Your own national AI weather model
Train a custom AI weather model on your national observations and run it on your own infrastructure, with no supercomputer to buy. We can also deliver the observation network that feeds it.
Meteorological institutions
AI-native weather infrastructure for national meteorological services
Built on 90 years of Vaisala atmospheric measurement and directed by your own meteorologists.
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Vaisala Xweather is a selected partner for the most demanding organizations
What we offer national services
National AI forecasting
Routine forecasts up to 40,000× faster than traditional NWP
100× higher resolution than traditional NWP models
Trained on your national observations
Observation networks
Network design and installation
Identifying which sensors and locations improve your forecasts the most
Built on Vaisala instruments your service already calibrates
Early warnings
Localized warnings for extreme weather
National coverage, deployed in months rather than years
Support for the UN Early Warnings for All target of full coverage by the end of 2027
Lightning detection
99.9% of thunderstorms detected
Over 2 billion lightning events recorded every year
Historical data from 2016 to the present
Validation and proof
Proven partner to global weather institutions
Decades of partnership: Meteorological institutions have trusted Vaisala instrumentation since the 1930s.
National-scale AI deployment: The Philippines achieved modern AI forecasting capability by co-operating with Atmo.ai.
Institutional adoption: Vaisala hardware and data support leading agencies worldwide, including NOAA and the UK Met Office.
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90 years
of measurement heritage
170 + countries
operating Vaisala instrumentation
300 + projects
delivered annually
National forecasting infrastructure
AI forecasting that works inside your national service
Vaisala Xweather works alongside your existing NWP infrastructure. By pairing 90 years of Vaisala atmospheric measurement with Xweather AI-native modeling, national services can raise spatial resolution, produce forecasts faster, and issue early warnings sooner.
How Xweather works with national MET services
01AI-augmented forecasting
Your meteorologists direct the AI layer, which runs routine forecasts up to 40,000× faster than traditional NWP. Human judgment stays at the center.
02Turnkey or DaaS deployment
Run a complete national forecasting stack, or access the models as Data-as-a-Service streams. Both can ingest Vaisala observations.
03Built on your network
Our models build on the instruments already in your network and help get more out of each one. They also show which new sensors would add the most value, and where.
04Public-private collaboration
National meteorological institutions lead the science. Xweather delivers the operational layer that helps agencies fulfill their public safety missions.
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Mission domains
Solutions for meteorological operators
User
National MET directors, regional forecasting teams, and government modernization leads.
Challenge
National services need modern forecasting and early warnings, but building AI capability in-house requires specialized skills, compute, and years of investment, on top of the daily work of delivering forecasts and warnings. For many, the UN Early Warnings for All initiative adds a deadline: full population coverage by the end of 2027.
Solution
Xweather gives agencies a turnkey AI forecasting stack, with national coverage and early warning capability, without building machine learning infrastructure from scratch. We can also design and install the observation network that feeds it. Both directly support the Early Warnings for All initiative.
Impact
Deploy modern AI-native forecasting in months rather than years.
Maintain full data ownership and national forecasting sovereignty.
Deliver localized early warnings for extreme weather events.
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