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Jie Feng and Xuguang Wang

Abstract

Although numerous studies have demonstrated that increasing model spatial resolution in free forecasts can potentially improve tropical cyclone (TC) intensity forecasts, studies on the impact of model resolution during data assimilation (DA) on TC prediction are lacking. In this study, using the ensemble-variational DA system for the Hurricane Weather Research and Forecasting (HWRF) Model, we investigated the individual impact of increasing the model resolution of first guess (FG) and background ensemble (BE) forecasts during DA on initial analyses and subsequent forecasts of Hurricane Patricia (2015). The impacts were compared between horizontal and vertical resolutions and also between the tropical storm (TS) and hurricane assimilation during Patricia. The results show that increasing the horizontal or vertical resolution in FG has a larger impact than increasing the resolution in BE on improving the analyzed TC intensity and structure for the hurricane stage. The result is reversed for the TS stage. These results are attributed to the effectiveness of increasing the FG resolution in intensifying the background vortex for the hurricane stage relative to the TS stage. Increasing the BE resolution contributes to improving the analyzed intensity through the better-resolved background correlation structure for both the hurricane and TS stages. Increasing horizontal resolution has an overall larger effect than increasing vertical resolution in improving the analysis at the hurricane stage and their effects are close for the analysis at the TS stage. Additionally, the more accurately analyzed primary circulation, secondary circulation, and warm-core structures via the increased resolution in DA lead to improved TC intensity forecasts.

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Robert G. Nystrom and Fuqing Zhang

Abstract

Hurricane Patricia (2015) was a record-breaking tropical cyclone that was difficult to forecast in real time by both operational numerical weather prediction models and operational forecasters. The current study examines the potential for improving intensity prediction for extreme cases like Hurricane Patricia. We find that Patricia’s intensity predictability is potentially limited by both initial conditions, related to the data assimilation, and model errors. First, convection-permitting assimilation of airborne Doppler radar radial velocity observations with an ensemble Kalman filter (EnKF) demonstrates notable intensity forecast improvements over assimilation of conventional observations alone. Second, decreasing the model horizontal grid spacing to 1 km and reducing the surface drag coefficient at high wind speed in the parameterization of the sea surface–atmosphere exchanges is also shown to notably improve intensity forecasts. The practical predictability of Patricia, its peak intensity, rapid intensification, and the underlying dynamics are further investigated through a high-resolution 60-member ensemble initialized with realistic initial condition uncertainties represented by the EnKF posterior analysis perturbations. Most of the ensemble members are able to predict the peak intensity of Patricia, but with greater uncertainty in the timing and rate of intensification; some members fail to reach the ultimate peak intensity before making landfall. Ensemble sensitivity analysis shows that initial differences in the region beyond the radius of maximum wind contributes the most to the differences between ensemble members in Patricia’s intensification. Ensemble members with stronger initial primary and secondary circulations beyond the radius of maximum wind intensify earlier, are able to maintain the intensification process for longer, and thus reach a greater and earlier peak intensity.

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Jie Feng and Xuguang Wang

Abstract

The dropsondes released during the Tropical Cyclone Intensity (TCI) field campaign provide high-resolution kinematic and thermodynamic measurements of tropical cyclones within the upper-level outflow and inner core. This study investigates the impact of these upper-level TCI dropsondes on analyses and prediction of Hurricane Patricia (2015) during its rapid intensification (RI) phase using an ensemble–variational data assimilation system. In the baseline experiment (BASE), both kinematic and thermodynamic observations of TCI dropsondes at all levels except the upper levels are assimilated. The upper-level wind and thermodynamic observations are assimilated in additional experiments to investigate their respective impacts. Compared to BASE, assimilating TCI upper-level wind observations improves the accuracy of outflow analyses verified against independent atmospheric motion vector (AMV) observations. It also strengthens the tangential and radial wind near the upper-level eyewall. The inertial stability within the upper-level eyewall is enhanced, and the maximum outflow is more aligned toward the inner core. Additionally, the analyses including the upper-level thermodynamic observations produce a warmer and drier core at high levels. Assimilating both upper-level kinematic and thermodynamic observations also improves the RI forecast. Compared to BASE, assimilating the upper-level wind induces more upright and inward-located eyewall convection, resulting in more latent heat release closer to the warm core. This process leads to stronger inner-core warming. Additionally, the initial warmer upper-level inner core produced by assimilating TCI thermodynamic observations also intensifies the convection and latent heat release within the eyewall, thus further contributing to the improved intensity forecasts.

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David R. Ryglicki, James D. Doyle, Daniel Hodyss, Joshua H. Cossuth, Yi Jin, Kevin C. Viner, and Jerome M. Schmidt

Abstract

Interactions between the upper-level outflow of a sheared, rapidly intensifying tropical cyclone (TC) and the background environmental flow in an idealized model are presented. The most important finding is that the divergent outflow from convection localized by the tilt of the vortex serves to divert the background environmental flow around the TC, thus reducing the local vertical wind shear. We show that this effect can be understood from basic theoretical arguments related to Bernoulli flow around an obstacle. In the simulation discussed, the environmental flow diversion by the outflow is limited to 2 km below the tropopause in the 12–14-km (250–150 hPa) layer. Synthetic water vapor satellite imagery confirms the presence of upshear arcs in the cloud field, matching satellite observations. These arcs, which exist in the same layer as the outflow, are caused by slow-moving wave features and serve as visual markers of the outflow–environment interface. The blocking effect where the outflow and the environmental winds meet creates a dynamic high pressure whose pressure gradient extends nearly 1000 km upwind, thus causing the environmental winds to slow down, to converge, and to sink. We discuss these results with respect to the first part of this three-part study, and apply them to another atypical rapid intensification hurricane: Matthew (2016).

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T. Connor Nelson, Lee Harrison, and Kristen L. Corbosiero

Abstract

The newly developed expendable digital dropsonde (XDD) allows for high spatial and temporal resolution data collection in tropical cyclones (TCs). In 2015, a total of 725 XDDs were launched into Hurricanes Marty (27–28 September), Joaquin (2–5 October), and Patricia (20–23 October) as part of the Tropical Cyclone Intensity (TCI) experiment. These dropsondes were launched from a NASA WB-57 at altitudes above 18 km, capturing the full depth of the TCs to the tropopause. This study documents the vertical velocity distributions observed in TCI using the XDDs and examines the distributions altitudinally, radially, and azimuthally. The strongest mean or median XDD-derived vertical velocities observed during TCI occurred in the upper levels and within the cores of the three TCs. There was little azimuthal signal in the vertical velocity distribution, likely due to sampling asymmetries and noise in the data. Downdrafts were strongest in Joaquin, while updrafts were strongest in Patricia, especially within the eyewall on 23 October. Patricia also had an impressive low-level (<2 km) updraft that exceeded 10 m s−1 associated with a shallow, overturning, radial circulation in the secondary eyewall.

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Xu Lu and Xuguang Wang

Abstract

Assimilating inner-core observations collected from recent field campaign programs such as Tropical Cyclone Intensity (TCI) and Intensity Forecasting Experiment (IFEX) together with the enhanced atmospheric motion vectors (AMVs) produce realistic three-dimensional (3D) analyses using the newly developed GSI-based, continuously cycled, dual-resolution hybrid ensemble–variational data assimilation (DA) system for the Hurricane Weather Research and Forecasting (HWRF) Model for Hurricane Patricia (2015). However, more persistent surface wind maximum spindown is found in the intensity forecast initialized from the realistic analyses produced by the DA system but not from the unrealistic initial conditions produced through vortex modification. Diagnostics in this study reveal that the spindown issue is likely attributed to the deficient HWRF Model physics that are unable to maintain the realistic 3D structures from the DA analysis. The horizontal diffusion is too strong to maintain the realistically observed vertical oscillation of radial wind near the eyewall region. The vertical diffusion profile cannot produce a sufficiently strong secondary circulation connecting the realistically elevated upper-level outflow produced in the DA analysis. Further investigations with different model physics parameterizations demonstrate that spindown can be alleviated by modifying model physics parameterizations. In particular, a modified turbulent mixing parameterization scheme together with a reduced horizontal diffusion is found to significantly alleviate the spindown issue and to improve the intensity forecast. Additional experiments show that the peak-simulated intensity and rapid intensification rate can be further improved by increasing the model resolution. But the model resolution is not as important as model physics in the spindown alleviation.

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David R. Ryglicki, Joshua H. Cossuth, Daniel Hodyss, and James D. Doyle

Abstract

A satellite-based investigation is performed of a class of tropical cyclones (TCs) that unexpectedly undergo rapid intensification (RI) in moderate vertical wind shear between 5 and 10 m s−1 calculated as 200–850-hPa shear. This study makes use of both infrared (IR; 11 μm) and water vapor (WV; 6.5 μm) geostationary satellite data, the Statistical Hurricane Prediction Intensity System (SHIPS), and model reanalyses to highlight commonalities of the six TCs. The commonalities serve as predictive guides for forecasters and common features that can be used to constrain and verify idealized modeling studies. Each of the TCs exhibits a convective cloud structure that is identified as a tilt-modulated convective asymmetry (TCA). These TCAs share similar shapes, upshear-relative positions, and IR cloud-top temperatures (below −70°C). They pulse over the core of the TC with a periodicity of between 4 and 8 h. Using WV satellite imagery, two additional features identified are asymmetric warming/drying upshear of the TC relative to downshear, as well as radially thin arc-shaped clouds on the upshear side. The WV brightness temperatures of these arcs are between −40° and −60°C. All of the TCs are sheared by upper-level anticyclones, which limits the strongest environmental winds to near the tropopause.

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David R. Ryglicki, James D. Doyle, Yi Jin, Daniel Hodyss, and Joshua H. Cossuth

Abstract

We investigate a class of tropical cyclones (TCs) that undergo rapid intensification (RI) in moderate vertical wind shear through analysis of a series of idealized model simulations. Two key findings derived from observational analysis are that the average 200–850-hPa shear value is 7.5 m s−1 and that the TCs displayed coherent cloud structures, deemed tilt-modulated convective asymmetries (TCA), which feature pulses of deep convection with periods of between 4 and 8 h. Additionally, all of the TCs are embedded in an environment that is characterized by shear associated with anticyclones, a factor that limits depth of the strongest environmental winds in the vertical. The idealized TC develops in the presence of relatively shallow environmental wind shear of an anticyclone. An analysis of the TC tilt in the vertical demonstrates that the source of the observed 4–8-h periodicity of the TCAs can be explained by smaller-scale nutations of the tilt on the longer, slower upshear precession. When the environmental wind shear occurs over a deeper layer similar to that of a trough, the TC does not develop. The TCAs are characterized as collections of updrafts that are buoyant throughout the depth of the TC since they rise into a cold anomaly caused by the tilting vortex. At 90 h into the simulation, RI occurs, and the tilt nutations (and hence the TCAs) cease to occur.

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Quanjia Zhong, Jianping Li, Lifeng Zhang, Ruiqiang Ding, and Baosheng Li

Abstract

The predictability limits of tropical cyclone (TC) intensity over the western North Pacific (WNP) are investigated using TC best track data. The results show that the predictability limit of the TC minimum central pressure (MCP) is ~102 h, comparable to that of the TC maximum sustained wind (MSW). The spatial distribution of the predictability limit of the TC MCP over the WNP is similar to that of the TC MSW, and both gradually decrease from the eastern WNP (EWNP) to the South China Sea (SCS). The predictability limits of the TC MCP and MSW are relatively high over the southeastern WNP where the modified accumulated cyclone energy (MACE) is relatively large, whereas they are relatively low over the SCS where the MACE is relatively small. The spatial patterns of the TC lifetime and the lifetime maximum intensity (LMI) are similar to that of the TC MACE. Strong and long-lived TCs, which have relatively long predictability, mainly form in the southwestern WNP. In contrast, weak and short-lived TCs, which have relatively short predictability, mainly form in the SCS. In addition to the dependence of the predictability limit on genesis location, the predictability limits of TC intensity also evolve in the TC life cycle. The predictability limit of the TC MCP (MSW) gradually decreases from 102 (108) h at genesis time (00 h) to 54 (84) h 4 days after TC genesis.

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Shixuan Zhang, Zhaoxia Pu, and Christopher Velden

Abstract

The impacts of enhanced satellite-derived atmospheric motion vectors (AMVs) on the numerical prediction of intensity changes during Hurricanes Gonzalo (2014) and Joaquin (2015) are examined. Enhanced AMVs benefit from special data-processing strategies and are examined for impact on model forecasts via assimilation experiments by employing the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecasting (HWRF) Model using a Gridpoint Statistical Interpolation analysis system (GSI)-based ensemble–variational hybrid system. Two different data assimilation (DA) configurations, one with and one without the use of vortex initialization (VI), are compared. It is found that the assimilation of enhanced AMVs can improve the HWRF track and intensity forecasts of Gonzalo and Joaquin during their intensity change phases. The degree of data impact depends on the DA configuration used. Overall, assimilation of enhanced AMVs in the innermost domain (e.g., storm inner-core region and its immediate vicinity) outperforms other DA configurations, both with and without VI, as it results in better track and intensity forecasts. Compared to the experiment with VI, assimilation of enhanced AMVs without VI reveals more notable data impact on the forecasts of Hurricane Gonzalo, as the VI before DA alters the first guess and reduces the actual number of AMV observations assimilated into the DA system. Even with VI, assimilation of enhanced AMVs in the inner-core region can at least partially mitigate the negative effect of VI on the intensity forecast of Hurricane Gonzalo and alleviate the unrealistic vortex weakening in the simulation by removing unrealistic outflow structure and unfavorable thermodynamic conditions, thus leading to improved intensity forecasts.

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