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in the TCI dataset, as well as to examine the updrafts and downdrafts observed by the XDDs. Hock and Franklin (1999) used RD-93 dropsondes to derive vertical velocity from GPS fall speeds and a single drag force estimate presumed to be representative for all individual sondes. This method is now routine, but more recent studies use a hydrostatic pressure-derived fall speed rather than the GPS fall speed (e.g., Wang et al. 2015 ). Sonde-derived vertical velocities have been used to examine the
in the TCI dataset, as well as to examine the updrafts and downdrafts observed by the XDDs. Hock and Franklin (1999) used RD-93 dropsondes to derive vertical velocity from GPS fall speeds and a single drag force estimate presumed to be representative for all individual sondes. This method is now routine, but more recent studies use a hydrostatic pressure-derived fall speed rather than the GPS fall speed (e.g., Wang et al. 2015 ). Sonde-derived vertical velocities have been used to examine the
-level forcing mechanism), and this study identifies and quantifies a set of key common features for this class of TCs. The analyses in this study primarily rely on satellite observations, since these data typically provide the most complete spatial and temporal coverage over the storms, given that aircraft observations of TCs are rare in the eastern North Pacific (EPAC) and western Pacific (WPAC; Knabb et al. 2008 ). Without in situ reconnaissance data, intensity estimations and analyses are primarily
-level forcing mechanism), and this study identifies and quantifies a set of key common features for this class of TCs. The analyses in this study primarily rely on satellite observations, since these data typically provide the most complete spatial and temporal coverage over the storms, given that aircraft observations of TCs are rare in the eastern North Pacific (EPAC) and western Pacific (WPAC; Knabb et al. 2008 ). Without in situ reconnaissance data, intensity estimations and analyses are primarily
-mean tangential winds (RMW) of 90 km. It also has a Gaussian-like decay in the vertical with the maximum wind speed at z = 1500 m. The environmental shear profile and how it is introduced in the model are described below. b. Time-varying point-downscaling method The large-scale environmental shear is incorporated into the model using the point-downscaling method (PDS; Nolan 2011 ). Using PDS, the initial environmental flow is balanced by an artificial force that is added to the momentum equation so that
-mean tangential winds (RMW) of 90 km. It also has a Gaussian-like decay in the vertical with the maximum wind speed at z = 1500 m. The environmental shear profile and how it is introduced in the model are described below. b. Time-varying point-downscaling method The large-scale environmental shear is incorporated into the model using the point-downscaling method (PDS; Nolan 2011 ). Using PDS, the initial environmental flow is balanced by an artificial force that is added to the momentum equation so that
the flow upwind of the convective cell and causing sinking. They noted ( Klemp and Wilhelmson 1978 , p. 1105), “The weak downdraft to the west of the updraft z = 2.25 km ([their] Fig.12) is possibly due to blocking westerly flow by the storm forcing the approaching air to descend rather than flow around the storm.” Fritsch and Maddox (1981) , in their analyses of upper-level observations of mesoscale convective complexes (MCC), noted the presence of mesohighs above the MCCs. While their
the flow upwind of the convective cell and causing sinking. They noted ( Klemp and Wilhelmson 1978 , p. 1105), “The weak downdraft to the west of the updraft z = 2.25 km ([their] Fig.12) is possibly due to blocking westerly flow by the storm forcing the approaching air to descend rather than flow around the storm.” Fritsch and Maddox (1981) , in their analyses of upper-level observations of mesoscale convective complexes (MCC), noted the presence of mesohighs above the MCCs. While their
1. Introduction The tropical cyclone (TC)–environmental flow interaction (TCEFI) plays an important role in TC structure and intensity change. Although some indices and methods exist to represent the TCEFI, they have mainly been developed in an axisymmetric framework. However, the azimuthally asymmetric interaction also needs to be considered, because the environmental flow is often highly asymmetric relative to the TC, thus creating an asymmetric forcing on the TC. Environmental features such
1. Introduction The tropical cyclone (TC)–environmental flow interaction (TCEFI) plays an important role in TC structure and intensity change. Although some indices and methods exist to represent the TCEFI, they have mainly been developed in an axisymmetric framework. However, the azimuthally asymmetric interaction also needs to be considered, because the environmental flow is often highly asymmetric relative to the TC, thus creating an asymmetric forcing on the TC. Environmental features such
,c,e,f , 9b–f ). In addition, the “DA without VI” can cause large initial errors for MSW ( Figs. 8f , 9f ), indicating that the enhanced AMVs data only are not enough to force the storm intensity to match the observed intensity for this case. However, it is worth noting that the assimilation of enhanced AMVs in the inner-core region produces smaller average MSLP and MSW errors over the whole 72-h forecasts in most of the cases (e.g., the blue numbers at the top of each panel in Figs. 8b,c , 8e,f , and
,c,e,f , 9b–f ). In addition, the “DA without VI” can cause large initial errors for MSW ( Figs. 8f , 9f ), indicating that the enhanced AMVs data only are not enough to force the storm intensity to match the observed intensity for this case. However, it is worth noting that the assimilation of enhanced AMVs in the inner-core region produces smaller average MSLP and MSW errors over the whole 72-h forecasts in most of the cases (e.g., the blue numbers at the top of each panel in Figs. 8b,c , 8e,f , and
–pressure relationship of TC vortices approximately satisfies the gradient wind balance. In this subsection, we evaluate the impact of increasing resolution during DA on wind–pressure relationship in the analyzed vortices for an initial-hurricane case. The metric used is the net radial force field F as defined by Smith et al. (2009) , Pu et al. (2016) , and Lu and Wang (2019) . A closer-to-zero value of F indicates a better approximation to the gradient wind balance. Figure 10 shows the net radial force
–pressure relationship of TC vortices approximately satisfies the gradient wind balance. In this subsection, we evaluate the impact of increasing resolution during DA on wind–pressure relationship in the analyzed vortices for an initial-hurricane case. The metric used is the net radial force field F as defined by Smith et al. (2009) , Pu et al. (2016) , and Lu and Wang (2019) . A closer-to-zero value of F indicates a better approximation to the gradient wind balance. Figure 10 shows the net radial force
Hurricane Imaging Radiometer (HIRAD) Wind Speed Retrievals and Validation Using Dropsondes; Rosenkranz and Staelin 1972 ). Since the increase in foam is correlated with surface wind speed ( Ross and Cardone 1974 ; Webster et al. 1976 ; Swift et al. 1984 ; Tanner et al. 1987 ), emissivity increases with surface wind speed. The sensitivity to wind speed is greatest at hurricane force (>33 m s −1 ) and is therefore particularly useful for measuring the strongest winds. The four C-band channels also have varying sensitivity to rain, so rain rate and wind speed can be retrieved simultaneously
; Rosenkranz and Staelin 1972 ). Since the increase in foam is correlated with surface wind speed ( Ross and Cardone 1974 ; Webster et al. 1976 ; Swift et al. 1984 ; Tanner et al. 1987 ), emissivity increases with surface wind speed. The sensitivity to wind speed is greatest at hurricane force (>33 m s −1 ) and is therefore particularly useful for measuring the strongest winds. The four C-band channels also have varying sensitivity to rain, so rain rate and wind speed can be retrieved simultaneously
). This stable stratification is a necessary ingredient for adiabatic subsidence to warm the eye. Regardless of the forcing mechanisms, the strong upper-tropospheric warming between 22 and 23 October was enough to completely eliminate the TIL within the eye, allowing the tropopause height and temperature to increase dramatically. Fig . 10. Vertical cross sections of (a),(b) storm-relative radial and (c),(d) tangential velocity (m s −1 ) and the cold-point tropopause height (green lines) in Hurricane
). This stable stratification is a necessary ingredient for adiabatic subsidence to warm the eye. Regardless of the forcing mechanisms, the strong upper-tropospheric warming between 22 and 23 October was enough to completely eliminate the TIL within the eye, allowing the tropopause height and temperature to increase dramatically. Fig . 10. Vertical cross sections of (a),(b) storm-relative radial and (c),(d) tangential velocity (m s −1 ) and the cold-point tropopause height (green lines) in Hurricane
72-h HWRF simulation. In addition, the adjustment of MSLP during the first 6-h forecast of RL-4D-TCI is not observed in RL-4DRES-TCI ( Fig. 10b ), implying that the high-resolution background error covariance and denser observational bin can possibly reduce the initial adjustment in HWRF after DA. Following Pu et al. (2016) , the net radial force field F , which is defined as the difference between the sum of the Coriolis and centrifugal forces and the radial pressure gradient force, is
72-h HWRF simulation. In addition, the adjustment of MSLP during the first 6-h forecast of RL-4D-TCI is not observed in RL-4DRES-TCI ( Fig. 10b ), implying that the high-resolution background error covariance and denser observational bin can possibly reduce the initial adjustment in HWRF after DA. Following Pu et al. (2016) , the net radial force field F , which is defined as the difference between the sum of the Coriolis and centrifugal forces and the radial pressure gradient force, is
