A Weighted Mean Temperature Model with Nonlinear Elevation Correction Using China as an Example

Hai Zhu,Guanwen Huang,Kejie Chen

doi:10.3390/rs13193887

Abstract

The weighted mean temperature (Tm) is a crucial parameter for determining the tropospheric delay in transforming precipitable water vapor. We used the reanalysis data provided by European Centre for Medium-Range Weather Forecasts (ECMWF) to analyze the distribution characteristics of Tm in the vertical direction in China. To address the problem that the precision of the traditional linear function model is limited in fitting the Tm profile, a scheme using the linear and Fourier functions to fit the Tm profile was constructed. Based on the least squares principle (LSQ) to fit the change in its coefficients over time, a Tm model for China with nonlinear elevation correction (CTm-h) was constructed. The experimental results show that, using ECMWF and radiosonde data to evaluate the precision of the CTm-h model, the RMS is 3.43 K and 4.64 K, respectively. Compared to GPT2w, the precision of the CTm-h model in China is increased by about 26.8%. The CTm-h model provides a significant improvement in the correction effect of Tm in the vertical direction, and the Tm profile calculated by the model is closer to the reference value.

Highlights

This paper proposes a linear and Fourier combined function method to fit the Tm profile, and based on this, a weighted mean temperature model considering nonlinear elevation correction is established
The RMS of the model with the nonlinear elevation correction was improved by about 60% compared to the linear model, and higher precision was obtained on all grid points in the study area
The European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data in 2019 that were not involved in the modeling were used to verify the precision of all grid points in the study area (55~15◦ N, 70~140◦ E)

Summary

Introduction

Water vapor plays an important role in the global energy balance and the water cycle. It is closely related to various meteorological and climatic disasters [1]. The precipitable water vapor (PWV) reflects the water vapor content in the atmosphere and has become an important monitoring object in the field of meteorology and climate [2]. Traditional atmospheric detection methods, such as radiosonde stations, microwave radiometers, etc., due to the observation costs and technical reasons, struggle to capture the temporal and spatial characteristics of water vapor.

Methods

Results

Conclusion