Articles | Volume 13, issue 9
Atmos. Meas. Tech., 13, 4669–4681, 2020
https://doi.org/10.5194/amt-13-4669-2020
Atmos. Meas. Tech., 13, 4669–4681, 2020
https://doi.org/10.5194/amt-13-4669-2020

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.

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Cited articles

Adesina, A. J., Kumar, K. R., Sivakumar, V., and Griffith, D.: Direct radiative forcing of urban aerosols over Pretoria (25.75 S, 28.28 E) using AERONET Sunphotometer data: first scientific results and environmental impact, J. Environ. Sci., 26, 2459–2474, https://doi.org/10.1016/j.jes.2014.04.006, 2014. 
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Boiyo, R., Kumar, K. R., Zhao, T., and Guo, J.: A 10-Year Record of Aerosol Optical Properties and Radiative Forcing Over Three Environmentally Distinct AERONET Sites in Kenya, East Africa, J. Geophys. Res.-Atmos., 124, 1596–1617, https://doi.org/10.1029/2018JD029461, 2019. 
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD”16, 785–794, ACM Press, New York, New York, USA, 2016. 
Chen, T. and He, T.: Higgs Boson Discovery with Boosted Trees, Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning, in: Proc. Mach. Learn. Res., 42, 69–80, 2015. 
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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.