A Scalable Way To Access Weather Data To Address Climate Risk
#EarthDay2017
Planet OS has been working with large-scale sensor data for 5 years.
We launched our public Datahub in early 2016, which today has over 2,100 variables accessible via a single API. Our goal is to enable simplified programmatic access to high-quality Earth Data (weather, climate, and environmental data) so that people can build great data-driven applications with less hassle.
This week, we are celebrating Earth Day, and we would like to highlight some of our most popular datasets both in business and scientific community.
Our user base varies greatly across domains, yet in the context of Earth Day, here are some of our biggest fans and weather buffs:
- Small business teams exploring opportunities for increasing the quality of their data-driven tools by combining and fusing various types of data;
- Regional fishermen communities using the API in applications to constantly monitor marine weather, sea level, and tides;
- Researchers building hyper-local weather models based on global forecasts and observations in their region of interest.
In addition, we have progressive enterprise customers from renewable energy, insurance, and agriculture industries that all need Earth Data for improving their operations to mitigate external risk.
Top Variables by API Requests in Planet OS Datahub
Top 5 Datasets by API Requests
Our customers include the renewable energy giant Innogy, climate research organization BAERI, and AgTech startup The Yield.
We are helping these companies with data integration, management, storage and of course accessing publicly available weather data, so that they can dramatically increase the speed of decision-making. By using the powerful Planet OS data platform, we remove the need for data crunching and formatting and make the data instantly accessible via a single consistent API.
Tools, guides and resources to get started
- First, I invite you to check out our Product Guide;
- In Planet OS GitHub page you’ll find useful code examples and Jupyter Notebooks;
- Here are some technical blog posts we’ve published recently:
- Calculating energy production from weather forecast in Python
- Insights from designing Datahub-ui in D3.js
- Creating weather forecast notebook with GFS model data and Planet OS API
- Querying and Rendering Weather Data with Python - Create a free Datahub account and try it out;
- Should you have any questions or comments, join our dedicated Slack channel or contact us via the chat tool on the website.
