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be truncated from the dataset as a result of old model configurations. For example, in this study for predicting cases of 2012, cases of 2011 and available cases of 2012 have been used. The training set for the 120-h forecast lead of 2012 contained 2958 cases, and 827 different cases were used to tune the bandwidth of the network. 4. Forecast results In this section, the results for intensity forecasts for the hurricane seasons from 2012 through 2016 are included. The GRNN algorithm was run using
be truncated from the dataset as a result of old model configurations. For example, in this study for predicting cases of 2012, cases of 2011 and available cases of 2012 have been used. The training set for the 120-h forecast lead of 2012 contained 2958 cases, and 827 different cases were used to tune the bandwidth of the network. 4. Forecast results In this section, the results for intensity forecasts for the hurricane seasons from 2012 through 2016 are included. The GRNN algorithm was run using
Li 2007 ). The NLLE allows the predictability limit of dynamical systems, such as a chaotic system, to be determined quantitatively. For a low-order chaotic system, the leading NLLE mainly describes the average growth rates of the initial error in the fastest-growing direction. Meanwhile, to assess the actual atmospheric predictability from observational data, a practical and efficient algorithm known as local dynamical analogs (LDAs), has been devised to enable the calculation of the NLLE ( Li
Li 2007 ). The NLLE allows the predictability limit of dynamical systems, such as a chaotic system, to be determined quantitatively. For a low-order chaotic system, the leading NLLE mainly describes the average growth rates of the initial error in the fastest-growing direction. Meanwhile, to assess the actual atmospheric predictability from observational data, a practical and efficient algorithm known as local dynamical analogs (LDAs), has been devised to enable the calculation of the NLLE ( Li
Hurricane Imaging Radiometer (HIRAD) Wind Speed Retrievals and Validation Using Dropsondesfrom an ensemble of possible wind–rain combinations ( Amarin et al. 2012 ). The treatment of surface emissivity as a function of wind speed follows the model of El-Nimri et al. (2010) . The microwave absorption by rain follows Klotz and Uhlhorn (2014) , using their Eq. (12) and the revised coefficients listed in their Table 3. The surface emissivity and rain absorption models are consistent with the operational algorithm for the SFMR ( Klotz and Uhlhorn 2014 ). The surface emissivity model also
from an ensemble of possible wind–rain combinations ( Amarin et al. 2012 ). The treatment of surface emissivity as a function of wind speed follows the model of El-Nimri et al. (2010) . The microwave absorption by rain follows Klotz and Uhlhorn (2014) , using their Eq. (12) and the revised coefficients listed in their Table 3. The surface emissivity and rain absorption models are consistent with the operational algorithm for the SFMR ( Klotz and Uhlhorn 2014 ). The surface emissivity model also
’s longitudinal axis. This separation allows for a dual-Doppler synthesis of the observed Doppler velocities from both the fore and aft scanning radars. The radar data are corrected for navigational errors using the method described by Cai et al. (2018) and subject to an automated quality control process following the “medium threshold” algorithm described by Bell et al. (2013) . Additional quality control of the radar observations is carried out manually to remove ocean returns, radar sidelobes, and
’s longitudinal axis. This separation allows for a dual-Doppler synthesis of the observed Doppler velocities from both the fore and aft scanning radars. The radar data are corrected for navigational errors using the method described by Cai et al. (2018) and subject to an automated quality control process following the “medium threshold” algorithm described by Bell et al. (2013) . Additional quality control of the radar observations is carried out manually to remove ocean returns, radar sidelobes, and
filtering. This technology becomes important at high altitudes (>18 km), where line-of-sight transmission pathlengths from aircraft to sonde can be >200 km from WB-57 or DC-8 flights. FEC algorithms such as Viterbi ( Gupta et al. 2010 ) use additional bandwidth to send along specially encoded extra data bits with the data payload. At the telemetry receiver, a mathematical decode algorithm reconstructs the dataset from a corrupted packet using the extra FEC data. The first XDD system tested at CIRPAS had
filtering. This technology becomes important at high altitudes (>18 km), where line-of-sight transmission pathlengths from aircraft to sonde can be >200 km from WB-57 or DC-8 flights. FEC algorithms such as Viterbi ( Gupta et al. 2010 ) use additional bandwidth to send along specially encoded extra data bits with the data payload. At the telemetry receiver, a mathematical decode algorithm reconstructs the dataset from a corrupted packet using the extra FEC data. The first XDD system tested at CIRPAS had
when AMVs were assimilated. Soden et al. (2001) demonstrated that the assimilation of AMVs led to a more accurate representation of the steering flow, thus improving TC track forecasts in the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane model. Pu et al. (2008) also found that the assimilation of GOES-11 rapid scan AMVs has a positive impact on numerical forecasts of TC intensity and precipitation. More recently, upgraded AMV processing algorithms and strategies have enabled
when AMVs were assimilated. Soden et al. (2001) demonstrated that the assimilation of AMVs led to a more accurate representation of the steering flow, thus improving TC track forecasts in the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane model. Pu et al. (2008) also found that the assimilation of GOES-11 rapid scan AMVs has a positive impact on numerical forecasts of TC intensity and precipitation. More recently, upgraded AMV processing algorithms and strategies have enabled
more weight is given to the ensemble background term. Detailed information regarding this hybrid algorithm can be seen in Wang (2010) . More recently, the GSI-3DEnVar system has been extended to include four-dimensional (4D) ensemble perturbations (e.g., GSI-4DEnVar). Following Kleist and Ide (2015) , the analysis increment in GSI hybrid 4DEnVar is obtained by minimizing a cost function: where the third term is extended to use the asynchronous observations up to K time levels compared with Eq
more weight is given to the ensemble background term. Detailed information regarding this hybrid algorithm can be seen in Wang (2010) . More recently, the GSI-3DEnVar system has been extended to include four-dimensional (4D) ensemble perturbations (e.g., GSI-4DEnVar). Following Kleist and Ide (2015) , the analysis increment in GSI hybrid 4DEnVar is obtained by minimizing a cost function: where the third term is extended to use the asynchronous observations up to K time levels compared with Eq
azimuthally averaged inertial stability in the analyses of the experiments BASE and ALLTCI and their differences. The algorithm to calculate inertial stability follows Chen and Zhang (2013) , namely, I 2 = ( f + 2 V / r )[ f + 1/ r ∂( rV )/∂ r ], where f , V , and r are the Coriolis parameter, the tangential wind, and the radius, respectively. It is expected that the lower levels typically have stronger inertial stability than upper levels ( Rappin et al. 2011 ), which is consistent with Figs. 7
azimuthally averaged inertial stability in the analyses of the experiments BASE and ALLTCI and their differences. The algorithm to calculate inertial stability follows Chen and Zhang (2013) , namely, I 2 = ( f + 2 V / r )[ f + 1/ r ∂( rV )/∂ r ], where f , V , and r are the Coriolis parameter, the tangential wind, and the radius, respectively. It is expected that the lower levels typically have stronger inertial stability than upper levels ( Rappin et al. 2011 ), which is consistent with Figs. 7
, which are very close to those in the best track. Then, an axisymmetric hurricane vortex within a radius of 600 km in the GFS-FNL data at the initial time is replaced by the spun-up axisymmetric hurricane vortex of the same size, based on the dynamical initialization (DI) algorithm developed by Wang et al. (2013) . In this way, any asymmetric flow component in the storm environment is still retained. It should be mentioned that we have performed sensitivity simulations, initialized with the 1
, which are very close to those in the best track. Then, an axisymmetric hurricane vortex within a radius of 600 km in the GFS-FNL data at the initial time is replaced by the spun-up axisymmetric hurricane vortex of the same size, based on the dynamical initialization (DI) algorithm developed by Wang et al. (2013) . In this way, any asymmetric flow component in the storm environment is still retained. It should be mentioned that we have performed sensitivity simulations, initialized with the 1
”), and structural changes associated with the tilt (“D”). A description of the numerical model used in this study, its setup, and the TC center-finding algorithms used to diagnose the vortex tilt are presented in section 2 . Section 3 describes the general diagnostics of the TC evolutions, such as the intensity and tilt of the sheared simulations. Section 4 explores the evolution of the TCA, including the “vortical” and buoyant structures of convective towers within the TCAs. The rationale for
”), and structural changes associated with the tilt (“D”). A description of the numerical model used in this study, its setup, and the TC center-finding algorithms used to diagnose the vortex tilt are presented in section 2 . Section 3 describes the general diagnostics of the TC evolutions, such as the intensity and tilt of the sheared simulations. Section 4 explores the evolution of the TCA, including the “vortical” and buoyant structures of convective towers within the TCAs. The rationale for
