Articles | Volume 15, issue 13
https://doi.org/10.5194/gmd-15-5337-2022
© Author(s) 2022. 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-15-5337-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
13 Jul 2022
Model description paper |
| 13 Jul 2022
| 13 Jul 2022
Development and evaluation of the Aerosol Forecast Member in the National Center for Environment Prediction (NCEP)'s Global Ensemble Forecast System (GEFS-Aerosols v1)
CIRES, University of Colorado, Boulder, CO, USA
Global Systems Laboratory, Earth System Research Laboratories, NOAA, Boulder, CO, USA
Raffaele Montuoro
CIRES, University of Colorado, Boulder, CO, USA
Global Systems Laboratory, Earth System Research Laboratories, NOAA, Boulder, CO, USA
Environmental Modeling Center, National Weather Service, College Park, MD, USA
Stuart A. McKeen
CIRES, University of Colorado, Boulder, CO, USA
Chemical Sciences Laboratory, Earth System Research Laboratories, NOAA, Boulder, CO, USA
Barry Baker
NOAA Air Resources Laboratory, College Park, MD, USA
Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD, USA
Partha S. Bhattacharjee
I.M. Systems Group at NCEP/NWS/EMC, College Park, MD, USA
Georg A. Grell
Global Systems Laboratory, Earth System Research Laboratories, NOAA, Boulder, CO, USA
Judy Henderson
Global Systems Laboratory, Earth System Research Laboratories, NOAA, Boulder, CO, USA
I.M. Systems Group at NCEP/NWS/EMC, College Park, MD, USA
Gregory J. Frost
Chemical Sciences Laboratory, Earth System Research Laboratories, NOAA, Boulder, CO, USA
Jeff McQueen
Environmental Modeling Center, National Weather Service, College Park, MD, USA
Rick Saylor
NOAA Air Resources Laboratory, Oak Ridge, TN, USA
Haiqin Li
CIRES, University of Colorado, Boulder, CO, USA
Global Systems Laboratory, Earth System Research Laboratories, NOAA, Boulder, CO, USA
Ravan Ahmadov
CIRES, University of Colorado, Boulder, CO, USA
Global Systems Laboratory, Earth System Research Laboratories, NOAA, Boulder, CO, USA
Jun Wang
Environmental Modeling Center, National Weather Service, College Park, MD, USA
Ivanka Stajner
Environmental Modeling Center, National Weather Service, College Park, MD, USA
Shobha Kondragunta
NOAA/NESDIS Center for Satellite Applications and Research, College Park, MD, USA
Xiaoyang Zhang
Geospatial Science Center of Excellence, Department of Geography & Geospatial Sciences, South Dakota State University, Brookings, SD, USA
Fangjun Li
Geospatial Science Center of Excellence, Department of Geography & Geospatial Sciences, South Dakota State University, Brookings, SD, USA
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Cited
17 citations as recorded by crossref.
- High-resolution anthropogenic emission inventories with deep learning in northern South America F. Antezana Lopez et al. https://doi.org/10.1016/j.rse.2025.114761
- UFS-RAQMS global atmospheric composition model: TROPOMI CO column assimilation M. Bruckner et al. https://doi.org/10.5194/gmd-18-8109-2025
- Quantifying CO emissions from boreal wildfires by assimilating TROPOMI and TCCON observations S. Voshtani et al. https://doi.org/10.5194/acp-25-15527-2025
- A simple and realistic aerosol emission approach for use in the Thompson–Eidhammer microphysics scheme in the NOAA UFS Weather Model (version GSL global-24Feb2022) H. Li et al. https://doi.org/10.5194/gmd-17-607-2024
- The prototype NOAA Aerosol Reanalysis version 1.0: description of the modeling system and its evaluation S. Wei et al. https://doi.org/10.5194/gmd-17-795-2024
- Global Ensemble Fire Emission Product Version 1.0 (EnsemFire V1.0) Y. Li et al. https://doi.org/10.1038/s41597-025-06429-z
- JEDI‐Based Three‐Dimensional Ensemble‐Variational Data Assimilation System for Global Aerosol Forecasting at NCEP B. Huang et al. https://doi.org/10.1029/2022MS003232
- Biomass burning emission estimation in the MODIS era: State-of-the-art and future directions M. Parrington et al. https://doi.org/10.1525/elementa.2024.00089
- Analysis of the GEFS-Aerosols annual budget to better understand aerosol predictions simulated in the model L. Pan et al. https://doi.org/10.5194/gmd-17-431-2024
- Impact of meteorological uncertainties on PM2.5 forecast: An ensemble air quality forecast study during 2022 Beijing Winter Olympics W. Wen et al. https://doi.org/10.1016/j.atmosenv.2025.121027
- Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS) A. Collow et al. https://doi.org/10.5194/gmd-17-1443-2024
- Aerosol, Clouds and Radiation Interactions in the NCEP Unified Forecast Systems A. Cheng & F. Yang https://doi.org/10.3390/meteorology4020014
- Updating and Evaluating Anthropogenic Emissions for NOAA’s Global Ensemble Forecast Systems for Aerosols (GEFS-Aerosols): Application of an SO2 Bias-Scaling Method G. Jeong et al. https://doi.org/10.3390/atmos14020234
- Synoptic-Scale Forcing and Its Role in a Rare Severe Rainfall Event over the UAE: A Case Study of 15–16 April 2024 N. AlShamsi et al. https://doi.org/10.3390/atmos16111267
- Development and Evaluation of a North America Ensemble Wildfire Air Quality Forecast: Initial Application to the 2020 Western United States “Gigafire” P. Makkaroon et al. https://doi.org/10.1029/2022JD037298
- Direct and Indirect Effects of Aerosols During the 2023 Canadian Wildfires A. Cheng et al. https://doi.org/10.3390/atmos17040337
- How accurate are operational dust models in predicting Particulate Matter (PM) levels in the Eastern Mediterranean Region? Insights from PM surface concentrations A. Eleftheriou et al. https://doi.org/10.5194/nhess-25-4961-2025
17 citations as recorded by crossref.
- High-resolution anthropogenic emission inventories with deep learning in northern South America F. Antezana Lopez et al. https://doi.org/10.1016/j.rse.2025.114761
- UFS-RAQMS global atmospheric composition model: TROPOMI CO column assimilation M. Bruckner et al. https://doi.org/10.5194/gmd-18-8109-2025
- Quantifying CO emissions from boreal wildfires by assimilating TROPOMI and TCCON observations S. Voshtani et al. https://doi.org/10.5194/acp-25-15527-2025
- A simple and realistic aerosol emission approach for use in the Thompson–Eidhammer microphysics scheme in the NOAA UFS Weather Model (version GSL global-24Feb2022) H. Li et al. https://doi.org/10.5194/gmd-17-607-2024
- The prototype NOAA Aerosol Reanalysis version 1.0: description of the modeling system and its evaluation S. Wei et al. https://doi.org/10.5194/gmd-17-795-2024
- Global Ensemble Fire Emission Product Version 1.0 (EnsemFire V1.0) Y. Li et al. https://doi.org/10.1038/s41597-025-06429-z
- JEDI‐Based Three‐Dimensional Ensemble‐Variational Data Assimilation System for Global Aerosol Forecasting at NCEP B. Huang et al. https://doi.org/10.1029/2022MS003232
- Biomass burning emission estimation in the MODIS era: State-of-the-art and future directions M. Parrington et al. https://doi.org/10.1525/elementa.2024.00089
- Analysis of the GEFS-Aerosols annual budget to better understand aerosol predictions simulated in the model L. Pan et al. https://doi.org/10.5194/gmd-17-431-2024
- Impact of meteorological uncertainties on PM2.5 forecast: An ensemble air quality forecast study during 2022 Beijing Winter Olympics W. Wen et al. https://doi.org/10.1016/j.atmosenv.2025.121027
- Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS) A. Collow et al. https://doi.org/10.5194/gmd-17-1443-2024
- Aerosol, Clouds and Radiation Interactions in the NCEP Unified Forecast Systems A. Cheng & F. Yang https://doi.org/10.3390/meteorology4020014
- Updating and Evaluating Anthropogenic Emissions for NOAA’s Global Ensemble Forecast Systems for Aerosols (GEFS-Aerosols): Application of an SO2 Bias-Scaling Method G. Jeong et al. https://doi.org/10.3390/atmos14020234
- Synoptic-Scale Forcing and Its Role in a Rare Severe Rainfall Event over the UAE: A Case Study of 15–16 April 2024 N. AlShamsi et al. https://doi.org/10.3390/atmos16111267
- Development and Evaluation of a North America Ensemble Wildfire Air Quality Forecast: Initial Application to the 2020 Western United States “Gigafire” P. Makkaroon et al. https://doi.org/10.1029/2022JD037298
- Direct and Indirect Effects of Aerosols During the 2023 Canadian Wildfires A. Cheng et al. https://doi.org/10.3390/atmos17040337
- How accurate are operational dust models in predicting Particulate Matter (PM) levels in the Eastern Mediterranean Region? Insights from PM surface concentrations A. Eleftheriou et al. https://doi.org/10.5194/nhess-25-4961-2025
Saved (final revised paper)
Latest update: 05 Jun 2026
Short summary
The NOAA’s air quality predictions contribute to protecting lives and health in the US, which requires sustainable development and improvement of forecast systems. GEFS-Aerosols v1 has been developed in a collaboration between the NOAA research laboratories for operational forecast since September 2020 in the NCEP. The predictions demonstrate substantial improvements for both composition and variability of aerosol distributions over those from the former operational system.
The NOAA’s air quality predictions contribute to protecting lives and health in the US, which...
