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Found 3 papers, 2 papers with code
Deep Generative Data Assimilation in Multimodal Setting
To our knowledge, our work is the first to apply deep generative framework for multimodal data assimilation using real-world datasets; an important step for building robust computational simulators, including the next-generation Earth system models.
Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming
Accurate representations of unknown and sub-grid physical processes through parameterizations (or closure) in numerical simulations with quantified uncertainty are critical for resolving the coarse-grained partial differential equations that govern many problems ranging from weather and climate prediction to turbulence simulations.
ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction
Thus, we propose ChaosBench, a large-scale, multi-channel, physics-based benchmark for S2S prediction.
