Products, help and information

meteoblue projects

meteoblue develops and participates to several projects with partners in order to improve the weather forecast and to adapt to the customers' needs.

iMeteo - station based weather forecast

iMeteo was developed by meteoblue and Pessl Instruments over two years on several thousands weather stations over all continents. It provides a weather forecast for the region, adjusted to the special conditions of the weather station. iMeteo weather forecast are available:

  • for on any place on Earth, where an i-metos meteorological station is installed;
  • with uniform high quality – regardless of station placement;
  • ready and running within 2 weeks of ordering;
  • through a personalised Web-interface.

You can order iMeteo with an i-metos weather station or contact us.

For more details about the iMeteo quality, please download the following document:

imeteo_forecast_quality_091104_EN.pdf (397.28 kB)


Vitimeteo Weatherdata

Weather-based disease and pest prediction system for viticulture

meteoblue participates in the vitimeteo project, which integrates weather station observations, weather forecasts and disease modelling for vineyards in Germany and Switzerland. It is conducted by the Staatliche Weinbauinstitut Freiburg, Germany, and the Staatliche Weinbauinstituts Agroscope in Switzerland.

ForeSite - forecasting for wind farms

Wind energy forecasting down to the windmill: a 2 year project to connect high resolution NMM models with local downscaling technology to produce best-in-class forecast for wind farms - worlwide.

Find more information with our Partner naturalpower. Download the ForeSite brochure for more information:

NaturalPower_ForeSite_Brochure_ecopy_2mb.pdf (1.62 MB)

Urban modelling

Extract about urban particles

Extract about urban particles

Urban PM10 modelling using neural network with weather forecast - an approach to urban pollution prevention.
"The overall models’ results illustrates a possibility of effective use on the operational level for forecasting PM10 concentrations one day in advance using Numerical Mesoscale Model (NMM) weather forecasts as input parameters."