Meteomatics Weather API

We use weather data to enable smarter business. 

What data can we provide

Easily select from our wide range of global and regional weather models including:

  • Worldwide historical model data from 1979 onwards
  • Radar and satellite data, including forecasts with over 280 updates daily
  • Temporal and spatial interpolation for each coordinate

How easy is it to work with?

Delivered as a Platform as a Service all the data are easily accessible thanks to the wide variety of connectors we provide for automated data, such as:

  • C++, Matlab, Python
  • ArcGis, QGIS
  • Google Spreadsheet, Google Maps

 

Why use the weather API?

The Weather API is our own platform that allows us to provide worldwide weather data – for any application, for any industry, for any institution and for any national weather service, faster then conventional weather database systems.

What data can we provide

Easily select from our wide range of global and regional weather models including:

  • Worldwide historical model data from 1979 onwards
  • Radar and satellite data, including forecasts with over 280 updates daily
  • Temporal and spatial interpolation for each coordinate

How easy is it to work with?

Delivered as a Platform as a Service all the data are easily accessible thanks to the wide variety of connectors we provide for automated data, such as:

  • C++, Matlab, Python
  • ArcGis, QGIS
  • Google Spreadsheet, Google Maps

Why use the weather API?

The Weather API is our own platform that allows us to provide worldwide weather data – for any application, for any industry, for any institution and for any national weather service, faster then conventional weather database systems.

What is modeling on the fly?

Pure observational data:

+ A weather station usually reflects reality, since data is actually measured
– This is only valid for the precise location of the weather station. Mostly there is no interest in that location, but different ones with completely different conditions (e.g. different elevation, different local winds, lee position, etc.).
– No forecasts

Pure model data:

+ Prognoses can be interpolated to any location
+ Easy handling within the API
– Forecast model output can significantly deviate from the truth, since it is based on a snapshot of measurements of the previous hours
– Resolution might be too low to resolve local phenomena

We want a combination of these two

→ This is done by combining observational data of the last few hours (weather stations, satellite images, radar), model data of the same time period and the NASA elevation grid. The result is a highly resolved representation of past and future time steps.
→ The output is an area-covering model that is supported by observational data.

What is modeling on the fly?

Pure observational data:

+ A weather station usually reflects reality, since data is actually measured
– This is only valid for the precise location of the weather station. Mostly there is no interest in that location, but different ones with completely different conditions (e.g. different elevation, different local winds, lee position, etc.).
– No forecasts

Pure model data:

+ Prognoses can be interpolated to any location
+ Easy handling within the API
– Forecast model output can significantly deviate from the truth, since it is based on a snapshot of measurements of the previous hours
– Resolution might be too low to resolve local phenomena

We want a combination of these two

→ This is done by combining observational data of the last few hours (weather stations, satellite images, radar), model data of the same time period and the NASA elevation grid. The result is a highly resolved representation of past and future time steps.
→ The output is an area-covering model that is supported by observational data.

“We use the Meteomatics API to enrich weather models with our machine learning algorithms and to create predictive models of consumer behavior according to the weather.
We particularly appreciate that Meteomatics manages all the data provision and handling which allows us to focus on our weather data transformation layer into business indicators.”

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(Metigate – smarter than weather)

“We use the Meteomatics API to enrich weather models with our machine learning algorithms and to create predictive models of consumer behavior according to the weather.
We particularly appreciate that Meteomatics manages all the data provision and handling which allows us to focus on our weather data transformation layer into business indicators.”

(Metigate – smarter than weather)