral Bin Model (SBM): Comparison of five methods using ICE-POP 2018 field experiment data
The Global Forest Fire Emissions Prediction System version 1.0
Impact of Multiple Radar Wind Profilers Data Assimilation on Convective Scale Short-Term Rainfall Forecasts: OSSE Studies over the Beijing-Tianjin-Hebei region
NEIVAv1.0: Next-generation Emissions InVentory expansion of Akagi et al. (2011) version 1.0
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
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This study evaluates the Weather Research and Forecasting Model (WRF) coupled with Chemistry (WRF-Chem) to predict a mega Asian dust storm (ADS) over South Korea on 28–29 March 2021. We assessed combinations of five dust emission and four land surface schemes by analyzing meteorological and air quality variables. The best scheme combination reduced the root mean square error (RMSE) for particulate matter 10 (PM10) by up to 29.6 %, demonstrating the highest performance.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025, https://doi.org/10.5194/gmd-18-2231-2025, 2025
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The effectiveness of this assimilation system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. This study advances our understanding of the selection of basic parameters in the four-dimensional local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate-matter-polluted environment.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
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To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
anomalous event.
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, https://doi.org/10.5194/gmd-18-1965-2025, 2025
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The MAR (Modèle Régional Atmosphérique) is a regional climate model used for weather forecasting and studying the climate over various regions. This paper presents an update of MAR thanks to which it can precisely decompose solar radiation, in particular in the UV (ultraviolet) and photosynthesis ranges, both being critical to human health and ecosystems. As a first application of this new capability, this paper presents a method for predicting UV indices with MAR.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
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Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025, https://doi.org/10.5194/gmd-18-1879-2025, 2025
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The High-Resolution Global Forecast Model (HGFM) is an advanced iteration of the operational Global Forecast System (GFS) model. HGFM can produce forecasts at a spatial scale of ~6 km in tropics. It demonstrates improved accuracy in short- to medium-range weather prediction over the Indian region, with notable success in predicting extreme events. Further, the model will be entrusted to operational forecasting agencies after validation and testing.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
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Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025, https://doi.org/10.5194/gmd-18-1809-2025, 2025
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Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025, https://doi.org/10.5194/gmd-18-1769-2025, 2025
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This study evaluates the performance of four planetary boundary layer (PBL) schemes in near-surface wind fields over the Sichuan Basin, China. Using 112 sensitivity experiments with the Weather Research and Forecasting (WRF) model and focusing on 28 wind events, it is found that wind direction was less sensitive to the PBL schemes. The quasi-normal scale elimination (QNSE) scheme captured temporal variations best, while the Mellor–Yamada–Janjić (MYJ) scheme had the least error in wind speed.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
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It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, and Raffaele Montuoro
Geosci. Model Dev., 18, 1635–1660, https://doi.org/10.5194/gmd-18-1635-2025, https://doi.org/10.5194/gmd-18-1635-2025, 2025
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The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
Yanwei Zhu, Aitor Atencia, Markus Dabernig, and Yong Wang
Geosci. Model Dev., 18, 1545–1559, https://doi.org/10.5194/gmd-18-1545-2025, https://doi.org/10.5194/gmd-18-1545-2025, 2025
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Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
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The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
Astrid Kerkweg, Timo Kirfel, Duong H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev., 18, 1265–1286, https://doi.org/10.5194/gmd-18-1265-2025, https://doi.org/10.5194/gmd-18-1265-2025, 2025
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Normally, the Modular Earth Submodel System (MESSy) is linked to complete dynamic models to create chemical climate models. However, the modular concept of MESSy and the newly developed DWARF component presented here make it possible to create simplified models that contain only one or a few process descriptions. This is very useful for technical optimisation, such as porting to GPUs, and can be used to create less complex models, such as a chemical box model.
Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
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An enhanced emission module has been developed for the PALM model system, improving flexibility and scalability of emission source representation across different sectors. A model for parametrized domestic emissions has also been included, for which an idealized model run is conducted for particulate matter (PM10). The results show that, in addition to individual sources and diurnal variations in energy consumption, vertical transport and urban topology play a role in concentration distribution.
Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, and Tobias Hell
Geosci. Model Dev., 18, 1141–1153, https://doi.org/10.5194/gmd-18-1141-2025, https://doi.org/10.5194/gmd-18-1141-2025, 2025
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As lightning is a brief and localized event, it is not explicitly resolved in atmospheric models. Instead, expert-based auxiliary descriptions are used to assess it. This study explores how AI can improve our understanding of lightning without relying on traditional expert knowledge. We reveal that AI independently identified the key factors known to experts as essential for lightning in the Alps region. This shows how knowledge discovery could be sped up in areas with limited expert knowledge.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev., 18, 1103–1118, https://doi.org/10.5194/gmd-18-1103-2025, https://doi.org/10.5194/gmd-18-1103-2025, 2025
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The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements at 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev., 18, 1017–1039, https://doi.org/10.5194/gmd-18-1017-2025, https://doi.org/10.5194/gmd-18-1017-2025, 2025
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In our study, we built upon previous work to investigate the patterns artificial intelligence (AI) learns to detect atmospheric features like tropical cyclones (TCs) and atmospheric rivers (ARs). As primary objective, we adopt a method to explain the AI used and investigate the plausibility of learned patterns. We find that plausible patterns are learned for both TCs and ARs. Hence, the chosen method is very useful for gaining confidence in the AI-based detection of atmospheric features.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
EGUsphere, https://doi.org/10.5194/egusphere-2024-3512, https://doi.org/10.5194/egusphere-2024-3512, 2025
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Non-thermal emission from chemical reactions in the Earth's middle und upper atmosphere strongly contributes to the brightness of the night sky below about 2.3 µm. The new Paranal Airglow Line and Continuum Emission model calculates the emission spectrum and its variability with an unprecedented accuracy. Relying on a large spectroscopic data set from astronomical spectrographs and theoretical molecular/atomic data, it is valuable for airglow research and astronomical observatories.
Felipe Cifuentes, Henk Eskes, Enrico Dammers, Charlotte Bryan, and Folkert Boersma
Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025, https://doi.org/10.5194/gmd-18-621-2025, 2025
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We tested the capability of the flux divergence approach (FDA) to reproduce known NOx emissions using synthetic NO2 satellite column retrievals from high-resolution model simulations. The FDA accurately reproduced NOx emissions when column observations were limited to the boundary layer and when the variability of the NO2 lifetime, the NOx : NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces strong model dependency, reducing the simplicity of the original FDA formulation.
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025, https://doi.org/10.5194/gmd-18-529-2025, 2025
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We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025, https://doi.org/10.5194/gmd-18-483-2025, 2025
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Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025, https://doi.org/10.5194/gmd-18-433-2025, 2025
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This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025, https://doi.org/10.5194/gmd-18-405-2025, 2025
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Radiation is relevant to the atmospheric impact on people and infrastructure in cities as it can influence the urban heat island, building energy consumption, and human thermal comfort. A new urban radiation model, assuming a more realistic form of urban morphology, is coupled to the urban climate model Town Energy Balance (TEB). The new TEB is evaluated with a reference radiation model for a variety of urban morphologies, and an improvement in the simulated radiative observables is found.
Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro
Geosci. Model Dev., 18, 253–286, https://doi.org/10.5194/gmd-18-253-2025, https://doi.org/10.5194/gmd-18-253-2025, 2025
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Contrails forming in some atmospheric conditions may persist and become strongly warming cirrus, while in other conditions may be neutral or cooling. We develop a contrail forecast model to predict contrail climate forcing for any arbitrary point in space and time and explore integration into flight planning and air traffic management. This approach enables contrail interventions to target high-probability high-climate-impact regions and reduce unintended consequences of contrail management.
Nils Eingrüber, Alina Domm, Wolfgang Korres, and Karl Schneider
Geosci. Model Dev., 18, 141–160, https://doi.org/10.5194/gmd-18-141-2025, https://doi.org/10.5194/gmd-18-141-2025, 2025
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Climate change adaptation measures like unsealings can reduce urban heat stress. As grass grid pavers have never been parameterized for microclimate model simulations with ENVI-met, a new parameterization was developed based on field measurements. To analyse the cooling potential, scenario analyses were performed for a densely developed area in Cologne. Statistically significant average cooling effects of up to −11.1 K were found for surface temperature and up to −2.9 K for 1 m air temperature.
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
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The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) was developed to improve the estimation of the aerosol direct radiation effect and was coupled online with a chemical weather model. The AI-NAOS scheme considers black carbon as fractal aggregates and soil dust as super-spheroids, encapsulated with hygroscopic aerosols. Real-case simulations emphasize the necessity of accurately representing nonspherical and inhomogeneous aerosols in chemical weather models.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025, https://doi.org/10.5194/gmd-18-101-2025, 2025
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The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Geosci. Model Dev., 18, 1–18, https://doi.org/10.5194/gmd-18-1-2025, https://doi.org/10.5194/gmd-18-1-2025, 2025
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The Modular and Integrated Data Assimilation System (MIDAS) software is described. The flexible design of MIDAS enables both deterministic and ensemble prediction applications for the atmosphere and several other Earth system components. It is currently used for all main operational weather prediction systems in Canada and also for sea ice and sea surface temperature analysis. The use of MIDAS for multiple Earth system components will facilitate future research on coupled data assimilation.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
EGUsphere, https://doi.org/10.5194/egusphere-2024-3690, https://doi.org/10.5194/egusphere-2024-3690, 2025
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We assess the relevance and utility indicators developed within FAIRMODE by evaluating 9 CAMS models in calculated air pollutant values. For NO2, the results highlight difficulties at traffic stations. For PM2.5 and PM10 the bias and Winter-Summer gradients reveal issues. O3 evaluation shows that e.g. seasonal gradients are useful. Overall, the indicators provide valuable insights into model limitations, yet there is a need to reconsider the strictness of some indicators for certain pollutants.
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024, https://doi.org/10.5194/gmd-17-8885-2024, 2024
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We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can reproduce PAH distribution well. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change in BaP is lower than that in PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although the Action Plan has been implemented.
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024, https://doi.org/10.5194/gmd-17-8773-2024, 2024
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We have developed a complete two-moment version of the LIMA (Liquid Ice Multiple Aerosols) microphysics scheme. We have focused on collection processes, where the hydrometeor number transfer is often estimated in proportion to the mass transfer. The impact of these parameterizations on a convective system and the prospects for more realistic estimates of secondary parameters (reflectivity, hydrometeor size) are shown in a first test on an idealized case.
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
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A new parameterisation for dynamic anthropogenic heat and electricity consumption is described. The model reproduced the temporal variation in and spatial distributions of electricity consumption and temperature well in summer and winter. The partial air conditioning was the most critical factor, significantly affecting the value of anthropogenic heat emission.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
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To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
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Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
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Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
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The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-201, https://doi.org/10.5194/gmd-2024-201, 2024
Revised manuscript accepted for GMD
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RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre and sub-km scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and improved representation of clouds and visibility.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
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Inadequate representation of surface–atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
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Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
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This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
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Marine cloud brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed. However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume-spreading model.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
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Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
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This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Markus Kunze, Christoph Zülicke, Tarique Adnan Siddiqui, Claudia Christine Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-191, https://doi.org/10.5194/gmd-2024-191, 2024
Revised manuscript accepted for GMD
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We present the Icosahedral Nonhydrostatic (ICON) general circulation model with upper atmosphere extension with the physics package for numerical weather prediction (UA-ICON(NWP)). The parameters for the gravity wave parameterizations were optimized, and realistic modelling of the thermal and dynamic state of the mesopause regions was achieved. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
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In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Wonbae Bang, Jacob Carlin, Kwonil Kim, Alexander Ryzhkov, Guosheng Liu, and Gyuwon Lee
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-179, https://doi.org/10.5194/gmd-2024-179, 2024
Revised manuscript accepted for GMD
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Microphysics model-based diagnosis such as the spectral bin model (SBM) recently has been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM have relatively higher accuracy about snow and wetsnow events whereas lower accuracy about rain event. When microphysics scheme in the SBM was optimized for the corresponding region, accuracy about rain events was improved.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
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The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-194, https://doi.org/10.5194/gmd-2024-194, 2024
Revised manuscript accepted for GMD
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A series of observing system simulation experiments are conducted to assess the impact of multiple radar wind profiler (RWP) networks on convective scale numerical weather prediction. Results from three southwest-type heavy rainfall cases in the Beijing-Tianjin-Hebei region suggest the added forecast skill of ridge and foothill networks associated with the Taihang Mountains over the existing RWP network. This research provides valuable guidance for designing optimal RWP networks in the region.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
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The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Cited articles
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado,
G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp,
A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M.,
Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C.,
Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P.,
Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P.,
Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: Large-Scale
Machine Learning on Heterogeneous Systems,
available at: https://www.tensorflow.org/ (last access: 10 March 2021), 2015. a
Bentayeb, M., Wagner, V., Stempfelet, M., Zins, M., Goldberg, M., Pascal, M.,
Larrieu, S., Beaudeau, P., Cassadou, S., Eilstein, D., Filleul, L., Le
Tertre, A., Medina, S., Pascal, L., Prouvost, H., Quénel, P., Zeghnoun, A.,
and Lefranc, A.: Association between long-term exposure to air pollution and
mortality in France: a 25-year follow-up study, Environ. Int.,
85, 5–14, https://doi.org/10.1016/j.envint.2015.08.006, 2015. a
Bishop, C. M.: Pattern recognition and machine learning, Springer, New York, 2006. a
Brunner, D., Savage, N., Jorba, O., Eder, B., Giordano, L., Badia, A.,
Balzarini, A., Baró, R., Bianconi, R., Chemel, C., Curci, G., Forkel, R.,
Jiménez-Guerrero, P., Hirtl, M., Hodzic, A., Honzak, L., Im, U., Knote, C.,
Makar, P., Manders-Groot, A., van Meijgaard, E., Neal, L., Pérez, J. L.,
Pirovano, G., San Jose, R., Schröder, W., Sokhi, R. S., Syrakov, D., Torian,
A., Tuccella, P., Werhahn, J., Wolke, R., Yahya, K., Zabkar, R., Zhang, Y.,
Hogrefe, C., and Galmarini, S.: Comparative analysis of meteorological
performance of coupled chemistry-meteorology models in the context of
AQMEII phase 2, Atmos. Environ., 115, 470–498,
https://doi.org/10.1016/j.atmosenv.2014.12.032, 2015. a
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F.,
Schwenk, H., and Bengio, Y.: Learning Phrase Representations using RNN
Encoder-Decoder for Statistical Machine Translation, arXiv:
1406.1078, available at: http://arxiv.org/abs/1406.1078 (last access: 10 March 2021), 2014. a
Cohen, A. J., Anderson, H. R., Ostro, B., Pandey, K. D., Krzyzanowski, M.,
Künzli, N., Gutschmidt, K., Pope, A., Romieu, I., Samet, J. M., and Smith,
K.: The Global Burden of Disease Due to Outdoor Air Pollution, J.
Toxicol. Env. Hea. A, 68, 1301–1307,
https://doi.org/10.1080/15287390590936166, 2005. a
Elliott, T.: The State of the Octoverse: machine learning, The GitHub Blog,
available at: https://github.blog/2019-01-24-the-state-of-the-octoverse-machine-learning/,
(last access: 23 June 2020), 2019. a
European Parliament and Council of the 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, Official Journal of the European Union,
available at: http://data.europa.eu/eli/dir/2008/50/oj (last access: 10 March 2021), 2008. a
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair,
S., Courville, A., and Bengio, Y.: Generative Adversarial Nets, in: Advances
in Neural Information Processing Systems 27, edited by: Ghahramani, Z.,
Welling, M., Cortes, C., Lawrence, N. D., and Weinberger, K. Q., pp.
2672–2680, Curran Associates, Inc.,
available at: http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf (last access: 10 March 2021),
2014. a
Goodfellow, I., Bengio, Y., and Courville, A.: Deep Learning, MIT Press,
http://www.deeplearningbook.org (last access: 10 March 2021), 2016. a
Gruber, J.: Markdown,
available at: https://daringfireball.net/projects/markdown/license,
(last access: 7 January 2021), 2004. a
Hochreiter, S. and Schmidhuber, J.: Long Short-Term Memory, Neural
Computation, 9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997. a
Hoyer, S. and Hamman, J.: xarray: N-D labeled arrays and datasets in
Python, J. Open Res. Softw., 5, 10, https://doi.org/10.5334/jors.148, 2017. a
Hoyer, S., Hamman, J., Roos, M., Fitzgerald, C., Cherian, D., Fujii, K.,
Maussion, F., crusaderky, Kleeman, A., Kluyver, T., Clark, S., Munroe, J.,
keewis, Hatfield-Dodds, Z., Nicholas, T., Abernathey, R., Wolfram, P. J.,
MaximilianR, Hauser, M., Markel, Gundersen, G., Signell, J., Helmus, J. J.,
Sinai, Y. B., Cable, P., Amici, A., lumbric, Rocklin, M., Rivera, G., and
Barna, A.: pydata/xarray v0.15.0, Zenodo, https://doi.org/10.5281/zenodo.3631851, 2020. a
ISO Central Secretary: Information technology – The JSON data interchange
syntax, Standard ISO/IEC 21778:2017, International Organization for
Standardization, Geneva, Switzerland,
available at: https://www.iso.org/standard/71616.html (last access: 10 March 2021), 2017. a
Kingma, D. P. and Welling, M.: Auto-Encoding Variational Bayes, arXiv:
1312.6114, available at: https://arxiv.org/abs/1312.6114 (last access: 10 March 2021), 2014. a
Kleinert, F., Leufen, L. H., and Schultz, M. G.: IntelliO3-ts v1.0: a neural network approach to predict near-surface ozone concentrations in Germany, Geosci. Model Dev., 14, 1–25, https://doi.org/10.5194/gmd-14-1-2021, 2021. a, b, c
Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B., Bussonnier, M.,
Frederic, J., Kelley, K., Hamrick, J., Grout, J., Corlay, S., Ivanov, P.,
Avila, D., Abdalla, S., Willing, C., and development team, J.: Jupyter
Notebooks – a publishing format for reproducible computational workflows, in:
Positioning and Power in Academic Publishing: Players, Agents and Agendas,
edited by: Loizides, F. and Scmidt, B., IOS Press, the Netherlands, 87–90,
available at: https://eprints.soton.ac.uk/403913/ (last access: 10 March 2021), 2016. a
Koranne, S.: Hierarchical data format 5: HDF5, in: Handbook of Open Source
Tools, 191–200, Springer, Boston, MA, HDF5 is maintained by The HDF Group,
http://www.hdfgroup.org/HDF5 (last access: 10 March 2021), 2011. a
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: ImageNet Classification with
Deep Convolutional Neural Networks, in: Advances in Neural Information
Processing Systems 25, edited by: Bartlett, P. L., Pereira, F. C. N., Burges,
C. J. C., Bottou, L., and Weinberger, K. Q., Curran
Associates, Inc., 1106–1114,
available at: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks (last access: 10 March 2021),
2012. a
LaTeX Project: LaTeX, available at: https://www.latex-project.org/,
(last access: 7 January 2021), 2005. a
Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P.: Gradient-based learning
applied to document recognition, Proceedings of the IEEE, 86, 2278–2324,
https://doi.org/10.1109/5.726791, 1998. a
Lefohn, A. S.,
Malley, C. S.,
Smith, L.,
Wells, B.,
Hazucha, M.,
Simon, H.,
Naik, V.,
Mills, G.,
Schultz, M. G.,
Paoletti, E.,
De Marco, A.,
Xu, X.,
Zhang, L.,
Wang, T.,
Neufeld, H. S.,
Musselman, R. C.,
Tarasick, D.,
Brauer, M.,
Feng, Z.,
Tang, H.,
Kobayashi, K.,
Sicard, P.,
Solberg, S., and
Gerosa, G.: Tropospheric ozone
assessment report: Global ozone metrics for climate change, human health, and
crop/ecosystem research, Elementa: Science of the Anthropocene, 1, 1,
https://doi.org/10.1525/elementa.279, 2018. a
Leufen, L. H., Kleinert, F., and Schultz, M. G.: MLAir (v1.0.0) – a tool to
enable fast and flexible machine learning on air data time series – Source
Code, EUDAT Collaborative Data Infrastructure,
https://doi.org/10.34730/fcc6b509d5394dad8cfdfc6e9fff2bec, 2020. a
Mills, G., Pleijel, H., Malley, C., Sinha, B., Cooper, O., Schultz, M.,
Neufeld, H., Simpson, D., Sharps, K., Feng, Z., Gerosa, G., Harmens, H.,
Kobayashi, K., Saxena, P., Paoletti, E., Sinha, V., and Xu, X.: Tropospheric
Ozone Assessment Report: Present-day tropospheric ozone distribution and
trends relevant to vegetation, Elementa: Science of the Anthropocene, 6, 47,
https://doi.org/10.1525/elementa.302, 2018. a
Murphy, A. H. and Daan, H.: Forecast evaluation, in: Probability, statistics,
and decision making in the atmospheric sciences, edited by: Murphy, A. H. and
Katz, R. W., Westview Press, Boulder, USA, 379–437, 1985. a
Murphy, A. H. and Winkler, R. L.: A General Framework for Forecast
Verification, Mon. Weather Rev., 115, 1330–1338,
https://doi.org/10.1175/1520-0493(1987)115<1330:AGFFFV>2.0.CO;2, 1987. a
Murphy, A. H., Brown, B. G., and Chen, Y.-S.: Diagnostic Verification of
Temperature Forecasts, Weather Forecast., 4, 485–501,
https://doi.org/10.1175/1520-0434(1989)004<0485:DVOTF>2.0.CO;2, 1989. a, b
Musgrave, K., Belongie, S., and Lim, S.-N.: A Metric Learning Reality Check,
arXiv: 2003.08505, available at: https://arxiv.org/abs/2003.08505 (last access: 10 March 2021), 2020. a
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen,
T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E.,
DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L.,
Bai, J., and Chintala, S.: PyTorch: An Imperative Style, High-Performance
Deep Learning Library, in: Advances in Neural Information Processing Systems
32, edited by: Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., and Garnett, R., Curran
Associates, Inc., Vancouver, Canada, 8024–8035,
available at: http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (last access: 10 March 2021),
2019. a
Python Software Foundation: Python Language Reference, release 3.6.8, PEP
494, available at: https://www.python.org/dev/peps/pep-0494/ (last access: 10 March 2021), 2018. a
Reback, J., McKinney, W., jbrockmendel, Van den Bossche, J., Augspurger, T.,
Cloud, P., gfyoung, Sinhrks, Klein, A., Roeschke, M., Tratner, J., She, C.,
Hawkins, S., Ayd, W., Petersen, T., Schendel, J., Hayden, A., Garcia, M.,
MomIsBestFriend, Jancauskas, V., Battiston, P., Seabold, S., chris-b1,
h-vetinari, Hoyer, S., Overmeire, W., alimcmaster1, Mehyar, M., Dong, K.,
and Whelan, C.: pandas-dev/pandas: Pandas v1.0.1, Zenodo,
https://doi.org/10.5281/zenodo.3644238, 2020. a
Rezende, D. J., Mohamed, S., and Wierstra, D.: Stochastic Backpropagation and
Approximate Inference in Deep Generative Models, arXiv: 1401.4082,
available at: https://arxiv.org/abs/1401.4082 (last access: 10 March 2021), 2014. a
Schultz, M. G.,
Schröder, S.,
Lyapina, O.,
Cooper, O. R.,
Galbally, I.,
Petropavlovskikh, I.,
von Schneidemesser, E.,
Tanimoto, H.,
Elshorbany, Y.,
Naja, M.,
Seguel, R. J.,
Dauert, U.,
Eckhardt, P.,
Feigenspan, S.,
Fiebig, M.,
Hjellbrekke, A.-G.,
Hong, Y.-D.,
Kjeld, P. C.,
Koide, H.,
Lear, G.,
Tarasick, D.,
Ueno, M.,
Wallasch, M.,
Baumgardner, D.,
Chuang, M.-T.,
Gillett, R.,
Lee, M.,
Molloy, S.,
Moolla, R.,
Wang, T.,
Sharps, K.,
Adame, J. A.,
Ancellet, G.,
Apadula, F.,
Artaxo, P.,
Barlasina, M. E.,
Bogucka, M.,
Bonasoni, P.,
Chang, L.,
Colomb, A.,
Cuevas-Agulló, E.,
Cupeiro, M.,
Degorska, A.,
Ding, A.,
Fröhlich, M.,
Frolova, M.,
Gadhavi, H.,
Gheusi, F.,
Gilge, S.,
Gonzalez, M. Y.,
Gros, V.,
Hamad, S. H.,
Helmig, D.,
Henriques, D.,
Hermansen, O.,
Holla, R.,
Hueber, J.,
Im, U.,
Jaffe, D. A.,
Komala, N.,
Kubistin, D.,
Lam, K.-S.,
Laurila, T.,
Lee, H.,
Levy, I.,
Mazzoleni, C.,
Mazzoleni, L. R.,
McClure-Begley, A.,
Mohamad, M.,
Murovec, M.,
Navarro-Comas, M.,
Nicodim, F.,
Parrish, D.,
Read, K. A.,
Reid, N.,
Ries, L.,
Saxena, P.,
Schwab, J. J.,
Scorgie, Y.,
Senik, I.,
Simmonds, P.,
Sinha, V.,
Skorokhod, A. I.,
Spain, G.,
Spangl, W.,
Spoor, R.,
Springston, S. R.,
Steer, K.,
Steinbacher, M.,
Suharguniyawan, E.,
Torre, P.,
Trickl, T.,
Weili, L.,
Weller, R.,
Xiaobin, X.,
Xue, L., and
Zhiqiang, M.: Tropospheric Ozone Assessment Report: Database and metrics
data of global surface ozone observations, Elementa: Science of the
Anthropocene, 5, 58, https://doi.org/10.1525/elementa.244, 2017a. a, b
Schultz, M. G., Schröder, S.,Lyapina, O., Cooper, O. R., Galbally, I., Petropavlovskikh, I., von Schneidemesser, E., Tanimoto, H., Elshorbany, Y., Naja, M., Seguel, R. J., Dauert, U., Eckhardt, P., Feigenspan, S., Fiebig, M., Hjellbrekke, A.-G., Hong, Y.-D., Kjeld, P. C., Koide, H., Lear, G., Tarasick, D., Ueno, M., Wallasch, M., Baumgardner, D., Chuang, M.-T., Gillett, R., Lee, M., Molloy, S., Moolla, R., Wang, T., Sharps, K., Adame, J. A., Ancellet, G.., Apadula, F., Artaxo, P., Barlasina, M. E., Bogucka, M., Bonasoni, P., Chang, L., Colomb, A., Cuevas-Agulló, E., Cupeiro, M., Degorska, A., Ding, A., Fröhlich, M., Frolova, M., Gadhavi, H., Gheusi, F., Gilge, S., Gonzalez, M. Y., Gros, V., Hamad, S. H., Helmig, D., Henriques, D., Hermansen, O., Holla, R., Hueber, J., Im, U., Jaffe, D. A., Komala, N., Kubistin, D., Lam, K.-S., Laurila, T., Lee, H.,Levy, I., Mazzoleni, C., Mazzoleni, L. R., McClure-Begley, A., Mohamad, M., Murovec, M., Navarro-Comas, M., Nicodim, F., Parrish, D., Read, K. A., Reid, N., Ries, L., Saxena, P., Schwab, J. J.,Scorgie, Y., Senik, I., Simmonds, P., Sinha, V., Skorokhod, A. I., Spain, G., Spangl, W., Spoor, R., Springston, S. R., Steer, K., Steinbacher, M., Suharguniyawan, E., Torre, P., Trickl, T., Weili, L., Weller, R., Xu, X., Xue, L., and Zhiqiang, M.: Tropospheric Ozone Assessment Report, links to Global
surface ozone datasets, PANGAEA, https://doi.org/10.1594/PANGAEA.876108, 2017b. a
Schultz, M. G., Betancourt, C., Gong, B., Kleinert, F., Langguth, M., Leufen,
L. H., Mozaffari, A., and Stadtler, S.: Can deep learning beat numerical
weather prediction?, Philos. T. Roy. Soc. A, 379, 2194,
https://doi.org/10.1098/rsta.2020.0097, 2021.
a, b
Szegedy, C., Wei Liu, Yangqing Jia, Sermanet, P., Reed, S., Anguelov, D.,
Erhan, D., Vanhoucke, V., and Rabinovich, A.: Going deeper with convolutions,
in: 2015 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), IEEE, Boston, MA, USA, 1–9,
https://doi.org/10.1109/CVPR.2015.7298594, 2015. a
TensorFlow: GPU support,
available at: https://www.tensorflow.org/install/gpu, last access: 6 June 2020. a
TOAR: Tropospheric Ozone Assessment Report (TOAR): Global metrics for climate
change, human health and crop/ecosystem research, International Global
Atmospheric Chemistry (IGAC),
available at: https://igacproject.org/activities/TOAR (last access: 29 January
2021), 2014–2021. a
US Environmental Protection Agency: Integrated science assessment for ozone
and related photochemical oxidants, US Environmental
Protection Agency, Washington, D.C., ePA-HQ-ORD-2018-0274, 2020. a
van der Walt, S., Colbert, S. C., and Varoquaux, G.: The NumPy Array: A
Structure for Efficient Numerical Computation, Comput. Sci.
Eng., 13, 22–30, https://doi.org/10.1109/MCSE.2011.37, 2011. a
Vautard, R.: Evaluation of the meteorological forcing used for the Air
Quality Model Evaluation International Initiative (AQMEII) air
quality simulations, Atmos. Environ., 53, 15–37,
https://doi.org/10.1016/j.atmosenv.2011.10.065, 2012. a
Wes McKinney: Data Structures for Statistical Computing in
Python, in: Proceedings of the 9th Python in Science Conference,
edited by: Stéfan van der Walt and Jarrod Millman, SciPy Organizers, Austin, Texas, 56–61,
https://doi.org/10.25080/Majora-92bf1922-00a, 2010. a
Wilks, D. S. (Ed.): Statistical methods in the atmospheric sciences, pp.
178–186, International Geophysics Series, Elsevier Academic Press,
Amsterdam, 3rd edn., 2011. a
World Health Organization: Health risks of air pollution in Europe – HRAPIE
project recommendations for concentration–response functions for
cost–benefit analysis of particulate matter, ozone and nitrogen dioxide,
Ozone and Nitrogen Dioxide,
available at: https://www.euro.who.int/__data/assets/pdf_file/0006/238956/Health_risks_air_pollution_HRAPIE_project.pdf (last access: 10 March 2021),
2013. a
Short summary
MLAir provides a coherent end-to-end structure for a typical time series analysis workflow using machine learning (ML). MLAir is adaptable to a wide range of ML use cases, focusing in particular on deep learning. The user has a free hand with the ML model itself and can select from different methods during preprocessing, training, and postprocessing. MLAir offers tools to track the experiment conduction, documents necessary ML parameters, and creates a variety of publication-ready plots.
MLAir provides a coherent end-to-end structure for a typical time series analysis workflow using...
