Official forecasts missed the human part of how weather feels locally.
The product needed to explain not just what the station measured, but whether people nearby were describing conditions as pleasant, windy, damp, or miserable.
When people plan around weather using only official readings, the risk is missing how conditions actually feel nearby. Flat18 combined forecasts with local public signals and an explainable felt score so users can judge a location with more context.
The interface starts with the layer people need to scan first.
Nearby posts help explain why the score looks the way it does.
The workflow stays readable when the screen gets smaller.
Flat18 treated Felt Weather as a context problem. The product had to keep the map simple while still showing the evidence behind the score, the local posts that support it, and the conditions underneath it.
The product needed to explain not just what the station measured, but whether people nearby were describing conditions as pleasant, windy, damp, or miserable.
The map had to stay easy to read while still showing the logic behind the score, the local posts that support it, and the conditions that sit underneath it.
The selected-location panel becomes a bottom sheet on smaller screens so people can keep checking the map without losing the evidence they need.
Felt Weather combines measured weather, public sentiment, and local evidence without making the interface feel like a research tool.
The heat layer combines local signals with a clear visual hierarchy so the first read stays simple.
The location panel keeps official weather, nearby posts, and the score in one place so the logic is inspectable.
The bottom sheet pattern makes the field view practical without hiding the evidence or the current location.
The interface exposes the evidence behind each score, so users can inspect the local posts and measured conditions before trusting the map signal.
Forecasts tell people what the weather station measured. Felt Weather adds the human context: whether nearby people are describing conditions as pleasant, windy, damp, fresh, disruptive, or miserable.
Official readings can be accurate while still missing microclimates, street-level comfort, and fast-moving local conditions.
Users needed weather data and human evidence together so they could understand what the conditions mean for travel, events, and local plans.
We built a map experience that combines Open-Meteo conditions, classified public posts, sentiment tags, heat layers, and selected-location evidence.
People and operators can compare official weather with local sentiment before they decide where to go or what to do next.
Flat18 can turn live data, public signals, and operational context into a product people can inspect, understand, and act on.
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