Artificial Intelligence Based Cloud Distributor (AI-CD): Probing Low Cloud Distribution with Generative Adversarial Neural Networks
Abstract
Here we introduce the artificial intelligence-based cloud distributor (AI-CD) approach to generate cloud fields across different scales and cloud types. We show that conditional generative adversarial nets (cGANs) can not only generate realistic cloud fields with corresponding meteorological variables, but also capture known physical relationship between cloud fields and meteorological variables such as sea surface temperature, atmospheric stability, and relative humidity etc. We demonstrate that this approach works across a large range of spatial scales: from individual grid points (sub-grid process modeling), multiple grids, to global scale. In addition, the AI-CD approach is stochastic in nature. We suggest the AI-CD approach can be used as a data-drive framework for stochastic cloud parameterization.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.A43E..05Y
- Keywords:
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- 3305 Climate change and variability;
- ATMOSPHERIC PROCESSES;
- 3337 Global climate models;
- ATMOSPHERIC PROCESSES;
- 3365 Subgrid-scale (SGS) parameterization;
- ATMOSPHERIC PROCESSES;
- 1942 Machine learning;
- INFORMATICS