Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Chaoxia YUAN"'
Autor:
Zixiong Shen, Qiming Sun, Xinyu Lu, Fenghua Ling, Yue Li, Jiye Wu, Jing-Jia Luo, Chaoxia Yuan
Publikováno v:
Applied Computing and Geosciences, Vol 24, Iss , Pp 100201- (2024)
The application of machine learning (ML) techniques to climate science has received significant attention, particularly in the field of climate predictions, ranging from sub-seasonal to decadal time scales. This paper reviews recent progress of ML te
Externí odkaz:
https://doaj.org/article/f825d5664425451ea991cd2cc7ad0189
Publikováno v:
Geophysical Research Letters, Vol 51, Iss 15, Pp n/a-n/a (2024)
Abstract In July and August 2022, the Yangtze River basin (YRB) experienced its hottest summer since 1961. The SINTEX‐F2 seasonal prediction system initialized in early May predicted the hotter‐than‐normal summer due to its successful predictio
Externí odkaz:
https://doaj.org/article/a7a306187d9e43f2896ab01d027f1a23
Autor:
Shixin Wang, Tiexi Chen, Jing-Jia Luo, Meng Gao, Hongchao Zuo, Fenghua Ling, Jianlin Hu, Chaoxia Yuan, Yuanjian Yang, Lina Wang, Huaming Huang, Naiang Wang, Yaojun Li, Toshio Yamagata
Publikováno v:
npj Climate and Atmospheric Science, Vol 7, Iss 1, Pp 1-12 (2024)
Abstract Understanding both positive and negative impacts of climate change is essential for comprehensively assessing and well adapting to the impacts of changing climate. Conventionally, climate warming is revealed to negatively impact human activi
Externí odkaz:
https://doaj.org/article/a959ea0822d5430eaffb467e4847bd9c
Publikováno v:
Geophysical Research Letters, Vol 51, Iss 13, Pp n/a-n/a (2024)
Abstract Accurate detection and attribution of past climate change are crucial for projecting future climate change and formulating proper policies. In this study, we show that the warming of the tropical Indian Ocean contributes to the observed wett
Externí odkaz:
https://doaj.org/article/0ca2173d6dd74a42be26a1d285c2ec54
Publikováno v:
Gaoyuan qixiang, Vol 42, Iss 6, Pp 1589-1603 (2023)
Subtropical anticyclone has an important influence in weather and climate in China， which is affected by the tropical sea surface temperature （SST） anomaly in the Indian Ocean for its formation.However， there is limited studies for the corres
Externí odkaz:
https://doaj.org/article/bea971ce433f4bd3b3bd7c5a69c57af6
Autor:
Anqi Chen, Chaoxia Yuan
Publikováno v:
Frontiers in Earth Science, Vol 12 (2024)
Resolution of global climate models (GCMs) significantly influences their capacity to simulate extreme weather such as tropical cyclones (TCs). However, improving the GCM resolution is computationally expensive and time-consuming, making it challengi
Externí odkaz:
https://doaj.org/article/98aee9a78ba540628ecbad16fd548879
Publikováno v:
Geophysical Research Letters, Vol 51, Iss 1, Pp n/a-n/a (2024)
Abstract Investigating the Indian Ocean Dipole (IOD) during the Last Interglacial (LIG) can advance knowledge of IOD behaviors in orbitally‐induced warmer‐than‐present scenarios. Based on multiple model outputs from the Paleoclimate Modeling In
Externí odkaz:
https://doaj.org/article/225ba684ab3040edace08d3696cbecb1
Publikováno v:
Statistical Theory and Related Fields, Vol 6, Iss 4, Pp 344-352 (2022)
Fragmentary data is becoming more and more popular in many areas which brings big challenges to researchers and data analysts. Most existing methods dealing with fragmentary data consider a continuous response while in many applications the response
Externí odkaz:
https://doaj.org/article/ed53a414deab43438dfa22b806566c51
Publikováno v:
Frontiers in Earth Science, Vol 11 (2023)
The El Niño/Southern Oscillation (ENSO) is the major driver of interannual variations of the western North Pacific (WNP) tropical cyclones (TCs). Realistic reproduction of ENSO-WNPTC teleconnection in coupled models (CGCMs) is thus crucial for impro
Externí odkaz:
https://doaj.org/article/f9c6e8fbf6c54902b7d54a7d5441bb75
Publikováno v:
Atmosphere, Vol 13, Iss 5, p 666 (2022)
It is challenging to predict the eastward-propagating Madden–Julian Oscillation (MJO) events across the Maritime Continent (MC) in models. We constructed an air–sea coupled numerical weather prediction model—a tropical channel model—to invest
Externí odkaz:
https://doaj.org/article/dc41eb8053c748c9ae661f3faf96bac1