A novel fusion framework embedded with zero-shot super-resolution and multivariate autoregression for precipitable water vapor across the continental Europe

作者：Jinhua Wu, Linyuan Xia, Ting On Chan, Joseph L. Awange, Peng Yuan, Bo Zhong, Qianxia Li · 发表于：Remote Sensing of Environment · 年份：2023 · DOI：10.1016/j.rse.2023.113783 · 被引用次数：14 · 研究领域：Atmospheric and Environmental Gas Dynamics、Climate variability and models、Meteorological Phenomena and Simulations

Precipitable water vapor (PWV), as the most abundant greenhouse gas, significantly impacts the evapotranspiration process and thus the global climate. However, the applicability of mainstream satellite PWV products is limited by the tradeoff between spatial and temporal resolutions, as well as some external factors such as cloud contamination. In this study, we proposed a novel PWV spatio-temporal fusion framework based on the zero-shot super-resolution and the multivariate autoregression models (ZSSR-ARF) to improve the accuracy and continuity of PWV. The framework is implemented in a way that the satellite-derived observations (MOD05) are fused with the reanalysis data (ERA5) to generate accurate and seamless PWV of high spatio-temporal resolution (0.01°, daily) across the European continent from 2001 to 2021. Firstly, the ZSSR approach is used to enhance the spatial resolution of ERA5 PWV based on the internal recurrence of image information. Secondly, the optimal ERA5-MOD05 image pairs are selected based on the image similarity as inputs to improve the fusion accuracy. Thirdly, the framework develops a multivariate autoregressive fusion approach to allocate weights adaptively for the high-resolution image prediction, which primely addresses the non-stationarity and autocorrelation of PWV. The results reveal that the accuracies of fused PWV are consistent with those of the GPS retrievals (r = 0.82–0.95 and RMSE = 2.21–4.01 mm), showing an enhancement in the accuracy and co...