Hurricane Intensity Prediction Research Articles

Tropical Cyclone Intensity Estimation in the North Atlantic Basin Using an Improved Deviation Angle Variance Technique

Abstract This paper describes results from an improvement to the objective deviation angle variance technique to estimate the intensity of tropical cyclones from satellite infrared imagery in the North Atlantic basin. The technique quantifies the level of organization of the infrared cloud signature of a tropical cyclone as an indirect measurement of its maximum wind speed. The major change described here is to use the National Hurricane Center’s best-track database to constrain the technique. Results are shown for the 2004–10 North Atlantic hurricane seasons and include an overall root-mean-square intensity error of 12.9 kt (6.6 m s−1, where 1 kt = 0.514 m s−1) and annual root-mean-square intensity errors ranging from 10.3 to 14.1 kt. A direct comparison between the previous version and the one reported here shows root-mean-square intensity error improvements in all years with a best improvement in 2009 from 17.9 to 10.6 kt and an overall improvement from 14.8 to 12.9 kt. In addition, samples from the 7-yr period are binned based on level of intensity and on the strength of environmental vertical wind shear as extracted from Statistical Hurricane Intensity Prediction Scheme (SHIPS) data. Preliminary results suggest that the deviation angle variance technique performs best at the weakest intensity categories of tropical storm through hurricane category 3, representing 90% of the samples, and then degrades in performance for hurricane categories 4 and 5. For environmental vertical wind shear, there is far less spread in the results with the technique performing better with increasing vertical wind shear.

Toward Improving High-Resolution Numerical Hurricane Forecasting: Influence of Model Horizontal Grid Resolution, Initialization, and Physics

Abstract This paper provides an account of the performance of an experimental version of the Hurricane Weather Research and Forecasting system (HWRFX) for 87 cases of Atlantic tropical cyclones during the 2005, 2007, and 2009 hurricane seasons. The HWRFX system was used to study the influence of model grid resolution, initial conditions, and physics. For each case, the model was run to produce 126 h of forecast with two versions of horizontal resolution, namely, (i) a parent domain at a resolution of about 27 km with a 9-km moving nest (27:9) and (ii) a parent domain at a resolution of 9 km with a 3-km moving nest (9:3). The former was selected to be consistent with the current operational resolution, while the latter is the first step in testing the impact of finer resolutions for future versions of the operational model. The two configurations were run with initial conditions for tropical cyclones obtained from the operational Geophysical Fluid Dynamics Laboratory (GFDL) and HWRF models. Sensitivity experiments were also conducted with the physical parameterization scheme. The study shows that the 9:3 HWRFX system using the GFDL initial conditions and a system of physics similar to the operational version (HWRF) provides the best results in terms of both track and intensity prediction. Use of the HWRF initial conditions in the HWRFX model provides reasonable skill, particularly when used in cases with initially strong storms (hurricane strength). However, initially weak storms (below hurricane strength) posed special challenges for the models. For the weaker storm cases, none of the predictions from the HWRFX runs or the operational GFDL forecasts provided any consistent improvement when compared to the operational Statistical Hurricane Intensity Prediction Scheme with an inland decay component (DSHIPS).

New Probabilistic Forecast Models for the Prediction of Tropical Cyclone Rapid Intensification

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.

Intensity forecast experiment of hurricane Rita (2005) with a cloud‐resolving, coupled hurricane–ocean modelling system

AbstractA cloud‐resolving, coupled hurricane‐ocean modelling system is developed under the Earth System Modeling Framework using state‐of‐the‐art numerical forecasting models of the atmosphere and the ocean. With this system, the importance of coupling to an eddy‐resolving ocean model and the high resolution of the atmospheric model for the prediction of intensity change of hurricane Rita (2005) is demonstrated through a set of numerical experiments. The erroneous intensification in the uncoupled experiments could be eliminated when taking account of the negative feedback of sea‐surface temperature cooling. Moreover, the deepening rate of Rita becomes larger with higher resolution of the atmospheric model. Nevertheless, the horizontal resolution of the atmospheric model with grid spacing at least less than ∼4 km is required in order to predict the rapid intensity changes of hurricane Rita in the fully coupled experiment. This strong dependence is found to arise from the potential interaction between the ocean coupling and internal processes of Rita, thus indicating that both high resolution and the ocean coupling are indispensable for the future improvement of hurricane intensity prediction. Copyright © 2011 Royal Meteorological Society

Association Rule Data Mining Applications for Atlantic Tropical Cyclone Intensity Changes

Abstract This study applies a data mining technique called association rule mining to the analysis of intensity changes of North Atlantic tropical cyclones (TCs). The “best track” data from the National Hurricane Center and the Statistical Hurricane Intensity Prediction Scheme databases were stratified into tropical depressions, tropical storms, and category 1–5 hurricanes based on the Saffir–Simpson hurricane scale. After stratification, the seven resulting groups of TCs plus two additional aggregation groups were further separated into intensifying, weakening, and stable TCs. The analysis of the stratified data for preprocessing revealed that faster northward storm motion (the meridional component of storm motion) favors tropical storm intensification but does not favor the intensification of hurricanes. Intensifying tropical storms are more strongly associated with a higher convergence in the upper atmosphere (200-hPa relative eddy momentum flux convergence) than weakening tropical storms, while intensifying hurricanes are more strongly associated with lower convergence values. The mined association rules showed that cofactors usually display higher-intensity prediction power in the stratified TC groups. The data mining results also identified a predictor set with fewer factors but improved probabilities of rapid intensification. This study found that the data mining technique not only sheds light on the roles of multiple-associated physical processes in tropical cyclone development—especially in rapid intensification processes—but also will help improve TC intensity forecasting. This paper provides an outline on how to use this data mining technique and how to overcome low occurrences of mined conditions in order to improve TC intensity forecasting capabilities.

Geometric Manifold Learning

We present algorithms for analyzing massive and high dimensional data sets motivated by theorems from geometry and topology. Optimization criteria for computing data projections are discussed and skew radial basis functions (sRBFs) for constructing nonlinear mappings with sharp transitions are demonstrated. Examples related to modeling dynamical systems, including hurricane intensity and financial time series prediction, are presented. The article represents an overview of the authors' and collaborators' work in manifold learning.

Oceanic Heat Content Variability in the Eastern Pacific Ocean for Hurricane Intensity Forecasting

Abstract Recent evidence supports the premise that the subsurface ocean structure plays an important role in modulating air–sea fluxes during hurricane passage, which in turn, affects intensity change. Given the generally sparse in situ data, it has been difficult to provide region-to-basin-wide estimates of isotherm depths and upper-ocean heat content (OHC). In this broader context, satellite-derived sea surface height anomalies (SSHAs) from multiple platforms carrying radar altimeters are blended, objectively analyzed, and combined with a hurricane-season climatology to estimate isotherm depths and OHC within the context of a reduced gravity model at 0.25° spatial intervals in the eastern Pacific Ocean where tropical cyclone intensity change occurs. Measurements from the Eastern Pacific Investigation of Climate in 2001, long-term tropical ocean atmosphere mooring network, and volunteer observing ship deploying expendable bathythermograph (XBT) profilers are used to carefully evaluate satellite-based measurements of upper-ocean variability. Regression statistics reveal small biases with slopes of 0.8–0.9 between the subsurface measurements compared with isotherm depths (20° and 26°C), and OHC fields derived from objectively analyzed SSHA field. Root-mean-square differences in OHC range between 10 and 15 kJ cm−2 or roughly 10%–15% of the mean signals. Similar values are found for isotherm depth differences between in situ and inferred satellite-derived values. Blended daily values are used in the Statistical Hurricane Intensity Prediction Scheme (SHIPS) forecasts as are OHC estimates for the Atlantic Ocean basin. An equivalent OHC variable is introduced that incorporates the strength of the thermocline at the base of the oceanic mixed layer using a climatological stratification parameter /No, which seems better correlated to hurricane intensity change than just anomalies as observed in Hurricane Juliette in 2001.

A Revised Tropical Cyclone Rapid Intensification Index for the Atlantic and Eastern North Pacific Basins

Abstract A revised rapid intensity index (RII) is developed for the Atlantic and eastern North Pacific basins. The RII uses large-scale predictors from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) to estimate the probability of rapid intensification (RI) over the succeeding 24 h utilizing linear discriminant analysis. Separate versions of the RII are developed for the 25-, 30-, and 35-kt RI thresholds, which represent the 90th (88th), 94th (92nd), and 97th (94th) percentiles of 24-h overwater intensity changes of tropical and subtropical cyclones in the Atlantic (eastern North Pacific) basins from 1989 to 2006, respectively. The revised RII became operational at the NHC prior to the 2008 hurricane season. The relative importance of the individual RI predictors is shown to differ between the two basins. Specifically, the previous 12-h intensity change, upper-level divergence, and vertical shear have the highest weights for the Atlantic basin, while the previous 12-h intensity change, symmetry of inner-core convection, and the difference in a system’s current and maximum potential intensity are weighted highest in the eastern North Pacific basin. A verification of independent forecasts from the 2006 and 2007 hurricane seasons shows that the probabilistic RII forecasts are generally skillful in both basins when compared to climatology. Moreover, when employed in a deterministic manner, the RII forecasts were superior to all other available operational intensity guidance in terms of the probability of detection (POD) and false alarm ratio (FAR). Specifically, the POD for the RII ranged from 15% to 59% (53% to 73%) while the FAR ranged from 71% to 85% (53% to 79%) in the Atlantic (eastern North Pacific) basins, respectively, for the three RI thresholds studied. Nevertheless, the modest POD and relatively high FAR of the RII and other intensity guidance demonstrate the difficulty of predicting RI, particularly in the Atlantic basin.

Effects of Moist Convection on Hurricane Predictability

Abstract This study exemplifies inherent uncertainties in deterministic prediction of hurricane formation and intensity. Such uncertainties could ultimately limit the predictability of hurricanes at all time scales. In particular, this study highlights the predictability limit due to the effects on moist convection of initial-condition errors with amplitudes far smaller than those of any observation or analysis system. Not only can small and arguably unobservable differences in the initial conditions result in different routes to tropical cyclogenesis, but they can also determine whether or not a tropical disturbance will significantly develop. The details of how the initial vortex is built can depend on chaotic interactions of mesoscale features, such as cold pools from moist convection, whose timing and placement may significantly vary with minute initial differences. Inherent uncertainties in hurricane forecasts illustrate the need for developing advanced ensemble prediction systems to provide event-dependent probabilistic forecasts and risk assessment.

A Simplified Dynamical System for Tropical Cyclone Intensity Prediction

Abstract A simplified dynamical system for tropical cyclone intensity prediction based on a logistic growth equation (LGE) is developed. The time tendency of the maximum sustained surface winds is proportional to the sum of two terms: a growth term and a term that limits the maximum wind to an upper bound. The maximum wind evolution over land is determined by an empirical inland wind decay formula. The LGE contains four free parameters, which are the time-dependent growth rate and maximum potential intensity (MPI), and two constants that determine how quickly the intensity relaxes toward the MPI. The MPI is estimated from an empirical formula as a function of sea surface temperature and storm translational speed. The adjoint of the LGE provides a method for finding the other three free parameters to make the predictions as close as possible to the National Hurricane Center best-track intensities. The growth rate is assumed to be a linear function of the vertical shear (S), a convective instability parameter (C) determined from an entraining plume, and their product, where both S and C use global model fields as input. This assumption reduces the parameter estimation problem to the selection of six constants. Results show that the LGE optimized for the full life cycle of individual storms can very accurately simulate the intensity variations out to as long as 15 days. For intensity prediction, single values of the six constants are found by fitting the model to more than 2400 Atlantic forecasts from 2001 to 2006. Results show that the observed intensity variations can be fit more accurately with the LGE than with the linear Statistical Hurricane Intensity Prediction Scheme (SHIPS) formulation, and with a much smaller number of constants. Results also show that LGE model solution (and some properties of real storms) can be explained by the evolution in the two-dimensional S–C phase space. Forecast and other applications of the LGE model are discussed.

Structural and Intensity Changes of Hurricane Bret (1999). Part I: Environmental Influences

Abstract Hurricane Bret underwent a rapid intensification (RI) and subsequent weakening between 1200 UTC 21 August and 1200 UTC 22 August 1999 before it made landfall on the Texas coast 12 h later. Its minimum sea level pressure fell 35 hPa from 979 to 944 hPa within 24 h. During this period, aircraft of the National Oceanic and Atmospheric Administration (NOAA) flew several research missions that sampled the environment and inner core of the storm. These datasets are combined with gridded data from the National Centers for Environmental Prediction (NCEP) Global Model and the NCEP–National Center for Atmospheric Research (NCAR) reanalyses to document Bret’s atmospheric and oceanic environment as well as their relation to the observed structural and intensity changes. Bret’s RI was linked to movement over a warm ocean eddy and high sea surface temperatures (SSTs) in the Gulf of Mexico coupled with a concurrent decrease in vertical wind shear. SSTs at the beginning of the storm’s RI were approximately 29°C and steadily increased to 30°C as it moved to the north. The vertical wind shear relaxed to less than 10 kt during this time. Mean values of oceanic heat content (OHC) beneath the storm were about 20% higher at the beginning of the RI period than 6 h prior. The subsequent weakening was linked to the cooling of near-coastal shelf waters (to between 25° and 26°C) by prestorm mixing combined with an increase in vertical wind shear. The available observations suggest no intrusion of dry air into the circulation core contributed to the intensity evolution. Sensitivity studies with the Statistical Hurricane Intensity Prediction Scheme (SHIPS) model were conducted to quantitatively describe the influence of environmental conditions on the intensity forecast. Four different cases with modified vertical wind shear and/or SSTs were studied. Differences between the four cases were relatively small because of the model design, but the greatest intensity changes resulted for much cooler prescribed SSTs. The results of this study underscore the importance of OHC and vertical wind shear as significant factors during RIs; however, internal dynamical processes appear to play a more critical role when a favorable environment is present.

A “sufficient” condition combination for rapid intensifications of tropical cyclones

Rapid Intensifications (RI) of tropical cyclones (TCs) provide major error sources in the challenging task of TC intensity forecasting. There are many factors that affect the RI processes of TCs, and identifying the combination of conditions most favorable to RI development is very time consuming when using traditional statistical data analysis methods. Data mining techniques are implemented to the data for SHIPS (Statistical Hurricane Intensity Prediction Scheme), an operational hurricane intensity forecasting model, to identify the “optimal” RI condition combinations when the number of affecting factors is given. One such combination (high latitude, low longitude, the TC being in an intensification phase, an initial intensity far away from the maximum potential intensity, high steering layer value, and low relative eddy flux convergence) gives such a high RI probability that the combination can be considered as a sufficient condition for RI, which almost guarantees that an RI will take place.

Application of Oceanic Heat Content Estimation to Operational Forecasting of Recent Atlantic Category 5 Hurricanes

Abstract Research investigating the importance of the subsurface ocean structure on tropical cyclone intensity change has been ongoing for several decades. While the emergence of altimetry-derived sea height observations from satellites dates back to the 1980s, it was difficult and uncertain as to how to utilize these measurements in operations as a result of the limited coverage. As the in situ measurement coverage expanded, it became possible to estimate the upper oceanic heat content (OHC) over most ocean regions. Beginning in 2002, daily OHC analyses have been generated at the National Hurricane Center (NHC). These analyses are used qualitatively for the official NHC intensity forecast, and quantitatively to adjust the Statistical Hurricane Intensity Prediction Scheme (SHIPS) forecasts. The primary purpose of this paper is to describe how upper-ocean structure information was transitioned from research to operations, and how it is being used to generate NHC’s hurricane intensity forecasts. Examples of the utility of this information for recent category 5 hurricanes (Isabel, Ivan, Emily, Katrina, Rita, and Wilma from the 2003–05 hurricane seasons) are also presented. Results show that for a large sample of Atlantic storms, the OHC variations have a small but positive impact on the intensity forecasts. However, for intense storms, the effect of the OHC is much more significant, suggestive of its importance on rapid intensification. The OHC input improved the average intensity errors of the SHIPS forecasts by up to 5% for all cases from the category 5 storms, and up to 20% for individual storms, with the maximum improvement for the 72–96-h forecasts. The qualitative use of the OHC information on the NHC intensity forecasts is also described. These results show that knowledge of the upper-ocean thermal structure is fundamental to accurately forecasting intensity changes of tropical cyclones, and that this knowledge is making its way into operations. The statistical results obtained here indicate that the OHC only becomes important when it has values much larger than that required to support a tropical cyclone. This result suggests that the OHC is providing a measure of the upper ocean’s influence on the storm and improving the forecast.

SHIPS-MI Forecast Analysis of Hurricanes Claudette (2003), Isabel (2003), and Dora (1999)

Abstract Three hurricanes, Claudette (2003), Isabel (2003), and Dora (1999), were selected to examine the Statistical Hurricane Intensity Prediction Scheme with Microwave Imagery (SHIPS-MI) forecast accuracy for three particular storm types. This research was conducted using model analyses and tropical cyclone best-track data, with forecasts generated from a dependent sample. The model analyses and best-track data are assumed to be a “perfect” representation of the actual event (e.g., perfect prog assumption). Analysis of intensity change forecasts indicated that SHIPS-MI performed best, compared to operational SHIPS output, for tropical cyclones that were intensifying from tropical storm to hurricane intensity. Passive microwave imagery, which is sensitive to the intensity and coverage of precipitation, improved intensity forecasts during these periods with a positive intensity change contribution resulting from above normal inner-core precipitation. Forecast improvement was greatest for 12–36-h forecasts, where the microwave contribution to SHIPS-MI was greatest. Once a storm reached an intensity close to its maximum potential intensity, as in the case of Isabel and Dora, both SHIPS and SHIPS-MI incorrectly forecast substantial weakening despite the positive contribution from microwave data. At least in Dora’s case, SHIPS-MI forecasts were slightly stronger than those of SHIPS. Other important contributions to SHIPS-MI forecasts were examined to determine their importance relative to the microwave inputs. Inputs related to sea surface temperature (SST) and persistence–climatology proved to be very important to intensity change forecasts, as expected. These predictors were the primary factor leading to the persistent weakening forecasts made by both models for Isabel and Dora. For Atlantic storms (Claudette and Isabel), the contribution from shear also proved important at characterizing the conduciveness of the environment toward intensification. However, the shear contribution was often small as a result of multiple offsetting shear-related predictors. Finally, it was observed that atmospheric parameters not included in SHIPS, such as eddy momentum flux, could substantially affect the intensity, leading to large forecast errors. This was especially true for the Claudette intensity change forecasts throughout its life cycle.

The Environmental and Inner-Core Conditions Governing the Intensity of Hurricane Erin (2001)

Abstract The evolution of Hurricane Erin (2001) is presented from the perspective of its environmental and inner-core conditions, particularly as they are characterized in the Statistical Hurricane Intensity Prediction Scheme with Microwave Imagery (SHIPS-MI). Erin can be described as having two very distinct periods. The first, which occurred between 1 and 6 September 2001, was characterized by a struggling tropical storm failing to intensify as the result of unfavorable environmental and inner-core conditions. The surrounding environment during this period was dominated by moderate shear and mid- to upper-level dry air, both caused in some part by the presence of a Saharan air layer (SAL). Further intensification was inhibited by the lack of sustained deep convection and latent heating near the low-level center. The authors attribute this in part to negative effects from the SAL. The thermodynamic conditions associated with the SAL were not well sampled by the SHIPS parameters, resulting in substantial overforecasting by both SHIPS and SHIPS-MI. Instead, the hostile conditions surrounding Erin caused its dissipation on 6 September. The second period began on 7 September when Erin re-formed north of the original center. Erin began to pull away from the SAL and moved over 29°C sea surface temperatures, beginning a rapid intensification phase and reaching 105 kt by 1800 UTC 9 September. SHIPS-MI forecasts called for substantial intensification as in the previous period, but this time the model underestimated the rate of intensification. The addition of inner-core characteristics from passive microwave data improved the skill somewhat compared to SHIPS, but still left much room for improvement. For this period, it appears that the increasingly favorable atmospheric conditions caused by Erin moving away from the SAL were not well sampled by SHIPS or SHIPS-MI. As a result, the intensity change forecasts were not able to take into account the more favorable environment.

Passive-Microwave-Enhanced Statistical Hurricane Intensity Prediction Scheme

Abstract The formulation and testing of an enhanced Statistical Hurricane Intensity Prediction Scheme (SHIPS) using new predictors derived from passive microwave imagery is presented. Passive microwave imagery is acquired for tropical cyclones in the Atlantic and eastern North Pacific basins between 1995 and 2003. Predictors relating to the inner-core (within 100 km of center) precipitation and convective characteristics of tropical cyclones are derived. These predictors are combined with the climatological and environmental predictors used by SHIPS in a simple linear regression model with change in tropical cyclone intensity as the predictand. Separate linear regression models are produced for forecast intervals of 12, 24, 36, 48, 60, and 72 h from the time of a microwave sensor overpass. Analysis of the resulting models indicates that microwave predictors, which provide an intensification signal to the model when above-average precipitation and convective signatures are present, have comparable importance to vertical wind shear and SST-related predictors. The addition of the microwave predictors produces a 2%–8% improvement in performance for the Atlantic and eastern North Pacific tropical cyclone intensity forecasts out to 72 h when compared with an environmental-only model trained from the same sample. Improvement is also observed when compared against the current version of SHIPS. The improvement in both basins is greatest for substantially intensifying or weakening tropical cyclones. Improvement statistics are based on calculating the forecast error for each tropical cyclone while it is held out of the training sample to approximate the use of independent data.

On the Decay of Tropical Cyclone Winds Crossing Narrow Landmasses

Abstract A method is developed to adjust the Kaplan and DeMaria tropical cyclone inland wind decay model for storms that move over narrow landmasses. The basic assumption that the wind speed decay rate after landfall is proportional to the wind speed is modified to include a factor equal to the fraction of the storm circulation that is over land. The storm circulation is defined as a circular area with a fixed radius. Application of the modified model to Atlantic Ocean cases from 1967 to 2003 showed that a circulation radius of 110 km minimizes the bias in the total sample of landfalling cases and reduces the mean absolute error of the predicted maximum winds by about 12%. This radius is about 2 times the radius of maximum wind of a typical Atlantic tropical cyclone. The modified decay model was applied to the Statistical Hurricane Intensity Prediction Scheme (SHIPS), which uses the Kaplan and DeMaria decay model to adjust the intensity for the portion of the predicted track that is over land. The modified decay model reduced the intensity forecast errors by up to 8% relative to the original decay model for cases from 2001 to 2004 in which the storm was within 500 km from land.

The Interaction of Supertyphoon Maemi (2003) with a Warm Ocean Eddy

Abstract Understanding the interaction of ocean eddies with tropical cyclones is critical for improving the understanding and prediction of the tropical cyclone intensity change. Here an investigation is presented of the interaction between Supertyphoon Maemi, the most intense tropical cyclone in 2003, and a warm ocean eddy in the western North Pacific. In September 2003, Maemi passed directly over a prominent (700 km × 500 km) warm ocean eddy when passing over the 22°N eddy-rich zone in the northwest Pacific Ocean. Analyses of satellite altimetry and the best-track data from the Joint Typhoon Warning Center show that during the 36 h of the Maemi–eddy encounter, Maemi’s intensity (in 1-min sustained wind) shot up from 41 m s−1 to its peak of 77 m s−1. Maemi subsequently devastated the southern Korean peninsula. Based on results from the Coupled Hurricane Intensity Prediction System and satellite microwave sea surface temperature observations, it is suggested that the warm eddies act as an effective insulator between typhoons and the deeper ocean cold water. The typhoon’s self-induced sea surface temperature cooling is suppressed owing to the presence of the thicker upper-ocean mixed layer in the warm eddy, which prevents the deeper cold water from being entrained into the upper-ocean mixed layer. As simulated using the Coupled Hurricane Intensity Prediction System, the incorporation of the eddy information yields an evident improvement on Maemi’s intensity evolution, with its peak intensity increased by one category and maintained at category-5 strength for a longer period (36 h) of time. Without the presence of the warm ocean eddy, the intensification is less rapid. This study can serve as a starting point in the largely speculative and unexplored field of typhoon–warm ocean eddy interaction in the western North Pacific. Given the abundance of ocean eddies and intense typhoons in the western North Pacific, these results highlight the importance of a systematic and in-depth investigation of the interaction between typhoons and western North Pacific eddies.

Further Improvements to the Statistical Hurricane Intensity Prediction Scheme (SHIPS)

Abstract Modifications to the Atlantic and east Pacific versions of the operational Statistical Hurricane Intensity Prediction Scheme (SHIPS) for each year from 1997 to 2003 are described. Major changes include the addition of a method to account for the storm decay over land in 2000, the extension of the forecasts from 3 to 5 days in 2001, and the use of an operational global model for the evaluation of the atmospheric predictors instead of a simple dry-adiabatic model beginning in 2001. A verification of the SHIPS operational intensity forecasts is presented. Results show that the 1997–2003 SHIPS forecasts had statistically significant skill (relative to climatology and persistence) out to 72 h in the Atlantic, and at 48 and 72 h in the east Pacific. The inclusion of the land effects reduced the intensity errors by up to 15% in the Atlantic, and up to 3% in the east Pacific, primarily for the shorter-range forecasts. The inclusion of land effects did not significantly degrade the forecasts at any time period. Results also showed that the 4–5-day forecasts that began in 2001 did not have skill in the Atlantic, but had some skill in the east Pacific. An experimental version of SHIPS that included satellite observations was tested during the 2002 and 2003 seasons. New predictors included brightness temperature information from Geostationary Operational Environmental Satellite (GOES) channel 4 (10.7 μm) imagery, and oceanic heat content (OHC) estimates inferred from satellite altimetry observations. The OHC estimates were only available for the Atlantic basin. The GOES data significantly improved the east Pacific forecasts by up to 7% at 12–72 h. The combination of GOES and satellite altimetry improved the Atlantic forecasts by up to 3.5% through 72 h for those storms west of 50°W.

Nonlinear interaction of axisymmetric circulation and nonaxisymmetric disturbances in hurricanes

The nonlinear interaction of axisymmetric circulation and nonaxisymmetric disturbances in hurricanes is numerically studied with a quasigeostrophic barotropic model of a higher resolution. It is pointed out that the interaction may be divided into two categories. In the first category, nonaxisymmetric disturbances decay, the coordinate locus of maximum relative vorticity ζ max is seemingly unordered, and the central pressure of hurricane rises; while in the second one, nonaxisymmetric disturbances develop, the locus of ζ max shows an ordered limit cycle pattern, and the central pressure falls remarkably. A succinct criterion is given to judge which category the interaction belongs to, i.e. the vortex beta Rossby number at the initial time R β R β > 1 to the developing one. Finally, practical applications of theoretical results of the rotational adaptation process presented by Zeng and numerical results in this paper to the hurricane intensity prediction in China are also discussed.