Last week I had the pleasure of attending the American Meteorological Society’s 97th annual meeting, where I gave a presentation on the challenges and successes of creating APIs for weather data. My talk focused not only on the challenges of wrangling many different scientific formats into developer friendly APIs, but also the reasons behind how and why we built what we call our “Weather Pipeline.” 

As many industries become more mature in their utilization of big data in attempt to better understand consumer behavior, productivity, and performance better, the use of data external to their routine operations is becoming more standard. Companies are increasingly aware of the need to use third-party data to create models for predicting future performance.

Weather is an external dataset that businesses are beginning to implement more frequently. Data scientists are seek historical and real-time weather information to build predictive models and implement product offerings for the industries served. The "Weather Pipeline" is a system we use to transform our model data and observations into consumer-ready formats using standard delivery mechanisms.

Much of the archived weather data is in industry-specific formats in a wide variety of geographical projections and a broad range of resolutions. Through trial and error, we examined various processes to create readily available global, historical and real-time datasets with our SkyWise APIs for developers in a variety of industries.

What once took 3-4 weeks can now be accomplished in a matter of minutes. Instead of creating multiple time-consuming special request datasets, we now have five main APIs covering everything from historical weather to point-specific current and forecast conditions. So, whether it’s a mobile or web application, GIS, or your Common Operating Picture, SkyWise APIs' weather data is ready to work for you. Try it out today!