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This image captures the output of the FV3-Chem model forecast for the distribution of the Saharan dust cloud for July 28

New NOAA research model improves dust, air quality forecasts

Running on the newest version of NOAA’s Global Forecast System, or GFS, the FV3-Chem model forecasts the distribution of some primary air pollutants: smoke, soot, organic carbon, sulfate, and large and small particles of dust and sea salt – collectively known as aerosols. Because these aerosols affect the weather, the model also provides weather forecasts.

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GSD researcher presenting at AeroCom in Barcelona, Spain

Sept 23-28 GSD scientist Georg Grell will present at the 18th AeroCom meeting in Barcelona, Spain. AeroCom is an open international initiative of scientists interested in the advancement of the understanding of global aerosol properties and aerosol impacts on climate, weather, and air quality. A central goal is to more strongly tie and constrain modeling efforts to observational data from satellite, ground-based, and aircraft observations. A major element for exchanges between data and modeling groups are annual meetings of AeroCom together with the satellite data oriented initiative AeroSAT. In addition to the comparisons among models and between models and data, AeroCom initiates and coordinates model experiments to target particular research topics, leading to joint research papers of synthesizing character.

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Meteorology and Climate – Modeling for Air Quality Conference

September 11-13: GSD Model Development Branch Chief Georg Grell is a Lead Chair for the biennial Meteorology and Climate – Modeling for Air Quality Conference (MAC-MAQ) hosted by the University of California, Davis. The biennial three day conference brings together research scientists, experts, and professionals from around the world to discuss a wide range of topics related to meteorology for air quality applications. The primary focus is on understanding and improving meteorological modeling, understanding what is “under the hood” in the models, how experimental data can be used to improve them, and the importance of meteorology in air quality modeling applications.

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