Articles | Volume 19, issue 17
https://doi.org/10.5194/gmd-19-8367-2026
© Author(s) 2026. 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-19-8367-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
09 Sep 2026
Methods for assessment of models |
| 09 Sep 2026
| 09 Sep 2026
Leveraging JEDI for atmospheric composition: a unified framework for evaluating observations and model forecasts
Shih-Wei Wei
CORRESPONDING AUTHOR
Joint Center for Satellite Data Assimilation, University Corporation for Atmospheric Research, Boulder, Colorado, USA
University at Albany, Albany, New York, USA
Jérôme Barré
Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Goddard Earth Sciences Technology and Research II, Morgan State University, Baltimore, Maryland, USA
Mesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA
Maryam Abdi-Oskouei
Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Goddard Earth Sciences Technology and Research II, Morgan State University, Baltimore, Maryland, USA
Benjamin Ménétrier
Norwegian Meteorological Institute, Oslo, Norway
Cheng Dang
Joint Center for Satellite Data Assimilation, University Corporation for Atmospheric Research, Boulder, Colorado, USA
Cheng-Hsuan Lu
Joint Center for Satellite Data Assimilation, University Corporation for Atmospheric Research, Boulder, Colorado, USA
University at Albany, Albany, New York, USA
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Vishal Bagadia and Cheng-Hsuan Lu
EGUsphere, https://doi.org/10.5194/egusphere-2026-2377, https://doi.org/10.5194/egusphere-2026-2377, 2026
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A new approach to retrieve stratospheric aerosol size distributions is developed to characterize smoke aerosol properties. Pyrocumulonimbus can strongly perturb stratospheric aerosol concentrations. Simulations are conducted to describe stratospheric aerosol properties for bimodal lognormal distributions. Observations from SAGEIII/ISS are used to determined stratospheric smoke aerosol size distributions and optical and microphysical properties and are validated against in situ measurements.
Hongli Wang, Cory Martin, Jérôme Barré, Ruifang Li, Steve Weygandt, Jianping Huang, Youhua Tang, Hyundeok Choi, Andrew Tangborn, Kai Wang, Haixia Liu, and Jeffrey Lee
Geosci. Model Dev., 19, 2479–2495, https://doi.org/10.5194/gmd-19-2479-2026, https://doi.org/10.5194/gmd-19-2479-2026, 2026
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This paper describes efforts to develop aerosol data assimilation capabilities for NOAA’s regional air quality modeling system by assimilating PM2.5 observations within the Joint Effort for Data Assimilation Integration framework. Results from the September 2020 Western U.S. wildfires show that assimilating AirNow and/or PurpleAir PM2.5 data reduces mean absolute errors of 1–24 h PM2.5 forecasts over the continental United States on average relative to a control without data assimilation.
Soyoung Ha, Jonathan J. Guerrette, Ivette Hernández Baños, William C. Skamarock, and Michael G. Duda
Geosci. Model Dev., 17, 4199–4211, https://doi.org/10.5194/gmd-17-4199-2024, https://doi.org/10.5194/gmd-17-4199-2024, 2024
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To mitigate the imbalances in the initial conditions, this study introduces our recent implementation of the incremental analysis update (IAU) in the Model for Prediction Across Scales – Atmospheric (MPAS-A) component coupled with the Joint Effort for Data assimilation Integration (JEDI) through the cycling system. A month-long cycling run demonstrates the successful implementation of the IAU capability in the MPAS–JEDI cycling system.
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
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This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Jonathan J. Guerrette, Zhiquan Liu, Chris Snyder, Byoung-Joo Jung, Craig S. Schwartz, Junmei Ban, Steven Vahl, Yali Wu, Ivette Hernández Baños, Yonggang G. Yu, Soyoung Ha, Yannick Trémolet, Thomas Auligné, Clementine Gas, Benjamin Ménétrier, Anna Shlyaeva, Mark Miesch, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
Geosci. Model Dev., 16, 7123–7142, https://doi.org/10.5194/gmd-16-7123-2023, https://doi.org/10.5194/gmd-16-7123-2023, 2023
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We demonstrate an ensemble of variational data assimilations (EDA) with the Model for Prediction Across Scales and the Joint Effort for Data assimilation Integration (JEDI) software framework. When compared to 20-member ensemble forecasts from operational initial conditions, those from 80-member EDA-generated initial conditions improve flow-dependent error covariances and subsequent 10 d forecasts. These experiments are repeatable for any atmospheric model with a JEDI interface.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
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Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Soyoung Ha
EGUsphere, https://doi.org/10.5194/egusphere-2022-371, https://doi.org/10.5194/egusphere-2022-371, 2022
Preprint withdrawn
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Heavy pollution events often occur in cloudy conditions, which is hard to observe. This study introduces a new 3D-Var analysis that can facilitate aerosol-cloud interactions through weakly coupled data assimilation using WRF-Chem/WRFDA.
Chloe A. Whicker, Mark G. Flanner, Cheng Dang, Charles S. Zender, Joseph M. Cook, and Alex S. Gardner
The Cryosphere, 16, 1197–1220, https://doi.org/10.5194/tc-16-1197-2022, https://doi.org/10.5194/tc-16-1197-2022, 2022
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Snow and ice surfaces are important to the global climate. Current climate models use measurements to determine the reflectivity of ice. This model uses physical properties to determine the reflectivity of snow, ice, and darkly pigmented impurities that reside within the snow and ice. Therefore, the modeled reflectivity is more accurate for snow/ice columns under varying climate conditions. This model paves the way for improvements in the portrayal of snow and ice within global climate models.
Soyoung Ha
Geosci. Model Dev., 15, 1769–1788, https://doi.org/10.5194/gmd-15-1769-2022, https://doi.org/10.5194/gmd-15-1769-2022, 2022
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In an effort to improve air quality forecasting, the WRFDA 3D-Var system is newly extended for the assimilation of surface PM2.5 and PM10 using the RACM/MADE-VBS chemistry in the WRF-Chem model. Through a case study during the Korea–United States Air Quality (KORUS-AQ) period, it is demonstrated that the analysis can lead to improving the prediction of surface PM concentrations up to 26 % for 24 h, diminishing most bias errors.
Dustin Francis Phillip Grogan, Cheng-Hsuan Lu, Shih-Wei Wei, and Sheng-Po Chen
Atmos. Chem. Phys., 22, 2385–2398, https://doi.org/10.5194/acp-22-2385-2022, https://doi.org/10.5194/acp-22-2385-2022, 2022
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This study shows that incorporating aerosols into satellite radiance calculations affects the representation of African easterly waves (AEWs), and their environment, over North Africa and the eastern Atlantic in a numerical weather model. These changes are driven by radiative effects of Saharan dust captured by the aerosol-affected radiances, which modify the initial fields and can improve the forecasting of AEWs.
Cheng-Hsuan Lu, Quanhua Liu, Shih-Wei Wei, Benjamin T. Johnson, Cheng Dang, Patrick G. Stegmann, Dustin Grogan, Guoqing Ge, Ming Hu, and Michael Lueken
Geosci. Model Dev., 15, 1317–1329, https://doi.org/10.5194/gmd-15-1317-2022, https://doi.org/10.5194/gmd-15-1317-2022, 2022
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This article is a technical note on the aerosol absorption and scattering calculations of the Community Radiative Transfer Model (CRTM) v2.2 and v2.3. It also provides guidance for prospective users of the CRTM aerosol option and Gridpoint Statistical Interpolation (GSI) aerosol-aware radiance assimilation. Scientific aspects of aerosol-affected BT in atmospheric data assimilation are also briefly discussed.
Mark G. Flanner, Julian B. Arnheim, Joseph M. Cook, Cheng Dang, Cenlin He, Xianglei Huang, Deepak Singh, S. McKenzie Skiles, Chloe A. Whicker, and Charles S. Zender
Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, https://doi.org/10.5194/gmd-14-7673-2021, 2021
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We present the technical formulation and evaluation of a publicly available code and web-based model to simulate the spectral albedo of snow. Our model accounts for numerous features of the snow state and ambient conditions, including the the presence of light-absorbing matter like black and brown carbon, mineral dust, volcanic ash, and snow algae. Carbon dioxide snow, found on Mars, is also represented. The model accurately reproduces spectral measurements of clean and contaminated snow.
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Short summary
This paper presents a flexible workflow using a unified data assimilation framework to evaluate atmospheric composition models. It enables comparison of observations with forecasts of trace gases and aerosols from different models. The system is consistent and adaptable, reducing repetitive work, supporting model validation and observation assessment, and aligning evaluation with operational data assimilation for research and practical applications.
This paper presents a flexible workflow using a unified data assimilation framework to evaluate...
