Deterministic Forecast

Deterministic forecasting aims to generate precise predictions without explicitly quantifying uncertainty, a limitation increasingly addressed by current research. Recent work focuses on improving deterministic forecast accuracy using diverse methods, including data-driven ordinary differential equation models, graph neural networks, and neural general circulation models, often leveraging large reanalysis datasets. These advancements are significant because they enhance the precision of predictions in fields like weather forecasting and fluid dynamics, while also informing the development of more robust probabilistic forecasting techniques that incorporate uncertainty estimates.