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Weather API

API weather

weather database

weather forecast api

weather web services

We deliver weather data wherever it can help solve business challenges

Weather API

API weather

weather database

weather forecast api

weather web services

We deliver weather data wherever it can help solve business challenges

We use weather data to enable smarter business

As businesses seek to work with the overwhelming sea of data coming from more and more connected ‘things’ there is a need to put those data in context, to deliver meaningful insights. 

We use weather data to enable smarter business

As businesses seek to work with the overwhelming sea of data coming from more and more connected ‘things’ there is a need to put those data in context, to deliver meaningful insights. 

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.

Connecting weather data leads to actionable insights

Applied forecasting begins with connected data. Organisations are striving to better leverage their internal data to create new business  insights and opportunities. The Weather API is our proprietary platform that enables us to deliver actionable weather data. It enables the straightforward integration of high quality weather data into all kinds of business tools and services for operational planning, business insights, decision-making systems for web, desktop or mobile/ app consumption or workforce management. 

We love helping our customers with our Applied Weather Data wherever it can help solve business challenges.   

Connecting weather data leads to actionable insights

Applied forecasting begins with connected data. Organisations are striving to better leverage their internal data to create new business  insights and opportunities. The Weather API is our proprietary platform that enables us to deliver actionable weather data. It enables the straightforward integration of high quality weather data into all kinds of business tools and services for operational planning, business insights, decision-making systems for web, desktop or mobile/ app consumption or workforce management. 

We love helping our customers with our Applied Weather Data wherever it can help solve business challenges.   

Are you ready to revolutionize your business?

Are you ready to revolutionize your business?

What is modeling on the fly?

Pure observational data:

+ A weather station usually delivers the truth, since the data were actually measured
– That is however only true for the precise location of the weather station and usually we are not interested in that location, but any other location with completely different conditions, e.g. different elevation, different local winds, behind a mountain, …
– obviously no forecasts
– varying base parameters measured that in the end describe the almost same thing (e.g. 24h-max of temperature, vs. 1h-mean-values, vs. 10min instant values,….)

Pure model data:

+ We can interpolate onto all locations
+ easy handling within the API
– forecast model output, meaning it can significantly deviate from the truth, since it is based on a snapshot of measurements, satellite images etc. that is always a few hours old
– resolution might be too low to represent local phenomena

We want a combination of these two

→ We do that by taking observational data of the last few hours, model data of these hours, the NASA-elevation-grid, shake it a few times and end up with a high resolution representation of a historic time step or forecast.
→ We get an output that is area-covering, as is the model output, but is ground-truthed by the observational data.
→ We get the 90m resolution also for these parameters, that most notably now includes 10m-Winds & Gusts

What is modeling on the fly?

Pure observational data:

+ A weather station usually delivers the truth, since the data were actually measured
– That is however only true for the precise location of the weather station and usually we are not interested in that location, but any other location with completely different conditions, e.g. different elevation, different local winds, behind a mountain, …
– obviously no forecasts
– varying base parameters measured that in the end describe the almost same thing (e.g. 24h-max of temperature, vs. 1h-mean-values, vs. 10min instant values,….)

Pure model data:

+ We can interpolate onto all locations
+ easy handling within the API
– forecast model output, meaning it can significantly deviate from the truth, since it is based on a snapshot of measurements, satellite images etc. that is always a few hours old
– resolution might be too low to represent local phenomena

We want a combination of these two

→ We do that by taking observational data of the last few hours, model data of these hours, the NASA-elevation-grid, shake it a few times and end up with a high resolution representation of a historic time step or forecast.
→ We get an output that is area-covering, as is the model output, but is ground-truthed by the observational data.
→ We get the 90m resolution also for these parameters, that most notably now includes 10m-Winds & Gusts

Because weather always occurs

Not only during our office hours. Our support (+41 71 222 00 02) is available to help with technical problems 24/7.

Optimized business processes

Because weather data only benefits if you can work with them. Our data connectors allow easy integration of weather data into existing processes.

Our expertise in a nutshell

The API documentation answers all questions about the API. From how to get started, how to query various parameters up to how to integrate.

Because weather always occurs

Not only during our office hours. Our support (+41 71 222 00 02) is available to help with technical problems 24/7.

Optimized business processes

Because weather data only benefits if you can work with them. Our data connectors allow easy integration of weather data into existing processes.

Our expertise in a nutshell

The API documentation answers all questions about the API. From how to get started, how to query various parameters up to how to integrate.