Creating a 1 km gridded daily air temperature dataset for quantifying urban thermal environment and its dynamics in Jing-Jin-Ji region, China

Keywords: Air temperature, Land surface temperature, Spatially varying coefficient models, Sign preservation, Jing-Jin-Ji region
Session Type: Virtual Lightning Paper Abstract
Day: Monday
Session Start / End Time: 2/28/2022 03:40 PM (Eastern Time (US & Canada)) - 2/28/2022 05:00 PM (Eastern Time (US & Canada))
Room: Virtual 78

Abstract

High spatiotemporal resolution air temperature (Ta) data is important for investigating urban thermal environment to understand urban food–energy–water systems. In this study, we created a 1 km gridded daily maximum and minimum Ta (Tmax and Tmin) dataset in the Jing-Jin-Ji (JJJ) region of China by using a novel regression method called the Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP) driven by weather station Ta data, and 1 km gridded elevation and seamless land surface temperature (LST) data. The new method can capture the proper relationships between Ta and explanatory variables (i.e., negative for elevation and positive for LST). First, we calculated the preset parameters of the bivariate spline surface. Second, we fitted the bivariate spline surface by using the input data at weather stations to get coefficients of explanatory variables in space. Third, we calculated the gridded Ta using the 1 km gridded elevation and LST data, and corresponding coefficients. We tested the algorithm in the JJJ region by using 10-fold cross validation. The results showed that the average root mean square error and mean absolute error based on the SVCM-SP are 1.13 ℃ and 0.89 ℃ for Tmax, and 1.58 ℃ and 1.27 ℃ for Tmin, respectively. The SVCM-SP method showed better performances than the traditionally and widely used geographically weighted regression method in terms of accuracy, signs of coefficients for explanatory variables, and efficiency. The resulting gridded daily Ta data (2013-2020) are essential to quantify urban thermal environment and its dynamics in the JJJ region.