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Comparison Matrix Block example
| Thunderstorm Manager | Xweather Insight | |
|---|---|---|
Add, edit, and display assets | ||
| Point | Alert area only | |
| Multi-point | ||
| Polygon | Alert area only | |
| Polyline | Alert area only | |
| Display assets without alerts | ||
| Search for assets by name | ||
User preferences | ||
| Light/dark mode UI | ||
| Unit preferences | Configured by Vaisala | |
| Regional date and time preferences | Configured by Vaisala | |
| Language preference | English / French / Spanish / Turkish / Chinese | English / more coming soon |
Security | ||
| Multi-factor authentication | Coming soon | |
| User access roles | Configured by Vaisala | |
Plan with confidence
Xcast is a machine learning-based weather prediction technology (MLWP) that underpins the Xweather Insight service. Xcast technology enhances short-term forecast accuracy and produces unique actionable insights by using local measurements and observations.
Xcast-powered hyperlocal forecasts are up to 50% more accurate than the short-term forecasts generated by traditional numerical weather prediction (NWP) models. What's more, Xcast also reduces the number of large errors (greater than 2.5 °C) in 24-hour temperature forecasts by up to 59%.
Xcast
Powered by local observations
Xcast is powered by precise, up-to-the-minute observations from on-site sensors at your key locations.
Hyperlocal forecasting
Xcast uses machine learning, trained on your local sensor observations, to produce highly accurate short-term forecasts.
Xcast technology
A more accurate forecast for the locations that matter to you
Xweather Insight is powered by Xcast, a technology that delivers a more accurate forecast for the locations that matter to you and your business. Xcast uses machine learning, trained on your local sensor observations, to go beyond traditional weather forecasting, delivering more accuracy where it matters most and giving you the weather confidence that your business demands.
Powered by observations from your local sensors
79% improvement
in temperature forecasting accuracy for a vineyard customer.
59% reduction
in large temperature forecast errors for a district heating customer.
50% improvement
in forecasting accuracy on average across all deployed devices.