Articles | Volume 9, issue 2
https://doi.org/10.5194/gmd-9-671-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/gmd-9-671-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
18 Feb 2016
Development and technical paper |
| 18 Feb 2016
Decadal evaluation of regional climate, air quality, and their interactions over the continental US and their interactions using WRF/Chem version 3.6.1
Khairunnisa Yahya
Department of Marine, Earth, and Atmospheric Sciences, North Carolina State
University, Raleigh, NC 27695, USA
Kai Wang
Department of Marine, Earth, and Atmospheric Sciences, North Carolina State
University, Raleigh, NC 27695, USA
Patrick Campbell
Department of Marine, Earth, and Atmospheric Sciences, North Carolina State
University, Raleigh, NC 27695, USA
Timothy Glotfelty
Department of Marine, Earth, and Atmospheric Sciences, North Carolina State
University, Raleigh, NC 27695, USA
Department of Marine, Earth, and Atmospheric Sciences, North Carolina State
University, Raleigh, NC 27695, USA
Yang Zhang
CORRESPONDING AUTHOR
Department of Marine, Earth, and Atmospheric Sciences, North Carolina State
University, Raleigh, NC 27695, USA
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Atmos. Chem. Phys., 14, 7485–7497, https://doi.org/10.5194/acp-14-7485-2014, https://doi.org/10.5194/acp-14-7485-2014, 2014
F. Yan, E. Winijkul, D. G. Streets, Z. Lu, T. C. Bond, and Y. Zhang
Atmos. Chem. Phys., 14, 5709–5733, https://doi.org/10.5194/acp-14-5709-2014, https://doi.org/10.5194/acp-14-5709-2014, 2014
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Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes
Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation
ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool
Coupling the urban canopy model TEB (SURFEXv9.0) with the radiation model SPARTACUS-Urbanv0.6.1 for more realistic urban radiative exchange calculation
Forecasting contrail climate forcing for flight planning and air traffic management applications: the CocipGrid model in pycontrails 0.51.0
Simulation of the heat mitigation potential of unsealing measures in cities by parameterizing grass grid pavers for urban microclimate modelling with ENVI-met (V5)
AI-NAOS: an AI-based nonspherical aerosol optical scheme for the chemical weather model GRAPES_Meso5.1/CUACE
Orbital-Radar v1.0.0: a tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data
Modelling extensive green roof CO2 exchanges in the TEB urban canopy model
The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1)
A new set of indicators for model evaluation complementing to FAIRMODE’s MQO
Modeling of polycyclic aromatic hydrocarbons (PAHs) from global to regional scales: model development (IAP-AACM_PAH v1.0) and investigation of health risks in 2013 and 2018 in China
LIMA (v2.0): A full two-moment cloud microphysical scheme for the mesoscale non-hydrostatic model Meso-NH v5-6
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 2: livestock farming
Least travel time ray tracer, version Two (LTT v2) adapted to the grid geometry of the OpenIFS atmospheric model
SLUCM+BEM (v1.0): a simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban (v4.3.2)
Carbon dioxide plume dispersion simulated at hectometer scale using DALES: model formulation and observational evaluation
NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components
Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ
Observational operator for fair model evaluation with ground NO2 measurements
Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting
Development of the CMA-GFS-AERO 4D-Var assimilation system v1.0 – Part 1: System description
The third Met Office Unified Model-JULES Regional Atmosphere and Land Configuration, RAL3
Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani
Geosci. Model Dev., 18, 3065–3079, https://doi.org/10.5194/gmd-18-3065-2025, https://doi.org/10.5194/gmd-18-3065-2025, 2025
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We developed ClimKern, a Python package and radiative kernel repository, to simplify calculating radiative feedbacks and make climate sensitivity studies more reproducible. Testing of ClimKern with sample climate model data reveals that radiative kernel choice may be more important than previously thought, especially in polar regions. Our work highlights the need for kernel sensitivity analyses to be included in future studies.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025, https://doi.org/10.5194/gmd-18-2983-2025, 2025
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Particle size is a key factor determining the properties of aerosol particles which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis
Geosci. Model Dev., 18, 2861–2890, https://doi.org/10.5194/gmd-18-2861-2025, https://doi.org/10.5194/gmd-18-2861-2025, 2025
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We estimate carbon monoxide emissions through inverse modeling, an approach where measurements of tracers in the atmosphere are fed to a model to calculate backwards in time (inverse) where the tracers came from. We introduce measurements from a new satellite instrument and show that, in most places globally, these on their own sufficiently constrain the emissions. This alleviates the need for additional datasets, which could shorten the delay for future carbon monoxide source estimates.
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025, https://doi.org/10.5194/gmd-18-2747-2025, 2025
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This paper introduces the Multi-Compartment Mercury (Hg) Modeling and Analysis Project (MCHgMAP) aimed at informing the effectiveness evaluations of two multilateral environmental agreements: the Minamata Convention on Mercury and the Convention on Long-Range Transboundary Air Pollution. The experimental design exploits a variety of models (atmospheric, land, oceanic ,and multimedia mass balance models) to assess the short- and long-term influences of anthropogenic Hg releases into the environment.
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025, https://doi.org/10.5194/gmd-18-2701-2025, 2025
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Machine learning has the potential to aid the identification of organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning models in atmospheric sciences.
Juan Escobar, Philippe Wautelet, Joris Pianezze, Florian Pantillon, Thibaut Dauhut, Christelle Barthe, and Jean-Pierre Chaboureau
Geosci. Model Dev., 18, 2679–2700, https://doi.org/10.5194/gmd-18-2679-2025, https://doi.org/10.5194/gmd-18-2679-2025, 2025
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The Meso-NH weather research code is adapted for GPUs using OpenACC, leading to significant performance and energy efficiency improvements. Called MESONH-v55-OpenACC, it includes enhanced memory management, communication optimizations and a new solver. On the AMD MI250X Adastra platform, it achieved up to 6× speedup and 2.3× energy efficiency gain compared to CPUs. Storm simulations at 100 m resolution show positive results, positioning the code for future use on exascale supercomputers.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
Geosci. Model Dev., 18, 2569–2586, https://doi.org/10.5194/gmd-18-2569-2025, https://doi.org/10.5194/gmd-18-2569-2025, 2025
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The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate this effect, the radiative kernel is constructed and applied. The results show that the kernels can reproduce aerosol radiative effects and are expected to simulate stratospheric aerosol radiative effects. This approach reduces computational expense, is consistent with radiative model calculations, and can be applied to atmospheric models with speed requirements.
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
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This study evaluates the Weather Research and Forecasting Model (WRF) coupled with Chemistry (WRF-Chem) to predict a mega Asian dust storm (ADS) over South Korea on 28–29 March 2021. We assessed combinations of five dust emission and four land surface schemes by analyzing meteorological and air quality variables. The best scheme combination reduced the root mean square error (RMSE) for particulate matter 10 (PM10) by up to 29.6 %, demonstrating the highest performance.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025, https://doi.org/10.5194/gmd-18-2231-2025, 2025
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The effectiveness of this assimilation system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. This study advances our understanding of the selection of basic parameters in the four-dimensional local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate-matter-polluted environment.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
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To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
anomalous event.
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, https://doi.org/10.5194/gmd-18-1965-2025, 2025
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The MAR (Modèle Régional Atmosphérique) is a regional climate model used for weather forecasting and studying the climate over various regions. This paper presents an update of MAR thanks to which it can precisely decompose solar radiation, in particular in the UV (ultraviolet) and photosynthesis ranges, both being critical to human health and ecosystems. As a first application of this new capability, this paper presents a method for predicting UV indices with MAR.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
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Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025, https://doi.org/10.5194/gmd-18-1879-2025, 2025
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The High-Resolution Global Forecast Model (HGFM) is an advanced iteration of the operational Global Forecast System (GFS) model. HGFM can produce forecasts at a spatial scale of ~6 km in tropics. It demonstrates improved accuracy in short- to medium-range weather prediction over the Indian region, with notable success in predicting extreme events. Further, the model will be entrusted to operational forecasting agencies after validation and testing.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
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Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025, https://doi.org/10.5194/gmd-18-1809-2025, 2025
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Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025, https://doi.org/10.5194/gmd-18-1769-2025, 2025
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This study evaluates the performance of four planetary boundary layer (PBL) schemes in near-surface wind fields over the Sichuan Basin, China. Using 112 sensitivity experiments with the Weather Research and Forecasting (WRF) model and focusing on 28 wind events, it is found that wind direction was less sensitive to the PBL schemes. The quasi-normal scale elimination (QNSE) scheme captured temporal variations best, while the Mellor–Yamada–Janjić (MYJ) scheme had the least error in wind speed.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
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It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, and Raffaele Montuoro
Geosci. Model Dev., 18, 1635–1660, https://doi.org/10.5194/gmd-18-1635-2025, https://doi.org/10.5194/gmd-18-1635-2025, 2025
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The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
Yanwei Zhu, Aitor Atencia, Markus Dabernig, and Yong Wang
Geosci. Model Dev., 18, 1545–1559, https://doi.org/10.5194/gmd-18-1545-2025, https://doi.org/10.5194/gmd-18-1545-2025, 2025
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Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
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The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
Astrid Kerkweg, Timo Kirfel, Duong H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev., 18, 1265–1286, https://doi.org/10.5194/gmd-18-1265-2025, https://doi.org/10.5194/gmd-18-1265-2025, 2025
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Normally, the Modular Earth Submodel System (MESSy) is linked to complete dynamic models to create chemical climate models. However, the modular concept of MESSy and the newly developed DWARF component presented here make it possible to create simplified models that contain only one or a few process descriptions. This is very useful for technical optimisation, such as porting to GPUs, and can be used to create less complex models, such as a chemical box model.
Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
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An enhanced emission module has been developed for the PALM model system, improving flexibility and scalability of emission source representation across different sectors. A model for parametrized domestic emissions has also been included, for which an idealized model run is conducted for particulate matter (PM10). The results show that, in addition to individual sources and diurnal variations in energy consumption, vertical transport and urban topology play a role in concentration distribution.
Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, and Tobias Hell
Geosci. Model Dev., 18, 1141–1153, https://doi.org/10.5194/gmd-18-1141-2025, https://doi.org/10.5194/gmd-18-1141-2025, 2025
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As lightning is a brief and localized event, it is not explicitly resolved in atmospheric models. Instead, expert-based auxiliary descriptions are used to assess it. This study explores how AI can improve our understanding of lightning without relying on traditional expert knowledge. We reveal that AI independently identified the key factors known to experts as essential for lightning in the Alps region. This shows how knowledge discovery could be sped up in areas with limited expert knowledge.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev., 18, 1103–1118, https://doi.org/10.5194/gmd-18-1103-2025, https://doi.org/10.5194/gmd-18-1103-2025, 2025
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The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements at 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev., 18, 1017–1039, https://doi.org/10.5194/gmd-18-1017-2025, https://doi.org/10.5194/gmd-18-1017-2025, 2025
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In our study, we built upon previous work to investigate the patterns artificial intelligence (AI) learns to detect atmospheric features like tropical cyclones (TCs) and atmospheric rivers (ARs). As primary objective, we adopt a method to explain the AI used and investigate the plausibility of learned patterns. We find that plausible patterns are learned for both TCs and ARs. Hence, the chosen method is very useful for gaining confidence in the AI-based detection of atmospheric features.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
EGUsphere, https://doi.org/10.5194/egusphere-2024-3512, https://doi.org/10.5194/egusphere-2024-3512, 2025
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Non-thermal emission from chemical reactions in the Earth's middle und upper atmosphere strongly contributes to the brightness of the night sky below about 2.3 µm. The new Paranal Airglow Line and Continuum Emission model calculates the emission spectrum and its variability with an unprecedented accuracy. Relying on a large spectroscopic data set from astronomical spectrographs and theoretical molecular/atomic data, it is valuable for airglow research and astronomical observatories.
Felipe Cifuentes, Henk Eskes, Enrico Dammers, Charlotte Bryan, and Folkert Boersma
Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025, https://doi.org/10.5194/gmd-18-621-2025, 2025
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We tested the capability of the flux divergence approach (FDA) to reproduce known NOx emissions using synthetic NO2 satellite column retrievals from high-resolution model simulations. The FDA accurately reproduced NOx emissions when column observations were limited to the boundary layer and when the variability of the NO2 lifetime, the NOx : NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces strong model dependency, reducing the simplicity of the original FDA formulation.
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025, https://doi.org/10.5194/gmd-18-529-2025, 2025
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We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025, https://doi.org/10.5194/gmd-18-483-2025, 2025
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Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025, https://doi.org/10.5194/gmd-18-433-2025, 2025
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This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025, https://doi.org/10.5194/gmd-18-405-2025, 2025
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Radiation is relevant to the atmospheric impact on people and infrastructure in cities as it can influence the urban heat island, building energy consumption, and human thermal comfort. A new urban radiation model, assuming a more realistic form of urban morphology, is coupled to the urban climate model Town Energy Balance (TEB). The new TEB is evaluated with a reference radiation model for a variety of urban morphologies, and an improvement in the simulated radiative observables is found.
Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro
Geosci. Model Dev., 18, 253–286, https://doi.org/10.5194/gmd-18-253-2025, https://doi.org/10.5194/gmd-18-253-2025, 2025
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Contrails forming in some atmospheric conditions may persist and become strongly warming cirrus, while in other conditions may be neutral or cooling. We develop a contrail forecast model to predict contrail climate forcing for any arbitrary point in space and time and explore integration into flight planning and air traffic management. This approach enables contrail interventions to target high-probability high-climate-impact regions and reduce unintended consequences of contrail management.
Nils Eingrüber, Alina Domm, Wolfgang Korres, and Karl Schneider
Geosci. Model Dev., 18, 141–160, https://doi.org/10.5194/gmd-18-141-2025, https://doi.org/10.5194/gmd-18-141-2025, 2025
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Climate change adaptation measures like unsealings can reduce urban heat stress. As grass grid pavers have never been parameterized for microclimate model simulations with ENVI-met, a new parameterization was developed based on field measurements. To analyse the cooling potential, scenario analyses were performed for a densely developed area in Cologne. Statistically significant average cooling effects of up to −11.1 K were found for surface temperature and up to −2.9 K for 1 m air temperature.
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
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The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) was developed to improve the estimation of the aerosol direct radiation effect and was coupled online with a chemical weather model. The AI-NAOS scheme considers black carbon as fractal aggregates and soil dust as super-spheroids, encapsulated with hygroscopic aerosols. Real-case simulations emphasize the necessity of accurately representing nonspherical and inhomogeneous aerosols in chemical weather models.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025, https://doi.org/10.5194/gmd-18-101-2025, 2025
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The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Aurélien Mirebeau, Cécile de Munck, Bertrand Bonan, Christine Delire, Aude Lemonsu, Valéry Masson, and Stephan Weber
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-233, https://doi.org/10.5194/gmd-2024-233, 2025
Revised manuscript accepted for GMD
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The greening of cities is recommended to limit the effects of climate change. In particular, green roofs can provide numerous environmental benefits, such as urban cooling, water retention and carbon sequestration. The aim of this research is to develop a new module for calculating green roof CO2 fluxes within a model that can already simulate hydrological and thermal processes of such roofs. The calibration and evaluation of this module take advantage of long term experimental data.
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Geosci. Model Dev., 18, 1–18, https://doi.org/10.5194/gmd-18-1-2025, https://doi.org/10.5194/gmd-18-1-2025, 2025
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The Modular and Integrated Data Assimilation System (MIDAS) software is described. The flexible design of MIDAS enables both deterministic and ensemble prediction applications for the atmosphere and several other Earth system components. It is currently used for all main operational weather prediction systems in Canada and also for sea ice and sea surface temperature analysis. The use of MIDAS for multiple Earth system components will facilitate future research on coupled data assimilation.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
EGUsphere, https://doi.org/10.5194/egusphere-2024-3690, https://doi.org/10.5194/egusphere-2024-3690, 2025
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We assess the relevance and utility indicators developed within FAIRMODE by evaluating 9 CAMS models in calculated air pollutant values. For NO2, the results highlight difficulties at traffic stations. For PM2.5 and PM10 the bias and Winter-Summer gradients reveal issues. O3 evaluation shows that e.g. seasonal gradients are useful. Overall, the indicators provide valuable insights into model limitations, yet there is a need to reconsider the strictness of some indicators for certain pollutants.
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024, https://doi.org/10.5194/gmd-17-8885-2024, 2024
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We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can reproduce PAH distribution well. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change in BaP is lower than that in PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although the Action Plan has been implemented.
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024, https://doi.org/10.5194/gmd-17-8773-2024, 2024
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We have developed a complete two-moment version of the LIMA (Liquid Ice Multiple Aerosols) microphysics scheme. We have focused on collection processes, where the hydrometeor number transfer is often estimated in proportion to the mass transfer. The impact of these parameterizations on a convective system and the prospects for more realistic estimates of secondary parameters (reflectivity, hydrometeor size) are shown in a first test on an idealized case.
Jize Jiang, David S. Stevenson, Aimable Uwizeye, Giuseppe Tempio, Alessandra Falcucci, Flavia Casu, and Mark A. Sutton
EGUsphere, https://doi.org/10.5194/egusphere-2024-3803, https://doi.org/10.5194/egusphere-2024-3803, 2024
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A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from livestock farming. It is estimated that about 30 % of excreted N from livestock was lost due to NH3 emissions from housing, manure management and land application of manure. High NH3 volatilization often occurred in hot regions, while poor management practices also result in significant N losses through NH3 emissions.
Maksym Vasiuta, Angel Navarro Trastoy, Sanam Motlaghzadeh, Lauri Tuppi, Torsten Mayer-Gürr, and Heikki Järvinen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-136, https://doi.org/10.5194/gmd-2024-136, 2024
Revised manuscript accepted for GMD
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Propagation of electromagnetic signals in the Earth's neutral atmosphere inflicts errors in space geodetic observations. To model these errors as accurately as possible, it is necessary to use a signal ray tracing algorithm which is informed of the state of the atmosphere. We developed such algorithm and tested it by modelling errors in GNSS network observations. Our algorithm's main advantage is loss-less utilization of atmospheric information provided by numerical weather prediction models.
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
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A new parameterisation for dynamic anthropogenic heat and electricity consumption is described. The model reproduced the temporal variation in and spatial distributions of electricity consumption and temperature well in summer and winter. The partial air conditioning was the most critical factor, significantly affecting the value of anthropogenic heat emission.
Arseniy Karagodin-Doyennel, Fredrik Jansson, Bart van Stratum, Hugo Denier van der Gon, Jordi Vilà-Guerau de Arellano, and Sander Houweling
EGUsphere, https://doi.org/10.5194/egusphere-2024-3721, https://doi.org/10.5194/egusphere-2024-3721, 2024
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We introduce a new simulation platform based on the Dutch Large-Eddy Simulation (DALES) to simulate carbon dioxide (CO2) emissions and their dispersion in the turbulent environments with hectometer resolution. This model incorporates both anthropogenic emission inventory and ecosystem exchanges. Simulation results for the main urban area in the Netherlands demonstrate a strong potential of DALES to enhance CO2 emission modeling, which is important for refining their reduction strategies.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
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To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
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Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
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Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
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The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Yongzhu Liu, Xiaoye Zhang, Wei Han, Chao Wang, Wenxing Jia, Deying Wang, Zhaorong Zhuang, and Xueshun Shen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-148, https://doi.org/10.5194/gmd-2024-148, 2024
Revised manuscript accepted for GMD
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In order to investigate the feedbacks of chemical data assimilation on meteorological forecasts, we developed a strongly coupled chemistry meteorology four-dimensional variational (4D-Var) assimilation system, CMA-GFS-AERO 4D-Var, based on the framework of the incremental analysis scheme of the CMA-GFS operational global numerical weather model. The results show that assimilating BC observations can generate analysis increments not only for BC but also for atmospheric variables.
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-201, https://doi.org/10.5194/gmd-2024-201, 2024
Revised manuscript accepted for GMD
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RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre and sub-km scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and improved representation of clouds and visibility.
Cited articles
Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation,
2. Multiple aerosol types, J. Geophys. Res., 105, 6837–6844, 2000.
Aitken, A. C., DeCarlo, P. F., Kroll, J. H., Worsnop, D. R., Huffman, J. A.,
Docherty, K. S., Ulbrich, I. M., Mohr, C., Kimmel, J. R., Sueper, D., Sun, Y.,
Zhang, Q., Trimborn, A., Northway, M., Ziemann, P. J., Canagaratna, M. R.,
Onasch, T. B., Alfarra, M. R., Prevot, A. S. H., Dommen, J., Duplissy, J.,
Metzger, A., Baltensperger, U., and Jimenez, J. L.: O/C and OM/OC ratios of
primary, secondary and ambient organic aerosols with high-resolution time of
flight aerosol mass spectrometry, Environ. Sci. Technol., 42, 4478–4485,
2008.
Ahmadov, R., McKeen, S. A., Robinson, A. L., Bareini, R., Middlebrook, A. M.,
De Gouw, J. A., Meagher, J., Hsie, E.-Y., Edgerton, E., Shaw, S., and Trainer,
M.: A volatility basis set model for summertime secondary organic aerosols
over the eastern United States in 2006, J. Geophys. Res. 117, D06301,
https://doi.org/10.1029/2011JD016831, 2012.
Alapaty, K., Herwehe, J., Nolte, C. G., Bullock, R. O., Otte, T. L., Mallard,
M. S., Dudhia, J., and Kain, J. S.: Introducing subgrid-scale cloud feedbacks
to radiation in WRF, the 13th WRF Users Workshop, Boulder, CO, 26–29 June 2012.
Beniston, M., Stephenson, D. B., Christensen, O. B., Ferro, C. A. T., Frei, C.,
Goyette, S., Halsnaes, K., Holt, T., Jylha, K., Koffi, B., Palutikof, J.,
Scholl, R., Semmler, T., and Woth, K.: Future extreme events in European
climate: an exploration of regional climate model projections, Clim. Change,
81, 71–95, https://doi.org/10.1007/s10584-006-9226-z, 2007.
Bennartz, R.: Global assessment of marine boundary layer cloud droplet
number concentration from satellite, J. Geophys. Res.-Atmos., 112, D02201,
https://doi.org/10.1029/2006JD007547, 2007.
Brunner, D., Savage, N., Jorba, O., Eder, B., Giordano, L., Badia, A.,
Balzarini, A., Baro, R., Bianconi, R., Chemel, C., Curci, G., Forkel, R.,
Jimenez-Guerrero, P., Hirtl, M., Hodzic, A., Hozak, L., Im, U., Knote, C.,
Makar, P., Manders-Groot, A., van Meijgaard, E., Neal, L., Perez, J. L.,
Pirovano, G., San Jose, R., Schroder, W., Sokhi, R. S., Syrakov, D., Torian,
A., Tuccella, P., Werhahn, J., Wolke, R., Yahya, K., Zabkar, R., Zhang, Y.,
Hogrefe, C., and Galmarini, S.: Comparative analysis of meteorological
performance of coupled chemistry-meteorology models in the context of AQMEII
phase 2, Atmos. Environ., 115, 470–498,
https://doi.org/10.1016/j.atmosenv.2014.12.032,
2015.
Caldwell, P., Chin, H.-N. S., Bader, D. C., and Bala, G.: Evaluation of a
WRF dynamical downscaling simulation over California, Clim. Change., 95,
499–521, 2009.
Campbell, P. C., Zhang, Y., Yahya, K., Wang, K., Hogrefe, C., Pouliot, G.,
Knote, C., Hodzic, A., San Jose, R., Perez, J., Jimenez-Guerrero, P., Baro,
R., and Makar, P.: A Multi-Model Assessment for the 2006 and 2010 Simulations
under the Air Quality Model Evaluation International Initiative (AQMEII)
Phase 2 over North America: Part I, Indicators of the Sensitivity of O3
and PM2.5 Formation Regimes, Atmos. Environ.,
https://doi.org/10.1016/j.atmosenv.2014.12.026, 115, 569–586, 2015.
Chen, F. and Dudhia, J.: Coupling an advanced land-surface/hydrology model
with the Penn State/NCAR MM5 modeling system. Part I: Model implementation
and sensitivity, Mon. Weather Rev., 129, 569–585, 2001.
Clough, S. A., Shephard, M. W., Mlawer, J. E., Delamere, J. S., Iacono, M. J.,
Cady-Pereira, K., Boukabara, S., and Brown, P. D.: Atmospheric radiative
transfer modeling: a summary of the AER codes, J. Quant. Spectrosc. R., 91, 233–244, https://doi.org/10.1016/j.qsrt.2004.05.058, 2005.
Cooper, O. R., Parrish, D. D., Ziemke, J., Balashov, N. V., Cupeiro, M.,
Galbally, I. E., Gilge, S., Horowitz, L., Jensen, N. R., Lamarque, J.-F.,
Naik, V., Oltmans, S. J., Schwab, J., Shindell, D. T., Thompson, A. M.,
Thouret, V., Wang, Y., and Zbinden, R. M.: Global distribution and trends of
tropospheric ozone: An observation-based review, Elem. Sci. Anth., 2,
000029, https://doi.org/10.12952/journal.elementa.000029, 2014.
Dasari, H. P., Salgado, R., Perdigao, J., and Challa, V. S.: A regional climate
simulation study using WRF-ARW model over Europe and evaluation for extreme
temperature weather events, Intl., J. Atmos. Sci., 2014, 704079,
https://doi.org/10.1155/2014/704079, 2014.
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V.,
Gayno, G., and Tarpley, J. D.: Implementation of NOAH land surface model
advances in the National Centers for Environmental Prediction operational
mesoscale model, J. Geophys. Res., 108, 8851, https://doi.org/10.1029/2002JD003296,
2003.
EPA.: Our Nation's Air – Status and Trends through 2010, Particle
Pollution, Report by the US EPA, 4 pp., available at: http://www.epa.gov/airtrends/2011 (last access: 6 July 2015)
2011.
Fan, F., Bradley, R. S., and Rawlins, M. A.: Climate change in the northeastern
U.S.: regional climate validation and climate change projections, Clim.
Dyn., 43, 145–161, https://doi.org/10.1007/s00382-014-2198-1, 2014.
Feser, F., Rockel, B., Von Storch, H., Winterfeldt, J., and Zahn, M.:
Regional climate models add value to global model data, B. Am. Meteorol. Soc., 92, 1181–1192, 2011.
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W., Cox, P.,
Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P., Guilyardi, E., Jakob, C., Kattsov, V.,
Reason, C., and Rummukainen, M.: Evaluation of Climate Models, in: Climate Change 2013: The
Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker,
T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels,
A., Xia, Y., Bex V., and Midgley, P. M., Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, 743–863, 2013.
Gao, Y., Fu, J. S., Drake, J. B., Liu, Y., and Lamarque, J. F.: Projected
changes of extreme weather events in the eastern United States based on a
high resolution climate modeling system, Environ. Res. Lett., 7, 044025,
https://doi.org/10.1088/1748-9326/7/4/044025,
2012.
Gao, Y., Fu, J. S., Drake, J. B., Lamarque, J.-F., and Liu, Y.: The impact of
emission and climate change on ozone in the United States under
representative concentration pathways (RCPs), Atmos. Chem. Phys., 13,
9607–9621, https://doi.org/10.5194/acp-13-9607-2013, 2013.
Glotfelty, T., He, J., and Zhang, Y.: Updated organic aerosol treatments in
CESM/CAM5: development and initial application, Atmos. Environ., in preparation,
2016.
Gong, S., Barrie, L. A., and Blanchet, J. P.: Modeling sea salt aerosols in the
atmosphere: 1. Model development, J. Geophys. Res., 102, 3805–3818,
https://doi.org/10.1029/96JD02953, 1997.
Grell, G. A. and Freitas, S. R.: A scale and aerosol aware stochastic
convective parameterization for weather and air quality modeling, Atmos.
Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014, 2014.
Grell, G. A., Knoche, R., Peckham, S. E., and McKeen, S. A.: Online vs.
offline air quality modeling on cloud-resolving time scales, Geophys. Res.
Lett., 31, L16117, https://doi.org/10.1029/2004GL020175, 2004.
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G.,
Skamarock, W. C., and Eder, B.: Fully coupled “online” chemistry within the
WRF model, Atmos. Environ., 39, 6957–6975, 2005.
Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and Geron,
C.: Estimates of global terrestrial isoprene emissions using MEGAN (Model of
Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys., 6,
3181–3210, https://doi.org/10.5194/acp-6-3181-2006, 2006.
He, J. and Zhang, Y.: Improvement and further development in CESM/CAM5:
gas-phase chemistry and inorganic aerosol treatments, Atmos. Chem. Phys., 14,
9171–9200, https://doi.org/10.5194/acp-14-9171-2014, 2014.
Hong, S.-Y.: A new stable boundary-layer mixing scheme and its impact on the
simulated East Asian summer monsoon, Q. J. Roy. Meteor. Soc., 136, 1481–1496, https://doi.org/0.1002/qj.665, 2010.
Hong, S.-Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with
an explicit treatment of entrainment processes, Mon. Weather Rev., 134,
2318–2341, 2006.
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner,
P. J., Lamarque, J.-F., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb,
W. H., Long, M. C., Mahowald, N., Marsh, D. R., Neale, R. B., Rasch, P., Vavrus,
S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl, J., and
Marshall, S.: The Community Earth System Model: A framework for
collaborative research, B. Am. Meteorol. Soc., 94, 1339–1360,
https://doi.org/10.1175/BAMS-D-12-00121.1, 2013.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shepard, M. W., Clough, S. A., and
Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys. Res., 113,
D13103, https://doi.org/10.1029/2008JD009944, 2008.
IPCC: Summary for Policymakers, in: Climate Change 2013: The Physical Science
Basis. Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin,
D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia,
Y., Bex, V., and Midley, P. M., Cambridge University Press, Cambridge, United
Kingdom and New York, NY, USA, 1–30,
https://doi.org/10.1017/CBO9781107415324.004,
2013.
Jacob, D., Barring, L., Christensen, O. B., Christensen, J. H., de Castro, M.,
Deque, M., Giorgi, F., Hagemann, S., Hirschi, M., Jones, R., Kjellstrom, E.,
Lenderink, G., Rockel, B., Sanchez, E., Schar, C., Seneviratne, S.I., Somot,
S., van Ulden, A., and van den Hurk, B.: An inter-comparison of regional
climate models for Europe: model performance in present-day climate, Clim.
Change, 81, 31–52, 2007.
Jimenez, P. A. and Dudhia, J.: Improving the representation of resolved and
unresolved topographic effects on surface wind in the WRF model, J. Appl.
Meteor. Climatol., 51, 300–316, 2012.
Jones, R. G., Noguer, M., Hassell, D. C., Hudson, D., Wilson, S. S., Jenkins
G. J., and Mitchell, J. F. B.: Generating high resolution climate change
scenarios using PRECIS, Met Office Hadley Centre, Exeter, UK, 40 pp., April
2004.
Jones, S. and Creighton, G.: AFWA dust emission scheme for WRF/Chem-GOCART,
2011 WRF workshop, 20–24 June Boulder, CO, USA, 2011.
Karamchandani, P., Zhang, Y., Chen, S.-Y., and Balmori-Bronson, R.:
Development of an extended chemical mechanism for global-through-urban
applications, Atmos. Poll. Res., 3, 1–24,
https://doi.org/10.5094/APR.2011.047, 2012.
Kim, J., Waliser, D. E., Mattmann, C. A., Mearns, L. O., Goodale, C. E., Hart,
A. F., Crichton, D. J., McGinnis, S., Lee, H., Loikith, P. C., and Boustani, M.:
Evaluation of the surface climatology over the conterminous United States in
the North American Regional Climate Change Assessment Program Hindcast
Experiment using a regional climate model evaluation system, J. Climate, 26,
5698–5715, 2013.
King, N. J., Bower, K. N., Crosier, J., and Crawford, I.: Evaluating MODIS
cloud retrievals with in situ observations from VOCALS-REx, Atmos. Chem.
Phys., 13, 191–209, https://doi.org/10.5194/acp-13-191-2013, 2013.
Legates, D. R. and McCabe Jr., G. J.: Evaluating the use of
“goodness-of-fit” measures in hydrologic and hydroclimatic model
validation, Water Resour. Res., 35, 233–241, https://doi.org/10.1029/1998WR900018, 1999.
Levy, R. C., Remer, L. A., and Dubovik, O.: Global aerosol optical properties
and application to Moderate Resolution Imaging Spectroradiometer aerosol
retrieval over land, J. Geophys. Res., 112, D13210, https://doi.org/10.1029/2006JD007815,
2007.
Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. A., Sayer, A. M., Patadia,
F., and Hsu, N. C.: The Collection 6 MODIS aerosol products over land and
ocean, Atmos. Meas. Tech., 6, 2989–3034, https://doi.org/10.5194/amt-6-2989-2013, 2013.
Leung, R. L., Qian, Y., and Bian, X.: Hydroclimate of the Western United
States based on Observations and Regional Climate Simulation of 1981–2000,
Part I: Seasonal Statistics, J. Clim., 16, 1892–1911, 2003.
Liu, X., Easter, R. C., Ghan, S. J., Zaveri, R., Rasch, P., Shi, X.,
Lamarque, J.-F., Gettelman, A., Morrison, H., Vitt, F., Conley, A., Park, S.,
Neale, R., Hannay, C., Ekman, A. M. L., Hess, P., Mahowald, N., Collins, W.,
Iacono, M. J., Bretherton, C. S., Flanner, M. G., and Mitchell, D.: Toward a
minimal representation of aerosols in climate models: description and
evaluation in the Community Atmosphere Model CAM5, Geosci. Model Dev., 5,
709–739, https://doi.org/10.5194/gmd-5-709-2012, 2012.
Loeb, N. G., Wielicki, B. A., Doelling, D. R., Smith, L., Keyes, D. F., Kato,
S., Manalo-Smith, N., and Wong, T.: Toward Optimal Closure of the earth's
top-of-atmosphere radiation budget, J. Climate, 22, 748–766, 2009.
Ma, P.-L., Rasch, P. J., Fast, J. D., Easter, R. C., Gustafson Jr., W. I.,
Liu, X., Ghan, S. J., and Singh, B.: Assessing the CAM5 physics suite in the
WRF-Chem model: implementation, resolution sensitivity, and a first
evaluation for a regional case study, Geosci. Model Dev., 7, 755–778,
https://doi.org/10.5194/gmd-7-755-2014, 2014.
Mass, C.: Improved subgrid drag or hyper PBL/vertical resolution? Dealing
with the stable PBL problems in WRF, presented at the 13th WRF Users'
Workshop, 26–29 June, Boulder, CO, 2012.
Molders, N., Bruyere, C. L., Gende, S., and Pirhala, M. A.: Assessment of the
2006–2012 Climatological Fields and Mesoscale Features from Regional
Downscaling of CESM Data by WRF/Chem over Southeast Alaska, Atmos. Clim.
Sci., 4, 589–613, 2014.
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of cloud microphysics
on the development of trailing stratiform precipitation in a simulated squall
line: Comparison of One- and Two-Moment Schemes, Mon. Weather Rev., 137,
991–1007, 2009.
Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van
Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A.,
Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R.
J., Thomson, A. M., Weyant, J. P., and Wilbanks, T. J.: The next generation
of scenarios for climate change research and assessment, Nature, 463,
747–756, https://doi.org/10.1038/nature08823, 2010.
Nasrollahi, N., AghaKouchak, A., Li, J., Gao, X., Hsu, K., and Sorooshian,
S.: Assessing the Impacts of Different WRF Precipitation Physics in Hurricane
Simulations, Weather Forecast., 27, 1003–1016, 2012.
Neale, R. B., Jadwiga, H. R., Conley, A. J., Park, S., Lauritzen, P. H.,
Gettelman, A., Williamson, D. L., Rasch, P., Vavrus, S. J., Taylor, M. A.,
Collins, W. D., Zhang, M., and Lin, S.-J.: Description of the NCAR Community
Atmosphere Model (CAM 5.0), NCAR Tech. Note NCAR/TN-486+STR, Natl. Cent.
for Atmos. Res., Boulder, CO, available at:
http://www.cesm.ucar.edu/models/cesm1.0/cam/docs/description/cam5_desc.pdf
(last access: 6 July 2015), 2010.
Otte, T. L., Nolte, C. G., Otte, M. J., and Bowden, J. H.: Does Nudging
squelch the extremes in regional climate modeling? J. Clim., 25, 7046–7066,
https://doi.org/10.1175/JCLI-D-12-00048.1, 2012.
Penrod, A., Zhang, Y., Wang, K., Wu, S.-Y., and Leung, R. L.: Impacts of
future climate and emission changes on U.S. air quality, Atmos. Environ., 89,
533–547, 2014.
Pietikäinen, J.-P., O'Donnell, D., Teichmann, C., Karstens, U., Pfeifer,
S., Kazil, J., Podzun, R., Fiedler, S., Kokkola, H., Birmili, W., O'Dowd, C.,
Baltensperger, U., Weingartner, E., Gehrig, R., Spindler, G., Kulmala, M.,
Feichter, J., Jacob, D., and Laaksonen, A.: The regional aerosol-climate
model REMO-HAM, Geosci. Model Dev., 5, 1323–1339,
https://doi.org/10.5194/gmd-5-1323-2012, 2012.
Pleim, J. E. and Gilliam, R.: An indirect data assimilation scheme for deep
soil temperature in the Pleim-Xiu Land Surface Model, J. Appl. Meteor.
Climatol., 48, 1362–1376, 2009.
Pouliot, G., van der Gon, H. A. C. D., Kuenen, J., Zhang, J., Moran, M., and
Makar, P.: Analysis of the Emission Inventories and Model-Ready Emission
Datasets of Europe and North America for Phase 2 of the AQMEII Project,
Atmos. Environ., 115, 345–360, https://doi.org/10.1016/j.atmosenv.2014.10.061,
2015.
Rawlins, M. A., Bradley, R. S., and Diaz, H. F.: Assessment of regional
climate model simulation estimates over the northeast United States, J.
Geophys. Res., 117, D23112, https://doi.org/10.1029/2012JD018137, 2012.
Refslund, J., Dellwik, E., Hahmann, A. N., Barlage, M. J., and Boegh, E.:
Development of satellite green vegetation fraction time series for use in
mesoscale modeling: application to the European heat wave 2006, Theor. Appl.
Climatol., 117, 377–392, https://doi.org/10.1007/s00704-013-1004-z, 2014.
Sarwar, G., Luecken, D. J., and Yarwood, G.: Developing and implementing an
updated chlorine chemistry into the Community Multiscale Air Quality Model,
presented at the 28th NATO/CCMS International Technical Meeting, Lepzig,
Germany, 15–19 May 2006.
Sarwar, G., Luecken, D., and Yarwood, G.: Chapter 2.9: Developing and
implementing an updated chlorine chemistry into the community multiscale air
quality model, Developments in Environmental Science, Volume 6, edited by:
Borrego, C. and Renner, E., Elsevier Ltd,
https://doi.org/10.1016/S1474-8177(07)06029-9, 168 pp., 2007.
Sarwar, G., Fahey, K., Napelenok, S., Roselle, S., and Mathur, R.: Examining
the impact of CMAQ model updates on aerosol sulfate predictions, the 10th
Annual CMAS Models-3 User's Conference, October, Chapel Hill, NC, 2011.
Shan, Z., Parol, F., Riedi, J., Cornet, C., and Thieuleux, F.: Examination of
POLDER/PARASOL and MODIS/Aqua cloud fractions and properties
representativeness, J. Climate, 24, 4435–4450, 2011.
Sievering, H.: Small-particle dry deposition under high wind speed
conditions: Eddy flux measurements at the boulder atmospheric observatory,
Atmos. Environ., 21, 2179–2185, 1987.
Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M. A., Mitchell, K., Ek,
M., Gayno, G., Wegiel, J., and Cuenca, R. H.: Implementation and verification
of the unified NOAH land surface model in the WRF model, 20th conference on
weather analysis and forecasting/16th conference on numerical weather
prediction, 11–15, 2004.
Tie, X., Madronich, S., Walters, S., Zhang, R., Rasch, P., and Collins, W.:
Effect of clouds on photolysis and oxidants in the troposphere, J. Geophys.
Res., 108, 4642, https://doi.org/10.1029/2003JD003659, 2003.
Toth, T. D., Zhang, J., Campbell, J. R., Reid, J. S., Shi, Y., Johnson, R.
S., Smirnov, A., Vaughan, M. A., and Winker, D. M.: Investigating enhanced
Aqua MODIS aerosol optical depth retrievals over the mid-to-high latitude
Southern Oceans through intercomparison with co-located CALIOP, MAN and
AERONET data sets, J. Geophys. Res.-Atmos., 18, 1–15, 2013.
van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard,
K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T.,
Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The
representative concentration pathways: an overview, Climate Change, 109,
5–31, https://doi.org/10.1007/s10584-011-0148-z, 2011.
Wang, K., Zhang, Y., Yahya, K., Wu, S.-Y., and Grell, G.:, Implementation and
initial application of new chemistry-aerosol options in WRF/Chem for
simulating secondary organic aerosols and aerosol indirect effects for
regional air quality, Atmos. Environ., 115, 371–388, https://doi.org/10.1016/j.atmosenv.2014.12.007, 2015a.
Wang, K., Yahya, K., Zhang, Y., Hogrefe, C., Pouliot, G., Knote, C., Hodzic,
A., San Jose, R., Perez, J. L., Guerrero, P. J., Baro, R., and Makar, P.:
Evaluation of Column Variable Predictions Using Satellite Data over the
Continental United States: A Multi-Model Assessment for the 2006 and 2010
Simulations under the Air Quality Model Evaluation International Initiative
(AQMEII) Phase 2, Atmos. Environ., 115, 587–603, https://doi.org/10.1016/j.atmosenv.2014.07.044,
2015b.
Warrach-Sagi, K., Schwitalla, T., Wulfmeyer, V., and Bauer, H.-S.: Evaluation
of a climate simulation in Europe based on the WRF-NOAH model system:
precipitation in Germany, Clim. Dyn., 41, 755–774,
https://doi.org/10.1007/s00382-013-1727-7, 2013.
Willmott, C. J.: On the validation of models, Phys. Geog., 2, 184–194, 1981.
Xing, J., Mathur, R., Pleim, J., Hogrefe, C., Gan, C.-M., Wong, D. C., Wei,
C., Gilliam, R., and Pouliot, G.: Observations and modeling of air quality
trends over 1990–2010 across the Northern Hemisphere: China, the United
States and Europe, Atmos. Chem. Phys., 15, 2723–2747,
https://doi.org/10.5194/acp-15-2723-2015, 2015.
Xu, Z. and Yang, Z.-L.: An improved dynamical downscaling method with GCM
Bias Corrections and Its Validation with 30 years of climate simulations, J.
Clim., 25, 6271–6286, 2012.
Yahya, K., Wang, K., Gudoshava, M., Glotfelty, T., and Zhang, Y.: Application
of WRF/Chem over North America under the AQMEII Phase 2. Part I.
Comprehensive Evaluation of 2006 Simulation, Atmos. Environ., 115, 733–755,
https://doi.org/10.1016/j.atmosenv.2014.08.063, 2015a.
Yahya, K., Wang, K., Zhang, Y., and Kleindienst, T. E.: Application of
WRF/Chem over North America under the AQMEII Phase 2 – Part 2: Evaluation of
2010 application and responses of air quality and meteorology-chemistry
interactions to changes in emissions and meteorology from 2006 to 2010,
Geosci. Model Dev., 8, 2095–2117, https://doi.org/10.5194/gmd-8-2095-2015,
2015b.
Yahya, K., He, J., and Zhang, Y.: Multi-Year Applications of WRF/Chem over
Continental U.S.: Model Evaluation, Variation Trend, and Impacts of Boundary
Conditions over CONUS, J. Geophy. Res., 120, 12748–12777, https://doi.org/10.1002/2015JD023819,
2015c.
Yarwood, G., Rao, S., Yocke, M., and Whitten, G. Z.: Final Report – Updates
to the Carbon Bond Chemical Mechanism: CB05, Rep. RT-04-00675, 246 pp., Yocke
and Co., Novato, Calif., 2005.
Yu, S., Dennis, R., Roselle, S., Nenes, A., Walker, J., Eder, B., Schere,
K., Swall, J., and Robarge, W.: An assessment of the ability of 3-D air
quality models with current thermodynamic equilibrium models to predict
aerosol NO3-, J. Geophys. Res., 110, D07S13, https://doi.org/10.1029/2004JD004718, 2005.
Yu, S., Eder, B., Dennis, R., Chu, S.-H., and Schwartz, S.: New unbiased
symmetric metrics for evaluation of air quality models, Atmos. Sci. Lett., 7,
26–34, 2006.
Yu, S., Mathur, R., Pleim, J., Wong, D., Gilliam, R., Alapaty, K., Zhao, C.,
and Liu, X.: Aerosol indirect effect on the grid-scale clouds in the two-way
coupled WRF-CMAQ: model description, development, evaluation and regional
analysis, Atmos. Chem. Phys., 14, 11247–11285,
https://doi.org/10.5194/acp-14-11247-2014, 2014.
Zhang, Y., Liu, P., Pun, B., and Seigneur, C.: A comprehensive performance
evaluation of MM5-CMAQ for summer 1999 Southern Oxidants Study Episode,
Part-I. Evaluation Protocols, Databases and Meteorological Predictions,
Atmos. Environ., 40, 4825–4838, 2006.
Zhang, Y., Wen, X.-Y., and Jang, C. J.: Simulating
chemistry-aerosol-cloud-radiation-climate feedbacks over the CONUS using the
online-coupled Weather Research Forecasting Model with chemistry (WRF/Chem),
Atmos. Environ., 44, 3568–3582, 2010.
Zhang, Y., Chen, Y.-C., Sarwar, G., and Schere, K.: Impact of Gas-Phase
Mechanisms on Weather Research Forecasting Model with Chemistry (WRF/Chem)
Predictions: Mechanism Implementation and Comparative Evaluation, J. Geophys.
Res., 117, D01301, https://doi.org/10.1029/2011JD015775, 2012a.
Zhang, Y., Karamchandani, P., Glotfelty, T., Streets, D. G., Grell, G.,
Nenes, A., Yu, F.-Q., and Bennartz, R.: Development and Initial Application
of the Global-Through-Urban Weather Research and Forecasting Model with
Chemistry (GU-WRF/Chem), J. Geophys. Res., 117, D20206,
https://doi.org/10.1029/2012JD017966, 2012b.
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Short summary
The Weather Research and Forecasting model with Chemistry (WRF/Chem) v3.6.1 is evaluated for its first decadal application during 2001 to 2010 using the Representative Concentration Pathway 8.5 emissions. The model evaluation shows acceptable performance for long-term climatological simulations of most meteorological variables and chemical concentrations. Larger biases exist for aerosol-cloud-radiation variables, which future model improvement should focus on.
The Weather Research and Forecasting model with Chemistry (WRF/Chem) v3.6.1 is evaluated for its...
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