Articles | Volume 16, issue 17
https://doi.org/10.5194/gmd-16-5251-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-5251-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
13 Sep 2023
Model description paper |
| 13 Sep 2023
| 13 Sep 2023
Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0
Junsu Gil
Department of Earth and Environmental Sciences, Korea University,
Seoul, South Korea
Department of Earth and Environmental Sciences, Korea University,
Seoul, South Korea
Jeonghwan Kim
Department of Environmental Science, Hankuk University of Foreign
Studies, Yongin, South Korea
Gangwoong Lee
Department of Environmental Science, Hankuk University of Foreign
Studies, Yongin, South Korea
Joonyoung Ahn
Climate and Air Quality Research
Department, Air Quality Forecasting Center, National Institute of Environmental Research (NIER), Incheon,
South Korea
Cheol-Hee Kim
Department of Atmospheric Sciences, Pusan National University, Busan, South Korea
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Preprint withdrawn
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Haeyoung Lee, Edward J. Dlugokencky, Jocelyn C. Turnbull, Sepyo Lee, Scott J. Lehman, John B. Miller, Gabrielle Pétron, Jeong-Sik Lim, Gang-Woong Lee, Sang-Sam Lee, and Young-San Park
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Lei Kong, Xiao Tang, Jiang Zhu, Zifa Wang, Joshua S. Fu, Xuemei Wang, Syuichi Itahashi, Kazuyo Yamaji, Tatsuya Nagashima, Hyo-Jung Lee, Cheol-Hee Kim, Chuan-Yao Lin, Lei Chen, Meigen Zhang, Zhining Tao, Jie Li, Mizuo Kajino, Hong Liao, Zhe Wang, Kengo Sudo, Yuesi Wang, Yuepeng Pan, Guiqian Tang, Meng Li, Qizhong Wu, Baozhu Ge, and Gregory R. Carmichael
Atmos. Chem. Phys., 20, 181–202, https://doi.org/10.5194/acp-20-181-2020, https://doi.org/10.5194/acp-20-181-2020, 2020
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Evaluation and uncertainty investigation of NO2, CO and NH3 modeling over China were conducted in this study using 14 chemical transport model results from MICS-Asia III. All models largely underestimated CO concentrations and showed very poor performance in reproducing the observed monthly variations of NH3 concentrations. Potential factors related to such deficiencies are investigated and discussed in this paper.
Yongjoo Choi, Yugo Kanaya, Seung-Myung Park, Atsushi Matsuki, Yasuhiro Sadanaga, Sang-Woo Kim, Itsushi Uno, Xiaole Pan, Meehye Lee, Hyunjae Kim, and Dong Hee Jung
Atmos. Chem. Phys., 20, 83–98, https://doi.org/10.5194/acp-20-83-2020, https://doi.org/10.5194/acp-20-83-2020, 2020
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Junsu Gil, Jeonghwan Kim, Meehye Lee, Gangwoong Lee, Dongsoo Lee, Jinsang Jung, Joonyeong An, Jinkyu Hong, Seogju Cho, Jeonghoon Lee, and Russell Long
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-1012, https://doi.org/10.5194/acp-2019-1012, 2019
Preprint withdrawn
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Jie Li, Tatsuya Nagashima, Lei Kong, Baozhu Ge, Kazuyo Yamaji, Joshua S. Fu, Xuemei Wang, Qi Fan, Syuichi Itahashi, Hyo-Jung Lee, Cheol-Hee Kim, Chuan-Yao Lin, Meigen Zhang, Zhining Tao, Mizuo Kajino, Hong Liao, Meng Li, Jung-Hun Woo, Jun-ichi Kurokawa, Zhe Wang, Qizhong Wu, Hajime Akimoto, Gregory R. Carmichael, and Zifa Wang
Atmos. Chem. Phys., 19, 12993–13015, https://doi.org/10.5194/acp-19-12993-2019, https://doi.org/10.5194/acp-19-12993-2019, 2019
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This study evaluated and intercompared 14 CTMs with ozone observations in East Asia, within the framework of the Model Inter-Comparison Study for ASIA Phase III (MICS-Asia III). Potential causes of the discrepancies between model results and observation were investigated by assessing the planetary boundary layer heights, emission fluxes, dry deposition, chemistry and vertical transport among models. Finally, a multi-model estimate of pollution distributions was provided.
Daun Jeong, Roger Seco, Dasa Gu, Youngro Lee, Benjamin A. Nault, Christoph J. Knote, Tom Mcgee, John T. Sullivan, Jose L. Jimenez, Pedro Campuzano-Jost, Donald R. Blake, Dianne Sanchez, Alex B. Guenther, David Tanner, L. Gregory Huey, Russell Long, Bruce E. Anderson, Samuel R. Hall, Kirk Ullmann, Hye-jung Shin, Scott C. Herndon, Youngjae Lee, Danbi Kim, Joonyoung Ahn, and Saewung Kim
Atmos. Chem. Phys., 19, 12779–12795, https://doi.org/10.5194/acp-19-12779-2019, https://doi.org/10.5194/acp-19-12779-2019, 2019
Haeyoung Lee, Sang-Ok Han, Sang-Boom Ryoo, Jeong-Soon Lee, and Gang-Woong Lee
Atmos. Chem. Phys., 19, 2149–2163, https://doi.org/10.5194/acp-19-2149-2019, https://doi.org/10.5194/acp-19-2149-2019, 2019
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We introduced our identical systems, which were installed at three S. Korea stations, using CRDS and a drying system. The measurement uncertainty was ~ 0.11 ppm across the stations. CO2 observed in the west of S. Korea was very sensitive to East Asia (e.g., China), indicating that data include CO2 flux information from East Asia. Through long-term comparison to other East Asian stations, it was suggested that they could be affected not only by local vegetation but also by measurement quality.
Xiaona Shang, Meehye Lee, Saehee Lim, Örjan Gustafsson, Gangwoong Lee, and Limseok Chang
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-721, https://doi.org/10.5194/acp-2018-721, 2018
Preprint withdrawn
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Eunha Kang, Meehye Lee, William H. Brune, Taehyoung Lee, Taehyun Park, Joonyoung Ahn, and Xiaona Shang
Atmos. Chem. Phys., 18, 6661–6677, https://doi.org/10.5194/acp-18-6661-2018, https://doi.org/10.5194/acp-18-6661-2018, 2018
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A potential aerosol mass (PAM) reactor expedites slow atmospheric oxidation reactions and enables the observation of chemical aging processes and the determination of the aerosol-forming power of an air mass. A PAM reactor was deployed at Baengnyeong Island in the Yellow Sea. Experimental results confirm the key role of SO2 in generating secondary aerosols in northeast Asia, and the contribution of organics to secondary aerosols is more variable during transport in the atmosphere.
Meng Gao, Zhiwei Han, Zirui Liu, Meng Li, Jinyuan Xin, Zhining Tao, Jiawei Li, Jeong-Eon Kang, Kan Huang, Xinyi Dong, Bingliang Zhuang, Shu Li, Baozhu Ge, Qizhong Wu, Yafang Cheng, Yuesi Wang, Hyo-Jung Lee, Cheol-Hee Kim, Joshua S. Fu, Tijian Wang, Mian Chin, Jung-Hun Woo, Qiang Zhang, Zifa Wang, and Gregory R. Carmichael
Atmos. Chem. Phys., 18, 4859–4884, https://doi.org/10.5194/acp-18-4859-2018, https://doi.org/10.5194/acp-18-4859-2018, 2018
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Topic 3 of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase III examines how online coupled air quality models perform in simulating high aerosol pollution in the North China Plain region during wintertime haze events and evaluates the importance of aerosol radiative and microphysical feedbacks. A comprehensive overview of the MICS-ASIA III Topic 3 study design is presented.
Xiaona Shang, Kai Zhang, Fan Meng, Shihao Wang, Meehye Lee, Inseon Suh, Daigon Kim, Kwonho Jeon, Hyunju Park, Xuezhong Wang, and Yuxi Zhao
Atmos. Chem. Phys., 18, 2573–2584, https://doi.org/10.5194/acp-18-2573-2018, https://doi.org/10.5194/acp-18-2573-2018, 2018
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The main sources of PM2.5 during the 2013–2014 winter period in Beijing were identified as soil dust, traffic emission, biomass combustion, industrial emission, and coal combustion. A red-alert haze was almost equally contributed by local traffic and transported coal combustion emissions from Beijing vicinities. This study emphasizes the role of weather condition in haze formation by building up stagnant condition that facilitates the transport of emissions from Beijing's neighboring cities.
Jihyun Han, Meehye Lee, Xiaona Shang, Gangwoong Lee, and Louisa K. Emmons
Atmos. Chem. Phys., 17, 10619–10631, https://doi.org/10.5194/acp-17-10619-2017, https://doi.org/10.5194/acp-17-10619-2017, 2017
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Peroxyacetyl nitrate (PAN) was first measured at Gosan Climate Observatory during the fall of 2010, when PAN was better correlated with PM10 than with O3. In particular, PAN and O3 concentrations were greatly elevated in haze and the Beijing plume and much higher than those from model simulation. This study highlights the decoupling of PAN from O3 in Chinese outflows and suggests PAN as a potential indicator of overall aerosol formation in aged air masses impacted by biomass burning.
Holger Baars, Thomas Kanitz, Ronny Engelmann, Dietrich Althausen, Birgit Heese, Mika Komppula, Jana Preißler, Matthias Tesche, Albert Ansmann, Ulla Wandinger, Jae-Hyun Lim, Joon Young Ahn, Iwona S. Stachlewska, Vassilis Amiridis, Eleni Marinou, Patric Seifert, Julian Hofer, Annett Skupin, Florian Schneider, Stephanie Bohlmann, Andreas Foth, Sebastian Bley, Anne Pfüller, Eleni Giannakaki, Heikki Lihavainen, Yrjö Viisanen, Rakesh Kumar Hooda, Sérgio Nepomuceno Pereira, Daniele Bortoli, Frank Wagner, Ina Mattis, Lucja Janicka, Krzysztof M. Markowicz, Peggy Achtert, Paulo Artaxo, Theotonio Pauliquevis, Rodrigo A. F. Souza, Ved Prakesh Sharma, Pieter Gideon van Zyl, Johan Paul Beukes, Junying Sun, Erich G. Rohwer, Ruru Deng, Rodanthi-Elisavet Mamouri, and Felix Zamorano
Atmos. Chem. Phys., 16, 5111–5137, https://doi.org/10.5194/acp-16-5111-2016, https://doi.org/10.5194/acp-16-5111-2016, 2016
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The findings from more than 10 years of global aerosol lidar measurements with Polly systems are summarized, and a data set of optical properties for specific aerosol types is given. An automated data retrieval algorithm for continuous Polly lidar observations is presented and discussed by means of a Saharan dust advection event in Leipzig, Germany. Finally, a statistic on the vertical aerosol distribution including the seasonal variability at PollyNET locations around the globe is presented.
S. Kundu, K. Kawamura, M. Kobayashi, E. Tachibana, M. Lee, P. Q. Fu, and J. Jung
Atmos. Chem. Phys., 16, 585–596, https://doi.org/10.5194/acp-16-585-2016, https://doi.org/10.5194/acp-16-585-2016, 2016
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Chemistry-transport models have predicted a change in secondary organic aerosols (SOA) in the future atmosphere with a large uncertainty. This study measures diacids, major water-soluble surrogates of SOA, on a sub-decadal scale in atmospheric aerosols in eastern Asia. Diacids are observed to increase by 3.9–47.4 % per year. The increases in the water-soluble organic acid fraction could modify the aerosol organic composition and its sensitivity to climate-relevant physical properties.
J. Han, B. Shin, M. Lee, G. Hwang, J. Kim, J. Shim, G. Lee, and C. Shim
Atmos. Chem. Phys., 15, 12611–12621, https://doi.org/10.5194/acp-15-12611-2015, https://doi.org/10.5194/acp-15-12611-2015, 2015
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In east Asia, emissions of O3 precursors have gradually increased and O3 concentrations are expected to increase in the near future. Ieodo Ocean Research Station (IORS), located in the East China Sea, is a unique research tower suitable for observing continental outflows from east Asia. In this study, we present long-term measurements of O3 at IORS, describe their characteristic variations, and evaluate their continental influence on the regional background concentrations of O3.
S. Kim, S.-Y. Kim, M. Lee, H. Shim, G. M. Wolfe, A. B. Guenther, A. He, Y. Hong, and J. Han
Atmos. Chem. Phys., 15, 4357–4371, https://doi.org/10.5194/acp-15-4357-2015, https://doi.org/10.5194/acp-15-4357-2015, 2015
S. Lim, M. Lee, S.-W. Kim, S.-C. Yoon, G. Lee, and Y. J. Lee
Atmos. Chem. Phys., 14, 7781–7793, https://doi.org/10.5194/acp-14-7781-2014, https://doi.org/10.5194/acp-14-7781-2014, 2014
E. N. Kirillova, A. Andersson, J. Han, M. Lee, and Ö. Gustafsson
Atmos. Chem. Phys., 14, 1413–1422, https://doi.org/10.5194/acp-14-1413-2014, https://doi.org/10.5194/acp-14-1413-2014, 2014
Related subject area
Atmospheric sciences
The sensitivity of aerosol data assimilation to vertical profiles: case study of dust storm assimilation with LOTOS-EUROS v2.2
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a neural network
Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching
A novel method for quantifying the contribution of regional transport to PM2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Diagnosis of winter precipitation types using the spectral bin model (version 1DSBM-19M): comparison of five methods using ICE-POP 2018 field experiment data
Improving winter condition simulations in SURFEX-TEB v9.0 with a multi-layer snow model and ice
UA-ICON with the NWP physics package (version ua-icon-2.1): mean state and variability of the middle atmosphere
Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study
Pochva: a new hydro-thermal process model in soil, snow, and vegetation for application in atmosphere numerical models
ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks
Accounting for effects of coagulation and model uncertainties in particle number concentration estimates based on measurements from sampling lines – a Bayesian inversion approach with SLIC v1.0
Top-down CO emission estimates using TROPOMI CO data in the TM5-4DVAR (r1258) inverse modeling suit
The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
Similarity-based analysis of atmospheric organic compounds for machine learning applications
Porting the Meso-NH atmospheric model on different GPU architectures for the next generation of supercomputers (version MESONH-v55-OpenACC)
Estimation of aerosol and cloud radiative heating rate in the tropical stratosphere using a radiative kernel method
Evaluation of dust emission and land surface schemes in predicting a mega Asian dust storm over South Korea using WRF-Chem
Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy
A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks
Inclusion of the ECMWF ecRad radiation scheme (v1.5.0) in the MAR (v3.14), regional evaluation for Belgium, and assessment of surface shortwave spectral fluxes at Uccle
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
NeuralMie (v1.0): an aerosol optics emulator
A REtrieval Method for optical and physical Aerosol Properties in the stratosphere (REMAPv1)
Simulation performance of planetary boundary layer schemes in WRF v4.3.1 for near-surface wind over the western Sichuan Basin: a single-site assessment
FootNet v1.0: development of a machine learning emulator of atmospheric transport
Updates and evaluation of NOAA's online-coupled air quality model version 7 (AQMv7) within the Unified Forecast System
Quantifying the analysis uncertainty for nowcasting application
Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01
The MESSy DWARF (based on MESSy v2.55.2)
Generalized local fractions – a method for the calculation of sensitivities to emissions from multiple sources for chemically active species, illustrated using the EMEP MSC-W model (rv5.5)
SanDyPALM v1.0: Static and Dynamic Drivers for the PALM-4U Model to Facilitate Realistic Urban Microclimate Simulations
An enhanced emission module for the PALM model system 23.10 with application for PM10 emission from urban domestic heating
Identifying lightning processes in ERA5 soundings with deep learning
Sensitivity of predicted ultrafine particle size distributions in Europe to different nucleation rate parameterizations using PMCAMx-UF v2.2
Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data
Accurate and fast prediction of radioactive pollution by Kriging coupled with Auto-Associative Models
Mitigating Hail Overforecasting in the 2-Moment Milbrandt-Yau Microphysics Scheme (v2.25.2_beta_04) in WRF (v4.5.1) by Incorporating the Graupel Spongy Wet Growth Process (MY2_GSWG v1.0)
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
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
Comprehensive evaluation of iAMAS (v1.0) in simulating Antarctic meteorological fields with observations and reanalysis
Forecasting contrail climate forcing for flight planning and air traffic management applications: the CocipGrid model in pycontrails 0.51.0
Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Batjargal Buyantogtokh, Yixuan Gu, Jiandong Li, Hai Xiang Lin, Hong Liao, and Wei Han
Geosci. Model Dev., 18, 3781–3798, https://doi.org/10.5194/gmd-18-3781-2025, https://doi.org/10.5194/gmd-18-3781-2025, 2025
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Aerosol data assimilation has gained popularity as it combines the advantages of modelling and observation. However, few studies have addressed the challenges in the prior vertical structure. Different observations are assimilated to examine the sensitivity of assimilation to vertical structure. Results show that assimilation can optimize the dust field in general. However, if the prior introduces an incorrect structure, the assimilation can significantly deteriorate the integrity of the aerosol profile.
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
Geosci. Model Dev., 18, 3707–3733, https://doi.org/10.5194/gmd-18-3707-2025, https://doi.org/10.5194/gmd-18-3707-2025, 2025
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This work focuses on the prediction of aerosol concentration values at the ground level, which are a strong indicator of air quality, using artificial neural networks. A study of different variables and their efficiency as inputs for these models is also proposed and reveals that the best results are obtained when using all of them. Comparison between network architectures and information fusion methods allows for the extraction of knowledge on the most efficient methods in the context of this study.
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025, https://doi.org/10.5194/gmd-18-3681-2025, 2025
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Tuning a climate model means adjusting uncertain parameters in the model to best match observations like the global radiation balance and cloud cover. This is usually done by running many simulations of the model with different settings, which can be time-consuming and relies heavily on expert knowledge. To make this process faster and more objective, we developed a machine learning emulator to create a large ensemble and apply a method called history matching to find the best settings.
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev., 18, 3623–3634, https://doi.org/10.5194/gmd-18-3623-2025, https://doi.org/10.5194/gmd-18-3623-2025, 2025
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This study combines machine learning with concentration-weighted trajectory analysis to quantify regional transport PM2.5. From 2013–2020, local emissions dominated Beijing's pollution events. The Air Pollution Prevention and Control Action Plan reduced regional transport pollution, but the eastern region showed the smallest decrease. Beijing should prioritize local emission reduction while considering the east region's contributions in future strategies.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025, https://doi.org/10.5194/gmd-18-3607-2025, 2025
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We developed a deep learning method to estimate CO2 emissions from power plants using satellite images. Trained and validated on simulated data, our model accurately predicts emissions despite challenges like cloud cover. When applied to real OCO3 satellite images, the results closely match reported emissions. This study shows that neural networks trained on simulations can effectively analyse real satellite data, offering a new way to monitor CO2 emissions from space.
Wonbae Bang, Jacob T. Carlin, Kwonil Kim, Alexander V. Ryzhkov, Guosheng Liu, and GyuWon Lee
Geosci. Model Dev., 18, 3559–3581, https://doi.org/10.5194/gmd-18-3559-2025, https://doi.org/10.5194/gmd-18-3559-2025, 2025
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Microphysics model-based diagnosis, such as the spectral bin model (SBM), has recently been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM has a relatively higher accuracy for dry-snow and wet-snow events, whereas it has lower accuracy for rain events. When the microphysics scheme in the SBM was optimized for the corresponding region, the accuracy for rain events improved.
Gabriel Colas, Valéry Masson, François Bouttier, Ludovic Bouilloud, Laura Pavan, and Virve Karsisto
Geosci. Model Dev., 18, 3453–3472, https://doi.org/10.5194/gmd-18-3453-2025, https://doi.org/10.5194/gmd-18-3453-2025, 2025
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In winter, snow- and ice-covered artificial surfaces are important aspects of the urban climate. They may influence the magnitude of the urban heat island effect, but this is still unclear. In this study, we improved the representation of the snow and ice cover in the Town Energy Balance (TEB) urban climate model. Evaluations have shown that the results are promising for using TEB to study the climate of cold cities.
Markus Kunze, Christoph Zülicke, Tarique A. Siddiqui, Claudia C. Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev., 18, 3359–3385, https://doi.org/10.5194/gmd-18-3359-2025, https://doi.org/10.5194/gmd-18-3359-2025, 2025
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We present the Icosahedral Nonhydrostatic (ICON) general circulation model with an upper-atmospheric extension with the physics package for numerical weather prediction (UA-ICON(NWP)). We optimized the parameters for the gravity wave parameterizations and achieved realistic modeling of the thermal and dynamic states of the mesopause regions. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
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Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025, https://doi.org/10.5194/gmd-18-3265-2025, 2025
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The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model setup, are discussed, and the official recommendations for the project are presented.
Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut
Geosci. Model Dev., 18, 3211–3239, https://doi.org/10.5194/gmd-18-3211-2025, https://doi.org/10.5194/gmd-18-3211-2025, 2025
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This study proposes using a statistical model to freeze errors due to differences in environmental forcing when evaluating the surface turbulent heat fluxes from numerical simulations with observations. The statistical model is first built with observations and then applied to the simulated environment to generate possibly observed fluxes. This novel method provides insight into differently evaluating the numerical formulation of turbulent heat fluxes with a long period of observational data.
Oxana Drofa
Geosci. Model Dev., 18, 3175–3209, https://doi.org/10.5194/gmd-18-3175-2025, https://doi.org/10.5194/gmd-18-3175-2025, 2025
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This paper presents the result of many years of effort of the author, who developed an original mathematical numerical model of heat and moisture exchange processes in soil, vegetation, and snow. The author relied on her 30 years of research experience in atmospheric numerical modelling. The presented model is the fruit of the author's research on physical processes at the surface–atmosphere interface and their numerical approximation and aims at improving numerical weather forecasting and climate simulations.
Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani
Geosci. Model Dev., 18, 3065–3079, https://doi.org/10.5194/gmd-18-3065-2025, https://doi.org/10.5194/gmd-18-3065-2025, 2025
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We developed ClimKern, a Python package and radiative kernel repository, to simplify calculating radiative feedbacks and make climate sensitivity studies more reproducible. Testing of ClimKern with sample climate model data reveals that radiative kernel choice may be more important than previously thought, especially in polar regions. Our work highlights the need for kernel sensitivity analyses to be included in future studies.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025, https://doi.org/10.5194/gmd-18-2983-2025, 2025
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Particle size is a key factor determining the properties of aerosol particles which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis
Geosci. Model Dev., 18, 2861–2890, https://doi.org/10.5194/gmd-18-2861-2025, https://doi.org/10.5194/gmd-18-2861-2025, 2025
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We estimate carbon monoxide emissions through inverse modeling, an approach where measurements of tracers in the atmosphere are fed to a model to calculate backwards in time (inverse) where the tracers came from. We introduce measurements from a new satellite instrument and show that, in most places globally, these on their own sufficiently constrain the emissions. This alleviates the need for additional datasets, which could shorten the delay for future carbon monoxide source estimates.
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025, https://doi.org/10.5194/gmd-18-2747-2025, 2025
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This paper introduces the Multi-Compartment Mercury (Hg) Modeling and Analysis Project (MCHgMAP) aimed at informing the effectiveness evaluations of two multilateral environmental agreements: the Minamata Convention on Mercury and the Convention on Long-Range Transboundary Air Pollution. The experimental design exploits a variety of models (atmospheric, land, oceanic ,and multimedia mass balance models) to assess the short- and long-term influences of anthropogenic Hg releases into the environment.
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025, https://doi.org/10.5194/gmd-18-2701-2025, 2025
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Machine learning has the potential to aid the identification of organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning models in atmospheric sciences.
Juan Escobar, Philippe Wautelet, Joris Pianezze, Florian Pantillon, Thibaut Dauhut, Christelle Barthe, and Jean-Pierre Chaboureau
Geosci. Model Dev., 18, 2679–2700, https://doi.org/10.5194/gmd-18-2679-2025, https://doi.org/10.5194/gmd-18-2679-2025, 2025
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The Meso-NH weather research code is adapted for GPUs using OpenACC, leading to significant performance and energy efficiency improvements. Called MESONH-v55-OpenACC, it includes enhanced memory management, communication optimizations and a new solver. On the AMD MI250X Adastra platform, it achieved up to 6× speedup and 2.3× energy efficiency gain compared to CPUs. Storm simulations at 100 m resolution show positive results, positioning the code for future use on exascale supercomputers.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
Geosci. Model Dev., 18, 2569–2586, https://doi.org/10.5194/gmd-18-2569-2025, https://doi.org/10.5194/gmd-18-2569-2025, 2025
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The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate this effect, the radiative kernel is constructed and applied. The results show that the kernels can reproduce aerosol radiative effects and are expected to simulate stratospheric aerosol radiative effects. This approach reduces computational expense, is consistent with radiative model calculations, and can be applied to atmospheric models with speed requirements.
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
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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.
Andrin Jörimann, Timofei Sukhodolov, Beiping Luo, Gabriel Chiodo, Graham Mann, and Thomas Peter
EGUsphere, https://doi.org/10.5194/egusphere-2025-145, https://doi.org/10.5194/egusphere-2025-145, 2025
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Aerosol particles in the stratosphere affect our climate. Climate models therefore need an accurate description of their properties and evolution. Satellites measure how strongly aerosol particles extinguish light passing through the stratosphere. We describe a method to use such aerosol extinction data to retrieve the number and sizes of the aerosol particles and calculate their optical effects. The resulting data sets for models are validated against ground-based and balloon observations.
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025, https://doi.org/10.5194/gmd-18-1769-2025, 2025
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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.
Peter Wind and Willem van Caspel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3571, https://doi.org/10.5194/egusphere-2024-3571, 2025
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This paper presents a numerical method to assess the origin of air pollution. Combined with a numerical air pollution transport and chemistry model, it can follow the contributions from a large number of emission sources. The result is a series of maps that give the relative contributions from for example all European countries at each point.
Julian Vogel, Sebastian Stadler, Ganesh Chockalingam, Afshin Afshari, Johanna Henning, and Matthias Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2025-144, https://doi.org/10.5194/egusphere-2025-144, 2025
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This study presents a toolkit to simplify input data creation for the urban microclimate model PALM-4U. It introduces novel methods to automate the use of open data sources. Our analysis of four test cases created from different geographic data sources shows variations in temperature, humidity, and wind speed, influenced by data quality. Validation indicates that the automated methods yield results comparable to expert-driven approaches, facilitating user-friendly urban climate modeling.
Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
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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.
Raphaël Périllat, Sylvain Girard, and Irène Korsakissok
EGUsphere, https://doi.org/10.5194/egusphere-2024-3838, https://doi.org/10.5194/egusphere-2024-3838, 2025
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We developed a method to improve decision-making during nuclear crises by predicting the spread of radiation more efficiently. Existing approaches are often too slow, especially when analyzing complex data like radiation maps. Our method combines techniques to simplify these maps and predict them quickly using statistical tools. This approach could help authorities respond faster and more accurately in emergencies, reducing risks to the population and the environment.
Shaofeng Hua, Gang Chen, Baojun Chen, Mingshan Li, and Xin Xu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3834, https://doi.org/10.5194/egusphere-2024-3834, 2025
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Hail forecasting using numerical models remains a challenge. In this study, we found that the commonly used graupel-to-hail conversion parameterization method led to hail overforecasting in heavy rainfall cases where no hail was observed. By incorporating the spongy wet growth process, we successfully mitigated hail overforecasting. The modified scheme also produced hail in real hail events. This research contributes to a better understanding of hail formation.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
EGUsphere, https://doi.org/10.5194/egusphere-2024-3512, https://doi.org/10.5194/egusphere-2024-3512, 2025
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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.
Qike Yang, Chun Zhao, Jiawang Feng, Gudongze Li, Jun Gu, Zihan Xia, Mingyue Xu, and Zining Yang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-229, https://doi.org/10.5194/gmd-2024-229, 2025
Revised manuscript accepted for GMD
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This study presents the first comprehensive evaluation of unstructured meshes using the iAMAS model over Antarctica, encompassing both surface and upper-level meteorological fields. Comparison with ERA5 and observational data reveals that the iAMAS model performs well in simulating the Antarctic atmosphere; iAMAS demonstrates comparable, and in some cases superior, performance in simulating temperature and wind speed in East Antarctica when compared to ERA5.
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.
Cited articles
Akimoto, H. and Tanimoto, H.: Review of Comprehensive Measurements of
Speciated NOy and its Chemistry: Need for Quantifying the Role of
Heterogeneous Processes of HNO3 and HONO, Aerosol Air Qual. Res., 21, 200395, https://doi.org/10.4209/aaqr.2020.07.0395,
2021.
Akimoto, H., Nagashima, T., Li, J., Fu, J. S., Ji, D., Tan, J., and Wang, Z.: Comparison of surface ozone simulation among selected regional models in MICS-Asia III – effects of chemistry and vertical transport for the causes of difference, Atmos. Chem. Phys., 19, 603–615, https://doi.org/10.5194/acp-19-603-2019, 2019.
Anderson, D. C., Loughner, C. P., Diskin, G., Weinheimer, A., Canty, T. P.,
Salawitch, R. J., Worden, H. M., Fried, A., Mikoviny, T., and Wisthaler, A.:
Measured and modeled CO and NOy in DISCOVER-AQ: An evaluation of emissions
and chemistry over the eastern US, Atmos. Environ., 96, 78–87, 2014.
Appel, K. W., Bash, J. O., Fahey, K. M., Foley, K. M., Gilliam, R. C., Hogrefe, C., Hutzell, W. T., Kang, D., Mathur, R., Murphy, B. N., Napelenok, S. L., Nolte, C. G., Pleim, J. E., Pouliot, G. A., Pye, H. O. T., Ran, L., Roselle, S. J., Sarwar, G., Schwede, D. B., Sidi, F. I., Spero, T. L., and Wong, D. C.: The Community Multiscale Air Quality (CMAQ) model versions 5.3 and 5.3.1: system updates and evaluation, Geosci. Model Dev., 14, 2867–2897, https://doi.org/10.5194/gmd-14-2867-2021, 2021.
Arcomano, T., Szunyogh, I., Wikner, A., Pathak, J., Hunt, B. R., and Ott,
E.: A Hybrid Approach to Atmospheric Modeling that Combines Machine Learning
with a Physics-Based Numerical Model, J. Adv. Model. Earth Sy., 14, e2021MS002712, https://doi.org/10.1029/2021MS002712,
2021.
Armante, R., Perrin, A., Kwabia Tchana, F., and Manceron, L.: The ν4 bands at 11 µm: linelists for the Trans- and Cis- conformer forms of nitrous acid (HONO) in the 2019 version of the GEISA database, Mol. Phys., 120,
e1951860, https://doi.org/10.1080/00268976.2021.1951860, 2021.
Arnell, N. W., Lowe, J. A., Challinor, A. J., and Osborn, T. J.: Global and
regional impacts of climate change at different levels of global temperature
increase, Climatic Change, 155, 377–391, 2019.
Baek, W.-K. and Jung, H.-S.: Performance Comparison of Oil Spill and Ship
Classification from X-Band Dual-and Single-Polarized SAR Image Using Support
Vector Machine, Random Forest, and Deep Neural Network, Remote Sens.-Basel,
13, 3203, https://doi.org/10.3390/rs13163203, 2021.
Bao, F., Cheng, Y., Kuhn, U., Li, G., Wang, W., Kratz, A. M., Weber, J.,
Weber, B., Pöschl, U., and Su, H.: Key Role of
Equilibrium HONO Concentration over Soil in Quantifying Soil–Atmosphere
HONO Fluxes, Environ. Sci. Technol., 56, 2204–2212, 2022.
Bengio, Y. and Grandvalet, Y.: No unbiased estimator of the variance of
K-fold cross-validation, in: Advances in Neural Information Processing Systems, vol. 16, edited by: Thrun, S., Saul, L., and Schölkopf, B., MIT Press, https://proceedings.neurips.cc/paper/2003/hash/e82c4b19b8151ddc25d4d93baf7b908f-Abstract.html, last access: 11 September 2003.
Bloss, W. J., Kramer, L., Crilley, L. R., Vu, T., Harrison, R. M., Shi, Z.,
Lee, J. D., Squires, F. A., Whalley, L. K., and Slater, E.: Insights into
air pollution chemistry and sulphate formation from nitrous acid (HONO)
measurements during haze events in Beijing, Faraday Discuss., 226, 223–238,
2021.
Brown, S. S., An, H., Lee, M., Park, J.-H., Lee, S.-D., Fibiger, D. L.,
McDuffie, E. E., Dubé, W. P., Wagner, N. L., and Min, K.-E.: Cavity
enhanced spectroscopy for measurement of nitrogen oxides in the
Anthropocene: results from the Seoul tower during MAPS 2015, Faraday Discuss., 200, 529–557, 2017.
Canty, T. P., Hembeck, L., Vinciguerra, T. P., Anderson, D. C., Goldberg, D. L., Carpenter, S. F., Allen, D. J., Loughner, C. P., Salawitch, R. J., and Dickerson, R. R.: Ozone and NOx chemistry in the eastern US: evaluation of CMAQ/CB05 with satellite (OMI) data, Atmos. Chem. Phys., 15, 10965–10982, https://doi.org/10.5194/acp-15-10965-2015, 2015.
Chen, G., Li, S., Knibbs, L. D., Hamm, N. A., Cao, W., Li, T., Guo, J., Ren,
H., Abramson, M. J., and Guo, Y.: A machine learning method to estimate PM2.
5 concentrations across China with remote sensing, meteorological and land
use information, Sci. Total Environ., 636, 52–60, 2018.
Chen, Y., Wolke, R., Ran, L., Birmili, W., Spindler, G., Schröder, W., Su, H., Cheng, Y., Tegen, I., and Wiedensohler, A.: A parameterization of the heterogeneous hydrolysis of N2O5 for mass-based aerosol models: improvement of particulate nitrate prediction, Atmos. Chem. Phys., 18, 673–689, https://doi.org/10.5194/acp-18-673-2018, 2018.
Cheng, P., Pour-Biazar, A., White, A. T., and McNider, R. T.: Improvement of
summertime surface ozone prediction by assimilating Geostationary
Operational Environmental Satellite cloud observations, Atmos. Environ., 268,
118751, https://doi.org/10.1016/j.atmosenv.2021.118751, 2022.
Clarisse, L., R'Honi, Y., Coheur, P. F., Hurtmans, D., and Clerbaux, C.:
Thermal infrared nadir observations of 24 atmospheric gases, Geophys. Res.
Lett., 38, L10802, https://doi.org/10.1029/2011GL047271, 2011.
Cui, L. and Wang, S.: Mapping the daily nitrous acid (HONO) concentrations
across China during 2006–2017 through ensemble machine-learning algorithm,
Sci. Total Environ., 785, 147325, https://doi.org/10.1016/j.scitotenv.2021.147325, 2021.
Dang, C., Liu, Y., Yue, H., Qian, J., and Zhu, R.: Autumn crop yield
prediction using data-driven approaches:-support vector machines, random
forest, and deep neural network methods, Can. J. Remote Sens., 47, 162–181,
2021.
Ding, B., Qian, H., and Zhou J.: Activation functions and their characteristics in deep neural networks, in: 2018 Chinese Control And Decision Conference (CCDC), Shenyang, China, 9 July 2018, 1836–1841, https://doi.org/10.1109/CCDC.2018.8407425, 2018.
Ge, B., Xu, X., Ma, Z., Pan, X., Wang, Z., Lin, W., Ouyang, B., Xu, D., Lee,
J., and Zheng, M.: Role of Ammonia on the Feedback Between AWC and Inorganic
Aerosol Formation During Heavy Pollution in the North China Plain, Astr. Soc.
P., 6, 1675–1693, 2019.
Gen, M., Liang, Z., Zhang, R., Mabato, B. R. G., and Chan, C. K.:
Particulate nitrate photolysis in the atmosphere, Environmental Science:
Atmospheres, 2022, 111–127, https://doi.org/10.1039/D1EA00087J, 2022.
Gil, J.: RNDv1.0 and example, Zenodo [code and data set], https://doi.org/10.5281/zenodo.5540180, 2021.
Gil, J., Son, J., Kang, S., Park, J., Lee, M., Jeon, E., and Shim, M.: HONO
measurement in Seoul during Summer 2018 and its Impact on Photochemistry,
Journal of Korean Society for Atmospheric Environment, 36, 579–588,
https://doi.org/10.5572/KOSAE.2020.36.5.579, 2020.
Gil, J., Kim, J., Lee, M., Lee, G., Ahn, J., Lee, D. S., Jung, J., Cho, S.,
Whitehill, A., Szykman, J., and Lee, J.: Characteristics of HONO and its
impact on O3 formation in the Seoul Metropolitan Area during the Korea-US
Air Quality study, Atmos. Environ., 247, 118182,
https://doi.org/10.1016/j.atmosenv.2020.118182, 2021.
Gil, J., Lee, M., Lee, H., and Jang, J.: Seasonal Characteristics of HONO
Variations in Seoul during 2021–2022, Journal of Korean
Society for Atmospheric Environment, 39, 308–319, 2023.
Gu, R., Wang, W., Peng, X., Xia, M., Zhao, M., Zhang, Y., Liu, Y., Shen, H.,
Xue, L., and Wang, T.: Nitrous acid in the polluted coastal atmosphere of
the South China Sea: Ship emissions, budgets, and impacts, Sci. Total Environ., 826, 153692, https://doi.org/10.1016/j.scitotenv.2022.153692, 2022.
IPCC: Summary for policymakers, in: Climate Change 2014: Impacts, Adaption,
and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of
Working Group II to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Field, C. B., Barros, V. R., Dokken, D. J., Mach, K. J., Mastrandrea, M. D., Bilir, T. E., Chatterjee, M., Ebi, K. L., Estrada, Y. O., Genova, R. C., Girma, B., Kissel, E. S., Levy, A. N., MacCracken, S., Mastrandrea, P. R.,
and White, L. L., Cambridge, United Kingdom and New York, NY, USA,
1–32, https://doi.org/10.1017/CBO9781107415379.003, 2014.
IPCC: Sections, in: Climate Change 2023: Synthesis Report. Contribution of
Working Groups I, II and III to the Sixth Assessment Report of the
Intergovernmental Panel on Climate Change, 35–115, https://www.ipcc.ch/report/sixth-assessment-report-cycle/, last access: 11 September 2023.
Jia, C., Tong, S., Zhang, W., Zhang, X., Li, W., Wang, Z., Wang, L., Liu,
Z., Hu, B., and Zhao, P.: Pollution characteristics and potential sources of
nitrous acid (HONO) in early autumn 2018 of Beijing, Sci. Total Environ., 735,
139317, https://doi.org/10.1016/j.scitotenv.2020.139317, 2020.
Joutsensaari, J., Ozon, M., Nieminen, T., Mikkonen, S., Lähivaara, T., Decesari, S., Facchini, M. C., Laaksonen, A., and Lehtinen, K. E. J.: Identification of new particle formation events with deep learning, Atmos. Chem. Phys., 18, 9597–9615, https://doi.org/10.5194/acp-18-9597-2018, 2018.
Kang, Y., Choi, H., Im, J., Park, S., Shin, M., Song, C.-K., and Kim, S.:
Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and
machine learning over East Asia, Environ. Pollut., 288, 117711, https://doi.org/10.1016/j.envpol.2021.117711, 2021.
Kim, H., Gil, J., Lee, M., Jung, J., Whitehill, A., Szykman, J., Lee, G.,
Kim, D.-S., Cho, S., and Ahn, J.-Y.: Factors controlling surface ozone in
the Seoul Metropolitan Area during the KORUS-AQ campaign, Elementa: Science
of the Anthropocene, 8, 46, https://doi.org/10.1525/elementa.444, 2020.
Kleffmann, J., Lörzer, J., Wiesen, P., Kern, C., Trick, S., Volkamer,
R., Rodenas, M., and Wirtz, K.: Intercomparison of the DOAS and LOPAP
techniques for the detection of nitrous acid (HONO), Atmos. Environ., 40,
3640–3652, 2006.
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.
Krishnamurthy, R., Newsom, R. K., Berg, L. K., Xiao, H., Ma, P.-L., and Turner, D. D.: On the estimation of boundary layer heights: a machine learning approach, Atmos. Meas. Tech., 14, 4403–4424, https://doi.org/10.5194/amt-14-4403-2021, 2021.
Lee, B. H., Wood, E. C., Zahniser, M. S., McManus, J. B., Nelson, D. D.,
Herndon, S. C., Santoni, G., Wofsy, S. C., and Munger, J. W.: Simultaneous
measurements of atmospheric HONO and NO2 via absorption spectroscopy using
tunable mid-infrared continuous-wave quantum cascade lasers, Appl. Phys. B,
102, 417–423, 2011.
Levy, M., Zhang, R., Zheng, J., Zhang, A. L., Xu, W., Gomez-Hernandez, M.,
Wang, Y., and Olaguer, E.: Measurements of nitrous acid (HONO) using ion
drift-chemical ionization mass spectrometry during the 2009 SHARP field
campaign, Atmos. Environ., 94, 231–240, 2014.
Li, S., Song, W., Zhan, H., Zhang, Y., Zhang, X., Li, W., Tong, S., Pei, C.,
Wang, Y., and Chen, Y.: Contribution of Vehicle Emission and NO2 Surface
Conversion to Nitrous Acid (HONO) in Urban Environments: Implications from
Tests in a Tunnel, Environ. Sci. Technol., 55, 15616–15624, 2021.
Li, Y., Wang, X., Wu, Z., Li, L., Wang, C., Li, H., Zhang, X., Zhang, Y.,
Li, J., and Gao, R.: Atmospheric nitrous acid (HONO) in an alternate process
of haze pollution and ozone pollution in urban Beijing in summertime:
Variations, sources and contribution to atmospheric photochemistry, Atmos.
Res., 260, 105689, https://doi.org/10.1016/j.atmosres.2021.105689, 2021.
Li, Z., Xie, P., Hu, R., Wang, D., Jin, H., Chen, H., Lin, C., and Liu, W.:
Observations of N2O5 and NO3 at a suburban environment in Yangtze river
delta in China: Estimating heterogeneous N2O5 uptake coefficients, J. Environ.
Sci., 95, 248–255, https://doi.org/10.1016/j.jes.2020.04.041, 2020.
Liebmann, J., Karu, E., Sobanski, N., Schuladen, J., Ehn, M., Schallhart, S., Quéléver, L., Hellen, H., Hakola, H., Hoffmann, T., Williams, J., Fischer, H., Lelieveld, J., and Crowley, J. N.: Direct measurement of NO3 radical reactivity in a boreal forest, Atmos. Chem. Phys., 18, 3799–3815, https://doi.org/10.5194/acp-18-3799-2018, 2018.
Liu, Y., Lu, K., Li, X., Dong, H., Tan, Z., Wang, H., Zou, Q., Wu, Y., Zeng,
L., and Hu, M.: A comprehensive model test of the HONO sources constrained
to field measurements at rural North China Plain, Environ. Sci. Technol., 53,
3517–3525, 2019.
Mallet, V. and Sportisse, B.: Uncertainty in a chemistry-transport model due
to physical parameterizations and numerical approximations: An ensemble
approach applied to ozone modeling, J. Geophys. Res.-Atmos., 111, D01302, https://doi.org/10.1029/2005JD006149, 2006.
Meng, F., Qin, M., Fang, W., Duan, J., Tang, K., Zhang, H., Shao, D., Liao,
Z., Feng, Y., and Huang, Y.: Measurement of HONO flux using the aerodynamic
gradient method over an agricultural field in the Huaihe River Basin, China,
J. Environ. Sci., 114, 297–307, https://doi.org/10.1016/j.jes.2021.09.005, 2022.
Monks, P. S., Archibald, A. T., Colette, A., Cooper, O., Coyle, M., Derwent, R., Fowler, D., Granier, C., Law, K. S., Mills, G. E., Stevenson, D. S., Tarasova, O., Thouret, V., von Schneidemesser, E., Sommariva, R., Wild, O., and Williams, M. L.: Tropospheric ozone and its precursors from the urban to the global scale from air quality to short-lived climate forcer, Atmos. Chem. Phys., 15, 8889–8973, https://doi.org/10.5194/acp-15-8889-2015, 2015.
Myhre, G., Aas, W., Cherian, R., Collins, W., Faluvegi, G., Flanner, M., Forster, P., Hodnebrog, Ø., Klimont, Z., Lund, M. T., Mülmenstädt, J., Lund Myhre, C., Olivié, D., Prather, M., Quaas, J., Samset, B. H., Schnell, J. L., Schulz, M., Shindell, D., Skeie, R. B., Takemura, T., and Tsyro, S.: Multi-model simulations of aerosol and ozone radiative forcing due to anthropogenic emission changes during the period 1990–2015, Atmos. Chem. Phys., 17, 2709–2720, https://doi.org/10.5194/acp-17-2709-2017, 2017.
NIER: Improvement of Air Quality Forecast based on the Measurement –Focused on Spring Episodes of PM2.5, 1–220, https://ecolibrary.me.go.kr/nier/#/search/detail/5711273?offset=1 (last access: 11 September 2023), 2020.
Pinto, J., Dibb, J., Lee, B., Rappenglück, B., Wood, E., Levy, M.,
Zhang, R. Y., Lefer, B., Ren, X. R., and Stutz, J.: Intercomparison of field
measurements of nitrous acid (HONO) during the SHARP campaign, J. Geophys.
Res.-Atmos., 119, 5583–5601, 2014.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., and
Carvalhais, N.: Deep learning and process understanding for data-driven
Earth system science, Nature, 566, 195–204, 2019.
Roberts, J. M., Veres, P., Warneke, C., Neuman, J. A., Washenfelder, R. A., Brown, S. S., Baasandorj, M., Burkholder, J. B., Burling, I. R., Johnson, T. J., Yokelson, R. J., and de Gouw, J.: Measurement of HONO, HNCO, and other inorganic acids by negative-ion proton-transfer chemical-ionization mass spectrometry (NI-PT-CIMS): application to biomass burning emissions, Atmos. Meas. Tech., 3, 981–990, https://doi.org/10.5194/amt-3-981-2010, 2010.
Schultz, M., Betancourt, C., Gong, B., Kleinert, F., Langguth, M., Leufen,
L., Mozaffari, A., and Stadtler, S.: Can deep learning beat numerical
weather prediction?, Philos. T. R. Soc. A, 379, 20200097, https://doi.org/10.1098/rsta.2020.0097, 2021.
Shahriar, S. A., Kayes, I., Hasan, K., Salam, M. A., and Chowdhury, S.:
Applicability of machine learning in modeling of atmospheric particle
pollution in Bangladesh, Air Qual. Atmos. Hlth., 13, 1247–1256, 2020.
Shareef, M. M., Husain, T., and Alharbi, B.: Studying the Effect of
Different Gas-Phase Chemical Kinetic Mechanisms on the Formation of
Oxidants, Nitrogen Compounds and Ozone in Arid Regions, Journal of
Environmental Protection, 10, 1006–1031, 2019.
Shindell, D. T., Lamarque, J.-F., Schulz, M., Flanner, M., Jiao, C., Chin, M., Young, P. J., Lee, Y. H., Rotstayn, L., Mahowald, N., Milly, G., Faluvegi, G., Balkanski, Y., Collins, W. J., Conley, A. J., Dalsoren, S., Easter, R., Ghan, S., Horowitz, L., Liu, X., Myhre, G., Nagashima, T., Naik, V., Rumbold, S. T., Skeie, R., Sudo, K., Szopa, S., Takemura, T., Voulgarakis, A., Yoon, J.-H., and Lo, F.: Radiative forcing in the ACCMIP historical and future climate simulations, Atmos. Chem. Phys., 13, 2939–2974, https://doi.org/10.5194/acp-13-2939-2013, 2013.
Stadtler, S., Simpson, D., Schröder, S., Taraborrelli, D., Bott, A., and Schultz, M.: Ozone impacts of gas–aerosol uptake in global chemistry transport models, Atmos. Chem. Phys., 18, 3147–3171, https://doi.org/10.5194/acp-18-3147-2018, 2018.
Stevenson, D. S., Young, P. J., Naik, V., Lamarque, J.-F., Shindell, D. T., Voulgarakis, A., Skeie, R. B., Dalsoren, S. B., Myhre, G., Berntsen, T. K., Folberth, G. A., Rumbold, S. T., Collins, W. J., MacKenzie, I. A., Doherty, R. M., Zeng, G., van Noije, T. P. C., Strunk, A., Bergmann, D., Cameron-Smith, P., Plummer, D. A., Strode, S. A., Horowitz, L., Lee, Y. H., Szopa, S., Sudo, K., Nagashima, T., Josse, B., Cionni, I., Righi, M., Eyring, V., Conley, A., Bowman, K. W., Wild, O., and Archibald, A.: Tropospheric ozone changes, radiative forcing and attribution to emissions in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP), Atmos. Chem. Phys., 13, 3063–3085, https://doi.org/10.5194/acp-13-3063-2013, 2013.
Sumathi, S. and Pugalendhi, G. K.: Cognition based spam mail text analysis
using combined approach of deep neural network classifier and random forest,
J. Amb. Intel. Hum. Comp., 12, 5721–5731, 2021.
Sun, Y., Wang, L., Wang, Y., Quan, L., and Zirui, L.: In situ measurements
of SO2, NOx, NOy, and O3 in Beijing, China during August 2008, Sci. Total Environ., 409, 933–940, 2011.
Theys, N., Volkamer, R., Müller, J.-F., Zarzana, K. J., Kille, N.,
Clarisse, L., De Smedt, I., Lerot, C., Finkenzeller, H., and Hendrick, F.:
Global nitrous acid emissions and levels of regional oxidants enhanced by
wildfires, Nat. Geosci., 13, 681–686, 2020.
Tie, X., Geng, F., Guenther, A., Cao, J., Greenberg, J., Zhang, R., Apel, E., Li, G., Weinheimer, A., Chen, J., and Cai, C.: Megacity impacts on regional ozone formation: observations and WRF-Chem modeling for the MIRAGE-Shanghai field campaign, Atmos. Chem. Phys., 13, 5655–5669, https://doi.org/10.5194/acp-13-5655-2013, 2013.
VandenBoer, T., Markovic, M., Sanders, J., Ren, X., Pusede, S., Browne, E.,
Cohen, R., Zhang, L., Thomas, J., and Brune, W. H.: Evidence for a nitrous
acid (HONO) reservoir at the ground surface in Bakersfield, CA, during
CalNex 2010, J. Geophys. Res.-Atmos., 119, 9093–9106, 2014.
Varotsos, K., Giannakopoulos, C., and Tombrou, M.: Assessment of the Impacts
of climate change on european ozone levels, Water Air Soil Poll., 224, 1596, https://doi.org/10.1007/s11270-013-1596-z,
2013.
Wang, T., Qin, Z., Zhu, M.: An ELU Network with Total Variation for Image Denoising, in: Neural Information Processing, edited by: Liu, D., Xie, S., Li, Y., Zhao, D., and El-Alfy, E., ICONIP 2017, Lecture Notes in Computer Science, vol. 10636, Springer, Cham, https://doi.org/10.1007/978-3-319-70090-8_24, 2017.
Wang, X., Wang, H., Xue, L., Wang, T., Wang, L., Gu, R., Wang, W., Tham, Y.
J., Wang, Z., and Yang, L.: Observations of N2O5 and ClNO2 at a polluted urban surface site in North China: High N2O5 uptake coefficients and low ClNO2 product yields, Atmos. Environ., 156, 125–134, 2017.
Wang, X., Dalton, E. Z., Payne, Z. C., Perrier, S., Riva, M., Raff, J. D.,
and George, C.: Superoxide and nitrous acid production from nitrate
photolysis is enhanced by dissolved aliphatic organic matter, Environ. Sci.
Tech. Let., 8, 53–58, 2020.
Wolfe, G. M., Marvin, M. R., Roberts, S. J., Travis, K. R., and Liao, J.: The Framework for 0-D Atmospheric Modeling (F0AM) v3.1, Geosci. Model Dev., 9, 3309–3319, https://doi.org/10.5194/gmd-9-3309-2016, 2016.
Xu, Z., Liu, Y., Nie, W., Sun, P., Chi, X., and Ding, A.: Evaluating the measurement interference of wet rotating-denuder–ion chromatography in measuring atmospheric HONO in a highly polluted area, Atmos. Meas. Tech., 12, 6737–6748, https://doi.org/10.5194/amt-12-6737-2019, 2019.
Xue, C., Ye, C., Ma, Z., Liu, P., Zhang, Y., Zhang, C., Tang, K., Zhang, W.,
Zhao, X., and Wang, Y.: Development of stripping coil-ion chromatograph
method and intercomparison with CEAS and LOPAP to measure atmospheric HONO,
Sci. Total Environ., 646, 187–195, 2019.
Ye, C., Zhou, X., Pu, D., Stutz, J., Festa, J., Spolaor, M., Tsai, C.,
Cantrell, C., Mauldin, R. L., and Campos, T.: Rapid cycling of reactive
nitrogen in the marine boundary layer, Nature, 532, 489–491, 2016.
Short summary
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.
In this study, the framework for calculating reactive nitrogen species using a deep neural...
