A Model for the Tropical Cyclone Wind Field Response to Idealized Landfall

The impacts of a tropical cyclone after landfall depend not only on storm intensity but also on the size and structure of the wind field. Hence, a simple predictive model for the wind field after landfall has significant potential value. This work tests existing theory for wind structure and size over the ocean against idealized axisymmetric landfall experiments in which the surface beneath a mature storm is instantaneously dried and roughened individually or simultaneously. Structure theory captures the response of the low-level wind field to different types of idealized landfalls given intensity and size response. Storm size, modeled to follow the ratio of simulated time-dependent storm intensity to the Coriolis parameter vm(t)/f, can generally predict the transient response of the storm gale wind radii r34kt to inland surface forcings, particularly for at least moderate surface roughening regardless of the level of drying. Given knowledge of the intensity evolution, the above results combine to yield a theoretical model that can predict the full tangential wind field response to idealized landfalls. An example application to a real world landfall is provided to demonstrate its potential utility for risk applications

A simple model for predicting the hurricane radius of maximum wind from outer size

The radius of maximum wind ($R_{max}$) in a hurricane governs the footprint of hazards, particularly damaging wind and rainfall. However, $R_{max}$ is noisy to observe directly and is poorly resolved in reanalyses and climate models. In contrast, outer wind radii are much less sensitive to such issues. Here we present a simple empirical model for predicting $R_{max}$ from the radius of 34-kt wind ($R_{17.5ms}$) that only requires as input quantities that are routinely estimated operationally: maximum wind speed, $R_{17.5ms}$, and latitude. The form of the empirical model takes advantage of our physical understanding of hurricane radial structure and is trained on the Extended Best Track database from the North Atlantic; results are similar for the TC-OBS database. The physics reduces the relationship between the two radii to a dependence on two physical parameters, while the observational data enables an optimal estimate of the quantitative dependence on those parameters. The model performs substantially better than existing operational methods for estimating $R_{max}$. The model reproduces the observed statistical increase in $R_{max}$ with latitude and demonstrates that this increase is driven by the increase in $R_{17.5ms}$ with latitude. Overall, the model offers a simple and fast first-order prediction of $R_{max}$ that can be used operationally and in risk models

A Semi-parametric Analysis of Technology, with an Application to U.S. Dairy Farms

This article proposes a semi-parametric stochastic frontier model (SPSF) in which components of the technology and of technical efficiency are represented using semi-parametric methods and estimated in a Bayesian framework. The approach is illustrated in an application to US farm data. The analysis shows important scale economies for small and medium herds and constant return to scale for larger herds. With the exception of labor, estimates of marginal products were close to the value expected under profit maximization. Finally, the results suggest important opportunities to increase productivity through reductions in technical inefficiencies.

Future changes in the vertical structure of severe convective storm environments over the U.S. central Great Plains

The effect of warming on severe convective storm potential is commonly explained in terms of changes in vertically-integrated ("bulk") environmental parameters, such as CAPE and 0--6 km shear. However, such events are known to depend on details of the vertical structure of the thermodynamic and kinematic environment that can change independently of these bulk parameters. This work examines how warming may affect the complete vertical structure of these environments for fixed ranges of values of high CAPE and bulk shear, using data over the central Great Plains from two high-performing climate models. Temperature profiles warm relatively uniformly with height, with a slight decrease in free tropospheric lapse rate, and the tropopause shifts upwards at constant temperature. The boundary layer becomes slightly drier (-2--4\% relative humidity) while the free troposphere becomes slightly moister (+2--3\%). Moist static energy (MSE) increases relatively uniformly with height with slightly larger increase within the boundary layer. Moist static energy deficit increases slightly above 4 km altitude. Wind shear and storm-relative helicity increase within the lowest 1.5 km associated with stronger hodograph curvature. Changes are broadly consistent between the two models despite differing biases relative to ERA5. The increased low-level shear and SRH suggests an increased potential for severe thunderstorms and tornadoes, while the slight increase in free tropospheric MSE deficit (enhanced entrainment) and decrease in boundary layer relative humidity (higher LCL) may oppose these effects. Evaluation of the net response of severe convective storm outcomes cannot be ascertained here but could be explored in simulation experiments

Tropical cyclone size in observations and in radiative-convective equilibrium

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 147-154).Tropical cyclone size remains an unsolved problem in tropical meteorology, yet size plays a significant role in the damage caused by tropical cyclones due to wind, storm surge, and inland freshwater flooding. This work explores size, defined as the radius of vanishing wind, in observations and at equilibrium in an idealized numerical model. First, a climatology of size is created from the QuikSCAT database of near-surface wind vectors for the years 1999-2008. Globally, the distribution of the outer radius is found to be log-normal, with statistically significant variation across ocean basins, but with minimal correlation with various dynamic and thermodynamic parameters. Second, the sensitivity of the structure of a numerically-simulated axisymmetric tropical cyclone at statistical equilibrium to the set of relevant model, initial, and environmental external parameters is explored. The analysis is performed in a highly-idealized state of radiative-convective equilibrium (RCE). The non-dimensional equilibrium radial wind profile is found to be modulated primarily by a single nondimensional parameter given by the ratio of the storm radial length scale to the parameterized eddy radial length scale. The relevant storm length scale is shown to be the ratio of the potential intensity to the Coriolis parameter, matching the prediction for the "natural" storm length scale in prevailing axisymmetric tropical cyclone theory. The outer storm circulation is further modulated by a second non-dimensional parameter that represents the non-dimensional Ekman suction rate. Third, size is explored in three-dimensional "tropical cyclone world" simulations, with preliminary results confirming the relevant length scale obtained in axisymmetry. Ultimately, the results of the equilibrium storm analysis are insufficient to explain the observed distribution of tropical cyclone size, but they provide the first steps toward a more fundamental understanding of the dynamics of size.by Daniel Robert Chavas.Ph.D

A simple model for predicting tropical cyclone minimum central pressure from intensity and size

Minimum central pressure ($P_{min}$) is an integrated measure of the tropical cyclone wind field and is known to be a useful indicator of storm damage potential. A simple model that predicts $P_{min}$ from routinely-estimated quantities, including storm size, would be of great value. Here we present a simple linear empirical model for predicting $P_{min}$ from maximum wind speed, the radius of 34-knot winds ($R_{34kt}$), storm-center latitude, and the environmental pressure. An empirical model for the pressure deficit is first developed that takes as predictors specific combinations of these quantities that are derived directly from theory, based on gradient wind balance and a modified-Rankine-type wind profile known to capture storm structure inside of $R_{34kt}$. Model coefficients are estimated using data from the southwestern North Atlantic and eastern North Pacific from 2004--2022 using aircraft-based estimates of $P_{min}$, Extended Best Track data, and estimates of environmental pressure from Global Forecast System (GFS) analyses. The model has near-zero conditional bias even for low $P_{min}$, explaining 94.4\% of the variance. Performance is superior to a variety of other model formulations, including a standard wind-pressure model that does not account for storm size or latitude (89.4\% variance explained). Model performance is also strong when applied to high-latitude data and data near coastlines. Finally, the model is shown to perform comparably well in an operations-like setting based solely on routinely-estimated variables, including the pressure of the outermost closed isobar. Case study applications to five impactful historical storms are discussed. Overall, the model offers a simple and fast prediction for $P_{min}$ for practical use in operations and research