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Safe Autonomous Flying – Thanks to Data from Meteomatics’ API

Alexander Stauch
Dr. Alexander Stauch
Head of Marketing

Sunflower Labs uses Meteomatics' Weather API to access hyperlocal real-time and forecast weather data, to enable Sunflower Labs’ drones to reliably and autonomously determine whether a planned flight is safe under current and future weather conditions. A prerequisite for operating drones autonomously. Learn why Sunflower Labs chose Meteomatics' API here.

"We tried numerous providers, but ultimately the conclusion was clear: the data from Meteomatics are reliable to keep our drone systems flying safely and on schedule," Yannik Nager, Robotics and Machine Learning Engineer at Sunflower Labs, tells us. To ensure the flight safety of the autonomous drone systems, Sunflower Labs has been using various weather parameters from Meteomatics' Weather API since August 2021. Sunflower Labs uses the following weather parameters to inform its flight planning: wind speed, wind gust speed, temperature, hail, and precipitation (5m interval).

Yannik Nager
Yannik Nager
Robotics and Machine Learning Engineer
Sunflower Labs
We tried numerous providers, but ultimately the conclusion was clear: the data from Meteomatics are reliable to keep our drone systems flying safely and on schedule.

In addition to wind speed and precipitation, snow and hail can have a significant impact on the flight operations of autonomous drone systems as well. As Yannik Nager explains in further detail; "Since precipitation has high temporal and spatial variability, accurate and frequently updated data is extremely important for us. To determine if a flight is safe, we need to make sure that the amount of precipitation is below our self-defined threshold during the very specific time window for the planned flight."

"Among the tested providers, Meteomatics is the only one that offers precipitation data with a measurement interval of only five minutes (precip_5m). Most other providers only offer intervals of one or more hours, which is too large a time frame and mostly completely unusable for our application" Yannik Nager further explains to us.

"In addition to determining for the safety of a flight, we also want to use advanced weather data to predict the maintenance of our drones. Drones deployed in areas with high salinity in the air (coastal regions), for example, require more regular maintenance than systems stationed in dry mountainous regions. Meteomatics allows us to use such data for any location around the world," Yannik Nager continues.

"The data from Meteomatics and the application of the Weather API has brought many benefits to Sunflower Labs" enthuses Yannik Nager. "In fact the extensive number of weather parameters, the high update frequency of the models (< 5 minutes) as well as the very impressive accuracy of the weather forecasts, have excited us from the beginning."

Visualization of the parameter Wind_Speed: Comparison of the high frequency Meteomatics data (red line) to other providers (yellow and green line).
Visualization of the parameter Wind_Speed: Comparison of the high frequency Meteomatics data (red line) to other providers (yellow and green line).

But it wasn't just Meteomatics' Data that won Sunflower Labs over; the simplicity of the API application made working with Meteomatics' Data easy and convenient from the start. "Here you can see that Meteomatics has put a lot of thought into the technical usability of its API. For example, there is only one API endpoint for all-weather parameters and data types (historical, current, forecasts), which allows us to integrate all their data quickly and through a single interface. Additionally, it is possible to handle whole areas (multiple locations at the same time) with only one data query. Meteomatics extensive documentation covering how to use the RESTful API, as well detailing the numerous weather parameters available, made using Meteomatics API and integrating Meteomatics Data so much easier".

About Sunflower Labs

Sunflower Labs makes an autonomous drone security system for outdoor use in residential, commercial and industrial settings that detects and deters unwanted visitors. Sunflower Labs was founded in 2016 by Alex Pachikov, Christian Eheim and Nicolas de Palézieux, and is backed by notable investors including General Catalyst, Gentian Investments and Stanley Ventures. The company is based in Zurich and San Francisco.

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