Friday, January 06, 2006

Forecast model of U.S. hurricanes 6 months in advance

Hurricanes are a serious social and economic threat to the
United States. Hurricane Katrina is a grim reminder of this fact.
Recent advances allow skillful forecasts of the U.S. hurricane
threat at (or near) the start of the Atlantic hurricane season.
Skillful forecasts of hurricane landfalls at longer lead times
(forecast horizons) for the complete hurricane season would greatly
benefit risk managers and others interested in acting on these
forecasts. Here we show a model that provides a 6-month forecast
horizon for annual hurricane counts along the U.S. coastline during
the June through November hurricane season. Forecast skill exceeds
that of climatology. The long-lead skill is linked to the
persistence of Atlantic sea-surface temperatures and to
teleconnections between North Atlantic sea-level pressures and
precipitation variability over North America and Europe. The model
is developed using Bayesian regression and therefore incorporates
the full set of Atlantic hurricane data extending back to 1851.

[with R.J. Murnane and T.H. Jagger]


Anonymous said...

How do you control for the disparity in temperature samples due to technological advances? i.e., today we refer to possible climate change in tenths of a degree, when 100 years ago such granularity wasn't possible.

The utility of such time series data sets in the face of such questions is dubious. I've seen discussions heralding catastrophic consequences from climate change of, say 0.8 degrees Fahrenheit, although such divisions of measurement were only made possible in the last couple or three decades.

I have a vested interest in this question, since I live in Pensacola, Florida. I seriously don't want another year to repeat the last two hurricane seasons.


James Elsner said...

Since we are using temperature samples to predict hurricane activity on an annual basis, the disparity in data due to technological advances is not a cause of significant concern.

In any event we check for trends (real or due to measurement bias) and control for these in our seasonal forecast model using standard regression techniques.