Influence of Changes in Meteorological Observational Environment on Urbanization Bias in Surface Air Temperature: A Review

Tao Shi,Dabing Sun,Yong Huang,Chune Shi,Yuanjian Yang

doi:10.3389/fclim.2021.781999

Abstract

It is important to quantify changes in the local meteorological observational environment (MOE) around weather stations if we are to obtain accurate assessments of the regional warming of the surface air temperature (SAT) in relation to urbanization bias. Current studies often use two-dimensional parameters (e.g., the land surface temperature, land use/land cover and the normalized difference vegetation index) to characterize the local MOE. Most of the existing models of the relationship between urbanization bias in SAT series and MOE parameters are linear regression models, which ignore the non-linear driving effect of MOE changes on SAT series. By contrast, there is a lack of three-dimensional parameters in the characterization of the morphological features of the MOE. Changes in the MOE related to urbanization lead to uncertainties in the contribution of SAT series on different scales and we need to introduce vertical structure indexes to enrich the three-dimensional spatial morphology of MOE parameters. The non-linear response of urbanization bias in SAT series to three-dimensional changes in the MOE and its scale dependence should be explored by coupling computational fluid dynamics model simulations with machine learning.

Highlights

The meteorological observational environment (MOE) around weather stations is fundamental to the accurate and continuous recording of the meteorological elements used for disaster prevention and mitigation, economic development, public health decision-making and climate change adaptation (Ren et al, 2007, 2015; Luo and Lau, 2018, 2019; Zheng et al, 2021)
The MOE of stations changes with rapid urbanization, which leads to inhomogeneities in the surface air temperature (SAT) series (Li, 2011; Yan et al, 2014; Cao et al, 2016; Du et al, 2020)
This paper summarizes and reviews recent progress in the characterization of the spatial morphology of the MOE, the relationship between changes in the MOE and the urbanization bias in SAT series, and considers future research directions

Summary

INTRODUCTION

The meteorological observational environment (MOE) around weather stations is fundamental to the accurate and continuous recording of the meteorological elements used for disaster prevention and mitigation, economic development, public health decision-making and climate change adaptation (Ren et al, 2007, 2015; Luo and Lau, 2018, 2019; Zheng et al, 2021). The rapid urbanization seen in recent years has meant that many meteorological stations previously located on the outskirts of cities with good observational environments have gradually moved into urban centers or are surrounded by builtup areas This has created biases in the SAT series, which cannot be ignored to regional warming in China (Ren et al, 2017). These studies show that it is not possible to finely quantify the effects of the three-dimensional morphology of the MOE on urbanization bias through linear statistics Another approach is numerical modeling, in which numerical simulations are used to study the impact of changes in the MOE caused by urbanization on the meteorological observational elements (Zhang et al, 2002, 2016; Liu and Zhou, 2007; Yang et al, 2016; Chen, 2021; Chen G. et al, 2021; Yu et al, 2021). A lack of in-depth consideration of the dependence on spatial scale increases the uncertainty in the impact of changes in the MOE on the urbanization bias in SAT series

Findings

DISCUSSION

SUMMARY