Atmospheric Composition Modeling

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (31 July 2018) | Viewed by 5770

Special Issue Editor

Finnish Meteorological Institute, Atmospheric Composition Research, Erik Palminen Aukio 1, 00560 Helsinki, Finland
Interests: model development; verification and comparison with measurements; data assimilation; statistical methodology for model validation; analysis of the measurement data; inverse and adjoint dispersion modeling; computer experiments
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Special Issue Information

Dear Colleagues,

Numerical modelling of atmospheric composition is an indispensable step in understanding the processes and governing factors that control the state and evolution of atmospheric composition from local to global scales. The impact of atmospheric composition on human health (air quality) and climate forcing by aerosols and reactive gases (short-lived climate forcers) are among the fastest-growing areas of atmospheric research. The interplay of the scales and troposphere-stratosphere interconnections are shown to have substantial impact across the globe. Recognition of air quality as a cancer risk factor gave a strong push towards development of air quality-related forecasting and assessment services. Both retrospective analysis of the composition and, especially, its forecasting require assimilation of direct and indirect observations of the composition and radiative effects. The data assimilation technology, initially based on the approaches developed for meteorology, is gradually shifting to hybrid methods, which are not limited with the model state assimilation but can adjust, e.g., the emission fluxes. Such developments, together with increasing resolution and complexity of the systems raised the topic of emission modelling. Such models are typically used for sea salt and desert dust, as well as for biogenic volatile compounds and primary aerosols. Their next step of development includes dynamic emission calculations for many anthropogenic sources, thus reflecting the impact of weather on human activities. Such models are often based on remote-sensing information, such as the hot-spot data used for computing the emission from biomass burning, and the vegetation state observations used for biogenic emission models.

Manuscripts related to all aspects of modelling of atmospheric composition are welcome to the Special Issue, including advances in modelling technology, emission modelling, model evaluation and data assimilation, as well as application to real-life cases.

Prof. Mikhail Sofiev
Guest Editor

Manuscript Submission Information

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Keywords

  • chemistry transport modelling
  • emission models
  • data assimilation
  • model evaluation
  • tropospheric chemistry
  • stratospheric chemistry
  • aerosol formation
  • inverse modelling
  • air quality forecasting

Published Papers (1 paper)

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Research

15 pages, 476 KiB  
Article
Advanced Ultraviolet Radiation and Ozone Retrieval for Applications (AURORA): A Project Overview
by Ugo Cortesi, Simone Ceccherini, Samuele Del Bianco, Marco Gai, Cecilia Tirelli, Nicola Zoppetti, Flavio Barbara, Marc Bonazountas, Argyros Argyridis, André Bós, Edo Loenen, Antti Arola, Jukka Kujanpää, Antti Lipponen, William Wandji Nyamsi, Ronald Van der A, Jacob Van Peet, Olaf Tuinder, Vincenzo Farruggia, Andrea Masini, Emilio Simeone, Rossana Dragani, Arno Keppens, Jean-Christopher Lambert, Michel Van Roozendael, Christophe Lerot, Huan Yu and Koen Verberneadd Show full author list remove Hide full author list
Atmosphere 2018, 9(11), 454; https://doi.org/10.3390/atmos9110454 - 18 Nov 2018
Cited by 11 | Viewed by 5396
Abstract
With the launch of the Sentinel-5 Precursor (S-5P, lifted-off on 13 October 2017), Sentinel-4 (S-4) and Sentinel-5 (S-5)(from 2021 and 2023 onwards, respectively) operational missions of the ESA/EU Copernicus program, a massive amount of atmospheric composition data with unprecedented quality will become available [...] Read more.
With the launch of the Sentinel-5 Precursor (S-5P, lifted-off on 13 October 2017), Sentinel-4 (S-4) and Sentinel-5 (S-5)(from 2021 and 2023 onwards, respectively) operational missions of the ESA/EU Copernicus program, a massive amount of atmospheric composition data with unprecedented quality will become available from geostationary (GEO) and low Earth orbit (LEO) observations. Enhanced observational capabilities are expected to foster deeper insight than ever before on key issues relevant for air quality, stratospheric ozone, solar radiation, and climate. A major potential strength of the Sentinel observations lies in the exploitation of complementary information that originates from simultaneous and independent satellite measurements of the same air mass. The core purpose of the AURORA (Advanced Ultraviolet Radiation and Ozone Retrieval for Applications) project is to investigate this exploitation from a novel approach for merging data acquired in different spectral regions from on board the GEO and LEO platforms. A data processing chain is implemented and tested on synthetic observations. A new data algorithm combines the ultraviolet, visible and thermal infrared ozone products into S-4 and S-5(P) fused profiles. These fused products are then ingested into state-of-the-art data assimilation systems to obtain a unique ozone profile in analyses and forecasts mode. A comparative evaluation and validation of fused products assimilation versus the assimilation of the operational products will seek to demonstrate the improvements achieved by the proposed approach. This contribution provides a first general overview of the project, and discusses both the challenges of developing a technological infrastructure for implementing the AURORA concept, and the potential for applications of AURORA derived products, such as tropospheric ozone and UV surface radiation, in sectors such as air quality monitoring and health. Full article
(This article belongs to the Special Issue Atmospheric Composition Modeling)
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