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    Status and future of numerical atmospheric aerosol prediction with a focus on data requirements
    (Katlenburg-Lindau : EGU, 2018) Benedetti, Angela; Reid, Jeffrey S.; Knippertz, Peter; Marsham, John H.; Di Giuseppe, Francesca; Rémy, Samuel; Basart, Sara; Boucher, Olivier; Brooks, Ian M.; Menut, Laurent; Mona, Lucia; Laj, Paolo; Pappalardo, Gelsomina; Wiedensohler, Alfred; Baklanov, Alexander; Brooks, Malcolm; Colarco, Peter R.; Cuevas, Emilio; da Silva, Arlindo; Escribano, Jeronimo; Flemming, Johannes; Huneeus, Nicolas; Jorba, Oriol; Kazadzis, Stelios; Kinne, Stefan; Popp, Thomas; Quinn, Patricia K.; Sekiyama, Thomas T.; Tanaka, Taichu; Terradellas, Enric
    Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centers. This development is due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation and military authorities, solar energy plant managers, climate services providers, and health professionals. Owing to the complexity of atmospheric aerosol processes and their sensitivity to the underlying meteorological conditions, the prediction of aerosol particle concentrations and properties in the numerical weather prediction (NWP) framework faces a number of challenges. The modeling of numerous aerosol-related parameters increases computational expense. Errors in aerosol prediction concern all processes involved in the aerosol life cycle including (a) errors on the source terms (for both anthropogenic and natural emissions), (b) errors directly dependent on the meteorology (e.g., mixing, transport, scavenging by precipitation), and (c) errors related to aerosol chemistry (e.g., nucleation, gas-aerosol partitioning, chemical transformation and growth, hygroscopicity). Finally, there are fundamental uncertainties and significant processing overhead in the diverse observations used for verification and assimilation within these systems. Indeed, a significant component of aerosol forecast development consists in streamlining aerosol-related observations and reducing the most important errors through model development and data assimilation. Aerosol particle observations from satellite- and ground-based platforms have been crucial to guide model development of the recent years and have been made more readily available for model evaluation and assimilation. However, for the sustainability of the aerosol particle prediction activities around the globe, it is crucial that quality aerosol observations continue to be made available from different platforms (space, near surface, and aircraft) and freely shared. This paper reviews current requirements for aerosol observations in the context of the operational activities carried out at various global and regional centers. While some of the requirements are equally applicable to aerosol-climate, the focus here is on global operational prediction of aerosol properties such as mass concentrations and optical parameters. It is also recognized that the term "requirements" is loosely used here given the diversity in global aerosol observing systems and that utilized data are typically not from operational sources. Most operational models are based on bulk schemes that do not predict the size distribution of the aerosol particles. Others are based on a mix of "bin" and bulk schemes with limited capability of simulating the size information. However the next generation of aerosol operational models will output both mass and number density concentration to provide a more complete description of the aerosol population. A brief overview of the state of the art is provided with an introduction on the importance of aerosol prediction activities. The criteria on which the requirements for aerosol observations are based are also outlined. Assimilation and evaluation aspects are discussed from the perspective of the user requirements.
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    Molecular distributions of dicarboxylic acids, oxocarboxylic acids and α-dicarbonyls in PM2.5 collected at the top of Mt. Tai, North China, during the wheat burning season of 2014
    (Katlenburg-Lindau : EGU, 2018) Zhu, Yanhong; Yang, Lingxiao; Chen, Jianmin; Kawamura, Kimitaka; Sato, Mamiko; Tilgner, Andreas; van Pinxteren, Dominik; Chen, Ying; Xue, Likun; Wang, Xinfeng; Simpson, Isobel J.; Herrmann, Hartmut; Blake, Donald R.; Wang, Wenxing
    Fine particulate matter (PM2.5) samples collected at Mount (Mt.) Tai in the North China Plain during summer 2014 were analyzed for dicarboxylic acids and related compounds (oxocarboxylic acids and α-dicarbonyls) (DCRCs). The total concentration of DCRCs was 1050±580 and 1040±490ng m-3 during the day and night, respectively. Although these concentrations were about 2 times lower than similar measurements in 2006, the concentrations reported here were about 1-13 times higher than previous measurements in other major cities in the world. Molecular distributions of DCRCs revealed that oxalic acid (C2) was the dominant species (50%), followed by succinic acid (C4) (12%) and malonic acid (C3) (8%). WRF modeling revealed that Mt. Tai was mostly in the free troposphere during the campaign and long-range transport was a major factor governing the distributions of the measured compounds at Mt. Tai. A majority of the samples (79%) had comparable concentrations during the day and night, with their day-night concentration ratios between 0.9 and 1.1. Multi-day transport was considered an important reason for the similar concentrations. Correlation analyses of DCRCs and their gas precursors and between C2 and sulfate indicated precursor emissions and aqueous-phase oxidations during long-range transport also likely play an important role, especially during the night. Source identification indicated that anthropogenic activities followed by photochemical aging accounted for about 60% of the total variance and were the dominant source at Mt. Tai. However, biomass burning was only important during the first half of the measurement period. Measurements of potassium (K+) and DCRCs were about 2 times higher than those from the second half of the measurement period. The concentration of levoglucosan, a biomass burning tracer, decreased by about 80% between 2006 and 2014, indicating that biomass burning may have decreased between 2006 and 2014.