Using Supercomputers To Examine How Water Vapor Uncertainty Shapes Cyclone Predictions

By Kimberly Mann Bruch, SDSC
Hurricane Irma as seen from space.

In the wake of devastating tropical cyclones, such as Hurricane Irma in 2017, scientists continue to delve into the intricate mechanisms governing their formation. A recent study by a research team at Pennsylvania State University (Penn State) has used ACCESS allocations to shed light on the crucial role of water vapor in the lower to middle troposphere in the genesis of these powerful storms.

Penn State’s Assistant Professor of Meteorology and Atmospheric Science Xingchao Chen recently led a study focused on the impact of local water vapor analysis uncertainty on the predictability of Hurricane Irma’s formation. In the new study, using sophisticated modeling techniques on Stampede2 at the Texas Advanced Computing Center (TACC), the team honed in on the incipient disturbance – the precursor to the cyclone – and assessed how uncertainties in atmospheric water vapor influenced the accuracy of ensemble forecasts.

Their work was recently published in the American Meteorological Society’s Monthly Weather Review journal.

“To simulate different scenarios, we used ACCESS allocations on Stampede2 to manipulate the magnitude of water vapor perturbations within the incipient disturbance using an ensemble-based data assimilation system,” Chen said. “This system assimilated satellite observed all-sky infrared and microwave radiances to constrain the initial moisture levels – providing a comprehensive view of atmospheric conditions.”

The study found that uncertainties in initial moisture levels within the incipient disturbance significantly impacted the forecasted intensity and formation timing of hurricanes. When ensemble forecasts were initialized with varying degrees of moisture uncertainty, the resulting intensity uncertainty exceeded half that of ensembles incorporating perturbations to all variables across the domain.

Satellite heat maps indicating moisture levels are depicted.
Advanced satellite data assimilation techniques have been used to improve the prediction of tropical cyclogenesis. (a) Meteosat-10 observed brightness temperature (color shading) of Hurricane Irma overlayed with observed atmospheric circulation (white lines); (b) output of a computer forecast model that did not assimilate satellite observations; (c) output of a computer forecast model that did assimilate satellite observations.

Interestingly, while ensembles with different moisture uncertainty levels followed similar pathways to genesis, significant variations were observed in the timing of hurricane formation. Moisture-rich members tended to exhibit earlier and faster spin-up of the low-level vortex, which influenced the onset of genesis.

“Our work highlighted the critical role of the first six to twelve hours of integration, during which differences in the position and intensity of mesoscale convective systems across ensemble members developed rapidly, particularly in scenarios with greater initial moisture uncertainty,” Chen explained. “Using our ACCESS allocations and the power of the Stampede2 supercomputer to run our complex simulations, we also found that the diurnal cycle is a potential modulator of the rapid growth of intensity forecast uncertainty.”

These findings underscore the importance of targeting incipient disturbance with high spatio-temporal water vapor observations for incorporation into data assimilation systems. By enhancing the accuracy of moisture measurements via ACCESS allocations, scientists like Chen are able to improve the predictability of tropical cyclone formation – ultimately contributing to better preparedness and mitigation efforts in vulnerable regions.

Project Details

Resource Provider Institution(s): Texas Advanced Computing Center (TACC)
Affiliations: Pennsylvania State University
Funding Agency: NSF
Grant or Allocation Number(s): ATM090042

The science story featured here was enabled by the U.S. National Science Foundation’s ACCESS program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.

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