European Centre for Medium-range Weather Forecast (ECMWF) operational forecast and analysis data used in this study were downloaded from the ECMWF Meteorological Archival and Retrieval System (MARS) on May 1, 2021. Access to the ECMWF archived data was provided by ECMWF's Data Services. Particularly, Emma Pidduck from the ECMWF's Data Services team is warmly thanked for her swift communication regarding data access.
NWP forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) High Resolution (HRES) model, as available in the ECMWF's Archive Catalogue, are offered to the energy forecasting community under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Access website：http://data.aicnic.cn/ECMWF/
NWP forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble (ENS) model, as available in the ECMWF's Archive Catalogue, are offered to the energy forecasting community under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Access website：http://data.aicnic.cn/ECMWF_ENS/
To free researchers from tedious resource management and environmental configuration, we propose a VenusAI platform for large-scale computing scenarios in scientific research, based on heterogeneous resources scheduling framework. The ECMWF website is hosted on VenusAI platform. Access website: http://data.aicnic.cn/
This is a compilation of papers related to the ECMWF. Since the ECMWF is not informed about any submitted paper with ECMWF content this list is by far not complete!
Paper 1 ：
Dazhi Yang, Wenting Wang, Tao Hong, A historical weather forecast dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) for energy forecasting, Solar Energy, Volume 232, 2022, Pages 263-274, ISSN 0038-092X, https://doi.org/10.1016/j.solener.2021.12.011. (https://www.sciencedirect.com/science/article/pii/S0038092X21010604)
Paper 2：
Tiechui Yao, Jue Wang, Meng Wan, Zhikuang Xin, Yangang Wang, Rongqiang Cao, Shigang Li, Xuebin Chi, VenusAI: An artificial intelligence platform for scientific discovery on supercomputers, Journal of Systems Architecture, 2022, 102550, ISSN 1383-7621, https://doi.org/10.1016/j.sysarc.2022.102550. (https://www.sciencedirect.com/science/article/pii/S1383762122001059)