
The Problem
AI weather models are constrained by inadequate data that limits their potential for breakthrough forecasting capabilities.
Sparse Training Data
Currently less than 0,1% of the atmosphere is measured with sensor-grade accuracy, creating massive data gaps that limit AI model training effectiveness and prediction accuracy.
Low-Resolution Inputs
Conventional weather data sources typically offer low spatial resolution and update intervals of 3 to 6 hours, different from high-precision AI models that require dense, real-time atmospheric profiling.
Performance Bottlenecks
AI weather models are constrained by data quality and density limitations, resulting in reduced short-term forecasting visibility, poor performance in data-sparse regions, and limited ability to capture rapid atmospheric changes.
The Missing Data Layer for
AI Weather Models
AI weather models need dense, high-frequency atmospheric data to reach their full potential. Current data sources create bottlenecks that limit model accuracy, more precise warning capabilities, and local forecasting accuracy. Skyfora provides the critical upstream data layer that AI models have been missing: foundation models requiring vast, diverse datasets and real-time prediction pipelines demanding ultra-low latency and high-frequency updates.
What You Get
Unlock unparalleled atmospheric insights with Skyfora's advanced data stream, designed for the demands of modern AI weather modeling.
Integration Made Simple
Note: Skyfora's data stream is currently available for research and pilot projects. Commercial availability is coming soon.
What you get:
Why Skyfora Data is Different
Skyfora redefines atmospheric data collection, offering unique advantages that set your AI models apart.
Key Use Cases for Skyfora Data
Real-world applications where our high-resolution atmospheric data drives AI model performance
Ready to Build with Better Data?
Join leading AI weather model developers who are transforming their forecasts with Skyfora's unparalleled atmospheric intelligence.