文献类型：期刊文献

英文题名：A method to calculate vertical temperature gradient and temperature advection based on flat-floating sounding data

作者：Chang, Shujie[1,2]; Huang, Sixun[3]; Li, Yongchi[1,2]

机构：[1] College of Ocean and Meteorology, South China Sea Institute of Marine Meteorology, Key Laboratory of Climate, Resources and Environment in Continental Shelf Sea and Deep Sea, Department of Education of Guangdong Province, Laboratory for Coastal Ocean Variation and Disaster Prediction, Guangdong Ocean University, Zhanjiang, 524088, China; [2] Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing, 100081, China; [3] College of Meteorology and Oceanology, National University of Defense Technology, Changsha, 410073, China

年份：2022

卷号：12259

外文期刊名：Proceedings of SPIE - The International Society for Optical Engineering

收录：EI(收录号：20222612274499)

基金：This work was supported by program for scientific research start-up funds of Guangdong Ocean University (060302032107), the National Natural Science Foundation of China (42005063), Projects (Platforms) for Construction of Top-ranking Disciplines of Guangdong Ocean University (231419022), and the Innovation Team Plan in the Universities of Guangdong Province (2019KCXTF021).

语种：英文

外文关键词：Atmospheric temperature - Differentiation (calculus) - Geophysics - Numerical methods - Thermal gradients

外文摘要：The vertical temperature gradient is an important indicator of atmospheric stratification, and the horizontal temperature gradient describes the variation in atmospheric temperature in a given horizontal direction. In the meanwhile, Temperature advection is a phenomenon whereby the temperature changes as a result of the horizontal air movements that plays an important role in the development of weather systems and weather phenomena. In this paper, a one-dimensional numerical differentiation algorithm is applied to calculate the temperature gradient and temperature advection from the temperature and wind fields obtained from flat-floating sounding data, and the results are compared with those of the central difference method. The comparison shows that the one-dimensional numerical differentiation algorithm is stable and feasible. However, in the calculation of the vertical temperature gradient, the advantages of the one-dimensional numerical differentiation algorithm are not apparent because of the high accuracy of the flat-floating sounding data. The relative error of the temperature-advection associated with the one-dimensional numerical differentiation algorithm is two orders of magnitude less than that associated with the central difference method. In addition, the relative error of the one-dimensional numerical differentiation algorithm is more stable. These results show that the issue of calculating partial derivatives based on observation data is ill-posed in mathematics and that the one-dimensional numerical differentiation algorithm is better suited to solve such issues than is the central difference method. ? 2022 SPIE