Observations of wind profiles within the tropical cyclone boundary layer until recently have been quite rare. However, the recent spate of observations from the GPS dropsonde have confirmed that a low-level wind speed maximum is a common feature of the tropical cyclone boundary layer. In Part I, a mechanism for producing such a maximum was proposed, whereby strong inward advection of angular momentum generates the supergradient flow. The processes that maintain the necessary inflow against the outward acceleration due to gradient wind imbalance were identified as being (i) vertical diffusion, (ii) vertical advection, and (iii) horizontal advection, and a linear analytical model of the boundary layer flow in a translating tropical cyclone was presented and used to diagnose the properties of the jet and the near-surface flow. A significant shortcoming was that the jet was too weak, which was argued to be due to the neglect of vertical advection. Here, a high-resolution, dry, hydrostatic, numerical model using the full primitive equations and driven by an imposed pressure gradient representative of a tropical cyclone is presented. It relaxes the constraint of linearity from Part I, includes the full advection terms, and produces a markedly stronger jet, more consistent with the observations. It is shown that the vertical advection of inflow is of major importance in jet dynamics, and that its neglect was the main reason that the linear model produced too weak a jet.

For applications such as windstorm underwriting or storm-surge forecasting, hurricane wind profiles are often approximated by continuous functions that are zero at the vortex center, increase to a maximum in the eyewall, and then decrease asymptotically to zero far from the center. Comparisons between the most commonly used functions and aircraft observations reveal systematic errors. Although winds near the peak are too strong, they decrease too rapidly with distance away from the peak. Pressure–wind relations for these profiles typically overestimate maximum winds. A promising alternative is a family of sectionally continuous profiles in which the wind increases as a power of radius inside the eye and decays exponentially outside the eye after a smooth polynomial tran-sition across the eyewall. Based upon a sample of 493 observed profiles, the mean exponent for the power law is 0.79 and the mean decay length is 243 km. The database actually contains 606 aircraft sorties, but 113 of these failed quality-control screening. Hurricanes stronger than Saffir–Simpson category 2 often require two exponentials to match the observed rapid decrease of wind with radius just outside the eye and slower decrease farther away. Experimentation showed that a fixed value of 25 km was satisfactory for the faster decay length. The mean value of the slower decay length was 295 km. The mean contribution of the faster exponential to the outer profile was 0.10, but for the most intense hurricanes it sometimes exceeded 0.5. The power-law exponent and proportion of the faster decay length increased with maximum wind speed and decreased with latitude, whereas the slower decay length decreased with intensity and increased with latitude, consistent with the qualitative observation that more intense hurricanes in lower latitudes usually have more sharply peaked wind profiles. 1.

Present parameterizations of air–sea momentum flux at high wind speed, including hurricane wind forcing, are based on extrapolation from field measurements in much weaker wind regimes. They predict monotonic increase of drag coefficient (Cd) with wind speed. Under hurricane wind forcing, the present numerical exper-iments using a coupled ocean wave and wave boundary layer model show that Cd at extreme wind speeds strongly depends on the wave field. Higher, longer, and more developed waves in the right-front quadrant of the storm produce higher sea drag; lower, shorter, and younger waves in the rear-left quadrant produce lower sea drag. Hurricane intensity, translation speed, as well as the asymmetry of wind forcing are major factors that determine the spatial distribution of Cd. At high winds above 30 m s21, the present model predicts a significant reduction of Cd and an overall tendency to level off and even decrease with wind speed. This tendency is consistent with recent observational, experimental, and theoretical results at very high wind speeds. 1.

Storm surge, the onshore rush of sea water caused by the high winds and low pressure associated with a hurricane, can compound the effects of inland flooding caused by rainfall, leading to loss of property and loss of life for residents of coastal areas. Numerical ocean models are essential for creating storm surge forecasts for coastal areas. These models are driven primarily by the surface wind forcings. Currently, the gridded wind fields used by ocean models are specified by deterministic formulas that are based on the central pressure and location of the storm center. While these equations incorporate important physical knowledge about the structure of hurricane surface wind fields, they cannot always capture the asymmetric and dynamic nature of a hurricane. A new Bayesian multivariate spatial statistical modeling framework is introduced combining data with physical knowledge about the wind fields to improve the estimation of the wind vectors. Many spatial models assume the data follow a Gaussian distribution. However, this may be overly-restrictive for wind fields data which often display erratic behavior, such as sudden changes in time or space. In this paper we develop a semiparametric multivariate spatial model for these data. Our model builds on the stick-breaking prior, which is frequently used in Bayesian modeling to capture uncertainty in the parametric form of an outcome. The stick-breaking prior is extended to the spatial setting by assigning each location a different, unknown distribution, and smoothing the distributions in space with a series of kernel functions. This semiparametric spatial model is shown to improve prediction compared to usual Bayesian Kriging methods for the wind field of Hurricane Ivan. 1. Introduction. Modeling

Objectively derived resolution-dependent criteria are defined for the detection of tropical cyclones in model simulations and observationally based analyses. These criteria are derived from the wind profiles of observed tropical cyclones, averaged at various resolutions. Both an analytical wind profile model and two-dimensional observed wind analyses are used. The results show that the threshold wind speed of an observed tropical cyclone varies roughly linearly with resolution. The criteria derived here are compared to the numerous different criteria previously employed in climate model simulations. The resulting method provides a simple means of comparing climate model simulations and reanalyses. 1.

Southern Louisiana is characterized by low-lying topography and an extensive network of sounds, bays, marshes, lakes, rivers, and inlets that permit widespread inundation during hurricanes. A basin- to channel-scale implementation of the Advanced Circulation (ADCIRC) unstructured grid hydrodynamic model has been developed that accurately simulates hurricane storm surge, tides, and river flow in this complex region. This is accomplished by defining a domain and computational resolution appropriate for the relevant processes, specifying realistic boundary conditions, and implementing accurate, robust, and highly parallel unstructured grid numerical algorithms. The model domain incorporates the western North Atlantic, the Gulf of Mexico, and the Caribbean Sea so that interactions between basins and the shelf are explicitly modeled and the boundary condition specification of tidal and hurricane processes can be readily defined at the deep water open boundary. The unstructured grid enables highly refined resolution of the complex overland region for modeling localized scales of flow while minimizing computational cost. Kinematic data assimilative or validated dynamic-modeled wind fields provide the hurricane wind and pressure field forcing. Wind fields are modified to incorporate directional boundary layer changes due to overland increases in surface roughness, reduction

New objective methods are introduced that use readily available data to estimate various aspects of the two-dimensional surface wind field structure in hurricanes. The methods correlate a variety of wind field metrics to combinations of storm intensity, storm position, storm age, and information derived from geo-stationary satellite infrared (IR) imagery. The first method estimates the radius of maximum wind (RMW) in special cases when a clear symmetric eye is identified in the IR imagery. The second method estimates RMW, and the additional critical wind radii of 34-, 50-, and 64-kt winds for the general case with no IR scene–type constraint. The third method estimates the entire two-dimensional surface wind field inside a storm-centered disk with a radius of 182 km. For each method, it is shown that the inclusion of infrared satellite data measurably reduces error. All of the methods can be transitioned to an operational setting or can be used as a postanalysis tool. 1.

Geostationary infrared (IR) satellite data are used to provide estimates of the symmetric and total low-level wind fields in tropical cyclones, constructed from estimations of an azimuthally averaged radius of maximum wind (RMAX), a symmetric tangential wind speed at a radius of 182 km (V182), a storm motion vector, and the maximum intensity (VMAX). The algorithm is derived using geostationary IR data from 405 cases from 87 tropical systems in the Atlantic and east Pacific Ocean basins during the 1995–2003 hurricane seasons that had corresponding aircraft data available. The algorithm is tested on 50 cases from seven tropical storms and hurricanes during the 2004 season. Aircraft-reconnaissance-measured RMAX and V182 are used as dependent variables in a multiple linear regression technique, and VMAX and the storm motion vector are estimated using conventional methods. Estimates of RMAX and V182 exhibit mean absolute errors (MAEs) of 27.3 km and 6.5 kt, respectively, for the dependent samples. A modified combined Rankine vortex model is used to estimate the one-dimensional symmetric tangential wind field from VMAX, RMAX, and V182. Next, the storm motion vector is added to the symmetric wind to produce estimates of the total wind field. The MAE of the IR total wind retrievals is 10.4 kt, and the variance explained is 53%, when compared with the two-dimensional wind fields from the aircraft data for the independent cases. 1.

HurricaneGustav (2008)made landfall in southern Louisiana on 1 September 2008 with its eye never closer than 75 km to New Orleans, but its waves and storm surge threatened to flood the city. Easterly tropical-storm-strength winds impacted the region east of theMississippi River for 12–15 h, allowing for early surge to develop up to 3.5 m there and enter the river and the city’s navigation canals. During landfall, winds shifted from easterly to southerly, resulting in late surge development and propagation over more than 70 km of marshes on the river’s west bank, over more than 40 km of Caernarvonmarsh on the east bank, and into Lake Pontchartrain to the north.Wind waves with estimated significant heights of 15 m developed in the deepGulf of Mexico but were reduced in size once they reached the continental shelf. The barrier islands further dissipated the waves, and locally generated seas existed behind these effective breaking zones. The hardening and innovative deployment of gauges since Hurricane Katrina (2005) resulted in a wealth of measured data for Gustav. A total of 39 wind wave time histories, 362 water level time histories, and 82 high

Abstract—The use of QuikSCAT data for wind retrievals of tropical cyclones is described. The evidence of QuikSCAT 0 dependence on wind direction for 30-m/s wind speeds is presented. The QuikSCAT 0 show a peak-to-peak wind direction modulation of 1 dB at 35-m/s wind speed, and the amplitude of modulation decreases with increasing wind speed. The decreasing directional sensitivity to wind speed agrees well with the trend of QSCAT1 model function at near 20 m/s. A correction of the QSCAT1 model function for above 23-m/s wind speed is proposed. We explored two microwave radiative transfer models to correct the attenuation and scattering effects of rain for wind retrievals. One is derived from the collocated QuikSCAT and Special Sensor Microwave/Imager (SSM/I) dataset, and the other one is a published parametric model developed for rain radars. These two radiative transfer models account for the effects of volume scattering, scattering from rain-roughened surfaces and rain attenuation. The models suggest that the 0 of wind-roughened sea surfaces for 40–50-m/s winds are comparable to the 0 of rain contributions for up to about 10–15 mm/h. Both radiative transfer models have been used to retrieve the ocean wind vectors from the collocated QuikSCAT and SSM/I rain rate data for several tropical cyclones. The resulting wind speed estimates of these tropical cyclones show improved agreement with the wind fields derived from the best track analysis and Holland’s model for up to about 15-mm/h SSM/I rain rate. A comparative analysis of maximum wind speed estimates suggests that other rain parameters likely have to be considered for further improvements. Index Terms—Hurricane, ocean wind, rain attenuation, rain scattering, scatterometer, tropical cyclone. I.