Impacts of Horizontal Resolution on Global Data Assimilation of Satellite Measurements for Tropospheric Chemistry Analysis

Takashi Sekiya*, Kazuyuki Miyazaki, Koji Ogochi, Kengo Sudo, Masayuki Takigawa, Henk Eskes, Folkert Boersma

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)

Abstract

We present the results from a global 0.56°-resolution chemical data assimilation that integrates satellite observations of ozone, NO2, CO, HNO3, and SO2 from OMI, GOME-2, SCIAMACHY, TES, MOPITT, and MLS. The assimilation is based on an ensemble Kalman filter technique and simultaneously optimizes ozone precursor emissions and concentrations of various species. The data assimilation at 0.56° resolution reduced model errors against independent surface, aircraft, and ozonesonde observations, which was larger than at coarser resolutions for many cases. By the data assimilation, surface model errors over major polluted regions were reduced by 33%–75% for NO2 and by 15%–18% for ozone. Agreements against assimilated observations for NO2 were improved using the data assimilation at 0.56° resolution by a factor of 1.5–3 compared to 2.8° resolution over major polluted regions. The estimated global total NOx emission over medium and strong source areas were smaller by 15% at 0.56° resolution than at 2.8° resolution associated with resolving small-scale transport and chemistry processes, while 2%–26% smaller emissions were found for regional total emissions over Europe, the United States, China, India, and South Africa, with larger differences over megacities such as Los Angeles (−41%). The estimated ship emissions were 5%–7% smaller at 0.56° resolution over the Pacific and Atlantic. The 0.56°-resolution data assimilation provides globally consistent analyses of the emissions and concentrations on a megacity scale, which benefit studies on air quality and its impact on human health at various spatial scales over different regions of the world.

Original languageEnglish
Article numbere2020MS002180
JournalJournal of Advances in Modeling Earth Systems
Volume13
Issue number6
Early online date30 Apr 2021
DOIs
Publication statusPublished - 2021

Keywords

  • atmospheric chemistry
  • data assimilation
  • emission inversion

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