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.
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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Hyun Cheol Kim, Soontae Kim, Mark Cohen, Changhan Bae, Dasom Lee, Rick Saylor, Minah Bae, Eunhye Kim, Byeong-Uk Kim, Jin-Ho Yoon, and Ariel Stein
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Xiaoyang Chen, Yang Zhang, Kai Wang, Daniel Tong, Pius Lee, Youhua Tang, Jianping Huang, Patrick C. Campbell, Jeff Mcqueen, Havala O. T. Pye, Benjamin N. Murphy, and Daiwen Kang
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Youhua Tang, Huisheng Bian, Zhining Tao, Luke D. Oman, Daniel Tong, Pius Lee, Patrick C. Campbell, Barry Baker, Cheng-Hsuan Lu, Li Pan, Jun Wang, Jeffery McQueen, and Ivanka Stajner
Atmos. Chem. Phys., 21, 2527–2550, https://doi.org/10.5194/acp-21-2527-2021, https://doi.org/10.5194/acp-21-2527-2021, 2021
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Alexander Ukhov, Ravan Ahmadov, Georg Grell, and Georgiy Stenchikov
Geosci. Model Dev., 14, 473–493, https://doi.org/10.5194/gmd-14-473-2021, https://doi.org/10.5194/gmd-14-473-2021, 2021
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We discuss and evaluate the effects of inconsistencies found in the WRF-Chem code when using the GOCART module. First, PM surface concentrations were miscalculated. Second, dust optical depth was underestimated by 25 %–30 %. Third, an inconsistency in the process of gravitational settling led to the overestimation of dust column loadings by 4 %–6 %, PM10 by 2 %–4 %, and the rate of gravitational dust settling by 5 %–10 %. We also presented diagnostics that can be used to estimate these effects.
Hai Zhang, Shobha Kondragunta, Istvan Laszlo, and Mi Zhou
Atmos. Meas. Tech., 13, 5955–5975, https://doi.org/10.5194/amt-13-5955-2020, https://doi.org/10.5194/amt-13-5955-2020, 2020
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Geostationary Operational Environmental Satellites (GOES) retrieve high temporal resolution aerosol optical depth, which is a measure of the aerosol quantity within the atmospheric column. This work introduces an algorithm that improves the accuracy of the aerosol optical depth retrievals from GOES. The resulting data product can be used in monitoring the air quality and climate change research.
Hyun Cheol Kim, Tianfeng Chai, Ariel Stein, and Shobha Kondragunta
Atmos. Chem. Phys., 20, 10259–10277, https://doi.org/10.5194/acp-20-10259-2020, https://doi.org/10.5194/acp-20-10259-2020, 2020
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Smoke forecasts have been challenged by high uncertainty in fire emission estimates. We develop an inverse modeling system, the HYSPLIT-based Emissions Inverse Modeling System for wildfires, that estimates wildfire emissions from the transport and dispersion of smoke plumes as measured by satellite observations. Using NOAA HYSPLIT and GOES Aerosol/Smoke Product (GASP), the system resolves smoke source strength as a function of time and vertical level and outperforms current operational system.
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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...