Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 13, issue 9
Articles | Volume 13, issue 9
https://doi.org/10.5194/amt-13-4669-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-4669-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 9
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, Yang Liu, Meytar Sorek-Hamer, Johnathan Rush, Michael Dorman, Robert Chatfield, Yujie Wang, Alexei Lyapustin, and Itai Kloog

Supplement

https://doi.org/10.5194/amt-13-4669-2020-supplement

Data sets

Gradient Boosting Machine Learning to Improve Satellite-Derived Column Water Vapor Measurement Error A. C. Just, Y. Liu, M. Sorek-Hamer, J. Rush, M. Dorman, R. Chatfield, Y. Wang, A. Lyapustin, and I. Kloog https://doi.org/10.5281/zenodo.3568449

Model code and software

Gradient Boosting Machine Learning to Improve Satellite-Derived Column Water Vapor Measurement Error A. C. Just, Y. Liu, M. Sorek-Hamer, J. Rush, M. Dorman, R. Chatfield, Y. Wang, A. Lyapustin, and I. Kloog https://doi.org/10.5281/zenodo.3568449

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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.
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