Introducing Leaf Weather

14 Mar, 2023

Introducing Leaf Weather

Introducing Leaf Weather

Catering to the needs of our clients is a key part of what we do at Leaf and, as Customer Success Manager, I speak with clients on a daily basis to make sure they have everything they need to be successful with the product they are working on. One of the recurring themes of these conversations is ‘weather’ - a lot of applications and other digital offerings incorporate some form of historic weather data or weather forecast, but accessing weather data feeds is not as straightforward as it should be.

Leaf clients told us that they need weather data that is:

  • Correct - for the obvious reason of trust by the user
  • Localized - meaning that users need to be able to choose a different per state, farm or even field
  • Redundant - with multiple models available in case one has an outage
  • Easy to use - with no post processing required and one unified API that provides access to all models at the same time
  • Affordable - businesses need to be able accurately forecast costs and be able to afford these.

With this in mind, we set out on the challenge of creating a solution that the food and agriculture industry needs in order to successfully incorporate weather data, both historic and forecast, in more digital products.


Leaf's API now contains a wide variety of Weather Data history & forecast models in hourly and daily weather variables. All of these variables can be provided at the field-level, creating weather data that is as accurate as possible.

Variables include:

· Temperature
· Humidity
· Wind
· Precipitation
· Clouds
· Many more

Weather variables are available from the following models:

· ICON (Germany)
· Arpege & Arome (France)
· IFS (EU)
· MET Nordic (Norway)

Leaf Weather combines local (1km resolution) and global (11km) weather models from national weather services to create the best forecast for every location on earth. Leaf Weather is always up to date, with the continuously correct weather forecast updated every hour at 1km resolution. The weather models use real-time measurements, airplane data, buoys, rain radar and satellite observations for predictions. A particular data source and weather model can be selected for a field, or multiple can be used at the same time to create an aggregated result.

Leaf's Weather service is available as a standalone service that accepts coordinates and returns weather data for that location. Leaf's Weather service is also integrated with Leaf's Field Boundary service, so you can get field level weather data by Field ID.

What’s next?

Leaf's Weather service already includes 7 models and will soon include compatibility with more 3rd party weather providers. This will bring even more flexibility to choosing weather providers. In the meantime, make sure to call or email myself or one of my colleagues for more information on how to get started with Leaf Weather!

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 Leaf | Data infrastructure for agriculture

Jace Klein

Customer Success Lead

Jace Klein, originally from Iowa, is the Customer Success Lead with Leaf. He started his career with ESE where he utilized his engineering background and project management skills to design water management systems for farms. From there, he moved to Granular where he was fortunate to be a part of a high performing Customer Success team that was crucial to Granular's success as a SaaS company. Currently, Jace is passionate about supporting Leaf customers through proactive engagement and strategic discussions as they utilize Leaf’s universal machine data API system.

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