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Yuanlong Li, Yuqing Wang, and Zhe-Min Tan

Abstract

The phenomenon that rapid contraction (RC) of the radius of maximum wind (RMW) could precede rapid intensification (RI) in tropical cyclones (TCs) has been found in several previous studies, but it is still unclear how frequently and to what extent RC precedes RI in rapidly intensifying and contracting TCs in observations. In this study, the statistical relationship between RMW RC and TC RI is examined based on the extended best track dataset for the North Atlantic and eastern North Pacific during 1999–2019. Results show that for more than ∼65% of available TCs, the time of the peak contraction rate precedes the time of the peak intensification rate, on average, by ∼10–15 h. With the quantitatively defined RC and RI, results show that ∼50% TCs with RC experience RI, and TCs with larger intensity and smaller RMW and embedded in more favorable environmental conditions tend to experience RI more readily following an RC. Among those TCs with RC and RI, more than ∼65% involve the onset of RC preceding the onset of RI, on average, by ∼15–25 h. The preceding time tends to be longer with lower TC intensity and larger RMW and shows weak correlations with environmental conditions. The qualitative results are insensitive to the time interval for the calculation of intensification/contraction rates and the definition of RI. The results from this study can improve our understanding of TC structure and intensity changes.

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Hao-Yan Liu and Zhe-Min Tan

Abstract

This paper reports on a dynamical initialization scheme for binary vortices (BVDI) that was developed to improve the initial conditions supplied to the models used to forecast binary tropical cyclones (TCs). For binary TCs, one TC can be regarded as the environment for the other TC’s development. Based on the dynamical initialization scheme for a single vortex (SVDI), a specified multistep iteration of SVDI was introduced in the BVDI scheme to ensure that each TC develops under conditions of realistic binary vortices interaction during the 6-h cycle run. In the BVDI scheme, each TC is initialized twice within a continuously adjusted environmental flow. Four clusters of forecast simulations with different initial conditions were run for 11 pairs of binary TCs over the northwest Pacific. The forecasts of binary TCs by the BVDI scheme reduced the position and intensity errors associated with the forecast TCs by 35.2% and 56.6%, respectively, compared with those without initialization, and also performed better than the direct extension of the SVDI scheme to binary TCs. The representation of binary vortices interaction will need to be improved for initialization and future forecasts of binary TCs.

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Yi-Peng Guo and Zhe-Min Tan

Abstract

The variation in the interannual relationship between the boreal winter Hadley circulation (HC) and El Niño–Southern Oscillation (ENSO) during 1948–2014 is investigated. The interannual variability of the HC is dominated by two principal modes: the equatorial asymmetric mode (AM) and the equatorial symmetric mode (SM). The AM of the HC during ENSO events mainly results from a combined effect of the ENSO sea surface temperature (SST) anomalies and the climatological background SST over the South Pacific convergence zone. Comparatively, the SM shows a steady and statistically significant relationship with ENSO; however, the interannual relationship between the AM and ENSO is strengthened during the mid-1970s, which leads to a HC regime change—that is, the interannual pulse of the HC intensity and its response to ENSO are stronger after the mid-1970s than before. The long-term warming trend of the tropical western Pacific since the 1950s and the increased ENSO amplitude play vital roles in the HC regime change. Although the tropical eastern Pacific also experienced a long-term warming trend, it has little influence on the HC regime change due to the climatologically cold background SST over the cold tongue region.

Open access
Yi-Peng Guo and Zhe-Min Tan

Abstract

El Niño–Southern Oscillation (ENSO), which features an equatorial quasi-symmetric sea surface temperature anomaly (SSTA), is related to both the symmetric and asymmetric components of the Hadley circulation (HC) variability. However, the mechanisms for such a nonlinear HC–ENSO relationship are still unclear. Using 36-yr monthly reanalysis datasets, this study shows that the month-to-month HC variability is dominated by two principal modes, the asymmetric mode (AM) and symmetric mode (SM), both of which are highly correlated with ENSO variability. Furthermore, the relationship between the HC principal modes and the ENSO SSTA is modulated by the western Pacific SST annual cycle. When the zonal mean western Pacific SST peaks off (on) the equator, the ENSO SSTA leads to the AM (SM) of HC variability. This is because the zonal mean western Pacific SST peak provides a warmer background favorable for the SSTA to stimulate convection, indicating the important role of the combined effect of the SST annual cycle and the ENSO SSTA in affecting the HC variability. Importantly, the western Pacific SST annual cycle has no such modulation effect during central Pacific El Niño or La Niña events. The results have important implications for simulating and predicting the climatic impacts of ENSO and HC variability.

Open access
Yi-Fan Wang and Zhe-Min Tan

Abstract

This study investigated the effects of vertical wind shear (VWS) with varying magnitudes on secondary eyewall formation (SEF). It turns out that weak-to-moderate VWS advances the timing of SEF. Strong VWS, however, is unfavorable for SEF in our idealized simulations.

VWS affecting SEF mainly lies on its influence on the outer rainbands (ORBs). Under weak-to-moderate VWS, ORBs develop more quickly in the downshear side and have distinct stratiform features in the upshear-left quadrant. The asymmetric inflow associated with the stratiform cooling descends into the boundary layer, reinforcing radial convergence at the radially inward side of ORBs. The radial convergence enhances the low-level convection, resulting in strengthened boundary layer inflow and accelerated low-level tangential wind jet. A budget analysis reveals that tangential advection extends a tangential wind jet further downwind, forming supergradient winds above the boundary layer in the upshear-right quadrant. As the ORBs propagate into the upshear-right quadrant, the pre-existing supergradient winds enhances the low-level convection, facilitating the closing of the secondary convective ring. The evolution in the upshear side exhibit quadrant-dependent interactions between ORBs and boundary layer. Following that, azimuthal-mean tangential wind acceleration becomes visible, forming the secondary tangential wind maximum.

Under strong VWS, the storm is weakened and the boundary layer in the upshear-left quadrant is invaded by low-entropy air, resulting in decreased conditional instability and low-level thermal buoyancy. The decreased stratiform precipitation due to weakened convective activity in the upshear-left quadrant prevents the upshear propagation of ORBs and thus is unfavorable for SEF.

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Lin Yao, Da Yang, and Zhe-Min Tan

Abstract

Convective self-aggregation refers to a phenomenon in which random convection can self-organize into large-scale clusters over an ocean surface with uniform temperature in cloud-resolving models. Previous literature studies convective aggregation primarily by analyzing vertically integrated (VI) moist static energy (MSE) variance. That is the global MSE variance, including both the local MSE variance at a given altitude and the covariance of MSE anomalies between different altitudes. Here we present a vertically resolved (VR) MSE framework that focuses on the local MSE variance to study convective self-aggregation. Using a cloud-resolving simulation, we show that the development of self-aggregation is associated with an increase of local MSE variance, and that the diabatic and adiabatic generation of the MSE variance is mainly dominated by the boundary layer (BL; the lowest 2 km). The results agree with recent numerical simulation results and the available potential energy analyses showing that the BL plays a key role in the development of self-aggregation. Additionally, we find that the lower free troposphere (2–4 km) also generates significant MSE variance in the first 15 days. We further present a detailed comparison between the global and local MSE variance frameworks in their mathematical formulation and diagnostic results, highlighting their differences.

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Yuqing Wang, Jing Xu, and Zhe-Min Tan

Abstract

Previous studies have demonstrated the contribution of dissipative heating (DH) to the maximum potential intensity (MPI) of tropical cyclones (TCs). Since DH is a function of near-surface wind speed and thus TC intensity, a natural question arises as to whether DH contributes to the intensity dependence of TC potential intensification rate (PIR). To address this issue, an attempt has been made to include DH in a recently developed time-dependent theory of TC intensification. With this addition, the theory predicts a shift of the maximum PIR toward the higher intensity side, which is consistent with the intensity dependence of TC intensification rate in observed strong TCs. Since the theory without DH predicts a dependence of TC PIR on the square of the MPI, the inclusion of DH results in an even higher PIR for strong TCs. Considering the projected increase in TC MPI under global warming, the theoretical work implies that as the climate continues to warm, TCs may intensify more rapidly. This may not only make the TC intensity forecasting more difficult, but also may increase the threats of TCs to the coastal populations if TCs intensify more rapidly just before they make landfall.

Significance Statement

Previous studies have demonstrated that dissipative heating (DH) can significantly contribute to the maximum potential intensity (MPI) that a tropical cyclone (TC) can achieve given favorable environmental thermodynamic conditions of the atmosphere and the underlying ocean. Here we show that because DH is a function of near-surface wind speed and thus TC intensity, DH can also significantly contribute to the intensity dependence of TC potential intensification rate (PIR). This has been demonstrated by introducing DH into a recently developed time-dependent theory of TC intensification. With DH the theory predicts a shift of the maximum PIR toward the higher intensity side as observed in strong TCs. Therefore, as the climate continues to warm, TCs may intensify more rapidly and become stronger.

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Jing-Yi Zhuo and Zhe-Min Tan

Abstract

A deep learning–based method augmented by prior knowledge of tropical cyclones (TCs), called DeepTCNet, is introduced to estimate TC intensity and wind radii from infrared (IR) imagery over the North Atlantic Ocean. While standard deep learning practices have many advantages over conventional analysis approaches and can produce reliable estimates of TCs, the data-driven models informed by machine-readable physical knowledge of TCs could achieve higher performance. To this end, two approaches are explored to develop the physics-augmented DeepTCNet: (i) infusing the auxiliary physical information of TCs into models for single-task learning and (ii) learning auxiliary physical tasks for multitask learning. More specifically, augmented by auxiliary information of TC fullness (a measure of the radial decay of the TC wind field), the DeepTCNet yields a 12% improvement in estimating TC intensity over the nonaugmented one. By learning TC wind radii and auxiliary TC intensity task simultaneously, the model’s wind radii estimation skill is improved by 6% over only learning four wind radii tasks and by 9% over separately learning a single wind radii task. The evaluation results showed that the DeepTCNet is in-line with the Satellite Consensus technique (SATCON) but systematically outperforms the advanced Dvorak technique (ADT) at all intensity scales with an averaged 39% enhancement in TC intensity estimation. The DeepTCNet also surpasses the Multiplatform Tropical Cyclone Surface Wind Analysis technique (MTCSWA) with an average improvement of 32% in wind radii estimation.

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Lili Lei, Zhongrui Wang, and Zhe-Min Tan

Abstract

Hybrid ensemble–variational assimilation methods that combine static and flow-dependent background error covariances have been widely applied for numerical weather predictions. The commonly used hybrid assimilation methods compute the analysis increment using a variational framework and update the ensemble perturbations by an ensemble Kalman filter (EnKF). To avoid the inconsistencies that result from performing separate variational and EnKF systems, two integrated hybrid EnKFs that update both the ensemble mean and ensemble perturbations by a hybrid background error covariance in the framework of EnKF are proposed here. The integrated hybrid EnKFs approximate the static background error covariance by use of climatological perturbations through augmentation or additive approaches. The integrated hybrid EnKFs are tested in the Lorenz05 model given different magnitudes of model errors. Results show that the static background error covariance can be sufficiently estimated by climatological perturbations with an order of hundreds. The integrated hybrid EnKFs are superior to the traditional hybrid assimilation methods, which demonstrates the benefit to update ensemble perturbations by the hybrid background error covariance. Sensitivity results reveal that the advantages of the integrated hybrid EnKFs over traditional hybrid assimilation methods are maintained with varying ensemble sizes, inflation values, and localization length scales.

Significance Statement

Data assimilation is critical for providing the best possible initial condition for forecast and improving the numerical weather predictions. The hybrid ensemble–variational data assimilation method has been widely adopted and developed by many operational centers. The hybrid ensemble–variational assimilation method combines the advantages of ensemble and variational methods and minimizes the weaknesses of the two methods, and thus it outperforms the stand-alone variational and ensemble assimilation methods. The hybrid ensemble–variational assimilation method often computes the control analysis using a variational solver with hybrid background error covariances, but generates the ensemble perturbations by an ensemble Kalman filter (EnKF) system with pure flow-dependent background error covariances. The inconsistencies that result from performing separate variational and EnKF systems can lead to suboptimality in the hybrid ensemble–variational assimilation method. Therefore, integrated hybrid EnKF methods that utilize the framework of an EnKF to update both the ensemble mean and ensemble perturbations by the hybrid background error covariance, are proposed. The integrated hybrid EnKFs use climatological ensemble perturbations to approximate the static background error covariance. The integrated hybrid EnKFs are superior to the traditional hybrid ensemble–variational assimilation methods by producing smaller errors, and the advantages are persistent with varying assimilation parameters.

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Zhe-Min Tan, Fuqing Zhang, Richard Rotunno, and Chris Snyder

Abstract

Recent papers by the authors demonstrated the possible influence of initial errors of small amplitude and scale on the numerical prediction of the “surprise” snowstorm of 24–25 January 2000. They found that initial errors grew rapidly at scales below 200 km, and that the rapid error growth was dependent on moist processes. In an attempt to generalize these results from a single case study, the present paper studies the error growth in an idealized baroclinic wave amplifying in a conditionally unstable atmosphere. The present results show that without the effects of moisture, there is little error growth in the short-term (0–36 h) forecast error (starting from random noise), even though the basic jet used here produces a rapidly growing synoptic-scale disturbance. With the effect of moisture included, the error is characterized by upscale growth, basically as found by the authors in their study of the numerical prediction of the surprise snowstorm.

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