Physical Initialization and Hurricane Ensemble Forecasts

Krishnamurti, T. N.; Correa-Torres, Ricardo; Rohaly, G. D.; Oosterhof, D.; Surgi, Naomi

doi:10.1175/1520-0434(1997)012<0503:piahef>2.0.co;2

Cited by 28 publications

(13 citation statements)

References 16 publications

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“…Tropical cyclone (TC) track forecast skill has steadily improved (Rogers et al 2006;Rappaport et al 2009), thanks to a number of factors, including increased model resolution (e.g., Miyoshi et al 2010), improved physical parameterizations and ensemble forecasting strategies (e.g., Krishnamurti et al 1997;Goerss 2000;Srinivas et al 2007;Fovell and Boucher 2009), and the availability of high-quality satellitederived information, especially over the data-sparse ocean (e.g., Leslie et al 1998). Enhanced initialization techniques, including data assimilation (e.g., Zou and Xiao 2000;Torn 2010) and TC bogussing (e.g., Kurihara et al 1990;Lord 1991;Leslie and Holland 1995) have resulted in crucial advances.…”

Section: Introductionmentioning

confidence: 99%

Tropical Cyclone Track and Structure Sensitivity to Initialization in Idealized Simulations: A Preliminary Study

Yang¹,

Fovell²,

Corbosiero³

2011

Terr. Atmos. Ocean. Sci.

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In the absence of environmental steering, tropical cyclone (TC) motion largely reflects "beta drift" owing to differential planetary vorticity advection by the storm's outer circulation. It is known that model physics choices (especially those relating to convection) can significantly alter these outer winds and thus the storm track. Here, semi-idealized simulations are used to explore the influence of the initialization on subsequent vortex evolution and motion. Specifically, TCs bred from a buoyant "bubble" are compared to bogussed vortices having a wide variety of parameterized shapes and sizes matching observations.As expected, the bogussed storms commencing with the strongest outer winds propagated fastest and, as a result, huge structure-dependent position differences quickly appeared. However, the forward speed variation among the initially bogussed TCs subsequently declined as a progressive homogenization harmonized the initially supplied structural differences. The homogenization likely involved model physics such as microphysics. This result casts doubt on the ability of models to retain and propagate forward information supplied at the initialization by advanced data assimilation techniques or parameterized vortex wind profiles.Asymmetries in near-core convective heating emerged as an important structural aspect that survived the homogenization tendency. The bubble and bogussed TCs developed markedly different heating patterns, which appear to help explain why the artificially-established storms tended to move about three times faster than their bubble counterparts. The reasons for this are not presently understood fully.

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Section: Introductionmentioning

confidence: 99%

Tropical Cyclone Track and Structure Sensitivity to Initialization in Idealized Simulations: A Preliminary Study

Yang¹,

Fovell²,

Corbosiero³

2011

Terr. Atmos. Ocean. Sci.

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“…For verifications also carried K K W Cheung Felix (1995) Krishnamurti et al, 1997). Aberson et al, 1998a).…”

Section: Work Performed In the Hurricanementioning

confidence: 99%

“…Recently, Krishnamurti et al (1997) studied an ensemble of hurricane forecasts using different analyses, which represents a combination of estimates of analysis error and perturbations to the model physics (from different assimilation procedures) at the initial time. The prognostic model used was the Florida State University global spectral model with a resolution of T63L15.…”

Section: Empirical Orthogonal Function (Eof) Analysis Of Tropical Dismentioning

confidence: 99%

“…Thus, it was argued that this reduction in the ensemble variance does not represent inadequate Aberson et al, 1998b). Frederic (1979 Krishnamurti et al, 1997).…”

Section: Empirical Orthogonal Function (Eof) Analysis Of Tropical Dismentioning

confidence: 99%

“…In addition, the ensemble tracks are more realistic than those from the barotropic model. As in Krishnamurti et al (1997), there was also discussion of the clustering of forecast tracks as a potential utility to produce probabilistic forecasts.…”

Section: Figure 9 Observed Best Track (Thick Line) and Ensemble Tracmentioning

confidence: 99%

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A review of ensemble forecasting techniques with a focus on tropical cyclone forecasting

Cheung

2001

Meteorological Applications

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This paper presents a general review of ensemble forecasting techniques, with a focus on short‐range and tropical cyclone predictions. The basic ideas and terminology of ensemble forecasting are introduced, and using four measures to evaluate an ensemble (ensemble mean forecast, consistency, spread versus skill, and inclusiveness), various potential utilities (e.g. dynamical probabilistic forecasts) are illustrated. Since the perturbation methodologies designed for medium‐range forecasts of mid‐latitude synoptic‐scale systems –singular vectors, bred modes, and so on –are already quite mature, they are only briefly described here. The general problems encountered in applying ensemble forecasting techniques to short‐range and tropical cyclone forecasts are diagnosed, and some recent studies on these topics reviewed. In general, the perturbation methodologies used for short‐range ensembles to date can have a skill comparable to or slightly higher than their corresponding high‐resolution control forecast. However, the complicated problem of the relationship between initial condition errors and model deficiencies persists. A similar situation also applies to ensembles designed for tropical cyclone forecasting. An additional difficulty is the different error characteristics encountered in the tropics, mainly the result of the strong convection in the area and the mutual interaction with the ocean. Studies from several research groups use quite different perturbation methodologies, but the results are encouraging. Most of them performed ensemble forecasting of tropical cyclone motion, but extensions to tropical cyclone intensity forecasts are also being developed. Copyright © 2001 Royal Meteorological Society

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Study on the Uncertainty of Ensemble Forecasting of Tropical Cyclone

Zhang

Zhu

Zhongfeng³

et al. 2007

Chinese J of Geophysics

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There are two main sources of estimate uncertainty in Numerical Weather Prediction (NWP), i.e., initial‐value‐related uncertainty, model‐related uncertainty. A 20‐member mesoscale ensemble forecasting system including these two kinds of uncertainty is set up to simulate tropical cyclone Danny 1997. The track of ensemble means is the best compared with all 20 members after 12 h. The sensitivity of simulation result to the two kinds of uncertainty is studied. The result shows that both kinds of uncertainty make an important role in simulation, but they dominate in the different time period during the simulation. The initial value‐related uncertainty dominates the simulation during 0~12 h of model integration. The model‐related uncertainty grows steadily during model integration. High sensitive area of uncertainty is located where severe weather occurs around the center of cyclone.

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Physical Initialization and Hurricane Ensemble Forecasts

Cited by 28 publications

References 16 publications

Tropical Cyclone Track and Structure Sensitivity to Initialization in Idealized Simulations: A Preliminary Study

Tropical Cyclone Track and Structure Sensitivity to Initialization in Idealized Simulations: A Preliminary Study

A review of ensemble forecasting techniques with a focus on tropical cyclone forecasting

Study on the Uncertainty of Ensemble Forecasting of Tropical Cyclone

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