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was 49–52 m s −1 at 61–67-km radius. The strong cyclonic circulation was very deep with 50 m s −1 winds reaching a height of 12 km. Weak-echo moats, such as the one shown in Fig. 1 , are often found in intense tropical cyclones (TCs). The tendency for convection to be suppressed in the moat region is generally attributed to mesoscale subsidence between two regions of strong upward motion. According to Dodge et al. (1999) , there appears to be lower tropospheric subsidence in the moat, but the
was 49–52 m s −1 at 61–67-km radius. The strong cyclonic circulation was very deep with 50 m s −1 winds reaching a height of 12 km. Weak-echo moats, such as the one shown in Fig. 1 , are often found in intense tropical cyclones (TCs). The tendency for convection to be suppressed in the moat region is generally attributed to mesoscale subsidence between two regions of strong upward motion. According to Dodge et al. (1999) , there appears to be lower tropospheric subsidence in the moat, but the
effects of inland winds from tropical cyclones. Motivated in part by Hurricane Hugo and with support from the U.S. Federal Emergency Management Agency, Kaplan and DeMaria (1995 , hereinafter KD95 ) developed a simple empirical model for estimating tropical cyclone winds after landfall in the United States for storms south of 37°N. This model assumes that the intensity decay rate at any time after landfall is proportional to the intensity at that time, which results in an exponential decay equation
effects of inland winds from tropical cyclones. Motivated in part by Hurricane Hugo and with support from the U.S. Federal Emergency Management Agency, Kaplan and DeMaria (1995 , hereinafter KD95 ) developed a simple empirical model for estimating tropical cyclone winds after landfall in the United States for storms south of 37°N. This model assumes that the intensity decay rate at any time after landfall is proportional to the intensity at that time, which results in an exponential decay equation
1. Introduction Tropical cyclone (TC) wind observations are obtained in a variety of ways. In situ surface observations include those from surface stations, buoys, and ships, and upper-air observations include those from radiosondes, aircraft reconnaissance, and global positioning system (GPS) dropsondes. Winds can also be measured using remote sensing such as Doppler radar, microwave scatterometers, and microwave radiometers. Microwave scatterometers provide the well-known sea surface wind
1. Introduction Tropical cyclone (TC) wind observations are obtained in a variety of ways. In situ surface observations include those from surface stations, buoys, and ships, and upper-air observations include those from radiosondes, aircraft reconnaissance, and global positioning system (GPS) dropsondes. Winds can also be measured using remote sensing such as Doppler radar, microwave scatterometers, and microwave radiometers. Microwave scatterometers provide the well-known sea surface wind
1. Introduction Radar reflectivities demonstrate that typical tropical cyclones with strong tangential wind have a symmetric eyewall structure and are accompanied by several spiral bands. Observational studies using aircraft have captured a characteristic structure that the spiral bands often generate a well-defined ring of heavy rainfall radially outside the primary eyewall and consequently form the secondary eyewall. This structure is generally called the concentric eyewall structure
1. Introduction Radar reflectivities demonstrate that typical tropical cyclones with strong tangential wind have a symmetric eyewall structure and are accompanied by several spiral bands. Observational studies using aircraft have captured a characteristic structure that the spiral bands often generate a well-defined ring of heavy rainfall radially outside the primary eyewall and consequently form the secondary eyewall. This structure is generally called the concentric eyewall structure
1. Introduction Over the past 5 yr, the extratropical transition (ET) life cycle has been defined based upon satellite signatures ( Klein et al. 2000 ) and objective measures of dynamic and thermal structure ( Evans and Hart 2003 ). Following ET, tropical cyclones (TCs) can become explosive cold-core cyclones, explosive warm-seclusion cyclones, or simply decay as a cold-core cyclone ( Hart 2003 ; Evans and Hart 2003 ). Each of these three evolution paths has dramatically different wind and
1. Introduction Over the past 5 yr, the extratropical transition (ET) life cycle has been defined based upon satellite signatures ( Klein et al. 2000 ) and objective measures of dynamic and thermal structure ( Evans and Hart 2003 ). Following ET, tropical cyclones (TCs) can become explosive cold-core cyclones, explosive warm-seclusion cyclones, or simply decay as a cold-core cyclone ( Hart 2003 ; Evans and Hart 2003 ). Each of these three evolution paths has dramatically different wind and
1. Introduction The capability of providing reflectivity and wind information with high temporal and spatial resolutions makes Doppler radar an important instrument for the observations of various severe weather phenomena such as tropical cyclones (TCs). Early applications of Doppler radar to the study of the vortex structure (e.g., Donaldson 1970 ), or the intensity and center position of an axisymmetric TC (e.g., Baynton 1979 ; Wood and Brown 1992 ), relied on the pattern recognition of
1. Introduction The capability of providing reflectivity and wind information with high temporal and spatial resolutions makes Doppler radar an important instrument for the observations of various severe weather phenomena such as tropical cyclones (TCs). Early applications of Doppler radar to the study of the vortex structure (e.g., Donaldson 1970 ), or the intensity and center position of an axisymmetric TC (e.g., Baynton 1979 ; Wood and Brown 1992 ), relied on the pattern recognition of
1. Introduction This study is aimed at documenting and understanding aspects of subseasonal variability of tropical cyclone (TC) genesis over the south Indian Ocean. We focus on the modulation of TC genesis by the various large-scale waves, or modes that exist coupled with convection in the tropical atmosphere. The modes considered are the Madden–Julian oscillation (MJO; e.g., Madden and Julian 1994 ) and the convectively coupled equatorial Rossby (ER), Kelvin, and mixed Rossby–gravity (MRG
1. Introduction This study is aimed at documenting and understanding aspects of subseasonal variability of tropical cyclone (TC) genesis over the south Indian Ocean. We focus on the modulation of TC genesis by the various large-scale waves, or modes that exist coupled with convection in the tropical atmosphere. The modes considered are the Madden–Julian oscillation (MJO; e.g., Madden and Julian 1994 ) and the convectively coupled equatorial Rossby (ER), Kelvin, and mixed Rossby–gravity (MRG
1. Introduction Tropical cyclone (TC) track forecasts in the Atlantic basin have steadily improved over the last 30 yr. Franklin et al. (2003) , updating the work of McAdie and Lawrence (2000) , found that position errors in the National Hurricane Center’s (NHC) official track forecasts for the Atlantic basin decreased at an average annual rate of 1.3%, 1.9%, and 2.0% at 24, 48, and 72 h, respectively, from 1970 to 2001. However, in contrast to the basinwide track forecast improvements
1. Introduction Tropical cyclone (TC) track forecasts in the Atlantic basin have steadily improved over the last 30 yr. Franklin et al. (2003) , updating the work of McAdie and Lawrence (2000) , found that position errors in the National Hurricane Center’s (NHC) official track forecasts for the Atlantic basin decreased at an average annual rate of 1.3%, 1.9%, and 2.0% at 24, 48, and 72 h, respectively, from 1970 to 2001. However, in contrast to the basinwide track forecast improvements
1. Introduction Possibly the most accurate and reliable measure of tropical cyclone (TC) intensity is the minimum sea level pressure (MSLP) either estimated from aircraft reconnaissance flight level or obtained via direct observation (surface or dropwindsonde). However, the destructive potential of TCs is better related to the maximum wind speed at or near the surface. For this reason, TC forecasts and advisories as well as climatological records are most useful when they describe TC intensity
1. Introduction Possibly the most accurate and reliable measure of tropical cyclone (TC) intensity is the minimum sea level pressure (MSLP) either estimated from aircraft reconnaissance flight level or obtained via direct observation (surface or dropwindsonde). However, the destructive potential of TCs is better related to the maximum wind speed at or near the surface. For this reason, TC forecasts and advisories as well as climatological records are most useful when they describe TC intensity
efficient tool for evaluating these forecast sensitivities ( Errico 1997 ). Conspicuously absent from the many prior applications of adjoint-based sensitivity analysis to tropical and extratropical cyclone issues are synoptic and dynamical interpretations of these sensitivities. When dynamical interpretation of sensitivities is provided, often what is offered is merely the observation that the distribution of sensitivities ( Vukicevic and Raeder 1995 ; Wu et al. 2007 ) or singular vectors ( Peng and
efficient tool for evaluating these forecast sensitivities ( Errico 1997 ). Conspicuously absent from the many prior applications of adjoint-based sensitivity analysis to tropical and extratropical cyclone issues are synoptic and dynamical interpretations of these sensitivities. When dynamical interpretation of sensitivities is provided, often what is offered is merely the observation that the distribution of sensitivities ( Vukicevic and Raeder 1995 ; Wu et al. 2007 ) or singular vectors ( Peng and
