Preprints
https://doi.org/10.5194/acp-2020-656
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-656
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
13 Oct 2020
13 Oct 2020
Review status: a revised version of this preprint is currently under review for the journal ACP.
The potential for geostationary remote sensing of NO2 to improve weather prediction
- 1Department of Earth and Planetary Science, University of California at Berkeley, Berkeley, CA, USA
- 2Atmospheric Chemistry Observation and Modeling Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
- 3Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research, Boulder, CO, USA
- 4Department of Chemistry, University of California at Berkeley, Berkeley, CA, USA
- anow at: NCAR/DAREs Visitor and at NASA Ames Research Center, USA
- 1Department of Earth and Planetary Science, University of California at Berkeley, Berkeley, CA, USA
- 2Atmospheric Chemistry Observation and Modeling Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
- 3Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research, Boulder, CO, USA
- 4Department of Chemistry, University of California at Berkeley, Berkeley, CA, USA
- anow at: NCAR/DAREs Visitor and at NASA Ames Research Center, USA
Abstract. Observations of winds in the planetary boundary layer remain sparse making it challenging to simulate and predict atmospheric conditions that are most important for describing and predicting urban air quality. Short-lived chemicals are observed as plumes whose location is affected by boundary layer winds and with a lifetime affected by boundary layer height and mixing. Here we investigate the application of data assimilation of NO2 columns as will be observed from geostationary orbit to improve predictions and retrospective analysis of wind fields in the boundary layer.
How to cite. Liu, X., Mizzi, A. P., Anderson, J. L., Fung, I., and Cohen, R. C.: The potential for geostationary remote sensing of NO2 to improve weather prediction, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2020-656, in review, 2020.
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Xueling Liu et al.
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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RC1: 'Review of Liu et al 2020', Anonymous Referee #2, 09 Dec 2020
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RC2: 'Review of acp-2020-656', Anonymous Referee #3, 23 Dec 2020
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AC1: 'Comment on acp-2020-656', Ronald Cohen, 27 Feb 2021
Xueling Liu et al.
Xueling Liu et al.
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Short summary
Observations of winds in the planetary boundary layer remain sparse making it challenging to simulate and predict atmospheric conditions that are most important for describing and predicting urban air quality. Here we investigate the application of data assimilation of NO2 columns as will be observed from geostationary orbit to improve predictions and retrospective analysis of wind fields in the boundary layer.
Observations of winds in the planetary boundary layer remain sparse making it challenging to...
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