Jiafei Xu, Zhizhao Liu

A Gradient Boosting Decision Tree Based Correction Model for AIRS Infrared Water Vapor Product

  • General Earth and Planetary Sciences
  • Geophysics

AbstractHigh‐quality precipitable water vapor (PWV) measurements have an essential role in climate change and weather prediction studies. The Atmospheric Infrared Sounder (AIRS) instrument provides an opportunity to measure PWV at infrared (IR) bands twice daily with nearly global coverage. However, AIRS IR PWV products are easily affected by the presence of clouds. We propose a Gradient Boosting Decision Tree (GBDT) based correction model (GBCorM) to enhance the accuracy of PWV products from AIRS IR observations in both clear‐sky and cloudy‐sky conditions. The GBCorM considers many dependence factors that are in association with the AIRS IR PWV's performance. The results show that the GBCorM greatly improves the all‐weather quality of AIRS IR PWV products, especially in dry atmospheric conditions. The GBCorM‐estimated PWV result in the presence of clouds shows an accuracy comparable with that of official AIRS IR PWV products in clear‐sky conditions, demonstrating the capability of the GBCorM model.

Need a simple solution for managing your BibTeX entries? Explore CiteDrive!

  • Web-based, modern reference management
  • Collaborate and share with fellow researchers
  • Integration with Overleaf
  • Comprehensive BibTeX/BibLaTeX support
  • Save articles and websites directly from your browser
  • Search for new articles from a database of tens of millions of references
Try out CiteDrive

More from our Archive