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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Sep 2020
Research article |
| 02 Sep 2020
| 02 Sep 2020
Gradient boosting machine learning to improve satellite-derived column water vapor measurement error
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
Meytar Sorek-Hamer
Universities Space Research Association (USRA), Mountain View, California, USA
NASA Ames Research Center, Mountain View, California, USA
Johnathan Rush
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
Michael Dorman
Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beersheba, Israel
Robert Chatfield
NASA Ames Research Center, Mountain View, California, USA
Yujie Wang
Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, Maryland, USA
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Alexei Lyapustin
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Itai Kloog
Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beersheba, Israel
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Latest update: 14 Nov 2024
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.
A flexible machine-learning model was fit to explain the differences between estimates of water...
