Articles | Volume 13, issue 9
Atmos. Meas. Tech., 13, 4669–4681, 2020
Atmos. Meas. Tech., 13, 4669–4681, 2020

Research article 02 Sep 2020

Research article | 02 Sep 2020

Gradient boosting machine learning to improve satellite-derived column water vapor measurement error

Allan C. Just et al.


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Allan Just on behalf of the Authors (05 May 2020)  Author's response    Manuscript
ED: Publish as is (06 May 2020) by Alexander Kokhanovsky
Short summary
A flexible machine-learning model was fit to explain the differences between estimates of water vapor from satellites versus ground stations in Northeastern USA. We use nine variables derived from the satellite acquisition and ground characteristics to explain this measurement error. Our results showed overall good agreement, but data from the Terra satellite were drifting too high in recent summers. Our model reduces measurement error and works well in new locations in the northeast.