Background

Modern weather models are extremely complex, and each forecast can only be simulated a limited number of times. This means forecasters often have just 50–100 versions of a prediction to work with, even though the atmosphere itself is vastly more complicated. With so few samples, important details can be missed. Traditional methods to “fill in the gaps” are either too expensive to run or introduce errors into the results. A faster, more affordable way to create realistic extra forecast scenarios is needed.

Technology Overview

Developed by researchers at The Ohio State University and the U.S. Naval Research Laboratory, PESE-GC and its localized version AL-PESE-GC are tools that expand a small set of weather forecasts into a much larger, realistic collection. Instead of running additional costly weather simulations, the algorithms take the existing forecasts, transform the data into a simplified space, generate new “virtual” forecasts, and then translate them back into physical weather variables. This process is highly efficient, running quickly on modern computers and easily scaling to large, complex models. The localized version, AL-PESE-GC, gives experts more control by letting them specify local weather relationships, which helps create realistic patterns without requiring massive amounts of memory. Together, these tools make it possible to create large, reliable ensembles that preserve the natural structure and balance of the atmosphere while dramatically reducing the time and cost of forecasting.

Benefits

• Big gains for low cost: An 80-member forecast set can be expanded with 240 more virtual members in just a few computing hours.
• Realistic results: The added forecasts capture key weather patterns and maintain the balance found in real atmospheric systems.
• More useful statistics: AL-PESE-GC spreads out the forecasts in meaningful ways, improving accuracy without losing realism.
• Scalable & flexible: Works efficiently with large models and allows users to fine-tune settings for their needs.

Applications

• Operational weather forecasting & data assimilation: Improves reliability of day-to-day weather predictions.
• Renewable energy planning: Expands wind and solar forecasts for better grid management.
• Air quality & health: Provides strong inputs for pollution, pollen, and allergy models.
• Climate & Earth-system science: Supplies diverse starting points for long-term climate and environmental simulations, as well as AI-driven models.