Grainswest - Fall 2026

Fall 2026 grainswest.com 27 A new Environment and Climate Change Canada (ECCC) initiative may give farmers just such an additional edge. The goal is to give meteorologists better information and, ultimately, to instill Canadians with greater confidence in the forecasts they rely on. On May 26, the department launched what it described as the world’s first operational hybrid weather forecasting system. It integrates the AI-based Global Environmental eMuLator to improve weather prediction accuracy. Rather than replace conventional forecasting, the approach combines AI with ECCC’s conventional physics- based weather modelling system to enhance forecast accuracy, particularly for medium- to long-range forecasts. Employed for decades, these systems use complex mathematical equations to simulate unfolding weather patterns. Unlike AI-based forecasting, these digital platforms calculate the evolution of atmospheric conditions without the use of machine learning. This process requires an accurate picture of current conditions created with the use of data collected by aircraft, radar facilities, satellites and weather stations. John Classen is chief meteorologist with CTV Edmonton. He pointed out these observations aren’t always immediately available. Additionally, to produce a forecast solely with the use of the older system requires enormous computing power and several hours of processing time, and the results aren’t always accurate. “As forecasts extend further into the future, the confidence in what the weather will look like is substantially reduced,” said Classen. TWO MODELS, ONE FORECAST “The AI modelling system is trained solely on data,” said Leo Separovic. An ECCC research scientist who helped develop the new system, he believes AI can strengthen prediction. Rather than calculate the complex equations that govern atmospheric physics, the AI system learns from patterns as it reanalyzes historical weather observations and forecast data collected over decades. By analyzing how weather has evolved in the past, the AI system learns to recognize patterns. Conventional physics continues to do the heavy lifting in the new AI system. It is used to predict weather system development. While it provides accurate local detail, the AI model adjusts the large-scale picture. The hybrid system produces global forecasts with substantially improved accuracy, particularly in the five- to seven-day range. This delivers earlier prediction of weather systems. “It combines the strengths of both approaches,” said Separovic. “The AI system has significant advantages, but it also has limitations. That’s why we use a hybrid model.” While AI excels at pattern recognition and reduction of forecast errors, it cannot yet interpret every aspect of atmospheric development. The hybrid system still relies on non-AI modelling to distinguish between precipitation head ther