A New Methodology for Estimating the Surface Temperature Lapse Rate Based on Grid Data and Its Application in China

Abstract

Land surface temperature (LST) is an important parameter in the study of the physical processes of land surface. Understanding the surface temperature lapse rate (TLR) can help to reveal the characteristics of mountainous climates and regional climate change. A methodology was developed to calculate and analyze land-surface TLR in China based on grid datasets of MODIS LST and digital elevation model (DEM), with a formula derived on the basis of the analysis of the temperature field and the height field, an image enhancement technique used to calculate gradient, and the fuzzy c-means (FCM) clustering applied to identify the seasonal pattern of the TLR. The results of the analysis through the methodology showed that surface temperature vertical gradient inversion widely occurred in Northeast, Northwest, and North China in winter, especially in the Xinjiang Autonomous Region, the northern and the western parts of the Greater Khingan Mountains, the Lesser Khingan Mountains, and the northern area of Northwest and North China. Summer generally witnessed the steepest TLR among the four seasons. The eastern Tibetan Plateau showed a distinctive seasonal pattern, where the steepest TLR happened in winter and spring, with a shallower TLR in summer. Large seasonal variations of TLR could be seen in Northeast China, where there was a steep TLR in spring and summer and a strong surface temperature vertical gradient inversion in winter. The smallest seasonal variation of TLR happened in Central and Southwest China, especially in the Ta-pa Mountains and the Qinling Mountains. The TLR at very high altitudes (>5 km) was usually steeper than at low altitudes, in all months of the year.

A newly developed South American Mapping of Temperature with estimated lapse rate corrections

“…The ELRT produces smaller rates for the decrease of temperature with height. This is not a peculiar behaviour for South America as it has been already identified for the Himalayas (Romshoo et al ., 2018), the Tibetan Plateau (Wang et al ., 2018; Zhang et al ., 2019), China (Qin et al ., 2018) and for the Arctic (Gardner et al ., 2009). The annual cycle is also well represented by all data sources used; however, SAMeT displayed values closest to the observations.…”

Analysis of Near-Surface Temperature Lapse Rates in Mountain Ecosystems of Northern Mexico Using Landsat-8 Satellite Images and ECOSTRESS

“…Nevertheless, it has been observed that some environmental variables (i.e., solar incidence, moisture, land cover characteristics) cause interference of NSTLR estimated from LST data [18,34]. Therefore, it has been suggested normalizing LST data with respect to environmental variables, potentially improving the accuracy of estimation of NSTLR from satellite information [8,35]. This approach has been less explored and requires further evaluation, particularly for the relatively less utilized medium resolution sensors.…”

Application of geographically weighted regression model in the estimation of surface air temperature lapse rate

“…In the aspect of estimation methods, the simple linear regression model solved with ordinary least squares (OLS) method was frequently used to calculate the SATLR (Rolland, 2003;Blandford et al, 2008;Kattel et al, 2018). As the simple linear regression model, a kind of global model, could lead to many problems, including the spatial non-stationarity, when there was complex terrain and climatic condition in a large region, observational stations were usually divided into several groups and the SATLRs of each group were estimated separately (Fotheringham et al, 2002;Zhai et al, 2016;Qin et al, 2018). The division criterion was not always the same between different literatures.…”