paper
2020
Data assimilation
Sub-seasonal to seasonal New model or method
Classical ML (trees, GP, linear, kernels)Physics-informed / physics-constrainedSeasonal to annualNew York University (M2LInES) Other
2020-01-21
022
Jan 2020
Dimitrios Giannakis, Joanna Sławińska, A. Ourmazd
preprint
New York University (M2LInES)
Quantum-mechanics-inspired, kernel-based data assimilation using Koopman operators, applied to Indo-Pacific SST and Niño 3.4 probabilistic forecasts. Novel but early-stage.
A Quantum Mechanical Approach for Data Assimilation in Climate Dynamics
In one sentence. Quantum-mechanics-inspired, kernel-based data assimilation using Koopman operators, applied to Indo-Pacific SST and Niño 3.4 probabilistic forecasts. Novel but early-stage.
Labels
- Topic
- Data assimilation, Sub-seasonal to seasonal
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels), Physics-informed / physics-constrained
- Time scale
- Seasonal to annual
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1002/essoar.10501976.1 OpenAlex:W3082589275
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.