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...
Review status: this preprint is currently under review for the journal ACP.
The potential for geostationary remote sensing of NO2 to improve weather prediction
Xueling Liu1,Arthur P. Mizzi2,a,Jeffrey L. Anderson3,Inez Fung1,and Ronald C. Cohen1,4Xueling Liu et al.Xueling Liu1,Arthur P. Mizzi2,a,Jeffrey L. Anderson3,Inez Fung1,and Ronald C. Cohen1,4
Received: 01 Jul 2020 – Accepted for review: 02 Oct 2020 – Discussion started: 13 Oct 2020
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.
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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...