Generative AI to quantify uncertainty in weather forecasting
We strive to create an environment conducive to many different types of research across many different time scales and levels of risk.
We present SEEDS, new AI technology to accelerate and improve weather forecasts using diffusion models. SEEDS enables significant reduction in computational cost for generating ensemble forecasts and better characterization of rare or extreme weather events.
Accurate weather forecasts can have a direct impact on people’s lives, from helping make routine decisions, like what to pack for a day’s activities, to informing urgent actions, for example, protecting people in the face of hazardous weather conditions. The importance of accurate and timely weather forecasts will only increase as the climate changes. Recognizing this, we at Google have been investing in weather and climate research to help ensure that the forecasting technology of tomorrow can meet the demand for reliable weather information. Some of our recent innovations include MetNet-3, Google's high-resolution forecasts up to 24-hours into the future, and GraphCast, a weather model that can predict weather up to 10 days ahead.
Weather is inherently stochastic. To quantify the uncertainty, traditional methods rely on physics-based simulation to generate an ensemble of forecasts. However, it is computationally costly to generate a large ensemble so that rare and extreme weather events can be discerned and characterized accurately.
