Articles | Volume 17, issue 7
https://doi.org/10.5194/gmd-17-2569-2024
https://doi.org/10.5194/gmd-17-2569-2024
Model evaluation paper
 | 
09 Apr 2024
Model evaluation paper |  | 09 Apr 2024

A grid model for vertical correction of precipitable water vapor over the Chinese mainland and surrounding areas using random forest

Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Huang, and Feijuan Li

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Cited articles

Adeyemi, B. and Joerg, S.: Analysis of Water Vapor over Nigeria Using Radiosonde and Satellite Data, J. Appl. Meteorol. Climatol., 51, 1855–1866, https://doi.org/10.1175/jamc-d-11-0119.1, 2012. 
Albergel, C., Dutra, E., Munier, S., Calvet, J.-C., Munoz-Sabater, J., de Rosnay, P., and Balsamo, G.: ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?, Hydrol. Earth Syst. Sci., 22, 3515–3532, https://doi.org/10.5194/hess-22-3515-2018, 2018. 
Alshawaf, F., Fersch, B., Hinz, S., Kunstmann, H., Mayer, M., and Meyer, F. J.: Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations, Hydrol. Earth Syst. Sci., 19, 4747–4764, https://doi.org/10.5194/hess-19-4747-2015, 2015. 
Bevis, M., Businger, S., Herring, T. A., Rocken, C., Anthes, R. A., and Ware, R. H.: GPS Meteorology – Remote-Sensing of Atmospheric Water-Vapor Using the Global Positioning System, J. Geophys. Res.-Atmos., 97, 15787–15801, https://doi.org/10.1029/92jd01517, 1992. 
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/a:1010933404324, 2001. 
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Short summary
In this study, we have developed a model (RF-PWV) to characterize precipitable water vapor (PWV) variation with altitude in the study area. RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.
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