Improved atmospheric weighted mean temperature modeling using sparse kernel learning

GPS Solutions ◽  
2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Liu Yang ◽  
Guobin Chang ◽  
Nijia Qian ◽  
Jingxiang Gao
Keyword(s):  
Kernel Learning ◽  
Weighted Mean ◽  
Temperature Modeling ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Sparse Kernel

A new global grid model for the determination of atmospheric weighted mean temperature in GPS precipitable water vapor

2018 ◽  
Vol 93 (2) ◽  
pp. 159-176 ◽  
Author(s):  
Liangke Huang ◽  
Weiping Jiang ◽  
Lilong Liu ◽  
Hua Chen ◽  
Shirong Ye
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Weighted Mean ◽  
Grid Model ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Global Grid

scholarly journals An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model

2020 ◽  
Vol 12 (7) ◽  
pp. 1098
Author(s):  
Pedro Mateus ◽  
João Catalão ◽  
Virgílio B. Mendes ◽  
Giovanni Nico
Keyword(s):  
Water Vapor ◽  
Air Temperature ◽  
Surface Pressure ◽  
Surface Air Temperature ◽  
Precipitable Water ◽  
Weighted Mean ◽  
Zenith Wet Delay ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Zenith Hydrostatic Delay

The Global Navigation Satellite System (GNSS) meteorology contribution to the comprehension of the Earth’s atmosphere’s global and regional variations is essential. In GNSS processing, the zenith wet delay is obtained using the difference between the zenith total delay and the zenith hydrostatic delay. The zenith wet delay can also be converted into precipitable water vapor by knowing the atmospheric weighted mean temperature profiles. Improving the accuracy of the zenith hydrostatic delay and the weighted mean temperature, normally obtained using modeled surface meteorological parameters at coarse scales, leads to a more accurate and precise zenith wet delay estimation, and consequently, to a better precipitable water vapor estimation. In this study, we developed an hourly global pressure and temperature (HGPT) model based on the full spatial and temporal resolution of the new ERA5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The HGPT model provides information regarding the surface pressure, surface air temperature, zenith hydrostatic delay, and weighted mean temperature. It is based on the time-segmentation concept and uses the annual and semi-annual periodicities for surface pressure, and annual, semi-annual, and quarterly periodicities for surface air temperature. The amplitudes and initial phase variations are estimated as a periodic function. The weighted mean temperature is determined using a 20-year time series of monthly data to understand its seasonality and geographic variability. We also introduced a linear trend to account for a global climate change scenario. Data from the year 2018 acquired from 510 radiosonde stations downloaded from the National Oceanic and Atmospheric Administration (NOAA) Integrated Global Radiosonde Archive were used to assess the model coefficients. Results show that the GNSS meteorology, hydrological models, Interferometric Synthetic Aperture Radar (InSAR) meteorology, climate studies, and other topics can significantly benefit from an ERA5 full-resolution model.

Sign in / Sign up

Export Citation Format

Share Document