This crowdsourcing app provides street-level weather forecasts that are up to 36 percent more accurate than weather professionals. Limiting the amount of issues people run into by incorrectly planning for the day's weather, this app uses the power of people to provide users with detailed forecasts, down to the very street they will be on.
By using a smartphone's built-in barometer sensors in tandem with a user-generated report, Sunshine provides weather reports that are garnered from both machines and people. While this is an improved way of getting accurate weather reports, there are other apps that similarly provide street level weather reports. 'Weather Signal' uses people's "mobile as a weather station," by garnering information from the same cell phone barometric sensors. However, Sunshine provides a human inputting component that reduces error by taking details into account that the sensors can read incorrectly.
Key Themes Behind This Trend
- Crowdsourced Weather Forecasts
- Using crowdsourcing and user-generated input to provide more accurate and localized weather forecasts.
- Integration of Sensor Data and Human Input
- Combining barometer sensor data from smartphones with user-generated reports to improve the accuracy of weather forecasts.
- Street-level Weather Apps
- Developing apps that offer weather forecasts specifically for individual streets and locations.
Where This Applies
- Weather Technology
- Applying crowdsourcing and sensor data integration to the field of weather forecasting.
- Mobile Applications
- Creating mobile apps that provide street-level weather forecasts for users.
- Data Collection and Analysis
- Utilizing user-generated reports and sensor data to gather and analyze weather information at a detailed level.
