Articles | Volume 5, issue 3
https://doi.org/10.5194/gmd-5-543-2012
© 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-5-543-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
A contrail cirrus prediction model
U. Schumann
Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Related subject area
Atmospheric sciences
On the use of Infrared Atmospheric Sounding Interferometer (IASI) spectrally resolved radiances to test the EC-Earth climate model (v3.3.3) in clear-sky conditions
Incorporation of aerosol into the COSPv2 satellite lidar simulator for climate model evaluation
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
Bayesian transdimensional inverse reconstruction of the Fukushima Daiichi caesium 137 release
Implementation of HONO into the chemistry–climate model CHASER (V4.0): roles in tropospheric chemistry
Isoprene and monoterpene simulations using the chemistry–climate model EMAC (v2.55) with interactive vegetation from LPJ-GUESS (v4.0)
A modern-day Mars climate in the Met Office Unified Model: dry simulations
The AirGAM 2022r1 air quality trend and prediction model
Evaluation of a cloudy cold-air pool in the Columbia River basin in different versions of the High-Resolution Rapid Refresh (HRRR) model
Comparing Sentinel-5P TROPOMI NO2 column observations with the CAMS regional air quality ensemble
Cross-evaluating WRF-Chem v4.1.2, TROPOMI, APEX, and in situ NO2 measurements over Antwerp, Belgium
Adapting a deep convolutional RNN model with imbalanced regression loss for improved spatio-temporal forecasting of extreme wind speed events in the short to medium range
ICLASS 1.1, a variational Inverse modelling framework for the Chemistry Land-surface Atmosphere Soil Slab model: description, validation, and application
Towards an improved representation of carbonaceous aerosols over the Indian monsoon region in a regional climate model: RegCM
The E3SM Diagnostics Package (E3SM Diags v2.7): a Python-based diagnostics package for Earth system model evaluation
A method for transporting cloud-resolving model variance in a multiscale modeling framework
The Mission Support System (MSS v7.0.4) and its use in planning for the SouthTRAC aircraft campaign
GENerator of reduced Organic Aerosol mechanism (GENOA v1.0): an automatic generation tool of semi-explicit mechanisms
Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework
Evaluation of high-resolution predictions of fine particulate matter and its composition in an urban area using PMCAMx-v2.0
A local data assimilation method (Local DA v1.0) and its application in a simulated typhoon case
Improved advection, resolution, performance, and community access in the new generation (version 13) of the high-performance GEOS-Chem global atmospheric chemistry model (GCHP)
Lightning assimilation in the WRF model (Version 4.1.1): technique updates and assessment of the applications from regional to hemispheric scales
Optimization of snow-related parameters in the Noah land surface model (v3.4.1) using a micro-genetic algorithm (v1.7a)
Development of an LSTM broadcasting deep-learning framework for regional air pollution forecast improvement
A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF
A comprehensive evaluation of the use of Lagrangian particle dispersion models for inverse modeling of greenhouse gas emissions
Importance of different parameterization changes for the updated dust cycle modeling in the Community Atmosphere Model (version 6.1)
An Improved Parameterization of Sea Spray-Mediated Heat Flux Using Gaussian Quadrature: Case Studies with a Coupled CFSv2.0-WW3 System
Evaluation of the NAQFC driven by the NOAA Global Forecast System (version 16): comparison with the WRF-CMAQ during the summer 2019 FIREX-AQ campaign
An inconsistency in aviation emissions between CMIP5 and CMIP6 and the implications for short-lived species and their radiative forcing
Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 1.0.0): EnVar implementation and evaluation
Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China
A lumped species approach for the simulation of secondary organic aerosol production from intermediate-volatility organic compounds (IVOCs): application to road transport in PMCAMx-iv (v1.0)
TrackMatcher – a tool for finding intercepts in tracks of geographical positions
Recovery of sparse urban greenhouse gas emissions
Tropospheric transport and unresolved convection: numerical experiments with CLaMS 2.0/MESSy
AMORE-Isoprene v1.0: A new reduced mechanism for gas-phase isoprene oxidation
MUNICH v2.0: a street-network model coupled with SSH-aerosol (v1.2) for multi-pollutant modelling
A preliminary evaluation of FY-4A visible radiance data assimilation by the WRF (ARW v4.1.1)/DART (Manhattan release v9.8.0)-RTTOV (v12.3) system for a tropical storm case
Repeatable high-resolution statistical downscaling through deep learning
The second Met Office Unified Model/JULES Regional Atmosphere and Land configuration, RAL2
Atmospherically Relevant Chemistry and Aerosol box model – ARCA box (version 1.2)
MultilayerPy (v1.0): a Python-based framework for building, running and optimising kinetic multi-layer models of aerosols and films
Introduction of the DISAMAR radiative transfer model: determining instrument specifications and analysing methods for atmospheric retrieval (version 4.1.5)
Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of a convective storm case study
Improving the representation of shallow cumulus convection with the Simplified Higher-Order Closure Mass-Flux (SHOC+MF v1.0) approach
Downscaling atmospheric chemistry simulations with physically consistent deep learning
Stefano Della Fera, Federico Fabiano, Piera Raspollini, Marco Ridolfi, Ugo Cortesi, Flavio Barbara, and Jost von Hardenberg
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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The long-term comparison between observed and simulated outgoing longwave radiances represents a strict test to evaluate climate model performance. In this work, 9 years of synthetic spectrally resolved radiances, simulated online on the basis of the atmospheric fields predicted by the EC-Earth global climate model (v3.3.3) in clear-sky conditions, are compared to IASI spectral radiance climatology in order to detect model biases in temperature and humidity at different atmospheric levels.
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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The Weather Forecasting and Research (WRF) model consists of many parameters and options that can be adapted to different conditions. This expansive sensitivity study uses a large-scale simulation system to determine the most suitable options for predicting cloud cover in Europe for deterministic and probabilistic weather predictions for day-ahead forecasting simulations.
Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan
Geosci. Model Dev., 16, 1039–1052, https://doi.org/10.5194/gmd-16-1039-2023, https://doi.org/10.5194/gmd-16-1039-2023, 2023
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When radionuclides are released into the atmosphere, the assessment of the consequences depends on the evaluation of the magnitude and temporal evolution of the release, which can be highly variable as in the case of Fukushima Daiichi.
Here, we propose Bayesian inverse modelling methods and the reversible-jump Markov chain Monte Carlo technique, which allows one to evaluate the temporal variability of the release and to integrate different types of information in the source reconstruction.
Phuc Thi Minh Ha, Yugo Kanaya, Fumikazu Taketani, Maria Dolores Andrés Hernández, Benjamin Schreiner, Klaus Pfeilsticker, and Kengo Sudo
Geosci. Model Dev., 16, 927–960, https://doi.org/10.5194/gmd-16-927-2023, https://doi.org/10.5194/gmd-16-927-2023, 2023
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HONO affects tropospheric oxidizing capacity; thus, it is implemented into the chemistry–climate model CHASER. The model substantially underpredicts daytime HONO, while nitrate photolysis on surfaces can supplement the daytime HONO budget. Current HONO chemistry predicts reductions of 20.4 % for global tropospheric NOx, 40–67 % for OH, and 30–45 % for O3 in the summer North Pacific. In contrast, OH and O3 winter levels in China are greatly enhanced.
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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We present results from the Met Office Unified Model (UM) to study the dry Martian climate. We describe our model set-up conditions and run two scenarios, with radiatively active/inactive dust. We compare both scenarios to results from an existing Mars climate model, the planetary climate model. We find good agreement in winds and air temperatures, but dust amounts differ between models. This study highlights the importance of using the UM for future Mars research.
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
Abramowitz, M. and Stegun, I. A.: Handbook of Mathematical Functions, Dover Publisher, New York, 1046 pp., 1964.
Adelfang, S. I.: On the relations between wind shears over various altitude intervals, J. Appl. Meteor., 10, 156–159, 1971.
Appleman, H.: The formation of exhaust contrails by jet aircraft, B. Am.\ Meteor. Soc., 34, 14–20, 1953.
Atlas, D. and Wang, Z.: Contrails of small and very large optical depth, J.\ Atmos. Sci., 67, 3065–3073, https://doi.org/10.1175/2010JAS3403.1, 2010.
Atlas, D., Wang, Z., and Duda, D. P.: Contrails to cirrus – Morphology, microphysics, and radiative properties, J. Appl. Meteor. Climatol., 45, 5–19, 2006.
Bakan, S., Betancor, M., Gayler, V., and Gra{ß}l, H.: Contrail frequency over Europe from NOAA-satellite images, Ann. Geophys., 12, 962–968, https://doi.org/10.1007/s00585-994-0962-y, 1994.
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities, Mon. Wea. Rev., 139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011.
Birner, T., Dörnbrack, A., and Schumann, U.: How sharp is the tropopause at midlatitudes?, Geophys. Res. Lett., 29, 45–1–45–4, https://doi.org/10.1029/2002gl015142, 2002.
Blanco-Muriel, M., Alarc{ó}n-Padilla, D. C., L{ó}pez-Moratella, T., and Lara-Coira, M.: Computing the solar vector, Solar Energy, 70, 431–441, 2001.
Brown, R. C., Miake-Lye, R. C., Anderson, M. R., and Kolb, C. E.: Aircraft sulphur emissions and the formation of visible contrails, Geophys. Res.\ Lett., 12, 385–388, https://doi.org/10.1029/97GL00107, 1997.
Burkhardt, U. and Kärcher, B.: Global radiative forcing from contrail cirrus, Nature Clim. Change, 1, 54–58, https://doi.org/10.1038/NCLIMATE1068, 2011.
Burkhardt, U., Kärcher, B., and Schumann, U.: Global modelling of the contrail and contrail cirrus climate impact, B. Am. Meteor. Soc., 91, 479–484, https://doi.org/10.1175/2009BAMS2656.1, 2010.
Busen, R. and Schumann, U.: Visible contrail formation from fuels with different sulfur contents, Geophys. Res. Lett., 22, 1357–1360, https://doi.org/10.1029/95GL01312, 1995.
Cariolle, D., Caro, D., Paoli, R., Hauglustaine, D. A., Cuenot, B., Cozic, A., and Paugam, R.: Parameterization of plume chemistry into large-scale atmospheric models: Application to aircraft NOx emissions, J.\ Geophys. Res., 114, D19302, https://doi.org/10.1029/2009JD011873, 2009.
Chlond, A.: Large eddy simulations of contrails, J. Atmos. Sci., 55, 796–819, 1998.
Clayson, C. A. and Kantha, L.: On turbulence and mixing in the free atmosphere inferred from high-resolution soundings, J. Atmos. Oceanic Technol., 25, 833–852, 2008.
Danielsen, E. F.: Trajectories: Isobaric, isentropic and actual, J. Meteorol., 18, 479–486, 1961.
Danilin, M., Ebel, A., Elbern, H., and Petry, H.: Evolution of the concentrations of trace species in an aircraft plume: Trajectory study, J.\ Geophys. Res., 99, 18951–18972, https://doi.org/10.1029/94JD01820, 1994.
deBruin, A. and Kannemans, H.: Analysis of NLR Cessna Citation flight test data for flight test-1 in AWIATOR project, Tech. rep., NLR, Techn. Report AW-NLR-113-010, 2004.
Delisi, D. P. and Robins, R.: Short-scale instabilities in trailing wake vortices in a stratified fluid, AIAA J., 38, 1916–1923, 2000.
Detwiler, A. and Pratt, R.: Clear-air seeding: Opportunities and strategies, J. Wea. Mod., 16, 46–60, 1984.
Dewan, E. M.: Stratospheric wave spectra resembling turbulence, Science, 204, 832–835, https://doi.org/10.1126/science.204.4395.832, 1979.
Dobbie, S. and Jonas, P.: Radiative influences on the structure and lifetime of cirrus clouds, Q. J. Roy. Meteor. Soc., 127, 2663–2682, 2001.
Dörnbrack, A. and Dürbeck, T.: Turbulent dispersion of aircraft exhausts in regions of breaking gravity waves, Atmos. Environ., 32, 3105–3112, 1998.
Duda, D., Minnis, P., and Nguyen, L.: Estimates of cloud radiative forcing in contrail clusters using GOES imagery, J. Geophys. Res., 106, 4927–4937, 2001.
Duda, D., Minnis, P., Nyuyen, L., and Palikonda, R.: A case study of the development of contrail clusters over the Great Lakes, J. Atmos. Sci., 61, 1132–1146, 2004.
Duda, D. P., Palikonda, R., and Minnis, P.: Relating observations of contrail persistence to numerical weather analysis output, Atmos. Chem. Phys., 9, 1357–1364, https://doi.org/10.5194/acp-9-1357-2009, 2009.
Dürbeck, T. and Gerz, T.: Large-eddy simulation of aircraft exhaust plumes in the free atmosphere: Effective diffusivities and cross-sections, Geophys.\ Res. Lett., 22, 3203–3206, https://doi.org/10.1029/95GL03021, 1995.
Dürbeck, T. and Gerz, T.: Dispersion of aircraft exhausts in the free atmosphere, J. Geophys. Res., 101, 26007–26015, 1996.
EUROCONTROL: Aircraft Performance Summary Tables for the Base of Aircraft Date (BADA), Revision 3.7, Tech. rep., European Organisation for the Safety of Air Navigation, 2009.
Eyers, C. J., Addleton, D., Atkinson, K., Broomhead, M. J., Christou, R., Elliff, T., Falk, R., Gee, I., Lee, D. S., Marizy, C., Michot, S., Middel, J., Newton, P., Norman, P., Plohr, M., Raper, D., and Stanciou, N.: AERO2k Global Aviation Emissions Inventories for 2002 and 2025, Tech. rep., QinetiQ for European Commission under Contract No. G4RD-CT-2000-00382, \urlprefixhttp://www.cate.mmu.ac.uk/aero2k.asp, 2004.
Fahey, D., Schumann, U., Ackerman, S., Artaxo, P., Boucher, O., Danilin, M. Y., Kärcher, B., Minnis, P., Nakajima, T., and Toon, O. B.: Aviation-produced aerosols and cloudiness, in: Aviation and the Global Atmosphere. A Special Report of IPCC Working Groups I and III, edited by: Penner, J. E., Lister, D. H., Griggs, D. J., Dokken, D. J., and McFarland, M., 65–120, Cambridge University Press, New York, \urlprefixhttp://www.ipcc.ch/ipccreports/sres/aviation/index.php?idp=0, 1999.
Febvre, G., Gayet, J.-F., Minikin, A., Schlager, H., Shcherbakov, V., Jourdan, O., Busen, R., Fiebig, M., Kärcher, B., and Schumann, U.: On optical and microphysical characteristics of contrails and cirrus, J. Geophys. Res., 114, D02204, https://doi.org/10.1029/2008JD010184, 2009.
Ferrone, A.: Aviation and climate change in Europe: from regional climate modelling to policy-options, Ph.D. thesis, Université Catholique de Louvain, 2011.
Fueglistaler, S., Legras, B., Beljaars, A., Morcrette, J.-J., Simmons, A., Tompkins, A. M., and Uppala, S.: The diabatic heat budget of the upper troposphere and lower/mid stratosphere in ECMWF reanalyses, Q. J. Roy. Meteor. Soc., 135, 21–37, https://doi.org/10.1002/qj.361, 2010.
Frehlich, R. and Sharman, R.: Climatology of velocity and temperature turbulence statistics determined from rawinsonde and ACARS/AMDAR data, J.\ Appl. Meteor. Climatol., 49, 1149–1169, https://doi.org/10.1175/2010JAMC2196, 2010.
Freudenthaler, V., Homburg, F., and Jäger, H.: Contrail observations by ground-based scanning lidar: Cross-sectional growth, Geophys. Res. Lett., 22, 3501–3504, https://doi.org/10.1029/95GL03549, 1995.
Garber, D. P., Minnis, P., and Costulis, P. K.: A commercial flight track database for upper tropospheric aircraft emission studies over the USA and southern Canada, Meteor. Z., 14, 445–452, 2005.
Gayet, J.-F., Febvre, G., Brogniez, G., Chepfer, H., Renger, W., and Wendling, P.: Microphysical and optical properties of cirrus and contrails, J. Atmos.\ Sci., 53, 126–138, 1996.
Gerz, T. and Ehret, T.: Wake dynamics and exhaust distribution behind cruising aircraft, in: The Characterization and Modification of Wakes from Lifting Vehicles in Fluids, 35.1–35.8, AGARD CP 584, 1996.
Gerz, T., Dürbeck, T., and Konopka, P.: Transport and effective diffusion of aircraft emissions, J. Geophys. Res., 103, https://doi.org/10.1029/98JD02282, 1998.
Gierens, K.: Numerical simulations of persistent contrails, J. Atmos. Sci., 53, 3333–3348, 1996.
Gierens, K. and Bretl, S.: Analytical treatment of ice sublimation and test of sublimation parameterisations in two-moment ice microphysics models, Atmos. Chem. Phys., 9, 7481–7490, https://doi.org/10.5194/acp-9-7481-2009, 2009.
Gierens, K. and Jensen, E.: A numerical study of the contrail-to-cirrus transition, Geophys. Res. Lett., 25, 4341–4344, 1998.
Gierens, K. and Spichtinger, P.: On the size distribution of ice-supersaturated regions in the upper troposphere and lowermost stratosphere, Ann. Geophys., 18, 499–504, https://doi.org/10.1007/s00585-000-0499-7, 2000.
Gierens, K., Schumann, U., Helten, M., Smit, H., and Marenco, A.: A distribution law for relative humidity in the upper troposphere and lower stratosphere derived from three years of MOZAIC measurements, Ann. Geophys., 17, 1218–1226, https://doi.org/10.1007/s00585-999-1218-7, 1999.
Gierens, K., Kärcher, B., Mannstein, H., and Mayer, B.: Aerodynamic contrails: Phenomenology and flow physics, J. Atmos. Sci., 66, 217–226, https://doi.org/10.1175/2008JAS2767.1, 2009.
Gierens, K. M.: The influence of radiation on the diffusional growth of ice crystals, Beitr. Phys. Atmos., 67, 181–193, 1994.
Green, J. E.: Greener by design – the technology challenge, Aeron. J., 106, 57–113, 2002.
Gultepe, I. and Starr, D. O.: Dynamical structure and turbulence in cirrus clouds: Aircraft observations during FIRE, J. Atmos. Sci., 52, 4159–4182, 1995.
Hansen, J. E. and Travis, L. D.: Light scattering in planetary atmospheres, Space Sci. Rev., 16, 527–610, 1974.
Haywood, J. M., Allan, R. P., Bornemann, J., Forster, P. M., Francis, P. N., Milton, S., Rädel, G., Rap, A., Shine, K. P., and Thorpe, R.: A case study of the radiative forcing of persistent contrails evolving into contrail-induced cirrus, J. Geophys. Res., 114, D24201, https://doi.org/10.1029/2009JD012650, 2009.
Hennemann, I.: Deformation und Zerfall von Flugzeugwirbelschleppen in turbulenter und stabil geschichteter Atmosphäre, Tech. rep., Deutsches Zentrum für Luft- und Raumfahrt, Forschungsbericht 2010–21, 2010.
Heymsfield, A. J., Lawson, R. P., and Sachse, G. W.: Growth of ice crystals in a precipitating contrail, Geophys. Res. Lett., 25, 1335–1338, 1998.
Holzäpfel, F.: Probabilistic two-phase wake vortex decay and transport model, J. Aircraft, 40, 323–331, 2003.
Holzäpfel, F. and Gerz, T.: Two-dimensional wake vortex physics in the stably stratified atmosphere, Aeros. Sci. Techn., 5, 261–270, 1999.
Holzäpfel, F., Misaka, T., and Hennemann, I.: Wake-vortex topology, circulation, and turbulent exchange processes, in: AIAA Paper 2010-7992, 1–16, 2010.
Houchi, K., Stoffelen, A., Marseille, G. J., and De Kloe, J.: Comparison of wind and wind shear climatologies derived from high-resolution radiosondes and the ECMWF model, J. Geophys. Res., 115, D22123, https://doi.org/10.1029/2009JD013196, 2010.
Huebsch, W. W. and Lewellen, D. C.: Sensitivity study on contrail evolution, in: 36th AIAA {F}luid D}ynamics {C}onference and {E}xhibit, {AIAA 2006–3749, 1–14, 2006.
Hunt, J. C. R.: Diffusion in the stably stratified atmospheric boundary layer, J. Climate Appl. Meteor., 24, 1187–1195, 1985.
Hunt, J. C. R., Stretch, D. D., and Britter, R. E.: Length scales in stably stratified turbulent flows and their use in turbulence models, in: Stably Stratified Flows and Dense Gas Dispersion, edited by: Puttock, J. S., 285–321, Clarendon, 1988.
ICAO: Manual of the ICAO Standard Atmosphere, Tech. rep., ICAO Document No. 7488, 2nd Edition, 1964.
Immler, F., Treffeisen, R., Engelbart, D., Krüger, K., and Schrems, O.: Cirrus, contrails, and ice supersaturated regions in high pressure systems at northern mid latitudes, Atmos. Chem. Phys., 8, 1689–1699, https://doi.org/10.5194/acp-8-1689-2008, 2008.
IPCC: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge Univ. Press, Cambridge, UK, 996 pp., 2007.
Iwabuchi, H., Yang, P., Liou, K. N., and Minnis, P.: Physical and optical properties of persistent contrails: Climatology and interpretation, J. Geophys. Res., 117, D06215, https://doi.org/10.1029/2011JD017020, 2012.
Jensen, E. J., Ackermann, A. S., Stevens, D. E., Toon, O. B., and Minnis, P.: Spreading and growth of contrails in a sheared environment, J. Geophys.\ Res., 103, 13557–13567, 1998{a}.
Jensen, E. J., Toon, O. B., Pueschel, R. F., Goodman, J., Sachse, G. W., Anderson, B. E., Chan, K. R., Baumgardner, D., and Miake-Lye, R. C.: Ice crystal nucleation and growth in contrails forming at low ambient temperatures, Geophys. Res. Lett., 25, 1371–1374, https://doi.org/10.1029/97GL03592, 1998{b}.
Jensen, E. J., Pfister, L., and Toon, O. B.: Impact of radiative heating, wind shear, temperature variability, and microphysical processes on the structure and evolution of thin cirrus in the tropical tropopause layer, J. Geophys. Res., 116, D12209, https://doi.org/10.1029/2010JD015417, 2011.
Kantha, L. and Hocking, W.: Dissipation rates of turbulence kinetic energy in the free atmosphere: MST radar and radiosondes, J. Atm. Sol. Terr. Phys., 73, 1043–1051, https://doi.org/10.1016/j.jastp.2010.11.024, 2011.
Kärcher, B.: A trajectory box model for aircraft exhaust plumes, J.\ Geophys. Res., 100, 18835–18844, https://doi.org/10.1029/95JD01638, 1995.
Kärcher, B.: Physicochemistry of aircraft-generated liquid aerosols, soot, and ice particles: 1. Model description, J. Geophys. Res., 103, 17111–17128, 1998.
Kärcher, B. and Ström, J.: The roles of dynamical variability and aerosols in cirrus cloud formation, Atmos. Chem. Phys., 3, 823–838, https://doi.org/10.5194/acp-3-823-2003, 2003.
Kärcher, B. and Yu, F.: Role of aircraft soot emissions in contrail formation, Geophys. Res. Lett., 36, L01804, https://doi.org/10.1029/2008GL036649, 2009.
Kärcher, B., Peter, T., Biermann, U. M., and Schumann, U.: The initial composition of jet condensation trails, J. Atmos. Sci., 53, 3066–3083, 1996.
Kärcher, B., Busen, R., Petzold, A., Schröder, F. P., Schumann, U., and Jensen, E. J.: Physicochemistry of aircraft-generated liquid aerosols, soot, and ice particles. 2. Comparison with observations and sensitivity studies, J. Geophys. Res., 103, 17129–17147, 1998.
Kärcher, B., Burkhardt, U., Unterstrasser, S., and Minnis, P.: Factors controlling contrail cirrus optical depth, Atmos. Chem. Phys., 9, 6229–6254, https://doi.org/10.5194/acp-9-6229-2009, 2009{a}.
Kärcher, B., Mayer, B., Gierens, K., Burkhardt, U., Mannstein, H., and Chatterjee, R.: Aerodynamic contrails: Microphysics and optical properties, J. Atmos. Sci., 66, 227–243, https://doi.org/10.1175/2008JAS2768.1, 2009{b}.
Karol, I. L., Ozolin, Y. E., and Rozanov, E. V.: Box and Gaussian plume models of the exhaust composition evolution of subsonic transport aircraft in- and out of the flight corridor, Ann. Geophys., 15, 88–96, https://doi.org/10.1007/s00585-997-0088-0, 1997.
Knollenberg, R.: Measurements of the growth of the ice budget in a persisting contrail, J. Atmos. Sci., 29, 1367–1374, 1972.
Koch, A., Nagel, B., Gollnick, V., Dahlmann, K., Grewe, V., Kärcher, B., and Schumann, U.: Integrated analysis and design environment for a climate compatible air transport system, in: 9th AIAA Aviation Technology, Integration and Operations (ATIO) Conference, pp. AIAA 2009–7050, AIAA, 2009.
Konopka, P.: Analytical Gaussian solutions for anisotropic diffusion in a linear shear flow, J. Non.-Equilib. Thermodyn., 20, 78–91, 1995.
Konrad, T. G. and Howard, J. C.: Multiple contrail streamers observed by radar, J. Appl. Meteor., 13, 563–572, 1974.
Koop, T., Luo, B., Tsias, A., and Peter, T.: Water activity as the determinant for homogeneous ice nucleation in aqueous solutions, Nature, 406, 611–614, 2000.
Kraabøl, A. G., Flatøy, F., and Stordal, F.: Impact of NOx emissions from subsonic aircraft: Inclusion of plume processes in a three-dimensional model covering Europe, North America, and the North Atlantic, J. Geophys. Res., 105, 3573–3581, 2000.
Krämer, M., Schiller, C., Afchine, A., Bauer, R., Gensch, I., Mangold, A., Schlicht, S., Spelten, N., Sitnikov, N., Borrmann, S., de Reus, M., and Spichtinger, P.: Ice supersaturations and cirrus cloud crystal numbers, Atmos. Chem. Phys., 9, 3505–3522, https://doi.org/10.5194/acp-9-3505-2009, 2009.
Lamquin, N., Gierens, K., Stubenrauch, C. J., and Chatterjee, R.: Evaluation of upper tropospheric humidity forecasts from ECMWF using AIRS and CALIPSO data, Atmos. Chem. Phys., 9, 1779–1793, https://doi.org/10.5194/acp-9-1779-2009, 2009.
Lamquin, N., Stubenrauch, C. J., Gierens, K., Burkhardt, U., and Smit, H.: A global climatology of upper-tropospheric ice supersaturation occurrence inferred from the Atmospheric Infrared Sounder calibrated by MOZAIC, Atmos. Chem. Phys., 12, 381–405, https://doi.org/10.5194/acp-12-381-2012, 2012.
Lee, D., Pitari, G., Grewe, V., Gierens, K., Penner, J. E., Petzold, A., Prather, M. J., Schumann, U., Bais, A., Berntsen, T., Iachetti, D., Lim, L. L., and Sausen, R.: Transport impacts on atmosphere and climate: Aviation, Atmos. Environ., 44, 4678–4734, https://doi.org/10.1016/j.atmosenv.2009.06.005, 2010.
Lewellen, D. C.: Analytic solutions for evolving size distributions of spherical crystals or droplets undergoing diffusional growth in different regimes, J. Atmos. Sci., 69, 417–434, https://doi.org/10.1175/JAS-D-11-029.1, 2012.
Lewellen, D. C. and Lewellen, W. S.: Large-eddy simulations of the vortex-pair breakup in aircraft wakes, AIAA J., 34, 2337–2345, 1996.
Lewellen, D. C. and Lewellen, W. S.: The effects of aircraft wake dynamics on contrail development, J. Atmos. Sci., 58, 390–406, 2001.
Lilly, D. K.: The representation of small-scale turbulence in numerical simulation experiments, in: IBM Sci. Comput. Symp. on Environm. Sci., 195–210, Thomas J. Watson Res. Center, Yorktown Heights, N.Y., IBM Form 320-1951, 1967.
Manney, G. L., Hegglin, M. I., Daffer, W. H., Santee, M. L., Ray, E. A., Pawson, S., Schwartz, M. J., Boone, C. D., Froidevaux, L., Livesey, N. J., Read, W. G., and Walker, K. A.: Jet characterization in the upper troposphere/lower stratosphere (UTLS): applications to climatology and transport studies, Atmos. Chem. Phys., 11, 6115–6137, https://doi.org/10.5194/acp-11-6115-2011, 2011.
Mannstein, H. and Schumann, U.: Aircraft induced contrail cirrus over Europe, Meteor. Z., 14, 549–554, 2005.
Mannstein, H., Meyer, R., and Wendling, P.: Operational detection of contrails from NOAA-AVHRR data, Int. J. Rem. Sens., 20, 1641–1660, 1999.
Mannstein, H., Spichtinger, P., and Gierens, K.: How to avoid contrail cirrus, Transp. Res., D 10, 421–426, 2005.
Mayer, B. and Kylling, A.: Technical note: The libRadtran software package for radiative transfer calculations – description and examples of use, Atmos. Chem. Phys., 5, 1855–1877, https://doi.org/10.5194/acp-5-1855-2005, 2005.
Meerkötter, R., Schumann, U., Doelling, D. R., Minnis, P., Nakajima, T., and Tsushima, Y.: Radiative forcing by contrails, Ann. Geophys., 17, 1080–1094, https://doi.org/10.1007/s00585-999-1080-7, 1999.
Meilinger, S. K., Kärcher, B., and Peter, Th.: Microphysics and heterogeneous chemistry in aircraft plumes - high sensitivity on local meteorology and atmospheric composition, Atmos. Chem. Phys., 5, 533–545, https://doi.org/10.5194/acp-5-533-2005, 2005.
Miake-Lye, R., Martinez-Sanchez, M., Brown, R., and Kolb, C. E.: Plume and wake dynamics, mixing and chemistry behind a high speed civil aircraft, J.\ Aircraft, 30, 467–479, 1993.
Minnis, P., Young, D. F., Garber, D. P., Nguyen, L., Smith Jr., W. L., and Palikonda, R.: Transformation of contrails into cirrus during SUCCESS, Geophys. Res. Lett., 25, 1157–1160, 1998.
Misaka, T., Holzäpfel, F., Hennemann, I., Gerz, T., Manhart, M., and Schwertfirm, F.: Vortex bursting and tracer transport of a counter-rotating vortex pair, Phys. Fluids, 24, 025104, https://doi.org/10.1063/1.3684990, 2012.
Mitchell, D. L. and Heymsfield, A. H.: Refinements in the treatment of ice particle terminal velocities, highlighting aggregates, J. Atmos. Sci., 62, 1637–1644, 2005.
Murphy, D. M. and Koop, T.: Review of the vapour pressures of ice and supercooled water for atmospheric applications, Q. J. Roy. Meteor.\ Soc., 131, 1539–1565, https://doi.org/10.1256/qj.04.94, 2005.
Naiman, A. D., Lele, S. K., Wilkerson, J. T., and Jacobson, M. Z.: Parameterization of subgrid plume dilution for use in large-scale atmospheric simulations, Atmos. Chem. Phys., 10, 2551–2560, https://doi.org/10.5194/acp-10-2551-2010, 2010.
Naiman, A. D., Lele, S. K., and Jacobson, M. Z.: Large eddy simulations of contrail development: Sensitivity to initial and ambient conditions over first twenty minutes, J.\ Geophys. Res., 116, D21208, https://doi.org/10.1029/2011JD015806, 2011.
Nair, R. D., Scroggs, J. S., and Semazzi, F. H. M.: A forward-trajectory global semi-Lagrangian transport scheme, J. Comput. Phys., 190, 275–294, https://doi.org/10.1016/S0021-9991(03)00274-2, 2003.
Nastrom, G. D. and Gage, K. S.: A climatology of atmospheric wavenumber spectra of wind and temperature observed by commerical aircraft, J. Atmos. Sci., 42, 950–960, 1985.
NIST: Guide for the use of the International System of Units (SI), Tech. rep., National Institute of Standards and Technology, Gaithersburg, MD 20899, NIST Special Publication 811, \urlprefixhttp://physics.nist.gov/cuu/pdf/sp811.pdf, 2008.
Paltridge, G. W. and Platt, C. M. R.: Radiative processes in meteorology and climatology, Elsevier, Amsterdam, 318 pp., 1976.
Paoli, R. and Garnier, F.: Interaction of exhaust jets and aircraft wake vortices: small-scale dynamics and potential microphysical-chemical transformations, Compt. Rend. Phys., 6, 525–547, 2005.
Paoli, R., Vancassel, X., Garnier, F., and Mirabel, P.: Large-eddy simulation of a turbulent jet and a vortex sheet interaction: particle formation and evolution in the near field of an aircraft wake, Meteor. Z., 17, 131–144, https://doi.org/10.1127/0941-2948/2008/0278, 2008.
Paoli, R., Cariolle, D., and Sausen, R.: Review of effective emissions modeling and computation, Geosci. Model Dev., 4, 643–667, https://doi.org/10.5194/gmd-4-643-2011, 2011.
Paugam, R., Paoli, R., and Cariolle, D.: Influence of vortex dynamics and atmospheric turbulence on the early evolution of a contrail, Atmos. Chem. Phys., 10, 3933–3952, https://doi.org/10.5194/acp-10-3933-2010, 2010.
Pavelin, E., Whiteway, J. A., Busen, R., and Hacker, J.: Airborne observations of turbulence, mixing, and gravity waves in the tropopause region, J.\ Geophys. Res., 107, 4084, https://doi.org/10.1029/2001JD000775, 2002.
Pisso, I., Real, E., Law, K. S., Legras, B., Bousserez, N., Attié, J. L., and Schlager, H.: Estimation of mixing in the troposphere from Lagrangian trace gas reconstructions during long-range pollution plume transport, J.\ Geophys. Res., 114, https://doi.org/10.1029/2008JD011289, 2009.
Ploeger, F., Fueglistaler, S., Groo\"s, J.-U., Günther, G., Konopka, P., Liu, Y. S., Müller, R., Ravegnani, F., Schiller, C., Ulanovski, A., and Riese, M.: Insight from ozone and water vapour on transport in the tropical tropopause layer (TTL), Atmos. Chem. Phys., 11, 407–419, https://doi.org/10.5194/acp-11-407-2011, 2011.
Ponater, M., Brinkop, S., Sausen, R., and Schumann, U.: Simulating the global atmospheric response to aircraft water vapour emissions and contrails: a first approach using a GCM, Ann. Geophys., 14, 941–960, https://doi.org/10.1007/s00585-996-0941-6, 1996.
Ponater, M., Marquart, S., and Sausen, R.: Contrails in a comprehensive global climate model: Parameterization and radiative forcing results, J. Geophys. Res., 107, 4164, https://doi.org/10.1029/2001JD000429, 2002.
Pruppacher, H. R. and Klett, J. D.: Microphysics of Clouds and Precipitation, Kluwer Academic Publ., Dordrecht, 954 pp., 1997.
Rädel, G. and Shine, K. P.: Evaluation of the use of radiosonde humidity data to predict the occurrence of persistent contrails, Q. J. Roy. Meteor.\ Soc., 133, 1413–1423, https://doi.org/10.1002/qj.128, 2007, 2007.
Riley, J. J. and Lindborg, E.: Stratified turbulence: A possible interpretation of some geophysical turbulence measurements, J. Atmos. Sci., 65, 2416–2424, https://doi.org/10.1175/2007JAS2455.1, 2008.
Sarpkaya, T.: Trailing vortices in homogeneous and density-stratified media, J. Fluid Mech., 136, 85–109, 1983.
Sassen, K.: Contrail-cirrus and their potential for regional climate change, B. Am. Meteor. Soc., 78, 1885–1903, 1997.
Sausen, R., Gierens, K., Ponater, M., and Schumann, U.: A diagnostic study of the global distribution of contrails. Part I: Present day climate, Theor.\ Appl. Clim., 61, 127–141, 1998.
Schiller, C., Krämer, M., Afchine, A., Spelten, N., and Sitnikov, N.: Ice water content of Arctic, midlatitude, and tropical cirrus, J. Geophys.\ Res., 113, D24208, https://doi.org/10.1029/2008JD010342, 2008.
Schlager, H., Konopka, P., Schulte, P., Schumann, U., Ziereis, H., Arnold, F., Klemm, M., Hagen, D. E., Whitefield, P. D., and Ovarlez, J.: In situ observations of air traffic emission signatures in the North Atlantic flight corridor, J. Geophys. Res., 102, 10739–10750, 1997.
Schmidt, E.: {D}ie {E}ntstehung von {E}isnebel aus den {A}uspuffgasen von {F}lugmotoren, in: {S}chriften der {D}eutschen {A}kademie der {L}uftfahrtforschung, Heft 44, 1–15, Verlag R. Oldenbourg, München, 1941.
Schoeberl, M. R., Douglass, A. R., Zhu, Z., and Pawson, S.: A comparison of the lower stratospheric age spectra derived from a general circulation model and two data assimilation systems, J. Geophys. Res., 108, 4113, https://doi.org/10.1029/2002JD002652, 2003.
Schröder, F., Kärcher, B., Duroure, C., Ström, J., Petzold, A., Gayet, J.-F., Strauss, B., Wendling, P., and Borrmann, S.: The transition of contrails into cirrus clouds, J. Atmos. Sci., 57, 464–480, 2000.
Schumann, U.: Subgrid length-scales for large-eddy simulation of stratified turbulence, Theor. Comput. Fluid Dyn., 2, 279–290, 1991.
Schumann, U.: On the effect of emissions from aircraft engines on the state of the atmosphere, Ann. Geophys., 12, 365–384, https://doi.org/10.1007/s00585-994-0365-0, 1994.
Schumann, U.: On conditions for contrail formation from aircraft exhausts, Meteor. Z., 5, 4–23, 1996.
Schumann, U.: Contrail cirrus, in: Cirrus, edited by Lynch, D. K., Sassen, K., O'C. Starr, D., and Stephens, G., 231–255, Oxford Univ. Press, Oxford, 2002.
Schumann, U.: A contrail cirrus prediction tool, in: Proceedings of the 2nd International Conference on Transport, Atmosphere and Climate (TAC-2), edited by: Sausen, R., van Velthoven, P. F. J., Brüning, C., and Blum, A., 69–74, Aachen, Germany, and Maastricht, The Netherlands, 22–25 June 2009, DLR-Forschungsbericht 2010-10, Cologne, Germany, ISSN 1434-8454, 2009.
Schumann, U. and Gerz:, T.: Turbulent mixing in stably stratified shear flows, J. Appl. Meteor., 34, 33–48, 1995.
Schumann, U. and Konopka, P.: A simple estimate of the concentration field in a flight corridor, in: Impact of Emissions from Aircraft and Spacecraft upon the Atmosphere. Proc. of an Intern. Sci. Colloquium, Köln (Cologne), Germany, April 18-20, 1994, edited by: Schumann, U. and Wurzel, D., 354–359, DLR-Mitt. 94-06, 1994.
Schumann, U. and Wendling, P.: Determination of contrails from satellite data and observational results, in: Air Traffic and the Environment – Background, Tendencies and Potential Global Atmospheric Effects, edited by Schumann, U., 138–153, Springer, 1990.
Schumann, U., Konopka, P., Baumann, R., Busen, R., Gerz, T., Schlager, H., Schulte, P., and Volkert, H.: Estimate of diffusion parameters of aircraft exhaust plumes near the tropopause from nitric oxide and turbulence measurements, J. Geophys. Res., 100, 14147–14162, 1995.
Schumann, U., Ström, J., Busen, R., Baumann, R., Gierens, K., Krautstrunk, M., Schröder, F. P., and Stingl, J.: In situ observations of particles in jet aircraft exhausts and contrails for different sulfur-containing fuels, J. Geophys. Res., 101, 6853–6870, https://doi.org/10.1029/95JD03405, 1996.
Schumann, U., Schlager, H., Arnold, F., Baumann, R., Haschberger, P., and Klemm, O.: Dilution of aircraft exhaust plumes at cruise altitudes, Atmos.\ Env., 32, 3097–3103, 1998.
Schumann, U., Arnold, F., Busen, R., Curtius, J., Kärcher, B., Petzold, A., Schlager, H., Schröder, F., and Wohlfrom, K. H.: Influence of fuel sulfur on the composition of aircraft exhaust plumes: The experiments SULFUR 1-7, J. Geophys. Res., 107, 4247, https://doi.org/10.1029/2001JD000813, 2002.
Schumann, U., Mayer, B., Graf, K., Mannstein, H., and Meerkötter, R.: A parametric radiative forcing model for cirrus and contrail cirrus, in: ESA Atmospheric Science Conference, Barcelona, Spain, ESA SP-676, edited by: Agency, E. S., 1–6, Frascati, Italy, 2009.
Schumann, U., Mayer, B., Hamann, U., and Graf, K.: Radiative heating in contrail cirrus, Geophys. Res. Abstr., 12, EGU2010–1501–1, 2010.
Schumann, U., Graf, K., and Mannstein, H.: Potential to reduce the climate impact of aviation by flight level changes, in: 3rd AIAA {A}tmospheric and S}pace {E}nvironments {C}onference, {AIAA paper 2011–3376, 1–22, 2011{a}.
Schumann, U., Mayer, B., Gierens, K., Unterstrasser, S., Jessberger, P., Petzold, A., Voigt, C., and Gayet, J.-F.: Effective radius of ice particles in cirrus and contrails, J. Atmos. Sci., 68, 300–321, https://doi.org/10.1175/2010JAS3562.1, 2011{b}.
Schumann, U., Mayer, B., Graf, K., and Mannstein, H.: A parametric radiative forcing model for contrail cirrus, J. Appl. Meteor. Climatol., 51, https://doi.org/10.1175/JAMC-D-11-0242.1, 2012.
Schwartz Dallara, E., Kroo, I. M., and Waitz, I.: Metric for comparing lifetime averaged climate impact of aircraft, AIAA J., 49, 1600–1613, 2011.
Scorer, R. S. and Davenport, L. J.: Contrails and aircraft downwash, J. Fluid\ Mech., 43, 451–464, 1970.
Sharman, R., Tebaldi, C., Wiener, G., and Wolff, J.: An integrated approach to mid- and upper-level turbulence forecasting, Weather Forecast., 21, 268–287, 2005.
Sölch, I. and Kärcher, B.: A large-eddy model for cirrus clouds with explicit aerosol and ice microphysics and Lagrangian ice particle tracking, Q. J. Roy. Meteor. Soc., 136B, 2074–2093, https://doi.org/10.1002/qj.689, 2010.
Sonntag, D.: Advancements in the field of hygrometry, Meteor. Z., 3, 51–66, 1994.
Spichtinger, P., Gierens, K., and Dörnbrack, A.: Formation of ice supersaturation by mesoscale gravity waves, Atmos. Chem. Phys., 5, 1243–1255, https://doi.org/10.5194/acp-5-1243-2005, 2005{a}.
Spichtinger, P., Gierens, K., and Wernli, H.: A case studies of the formation and evolution of ice supersaturation in the vicinity of a warm conveyor belt's outflow region., Atmos. Chem. Phys., 5, 973–987, 2005{b}.
Spinhirne, J. D., Hart, W. D., and Duda, D. P.: Evolution of the morphology and microphysics of contrail cirrus from airborne remote sensing, Geophys. Res.\ Lett., 25, 1153–1156, 1998.
Stohl, A., Hittenberger, M., and Wotawa, G.: Validation of the Lagrangian particle dispersion model FLEXPART against large-scale tracer experiment data, Atmos. Environ., 32, 4245–4264, 1998.
Stohl, A., Haimberger, L., Scheele, M. P., and Wernli, H.: An intercomparison of results from three trajectory models, Meteor. Appl., 8, 127–135, 2001.
Sun, Z.: Reply to comments by Greg M. McFarquhar, Q. J. Roy. Meteor.\ Soc., 127, 267–271, 2001.
Sun, Z. and Rikus, L.: Parametrization of effective sizes of cirrus-cloud particles and its verification against observations, Q. J. Roy.\ Meteor. Soc., 125, 3037–3055, 1999.
Sussmann, R. and Gierens, K.: Lidar and numerical studies on the different evolution of vortex pair and secondary wake in young contrails, J. Geophys.\ Res., 104, 2131–2142, 1999.
Sussmann, R. and Gierens, K.: Differences in early contrail evolution of two-engine versus four-engine aircraft: Lidar measurements and numerical simulations, J. Geophys. Res., 106, 4899–4911, 2001.
Tompkins, A., Gierens, K., and Rädel, G.: Ice supersaturation in the ECMWF Integrated Forecast System, Q. J. Roy. Meteor. Soc., 133, 53–63, https://doi.org/10.1002/qj.14, 2007.
Unterstrasser, S.: Numerische Simulationen von Kondensstreifen und deren Übergang in Zirren, Ph.D. thesis, University of Munich, Department of Physics, http://edoc.ub.uni-muenchen.de/9464, DLR-FB 2009-15, 2008.
Unterstrasser, S. and Gierens, K.: Numerical simulations of contrail-to-cirrus transition – Part 1: An extensive parametric study, Atmos. Chem. Phys., 10, 2017–2036, https://doi.org/10.5194/acp-10-2017-2010, 2010{a}.
Unterstrasser, S. and Gierens, K.: Numerical simulations of contrail-to-cirrus transition – Part 2: Impact of initial ice crystal number, radiation, stratification, secondary nucleation and layer depth, Atmos. Chem. Phys., 10, 2037–2051, https://doi.org/10.5194/acp-10-2037-2010, 2010{b}.
Unterstrasser, S. and Sölch, I.: Study of contrail microphysics in the vortex phase with a Lagrangian particle tracking model, Atmos. Chem. Phys., 10, 10003–10015, https://doi.org/10.5194/acp-10-10003-2010, 2010.
Unterstrasser, S., Gierens, K., and Spichtinger, P.: The evolution of contrail microphysics in the vortex phase, Meteor. Z., 17, 145–156, 2008.
van de Hulst, H. C.: Light Scattering by Small Particles, Wiley, New York, 470 pp., 1957.
Vazquez-Navarro, M. R.: Life cycle of contrails from a time series of geostationary satellite images, Ph.D. thesis, University of Munich, Department of Physics, http://edoc.ub.uni-muenchen.de/10913/, DLR-FB 2010-19, 2009.
Voigt, C., Schumann, U., Jurkat, T., Schäuble, D., Schlager, H., Petzold, A., Gayet, J.-F., Krämer, M., Schneider, J., Borrmann, S., Schmale, J., Jessberger, P., Hamburger, T., Lichtenstern, M., Scheibe, M., Gourbeyre, C., Meyer, J., Kübbeler, M., Frey, W., Kalesse, H., Butler, T., Lawrence, M. G., Holzäpfel, F., Arnold, F., Wendisch, M., Döpelheuer, A., Gottschaldt, K., Baumann, R., Zöger, M., Sölch, I., Rautenhaus, M., and Dörnbrack, A.: In-situ observations of young contrails – overview and selected results from the CONCERT campaign, Atmos. Chem. Phys., 10, 9039–9056, https://doi.org/10.5194/acp-10-9039-2010, 2010.
Voigt, C., Schumann, U., Jessberger, P., Jurkat, T., Petzold, A., Gayet, J.-F., Krämer, M., Thornberry, T., and Fahey, D. W.: Extinction and optical depth of contrails, Geophys. Res. Lett., 38, L11806, https://doi.org/10.1029/2011GL047189, 2011.
Wernli, H. and Davies, H. C.: A Lagrangian-based analysis of extratropical cyclones. I: The method and some applications, Q. J. Roy. Meteor.\ Soc., 123, 467–489, 1997.
Wilkerson, J. T., Jacobson, M. Z., Malwitz, A., Balasubramanian, S., Wayson, R., Fleming, G., Naiman, A. D., and Lele, S. K.: Analysis of emission data from global commercial aviation: 2004 and 2006, Atmos. Chem. Phys., 10, 6391–6408, https://doi.org/10.5194/acp-10-6391-2010, 2010.
Wong, H.-W. and Miake-Lye, R. C.: Parametric studies of contrail ice particle formation in jet regime using microphysical parcel modeling, Atmos. Chem. Phys., 10, 3261–3272, https://doi.org/10.5194/acp-10-3261-2010, 2010.