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Christopher M. Rozoff and James P. Kossin

Abstract

The National Hurricane Center currently employs a skillful probabilistic rapid intensification index (RII) based on linear discriminant analysis of the environmental and satellite-derived features from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) dataset. Probabilistic prediction of rapid intensity change in tropical cyclones is revisited here using two additional models: one based on logistic regression and the other on a naïve Bayesian framework. Each model incorporates data from the SHIPS dataset over both the North Atlantic and eastern North Pacific Ocean basins to provide the probability of exceeding the standard rapid intensification thresholds [25, 30, and 35 kt (24 h)−1] for 24 h into the future. The optimal SHIPS and satellite-based predictors of rapid intensification differ slightly between each probabilistic model and ocean basin, but each set of optimal predictors incorporates thermodynamic and dynamic aspects of the tropical cyclone’s environment (such as vertical wind shear) and its structure (such as departure from convective axisymmetry). Cross validation shows that both the logistic regression and Bayesian probabilistic models are skillful relative to climatology. Dependent testing indicates both models exhibit forecast skill that generally exceeds the skill of the present operational SHIPS-RII and a simple average of the probabilities provided by the logistic regression, Bayesian, and SHIPS-RII models provides greater skill than any individual model. For the rapid intensification threshold of 25 kt (24 h)−1, the three-member ensemble mean improves the Brier skill scores of the current operational SHIPS-RII by 33% in the North Atlantic and 52% in the eastern North Pacific.

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Matthew Sitkowski, James P. Kossin, and Christopher M. Rozoff

Abstract

A flight-level aircraft dataset consisting of 79 Atlantic basin hurricanes from 1977 to 2007 was used to develop an unprecedented climatology of inner-core intensity and structure changes associated with eyewall replacement cycles (ERCs). During an ERC, the inner-core structure was found to undergo dramatic changes that result in an intensity oscillation and rapid broadening of the wind field. Concentrated temporal sampling by reconnaissance aircraft in 14 of the 79 hurricanes captured virtually the entire evolution of 24 ERC events. The analysis of this large dataset extends the phenomenological paradigm of ERCs described in previous observational case studies by identifying and exploring three distinct phases of ERCs: intensification, weakening, and reintensification. In general, hurricanes intensify, sometimes rapidly, when outer wind maxima are first encountered by aircraft. The mean locations of the inner and outer wind maximum at the start of an ERC are 35 and 106 km from storm center, respectively. The intensification rate of the inner wind maximum begins to slow and the storm ultimately weakens as the inner-core structure begins to organize into concentric rings. On average, the inner wind maximum weakens 10 m s−1 before the outer wind maximum surpasses the inner wind maximum as it continues to intensify. This reintensification can be quite dramatic and often brings the storm to its maximum lifetime intensity. The entire ERC lasts 36 h on average.

Comparison of flight-level data and microwave imagery reveals that the first appearance of an outer wind maximum, often associated with a spiral rainband, typically precedes the weakening of the storm by roughly 9 h, but the weakening is already well under way by the time a secondary convective ring with a well-defined moat appears in microwave imagery. The data also show that winds beyond the outer wind maximum remain elevated even after the outer wind maximum contracts inward. Additionally, the contraction of the outer wind maximum usually ceases at a radius larger than the location of the inner wind maximum at the start of the ERC. The combination of a larger primary eyewall and expanded outer wind field increase the integrated kinetic energy by an average of 28% over the course of a complete ERC despite little change in the maximum intensity between the times of onset and completion of the event.

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Christopher M. Rozoff, William R. Cotton, and Jimmy O. Adegoke

Abstract

A storm-resolving version of the Regional Atmospheric Modeling System is executed over St. Louis, Missouri, on 8 June 1999, along with sophisticated boundary conditions, to simulate the urban atmosphere and its role in deep, moist convection. In particular, surface-driven low-level convergence mechanisms are investigated. Sensitivity experiments show that the urban heat island (UHI) plays the largest role in initiating deep, moist convection downwind of the city. Surface convergence is enhanced on the leeward side of the city. Increased momentum drag over the city induces convergence on the windward side of the city, but this convergence is not strong enough to initiate storms. The nonlinear interaction of urban momentum drag and the UHI causes downwind convection to erupt later, because momentum drag over the city regulates the strength of the UHI. In all simulations including a UHI, precipitation totals are enhanced downwind of St. Louis. Topography around St. Louis also affects storm development. There is a large sensitivity of simulated urban-enhanced convection to the details of the urban surface model.

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Sarah A. Monette, Christopher S. Velden, Kyle S. Griffin, and Christopher M. Rozoff

Abstract

A geostationary satellite–derived cloud product that is based on a tropical-overshooting-top (TOT) detection algorithm is described for applications over tropical oceans. TOTs are identified using a modified version of a midlatitude overshooting-top detection algorithm developed for severe-weather applications. The algorithm is applied to identify TOT activity associated with Atlantic Ocean tropical cyclones (TCs). The detected TOTs can serve as a proxy for “hot towers,” which represent intense convection with possible links to TC rapid intensification (RI). The purpose of this study is to describe the adaptation of the midlatitude overshooting-top detection algorithm to the tropics and to provide an initial exploration of possible correlations between TOT trends in developing TCs and subsequent RI. This is followed by a cursory examination of the TOT parameter’s potential as a predictor of RI both on its own and in multiparameter RI forecast schemes. RI forecast skill potential is investigated by examining empirical thresholds of TOT activity and trends within prescribed radii of a large sample of developing North Atlantic TC centers. An independent test on Atlantic TCs in 2006–07 reveals that an empirically based TOT scheme has potential as a predictor for RI occurring in the subsequent 24 h, especially for RI maximum wind thresholds of 25 and 30 kt (24 h)−1 (1 kt ≈ 0.5 m s−1). As expected, the stand-alone TOT-based RI scheme is comparatively less accurate than existing objective multiparameter RI prediction methods. A preliminary experiment that adds TOT-based predictors to an objective logistic regression-based scheme is shown to improve slightly the forecast skill of RI, however.

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Christopher M. Rozoff, Christopher S. Velden, John Kaplan, James P. Kossin, and Anthony J. Wimmers

Abstract

The probabilistic prediction of tropical cyclone (TC) rapid intensification (RI) in the Atlantic and eastern Pacific Ocean basins is examined here using a series of logistic regression models trained on environmental and infrared satellite-derived features. The environmental predictors are based on averaged values over a 24-h period following the forecast time. These models are compared against equivalent models enhanced with additional TC predictors created from passive satellite microwave imagery (MI). Leave-one-year-out cross validation on the developmental dataset shows that the inclusion of MI-based predictors yields more skillful RI models for a variety of RI and intensity thresholds. Compared with the baseline forecast skill of the non-MI-based RI models, the relative skill improvements from including MI-based predictors range from 10.6% to 44.9%. Using archived real-time data during the period 2004–13, evaluation of simulated real-time models is also carried out. Unlike in the model development stage, the simulated real-time setting involves using Global Forecast System forecasts for the non-satellite-based predictors instead of “perfect” observational-based predictors in the developmental data. In this case, the MI-based RI models still generate superior skill to the baseline RI models lacking MI-based predictors. The relative improvements gained in adding MI-based predictors are most notable in the Atlantic, where the non-MI versions of the models suffer acutely from the use of imperfect real-time data. In the Atlantic, relative skill improvements provided from the inclusion of MI-based predictors range from 53.5% to 103.0%. The eastern Pacific relative improvements are less impressive but are still uniformly positive.

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Sarah M. Griffin, Jason A. Otkin, Christopher M. Rozoff, Justin M. Sieglaff, Lee M. Cronce, and Curtis R. Alexander

Abstract

In this study, the utility of dimensioned, neighborhood-based, and object-based forecast verification metrics for cloud verification is assessed using output from the experimental High Resolution Rapid Refresh (HRRRx) model over a 1-day period containing different modes of convection. This is accomplished by comparing observed and simulated Geostationary Operational Environmental Satellite (GOES) 10.7-μm brightness temperatures (BTs). Traditional dimensioned metrics such as mean absolute error (MAE) and mean bias error (MBE) were used to assess the overall model accuracy. The MBE showed that the HRRRx BTs for forecast hours 0 and 1 are too warm compared with the observations, indicating a lack of cloud cover, but rapidly become too cold in subsequent hours because of the generation of excessive upper-level cloudiness. Neighborhood and object-based statistics were used to investigate the source of the HRRRx cloud cover errors. The neighborhood statistic fractions skill score (FSS) showed that displacement errors between cloud objects identified in the HRRRx and GOES BTs increased with time. Combined with the MBE, the FSS distinguished when changes in MAE were due to differences in the HRRRx BT bias or displacement in cloud features. The Method for Object-Based Diagnostic Evaluation (MODE) analyzed the similarity between HRRRx and GOES cloud features in shape and location. The similarity was summarized using the newly defined MODE composite score (MCS), an area-weighted calculation using the cloud feature match value from MODE. Combined with the FSS, the MCS indicated if HRRRx forecast error is the result of cloud shape, since the MCS is moderately large when forecast and observation objects are similar in size.

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Matthew Sitkowski, James P. Kossin, Christopher M. Rozoff, and John A. Knaff

Abstract

Flight-level aircraft data are used to examine inner-core thermodynamic changes during eyewall replacement cycles (ERCs) and the role of the relict inner eyewall circulation on the evolution of a hurricane during and following an ERC. Near the end of an ERC, the eye comprises two thermodynamically and kinematically distinct air masses separated by a relict wind maximum, inside of which high inertial stability restricts radial motion creating a “containment vessel” that confines the old-eye air mass. Restricted radial flow aloft also reduces subsidence within this confined region. Subsidence-induced warming is thus focused along the outer periphery of the developing post-ERC eye, which leads to a flattening of the pressure profile within the eye and a steepening of the gradient at the eyewall. This then causes a local intensification of the winds in the eyewall. The cessation of active convection and subsidence near the storm center, which has been occurring over the course of the ERC, leads to an increase in minimum pressure. The increase in minimum pressure concurrent with the increase of winds in the developing eyewall can create a highly anomalous pressure–wind relationship. When the relict inner eyewall circulation dissipates, the air masses are free to mix and subsidence can resume more uniformly over the entire eye.

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Stefano Alessandrini, Luca Delle Monache, Christopher M. Rozoff, and William E. Lewis

Abstract

An analog ensemble (AnEn) technique is applied to the prediction of tropical cyclone (TC) intensity (i.e., maximum 1-min averaged 10-m wind speed). The AnEn is an inexpensive, naturally calibrated ensemble prediction of TC intensity derived from a training dataset of deterministic Hurricane Weather Research and Forecasting (HWRF; 2015 version) Model forecasts. In this implementation of the AnEn, a set of analog forecasts is generated by searching an HWRF archive for forecasts sharing key features with the current HWRF forecast. The forecast training period spans 2011–15. The similarity of a current forecast with past forecasts is estimated using predictors derived from the HWRF reforecasts that capture thermodynamic and kinematic properties of a TC’s environment and its inner core. Additionally, the value of adding a multimodel intensity consensus forecast as an AnEn predictor is examined. Once analogs are identified, the verifying intensity observations corresponding to each analog HWRF forecast are used to produce the AnEn intensity prediction. In this work, the AnEn is developed for both the eastern Pacific and Atlantic Ocean basins. The AnEn’s performance with respect to mean absolute error (MAE) is compared with the raw HWRF output, the official National Hurricane Center (NHC) forecast, and other top-performing NHC models. Also, probabilistic intensity forecasts are compared with a quantile mapping model based on the HWRF’s intensity forecast. In terms of MAE, the AnEn outperforms HWRF in the eastern Pacific at all lead times examined and up to 24-h lead time in the Atlantic. Also, unlike traditional dynamical ensembles, the AnEn produces an excellent spread–skill relationship.

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Christopher M. Rozoff, David S. Nolan, James P. Kossin, Fuqing Zhang, and Juan Fang

Abstract

The Weather and Research and Forecasting Model (WRF) is used to simulate secondary eyewall formation (SEF) in a tropical cyclone (TC) on the β plane. The simulated SEF process is accompanied by an outward expansion of kinetic energy and the TC warm core. An absolute angular momentum budget demonstrates that this outward expansion is predominantly a symmetric response to the azimuthal-mean and wavenumber-1 components of the transverse circulation. As the kinetic energy expands outward, the kinetic energy efficiency in which latent heating can be retained as local kinetic energy increases near the developing outer eyewall.

The kinetic energy efficiency associated with SEF is examined further using a symmetric linearized, nonhydrostatic vortex model that is configured as a balanced vortex model. Given the symmetric tangential wind and temperature structure from WRF, which is close to a state of thermal wind balance above the boundary layer, the idealized model provides the transverse circulation associated with the symmetric latent heating and friction prescribed from WRF. In a number of ways, this vortex response matches the azimuthal-mean secondary circulation in WRF. These calculations suggest that sustained azimuthal-mean latent heating outside of the primary eyewall will eventually lead to SEF. Sensitivity experiments with the balanced vortex model show that, for a fixed amount of heating, SEF is facilitated by a broadening TC wind field.

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Christopher M. Rozoff, Wayne H. Schubert, Brian D. McNoldy, and James P. Kossin

Abstract

Intense tropical cyclones often possess relatively little convection around their cores. In radar composites, this surrounding region is usually echo-free or contains light stratiform precipitation. While subsidence is typically quite pronounced in this region, it is not the only mechanism suppressing convection. Another possible mechanism leading to weak-echo moats is presented in this paper. The basic idea is that the strain-dominated flow surrounding an intense vortex core creates an unfavorable environment for sustained deep, moist convection. Strain-dominated regions of a tropical cyclone can be distinguished from rotation-dominated regions by the sign of S 2 1 + S 2 2ζ 2, where S 1 = uxυy and S 2 = υx + uy are the rates of strain and ζ = υxuy is the relative vorticity. Within the radius of maximum tangential wind, the flow tends to be rotation-dominated (ζ 2 > S 2 1 + S 2 2), so that coherent structures, such as mesovortices, can survive for long periods of time. Outside the radius of maximum tangential wind, the flow tends to be strain-dominated (S 2 1 + S 2 2 > ζ 2), resulting in filaments of anomalous vorticity. In the regions of strain-dominated flow the filamentation time is defined as τ fil = 2(S 2 1 + S 2 2ζ 2)−1/2. In a tropical cyclone, an approximately 30-km-wide annular region can exist just outside the radius of maximum tangential wind, where τ fil is less than 30 min and even as small as 5 min. This region is defined as the rapid filamentation zone. Since the time scale for deep moist convective overturning is approximately 30 min, deep convection can be significantly distorted and even suppressed in the rapid filamentation zone. A nondivergent barotropic model illustrates the effects of rapid filamentation zones in category 1–5 hurricanes and demonstrates the evolution of such zones during binary vortex interaction and mesovortex formation from a thin annular ring of enhanced vorticity.

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