Explore and apply new technologies and methodologies in computing, modeling, visualization, data access and delivery to advance NOAA’s earth system prediction and decision support capabilities
Depiction of a machine learning algorithm used to identify hurricanes and typhoons (red boxes) in GOES satellite data →
Innovative numerical methods and software design that improve the performance, portability, and scientific accuracy of models running on next-generation exascale computers
Improve cloud computing capabilities to address computing and data challenges of the end-to-end Unified Forecast System used by NOAA, EPIC, and the research community
Develop machine learning algorithms to increase the utilization of observations in data assimilation, improve model prediction capabilities, and enable a better understanding of diverse, dense and complex data
Investigate new techniques to analyze, integrate, display, and interpret data to serve diverse applications and end-user requirements
Explore advanced data assimilation methodologies and other techniques that improve the scientific accuracy and computational efficiency of assimilation models
Technologies being Explored
High-Performance Computing with CPU, GPU, and ARM processors
SOS Explorer™ (SOSx) is a flat-screen desktop version of SOS. SOSx comes with an intuitive touchscreen interface so users choose which visualizations they want to explore. The datasets can be viewed on a virtual globe, as a flat map, or in 3D with virtual reality goggles.
Central Weather Bureau of Taiwan
GSL works with the Central Weather Bureau of Taiwan to improve their hazardous weather monitoring and forecasting and develops high-resolution forecast product generation assistance tools.
MADIS is now operational, but research continues to enhance the system to fully leverage advances in technology and science.
The AQPI System is a precipitation monitoring, alerting, and hydrological information system tailored for operational use by water agencies in the San Francisco Bay (SF-Bay) area. What to monitor, when to alert, and what information to provide will be driven by each water agency.