Sustainability
Dedicated to using technology for good
Improving weather forecasts is essential for the safety and well-being of people. The need to measure, predict, and adapt to extreme weather has become increasingly urgent worldwide due to climate change.
Skyfora provides an environmentally friendly way to access better weather insights
Skyfora’s solutions allow various industries to obtain improved weather forecasts boosted by AI and unique weather observation data without the need of expensive new infrastructure investments.
Optimized weather forecasts are crucial in the fight against climate change. Our industrial customers improve their operations, reduce their CO2 emission and we empower renewable energy sources to become more profitable.
Skyfora’s ultralight and eco-friendly weather instruments use 30% less helium or hydrogen and reduce environmental waste by 40kg per weather station annually when compared to widely-used instruments today.
Accurate weather predictions have numerous benefits across multiple industries.
Saving lives and reducing the economic and environmental impacts of natural disasters such as hurricanes, tornadoes, floods, and wildfires by providing timely and accurate weather predictions.
Boosting renewable energy generation by optimizing solar and wind farms for predictable weather patterns.
Reducing fuel consumption, greenhouse gas emissions, and travel time by optimizing transportation routes and schedules.
Aiding in climate modeling and understanding long-term climate trends, which is vital for formulating policies and strategies to mitigate climate change and adapt to its effects.
Improving preparedness against natural disasters for the insurance industry through better weather forecasts and risk assessments.
Efficient management of natural resources like water, crucial for agriculture, industry, and households, by predicting precipitation patterns and droughts.
Join our journey
Check our open positions and join us to build next-generation weather forecasting and contribute to climate change adaptation.