Status and future of Numerical Atmospheric Aerosol Prediction with a focus on data requirements

Autor: A. Benedetti, J. S. Reid, P. Knippertz, J. H. Marsham, F. Di Giuseppe, S. Rémy, S. Basart, O. Boucher, I. M. Brooks, L. Menut, L. Mona, P. Laj, G. Pappalardo, A. Wiedensohler, A. Baklanov, M. Brooks, P. R. Colarco, E. Cuevas, A. da Silva, J. Escribano, J. Flemming, N. Huneeus, O. Jorba, S. Kazadzis, S. Kinne, T. Popp, P. K. Quinn, T. T. Sekiyama, T. Tanaka, E. Terradellas
Přispěvatelé: European Centre for Medium-Range Weather Forecasts (ECMWF), Institute for Meteorology and Climate Research (IMK), Karlsruhe Institute of Technology (KIT), School of Earth and Environment [Leeds] (SEE), University of Leeds, Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS Paris)-École normale supérieure - Paris (ENS Paris), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS Paris)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Istituto di Metodologie per l'Analisi Ambientale (IMAA), Consiglio Nazionale delle Ricerche [Potenza] (CNR), Laboratoire de glaciologie et géophysique de l'environnement (LGGE), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS), Leibniz Institute for Tropospheric Research (TROPOS), Danish Meteorological Institute (DMI), Atmospheric Chemistry and Dynamics Branch [ Greenbelt], NASA Goddard Space Flight Center (GSFC), Izaña Atmospheric Research Center (IARC), Agencia Estatal de Meteorología (AEMet), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, Max-Planck-Institut für Meteorologie (MPI-M), Max-Planck-Gesellschaft, Deutsches Fernerkundungsdatenzentrum / German Remote Sensing Data Center (DFD), Deutsches Zentrum für Luft- und Raumfahrt [Oberpfaffenhofen-Wessling] (DLR), NOAA Pacific Marine Environmental Laboratory [Seattle] (PMEL), National Oceanic and Atmospheric Administration (NOAA), Japan Meteorological Agency (JMA), Meteorological Research Institute [Tsukuba] (MRI), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Département des Géosciences - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École des Ponts ParisTech (ENPC)-École polytechnique (X)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Department of Physics
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Atmospheric Science
010504 meteorology & atmospheric sciences
010502 geochemistry & geophysics
computer.software_genre
01 natural sciences
lcsh:Chemistry
Data assimilation
Satellite measurements
Atmospheric composition prediction
ddc:550
requirements
FIRE RADIATIVE POWER
ComputingMilieux_MISCELLANEOUS
education.field_of_study
MINERAL DUST
Numerical prediction
REMOTE-SENSING OBSERVATIONS
VIIRS DAY/NIGHT BAND
OPTICAL DEPTH RETRIEVALS
lcsh:QC1-999
Geography
[SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology
SEA-SURFACE TEMPERATURE
[SDE]Environmental Sciences
Atmosphäre
Aerosol prediction
Aerosol particle
Meteorology
Population
Weather forecasting
Context (language use)
User requirements document
114 Physical sciences
modelling
DATA ASSIMILATION
education
DUST EMISSION
Air quality index
0105 earth and related environmental sciences
Numerical weather prediction
Aerosol
Earth sciences
lcsh:QD1-999
13. Climate action
[SDU]Sciences of the Universe [physics]
SUMMERTIME WEST-AFRICA
computer
lcsh:Physics
SAHELIAN DUST
Zdroj: Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics, European Geosciences Union, 2018, 18 (14), pp.10615-10643. ⟨10.5194/acp-18-10615-2018⟩
Atmospheric Chemistry and Physics, 2018, 18 (14), pp.10615-10643. ⟨10.5194/acp-18-10615-2018⟩
Atmospheric Chemistry and Physics, Vol 18, Pp 10615-10643 (2018)
Atmospheric chemistry and physics, 18 (14), 10615–10643
Atmospheric Chemistry and Physics Discussions
Atmospheric chemistry and physics
18 (2018): 10615–10643. doi:10.5194/acp-18-10615-2018
info:cnr-pdr/source/autori:Benedetti, Angela; Reid, Jeffrey S.; Knippertz, Peter; Marsham, John H.; Di Giuseppe, Francesca; Remy, 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/titolo:Status and future of numerical atmospheric aerosol prediction with a focus on data requirements/doi:10.5194%2Facp-18-10615-2018/rivista:Atmospheric chemistry and physics (Print)/anno:2018/pagina_da:10615/pagina_a:10643/intervallo_pagine:10615–10643/volume:18
ARCIMIS. Archivo Climatológico y Meteorológico Institucional (AEMET)
Agencia Estatal de Meteorología (AEMET)
ISSN: 1680-7367
1680-7316
1680-7324
DOI: 10.5194/acp-18-10615-2018⟩
Popis: 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.
Databáze: OpenAIRE
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