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      Rapid night-time nanoparticle growth in Delhi driven by biomass-burning emissions

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          Chemical and microphysical characterization of ambient aerosols with the aerodyne aerosol mass spectrometer.

          The application of mass spectrometric techniques to the real-time measurement and characterization of aerosols represents a significant advance in the field of atmospheric science. This review focuses on the aerosol mass spectrometer (AMS), an instrument designed and developed at Aerodyne Research, Inc. (ARI) that is the most widely used thermal vaporization AMS. The AMS uses aerodynamic lens inlet technology together with thermal vaporization and electron-impact mass spectrometry to measure the real-time non-refractory (NR) chemical speciation and mass loading as a function of particle size of fine aerosol particles with aerodynamic diameters between approximately 50 and 1,000 nm. The original AMS utilizes a quadrupole mass spectrometer (Q) with electron impact (EI) ionization and produces ensemble average data of particle properties. Later versions employ time-of-flight (ToF) mass spectrometers and can produce full mass spectral data for single particles. This manuscript presents a detailed discussion of the strengths and limitations of the AMS measurement approach and reviews how the measurements are used to characterize particle properties. Results from selected laboratory experiments and field measurement campaigns are also presented to highlight the different applications of this instrument. Recent instrumental developments, such as the incorporation of softer ionization techniques (vacuum ultraviolet (VUV) photo-ionization, Li+ ion, and electron attachment) and high-resolution ToF mass spectrometers, that yield more detailed information about the organic aerosol component are also described. (c) 2007 Wiley Periodicals, Inc.
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            Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2

            The Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA2) version of the Goddard Earth Observing System-5 (GEOS-5) atmospheric general circulation model (AGCM) is currently in use in the NASA Global Modeling and Assimilation Office (GMAO) at a wide range of resolutions for a variety of applications. Details of the changes in parameterizations subsequent to the version in the original MERRA reanalysis are presented here. Results of a series of atmosphere-only sensitivity studies are shown to demonstrate changes in simulated climate associated with specific changes in physical parameterizations, and the impact of the newly implemented resolution-aware behavior on simulations at different resolutions is demonstrated. The GEOS-5 AGCM presented here is the model used as part of the GMAO MERRA2 reanalysis, global mesoscale simulations at 10 km resolution through 1.5 km resolution, the real-time numerical weather prediction system, and for atmosphere-only, coupled ocean-atmosphere and coupled atmosphere-chemistry simulations. The seasonal mean climate of the MERRA2 version of the GEOS-5 AGCM represents a substantial improvement over the simulated climate of the MERRA version at all resolutions and for all applications. Fundamental improvements in simulated climate are associated with the increased re-evaporation of frozen precipitation and cloud condensate, resulting in a wetter atmosphere. Improvements in simulated climate are also shown to be attributable to changes in the background gravity wave drag, and to upgrades in the relationship between the ocean surface stress and the ocean roughness. The series of resolution-aware parameters related to the moist physics was shown to result in improvements at higher resolutions and result in AGCM simulations that exhibit seamless behavior across different resolutions and applications.
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              Emission of trace gases and aerosols from biomass burning

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                Journal
                Nature Geoscience
                Nat. Geosci.
                Springer Science and Business Media LLC
                1752-0894
                1752-0908
                March 2023
                March 10 2023
                March 2023
                : 16
                : 3
                : 224-230
                Article
                10.1038/s41561-023-01138-x
                ac52463d-706c-415b-831a-2893a0fdfc4a
                © 2023

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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