June, 2014
Beginning June 25 and running through late August, the NOAA Air Resources Laboratory will conduct a field study in the Southeastern U.S. to improve predictions of Convective Initiation (CI). The study is being done in collaboration with the University of Alabama-Huntsville. Surface and boundary layer observations will be used to validate model predictions of convection and surface fluxes and also complement a training database of actual CI occurrences and identify critical surface and boundary layer processes associated with CI. The study team also will rely heavily on data from NOAA and NASA satellite sensors, including surface temperatures, cloud optical depth and effective radius, land use, elevation, topography, Normalized Difference Vegetation Index, Leaf Area Index, and established algorithms that retrieve sensible heating, evapotranspiration and soil moisture, and atmospheric water vapor content.
Background: In simple terms, Convective Initiation is defined as the birth of a thunderstorm. Despite extensive research on CI, the key mechanisms involved in the initial development of CI are not well understood. One approach to improve our capability of predicting severe storm events is to study the events during the warm season, May–September, where local topography, land use, and soil moisture often play significant roles in the development of clouds and convective precipitation. Two major goals for the study are to develop a probabilistic 0-6 hour product using machine-learning approaches operating on real-time observations and to improve the models that predict CI.
Significance: Understanding when and where CI occurs, and the mechanisms that cause CI, is vital to forecasters for early warnings to the public of severe weather and to take action. Advances in our ability to predict the location and timing of local convective storms can help reduce risks in a number of areas, including aviation safety, flood forecasts and other wind- and precipitation-sensitive industries.
For More Information, contact: Tilden Meyers