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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:MISSISSIPPI STATE UNIVERSITY
Doing Business As Name:Mississippi State University
PD/PI:
  • Kimberly M Wood
  • (662) 325-0602
  • kimberly.wood@msstate.edu
Award Date:06/24/2020
Estimated Total Award Amount: $ 203,960
Funds Obligated to Date: $ 203,960
  • FY 2020=$203,960
Start Date:07/01/2020
End Date:06/30/2023
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.050
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Collaborative Research: An Object-Oriented Approach to Assess the Rainfall Evolution of Tropical Cyclones in Varying Moisture Environments
Federal Award ID Number:2011812
DUNS ID:075461814
Parent DUNS ID:075461814
Program:Physical & Dynamic Meteorology
Program Officer:
  • Chungu Lu
  • (703) 292-7110
  • clu@nsf.gov

Awardee Location

Street:PO Box 6156
City:MISSISSIPPI STATE
State:MS
ZIP:39762-9662
County:Mississippi State
Country:US
Awardee Cong. District:03

Primary Place of Performance

Organization Name:Mississippi State University
Street:
City:
State:MS
ZIP:39762-5448
County:Mississippi State
Country:US
Cong. District:03

Abstract at Time of Award

Tropical storms and hurricanes can produce more than five feet of rain as occurred in 2017 during Hurricane Harvey. Climate models indicate increasing temperatures and atmospheric moisture in the future, factors which can lead to stronger storms that produce more rain. To better understand how moisture is tied to rainfall in tropical systems, it is essential to assess the structures that produce rain within the storm – the rainbands – and the impact of moisture on those rainbands. This project will use geographic methods to measure storm structure and analyze how atmospheric moisture (also referred to as humidity) affects that structure. Rainbands will be analyzed in dozens of tropical storms using ground-based radar and polar-orbiting satellite data. Metrics that quantify the shape, size, and evolution of rainbands are applied to these observations. By comparing rainband evolution in different moisture environments, this research will describe how rainband structural changes occur and the environmental moisture regimes that lead to high rain rates. Through collaboration with scientists from the National Oceanic and Atmospheric Administration (NOAA), this project’s results will enable assessment of how accurately hurricane model forecasts depict rainband structure, an assessment that will help improve hurricane rainfall predictions. In addition to funding graduate student research, each investigator will simultaneously teach a course that provides hands-on training in state-of-the art methods and includes collaborative learning opportunities for students to discuss research among the three universities. This project will integrate geographic and meteorological methods to investigate two fundamental research questions about tropical cyclone (TC) size and structure: (1) How skillful are satellite and modeling datasets in representing cloud and precipitation structure and which three-dimensional object-based metrics best quantify these structures? (2) How does large-scale environmental moisture impact TC rainband development and rainfall production? Despite research that details the importance of environmental moisture at the synoptic-scale and within the TC inner core, few studies have combined radar, satellite, and modeling data to examine the influence of variable moisture on synoptic and mesoscale processes that impact TC size and structure (e.g, ventilation and shear-induced asymmetric circulations). This research will provides crucial insight into model TC forecasts. By employing a novel shape-identification algorithm that is scalable across datasets with multiple spatial resolutions, this project will identify rainbands and tracks changes in rainband configuration to then identify how rainbands, and TC spatial extent more generally, are impacted by the TC’s moisture environment. The results from these analyses will be used to establish a multi-scale conceptual model of TC size and structure based on large-scale environmental moisture. Finally, object-based metrics will be applied to evaluate rainfall forecasts from current operational and experimental models by collaborating with the Hurricane Research Division of NOAA. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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