Artificial Intelligence and Big Data for Prediction

Precise Location-Based Weather Forecasts Wherever You Are

RAL has been a leader in the development of intelligent weather prediction systems that blend data from numerical weather prediction models, statistics datasets, real–time observations, and human intelligence to optimize forecasts at user–defined locations. The Dynamic Integrated Forecast System (DICast®) is an example of this technology and it is currently being used by three of the nation's largest private sector weather service companies. There is a growing desire in industry to have fine–tuned forecasts for specific user–defined locations. This trend is clear in the energy, transportation, agriculture, and location–based service industries. RAL's expertise in meteorology, engineering, and applied mathematics and statistics, is being utilized to address society's growing need for accurate weather information.

Partners

  • EPRI
  • Xcel
  • Global Weather Corporation (GWC)
  • Schneider Electric (Telvent) 

Representative Projects

  • Advancing Weather Analysis and Forecasting TechnologiesThe DICast® system combines NWP model data and artificial intelligence to optimize weather forecasting as well as probabilistic forecasting that is completely automated, updates as frequently as necessary and produces forecasts out to customized forecast extents and temporal resolutions.
  • Solar Power Forecasting Partnership: Led a partnership to advance the state-of-the-science of solar power forecasting which involved cutting edge research, testing the forecasts in several geographically- and climatologically-diverse high-penetration solar utilities, and widely disseminating the research results to improve solar power forecasting technology.

Search through all publications in NCAR's OpenSky Library.

  • Ruiz-Arias, J.A., Dudhia, J., Santos-Alamillos, F.J., Pozo-Vázquez, D. (2013)Surface clear-sky shortwave radiative closure intercomparisons in the Weather Research and Forecasting model, J. Geophys. Res: Atmos., Vol. 118, pp: 1-13, doi:10.1002/jgrd.50778.
  • Troccoli, A., S.E. Haupt, and L. Dubus, eds., 2014: Weather Matters for Energy, Springer, 528 pp.

Artificial Intelligence and Big Data for Prediction