学位论文详细信息
Cloud condensation nuclei spectra: measurement techniques and ambient sampling in the southeastern United States
Aerosols-cloud interactions;Atmospheric aerosols;Cloud condensation nuclei;Cloud microphysics;Hygroscopicity;Instrumentation
Lin, Jack Jie ; Earth and Atmospheric Sciences Nenes, Athanasios Ng, Nga Lee Russell, Armistead G. Wang, Yuhang Weber, Rodney J.
University:Georgia Institute of Technology
Department:Earth and Atmospheric Sciences
关键词: Aerosols-cloud interactions;    Atmospheric aerosols;    Cloud condensation nuclei;    Cloud microphysics;    Hygroscopicity;    Instrumentation;   
Others  :  https://smartech.gatech.edu/bitstream/1853/58599/1/LIN-DISSERTATION-2016.pdf
美国|英语
来源: SMARTech Repository
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【 摘 要 】
Measurements of the ability of aerosols to act as cloud condensation nuclei (CCN) continue to be necessary to improve our understanding of the interaction between aerosols and clouds. Hardware improvements to the Droplet Measurement Technologies CCN Counter in the form of an Arduino microcontroller and a mass flow controller (MFC) have increased the robustness and flexibility of CCN spectra measurements. The supersaturation generated during dynamic flow operation was comprehensively characterized. A set of calibration experiments and instrument model simulations were used to assess instrument performance for a wide range of operating conditions, including instrument pressure, flow rate waveform, and flow rate period for both laboratory and ambient sampling conditions.CCN spectra, along with aerosol size and composition, were measured aboard the National Oceanic and Atmospheric Administration WP-3D aircraft during the SENEX field campaign in the summer of 2013. Measured aerosol properties were evaluated against sim- ulations using the Community Atmosphere Model and Community Multi-scale Air Quality Model. Aerosol in the southeastern United States was found to mostly composed of organics, but the composition of Aitken mode aerosol was found to have hygroscopicity similar to pure inorganic compounds. Both models fail to capture key characteristics of the observed aerosol. In particular, both models largely underestimating the total aerosol number. Under typical observed updraft values, however, the resulting cloud droplet number is only underestimated by a factor of two. Using the sensitivities of cloud droplet number to relevant input parameters, it was determined that the primary driver of discrepancy between the cloud droplet number computed from the observed and modeled aerosol parameters is the total aerosol number.
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