Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Ruian Tie"'
Publikováno v:
Remote Sensing, Vol 16, Iss 17, p 3327 (2024)
Snow detection is imperative in remote sensing for various applications, including climate change monitoring, water resources management, and disaster warning. Recognizing the limitations of current deep learning algorithms in cloud and snow boundary
Externí odkaz:
https://doaj.org/article/f99c26461feb482ca7587ec57bbbf935
Autor:
Jieli Liu, Chunxiang Shi, Lingling Ge, Ruian Tie, Xiaojian Chen, Tao Zhou, Xiang Gu, Zhanfei Shen
Publikováno v:
Remote Sensing, Vol 16, Iss 11, p 1867 (2024)
Before 2008, China lacked high-coverage regional surface observation data, making it difficult for the China Meteorological Administration Land Data Assimilation System (CLDAS) to directly backtrack high-resolution, high-quality land assimilation pro
Externí odkaz:
https://doaj.org/article/14c06bad1d364a3ca4eb63eb441e6b6c
Spatial Downscaling of Near-Surface Air Temperature Based on Deep Learning Cross-Attention Mechanism
Publikováno v:
Remote Sensing, Vol 15, Iss 21, p 5084 (2023)
Deep learning methods can achieve a finer refinement required for downscaling meteorological elements, but their performance in terms of bias still lags behind physical methods. This paper proposes a statistical downscaling network based on Light-CLD
Externí odkaz:
https://doaj.org/article/61709470da6f44058f61cee5a623de3f
Publikováno v:
Journal of Atmospheric & Oceanic Technology. Apr2022, Vol. 39 Issue 4, p479-490. 12p.
Publikováno v:
Advances in Atmospheric Sciences. 39:117-130
Before 2008, the number of surface observation stations in China was small. Thus, the surface observation data were too sparse to effectively support the High-resolution China Meteorological Administration's Land Assimilation System (HRCLDAS) which u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::99b3c7a6b25b226e33118f35de6d9c67
https://doi.org/10.1007/s00376-021-0438-y
https://doi.org/10.1007/s00376-021-0438-y