Systems and methods for selecting global climate simulation models for training neural network climate forecasting models
US11835677B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 14, 2020 |
| Grant date | Dec 5, 2023 |
| Priority date | — |
| Expiry date | Aug 5, 2041 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02A90/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems for generating a multi-model ensemble of global climate simulation data from a plurality of pre-existing global climate simulation model (GCM) datasets, are disclosed. The methods and systems perform steps of computing a GCM dataset validation measure based on at least one sample statistic for at least one climate variable from the pre-existing GCM dataset; selecting a validated subset of the plurality of pre-existing GCM datasets; selecting a subset of GCM datasets; generating one or more candidate ensembles of GCM datasets; computing an ensemble forecast skill score for each candidate ensemble of GCM datasets; generating the multi-model ensemble of GCM datasets by selecting a candidate ensemble of GCM datasets with a best ensemble forecast skill score; and training the NN-based climate forecasting model using the multi-model ensemble of GCM datasets. Embodiments of the present invention enable accurate climate forecasting without the need to run new dynamical global climate simulations on supercomputers.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.