Articles | Volume 5, issue 1
https://doi.org/10.5194/gmd-5-167-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-5-167-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
26 Jan 2012
Model description paper |
| 26 Jan 2012
CELLS v1.0: updated and parallelized version of an electrical scheme to simulate multiple electrified clouds and flashes over large domains
C. Barthe
Laboratoire de l'Atmosphère et des Cyclones (LACy) – UMR8105, CNRS, Université de la Réunion and Météo-France, Saint-Denis, La Réunion, France
M. Chong
Laboratoire d'Aérologie, CNRS and Université de Toulouse, Toulouse, France
J.-P. Pinty
Laboratoire d'Aérologie, CNRS and Université de Toulouse, Toulouse, France
C. Bovalo
Laboratoire de l'Atmosphère et des Cyclones (LACy) – UMR8105, CNRS, Université de la Réunion and Météo-France, Saint-Denis, La Réunion, France
J. Escobar
Laboratoire d'Aérologie, CNRS and Université de Toulouse, Toulouse, France
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The impact of altering emission data precision on compression efficiency and accuracy of simulations of the community multiscale air quality model
AerSett v1.0: a simple and straightforward model for the settling speed of big spherical atmospheric aerosols
Optimization of weather forecasting for cloud cover over the European domain using the meteorological component of the Ensemble for Stochastic Integration of Atmospheric Simulations version 1.0
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Geosci. Model Dev., 16, 1379–1394, https://doi.org/10.5194/gmd-16-1379-2023, https://doi.org/10.5194/gmd-16-1379-2023, 2023
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Marine Bonazzola, Hélène Chepfer, Po-Lun Ma, Johannes Quaas, David M. Winker, Artem Feofilov, and Nick Schutgens
Geosci. Model Dev., 16, 1359–1377, https://doi.org/10.5194/gmd-16-1359-2023, https://doi.org/10.5194/gmd-16-1359-2023, 2023
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Aerosol has a large impact on climate. Using a lidar aerosol simulator ensures consistent comparisons between modeled and observed aerosol. We present a lidar aerosol simulator that applies a cloud masking and an aerosol detection threshold. We estimate the lidar signals that would be observed at 532 nm by the Cloud-Aerosol Lidar with Orthogonal Polarization overflying the atmosphere predicted by a climate model. Our comparison at the seasonal timescale shows a discrepancy in the Southern Ocean.
Michael S. Walters and David C. Wong
Geosci. Model Dev., 16, 1179–1190, https://doi.org/10.5194/gmd-16-1179-2023, https://doi.org/10.5194/gmd-16-1179-2023, 2023
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A typical numerical simulation that associates with a large amount of input and output data, applying popular compression software, gzip or bzip2, on data is one good way to mitigate data storage burden. This article proposes a simple technique to alter input, output, or input and output by keeping a specific number of significant digits in data and demonstrates an enhancement in compression efficiency on the altered data but maintains similar statistical performance of the numerical simulation.
Sylvain Mailler, Laurent Menut, Arineh Cholakian, and Romain Pennel
Geosci. Model Dev., 16, 1119–1127, https://doi.org/10.5194/gmd-16-1119-2023, https://doi.org/10.5194/gmd-16-1119-2023, 2023
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Large or even
giantparticles of mineral dust exist in the atmosphere but, so far, solving an non-linear equation was needed to calculate the speed at which they fall in the atmosphere. The model we present, AerSett v1.0 (AERosol SETTling version 1.0), provides a new and simple way of calculating their free-fall velocity in the atmosphere, which will be useful to anyone trying to understand and represent adequately the transport of giant dust particles by the wind.
Yen-Sen Lu, Garrett H. Good, and Hendrik Elbern
Geosci. Model Dev., 16, 1083–1104, https://doi.org/10.5194/gmd-16-1083-2023, https://doi.org/10.5194/gmd-16-1083-2023, 2023
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Ryan Vella, Matthew Forrest, Jos Lelieveld, and Holger Tost
Geosci. Model Dev., 16, 885–906, https://doi.org/10.5194/gmd-16-885-2023, https://doi.org/10.5194/gmd-16-885-2023, 2023
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Biogenic volatile organic compounds (BVOCs) are released by vegetation and have a major impact on atmospheric chemistry and aerosol formation. Non-interacting vegetation constrains the majority of numerical models used to estimate global BVOC emissions, and thus, the effects of changing vegetation on emissions are not addressed. In this work, we replace the offline vegetation with dynamic vegetation states by linking a chemistry–climate model with a global dynamic vegetation model.
Danny McCulloch, Denis E. Sergeev, Nathan Mayne, Matthew Bate, James Manners, Ian Boutle, Benjamin Drummond, and Kristzian Kohary
Geosci. Model Dev., 16, 621–657, https://doi.org/10.5194/gmd-16-621-2023, https://doi.org/10.5194/gmd-16-621-2023, 2023
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Sam-Erik Walker, Sverre Solberg, Philipp Schneider, and Cristina Guerreiro
Geosci. Model Dev., 16, 573–595, https://doi.org/10.5194/gmd-16-573-2023, https://doi.org/10.5194/gmd-16-573-2023, 2023
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We have developed a statistical model for estimating trends in the daily air quality observations of NO2, O3, PM10 and PM2.5, adjusting for trends and short-term variations in meteorology. The model is general and may also be used for prediction purposes, including forecasting. It has been applied in a recent comprehensive study in Europe. Significant declines are shown for the pollutants from 2005 to 2019, mainly due to reductions in emissions not attributable to changes in meteorology.
Bianca Adler, James M. Wilczak, Jaymes Kenyon, Laura Bianco, Irina V. Djalalova, Joseph B. Olson, and David D. Turner
Geosci. Model Dev., 16, 597–619, https://doi.org/10.5194/gmd-16-597-2023, https://doi.org/10.5194/gmd-16-597-2023, 2023
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Rapid changes in wind speed make the integration of wind energy produced during persistent orographic cold-air pools difficult to integrate into the electrical grid. By evaluating three versions of NOAA’s High-Resolution Rapid Refresh model, we demonstrate how model developments targeted during the second Wind Forecast Improvement Project improve the forecast of a persistent cold-air pool event.
John Douros, Henk Eskes, Jos van Geffen, K. Folkert Boersma, Steven Compernolle, Gaia Pinardi, Anne-Marlene Blechschmidt, Vincent-Henri Peuch, Augustin Colette, and Pepijn Veefkind
Geosci. Model Dev., 16, 509–534, https://doi.org/10.5194/gmd-16-509-2023, https://doi.org/10.5194/gmd-16-509-2023, 2023
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We focus on the challenges associated with comparing atmospheric composition models with satellite products such as tropospheric NO2 columns. The aim is to highlight the methodological difficulties and propose sound ways of doing such comparisons. Building on the comparisons, a new satellite product is proposed and made available, which takes advantage of higher-resolution, regional atmospheric modelling to improve estimates of troposheric NO2 columns over Europe.
Catalina Poraicu, Jean-François Müller, Trissevgeni Stavrakou, Dominique Fonteyn, Frederik Tack, Felix Deutsch, Quentin Laffineur, Roeland Van Malderen, and Nele Veldeman
Geosci. Model Dev., 16, 479–508, https://doi.org/10.5194/gmd-16-479-2023, https://doi.org/10.5194/gmd-16-479-2023, 2023
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High-resolution WRF-Chem simulations are conducted over Antwerp, Belgium, in June 2019 and evaluated using meteorological data and in situ, airborne, and spaceborne NO2 measurements. An intercomparison of model, aircraft, and TROPOMI NO2 columns is conducted to characterize biases in versions 1.3.1 and 2.3.1 of the satellite product. A mass balance method is implemented to provide improved emissions for simulating NO2 distribution over the study area.
Daan R. Scheepens, Irene Schicker, Kateřina Hlaváčková-Schindler, and Claudia Plant
Geosci. Model Dev., 16, 251–270, https://doi.org/10.5194/gmd-16-251-2023, https://doi.org/10.5194/gmd-16-251-2023, 2023
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The production of wind energy is increasing rapidly and relies heavily on atmospheric conditions. To ensure power grid stability, accurate predictions of wind speed are needed, especially in the short range and for extreme wind speed ranges. In this work, we demonstrate the forecasting skills of a data-driven deep learning model with model adaptations to suit higher wind speed ranges. The resulting model can be applied to other data and parameters, too, to improve nowcasting predictions.
Peter J. M. Bosman and Maarten C. Krol
Geosci. Model Dev., 16, 47–74, https://doi.org/10.5194/gmd-16-47-2023, https://doi.org/10.5194/gmd-16-47-2023, 2023
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We describe an inverse modelling framework constructed around a simple model for the atmospheric boundary layer. This framework can be fed with various observation types to study the boundary layer and land–atmosphere exchange. With this framework, it is possible to estimate model parameters and the associated uncertainties. Some of these parameters are difficult to obtain directly by observations. An example application for a grassland in the Netherlands is included.
Sudipta Ghosh, Sagnik Dey, Sushant Das, Nicole Riemer, Graziano Giuliani, Dilip Ganguly, Chandra Venkataraman, Filippo Giorgi, Sachchida Nand Tripathi, Srikanthan Ramachandran, Thazhathakal Ayyappen Rajesh, Harish Gadhavi, and Atul Kumar Srivastava
Geosci. Model Dev., 16, 1–15, https://doi.org/10.5194/gmd-16-1-2023, https://doi.org/10.5194/gmd-16-1-2023, 2023
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Accurate representation of aerosols in climate models is critical for minimizing the uncertainty in climate projections. Here, we implement region-specific emission fluxes and a more accurate scheme for carbonaceous aerosol ageing processes in a regional climate model (RegCM4) and show that it improves model performance significantly against in situ, reanalysis, and satellite data over the Indian subcontinent. We recommend improving the model performance before using them for climate studies.
Chengzhu Zhang, Jean-Christophe Golaz, Ryan Forsyth, Tom Vo, Shaocheng Xie, Zeshawn Shaheen, Gerald L. Potter, Xylar S. Asay-Davis, Charles S. Zender, Wuyin Lin, Chih-Chieh Chen, Chris R. Terai, Salil Mahajan, Tian Zhou, Karthik Balaguru, Qi Tang, Cheng Tao, Yuying Zhang, Todd Emmenegger, Susannah Burrows, and Paul A. Ullrich
Geosci. Model Dev., 15, 9031–9056, https://doi.org/10.5194/gmd-15-9031-2022, https://doi.org/10.5194/gmd-15-9031-2022, 2022
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Earth system model (ESM) developers run automated analysis tools on data from candidate models to inform model development. This paper introduces a new Python package, E3SM Diags, that has been developed to support ESM development and use routinely in the development of DOE's Energy Exascale Earth System Model. This tool covers a set of essential diagnostics to evaluate the mean physical climate from simulations, as well as several process-oriented and phenomenon-based evaluation diagnostics.
Walter Hannah and Kyle Pressel
Geosci. Model Dev., 15, 8999–9013, https://doi.org/10.5194/gmd-15-8999-2022, https://doi.org/10.5194/gmd-15-8999-2022, 2022
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A multiscale modeling framework couples two models of the atmosphere that each cover different scale ranges. Traditionally, fluctuations in the small-scale model are not transported by the flow on the large-scale model grid, but this is hypothesized to be responsible for a persistent, unphysical checkerboard pattern. A method is presented to facilitate the transport of these small-scale fluctuations, analogous to how small-scale clouds and turbulence are transported in the real atmosphere.
Reimar Bauer, Jens-Uwe Grooß, Jörn Ungermann, May Bär, Markus Geldenhuys, and Lars Hoffmann
Geosci. Model Dev., 15, 8983–8997, https://doi.org/10.5194/gmd-15-8983-2022, https://doi.org/10.5194/gmd-15-8983-2022, 2022
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The Mission Support System (MSS) is an open source software package that has been used for planning flight tracks of scientific aircraft in multiple measurement campaigns during the last decade. Here, we describe the MSS software and its use during the SouthTRAC measurement campaign in 2019. As an example for how the MSS software is used in conjunction with many datasets, we describe the planning of a single flight probing orographic gravity waves propagating up into the lower mesosphere.
Zhizhao Wang, Florian Couvidat, and Karine Sartelet
Geosci. Model Dev., 15, 8957–8982, https://doi.org/10.5194/gmd-15-8957-2022, https://doi.org/10.5194/gmd-15-8957-2022, 2022
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Air quality models need to reliably predict secondary organic aerosols (SOAs) at a reasonable computational cost. Thus, we developed GENOA v1.0, a mechanism reduction algorithm that preserves the accuracy of detailed gas-phase chemical mechanisms for SOA formation, thereby improving the practical use of actual chemistry in SOA models. With GENOA, a near-explicit chemical scheme was reduced to 2 % of its original size and computational time, with an average error of less than 3 %.
Felix Kleinert, Lukas H. Leufen, Aurelia Lupascu, Tim Butler, and Martin G. Schultz
Geosci. Model Dev., 15, 8913–8930, https://doi.org/10.5194/gmd-15-8913-2022, https://doi.org/10.5194/gmd-15-8913-2022, 2022
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We examine the effects of spatially aggregated upstream information as input for a deep learning model forecasting near-surface ozone levels. Using aggregated data from one upstream sector (45°) improves the forecast by ~ 10 % for 4 prediction days. Three upstream sectors improve the forecasts by ~ 14 % on the first 2 d only. Our results serve as an orientation for other researchers or environmental agencies focusing on pointwise time-series predictions, for example, due to regulatory purposes.
Brian T. Dinkelacker, Pablo Garcia Rivera, Ioannis Kioutsioukis, Peter J. Adams, and Spyros N. Pandis
Geosci. Model Dev., 15, 8899–8912, https://doi.org/10.5194/gmd-15-8899-2022, https://doi.org/10.5194/gmd-15-8899-2022, 2022
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The performance of a chemical transport model in reproducing PM2.5 concentrations and composition was evaluated at the finest scale using measurements from regulatory sites as well as a network of low-cost monitors. Total PM2.5 mass is reproduced well by the model during the winter when compared to regulatory measurements, but in the summer PM2.5 is underpredicted, mainly due to difficulties in reproducing regional secondary organic aerosol levels.
Shizhang Wang and Xiaoshi Qiao
Geosci. Model Dev., 15, 8869–8897, https://doi.org/10.5194/gmd-15-8869-2022, https://doi.org/10.5194/gmd-15-8869-2022, 2022
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A local data assimilation scheme (Local DA v1.0) was proposed to leverage the advantage of hybrid covariance, multiscale localization, and parallel computation. The Local DA can perform covariance localization in model space, observation space, or both spaces. The Local DA that used the hybrid covariance and double-space localization produced the lowest analysis and forecast errors among all observing system simulation experiments.
Randall V. Martin, Sebastian D. Eastham, Liam Bindle, Elizabeth W. Lundgren, Thomas L. Clune, Christoph A. Keller, William Downs, Dandan Zhang, Robert A. Lucchesi, Melissa P. Sulprizio, Robert M. Yantosca, Yanshun Li, Lucas Estrada, William M. Putman, Benjamin M. Auer, Atanas L. Trayanov, Steven Pawson, and Daniel J. Jacob
Geosci. Model Dev., 15, 8731–8748, https://doi.org/10.5194/gmd-15-8731-2022, https://doi.org/10.5194/gmd-15-8731-2022, 2022
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Atmospheric chemistry models must be able to operate both online as components of Earth system models and offline as standalone models. The widely used GEOS-Chem model operates both online and offline, but the classic offline version is not suitable for massively parallel simulations. We describe a new generation of the offline high-performance GEOS-Chem (GCHP) that enables high-resolution simulations on thousands of cores, including on the cloud, with improved access, performance, and accuracy.
Daiwen Kang, Nicholas K. Heath, Robert C. Gilliam, Tanya L. Spero, and Jonathan E. Pleim
Geosci. Model Dev., 15, 8561–8579, https://doi.org/10.5194/gmd-15-8561-2022, https://doi.org/10.5194/gmd-15-8561-2022, 2022
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A lightning assimilation (LTA) technique implemented in the WRF model's Kain–Fritsch (KF) convective scheme is updated and applied to simulations from regional to hemispheric scales using observed lightning flashes from ground-based lightning detection networks. Different user-toggled options associated with the KF scheme on simulations with and without LTA are assessed. The model's performance is improved significantly by LTA, but it is sensitive to various factors.
Sujeong Lim, Hyeon-Ju Gim, Ebony Lee, Seungyeon Lee, Won Young Lee, Yong Hee Lee, Claudio Cassardo, and Seon Ki Park
Geosci. Model Dev., 15, 8541–8559, https://doi.org/10.5194/gmd-15-8541-2022, https://doi.org/10.5194/gmd-15-8541-2022, 2022
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The land surface model (LSM) contains various uncertain parameters, which are obtained by the empirical relations reflecting the specific local region and can be a source of uncertainty. To seek the optimal parameter values in the snow-related processes of the Noah LSM over South Korea, we have implemented an optimization algorithm, a micro-genetic algorithm using the observations. As a result, the optimized snow parameters improve snowfall prediction.
Haochen Sun, Jimmy C. H. Fung, Yiang Chen, Zhenning Li, Dehao Yuan, Wanying Chen, and Xingcheng Lu
Geosci. Model Dev., 15, 8439–8452, https://doi.org/10.5194/gmd-15-8439-2022, https://doi.org/10.5194/gmd-15-8439-2022, 2022
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This study developed a novel deep-learning layer, the broadcasting layer, to build an end-to-end LSTM-based deep-learning model for regional air pollution forecast. By combining the ground observation, WRF-CMAQ simulation, and the broadcasting LSTM deep-learning model, forecast accuracy has been significantly improved when compared to other methods. The broadcasting layer and its variants can also be applied in other research areas to supersede the traditional numerical interpolation methods.
Shunji Kotsuki, Takemasa Miyoshi, Keiichi Kondo, and Roland Potthast
Geosci. Model Dev., 15, 8325–8348, https://doi.org/10.5194/gmd-15-8325-2022, https://doi.org/10.5194/gmd-15-8325-2022, 2022
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Data assimilation plays an important part in numerical weather prediction (NWP) in terms of combining forecasted states and observations. While data assimilation methods in NWP usually assume the Gaussian error distribution, some variables in the atmosphere, such as precipitation, are known to have non-Gaussian error statistics. This study extended a widely used ensemble data assimilation algorithm to enable the assimilation of more non-Gaussian observations.
Martin Vojta, Andreas Plach, Rona L. Thompson, and Andreas Stohl
Geosci. Model Dev., 15, 8295–8323, https://doi.org/10.5194/gmd-15-8295-2022, https://doi.org/10.5194/gmd-15-8295-2022, 2022
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In light of recent global warming, we aim to improve methods for modeling greenhouse gas emissions in order to support the successful implementation of the Paris Agreement. In this study, we investigate certain aspects of a Bayesian inversion method that uses computer simulations and atmospheric observations to improve estimates of greenhouse gas emissions. We explore method limitations, discuss problems, and suggest improvements.
Longlei Li, Natalie M. Mahowald, Jasper F. Kok, Xiaohong Liu, Mingxuan Wu, Danny M. Leung, Douglas S. Hamilton, Louisa K. Emmons, Yue Huang, Neil Sexton, Jun Meng, and Jessica Wan
Geosci. Model Dev., 15, 8181–8219, https://doi.org/10.5194/gmd-15-8181-2022, https://doi.org/10.5194/gmd-15-8181-2022, 2022
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This study advances mineral dust parameterizations in the Community Atmospheric Model (CAM; version 6.1). Efforts include 1) incorporating a more physically based dust emission scheme; 2) updating the dry deposition scheme; and 3) revising the gravitational settling velocity to account for dust asphericity. Substantial improvements achieved with these updates can help accurately quantify dust–climate interactions using CAM, such as the dust-radiation and dust–cloud interactions.
Ruizi Shi and Fanghua Xu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-233, https://doi.org/10.5194/gmd-2022-233, 2022
Revised manuscript accepted for GMD
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Based on Gaussian Quadrature method, a fast parameterization scheme of sea spray-mediated heat flux is developed. Compared with the widely-used single-radius scheme, the new scheme shows a better agreement with the full spectrum integral of spray-flux. The new scheme is evaluated in a coupled modeling system, and the simulations of sea surface temperature, wind speed and wave height are improved. Thereby, the new scheme has a great potential to be used in coupled modeling systems.
Youhua Tang, Patrick C. Campbell, Pius Lee, Rick Saylor, Fanglin Yang, Barry Baker, Daniel Tong, Ariel Stein, Jianping Huang, Ho-Chun Huang, Li Pan, Jeff McQueen, Ivanka Stajner, Jose Tirado-Delgado, Youngsun Jung, Melissa Yang, Ilann Bourgeois, Jeff Peischl, Tom Ryerson, Donald Blake, Joshua Schwarz, Jose-Luis Jimenez, James Crawford, Glenn Diskin, Richard Moore, Johnathan Hair, Greg Huey, Andrew Rollins, Jack Dibb, and Xiaoyang Zhang
Geosci. Model Dev., 15, 7977–7999, https://doi.org/10.5194/gmd-15-7977-2022, https://doi.org/10.5194/gmd-15-7977-2022, 2022
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This paper compares two meteorological datasets for driving a regional air quality model: a regional meteorological model using WRF (WRF-CMAQ) and direct interpolation from an operational global model (GFS-CMAQ). In the comparison with surface measurements and aircraft data in summer 2019, these two methods show mixed performance depending on the corresponding meteorological settings. Direct interpolation is found to be a viable method to drive air quality models.
Robin N. Thor, Mariano Mertens, Sigrun Matthes, Mattia Righi, Johannes Hendricks, Sabine Brinkop, Phoebe Graf, Volker Grewe, Patrick Jöckel, and Steven Smith
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-250, https://doi.org/10.5194/gmd-2022-250, 2022
Revised manuscript accepted for GMD
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We report on an inconsistency in the latitudinal distribution of aviation emissions between two versions of a data product which is widely used by researchers. From the available documentation, we do not expect such an inconsistency. We run a chemistry-climate model to compute the effect of the inconsistency in emissions on atmospheric chemistry and radiation and find that the radiative forcing associated for aviation ozone is 7.6 % higher when using the less recent version of the data.
Zhiquan Liu, Chris Snyder, Jonathan J. Guerrette, Byoung-Joo Jung, Junmei Ban, Steven Vahl, Yali Wu, Yannick Trémolet, Thomas Auligné, Benjamin Ménétrier, Anna Shlyaeva, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
Geosci. Model Dev., 15, 7859–7878, https://doi.org/10.5194/gmd-15-7859-2022, https://doi.org/10.5194/gmd-15-7859-2022, 2022
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JEDI-MPAS 1.0.0, a new data assimilation (DA) system for the MPAS model, was publicly released for community use. This article describes JEDI-MPAS's implementation of the ensemble–variational DA technique and demonstrates its robustness and credible performance by incrementally adding three types of microwave radiances (clear-sky AMSU-A, all-sky AMSU-A, clear-sky MHS) to a non-radiance DA experiment. We intend to periodically release new and improved versions of JEDI-MPAS in upcoming years.
Li Fang, Jianbing Jin, Arjo Segers, Hai Xiang Lin, Mijie Pang, Cong Xiao, Tuo Deng, and Hong Liao
Geosci. Model Dev., 15, 7791–7807, https://doi.org/10.5194/gmd-15-7791-2022, https://doi.org/10.5194/gmd-15-7791-2022, 2022
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This study proposes a regional feature selection-based machine learning system to predict short-term air quality in China. The system has a tool that can figure out the importance of input data for better prediction. It provides large-scale air quality prediction that exhibits improved interpretability, fewer training costs, and higher accuracy compared with a standard machine learning system. It can act as an early warning for citizens and reduce exposure to PM2.5 and other air pollutants.
Stella E. I. Manavi and Spyros N. Pandis
Geosci. Model Dev., 15, 7731–7749, https://doi.org/10.5194/gmd-15-7731-2022, https://doi.org/10.5194/gmd-15-7731-2022, 2022
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The paper describes the first step towards the development of a simulation framework for the chemistry and secondary organic aerosol production of intermediate-volatility organic compounds (IVOCs). These compounds can be a significant source of organic particulate matter. Our approach treats IVOCs as lumped compounds that retain their chemical characteristics. Estimated IVOC emissions from road transport were a factor of 8 higher than emissions used in previous applications.
Peter Bräuer and Matthias Tesche
Geosci. Model Dev., 15, 7557–7572, https://doi.org/10.5194/gmd-15-7557-2022, https://doi.org/10.5194/gmd-15-7557-2022, 2022
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This paper presents a tool for (i) finding temporally and spatially resolved intersections between two- or three-dimensional geographical tracks (trajectories) and (ii) extracting of data in the vicinity of intersections to achieve the optimal combination of various data sets.
Benjamin Zanger, Jia Chen, Man Sun, and Florian Dietrich
Geosci. Model Dev., 15, 7533–7556, https://doi.org/10.5194/gmd-15-7533-2022, https://doi.org/10.5194/gmd-15-7533-2022, 2022
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Gaussian priors (GPs) used in least squares inversion do not reflect the true distributions of greenhouse gas emissions well. A method that does not rely on GPs is sparse reconstruction (SR). We show that necessary conditions for SR are satisfied for cities and that the application of a wavelet transform can further enhance sparsity. We apply the theory of compressed sensing to SR. Our results show that SR needs fewer measurements and is superior for assessing unknown emitters compared to GPs.
Paul Konopka, Mengchu Tao, Marc von Hobe, Lars Hoffmann, Corinna Kloss, Fabrizio Ravegnani, C. Michael Volk, Valentin Lauther, Andreas Zahn, Peter Hoor, and Felix Ploeger
Geosci. Model Dev., 15, 7471–7487, https://doi.org/10.5194/gmd-15-7471-2022, https://doi.org/10.5194/gmd-15-7471-2022, 2022
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Pure trajectory-based transport models driven by meteorology derived from reanalysis products (ERA5) take into account only the resolved, advective part of transport. That means neither mixing processes nor unresolved subgrid-scale advective processes like convection are included. The Chemical Lagrangian Model of the Stratosphere (CLaMS) includes these processes. We show that isentropic mixing dominates unresolved transport. The second most important transport process is unresolved convection.
Forwood Wiser, Bryan Place, Siddhartha Sen, Havala O. T. Pye, Benjamin Yang, Daniel M. Westervelt, Daven K. Henze, Arlene M. Fiore, and V. Faye McNeill
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-240, https://doi.org/10.5194/gmd-2022-240, 2022
Revised manuscript accepted for GMD
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We developed an automated method, AMORE, to simplify complex chemical mechanisms. We applied AMORE to the oxidation mechanism for isoprene, an abundant biogenic volatile organic compound. Using AMORE with minimal manual adjustments to the output, we created the AMORE-isoprene mechanism, with improved accuracy and similar size to other reduced isoprene mechanisms. AMORE-Isoprene improved the accuracy of EPA’s CMAQ model compared to observations.
Youngseob Kim, Lya Lugon, Alice Maison, Thibaud Sarica, Yelva Roustan, Myrto Valari, Yang Zhang, Michel André, and Karine Sartelet
Geosci. Model Dev., 15, 7371–7396, https://doi.org/10.5194/gmd-15-7371-2022, https://doi.org/10.5194/gmd-15-7371-2022, 2022
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This paper presents the latest version of the street-network model MUNICH, v2.0. The description of MUNICH v1.0, which models gas-phase pollutants in a street network, was published in GMD in 2018. Since then, major modifications have been made to MUNICH. The comprehensive aerosol model SSH-aerosol is now coupled to MUNICH to simulate primary and secondary aerosol concentrations. New parameterisations have also been introduced. Test cases are defined to illustrate the new model functionalities.
Yongbo Zhou, Yubao Liu, Zhaoyang Huo, and Yang Li
Geosci. Model Dev., 15, 7397–7420, https://doi.org/10.5194/gmd-15-7397-2022, https://doi.org/10.5194/gmd-15-7397-2022, 2022
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The study evaluates the performance of the Data Assimilation Research Testbed (DART), equipped with the recently added forward operator Radiative Transfer for TOVS (RTTOV), in assimilating FY-4A visible images into the Weather Research and Forecasting (WRF) model. The ability of the WRF-DART/RTTOV system to improve the forecasting skills for a tropical storm over East Asia and the Western Pacific is demonstrated in an Observing System Simulation Experiment framework.
Dánnell Quesada-Chacón, Klemens Barfus, and Christian Bernhofer
Geosci. Model Dev., 15, 7353–7370, https://doi.org/10.5194/gmd-15-7353-2022, https://doi.org/10.5194/gmd-15-7353-2022, 2022
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We improved the performance of past perfect prognosis statistical downscaling methods while achieving full model repeatability with GPU-calculated deep learning models using the TensorFlow, climate4R, and VALUE frameworks. We employed the ERA5 reanalysis as predictors and ReKIS (eastern Ore Mountains, Germany, 1 km resolution) as precipitation predictand, while incorporating modern deep learning architectures. The achieved repeatability is key to accomplish further milestones with deep learning.
Mike Bush, Ian Boutle, John Edwards, Anke Finnenkoetter, Charmaine Franklin, Kirsty Hanley, Aravindakshan Jayakumar, Huw Lewis, Adrian Lock, Marion Mittermaier, Saji Mohandas, Rachel North, Aurore Porson, Belinda Roux, Stuart Webster, and Mark Weeks
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-209, https://doi.org/10.5194/gmd-2022-209, 2022
Revised manuscript accepted for GMD
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Building on the baseline of RAL1, the RAL2 science configuration is used for regional modelling around the UM Partnership and in operations at the Met Office. RAL2 has been tested in different parts of the world including Australia, India and the U.K. RAL2 increases medium and low cloud amounts in the mid-latitudes compared to RAL1, leading to improved cloud forecasts and a reduced diurnal cycle of screen temperature. There is also a reduction in the frequency of heavier precipitation rates.
Petri Clusius, Carlton Xavier, Lukas Pichelstorfer, Putian Zhou, Tinja Olenius, Pontus Roldin, and Michael Boy
Geosci. Model Dev., 15, 7257–7286, https://doi.org/10.5194/gmd-15-7257-2022, https://doi.org/10.5194/gmd-15-7257-2022, 2022
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Atmospheric chemistry and aerosol processes form a dynamic and sensitively balanced system, and solving problems regarding air quality or climate requires detailed modelling and coupling of the processes. The models involved are often very complex to use. We have addressed this problem with the new ARCA box model. It puts much of the current knowledge of the nano- and microscale aerosol dynamics and chemistry into usable software and has the potential to become a valuable tool in the community.
Adam Milsom, Amy Lees, Adam M. Squires, and Christian Pfrang
Geosci. Model Dev., 15, 7139–7151, https://doi.org/10.5194/gmd-15-7139-2022, https://doi.org/10.5194/gmd-15-7139-2022, 2022
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MultilayerPy is a Python-based framework facilitating the creation, running and optimisation of state-of-the-art kinetic multi-layer models of aerosol and film processes. Models can be fit to data with local and global optimisation algorithms along with a statistical sampling algorithm, which quantifies the uncertainty in optimised model parameters. This “modelling study in a box” enables more reproducible and reliable results, with model code and outputs produced in a human-readable way.
Johan F. de Haan, Ping Wang, Maarten Sneep, J. Pepijn Veefkind, and Piet Stammes
Geosci. Model Dev., 15, 7031–7050, https://doi.org/10.5194/gmd-15-7031-2022, https://doi.org/10.5194/gmd-15-7031-2022, 2022
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We present an overview of the DISAMAR radiative transfer code, highlighting the novel semi-analytical derivatives for the doubling–adding formulae and the new DISMAS technique for weak absorbers. DISAMAR includes forward simulations and retrievals for satellite spectral measurements from 270 to 2400 nm to determine instrument specifications for passive remote sensing. It has been used in various Sentinel-4/5P/5 projects and in the TROPOMI aerosol layer height and ozone profile products.
Ivette H. Banos, Will D. Mayfield, Guoqing Ge, Luiz F. Sapucci, Jacob R. Carley, and Louisa Nance
Geosci. Model Dev., 15, 6891–6917, https://doi.org/10.5194/gmd-15-6891-2022, https://doi.org/10.5194/gmd-15-6891-2022, 2022
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A prototype data assimilation system for NOAA’s next-generation rapidly updated, convection-allowing forecast system, or Rapid Refresh Forecast System (RRFS) v0.1, is tested and evaluated. The impact of using data assimilation with a convective storm case study is examined. Although the convection in RRFS tends to be overestimated in intensity and underestimated in extent, the use of data assimilation proves to be crucial to improve short-term forecasts of storms and precipitation.
Maria J. Chinita, Mikael Witte, Marcin J. Kurowski, Joao Teixeira, Kay Suselj, Georgios Matheou, and Peter Bogenschutz
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-162, https://doi.org/10.5194/gmd-2022-162, 2022
Revised manuscript accepted for GMD
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Low clouds are one of the largest sources of uncertainty in climate prediction. In this paper, we introduce the first version of the unified turbulence and shallow convection parameterization named SHOC+MF developed to improve the representation of shallow cumulus clouds in the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM). Here, we also show promising preliminary results in a single-column model framework for two benchmark cases of shallow cumulus convection.
Andrew Geiss, Sam J. Silva, and Joseph C. Hardin
Geosci. Model Dev., 15, 6677–6694, https://doi.org/10.5194/gmd-15-6677-2022, https://doi.org/10.5194/gmd-15-6677-2022, 2022
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This work demonstrates the use of modern machine learning techniques to enhance the resolution of atmospheric chemistry simulations. We evaluate the schemes for an 8 x 10 increase in resolution and find that they perform substantially better than conventional methods. Methods are introduced to target machine learning methods towards this type of problem, most notably by ensuring they do not break known physical constraints.
Cited articles
Altaratz, O., Reisin, T., and Levin, Z.: Simulation of the electrification of winter thunderclouds using the three-dimensional Regional Atmospheric Modeling System ({RAMS}) model: Single cloud simulations, J. Geophys. Res., 110, D20205, https://doi.org/10.1029/2004JD005616, 2005.
Avila, E. E., Varela, G. G. A., and Caranti, G. M.: Temperature dependance of static charging in ice growing by riming, J. Atmos. Sci., 52, 4515–4520, 1995.
Barth, M. C., Stuart, A. L., and Skamarock, W. C.: Numerical simulations of the {J}uly 10, 1996, {S}tratospheric-{T}ropospheric {E}xperiment: {R}adiation, {A}erosols, and {O}zone {STERAO}- {D}eep convection experiment storm: {R}edistribution of soluble tracers, J. Geophys. Res., 106, 12381–12400, https://doi.org/10.1029/2001JD900139, 2001.
Barth, M. C., Kim, S.-W., Wang, C., Pickering, K. E., Ott, L. E., Stenchikov, G., Leriche, M., Cautenet, S., Pinty, J.-P., Barthe, Ch., Mari, C., Helsdon, J. H., Farley, R. D., Fridlind, A. M., Ackerman, A. S., Spiridonov, V., and Telenta, B.: Cloud-scale model intercomparison of chemical constituent transport in deep convection, Atmos. Chem. Phys., 7, 4709–4731, https://doi.org/10.5194/acp-7-4709-2007, 2007.
Barthe, C. and Barth, M. C.: Evaluation of a new lightning-produced NO$_{\rm x}$ parameterization for cloud resolving models and its associated uncertainties, Atmos. Chem. Phys., 8, 4691–4710, https://doi.org/10.5194/acp-8-4691-2008, 2008.
Barthe, C. and Pinty, J.-P.: Simulation of electrified storms with comparison of the charge structure and lightning efficiency, J. Geophys. Res., 112, D19204, https://doi.org/10.1029/2006JD008241, 2007{a}.
Barthe, C. and Pinty, J.-P.: Simulation of a supercellular storm using a three-dimensional mesoscale model with an explicit lightning flash scheme, J. Geophys. Res., 112, D06210, https://doi.org/10.1029/2006JD007484, 2007{b}.
Barthe, C., Molinié, G., and Pinty, J.-P.: Description and first results of an explicit electrical scheme in a 3{D} cloud resolving model, Atmos. Res., 76, 95–113, 2005.
Barthe, C., Pinty, J.-P., and Mari, C.: Lightning-produced NO}$\rm{_x}$ in an explicit electrical scheme: a {STERAO case study, J. Geophys. Res., 112, D04302, https://doi.org/10.1029/2006JD007402, 2007.
Barthe, C., Deierling, W., and Barth, M. C.: Estimation of total lightning from various storm parameters: A cloud-resolving model study, J. Geophys. Res., 115, D24202, https://doi.org/10.1029/2010JD014405, 2010.
Blyth, A. M., Jr., H. J. C., Driscoll, K., Gadian, A. M., and Latham, J.: Determination of ice precipitation rates and thunderstorm anvil ice contents from satellite observations of lightning, Atmos. Res., 59–60, 217–229, 2001.
Bruning, E. C., Rust, W. D., Schuur, T. J., MacGorman, D. R., Krehbiel, P. R., and Rison, W.: Electrical and polarimetric radar observations of a multicell storm in TELEX, Mon. Weather Rev., 135, 2525–2544, 2007.
Cecil, D. J. and Zipser, E. J.: Relationships between tropical cyclone intensity and satellite-based indicators of inner core convection: 85-{GH}z ice-scattering signature and lightning, Mon. Weather Rev., 127, 103–123, 1999.
Chiu, C.-S.: Numerical study of cloud electrification in a axisymmetric, time-dependant cloud model, J. Geophys. Res., 83, 5025–5049, 1978.
Cuxart, J., Bougeault, P., and Redelsperger, J.-L.: A turbulence scheme for mesoscale and large-eddy simulations, Quart. J. Roy. Meteor. Soc., 126, 1–30, 2000.
Darden, C. B., Nadler, D. J., Carcione, B. C., Blakeslee, R. J., Stano, G. T., and Buechler, D. E.: Utilizing total lightning information to diagnose convective trends, Bull. Am. Meteorol. Soc., 91, 167–175, https://doi.org/10.1175/2009BAMS2808.1, 2010.
Defer, E., Blanchet, P., Théry, C., Laroche, P., Dye, J. E., Venticinque, M., and Cummins, K. L.: Lightning activity for the J}uly 10, 1996, storm during the {S}tratosphere-{T}roposphere {E}xperiment: {R}adiation, {A}erosol, and {O}zone-{A ({STERAO}-{A}) experiment, J. Geophys. Res., 106, 10151–10172, https://doi.org/10.1029/2000JD900849, 2001.
Defer, E., Laroche, P., Dye, J. E., and Skamarock, W. C.: Use of total lightning lengths to estimate {NO}$\rm{_x}$ production in a {C}olorado thunderstorm, in: Proceedings of the 12th {I}nternational {C}onference on {A}tmospheric {E}lectricity, 9–13 June 2003, {I}nt. {C}omm. on {A}tmos. {E}lectr., Versailles, France, 2003.
Deierling, W., Petersen, W. A., Latham, J., Ellis, S., and Christian, H. J.: The relationship between lightning activity and ice fluxes in thunderstorms, J. Geophys. Res., 113, D15210, https://doi.org/10.1029/2007JD009700, 2008.
Dye, J. E., Jones, J. J., and Winn, W. P.: Observations within two regions of charge during initial thunderstorm electrification, Quart. J. Roy. Meteor. Soc., 114, 1271–1290, 1989.
Dye, J. E., Ridley, B. A., Skamarock, W., Barth, M., Venticinque, M., Defer, E., Blanchet, P., Théry, C., Laroche, P., Baumann, K., Hubler, G., Parrish, D. D., Ryerson, T., Trainer, M., Frost, G., Holloway, J. S., Matejka, T., Bartels, D., Fehsenfeld, F. C., Tuck, A., Rutledge, S. A., Lang, T., Stith, J., and Zerr, R.: An overview of the {S}tratospheric-{T}ropospheric {E}xperiment: {R}adiation, {A}erosols, and {O}zone ({STERAO})-{D}eep {C}onvection experiment with results for the {J}uly 10, 1996 storm, J. Geophys. Res., 105, 10023–10045, https://doi.org/10.1029/1999JD901116, 2000.
Emersic, C., Heinselman, P. L., MacGorman, D. R., and Bruning, E. C.: Lightning activity in a hail-producing storm observed with phased-array radar, Mon. Weather Rev., 139, 1809–1825, https://doi.org/10.1175/2010MWR3574.1, 2011.
Fehr, T., H{ö}ller, H., and Huntrieser, H.: Model study on production and transport of lightning-produced NO}x in a {EULINOX supercell storm, J. Geophys. Res., 109, 1–17, https://doi.org/10.1029/2003JD003935, 2004.
Fierro, A. O., Leslie, L., Mansell, E., Straka, J., MacGorman, D., and Ziegler, C.: A high-resolution simulation of microphysics and electrification in an idealized hurricane-like vortex, Meteor. Atmos. Phys., 98, 13–33, 2007.
Gardiner, B. A., Lamb, D., Pitter, R., Hallett, J., and Saunders, C.: Measurements of initial potential gradient and particle charge in a {M}ontana summer thunderstorm, J. Geophys. Res., 90, 6079–6086, 1985.
Goodman, S. J., Buechler, D. E., Wright, P. D., and Rust, W. D.: Lightning and precipitation history of a microburst-producing storm, Geophys. Res. Lett., 15, 1185–1188, https://doi.org/10.1029/GL015i011p01185, 1988.
Helsdon, J. H. and Farley, R. D.: A numerical modeling study of a {M}ontana thunderstorm: {P}art 2 : {M}odel results versus observations involving electrical aspects, J. Geophys. Res., 92, 5661–5675, 1987.
Helsdon, J. H., Wu, G., and Farley, R. D.: An intracloud lightning parameterization scheme for a storm electrification model, J. Geophys. Res., 97, 5865–5884, 1992.
Helsdon, J. H., Wojcik, W. A., and Farley, R. D.: An examination of thunderstorm-charging mechanism using a two-dimensional storm electrification model, J. Geophys. Res., 106, 1165–1192, https://doi.org/10.1029/2000JD900532, 2001.
Helsdon, J. H., Gattaleeradapan, S., Farley, R. D., and Waits, C. C.: An examination of the convective charging hypothesis : {C}harge structure, electric fields, and {M}axwell currents, J. Geophys. Res., 107, 4630, https://doi.org/10.1029/2001JD001495, 2002.
Hou, T., Lei, H., and Hu, Z.: Numerical simulation of the relationship between electrification and microphysics in the prelightning stage of thunderstorms, Atmos. Res., 91, 281–291, https://doi.org/10.1016/j.atmosres.2008.04.009, 2009.
Huntrieser, H., Feigl, C., Schlager, H., Schroder, F., Gerbig, C., van Velthoven, P., Flatoy, F., Théry, C., Petzold, A., H{ö}ller, H., and Anann, U.: Airborne measurements of {NO}x, tracer species and small particles during the {E}uropeean {L}ightning {N}itrogen {O}xides {E}xperiment, J. Geophys. Res., 107, D11, https://doi.org/10.1029/2000JD000209, 2002.
Jabouille, P., Guivarch, R., Kloos, P., Gazen, D., Gicquel, N., Giraud, L., Asencio, N., Ducrocq, V., Escobar, J., Redelsperger, J.-L., Stein, J., and Pinty, J.-P.: Parallelization of the French Meteorological Mesoscale Model MésoNH, in: Proceedings of the 5th International Euro-Par Conference on Parallel Processing, Euro-Par '99, 1417–1422, Springer-Verlag, London, UK, UK, available at: http://portal.acm.org/citation.cfm?id=646664.701041, 1999.
Jayaratne, R., Saunders, C. P. R., and Hallet, J.: Laboratory studies of the charging of soft hail during ice crystal interactions, Quart. J. Roy. Meteor. Soc., 103, 609–630, 1983.
Kajikawa, M. and Heymsfield, A. J.: Aggregation of ice crystals in cirrus, J. Atmos. Sci., 46, 3108–3121, 1989.
Kasemir, H. W.: A contribution to the electrostatic theory of a lightning discharge, J. Geophys. Res., 65, 1873–1878, 1960.
Kawasaki, Z. and Matsuura, K.: Does a lightning channel show a fractal?, Appl. Energy, 67, 147–158, 2000.
Keith, W. D. and Saunders, C. P. R.: Further laboratory studies of the charging of graupel during ice crystal interactions, Atmos. Res., 25, 4345–4464, 1990.
Krehbiel, P. R.: An analysis of the electric field change produced by lightning, Ph.D. thesis, Univ. of Manchester Inst. of Sci. and Technol., Manchester, UK, 1981.
Kuhlman, K. M., Ziegler, C. L., Mansell, E. R., MacGorman, D. R., and Straka, J. M.: Numerically simulated electrification and lightning of the 29 J}une 2000 {STEPS supercell storm, Mon. Weather Rev., 134, 2734–2757, 2006.
Lafore, J. P., Stein, J., Asencio, N., Bougeault, P., Ducrocq, V., Duron, J., Fischer, C., Héreil, P., Mascart, P., Masson, V., Pinty, J. P., Redelsperger, J. L., Richard, E., and Vilà-Guerau de Arellano, J.: The Meso-NH Atmospheric Simulation System. Part I: adiabatic formulation and control simulations, Ann. Geophys., 16, 90–109, https://doi.org/10.1007/s00585-997-0090-6, 1998.
Latham, J., Petersen, W. A., Deierling, W., and Christian, H. J.: Field identification of a unique globally dominant mechanism of thunderstorm electrification, Quart. J. Roy. Meteor. Soc., 133, 1453–1457, 2007.
MacGorman, D. R. and Rust, W. D.: The electrical nature of storms, Oxford Univ. Press, 1998.
MacGorman, D. R., Straka, J. M., and Ziegler, C. L.: A lightning parameterization for numerical cloud model, J. Appl. Meteor., 40, 459–478, 2001.
Mansell, E. R., MacGorman, D. R., Ziegler, C. L., and Straka, J. M.: Charge structure and lightning sensitivity in a simulated multicell thunderstorm, J. Geophys. Res., 110, D12101, https://doi.org/10.1029/2004JD005287, 2005.
Mansell, E. R.: Electrification and Lightning in Simulated Supercell and Non-supercell Thunderstorms, Ph.D. thesis, Univ. Oklahoma, 2000.
Mansell, E. R., MacGorman, D., Ziegler, C. L., and Straka, J. M.: Simulated three-dimensional branched lightning in a numerical thunderstorm model, J. Geophys. Res., 107, D9, https://doi.org/10.1029/2000JD000244, 2002.
Mansell, E. R., Ziegler, C. L., and Bruning, E. C.: Simulated electrification of a small thunderstorm with two-moment bulk microphysics, J. Atmos. Sci., 67, 171–194, https://doi.org/10.1175/2009JAS2965.1, 2010.
Marshall, T. C., MacCarthy, M. P., and Rust, W. D.: Electric field magnitudes and lightning initiation in thunderstorms, J. Geophys. Res., 100, 7097–7103, 1995.
Mazur, V. and Ruhnke, L. H.: Model of electric charges in thunderstorms and associated lightning, J. Geophys. Res., 103, 23299–23308, 1998.
Niemeyer, L., Pietronero, L., and Wiesmann, H. J.: Fractal dimension of dielectric breakdown, Phys. Rev. Let., 52, 1033–1036, 1984.
Ott, L. E., Pickering, K. E., Stenchikov, G., Huntrieser, H., and Schumann, U.: Effects of lightning {NO}x production during the 21 {J}uly {E}uropean {L}ightning {N}itrogen {O}xides {P}roject storm studied with a three-dimensional cloud-scale chemical transport model, J. Geophys. Res., 112, D05307, https://doi.org/1029/2006JD007365, 2007.
Petersen, W. A., Christian, H. J., and Rutledge, S. A.: TRMM observations of the global relationship between ice water content and lightning, Geophys. Res. Lett., 32, L14819, https://doi.org/10.1029/2005GL023236, 2005.
Petrov, N. I. and Petrova, G. N.: Physical mechanisms for intracloud lightning discharges, Tech. Physics, 44, 472–475, 1993.
Pinty, J.-P. and Barthe, C.: Ensemble simulation of the lightning flash variability in a 3-D cloud model with parameterizations of cloud electrification and lightning flashes, Mon. Weather Rev., 136, 380–387, 2008.
Pinty, J.-P. and Jabouille, P.: A mixed-phase cloud parameterization for use in mesoscale non hydrostatic model : simulations of a squall line and of orographic precipitations, in: Conference of Cloud Physics, Everett, WA, USA, 217–220, 1998.
Price, C., Asfur, M., and Yair, Y.: Maximum hurricane intensity preceded by increase in lightning frequency, Nat. Geosci., 2, 329–332, https://doi.org/10.1038/NGEO477, 2009.
Prigent, C., Defer, E., Pardo, J. R., Pearl, C., Rossow, W. B., and Pinty, J. P.: Relations of polarized scattering signatures observed by the TRMM {M}icrowave {I}nstrument with electrical processes in cloud systems, Geophys. Res. Lett., 32, L04810, https://doi.org/10.1029/2004GL022225, 2005.
Rakov, V. and Uman, M.: Lightning: Physics and Effects, Cambridge University Press, 2003.
Rawlins, F.: A numerical study of thunderstorm electrification using a three-dimensional model incorporating the ice phase, Quart. J. Roy. Meteor. Soc., 108, 779–800, 1982.
Reynolds, S. E., Brooks, M., and Gourley, M. F.: Thunderstorm charge separation, J. Meteor., 14, 163–178, 1957.
Riousset, J. A., Pasko, V. P., Krehbiel, P. R., Thomas, R. J., and Rison, W.: Three-dimensional fractal modeling of intracloud lightning discharge in a {N}ew {M}exico thunderstorm and comparison with lightning mapping observations, J. Geophys. Res., 112, D11, https://doi.org/10.1029/2006JD007621, 2007.
Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., and Harlin, J.: A {GPS}-based three-dimensional lightning mapping system : initial observations in {C}entral {N}ew-{M}exico, Geophys. Res. Lett., 26, 3573–3576, 1999.
Sañudo, J., Gómez, J. B., Castaño, F., and Pacheco, A. F.: Fractal dimension of lightning discharge, Nonlin. Processes Geophys., 2, 101–106, https://doi.org/10.5194/npg-2-101-1995, 1995.
Saunders, C. P. R. and Brooks, I.: The effects of high liquid water content on thunderstorm charging, J. Geophys. Res., 97, 14671–14676, 1992.
Saunders, C. P. R. and Peck, S. L.: Laboratory studies of the influence of the rime accretion rate on charge transfer during crystal/graupel collisions, J. Geophys. Res., 103, 13949–13956, 1998.
Saunders, C. P. R., Keith, W. D., and Mitzeva, R. P.: The effect of liquid water on thunderstorm charging, J. Geophys. Res., 96, 11007–11017, 1991.
Schumann, U. and Huntrieser, H.: The global lightning-induced nitrogen oxides source, Atmos. Chem. Phys., 7, 3823–3907, https://doi.org/10.5194/acp-7-3823-2007, 2007.
Shao, X. M. and Krehbiel, P. R.: The spatial and temporal development of intracloud lightning, J. Geophys. Res., 101, 26641–26668, 1996.
Skamarock, W. C., Powers, J. G., Barth, M., Dye, J. E., Matejka, T., Bartels, D., Baumann, K., Stith, J., Parrish, D. D., and H{ü}bler, G.: Numerical simulations of the {J}uly 10 {S}tratospheric-{T}ropospheric {E}xperiment: {R}adiation, {A}erosol, and {O}zone/{D}eep {C}onvection {E}xperiment convective system: {K}inematics and transport, J. Geophys. Res., 105, 19973–19990, 2000.
Skamarock, W. C., Dye, J. E., Defer, E., Barth, M. C., Stith, J. L., and Ridley, B. A.: Observational- and modeling-based budget of lightning-produced {NO}x in a continental thunderstorm, J. Geophys. Res., 108, 4305, https://doi.org/10.1029/2002JD002163, 2003.
Solomon, R. and Baker, M.: A one-dimensional lightning parametrization, J. Geophys. Res., 101, D10, https://doi.org/10.1029/96JD00941, 1996.
Squires, K. and Businger, S.: The Morphology of Eyewall Lightning Outbreaks in Two Category 5 Hurricanes, Mon. Weather Rev., 136, 1706–1725, 2008.
Sun, A., Chun, H.-Y., Baik, J.-J., and Yan, M.: Influence of electrification on microphysical and dynamical processes in a numerically simulated thunderstorm, J. Appl. Meteor., 41, 1112–1127, 2002.
Takahashi, T.: Riming electrification as a charge generation mechanism in thunderstorms, J. Atmos. Sci., 35, 1536–1548, 1978.
Takahashi, T.: Determination of lightning origins in a thunderstorm model, J. Meteorol. Soc. Jpn., 65, 777–794, 1987.
Thomas, R. J., Krehbiel, P. R., Rison, W., Hamlin, T., Harlin, J., and Shown, D.: Observations of VHF source powers radiated by lightning, Geophys. Res. Lett., 28, 143–146, 2001.
Tsonis, A. A. and Elsner, J. B.: Fractal characterization and simulation of lightning, Beitr. Phys. Atmos., 60, 187–192, 1987.
Wiens, K. C., Rutledge, S. A., and Tessendorf, S. A.: The 29 J}une 2000 supercell observed during {STEPS. {P}art {II}: {L}ightning and charge structure, J. Atmos. Sci., 62, 4151–4177, 2005.
Wiesmann, H. J.: Realistic models of dielectric breakdown, Fractals physical origin and properties, Plenum, New York, 243–257, 1988.
Wiesmann, H. J. and Zeller, H. R.: A fractal model of dielectric breakdown and prebreakdown in solid dielectrics, J. Appl. Phys., 60, 1770–1773, 1986.
Williams, E. R. and Lhermitte, R. M.: Radar tests of the precipitation hypothesis for thunderstorm electrification, J. Geophys. Res., 88, 10984–10992, 1983.
Williams, E. R., Cooke, C. M., and Wright, K. A.: Electrical discharge propagation in and around space charge clouds, J. Geophys. Res., 90, 6054–6070, 1985.
Zhang, X., Helsdon, J. H., and Farley, R. D.: Numerical modeling of lightning-produced {NO}x using an explicit lightning scheme: 1. {T}wo-dimensional simulation as a proof of concept, J. Geophys. Res., 108, https://doi.org/10.1029/2002JD003224, 2003a.
Zhang, X., Helsdon, J. H., and Farley, R. D.: Numerical modeling of lightning-produced {NO}x using an explicit lightning scheme: 2. {T}hree-dimensional simulation and expanded chemistry, J. Geophys. Res., 108, https://doi.org/10.1029/2002JD003225, 2003b.
Ziegler, C. L. and MacGorman, D. R.: Observed lightning morphology relative to modeled space charge and electric field distributions in a tornadic storm, J. Atmos. Sci., 51, 833–851, 1994.
Ziegler, C. L., MacGorman, D. R., Dye, J. E., and Ray, P. S.: A model evaluation of noninductive graupel-ice charging in the early electrification of a mountain thunderstorm, J. Geophys. Res., 96, 12833–12855, 1991.
