Space Science and Engineering Center University of Wisconsin-Madison

SSEC scientists conduct research to improve satellite data assimilation techniques in operational weather forecast models.

SSEC scientists have developed an automated ranking method to quickly assess the accuracy of High Resolution Rapid Refresh (HRRR) model forecasts through comparison of observed and simulated Geostationary Operational Environmental Satellite (GOES) infrared brightness temperatures.

Hazus-MH, created in partnership with the Federal Emergency Management Administration (FEMA), uses Geographic Information Systems (GIS) technology to estimate physical, economic, and social impacts of disasters such as earthquakes, hurricane winds, and floods.

Researchers will work with NOAA to support the transition of algorithms to operations.

Using ultra-high resolution simulations of supercell thunderstorms, as well as state-of-the-art visualization and analysis software, researchers are studying how these storms evolve and produce tornadoes and downbursts.

MIMIC-TPW is a technique that combines microwave observations from polar orbiting satellites to create near-seamless hourly imagery of global total precipitable water (TPW).

Utilizing infrared water vapor data from GOES, Nearcasting is used to predict severe weather outbursts one to six hours in advance. This technique fills the gap between radar nowcasts, which predict weather from the zero to one-hour range, and NWP forecasting models which predict weather more than eight hours in advance.

ProbSevere is a statistical model that predicts the likelihood of a storm producing severe weather within the next 60 minutes.

The Real-time Air Quality Modeling System (RAQMS) is an online system for assimilating and forecasting global, ambient chemicals, and aerosols in the stratosphere and troposphere.

The Satellite Data Assimilation for Tropical storm is a near realtime experimental data assimilation system that uses conventional and satellite data to demonstrate the use of Joint Polar Satellite System sounder observations for improving tropical cyclone track and intensity forecasts.

CIMSS scientists have been investigating how they can use AI to improve short-term forecasts of hurricanes, and in particular their intensity.