The Science Behind Nowcasting: 0-2 Hour Predictions

The Gap Between "Now" and "Later"

In the world of meteorology, there is a notorious blind spot. We have excellent radar to tell us what is happening right now, and we have powerful supercomputer models to tell us what will happen tomorrow.

But what about the next 90 minutes, the next few hours?

This 0-6 hour window, known as Nowcasting, is the most critical period for operational decision-making. It determines if an oil rigg needs to be evacuated, if a plane can land, or if a flash flood barrier needs to be raised. Yet, traditionally, this has been the hardest forecast to get right.

Why? because traditional numerical weather prediction (NWP) models are too slow. By the time a supercomputer ingests data, runs the physics equations, and outputs a forecast, 3 to 6 hours have passed. The first hours in a forecast is obsolete before it is even published.

The "Spin-Up" Problem

To predict the next hour, meteorologists have traditionally relied on linear extrapolation of radar data. Essentially: "That rain blob is moving east at 30mph; therefore, in one hour, it will be 30 miles east."

This works fine for stable, stratiform rain. It fails miserably for dynamic weather. Storms intensify, grow, decay, split, and merge.

When scientists try to force rapid updates into standard models, they encounter the "spin-up" problem. The model needs time to equilibrate the mathematical atmosphere. For the first few hours of a model run, the data is often noisy and unreliable. This creates a paradoxical gap: our high-tech models are often blindest when looking at the immediate future.

Deep Dive: The shift to Data Assimilation

True nowcasting requires a different approach: Rapid Update Cycling (RUC) and advanced Data Assimilation.

Instead of a cold start every 6 hours, the system is in a constant state of update. It ingests new observational data continuously, nudging the model's current state closer to reality without a full reset.

However, this approach is data-hungry. Radar provides precipitation data, but it lacks the thermodynamic variables (temperature, humidity, pressure) necessary to predict growth or decay. You might see the rain, but without knowing the humidity profile ahead of the storm, you don't know if it will explode into a severe thunderstorm or fizzle out.

Skyfora's Advantage: 4D Atmospheric Imaging

Skyfora solves the thermodynamic data gap in nowcasting.

Because our GNSS tomography updates continuously (rather than in 12-hour balloon cycles), we provide the "fuel gauge" measurements, specifically water vapor, in real-time.

1. Latency: Our processing pipeline delivers atmospheric profiles within minutes of signal reception.
2. 4D Resolution: We track changes in X, Y, Z, and Time. This allows nowcasting algorithms to see not just the storm's position, but the rate of change in the environment feeding the storm.

Practical Applications

• Formula 1 & Sports: Teams use nowcasting to decide on tire changes (wet vs. intermediate) with lap-by-lap precision.
• Ride-Sharing: Platforms can activate surge pricing or driver incentives 30 minutes before the rain starts, balancing supply before the demand spike hits.
• Airport Ramp Safety: Ground crews are legally required to stop work during lightning. Precise nowcasting minimizes downtime, saving thousands in delay costs per minute.

Conclusion

The future of weather isn't just about looking further ahead; it's about seeing the "now" with absolute clarity. By combining the speed of AI with the continuous thermodynamic data from GNSS, we are finally closing the 2-hour blind spot that has plagued meteorology for decades.