// WEATHER INTELLIGENCE
A cyclone prediction and alert experience combining weather data, machine learning, and offline-first delivery.
A weather intelligence concept designed to turn complex cyclone information into clearer decisions. The system separates data ingestion, prediction, alerting, and presentation so the public experience can remain simple even as the underlying model becomes more sophisticated.
Sky Pulse is an offline-capable weather intelligence interface. The frontend is intentionally simple: location, current risk, forecast movement, and actionable alerts. Prediction infrastructure can evolve independently from the public interface.
“Show the signal. Hide the noise.”
Cyclone forecasts expose users to large amounts of technical information.
Connectivity can become unreliable during severe weather.
Prediction outputs need context before they become useful.
// 011 / RESULT
Explore more work, or move from the case study into a direct conversation about the next build.
VIEW MORE PROJECTSAlert systems need clear thresholds and trustworthy data flow.
Reduce cognitive load during severe weather.
Provide useful information when connectivity is poor.
Create a clean boundary between ML and product UI.
Make alerts explainable.
Mapped the weather information users actually need during a cyclone event.
Designed a separate model service for prediction and confidence outputs.
Built a PWA path for caching and offline access.
Translated risk states into concise, actionable notifications.
“The strongest result is a system that remains understandable after the first release.”
Evaluate prediction quality against historical cyclone data.
Add location-aware notification thresholds.
Improve cache strategies for degraded connectivity.
The next stage is deliberately defined as a set of measurable product and engineering questions rather than a promise to add features indefinitely.
