Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Jifeng Qi"'
Autor:
Delei Li, Jakob Zscheischler, Yang Chen, Baoshu Yin, Jianlong Feng, Mandy Freund, Jifeng Qi, Yuchao Zhu, Emanuele Bevacqua
Publikováno v:
Geophysical Research Letters, Vol 51, Iss 11, Pp n/a-n/a (2024)
Abstract Compound wind and precipitation extremes (CWPEs) can severely impact natural and socioeconomic systems. However, our understanding of CWPE future changes, drivers, and uncertainties under a warmer climate is limited. Here, by analyzing the e
Externí odkaz:
https://doaj.org/article/132972a379a0472984dee7a88c6bb0a5
Autor:
Liu ZHANG, Jifeng QI, Yanju JIA, Qinghai SHENG, Wei ZHAO, Aixia ZHANG, Jingke LIU, Shaohui LI
Publikováno v:
Shipin gongye ke-ji, Vol 44, Iss 5, Pp 429-436 (2023)
Pretreatment methods affect the properties of bean starch and thus the application of bean products. This article reviewed the effects of different pretreatment methods on the properties of bean starch such as particle morphology, molecular structure
Externí odkaz:
https://doaj.org/article/45b647f359224a04ab78b1de8b37144b
Understanding the compound marine heatwave and low-chlorophyll extremes in the western Pacific Ocean
Publikováno v:
Frontiers in Marine Science, Vol 10 (2023)
The western Pacific Ocean is the global center for marine biodiversity, with high vulnerability to climate change. A better understanding of the spatiotemporal characteristics and potential drivers of compound marine heatwaves (MHWs) and low-chloroph
Externí odkaz:
https://doaj.org/article/b6b2847ceac044eab41913bbad960f3f
Publikováno v:
Environmental Research Letters, Vol 19, Iss 7, p 074066 (2024)
In boreal summer (July–August) 2022, an unprecedented marine heatwave (MHW) occurred in the northwest Pacific Ocean (NWP), while a record-breaking terrestrial heatwave (THW) hit the Yangtze River Basin (YRB). The temperature anomalies caused by thi
Externí odkaz:
https://doaj.org/article/448397e1a4804c16a8212e003b5faec3
Publikováno v:
Atmosphere, Vol 15, Iss 1, p 86 (2024)
Accurate sea surface temperature (SST) prediction is vital for disaster prevention, ocean circulation, and climate change. Traditional SST prediction methods, predominantly reliant on time-intensive numerical models, face challenges in terms of speed
Externí odkaz:
https://doaj.org/article/69dbc54191a741a19242a3a52fcfb367
Publikováno v:
Frontiers in Marine Science, Vol 10 (2023)
Accurately estimating the ocean’s subsurface thermohaline structure is essential for advancing our understanding of regional and global ocean dynamics. In this study, we propose a novel neural network model based on Convolutional Block Attention Mo
Externí odkaz:
https://doaj.org/article/1d51cffbc86d4402a96de0ba857074fa
Autor:
Delei Li, Jianlong Feng, Yuchao Zhu, Joanna Staneva, Jifeng Qi, Arno Behrens, Donghyun Lee, Seung-Ki Min, Baoshu Yin
Publikováno v:
Frontiers in Marine Science, Vol 9 (2022)
Few studies have focused on the projected future changes in wave climate in the Chinese marginal seas. For the first time, we investigate the projected changes of the mean and extreme wave climate over the Bohai Sea, Yellow Sea, and East China Sea (B
Externí odkaz:
https://doaj.org/article/2c7e3a48e59746b08203c775fe322279
Publikováno v:
Environmental Research Communications, Vol 5, Iss 9, p 091005 (2023)
Accurately estimating the barrier layer thickness (BLT) is crucial for enhancing our understanding of the ocean’s role in climate variability on both regional and global scales. Here, we propose a meta-learning-based ensemble model to estimate the
Externí odkaz:
https://doaj.org/article/f65b4aaae3f5421491dd6383957b0dac
Autor:
Lin Dong, Jifeng Qi, Baoshu Yin, Hai Zhi, Delei Li, Shuguo Yang, Wenwu Wang, Hong Cai, Bowen Xie
Publikováno v:
Remote Sensing, Vol 14, Iss 14, p 3494 (2022)
Accurately estimating the ocean’s interior structures using sea surface data is of vital importance for understanding the complexities of dynamic ocean processes. In this study, we proposed an advanced machine-learning method, the Light Gradient Bo
Externí odkaz:
https://doaj.org/article/944470d9c48845e288a538d889d2c486
Publikováno v:
Remote Sensing, Vol 14, Iss 13, p 3207 (2022)
Reconstructing the vertical structures of the ocean from sea surface information is of great importance for ocean and climate studies. In this study, an ensemble machine learning (Ens-ML) model is proposed to retrieve ocean subsurface thermal structu
Externí odkaz:
https://doaj.org/article/3406660934254945a8054c0fdda2bd9c