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

Wu, Jinhua; Xia, Linyuan; Chan, Ting On; Awange, Joseph; Yuan, Peng 1; Zhong, Bo; Li, Qianxia
1 Geodätisches Institut (GIK), Karlsruher Institut für Technologie (KIT)

Abstract:

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. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000161993
Veröffentlicht am 11.09.2023
Originalveröffentlichung
DOI: 10.1016/j.rse.2023.113783
Scopus
Zitationen: 2
Web of Science
Zitationen: 1
Dimensions
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Geodätisches Institut (GIK)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 11.2023
Sprache Englisch
Identifikator ISSN: 0034-4257, 1879-0704
KITopen-ID: 1000161993
Erschienen in Remote Sensing of Environment
Verlag Elsevier
Band 297
Seiten Art.-Nr.: 113783
Vorab online veröffentlicht am 28.08.2023
Schlagwörter Precipitable water vapor, Data fusion, Super-resolution, MODIS, ERA5
Nachgewiesen in Scopus
Dimensions
Web of Science
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