The new standard in 2- to 52-week weather analytics

Inertia: the true driver of seasonal weather

Conventional forecasts are based on numerical models of atmospheric conditions. However, due to the chaos of the atmosphere, these models provide little skill for subseasonal-to-seasonal weather predictions. And while some statistical models of ENSO cycles provide modest skill at seasonal timescales, there is much more to the ocean than just ENSO. Our decades of research reveal that ocean and land-surface conditions—two global features with greater inertia and heat capacity—have the largest influence on seasonal weather patterns.

A global-scale machine learning platform

Salient uses deep neural networks to analyze a wide range of climate data. Our models are statistical in nature, so they don’t get bogged down by the details of atmospheric physics—and they have the scale and complexity to find predictability in all aspects of the climate system.