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Improving Seasonal Forecasts of Tropical Cyclone Activity


Tropical cyclones are one of the biggest natural threats to society, causing substantial economic damage and loss of life annually. Accurate and reliable seasonal predictions of tropical cyclone (TC) activity are essential for disaster preparedness, but remain challenging for climate scientists.
New research funded by the Climate Program Office’s Modeling, Analysis, Predictions, and Projections (MAPP) Program indicates that the potential for high-resolution coupled (atmosphere-ocean) modeling to improve seasonal forecasting of tropical cyclone activity may be greater than previously believed.
The study led by Julia V. Manganello (Center for Ocean-Land-Atmosphere Studies; COLA) assessed the TC forecast skill of Minerva, an experimental coupled prediction system, at three different high atmospheric resolutions.
As part of Project Minerva, a collaboration between COLA and the European Centre for Medium-Range Weather Forecasts (ECMWF), the system was evaluated using seven-month hindcasts, or retrospective forecasts, from 1980-2011 of seasonal, basin-wide, and regional TC activity at 62km, 31km, and 16km atmospheric resolutions (spacing between model grid points) with at least 15 ensemble members.
The research team found that using a higher resolution improved prediction skill for accumulated cyclone energy, a measure representing total intensity and duration of seasonal TC activity, and to a lesser degree, for TC frequency. The biggest improvement occurred when the model grid spacing (resolution) was reduced from 62 km to 31 km. Overall, the research indicates that high-resolution seasonal TC prediction could make substantial gains from more research on model improvement to overcome model biases.
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