Data Assimilation

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Assimilation of diverse observation-based data sources can improve climate models, and machine learning can transform raw sensor output into more relevant derived data. Relevant applications include sensor calibration and analyzing information in remote sensing data or assimilating climate model output with the observations[1]. Well-curated benchmark datasets have the potential to advance several geoscience problems[2][3].

Background Readings[edit | edit source]

Conferences, Journals, and Professional Organizations[edit | edit source]

Libraries and Tools[edit | edit source]

Data[edit | edit source]

Future Directions[edit | edit source]

Relevant Groups and Organizations[edit | edit source]

References[edit | edit source]

  1. Yuan, Qiangqiang; Shen, Huanfeng; Li, Tongwen; Li, Zhiwei; Li, Shuwen; Jiang, Yun; Xu, Hongzhang; Tan, Weiwei; Yang, Qianqian; Wang, Jiwen; Gao, Jianhao (2020-05-01). "Deep learning in environmental remote sensing: Achievements and challenges". Remote Sensing of Environment. 241: 111716. doi:10.1016/j.rse.2020.111716. ISSN 0034-4257.
  2. Rasp, S., et al., (2020). "WeatherBench: A benchmark dataset for data-driven weatherforecasting" (PDF). arXiv.CS1 maint: extra punctuation (link)
  3. Düben, Peter; Modigliani, Umberto; Geer, Alan; Siemen, Stephan; Pappenberger, Florian; Bauer, Peter; Brown, Andy; Palkovic, Martin; Raoult, Baudouin (2021). "Machine learning at ECMWF: A roadmap for the next 10 years". Retrieved 2021-01-25.