Articles | Volume 14, issue 1
https://doi.org/10.5194/gmd-14-73-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-73-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
07 Jan 2021
Model experiment description paper |
| 07 Jan 2021
| 07 Jan 2021
A multi-year short-range hindcast experiment with CESM1 for evaluating climate model moist processes from diurnal to interannual timescales
Lawrence Livermore National Laboratory, Livermore, CA, USA
Chen Zhou
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Yunyan Zhang
Lawrence Livermore National Laboratory, Livermore, CA, USA
Stephen A. Klein
Lawrence Livermore National Laboratory, Livermore, CA, USA
Mark D. Zelinka
Lawrence Livermore National Laboratory, Livermore, CA, USA
Xue Zheng
Lawrence Livermore National Laboratory, Livermore, CA, USA
Shaocheng Xie
Lawrence Livermore National Laboratory, Livermore, CA, USA
Wei-Ting Chen
Department of Atmospheric Sciences, National Taiwan University,
Taipei, Taiwan
Chien-Ming Wu
Department of Atmospheric Sciences, National Taiwan University,
Taipei, Taiwan
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Kai Zhang, Wentao Zhang, Hui Wan, Philip J. Rasch, Steven J. Ghan, Richard C. Easter, Xiangjun Shi, Yong Wang, Hailong Wang, Po-Lun Ma, Shixuan Zhang, Jian Sun, Susannah M. Burrows, Manish Shrivastava, Balwinder Singh, Yun Qian, Xiaohong Liu, Jean-Christophe Golaz, Qi Tang, Xue Zheng, Shaocheng Xie, Wuyin Lin, Yan Feng, Minghuai Wang, Jin-Ho Yoon, and L. Ruby Leung
Atmos. Chem. Phys., 22, 9129–9160, https://doi.org/10.5194/acp-22-9129-2022, https://doi.org/10.5194/acp-22-9129-2022, 2022
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Xue Zheng, Qing Li, Tian Zhou, Qi Tang, Luke P. Van Roekel, Jean-Christophe Golaz, Hailong Wang, and Philip Cameron-Smith
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Aurore Voldoire, Romain Roehrig, Hervé Giordani, Robin Waldman, Yunyan Zhang, Shaocheng Xie, and Marie-Nöelle Bouin
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Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
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Yu-Hung Chang, Wei-Ting Chen, Chien-Ming Wu, Christopher Moseley, and Chia-Chun Wu
Atmos. Chem. Phys., 21, 16709–16725, https://doi.org/10.5194/acp-21-16709-2021, https://doi.org/10.5194/acp-21-16709-2021, 2021
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Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
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The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Yong Wang, Guang J. Zhang, Shaocheng Xie, Wuyin Lin, George C. Craig, Qi Tang, and Hsi-Yen Ma
Geosci. Model Dev., 14, 1575–1593, https://doi.org/10.5194/gmd-14-1575-2021, https://doi.org/10.5194/gmd-14-1575-2021, 2021
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Qi Tang, Michael J. Prather, Juno Hsu, Daniel J. Ruiz, Philip J. Cameron-Smith, Shaocheng Xie, and Jean-Christophe Golaz
Geosci. Model Dev., 14, 1219–1236, https://doi.org/10.5194/gmd-14-1219-2021, https://doi.org/10.5194/gmd-14-1219-2021, 2021
Peter A. Bogenschutz, Shuaiqi Tang, Peter M. Caldwell, Shaocheng Xie, Wuyin Lin, and Yao-Sheng Chen
Geosci. Model Dev., 13, 4443–4458, https://doi.org/10.5194/gmd-13-4443-2020, https://doi.org/10.5194/gmd-13-4443-2020, 2020
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This paper documents a tool that has been developed that can be used to accelerate the development and understanding of climate models. This version of the model, known as a the single-column model, is much faster to run than the full climate model, and we demonstrate that this tool can be used to quickly exploit model biases that arise due to physical processes. We show examples of how this single-column model can directly benefit the field.
Qi Tang, Stephen A. Klein, Shaocheng Xie, Wuyin Lin, Jean-Christophe Golaz, Erika L. Roesler, Mark A. Taylor, Philip J. Rasch, David C. Bader, Larry K. Berg, Peter Caldwell, Scott E. Giangrande, Richard B. Neale, Yun Qian, Laura D. Riihimaki, Charles S. Zender, Yuying Zhang, and Xue Zheng
Geosci. Model Dev., 12, 2679–2706, https://doi.org/10.5194/gmd-12-2679-2019, https://doi.org/10.5194/gmd-12-2679-2019, 2019
Daniel T. McCoy, Paul R. Field, Gregory S. Elsaesser, Alejandro Bodas-Salcedo, Brian H. Kahn, Mark D. Zelinka, Chihiro Kodama, Thorsten Mauritsen, Benoit Vanniere, Malcolm Roberts, Pier L. Vidale, David Saint-Martin, Aurore Voldoire, Rein Haarsma, Adrian Hill, Ben Shipway, and Jonathan Wilkinson
Atmos. Chem. Phys., 19, 1147–1172, https://doi.org/10.5194/acp-19-1147-2019, https://doi.org/10.5194/acp-19-1147-2019, 2019
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The largest single source of uncertainty in the climate sensitivity predicted by global climate models is how much low-altitude clouds change as the climate warms. Models predict that the amount of liquid within and the brightness of low-altitude clouds increase in the extratropics with warming. We show that increased fluxes of moisture into extratropical storms in the midlatitudes explain the majority of the observed trend and the modeled increase in liquid water within these storms.
Tao Zhang, Minghua Zhang, Wuyin Lin, Yanluan Lin, Wei Xue, Haiyang Yu, Juanxiong He, Xiaoge Xin, Hsi-Yen Ma, Shaocheng Xie, and Weimin Zheng
Geosci. Model Dev., 11, 5189–5201, https://doi.org/10.5194/gmd-11-5189-2018, https://doi.org/10.5194/gmd-11-5189-2018, 2018
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Kai Zhang, Philip J. Rasch, Mark A. Taylor, Hui Wan, Ruby Leung, Po-Lun Ma, Jean-Christophe Golaz, Jon Wolfe, Wuyin Lin, Balwinder Singh, Susannah Burrows, Jin-Ho Yoon, Hailong Wang, Yun Qian, Qi Tang, Peter Caldwell, and Shaocheng Xie
Geosci. Model Dev., 11, 1971–1988, https://doi.org/10.5194/gmd-11-1971-2018, https://doi.org/10.5194/gmd-11-1971-2018, 2018
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The conservation of total water is an important numerical feature for global Earth system models. Even small conservation problems in the water budget can lead to systematic errors in century-long simulations for sea level rise projection. This study quantifies and reduces various sources of water conservation error in the atmosphere component of the Energy Exascale Earth System Model.
Scott E. Giangrande, Zhe Feng, Michael P. Jensen, Jennifer M. Comstock, Karen L. Johnson, Tami Toto, Meng Wang, Casey Burleyson, Nitin Bharadwaj, Fan Mei, Luiz A. T. Machado, Antonio O. Manzi, Shaocheng Xie, Shuaiqi Tang, Maria Assuncao F. Silva Dias, Rodrigo A. F de Souza, Courtney Schumacher, and Scot T. Martin
Atmos. Chem. Phys., 17, 14519–14541, https://doi.org/10.5194/acp-17-14519-2017, https://doi.org/10.5194/acp-17-14519-2017, 2017
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The Amazon forest is the largest tropical rain forest on the planet, featuring
prolific and diverse cloud conditions. The Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) experiment was motivated by demands to gain a better understanding of aerosol and cloud interactions on climate and the global circulation. The routine DOE ARM observations from this 2-year campaign are summarized to help quantify controls on clouds and precipitation over this undersampled region.
Yoko Tsushima, Florent Brient, Stephen A. Klein, Dimitra Konsta, Christine C. Nam, Xin Qu, Keith D. Williams, Steven C. Sherwood, Kentaroh Suzuki, and Mark D. Zelinka
Geosci. Model Dev., 10, 4285–4305, https://doi.org/10.5194/gmd-10-4285-2017, https://doi.org/10.5194/gmd-10-4285-2017, 2017
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Cloud feedback is the largest uncertainty associated with estimates of climate sensitivity. Diagnostics have been developed to evaluate cloud processes in climate models. For this understanding to be reflected in better estimates of cloud feedbacks, it is vital to continue to develop such tools and to exploit them fully during the model development process. Code repositories have been created to store and document the programs which will allow climate modellers to compute these diagnostics.
Mark J. Webb, Timothy Andrews, Alejandro Bodas-Salcedo, Sandrine Bony, Christopher S. Bretherton, Robin Chadwick, Hélène Chepfer, Hervé Douville, Peter Good, Jennifer E. Kay, Stephen A. Klein, Roger Marchand, Brian Medeiros, A. Pier Siebesma, Christopher B. Skinner, Bjorn Stevens, George Tselioudis, Yoko Tsushima, and Masahiro Watanabe
Geosci. Model Dev., 10, 359–384, https://doi.org/10.5194/gmd-10-359-2017, https://doi.org/10.5194/gmd-10-359-2017, 2017
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The Cloud Feedback Model Intercomparison Project (CFMIP) aims to improve understanding of cloud-climate feedback mechanisms and evaluation of cloud processes and cloud feedbacks in climate models. CFMIP also aims to improve understanding of circulation, regional-scale precipitation and non-linear changes. CFMIP is contributing to the 6th phase of the Coupled Model Intercomparison Project (CMIP6) by coordinating a hierarchy of targeted experiments with cloud-related model outputs.
Shuaiqi Tang, Shaocheng Xie, Yunyan Zhang, Minghua Zhang, Courtney Schumacher, Hannah Upton, Michael P. Jensen, Karen L. Johnson, Meng Wang, Maike Ahlgrimm, Zhe Feng, Patrick Minnis, and Mandana Thieman
Atmos. Chem. Phys., 16, 14249–14264, https://doi.org/10.5194/acp-16-14249-2016, https://doi.org/10.5194/acp-16-14249-2016, 2016
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Data observed during the Green Ocean Amazon (GoAmazon2014/5) experiment are used to derive the large-scale fields in this study. The morning propagating convective systems are active during the wet season but rare during the dry season. The afternoon convections are active in both seasons, with heating and moistening in the lower level corresponding to the vertical convergence of eddy fluxes. Case study shows distinguish large-scale environments for three types of convective systems in Amazonia.
M. P. Jensen, T. Toto, D. Troyan, P. E. Ciesielski, D. Holdridge, J. Kyrouac, J. Schatz, Y. Zhang, and S. Xie
Atmos. Meas. Tech., 8, 421–434, https://doi.org/10.5194/amt-8-421-2015, https://doi.org/10.5194/amt-8-421-2015, 2015
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A major component of the 2011 Midlatitude Continental Convective Clouds Experiment (MC3E) was a six-site radiosonde array designed to capture the large-scale variability of the atmospheric state. This manuscript describes the details of the MC3E radiosonde operations including the instrumentation, data processing and analysis of the impacts of bias correction and algorithm assumptions on the determination of forcing data sets.
G. de Boer, M. D. Shupe, P. M. Caldwell, S. E. Bauer, O. Persson, J. S. Boyle, M. Kelley, S. A. Klein, and M. Tjernström
Atmos. Chem. Phys., 14, 427–445, https://doi.org/10.5194/acp-14-427-2014, https://doi.org/10.5194/acp-14-427-2014, 2014
M. S. Johnston, S. Eliasson, P. Eriksson, R. M. Forbes, K. Wyser, and M. D. Zelinka
Atmos. Chem. Phys., 13, 12043–12058, https://doi.org/10.5194/acp-13-12043-2013, https://doi.org/10.5194/acp-13-12043-2013, 2013
Related subject area
Atmospheric sciences
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
NeuralMie (v1.0): an aerosol optics emulator
Simulation performance of planetary boundary layer schemes in WRF v4.3.1 for near-surface wind over the western Sichuan Basin: a single-site assessment
FootNet v1.0: development of a machine learning emulator of atmospheric transport
Updates and evaluation of NOAA's online-coupled air quality model version 7 (AQMv7) within the Unified Forecast System
Quantifying the analysis uncertainty for nowcasting application
Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01
The MESSy DWARF (based on MESSy v2.55.2)
An enhanced emission module for the PALM model system 23.10 with application for PM10 emission from urban domestic heating
Identifying lightning processes in ERA5 soundings with deep learning
Sensitivity of predicted ultrafine particle size distributions in Europe to different nucleation rate parameterizations using PMCAMx-UF v2.2
Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes
Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation
ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool
Coupling the urban canopy model TEB (SURFEXv9.0) with the radiation model SPARTACUS-Urbanv0.6.1 for more realistic urban radiative exchange calculation
Forecasting contrail climate forcing for flight planning and air traffic management applications: the CocipGrid model in pycontrails 0.51.0
Simulation of the heat mitigation potential of unsealing measures in cities by parameterizing grass grid pavers for urban microclimate modelling with ENVI-met (V5)
AI-NAOS: an AI-based nonspherical aerosol optical scheme for the chemical weather model GRAPES_Meso5.1/CUACE
Orbital-Radar v1.0.0: a tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data
The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1)
Modeling of polycyclic aromatic hydrocarbons (PAHs) from global to regional scales: model development (IAP-AACM_PAH v1.0) and investigation of health risks in 2013 and 2018 in China
LIMA (v2.0): A full two-moment cloud microphysical scheme for the mesoscale non-hydrostatic model Meso-NH v5-6
SLUCM+BEM (v1.0): a simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban (v4.3.2)
NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components
Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ
Observational operator for fair model evaluation with ground NO2 measurements
Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting
The third Met Office Unified Model-JULES Regional Atmosphere and Land Configuration, RAL3
An updated parameterization of the unstable atmospheric surface layer in the Weather Research and Forecasting (WRF) modeling system
The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators
An updated aerosol simulation in the Community Earth System Model (v2.1.3): dust and marine aerosol emissions and secondary organic aerosol formation
Exploring ship track spreading rates with a physics-informed Langevin particle parameterization
Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast
Development of the MPAS-CMAQ coupled system (V1.0) for multiscale global air quality modeling
UA-ICON with NWP physics package (version: ua-icon-2.1): mean state and variability of the middle atmosphere
Assessment of object-based indices to identify convective organization
Diagnosis of winter precipitation types using Spectral Bin Model (SBM): Comparison of five methods using ICE-POP 2018 field experiment data
The Global Forest Fire Emissions Prediction System version 1.0
Sensitivity Studies of Four‐Dimensional Local Ensemble Transform Kalman Filter Coupled With WRF-Chem Version 3.9.1 for Improving Particulate Matter Simulation Accuracy
Development of A Fast Radiative Transfer Model for Ground-based Microwave Radiometers (ARMS-gb v1.0): Validation and Comparison to RTTOV-gb
NEIVAv1.0: Next-generation Emissions InVentory expansion of Akagi et al. (2011) version 1.0
FLEXPART version 11: improved accuracy, efficiency, and flexibility
Low-level jets in the North and Baltic Seas: Mesoscale Model Sensitivity and Climatology
Challenges of high-fidelity air quality modeling in urban environments – PALM sensitivity study during stable conditions
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a Neural Network
Air quality modeling intercomparison and multiscale ensemble chain for Latin America
Recommended coupling to global meteorological fields for long-term tracer simulations with WRF-GHG
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.
Felipe Cifuentes, Henk Eskes, Enrico Dammers, Charlotte Bryan, and Folkert Boersma
Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025, https://doi.org/10.5194/gmd-18-621-2025, 2025
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We tested the capability of the flux divergence approach (FDA) to reproduce known NOx emissions using synthetic NO2 satellite column retrievals from high-resolution model simulations. The FDA accurately reproduced NOx emissions when column observations were limited to the boundary layer and when the variability of the NO2 lifetime, the NOx : NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces strong model dependency, reducing the simplicity of the original FDA formulation.
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025, https://doi.org/10.5194/gmd-18-529-2025, 2025
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We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025, https://doi.org/10.5194/gmd-18-483-2025, 2025
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Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025, https://doi.org/10.5194/gmd-18-433-2025, 2025
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This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025, https://doi.org/10.5194/gmd-18-405-2025, 2025
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Radiation is relevant to the atmospheric impact on people and infrastructure in cities as it can influence the urban heat island, building energy consumption, and human thermal comfort. A new urban radiation model, assuming a more realistic form of urban morphology, is coupled to the urban climate model Town Energy Balance (TEB). The new TEB is evaluated with a reference radiation model for a variety of urban morphologies, and an improvement in the simulated radiative observables is found.
Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro
Geosci. Model Dev., 18, 253–286, https://doi.org/10.5194/gmd-18-253-2025, https://doi.org/10.5194/gmd-18-253-2025, 2025
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Contrails forming in some atmospheric conditions may persist and become strongly warming cirrus, while in other conditions may be neutral or cooling. We develop a contrail forecast model to predict contrail climate forcing for any arbitrary point in space and time and explore integration into flight planning and air traffic management. This approach enables contrail interventions to target high-probability high-climate-impact regions and reduce unintended consequences of contrail management.
Nils Eingrüber, Alina Domm, Wolfgang Korres, and Karl Schneider
Geosci. Model Dev., 18, 141–160, https://doi.org/10.5194/gmd-18-141-2025, https://doi.org/10.5194/gmd-18-141-2025, 2025
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Climate change adaptation measures like unsealings can reduce urban heat stress. As grass grid pavers have never been parameterized for microclimate model simulations with ENVI-met, a new parameterization was developed based on field measurements. To analyse the cooling potential, scenario analyses were performed for a densely developed area in Cologne. Statistically significant average cooling effects of up to −11.1 K were found for surface temperature and up to −2.9 K for 1 m air temperature.
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
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The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) was developed to improve the estimation of the aerosol direct radiation effect and was coupled online with a chemical weather model. The AI-NAOS scheme considers black carbon as fractal aggregates and soil dust as super-spheroids, encapsulated with hygroscopic aerosols. Real-case simulations emphasize the necessity of accurately representing nonspherical and inhomogeneous aerosols in chemical weather models.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025, https://doi.org/10.5194/gmd-18-101-2025, 2025
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The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Geosci. Model Dev., 18, 1–18, https://doi.org/10.5194/gmd-18-1-2025, https://doi.org/10.5194/gmd-18-1-2025, 2025
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The Modular and Integrated Data Assimilation System (MIDAS) software is described. The flexible design of MIDAS enables both deterministic and ensemble prediction applications for the atmosphere and several other Earth system components. It is currently used for all main operational weather prediction systems in Canada and also for sea ice and sea surface temperature analysis. The use of MIDAS for multiple Earth system components will facilitate future research on coupled data assimilation.
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024, https://doi.org/10.5194/gmd-17-8885-2024, 2024
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We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can reproduce PAH distribution well. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change in BaP is lower than that in PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although the Action Plan has been implemented.
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024, https://doi.org/10.5194/gmd-17-8773-2024, 2024
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We have developed a complete two-moment version of the LIMA (Liquid Ice Multiple Aerosols) microphysics scheme. We have focused on collection processes, where the hydrometeor number transfer is often estimated in proportion to the mass transfer. The impact of these parameterizations on a convective system and the prospects for more realistic estimates of secondary parameters (reflectivity, hydrometeor size) are shown in a first test on an idealized case.
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
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A new parameterisation for dynamic anthropogenic heat and electricity consumption is described. The model reproduced the temporal variation in and spatial distributions of electricity consumption and temperature well in summer and winter. The partial air conditioning was the most critical factor, significantly affecting the value of anthropogenic heat emission.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
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To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
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Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
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Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
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The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-201, https://doi.org/10.5194/gmd-2024-201, 2024
Revised manuscript accepted for GMD
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RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre and sub-km scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and improved representation of clouds and visibility.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
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Inadequate representation of surface–atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
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Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
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This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
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Marine cloud brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed. However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume-spreading model.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
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Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
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This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Markus Kunze, Christoph Zülicke, Tarique Adnan Siddiqui, Claudia Christine Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-191, https://doi.org/10.5194/gmd-2024-191, 2024
Revised manuscript accepted for GMD
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We present the Icosahedral Nonhydrostatic (ICON) general circulation model with upper atmosphere extension with the physics package for numerical weather prediction (UA-ICON(NWP)). The parameters for the gravity wave parameterizations were optimized, and realistic modelling of the thermal and dynamic state of the mesopause regions was achieved. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
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In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Wonbae Bang, Jacob Carlin, Kwonil Kim, Alexander Ryzhkov, Guosheng Liu, and Gyuwon Lee
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-179, https://doi.org/10.5194/gmd-2024-179, 2024
Revised manuscript accepted for GMD
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Microphysics model-based diagnosis such as the spectral bin model (SBM) recently has been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM have relatively higher accuracy about snow and wetsnow events whereas lower accuracy about rain event. When microphysics scheme in the SBM was optimized for the corresponding region, accuracy about rain events was improved.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
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The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
EGUsphere, https://doi.org/10.5194/egusphere-2024-3321, https://doi.org/10.5194/egusphere-2024-3321, 2024
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The effectiveness of assimilation system and its sensitivity to ensemble member size and length of assimilation window have been investigated. This study advances our understanding about the selection of basic parameters in the four-dimension local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate matter polluted environment.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
EGUsphere, https://doi.org/10.5194/egusphere-2024-2884, https://doi.org/10.5194/egusphere-2024-2884, 2024
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Assimilating Ground-based microwave radiometers' observations into numerical weather prediction models holds significant promise for enhancing forecast accuracy. Radiative transfer models (RTM) are crucial for direct data assimilation. We propose a new RTM capable of simulating brightness temperatures observed by GMRs and their Jacobians. Several improvements are introduced to achieve higher accuracy.The RTM align with RTTOV-gb well and can achieve smaller STD in water vapor absorption channels.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
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The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
Geosci. Model Dev., 17, 7595–7627, https://doi.org/10.5194/gmd-17-7595-2024, https://doi.org/10.5194/gmd-17-7595-2024, 2024
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Computer models are essential for improving our understanding of how gases and particles move in the atmosphere. We present an update of the atmospheric transport model FLEXPART. FLEXPART 11 is more accurate due to a reduced number of interpolations and a new scheme for wet deposition. It can simulate non-spherical aerosols and includes linear chemical reactions. It is parallelised using OpenMP and includes new user options. A new user manual details how to use FLEXPART 11.
Bjarke Tobias Eisensøe Olsen, Andrea Noemi Hahmann, Nicolás González Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
EGUsphere, https://doi.org/10.5194/egusphere-2024-3123, https://doi.org/10.5194/egusphere-2024-3123, 2024
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Low-level jets (LLJs) are strong winds in the lower atmosphere, important for wind energy as turbines get taller. This study compares a weather model (WRF) with real data across five North and Baltic Sea sites. Adjusting the model improved accuracy over the widely-used ERA5. In key offshore regions, LLJs occur 10–15 % of the time and significantly boost wind power, especially in spring and summer, contributing up to 30 % of total capacity in some areas.
Jaroslav Resler, Petra Bauerová, Michal Belda, Martin Bureš, Kryštof Eben, Vladimír Fuka, Jan Geletič, Radek Jareš, Jan Karel, Josef Keder, Pavel Krč, William Patiño, Jelena Radović, Hynek Řezníček, Matthias Sühring, Adriana Šindelářová, and Ondřej Vlček
Geosci. Model Dev., 17, 7513–7537, https://doi.org/10.5194/gmd-17-7513-2024, https://doi.org/10.5194/gmd-17-7513-2024, 2024
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Detailed modeling of urban air quality in stable conditions is a challenge. We show the unprecedented sensitivity of a large eddy simulation (LES) model to meteorological boundary conditions and model parameters in an urban environment under stable conditions. We demonstrate the crucial role of boundary conditions for the comparability of results with observations. The study reveals a strong sensitivity of the results to model parameters and model numerical instabilities during such conditions.
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
EGUsphere, https://doi.org/10.5194/egusphere-2024-2676, https://doi.org/10.5194/egusphere-2024-2676, 2024
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This work focuses on the prediction of aerosol concentration values at ground level, which are a strong indicator of air quality, using Artificial Neural Networks. A study of different variables and their efficiency as inputs for these models is also proposed, and reveals that the best results are obtained when using all of them. Comparison of networks architectures and information fusion methods allows the extraction of knowledge on the most efficient methods in the context of this study.
Jorge E. Pachón, Mariel A. Opazo, Pablo Lichtig, Nicolas Huneeus, Idir Bouarar, Guy Brasseur, Cathy W. Y. Li, Johannes Flemming, Laurent Menut, Camilo Menares, Laura Gallardo, Michael Gauss, Mikhail Sofiev, Rostislav Kouznetsov, Julia Palamarchuk, Andreas Uppstu, Laura Dawidowski, Nestor Y. Rojas, María de Fátima Andrade, Mario E. Gavidia-Calderón, Alejandro H. Delgado Peralta, and Daniel Schuch
Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024, https://doi.org/10.5194/gmd-17-7467-2024, 2024
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Latin America (LAC) has some of the most populated urban areas in the world, with high levels of air pollution. Air quality management in LAC has been traditionally focused on surveillance and building emission inventories. This study performed the first intercomparison and model evaluation in LAC, with interesting and insightful findings for the region. A multiscale modeling ensemble chain was assembled as a first step towards an air quality forecasting system.
David Ho, Michał Gałkowski, Friedemann Reum, Santiago Botía, Julia Marshall, Kai Uwe Totsche, and Christoph Gerbig
Geosci. Model Dev., 17, 7401–7422, https://doi.org/10.5194/gmd-17-7401-2024, https://doi.org/10.5194/gmd-17-7401-2024, 2024
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Atmospheric model users often overlook the impact of the land–atmosphere interaction. This study accessed various setups of WRF-GHG simulations that ensure consistency between the model and driving reanalysis fields. We found that a combination of nudging and frequent re-initialization allows certain improvement by constraining the soil moisture fields and, through its impact on atmospheric mixing, improves atmospheric transport.
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Short summary
We propose an experimental design of a suite of multi-year, short-term hindcasts and compare them with corresponding observations or measurements for periods based on different weather and climate phenomena. This atypical way of evaluating model performance is particularly useful and beneficial, as these hindcasts can give scientists a robust picture of modeled precipitation, and cloud and radiation processes from their diurnal variation to year-to-year variability.
We propose an experimental design of a suite of multi-year, short-term hindcasts and compare...
