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Found 7 papers, 5 papers with code
Skillful joint probabilistic weather forecasting from marginals
Machine learning (ML)-based weather models have rapidly risen to prominence due to their greater accuracy and speed than traditional forecasts based on numerical weather prediction (NWP), recently outperforming traditional ensembles in global probabilistic weather forecasting.
Aardvark weather: end-to-end data-driven weather forecasting
Local station forecasts are skillful up to ten days lead time and achieve comparable and often lower errors than a post-processed global NWP baseline and a state-of-the-art end-to-end forecasting system with input from human forecasters.
GenCast: Diffusion-based ensemble forecasting for medium-range weather
Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather, to planning renewable energy use.
Sim2Real for Environmental Neural Processes
On held-out weather stations, Sim2Real training substantially outperforms the same model architecture trained only with reanalysis data or only with station data, showing that reanalysis data can serve as a stepping stone for learning from real observations.
Autoregressive Conditional Neural Processes
Our work provides an example of how ideas from neural distribution estimation can benefit neural processes, and motivates research into the AR deployment of other neural process models.
Environmental Sensor Placement with Convolutional Gaussian Neural Processes
This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
Ice Core Dating using Probabilistic Programming
Ice cores record crucial information about past climate.
