Each drone flies its own lane in a boustrophedon ("lawnmower") sweep across the
survey area. As it passes over a grid cell it samples the true underlying field plus
sensor noise, and that cell lights up on the live weather map.
reading(cell) = field(x,z) + noise * randNormal()
field(x,z) = sum of smooth sinusoids (synthetic atmosphere)
coverage = cellsVisited / totalCells
- Temp / Humidity / Pressure — switches which synthetic atmospheric variable is being mapped, changing the underlying field and color scale.
- Swarm size (lanes) — number of drones, each covering one parallel lane; more drones finish the survey faster.
- Sweep speed — forward flight speed along each lane.
- Sensor noise — random measurement error added to each sample, visible as speckling in the map.
This coordinated-coverage pattern is exactly how real research drone fleets build
high-resolution weather maps far faster and cheaper than a single aircraft or
satellite pass, useful for storm nowcasting and microclimate studies.