Is Tropical Cyclone Intensity Guidance Improving?

DeMaria, Mark; Sampson, Charles R.; Knaff, John A.; Musgrave, Kate D.

doi:10.1175/bams-d-12-00240.1

Cited by 376 publications

(271 citation statements)

References 23 publications

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“…Much attention has been directed in recent years toward improving tropical cyclone intensity forecasts (Gall et al 2013), especially since there has been comparatively little improvement in measures of intensity skill in the last few decades (DeMaria et al 2014). Within the community of scientists working on tropical cyclones, efforts have been directed toward improving dedicated tropical cyclone models (e.g., Gopalakrishnan et al 2011), real-time in situ and remote sensing observations of storms (e.g., Ruf et al 2016), assimilation of those observations into models (Zhang and Weng 2015;Weng and Zhang 2016;Zhang et al 2016), and statistical forecast models, which are still competitive with deterministic models (Kaplan et al 2015).…”

Section: Introductionmentioning

confidence: 99%

On the Predictability and Error Sources of Tropical Cyclone Intensity Forecasts

Emanuel

Zhang

2016

Journal of the Atmospheric Sciences

131

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The skill of tropical cyclone intensity forecasts has improved slowly since such forecasts became routine, even though track forecast skill has increased markedly over the same period. In deciding whether or how best to improve intensity forecasts, it is useful to estimate fundamental predictability limits as well as sources of intensity error. Toward that end, the authors estimate rates of error growth in a ''perfect model'' framework in which the same model is used to explore the sensitivities of tropical cyclone intensity to perturbations in the initial storm intensity and large-scale environment. These are compared to estimates made in previous studies and to intensity error growth in real-time forecasts made using the same model, in which model error also plays an important role. The authors find that error growth over approximately the first few days in the perfect model framework is dominated by errors in initial intensity, after which errors in forecasting the track and large-scale kinematic environment become more pronounced. Errors owing solely to misgauging initial intensity are particularly large for storms about to undergo rapid intensification and are systematically larger when initial intensity is underestimated compared to overestimating initial intensity by the same amount. There remains an appreciable gap between actual and realistically achievable forecast skill, which this study suggests can best be closed by improved models, better observations, and superior data assimilation techniques.

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Section: Introductionmentioning

confidence: 99%

On the Predictability and Error Sources of Tropical Cyclone Intensity Forecasts

Emanuel

Zhang

2016

Journal of the Atmospheric Sciences

131

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“…However, the ability to predict their strength has not progressed as quickly (DeMaria et al 2013). This is partially because of the incomplete understanding of turbulent upperocean mixing.…”

Section: Introductionmentioning

confidence: 99%

Langmuir Turbulence under Hurricane Gustav (2008)

Rabe

Kukulka

Ginis

et al. 2015

Journal of Physical Oceanography

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Extreme winds and complex wave fields drive upper-ocean turbulence in tropical cyclone conditions. Motivated by Lagrangian float observations of bulk vertical velocity variance (VVV) under Hurricane Gustav (2008), upper-ocean turbulence is investigated based on large-eddy simulation (LES) of the wave-averaged Navier-Stokes equations. To realistically capture wind-and wave-driven Langmuir turbulence (LT), the LES model imposes the Stokes drift vector from spectral wave simulations; both the LES and wave model are forced by the NOAA Hurricane Research Division (HRD) surface wind analysis product. Results strongly suggest that without LT effects simulated VVV underestimates the observed VVV. LT increases the VVV, indicating that it plays a significant role in upper-ocean turbulence dynamics. Consistent with observations, the LES predicts a suppression of VVV near the hurricane eye due to wind-wave misalignment. However, this decrease is weaker and of shorter duration than that observed, potentially due to large-scale horizontal advection not present in the LES. Both observations and simulations are consistent with a highly variable upper ocean turbulence field beneath tropical cyclone cores. Bulk VVV, a TKE budget analysis, and anisotropy coefficient (ratio of horizontal to vertical velocity variances) profiles all indicate that LT is suppressed to levels closer to that of shear turbulence (ST) due to misaligned wind and wave fields. VVV approximately scales with the directional surface layer Langmuir number. Such a scaling provides guidance for the development of an upper-ocean boundary layer parameterization that explicitly depends on sea state.

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“…Unfortunately, there has been little improvement in tropical cyclone intensity forecasts over the period from 1990 to the present (DeMaria et al 2014). While hurricane track forecasts using numerical prediction models have steadily improved, there has been only slow improvement in forecasts of intensity by these same models.…”

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confidence: 99%

Will Global Warming Make Hurricane Forecasting More Difficult?

Emanuel

2017

Bulletin of the American Meteorological Society

161

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Hurricane track forecasts have improved steadily over the past few decades, yet forecasting hurricane intensity remains challenging. Of special concern are the rare instances of tropical cyclones that intensify rapidly just before landfall, catching forecasters and populations off guard, thereby risking large casualties. Here, we review two historical examples of such events and use scaling arguments and models to show that rapid intensification just before landfall is likely to become increasingly frequent and severe as the globe warms.

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Is Tropical Cyclone Intensity Guidance Improving?

Abstract: Contrary to what is often reported in the literature, tropical cyclone intensity forecast models have improved over the past two decades at a rate that is statistically significant.

Cited by 376 publications

References 23 publications

On the Predictability and Error Sources of Tropical Cyclone Intensity Forecasts

On the Predictability and Error Sources of Tropical Cyclone Intensity Forecasts

Langmuir Turbulence under Hurricane Gustav (2008)

Will Global Warming Make Hurricane Forecasting More Difficult?

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