Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Yilun Han"'
Publikováno v:
Geophysical Research Letters, Vol 51, Iss 13, Pp n/a-n/a (2024)
Abstract Current global climate models (GCMs), limited to grid‐scale land‐atmosphere coupling, cannot represent subgrid urban‐rural precipitation contrasts. This study develops an innovative two‐way subgrid land‐atmosphere coupling framewor
Externí odkaz:
https://doaj.org/article/ee246eaa4ef6461991c2725f3f6d9cb3
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 15, Iss 10, Pp n/a-n/a (2023)
Abstract With the recent advances in data science, machine learning has been increasingly applied to convection and cloud parameterizations in global climate models (GCMs). This study extends the work of Han et al. (2020, https://doi.org/10.1029/2020
Externí odkaz:
https://doaj.org/article/e9a8e7d7697e481e8ca08a75d59eafe7
Publikováno v:
Remote Sensing, Vol 15, Iss 18, p 4537 (2023)
In this study, we used Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol products acquired from 2006 to 2016 to identify global long-range aerosol transport pathways, including the trans-Atlantic, the trans-Pacific, and the trans-Arab
Externí odkaz:
https://doaj.org/article/dc95d55c759b40a5bc06a6a3642e0ba4
Autor:
Xiaoying Li, Chensheng Huang, Huijun Jin, Yilun Han, Siqi Kang, Jing Liu, Huiying Cai, Tongxin Hu, Guang Yang, Hongzhou Yu, Long Sun
Publikováno v:
Frontiers in Earth Science, Vol 10 (2022)
Carbon storage is an important component of ecosystem services. Under climate warming and human activities, land use/land cover (LULC) have been undergoing tremendous change, leading to spatio-temporal variations in carbon storage. Based on seven ser
Externí odkaz:
https://doaj.org/article/0f7ab3c8048c4ef2bea6a3ec1d641896
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 12, Iss 9, Pp n/a-n/a (2020)
Abstract Current moist physics parameterization schemes in general circulation models (GCMs) are the main source of biases in simulated precipitation and atmospheric circulation. Recent advances in machine learning make it possible to explore data‐
Externí odkaz:
https://doaj.org/article/b72fefa36e5247fb9e0e3f5bff33b355
Publikováno v:
Geoscientific Model Development. 15:3923-3940
In climate models, subgrid parameterizations of convection and clouds are one of the main causes of the biases in precipitation and atmospheric circulation simulations. In recent years, due to the rapid development of data science, machine learning (
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f56edf0728a4541b702cb35e3cf4d34f
https://doi.org/10.5194/gmd-15-3923-2022
https://doi.org/10.5194/gmd-15-3923-2022
All current global climate models (GCMs) utilize only grid-averaged surface heat fluxes to drive the atmosphere, and thus their subgrid horizontal variations and partitioning are absent. This can result in many simulation biases. To address this shor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::47f2bede81a0eb3660386de4b220d279
https://gmd.copernicus.org/articles/16/135/2023/
https://gmd.copernicus.org/articles/16/135/2023/
Publikováno v:
Journal of Ambient Intelligence and Humanized Computing. 13:3655-3667
For oil fields, shooting target range, military forbidden zones and other special complex scenarios, it is not only difficult to deploy communication infrastructure because of its remote location and difficult environment, but also difficult to trans
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::afe35ba2531675731b90f98d565f9b39
https://doi.org/10.1007/s12652-020-02140-6
https://doi.org/10.1007/s12652-020-02140-6
Autor:
Yilun Han
Publikováno v:
Proceedings of the 11th International Conference on Biomedical Engineering and Bioinformatics.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f7b102b0ebbdb75738563faee09b2a55
https://doi.org/10.5220/0011375400003443
https://doi.org/10.5220/0011375400003443
Publikováno v:
International Conference on Intelligent Equipment and Special Robots (ICIESR 2021).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::098259daf4ed61a81f38eed3eff5d609
https://doi.org/10.1117/12.2625275
https://doi.org/10.1117/12.2625275