Central Pressure Deficit Research Articles

Comparative study of typhoon wind hazard estimation in coastal region of China using different wind field parameter models

Radius to maximum wind speed (Rmax) and Holland pressure profile parameter (B) are two key parameters for parametric typhoon or hurricane wind field modelling and hazard estimation. They are difficult to be observed or estimated for all historical storms and therefore always unavailable in most best track datasets. The statistical models were developed to correlate these two parameters with other available parameters based on limited real observation data and widely employed in typhoon hazard estimation. The applicability of these models to different ocean regions remains questionable. In this study, series of statistical models of Rmax and B are revisited and classified based on their regions of application, i.e., Gulf of Mexico and Atlantic Basin (GM&AB), South Pacific (SP), Bay of Bengal (BB) and Western North Pacific (WNP, 0°N-60°N, 100°E−180°E). Moreover, recursive models of Rmax and B in WNP are also introduced. The comparison of variation characteristics of different models for these two key parameters along with the change of typhoon surface central pressure deficit ΔPs are conducted. The stochastic typhoon tracking and intensity models are then coupled with an analytical wind field model to estimate the typhoon wind hazard of five coastal cities of China. The probability of exceedance and return period curves of wind speed are compared using different Rmax and B parameter models. The use of different Rmax and B models is shown to have significant effects on wind hazard curves. The extreme wind speeds estimated using parameter models from GM&AB and wind field model in this study are almost consistent with each other. Different models in WNP produce significant differences of wind hazard curve. The new findings in this study provide a forward step to the rational estimation of typhoon wind hazard in WNP by using the appropriate wind field parameter models.

Probabilistic flutter analysis of a long-span bridge in typhoon-prone regions considering climate change and structural deterioration

In this paper, a long-term probabilistic flutter analysis of long-span bridges, with consideration of the time-variant probability density function (PDF) of annual maximum wind speed caused by climate change and the deterioration effects of dynamic properties obtained by field monitoring data, is investigated. Estimated uncertainty, which occurs in probabilistic flutter analysis, is quantified by the generalized density evolution equation (GDEE). A suspension bridge, with the center span of 1650 m, is chosen as application example. Long-term deterioration and inter-seasonal varying characteristics of modal frequencies and damping ratios are discussed. An implicit formulation among radius to maximum winds Rmax, central pressure deficit Δp, latitude ψ and sea surface temperature SST is set up by training a two-layer feed-forward artificial neural network (ANN) with historical records, and then the full-track typhoon simulation is conducted based on Vickery’s empirical model. Lastly, long-term probabilistic flutter analysis is conducted in conjunction with three prospective climate change scenarios, RCP2.6, RCP4.5 and RCP8.5, showing that the likelihood of annual flutter failure will increase greatly mainly due to higher annual maximum wind speed in a warming climate.

Long-Lead-Time Prediction of Storm Surge Using Artificial Neural Networks and Effective Typhoon Parameters: Revisit and Deeper Insight

Storm surge induced by severe typhoons has caused many catastrophic tragedies to coastal communities over past decades. Accurate and efficient prediction/assessment of storm surge is still an important task in order to achieve coastal disaster mitigation especially under the influence of climate change. This study revisits storm surge predictions using artificial neural networks (ANN) and effective typhoon parameters. Recent progress of storm surge modeling and some remaining unresolved issues are reviewed. In this paper, we chose the northeastern region of Taiwan as the study area, where the largest storm surge record (over 1.8 m) has been observed. To develop the ANN-based storm surge model for various lead-times (from 1 to 12 h), typhoon parameters are carefully examined and selected by analogy with the physical modeling approach. A knowledge extraction method (KEM) with backward tracking and forward exploration procedures is also proposed to analyze the roles of hidden neurons and typhoon parameters in storm surge prediction, as well as to reveal the abundant, useful information covered in the fully-trained artificial brain. Finally, the capability of ANN model for long-lead-time predictions and influences in controlling parameters are investigated. Overall, excellent agreement with observations (i.e., the coefficient of efficiency CE > 0.95 for training and CE > 0.90 for validation) is achieved in one-hour-ahead prediction. When the typhoon affects coastal waters, contributions of wind speed, central pressure deficit, and relative angle are clarified via influential hidden neurons. A general pattern of maximum storm surge under various scenarios is also obtained. Moreover, satisfactory accuracy is successfully extended to a much longer lead time (i.e., CE > 0.85 for training and CE > 0.75 for validation in 12-h-ahead prediction). Possible reasons for further accuracy improvement compared to earlier works are addressed.

A Rapid Forecasting and Mapping System of Storm Surge and Coastal Flooding

AbstractA prototype of an efficient and accurate rapid forecasting and mapping system (RFMS) of storm surge is presented. Given a storm advisory from the National Hurricane Center, the RFMS can generate a coastal inundation map on a high-resolution grid in 1 min (reference system Intel Core i7–3770K). The foundation of the RFMS is a storm surge database consisting of high-resolution simulations of 490 optimal storms generated by a robust storm surge modeling system, Curvilinear-Grid Hydrodynamics in 3D (CH3D-SSMS). The RFMS uses an efficient quick kriging interpolation scheme to interpolate the surge response from the storm surge database, which considers tens of thousands of combinations of five landfall parameters of storms: central pressure deficit, radius to maximum wind, forward speed, heading direction, and landfall location. The RFMS is applied to southwest Florida using data from Hurricane Charley in 2004 and Hurricane Irma in 2017, and to the Florida Panhandle using data from Hurricane Michael in 2018 and validated with observed high water mark data. The RFMS results agree well with observation and direct simulation of the high-resolution CH3D-SSMS. The RFMS can be used for real-time forecasting during a hurricane or “what-if” scenarios for mitigation planning and preparedness training, or to produce a probabilistic flood map. The RFMS can provide more accurate surge prediction with uncertainties if NHC can provide more accurate storm forecasts in the future. By incorporating storms for future climate and sea level rise, the RFMS could be used to generate future flood maps for coastal resilience and adaptation planning.

Physical understanding of the tropical cyclone wind-pressure relationship

The relationship between the two common measures of tropical cyclone intensity, the central pressure deficit and the peak near-surface wind speed, is a long-standing problem in tropical meteorology that has been approximated empirically yet lacks physical understanding. Here we provide theoretical grounding for this relationship. We first demonstrate that the central pressure deficit is highly predictable from the low-level wind field via gradient wind balance. We then show that this relationship reduces to a dependence on two velocity scales: the maximum azimuthal-mean azimuthal wind speed and half the product of the Coriolis parameter and outer storm size. This simple theory is found to hold across a hierarchy of models spanning reduced-complexity and Earth-like global simulations and observations. Thus, the central pressure deficit is an intensity measure that combines maximum wind speed, storm size, and background rotation rate. This work has significant implications for both fundamental understanding and risk analysis, including why the central pressure better explains historical economic damages than does maximum wind speed.

Influences of Larger-Scale Vortex Variability on Tornado Pressure Minima in an Outer-Flow Region: Explorations Using a Parametric Tangential Wind Model

Abstract Previous studies have neglected to distinguish between a central pressure deficit due to a tornado itself and due to a parent mesocyclone in which the tornado is embedded. To obtain improved understanding of the influences of larger-scale vortex variability on smaller-scale tornado pressure deficits, a parametric tangential wind model supplemented with a cyclostrophic speed equation was used to explore the role that the variability plays in influencing radial pressure deficits by deducing radial pressure deficit distributions from radial profiles of hypothetically superpositioned, dual-maxima tangential velocities in the free atmosphere, where a dominant swirling flow was in approximate cyclostrophic balance. The cyclostrophic approximation was partitioned into two separate components, allowing one to scrutinize and determine which of the concentric vortices contributes most significantly to the tornado pressure minima. The model parametrically constructed a smaller-scale, stronger vortex (rapidly swirling flow) that was centered within a larger-scale, weaker vortex (slowly swirling flow) to represent a tornado centered within a supercell, low-level, parent mesocyclone above a tornado boundary layer. The radial pressure deficit fluctuations were varied by changing one of five key velocity-controlling parameters assigned to one vortex to represent a variety of vortex strengths. Based on eight experiments, the larger-scale, weaker (smaller scale, stronger) vortex contributed less (more) to the total pressure deficit than the smaller-scale, stronger (larger scale, weaker) vortex. The stronger vortex centered within the larger-scale, weaker vortex has a larger central pressure minimum than it does in the absence of the larger-scale vortex.

Observed Tropical Cyclone Size Revisited

Abstract This work revisits the statistics of observed tropical cyclone outer size in the context of recent advances in our theoretical understanding of the storm wind field. The authors create a new dataset of the radius of 12 m s−1 winds based on a recently updated version of the QuikSCAT ocean wind vector database and apply an improved analytical outer wind model to estimate the outer radius of vanishing wind. The dataset is then applied to analyze the statistical distributions of the two size metrics as well as their dependence on environmental parameters, with a specific focus on testing recently identified parameters possessing credible theoretical relationships with tropical cyclone size. The ratio of the potential intensity to the Coriolis parameter is found to perform poorly in explaining variation of size, with the possible exception of its upper bound, the latter of which is in line with existing theory. The rotating radiative–convective equilibrium scaling of Khairoutdinov and Emanuel is also found to perform poorly. Meanwhile, mean storm size is found to increase systematically with the relative sea surface temperature, in quantitative agreement with the results of a recent study of storm size based on precipitation area. Implications of these results are discussed in the context of existing tropical climate theory. Finally, an empirical dependence of the central pressure deficit on outer size is found in line with past work.

A Parametric Wind–Pressure Relationship for Rankine versus Non-Rankine Cyclostrophic Vortices

AbstractA parametric tangential wind profile model is presented for depicting representative pressure deficit profiles corresponding to varying tangential wind profiles of a cyclostrophic, axisymmetric vortex. The model employs five key parameters per wind profile: tangential velocity maximum, radius of the maximum, and three shape parameters that control different portions of the profile. The model coupled with the cyclostrophic balance assumption offers a diagnostic tool for estimating and examining a radial profile of pressure deficit deduced from a theoretical superimposing tangential wind profile in the vortex. Analytical results show that the shape parameters for a given tangential wind maximum of a non-Rankine vortex have an important modulating influence on the behavior of realistic tangential wind and corresponding pressure deficit profiles. The first parameter designed for changing the wind profile from sharply to broadly peaked produces the corresponding central pressure fall. An increase in the second (third) parameter yields the pressure rise by lowering the inner (outer) wind profile inside (outside) the radius of the maximum. Compared to the Rankine vortex, the parametrically constructed non-Rankine vortices have a larger central pressure deficit. It is suggested that the parametric model of non-Rankine vortex tangential winds has good potential for diagnosing the pressure features arising in dust devils, waterspouts, tornadoes, tornado cyclones, and mesocyclones. Finally, presented are two examples in which the parametric model is fitted to a tangential velocity profile, one derived from an idealized numerical simulation and the other derived from high-resolution Doppler radar data collected in a real tornado.

Statistical Models of Holland Pressure Profile Parameter and Radius to Maximum Winds of Hurricanes from Flight-Level Pressure and H*Wind Data

Abstract In many hurricane risk models the inclusion of the Holland B parameter plays an important role in the risk prediction methodology. This paper presents an analysis of the relationship between B and a nondimensional intensity parameter. The nondimensional parameter includes the strong negative correlation of B with increasing hurricane size [as defined by the radius to maximum winds (RMW)] and latitude as well as a positive correlation with sea surface temperature. A weak positive correlation between central pressure deficit and B is also included in the single parameter term. Alternate statistical models relating B to RMW and latitude are also developed. Estimates of B are derived using pressure data collected during hurricane reconnaissance flights, coupled with additional information derived from the Hurricane Research Division’s H*Wind snapshots of hurricane wind fields. The reconnaissance data incorporate flights encompassing the time period 1977 through 2001, but the analysis was limited to include only those data collected at the 700-hPa-or-higher level. Statistical models relating RMW to latitude and central pressure derived from the dataset are compared to those derived for U.S. landfalling storms during the period 1900–2005. The authors find that for the Gulf of Mexico, using only the landfall hurricanes, the data suggest that there is no inverse relationship between RMW and the central pressure deficit. The RMW data also demonstrate that Gulf of Mexico hurricanes are, on average, smaller than Atlantic Ocean hurricanes. A qualitative examination of the variation of B, central pressure, and radius to maximum winds as a function of time suggests that along the Gulf of Mexico coastline (excluding southwest Florida), during the final 6–24 h before landfall, the hurricanes weaken as characterized by both an increase in central pressure and the radius to maximum winds and a decrease in B. This weakening characteristic of landfalling storms is not evident for hurricanes making landfall elsewhere along the U.S. coastline.

A Simple Empirical Model for Estimating the Intensity Change of Tropical Cyclones after Landfall along the South China Coast

Abstract The intensity change of past (1976–2005) tropical cyclones that made landfall along the south China coast (110.5°–117.5°E) is examined in this study using the best-track data from the Hong Kong Observatory. The change in the central pressure deficit (environmental pressure minus central pressure) and maximum surface wind after landfall are found to fit fairly well with an exponential decay model. Of the various potential predictors, the landfall intensity, landward speed, and excess of 850-hPa moist static energy have significant influence on the decay rates. Prediction equations for the exponential decay constants are developed based on these predictors.

Martian and terrestrial dust devils: Test of a scaling theory using Pathfinder data

The Mars Pathfinder meteorological station recorded wind, pressure, and temperature fluctuations that have been interpreted as dust devils: warm‐core vortices that form at the bottom of connective plumes. We apply a scaling theory [Rennó et al., 1998], developed to explain terrestrial dust devil observations, to test the validity of this interpretation and to provide a simple physical interpretation of the general characteristics of Martian dust devils. The theory is based on the thermodynamics of heat engines and predicts the central pressure and the wind speed of the connective vortices. Our findings are as follows: For the best documented candidate event (sol 25), observed wind and pressure fluctuations are consistent with those predicted by the model and hence strengthen the interpretation of this case as a dust devil. A number of other candidates, less well documented, however, also are consistent with passage of a dust devil over or near the lander. Temperature fluctuations observed on other sols permit dust devils an order of magnitude larger than the ones measured by the meteorology package. The strongest dust devils predicted by our theory have a central pressure deficit of about 50 Pa and wind speed of about 60 m s−1. The strongest dust devils are capable of lofting dust and hence support the interpretation of selected Pathfinder images as showing the passage of dust devils within sight of the lander.

The Effects of Dissipative Heating on Hurricane Intensity

The effects of dissipative heating on hurricane intensity are examined using a 72-h explicit simulation of Hurricane Andrew (1992) with a state-of-the-art, three-dimensional, nonhydrostatic mesoscale (cloud resolving) model (i.e., MM5). It is found that the inclusion of dissipative heating increases the central pressure deficit of the storm by 5–7 hPa and its maximum surface wind by about 10% prior to landfall. It is shown that dissipative heating tends to warm the surface layer, causing a decrease (increase) in sensible heat flux at the sea surface (the top of the surface layer) that acts to cool the surface layer, although the net (sensible plus dissipative) heating rates are still 30%–40% greater than the sensible heating rates in the control simulation. Finally, the potential effects of energy transfer into the ocean, sea surface temperature changes within the inner core, and evaporation of sea spray, interacting with dissipative heating, on hurricane intensity are discussed.

Fast hurricane wind analysis using the orthogonal decomposition method

An efficient diagnostic approach is presented to solve the risk analysis problem for the exposure of large insurance portfolios to hurricane wind hazard. Efficiency is gained from replacing the complex numerical solution for the wind profile with a system of orthogonal modes. The orthogonal decomposition method (ODM) is applied to the windfield predicted by a numerical model which follows the work done by Shapiro [J. Fluid Mech. Yo (1983)]. Using the ODM, the wind speed data is observed in a new coordinate system, in which each axis is aligned along the direction of maximum joint variability. In this way, using the first mode, first two modes, and the first three modes 87%, 97% and 99.8% of the variance may be represented. It should be mentioned that using only the first mode captures about 98% of the mean-square wind speed, and the second and third modes contribute much less. Based on this analysis, it is shown that the central pressure deficit, Δp, contributes the most to the wind speed. Radius to maximum wind, RMAX, and track speed, VT, in turn, have smaller contribution to the wind speed.

Data Base of Maximum Sea States During Hurricanes

A simple method is developed for calculating a data base of maximum sea states at a given location in deep water under hurricane conditions. The long‐term statistics of maximum sea states under hurricane conditions can then be estimated from this data base. The method is. based on (1) A parameterized time‐ and space‐dependent wind‐wave model that allows the evaluation of the sea state at a given, fixed location as a function of hurricane parameters (forward velocity, radius of maximum wind, central pressure deficit) and the location of the site relative to the storm track; and (2) the historical record of hurricane parameters for storms that have passed within 100 nautical mi of the selected site. The method differs from the past formulations of equivalent simplicity in two ways: (1) The inclusion of storm‐track distribution as a parameter affecting the sea‐state statistics; and (2) the choice of the wind‐wave model used in hindcasting the historical storms. The method is applied to a site in the Gulf of Mexico.

Measurements of Axial Pressures in Tornado-like Vortices

Abstract The results of a series of measurements of centerline pressure deficit in tornado-like vortices are described. These measurements were undertaken for the purpose of determining 1) how the magnitude of the central pressure deficit in a columnar vortex varies with height, and 2) what functional relationships exist between them deficits and the dynamic and geometric parameters characterizing the flow. The results graphically show the complicated variation of central pressure deficit with height in both laminar and turbulent vortices In low-swirl vortices, the largest deficits are found aloft, not at surface. Further, the low-swirl vortices have generally greater central pressure deficits than moderate-swirl events. The greatest deficits are tied to the approach of the vortex breakdown to the lower surface. The data also indicate a cubic dependence of the central pressure deficit on applied circulation.