Groundhogs and Superstorms


Punxsutawney Phil didn’t see it coming, but this weekend’s New England blizzard is actually a triumph for modern weather forecasting. Specifically, it’s a triumph for numerical weather prediction models—the primary method used to forecast the weather one to two weeks in advance. These computer-generated simulations are run primarily by large governmental agencies. The National Center for Environmental Prediction (NCEP) in the United States and the European Center for Medium-range Weather Forecasting (ECMWF) in the United Kingdom are the two primary global centers for numerical weather prediction.

Both Superstorm Sandy and what The Weather Channel is now calling Winterstorm Nemo were well forecast by the ECMWF several days before they occurred. Meteorologists have been keenly watching Nemo with every new run of NCEP’s Global Forecast System (GFS) and the ECMWF forecasts (there are twelve new model runs each day), and many have been calling for a “storm of historic proportions” since early in the week.

Many people are discussing the similarities between this storm and Superstorm Sandy. Why is this happening? Is it just a coincidence?

As a meteorologist, I must say that Sandy and Nemo are not physically or dynamically connected. The ingredients that produced Sandy are entirely independent of those leading to Nemo. With that said, climate science has produced a volume of robust research over the past five to 10 years suggesting that extreme weather events like Sandy and Nemo might become more frequent and/or more intense in the decades ahead. This research suggests the changing global climate is likely to change how weather patterns evolve. It’s controversial, but certainly possible, that Sandy and Nemo could be symptoms of a broader global condition.

For residents of New Jersey and Long Island, however, it’s just bad luck. Two super storms within a few months of one another, following very similar paths is just like hitting blackjack in the casino on two hands in a row. Sure it’s possible—but it’s unlikely. On the other hand, play long enough and you’re sure to see it happen.

At EarthRisk Technologies, a San Diego startup, we’re using “big data” analytics and statistical and empirical techniques to forecast beyond the limits of reliable numerical weather prediction. TempRisk, our company’s flagship product, uses more than 60 years of global weather data, along with pattern recognition algorithms and current observational data to objectively quantify the risk for extreme temperature events—by as many as 40 days in advance. Our goal is to provide ample notice for businesses to prepare for extreme cold that often accompanies storms like Nemo.

Xconomy explained the potential commercial value of such long-range weather forecasts last year. More recently, EarthRisk partnered with scientists at the University at Albany in New York to develop a new approach to long-term forecasting that strives to quantify how energetic the global atmosphere may be at any point in time. This new system is based on something called the Global Wind Oscillation (GWO) index.

The GWO is an integrated measure of global atmospheric variability, which is usually associated with the position of jet streams around the globe. At times, this variability can lead to episodes of extreme weather and rapidly changing weather patterns.

Developed by Dr. Klaus Weickmann and Ed Berry in 2009 while both were scientists at the National Oceanic and Atmospheric Administration (NOAA), the GWO index identifies conditions that could lead to outbreaks of anomalous cold weather in the big population centers of the U.S. and Europe. Conversely, other GWO variations can lead to extreme periods of warm weather.

EarthRisk and the University at Albany are working together to develop algorithms that forecast the GWO. Ph.D. student Nick Schiraldi developed the process under the direction of Professor Paul Roundy and EarthRisk’s David Margolin. The system currently ingests data from NCEP’s GFS. As the system matures, we plan to introduce other inputs, such as the ECMWF.

So were we able to use the GWO index to forecast Nemo? Not directly.

The GWO Index was in what we’d call a “breakout pattern” during the latter half of January. This breakout told us to be alert for … Next Page »

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Stephen Bennett, J.D., is a founding partner and the chief science and products officer for San Diego-based EarthRisk Technologies. Prior to founding EarthRisk, Steve spent three years at the Scripps Institution of Oceanography at UC San Diego, forging ties between earth systems research and energy, insurance, and financial firms. He has been a meteorologist since 1995. Follow @

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