AUTHOR=Feng Aixia , Gao Feng , Wang Qiguang , Feng Aiqing , Zhang Qiang , Shi Yan , Gong Zhiqiang , Feng Guolin , Zhao Yufei 

TITLE=Combining Snow Depth From FY-3C and In Situ Data Over the Tibetan Plateau Using a Nonlinear Analysis Method

JOURNAL=Frontiers in Physics

VOLUME=Volume 9 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2021.672288

DOI=10.3389/fphy.2021.672288

ISSN=2296-424X

ABSTRACT=The snow cover over the Tibetan Plateau plays a vital role in the regional and global climate system because it affects not only the climate but also the hydrological cycle and ecosystem. Hence, high-quality snow data over this region are essential to improve our understanding of these fields. However, this is hindered due to the sparse of the observation networks and complex terrain in the Tibetan Plateau. In this study, a nonlinear time series analysis method called phase space reconstruction was used to retrieve the Tibetan Plateau snow depth by combining the FY-3C Satellite Data  and In-situ Data in the period 2014-2017. The method features on capturing the evolutionary characters of the time series. The results show that the integrated Tibetan Plateau  snow depth (ITPSD )  has low average bias of -1.35 cm and 1.14 cm, standard deviation of the bias 3.96cm and 5.67cm, and root-mean-square error of 4.18 cm and 5.79cm compared with In-situ Data and FY-3C Satellite Data  respectively. ITPSD expressed the issue that snow depth is usually overestimated in mountain regions by satellites. This can be contributed to the application of the nonlinear method to adjust the bias. ITPSD is more effective to monitor the snow cover over the  Tibetan Plateau  and the method is a robust approach on combining observations from multiple source.