A weighted mean temperature model using principal component analysis for Greenland

Shengkai Zhang, Li Gong, Wenliang Gao, Qi Zeng, Feng Xiao, Zhizhao Liu, Jintao Lei

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)

Abstract

The weighted mean temperature (Tm) is an important parameter to convert the tropospheric zenith wet delay (ZWD) extracted from the global navigation satellite system (GNSS) signal into precipitable water vapor (PWV). The computation of Tm requires vertical or ground meteorological parameters. However, most GNSS stations in Greenland lack in situ meteorological data, resulting in an accuracy degradation of the derived PWV. Using the most recent ERA5 reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) from 1990 to 2018, we extract the principal components from a large amount of reanalysis data and then model Tm using a data-driven principal component analysis (PCA) method. In comparison with classic periodic modeling approaches, our PCA model uses fewer parameters and considers temperature fluctuation with height. The proposed model is validated using observations from 11 radiosonde stations in Greenland from 2015 to 2019. The model’s bias and RMSE are − 0.110 and 4.447 K, respectively. The new model is also compared to the global pressure and temperature 3 (GPT3) and GTrop traditional grid models. The bias is reduced by 0.339 and 0.422 K, respectively, and the RMSE is reduced by 0.197 and 0.045 K, respectively.

Original languageEnglish
Article number57
JournalGPS Solutions
Volume27
Issue number1
DOIs
Publication statusPublished - Jan 2023

Keywords

  • ERA5
  • Greenland
  • PCA
  • Weighted mean temperature

ASJC Scopus subject areas

  • General Earth and Planetary Sciences

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