Leveraging Analytics of Big Data to Support Weather Knowledge and Decision Making

Significant shifts in the way weather data is collected and processed are being driven by the emergence of new technologies combined with the significant increase in the amount of weather and environmental data being generated. Analysis techniques that rely on interactive display of weather on meteorologists' workstations cannot keep up with the rapid increase of weather data and products made available to the forecaster. New techniques to effectively ingest, process, and analyze weather data for a variety of decision support tools are being developed. Upgrades to the Nation's NEXRAD Doppler Radar network, increased resolution in forecast models, additional sensors aboard mobile platforms, and GOES-R are just some of the expected increases of weather data and information.

The use of analytics on very large data sets can yield significant results for all weather consumers both in the federal space as well as industries such as energy, emergency management, commerce and transportation, and commercial business. The increase in the amount and size of the data calls for more effective techniques to mine the data and make better and more accurate decisions. We will demonstrate how machine learning, including analytics and software algorithms can yield significant advances in knowledge development and provide benefits to various decision support systems.