Data like this is more accessible; these details are being driven largely by faster computers, far more data, and artificial intelligence, changing the way we manage risk on the ranch.
THE FORECAST HAS CHANGED: HOW AI AND ADVANCED MODELING ARE RESHAPING WEATHER FOR PRODUCERS
By Matt Makens, Atmospheric Scientist
One of the questions I get asked the most right now is about AI. Artificial intelligence is doing for weather forecasting what GPS did for navigation, and we are starting to see those benefits show up quickly.
You have all been there— pulling out your phone while trying to make a decision on the ranch, from cold threats during calving to when to rotate pasture to trying to move cattle before a storm hits. You’ re not looking for the generic 10-day forecast that’ s been wrong most of the week, but for an hourly, mile-by-mile breakdown of where the thunderstorms are tracking, whether the next three days will push heat-stress thresholds and how much water you’ ll need to truck in, or when the drought gripping pastures might finally break. Ten years ago, that level of detail either did not exist or was not easy to access. Today, data like this is more accessible; these details are being driven largely by faster computers, far more data, and artificial intelligence, changing the way we manage risk on the ranch.
The weather has always been the variable no one can control. Still, for the first time, nextgeneration atmospheric modeling( powered by AI) is giving producers something close to the next best thing: the ability to see it coming with greater clarity, further in advance, and at scales that actually match the geography of your ranch. A New Kind of Model For decades, weather forecasting has relied on numerical weather prediction— complex, physics-based equations that simulate the atmosphere. These models, run by supercomputers, have improved steadily as computing power has grown, but they’ re limited. They are computationally expensive, slow to update, and often struggle to capture the localized details that matter for managing cattle in a specific county.
AI-based models work differently. Trained on decades of historical atmospheric data, they learn the patterns and relationships among variables( pressure, temperature, humidity, wind) not necessarily by solving equations, but by recognizing which weather setups tend to lead to others. The result is a system that can generate forecasts at extraordinary speed, often with comparable or better accuracy than traditional models, especially at short- to medium-range time frames.
In head-to-head comparisons, AI-driven models have matched or outperformed conventional forecasts for temperature, precipitation and wind— sometimes at a fraction
of the computational cost. That efficiency matters because it allows forecasts to be updated more frequently, giving producers more current information during fast-moving weather situations. I have seen AI models showing stronger skills for forecasts made over the next two weeks or so, compared to their“ traditional” model counterparts. The longer ranges— times farther out than two weeks— need a lot of work in both modeling types, but we will get there. Drought and the Long View Few threats loom larger than drought. Decisions about herd size, hay supply and water infrastructure all hinge on knowing whether rainfall is actually going to show up or if conditions are worsening.
AI is improving what meteorologists call sub-seasonal-toseasonal forecasting, the notoriously difficult window of two weeks to three months. By identifying climate patterns like ocean temperature anomalies and atmospheric oscillations that precede drought conditions, AI models are beginning to extend meaningful drought outlooks further into the future. Some experimental systems have demonstrated the ability to anticipate drought several weeks ahead, giving producers more lead time for decisions on early culling or feed procurement before markets tighten.
Satellite-derived soil moisture data, updated continuously and processed with machine learning, adds another layer. Producers can now access maps showing( relatively highresolution) trends in soil moisture across a region, helping them anticipate forage conditions before pastures show visible stress. Heat Stress: Knowing Before It Hits The challenge has always been anticipating multi-day heat events with enough lead time to intervene— adjusting grazing schedules, ensuring adequate water, and timing breeding.
AI forecasting models are improving the precision and lead time of heat-event predictions. Because these systems evaluate far more atmospheric variables at once than traditional models, they can better capture the factors that produce prolonged heat domes. These stagnant high-pressure systems bake the southern and central Plains for days at a time. The Cattle Health Index I have built accounts for the animals’ expected stress levels more accurately. Severe Storms: Minutes Matter Severe thunderstorms remain the most immediate weather
Data like this is more accessible; these details are being driven largely by faster computers, far more data, and artificial intelligence, changing the way we manage risk on the ranch.
20 APRIL 2026 www. NCBA. org