Atmo provides ultra-precise weather forecasting by leveraging Deep Learning models trained on real-time global atmospheric data. These AI models deliver forecasts up to 40,000 times faster than traditional methods, achieving up to 50% greater accuracy across nowcasting and medium-range time scales. The service offers extreme resolution down to 1km by 1km, enabling detailed microclimate prediction for governments and industries.
Funding
$28.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
TCFounders
Product
Problem
Existing weather forecasting models often lack the precision and speed required for timely decision-making in rapidly changing weather conditions, particularly at the local level. Traditional models can be slow to process data and may not capture the nuances of microclimates, leading to inaccurate predictions.
Solution
Atmo provides ultra-precise weather forecasting through deep learning algorithms that analyze real-time data from a multitude of sources, including weather satellites, ground stations, radars, and ocean buoys. These AI-powered models deliver forecasts with a resolution of up to 1km, offering unprecedented clarity in predicting weather patterns for microclimates. Atmo's technology enables governments, militaries, and enterprises to make informed decisions, protect assets, and harness the power of nature with superior weather prediction tools. The forecasts are up to 50% more accurate than today’s most advanced forecasts across major prognostic and diagnostic variables, for time scales ranging from nowcasting (24 hours) to medium-range (14 days). Atmo's AI weather models deliver forecasts up to 40,000 times quicker than traditional models.
Target Audience
Atmo's primary customers include governments, militaries, and enterprises that require accurate and timely weather forecasts for decision-making, asset protection, and resource management.
Features
- AI-driven weather forecasting models that utilize deep learning techniques.
- Real-time data ingestion from a global network of weather satellites, ground stations, radars, and ocean buoys.
- High-resolution forecasts with a granularity of up to 1km for precise microclimate predictions.
- Rapid forecast generation, up to 40,000 times faster than traditional weather models.
- Benchmarked accuracy, demonstrating up to 50% improvement over existing forecasting methods.
- Tailored forecasts for challenging environments, suitable for government, military, and industrial applications.