Evolving R2O in the Era of “Big Data” Meteorology

For meteorology, “big data” is a blessing and a challenge. The ability to resolve atmospheric features at high spatial and temporal resolutions from new remote sensing systems and numerical weather prediction models presents an opportunity to refine our understanding of the atmosphere and improve forecasts and warnings. At the same time, the additional information from observation platforms that produce “big data”, such as new-generation weather satellites, can strain operational meteorologists in their effort to interpret and comprehend the sometimes subtle development of decision-altering weather conditions. Furthermore, this “big data” can stress networks and user-facing technical systems that are responsible for delivering, storing, and displaying the observations and other meteorological information, like model output.

This presentation will discuss how the nature of research products produced from large datasets containing observations at high spatial and temporal resolutions will need to evolve over the coming decade. The transition of research into an operational environment already saturated with information will also transform as the impact of human meteorologists is refocused in areas where they can provide demonstrable value to the forecast and warning process. In doing so, difficult questions about limitations on the first-order usability of raw data and the appropriate balance between raw data and derived products will be posed, with implications for the new era of weather satellites that includes the Geostationary Operational Environmental Satellite R-Series (GOES-R) and Joint Polar Satellite System (JPSS).