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http://hdl.handle.net/20.500.11765/8806
Status and future of Numerical Atmospheric Aerosol Prediction with a focus on data requirements
Title: | Status and future of Numerical Atmospheric Aerosol Prediction with a focus on data requirements |
Authors: | Benedetti, Angela; Reid, Jeffrey S.; Baklanov, Alexander; Basart, Sara
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Keywords: | Aerosol particle; Numerical prediction; Aerosol prediction; Atmospheric composition prediction; Satellite measurements |
Issue Date: | 2018 |
Publisher: | European Geosciences Union |
Citation: | Atmospheric Chemistry and Physics. 2018, 18(14), p. 10615–10643 |
Publisher version: | https://doi.org/10.5194/acp-18-10615-2018 |
Abstract: | Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centres due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation and military authorities, solar energy plant managers, providers of climate services, and health professionals. The prediction of aerosol particle properties in Numerical Weather Prediction (NWP) models faces a number of challenges owing to the complexity of atmospheric aerosol processes and their sensitivity to the underlying meteorological conditions. Errors in aerosol prediction concern all processes involved in the aerosol life cycle. These include errors on the source terms (for both anthropogenic and natural emissions), errors directly dependent on the meteorology (e.g., mixing, transport, scavenging by precipitation), as well as errors related to aerosol chemistry (e.g., nucleation, gas-aerosol partitioning, chemical transformation and growth, hygroscopicity). The main goal of current research on aerosol forecast consists in prioritizing these errors and trying to reduce the most important ones 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 (...) |
Sponsorship : | Angela Benedetti has received funding from the H2020 Aerosols, Clouds, and Trace gases Research InfraStructure (ACTRIS2, Grant Number 654109). |
URI: | http://hdl.handle.net/20.500.11765/8806 |
ISSN: | 1680-7316 1680-7324 |
Appears in Collections: | Artículos científicos 2015-2018 |
Files in This Item:
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![]() | acp-18-10615-2018.pdf | 1,02 MB | Adobe PDF | ![]() View/Open |
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