| description abstract | Tropical cyclone (TC) intensity has been shown to have limited predictability in numerical weather prediction models; therefore, ensemble forecasting may be critical. An ensemble prediction system (EPS) should ideally cover all sources of uncertainty; however, most meso- and convective-scale EPSs typically consider initial-condition uncertainty alone, with limited treatment of model uncertainty, even though the evolution of mesoscale features is highly dependent on uncertain parameterization schemes. The role of stochastic treatment of model error in the Hurricane Weather Research and Forecasting (HWRF) EPS is evaluated by applying independent stochastically perturbed parameterization (iSPPT) scheme to individual parameterization schemes for four TCs from 2017 to 2018. Experiments with Hurricane Irma (2017) indicate that TC intensity ensemble standard deviation is most sensitive to the amplitude of the stochastic perturbation field, with smaller impact from adjusting the decorrelation time scale and spatial length scale. Results from all four TC cases show that stochastic perturbations to the turbulent mixing scheme can increase the ensemble standard deviation in intensity metrics over a 72-h simulation without introducing significant differences in mean error or bias. By contrast, stochastic perturbations to the microphysics, radiation, and cumulus tendencies have negligible effects on intensity standard deviation. | |
