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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/acp-2018-42
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Review article
27 Feb 2018
Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Atmospheric Chemistry and Physics (ACP).
Status and future of Numerical Atmospheric Aerosol Prediction with a focus on data requirements
Angela Benedetti1, Jeffrey S. Reid2, Alexander Baklanov3, Sara Basart4, Olivier Boucher5, Ian M. Brooks6, Malcolm Brooks7, Peter R. Colarco8, Emilio Cuevas9, Arlindo da Silva8, Francesca Di Giuseppe1, Jeronimo Escribano5, Johannes Flemming1, Nicolas Huneeus10,11, Oriol Jorba4, Stelios Kazadzis12,13, Stefan Kinne14, Peter Knippertz15, Paolo Laj16, John H. Marsham6,17, Laurent Menut18, Lucia Mona19, Thomas Popp20, Patricia K. Quinn24, Samuel Rémy5, Thomas S. Sekiyama21, Taichu Tanaka21, Enric Terradellas22, and Alfred Wiedensohler23 1European Centre for Medium-Range Weather Forecasts, Reading, UK
2Naval Research Laboratory, Monterey, CA, USA
3World Meteorological Organisation, Switzerland
4Barcelona Supercomputing Center, BSC, Barcelona, Spain
5Institut Pierre-Simon Laplace, CNRS / Sorbonne Université, Paris, France
6University of Leeds, Leeds, UK
7UK Met Office, Exeter, UK
8NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
9Izaña Atmospheric Research Centre, AEMET, Santa Cruz de Tenerife, Spain
10Geophysics Department, University of Chile, Santiago, Chile
11Center for Climate and Resilience Research (CR)2, Santiago, Chile
12Physikalisch-Meteorologisches Observatorium Davos, World Radiation Center, Switzerland, Davos, Switzerland
13National Observatory of Athens, Greece
14Max-Planck-Institut für Meteorologie, Hamburg, Germany
15Karlsruhe Institute of Technology, Karlsruhe, Germany
16Institut des Géosciences de l'Environnement, Grenoble
17National Centre for Atmospheric Science, UK
18Laboratoire de Météorologie Dynamique, Ecole Polytechnique, IPSL Research University, Ecole Normale Supérieure, Université Paris-Saclay, Sorbonne Universités, UPMC Univ Paris 06, CNRS, Palaiseau, France
19Centro Nazionale Ricerche, Italy
20German Aerospace Center (DLR), German Remote Sensing Data Center Atmosphere, Oberpfaffenhofen, Germany
21Japan Meteorological Agency/Meteorological Research Institute, Tsukuba, Japan
22Spanish Meteorological Agency, AEMET, Barcelona, Spain
23Leibniz Institute for Tropospheric Research, Leipzig, Germany
24National Oceanic and Atmospheric Administration, Pacific Marine Environmental Laboratory, Seattle, USA
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. 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 white paper reviews current requirements for aerosol observations in the context of the operational activities carried out at various global and regional centres. Some of the requirements are equally applicable to aerosol-climate research. However, the focus here is on the global operational prediction of aerosol properties such as mass concentrations and optical parameters. 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 to simulate the size information. However the next generation of aerosol operational models will have the capability to predict both mass and number density which will provide a more complete description of the aerosols properties. 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.
Citation: Benedetti, A., Reid, J. S., Baklanov, A., Basart, S., Boucher, O., Brooks, I. M., Brooks, M., Colarco, P. R., Cuevas, E., da Silva, A., Di Giuseppe, F., Escribano, J., Flemming, J., Huneeus, N., Jorba, O., Kazadzis, S., Kinne, S., Knippertz, P., Laj, P., Marsham, J. H., Menut, L., Mona, L., Popp, T., Quinn, P. K., Rémy, S., Sekiyama, T. S., Tanaka, T., Terradellas, E., and Wiedensohler, A.: Status and future of Numerical Atmospheric Aerosol Prediction with a focus on data requirements, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-42, in review, 2018.
Angela Benedetti et al.
Angela Benedetti et al.
Angela Benedetti et al.

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