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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 14 | Copyright
Atmos. Chem. Phys., 18, 10615-10643, 2018
https://doi.org/10.5194/acp-18-10615-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Review article 25 Jul 2018

Review article | 25 Jul 2018

Status and future of numerical atmospheric aerosol prediction with a focus on data requirements

Angela Benedetti1, Jeffrey S. Reid2, Peter Knippertz15, John H. Marsham6,17, Francesca Di Giuseppe1, Samuel Rémy5, Sara Basart4, Olivier Boucher5, Ian M. Brooks6, Laurent Menut18, Lucia Mona19, Paolo Laj16,25, Gelsomina Pappalardo19, Alfred Wiedensohler23, Alexander Baklanov3, Malcolm Brooks7, Peter R. Colarco8, Emilio Cuevas9, Arlindo da Silva8, Jeronimo Escribano5, Johannes Flemming1, Nicolas Huneeus10,11, Oriol Jorba4, Stelios Kazadzis12,13, Stefan Kinne14, Thomas Popp20, Patricia K. Quinn24, Thomas T. Sekiyama21, Taichu Tanaka21, and Enric Terradellas22 Angela Benedetti et al.
  • 1European Centre for Medium-Range Weather Forecasts, Reading, UK
  • 2Naval Research Laboratory, Monterey, CA, USA
  • 3World Meteorological Organization, Geneva, 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
  • 16Univ. Grenoble-alpes, IGE, CNRS, IRD, Grenoble INP, Grenoble, France
  • 17National Centre for Atmospheric Science, Leeds, 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
  • 19Consiglio Nazionale delle Ricerche, Istituto di Metodologie per l'Analisi Ambientale (CNR-IMAA), C. da S. Loja, Tito Scalo (PZ), 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, WA, USA
  • 25Department of Physics, University of Helsinki, Helsinki, Finland

Abstract. 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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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 authorities, solar energy plant managers, climate service providers, and health professionals. This paper describes the advances in the field and sets out requirements for observations for the sustainability of these activities.
Numerical prediction of aerosol particle properties has become an important activity at many...
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