N.C. State – NWS CSTAR Project to Examine Low-Topped Severe Convection in the Southeast Selected for Funding

cstar.v.logo.alternate.smallThe project entitled “Improving Understanding and Prediction of High Impact Weather Associated with Low-Topped Severe Convection in the Southeastern U.S.” will be led by Drs. Matthew D. Parker, Gary M. Lackmann, and Lian Xie of N.C. State University in collaboration with nearly a dozen WFOs in the Southeast along with the Storm Prediction Center. The three-year project is being funded as a part of the NOAA/NWS Collaborative Science, Technology, and Applied Research (CSTAR) Program. This project will build off of previous collaborative research between N.C. State and  the NWS which have had very successful research to operations results along with the integration of students into NOAA and the NWS.

Severe convective storms in environments with large vertical wind shear and marginal instability (so-called “high-shear low-CAPE”, or “HSLC” events) represent a significant short-term, high-impact forecasting and warning challenge, particularly in the Southeastern and Mid-Atlantic states of the U.S. Such environments account for a substantial fraction of severe wind and tornado reports in the region, and they are present for many hours each year. The long-range goal of the research is to improve predictions and warnings for hazardous weather in HSLC environments.

The research will be conducted through a set of complementary collaborative research studies including:
(i) The project intends to advance the understanding and interpretation of HSLC radar imagery by performing idealized simulations of HSLC convective storms, within which we will study the dynamical processes at work and compare them to pseudo-radar measurements of the simulated storms.
(ii) The project also intends to improve short range prediction and situational awareness of HSLC scenarios by evaluating a suite of convection-allowing hindcasts of notable HSLC events and nulls and testing the sensitivity of these hindcasts to grid spacing and model configuration.
(iii) The project hopes to improve short-to-medium range prediction and situational awareness of HSLC scenarios by applying dynamically-based statistical downscaling techniques in order to exploit the information available from operational model ensembles.
(iv) Finally, the project intends to improve operational “best practices” in HSLC environments by coordinating an assessment of a number of experimental HSLC diagnosis and forecast products within NOAA.

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