鶹Ů Technology Makes ‘Weathering the Storm’ More Precise
Researchers have installed 92 automated weather monitoring stations in seven counties in Florida, including Broward, Palm Beach and Martin counties; one county in Georgia; 12 counties in South Carolina; and in San Juan, Puerto Rico.
The Atlantic Basin hurricane season officially kicked off this month with 13 to 19 named storms predicted. Hurricanes Joaquin and Matthew, in 2015 and 2016 respectively, are widely regarded as “near misses.” Yet, both hurricanes resulted in significant inland flooding. In the last several decades, more than half of the deaths associated with tropical cyclones in the United States were due to inland flooding. Unfortunately, current forecasting capabilities are limited – and failure to predict significant flooding is of great concern. So is continuously overstating forecasts, which people may grow to ignore.
Researchers from ’s Institute for Sensing and Embedded Network Systems Engineering (I-SENSE) and 鶹Ů’s , in collaboration with ’s Burroughs and Chapin Center for Marine and Wetland Studies, are developing a warning system for more accurate and timely detection and forecasting of inland and coastal floods, under a variety of precipitation regimes. The technology will enable local and state governments to more effectively plan and respond to tropical storms.
“Our system is based on a new family of micro-drifters,” said , Ph.D., principal investigator, director of I-SENSE and a professor in 鶹Ů’s . “We deploy these drifters in groups to measure surface water dynamics before, during, and after tropical storms, with the goal of providing rapid guidance for use by local and state governments and decision-makers. Data collected from the micro-drifters drives numerical simulations developed by our partners at Coastal Carolina University.”
The projects, which are funded by the National Science Foundation (NSF), provide measurement tools and a computational framework to measure and simulate inland and coastal flooding processes. By collecting and calibrating key hydrologic variables, both in baseline and in advance of oncoming storms, researchers focus on surface water velocity measurements near key population locales, particularly as the river basins transition from storage modes to discharge modes. Data is collected through static and mobile sensing arrays, including atmospheric stations, photogrammetry drones, and micro-drifters.
“Increased spatial density of real-time observations are needed to support validation and assimilation into the interactively coupled model systems to drive forecasts to be more accurate and applicable, ultimately, on a block-by-block scale to aid coastal and emergency managers,” said Paul Gayes, Ph.D., Palmetto Professor of marine science and geology and executive director, Burroughs and Chapin Center for Marine and Wetland Studies at CCU. “The partnership between 鶹Ů and CCU has really closed the loop between observations, modeling, applications and technology for critically needed guidance to coastal states and communities.”
In addition to these NSF projects, 鶹Ů’s I-SENSE is the lead technology provider for the , a large regional network of weather monitoring stations, also managed in cooperation with Coastal Carolina University. The volumes of data collected are used by the National Oceanic and Atmospheric Administration’s (NOAA) weather modeling systems to increase the accuracy of its weather forecasting systems along the Atlantic coast. 鶹Ů’s I-SENSE is helping to build the southeastern region by deploying sensing assets in local municipalities
The team has installed 92 of these automated weather monitoring stations in seven counties in Florida, including Broward, Palm Beach and Martin counties; one county in Georgia; 12 counties in South Carolina; and in San Juan, Puerto Rico. The team also is collaborating with in Boca Raton, a leading independent owner and operator of wireless communications infrastructure, including towers, buildings, rooftops, distributed antenna systems and small cells. SBA provides tower access for a number of the monitoring stations.
These sensors collect weather data – rain, wind velocity, barometric pressure, temperature and other parameters, as well as other environmental parameters. The system integrates with NOAA’s system, which the National Weather Service uses for analysis, forecasting and climate modeling.
The South East Atlantic Econet is part of the , a multi-functional, multi-faceted observational weather “network of networks” that delivers critical information required for improved weather prediction and warnings across the nation. This is a network of non-federally owned weather stations that deliver real-time data for NOAA’s National Weather Service. Universities, the private sector and local municipalities partner to install weather stations across the country.
“All of the sensors being developed and deployed by I-SENSE and our collaborators provide a rich source of information for communities in local weather forecasting, emergency preparedness and management,” said , Ph.D., dean of 鶹Ů’s College of Engineering and Computer Science. “This technology and these services are becoming increasingly important because more than half of the population in the United States lives in coastal communities, with growth forecasted to continue.”
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