Big Data Watch

Big data key to bringing hyperlocal weather forecasts to Georgia farmers

Researchers say tools would help Flint River Vally farmers conserve water and increase long-term crop yield

Farmers in southwest Georgia's Flint River Valley could one day get accurate, hyperlocal weather forecasts just for their individual farms up to three days in advance.

A team of researchers from the Flint River Soil and Water Conservation District, the U.S. Department of Agriculture, the University of Georgia and IBM is using sophisticated big data tools to analyze large volumes of meteorological, geographical, historical and other data. The goal is to model weather behavior with a higher degree of accuracy and localization than can be done today.

Such forecasting would help farmers make more informed irrigation, seeding, harvesting and fertilizer scheduling decisions, which in turn would enable them to conserve water and increase long-term crop yield.

"With data and data-driven solutions, we are looking at the next generation of agriculture," says David Reckford, director of The Flint River Partnership project. "We are beginning to apply a level of science to the system that will allow us to grow more with less."

The Flint River Valley is an important part of Georgia's agricultural industry. Farms in the 27-county region contribute roughly $2 billion annually in farm-based revenue.

About 10 years ago, the region's water conservation authority teamed up with the U.S Department of Agriculture, University of Georgia, and other local, regional and state-level agencies to promote water conservation practices among farmers in the area. The effort has already paid dividends.

A so-called Variable Rate Irrigation (VRI) technology developed by researchers at the University of Georgia moved quickly from concept to commercial product as the result of the work done by the Flint River Soil and Water Conservation District and the same groups involved in the data analytics effort. The GPS-based technology allows farmers to set irrigation sprinklers so that the nozzles turn water off over areas that don't need water and turn them back on over areas that need irrigation.

Reckford is confident that hyperlocal forecasts enabled by big data analytics and sophisticated modeling technologies will one day yield similar benefits.

Weather forecast models in the U.S. typically have a horizontal resolution of 12 kilometers, meaning they are based on data gathered from grid points spaced 12 kilometers apart. By building weather models with a 1.5 kilometer resolution, the Flint River Partnership project is looking to provide farmers in the area with much more granular weather information.

That would lead to more informed decisions regarding irrigation, seeding, harvesting and fertilizer application, Reckford said.

Central to the effort is IBM's Deep Thunder short-term forecasting and customized weather modeling technology. The technology, which is built around a parallel processing supercomputer, is deigned to help organizations forecast weather down to a square kilometer -- and even smaller -- geographic area.

Deep Thunder was created jointly by IBM and the National Oceanic and Atmospheric Administration (NOAA) as part of a project launched in 1996. After years in IBM's research labs, the technology is now sold to utility companies, city governments and others as a cloud-based offering that's used for precision weather forecasting.

The Flint River project will ingest and crunch data from numerous sources, including NOAA, several weather stations set up in the region by the University of Georgia, and the Earth Network WeatherBug system. The system will also evaluate land use, vegetation, topography and other geographic data from the U.S. Geological Survey and NASA.

Deep Thunder
Researchers from the Flint River Soil and Water Conservation District, the Department of Agriculture, the University of Georgia and IBM are using sophisticated big data analytics to produce detailed weather forecasts.
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