A new model from North Carolina State University incorporates machine learning to create long-range hurricane forecasts with similar accuracy to those currently in use.
Most preseason hurricane predictions are made using statistical models that use data from sea level pressure, sea surface temperatures, and other historical information. However, these predictions are made from climatic readings from one location or averaged over a particular area and time period.
Lian Xie, professor of marine, earth, and atmospheric sciences at NC State and corresponding author of a paper describing the work, describes these older models as “one dimensional.”
“We were looking at each predictor time series at a location averaged over a certain period of time each year: for example, sea surface temperature anomalies averaged over some parts of the tropical Pacific during February,” Xie says.
“In contrast, the new model looks at data taken from many specific locations, and for each location, it utilizes two data points per month – adding an important spatial component to the forecasting.”
“Hurricane systems are outrageously complex,” says coauthor Hamid Krim, professor of electrical and computer engineering at NC State. “We know that what happens at distant locations will and does impact other places through the connectivity of weather systems. So a Spatio-temporal model gives us a much more accurate picture of the dynamics of a hurricane system.”
The new model incorporates historical data from distant meteorological events like El Niño and La Niña, as well as data from multiple locations at several time points. To train the model, the researchers used data from 1951 to 2010.
The researchers want to use the new model to forecast how active an upcoming season may be.
Xie describes it as a “different way of measuring how active a hurricane season is, beyond just trying to give a number of storms.”
The researchers validated their new model in time windows of three, six, nine, 12, and 18 months against seven years of hurricane data. For all forecasts, the model demonstrated accuracy comparable to that achieved by models currently in use. For the upcoming 2021 season, they plan to use a combination of both traditional forecasting and the new model, focusing on more than just numbers of storms.
Xie says the initial results for the longer-term forecasts look promising.
“There are of course errors with the model, but its accuracy is comparable to other forecasts, with the advantage of gaining a longer lead time. This is really just the starting point. We hope that we can continue to improve it over time.”
