scholarly journals Can Existing Theory Predict the Response of Tropical Cyclone Intensity to Idealized Landfall?

Author(s):  
Jie Chen ◽  
Daniel R. Chavas

AbstractTropical cyclones cause significant inland hazards, including wind damage and freshwater flooding, that depend strongly on how storm intensity evolves after landfall. Existing theoretical predictions for storm intensification and equilibrium storm intensity have been tested over the open ocean but have not yet been applied to storms after landfall. Recent work examined the transient response of the tropical cyclone low-level wind field to instantaneous surface roughening or drying in idealized axisymmetric f -plane simulations. Here, experiments testing combined surface roughening and drying with varying magnitudes of each are used to test theoretical predictions for the intensity response. The transient response to combined surface forcings can be reproduced by the product of their individual responses, in line with traditional potential intensity theory. Existing intensification theory is generalized to weakening and found capable of reproducing the time-dependent inland intensity decay. The initial (0-10min) rapid decay of near-surface wind caused by surface roughening is not captured by existing theory but can be reproduced by a simple frictional spin-down model, where the decay rate is a function of surface drag coefficient. Finally, the theory is shown to compare well with the prevailing empirical decay model for real-world storms. Overall, results indicate the potential for existing theory to predict how tropical cyclone intensity evolves after landfall.

2019 ◽  
Vol 34 (4) ◽  
pp. 905-922 ◽  
Author(s):  
Timothy L. Olander ◽  
Christopher S. Velden

Abstract The advanced Dvorak technique (ADT) is used operationally by tropical cyclone forecast centers worldwide to help estimate the intensity of tropical cyclones (TCs) from operational geostationary meteorological satellites. New enhancements to the objective ADT have been implemented by the algorithm development team to further expand its capabilities and precision. The advancements include the following: 1) finer tuning to aircraft-based TC intensity estimates in an expanded development sample, 2) the incorporation of satellite-based microwave information into the intensity estimation scheme, 3) more sophisticated automated TC center-fixing routines, 4) adjustments to the intensity estimates for subtropical systems and TCs undergoing extratropical transition, and 5) addition of a surface wind radii estimation routine. The goals of these upgrades and others are to provide TC analysts/forecasters with an expanded objective guidance tool to more accurately estimate the intensity of TCs and those storms forming from, or converting into, hybrid/nontropical systems. The 2018 TC season is used to illustrate the performance characteristics of the upgraded ADT.


2017 ◽  
Vol 74 (7) ◽  
pp. 2315-2324 ◽  
Author(s):  
Kerry Emanuel ◽  
Fuqing Zhang

Abstract Errors in tropical cyclone intensity forecasts are dominated by initial-condition errors out to at least a few days. Initialization errors are usually thought of in terms of position and intensity, but here it is shown that growth of intensity error is at least as sensitive to the specification of inner-core moisture as to that of the wind field. Implications of this finding for tropical cyclone observational strategies and for overall predictability of storm intensity are discussed.


2013 ◽  
Vol 118 (5) ◽  
pp. 2367-2377 ◽  
Author(s):  
Yu-Chia Chang ◽  
Guan-Yu Chen ◽  
Ruo-Shan Tseng ◽  
Luca R. Centurioni ◽  
Peter C. Chu

2015 ◽  
Vol 30 (3) ◽  
pp. 692-701 ◽  
Author(s):  
Jing Xu ◽  
Yuqing Wang

Abstract The dependence of tropical cyclone (TC) intensification rate IR on storm intensity and size was statistically analyzed for North Atlantic TCs during 1988–2012. The results show that IR is positively (negatively) correlated with storm intensity (the maximum sustained near-surface wind speed Vmax) when Vmax is below (above) 70–80 knots (kt; 1 kt = 0.51 m s−1), and negatively correlated with storm size in terms of the radius of maximum wind (RMW), the average radius of gale-force wind (AR34), and the outer-core wind skirt parameter DR34 (=AR34 − RMW). The turning point for Vmax of 70–80 kt is explained as a balance between the potential intensification and the maximum potential intensity (MPI). The highest IR occurs for Vmax = 80 kt, RMW ≤ 40 km, and AR34 = DR34 = 150 km. The high frequency of occurrence of intensifying TCs occurs for Vmax ≤ 80 kt and RMW between 20 and 60 km, AR34 ≤ 200 km, and DR34 ≤ 150 km. Rapid intensification (RI) often occurs in a relatively narrow parameter space in storm intensity and both inner- and outer-core sizes. In addition, a theoretical basis for the intensity dependency has also been provided based on a previously constructed simplified dynamical system for TC intensity prediction.


2019 ◽  
Vol 32 (22) ◽  
pp. 7837-7855 ◽  
Author(s):  
Renzhi Jing ◽  
Ning Lin

Abstract A hidden Markov model is developed to simulate tropical cyclone intensity evolution dependent on the surrounding large-scale environment. The model considers three unobserved (hidden) discrete states of storm intensity change and associates each state with a probability distribution of intensity change. The storm’s transit from one state to another is described as a Markov chain. Both the intensity change and state transit components of the model are dependent on environmental variables including potential intensity, vertical wind shear, relative humidity, and ocean feedback. This Markov Environment-Dependent Hurricane Intensity Model (MeHiM) is used to simulate the evolution of storm intensity along the storm track over the ocean, and a simple decay model is added to estimate the intensity change when the storm moves over land. Data for the North Atlantic (NA) basin from 1979 to 2014 (555 storms) are used for model development and evaluation. Probability distributions of 6- and 24-h intensity change, lifetime maximum intensity, and landfall intensity based on model simulations and observations compare well. Although the MeHiM is still limited in fully describing rapid intensification, it shows a significant improvement over previous statistical models (e.g., linear, nonlinear, and finite mixture models).


2019 ◽  
Vol 34 (3) ◽  
pp. 521-538 ◽  
Author(s):  
Shixuan Zhang ◽  
Zhaoxia Pu

Abstract Observations from High-Definition Sounding System (HDSS) dropsondes, collected for Hurricane Joaquin during the Office of Naval Research Tropical Cyclone Intensity (TCI) field experiment in 2015, are assimilated into the NCEP Hurricane Weather Research and Forecasting (HWRF) Model. The Gridpoint Statistical Interpolation (GSI)-based hybrid three-dimensional and four-dimensional ensemble–variational (3DEnVar and 4DEnVar) data assimilation configurations are compared. The assimilation of HDSS dropsonde observations can help HWRF initialization by generating consistent analysis between wind and pressure fields and can also compensate for the initial maximum surface wind errors in the absence of initial vortex intensity correction. Compared with GSI–3DEnVar, the assimilation of HDSS dropsonde observations using GSI–4DEnVar generates a more realistic initial vortex intensity and reproduces the rapid weakening (RW) of Hurricane Joaquin, suggesting that the assimilation of high-resolution inner-core observations (e.g., HDSS dropsonde data) based on an advanced data assimilation method (e.g., 4DEnVar) can potentially outperform the vortex initialization scheme currently used in HWRF. Additionally, the assimilation of HDSS dropsonde observations can improve the simulation of vortex structure changes and the accuracy of the vertical motion within the TC inner-core region, which is essential to the successful simulation of the RW of Hurricane Joaquin with HWRF. Additional experiments with GSI–4DEnVar in different configurations also indicate that the performance of GSI–4DEnVar can be further improved with a high-resolution background error covariance and a denser observational bin.


2011 ◽  
Author(s):  
Wayne H. Schubert ◽  
Mark DeMaria ◽  
Charles R. Sampson ◽  
James Cummings

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