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Chia-Ying Lee and Shuyi S. Chen
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Chia-Ying Lee and Shuyi S. Chen

Abstract

It is widely accepted that air–sea interaction is one of the key factors in controlling tropical cyclone (TC) intensity. However, the physical mechanisms for connecting the upper ocean and air–sea interface with storm structure through the atmospheric boundary layer in TCs are not well understood. This study investigates the air–sea coupling processes using a fully coupled atmosphere–wave–ocean model, especially the coupling-induced asymmetry in surface winds, sea surface temperature, air–sea fluxes, and their impacts on the structure of the hurricane boundary layer (HBL). Numerical experiments of Hurricane Frances (2004) with and without coupling to an ocean model and/or a surface wave model are used to examine the impacts of the ocean and wave coupling, respectively. Model results are compared with the airborne dropsonde and surface wind measurements on board the NOAA WP-3D aircraft. The atmosphere–ocean coupling reduces the mixed-layer depth in the rear-right quadrant due to storm-induced ocean cooling, whereas the wind–wave coupling enhances boundary inflow outside the radius of maximum wind. Storm motion and deep tropospheric inflow create a significant front-to-back asymmetry in the depth of the inflow layer. These results are consistent with the dropsonde observations. The azimuthally averaged inflow layer and the mixed layer, as documented in previous studies, are not representative of the asymmetric HBL. The complex, three-dimensional asymmetric structure in both thermodynamic and dynamic properties of the HBL indicates that it would be difficult to parameterize the effects of air–sea coupling without a fully coupled model.

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Chia-Ying Lee and Shuyi S. Chen

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The atmospheric boundary layer (BL) in tropical cyclones (TCs) connects deep convection within rainbands and the eyewall to the air–sea interface. Although the importance of the BL in TCs has been widely recognized in recent studies, how physical processes affect TC structure and intensity are still not well understood. This study focuses on a particular physical mechanism through which a TC-induced upper-ocean cooling within the core circulation of the TC can affect the BL and TC structure. A coupled atmosphere–ocean model forecast of Typhoon Choi-Wan (2009) is used to better understand the physical processes of air–sea interaction in TCs. A persistent stable boundary layer (SBL) is found to form over the cold wake within the TC’s right-rear quadrant, which influences TC structure by suppressing convection in rainbands downstream of the cold wake and enhancing the BL inflow into the inner core by increasing inflow angles over strong SST and pressure gradients. Tracer and trajectory analyses show that the air in the SBL stays in the BL longer and gains extra energy from surface heat and moisture fluxes. The enhanced inflow helps transport air in the SBL into the eyewall. In contrast, in the absence of a TC-induced cold wake and an SBL in an uncoupled atmosphere model forecast, the air in the right-rear quadrant within the BL tends to rise into local rainbands. The SBL formed over the cold wake in the coupled model seems to be a key feature that enhances the transport of high energy air into the TC inner core and may increase the storm efficiency.

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Chun-Chieh Wu, Chia-Ying Lee, and I-I. Lin

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The rapid intensification of Hurricane Katrina followed by the devastation of the U.S. Gulf States highlights the critical role played by an upper-oceanic thermal structure (such as the ocean eddy or Loop Current) in affecting the development of tropical cyclones. In this paper, the impact of the ocean eddy on tropical cyclone intensity is investigated using a simple hurricane–ocean coupled model. Numerical experiments with different oceanic thermal structures are designed to elucidate the responses of tropical cyclones to the ocean eddy and the effects of tropical cyclones on the ocean. This simple model shows that rapid intensification occurs as a storm encounters the ocean eddy because of enhanced heat flux. While strong winds usually cause strong mixing in the mixed layer and thus cool down the sea surface, negative feedback to the storm intensity of this kind is limited by the presence of a warm ocean eddy, which provides an insulating effect against the storm-induced mixing and cooling.

Two eddy factors, F EDDY-S and F EDDY-T, are defined to evaluate the effect of the eddy on tropical cyclone intensity. The efficiency of the eddy feedback effect depends on both the oceanic structure and other environmental parameters, including properties of the tropical cyclone. Analysis of the functionality of F EDDY-T shows that the mixed layer depth associated with either the large-scale ocean or the eddy is the most important factor in determining the magnitude of eddy feedback effects. Next to them are the storm’s translation speed and the ambient relative humidity.

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Qidong Yang, Chia-Ying Lee, and Michael K. Tippett

ABSTRACT

Rapid intensification (RI) is an outstanding source of error in tropical cyclone (TC) intensity predictions. RI is generally defined as a 24-h increase in TC maximum sustained surface wind speed greater than some threshold, typically 25, 30, or 35 kt (1 kt ≈ 0.51 m s−1). Here, a long short-term memory (LSTM) model for probabilistic RI predictions is developed and evaluated. The variables (features) of the model include storm characteristics (e.g., storm intensity) and environmental variables (e.g., vertical shear) over the previous 48 h. A basin-aware RI prediction model is trained (1981–2009), validated (2010–13), and tested (2014–17) on global data. Models are trained on overlapping 48-h data, which allows multiple training examples for each storm. A challenge is that the data are highly unbalanced in the sense that there are many more non-RI cases than RI cases. To cope with this data imbalance, the synthetic minority-oversampling technique (SMOTE) is used to balance the training data by generating artificial RI cases. Model ensembling is also applied to improve prediction skill further. The model’s Brier skill scores in the Atlantic and eastern North Pacific are higher than those of operational predictions for RI thresholds of 25 and 30 kt and comparable for 35 kt on the independent test data. Composites of the features associated with RI and non-RI situations provide physical insights for how the model discriminates between RI and non-RI cases. Prediction case studies are presented for some recent storms.

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Chia-Ying Lee, Michael K. Tippett, Suzana J. Camargo, and Adam H. Sobel

Abstract

The authors describe the development and verification of a statistical model relating tropical cyclone (TC) intensity to the local large-scale environment. A multiple linear regression framework is used to estimate the expected intensity of a tropical cyclone given the environmental and storm conditions. The uncertainty of the estimate is constructed from the empirical distribution of model errors. NCEP–NCAR reanalysis fields and historical hurricane data from 1981 to 1999 are used for model development, and data from 2000 to 2012 are used to evaluate model performance. Seven predictors are selected: initial storm intensity, the change of storm intensity over the past 12 h, the storm translation speed, the difference between initial storm intensity and its corresponding potential intensity, deep-layer (850–200 hPa) vertical shear, atmospheric stability, and 200-hPa divergence. The system developed here models storm intensity changes in response to changes in the surrounding environment with skill comparable to existing operational forecast tools. Since one application of such a model is to predict changes in TC activity in response to natural or anthropogenic climate change, the authors examine the performance of the model using data that is most readily available from global climate models, that is, monthly averages. It is found that statistical models based on monthly data (as opposed to daily) with only a few essential predictors, for example, the difference between storm intensity and potential intensity, perform nearly as well at short leads as when daily predictors are used.

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Chung-Chuan Yang, Chun-Chieh Wu, Kun-Hsuan Chou, and Chia-Ying Lee

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A cyclonic loop was observed in the track of Typhoon Fungwong (2002) when it was about 765 n mi from Supertyphoon Fengshen (2002). It is shown that Fungwong’s special path is associated with the circulation of Fengshen, and such an association is regarded as an indication of binary interaction. In this paper, the binary interaction between Fengshen and Fungwong is studied based on the potential vorticity diagnosis. The impacts of large-scale flow fields on their motions are also investigated. Furthermore, the sensitivity of the storm characteristics to the binary interaction is demonstrated by the mesoscale numerical model simulations with different sizes and intensities for the initial bogused storms. Results of the study show that before Fungwong and Fengshen interacted with each other, their motions were governed by the large-scale environmental flow, that is, mainly associated with the subtropical high. During this binary interaction, Fungwong’s looping is partly attributed to Fengshen’s steering flow. This pattern shows up first as a case of one-way interaction in the early period, and then develops into a mutual interaction during the later stages. The numerical experiments show the sensitivity of the storm size and intensity to the binary interaction, implicating that a good representation of the initial storm vortex is important for the prediction of binary storms. Further analyses also indicate the influence of the monsoon trough and subtropical high systems on the binary interaction. These results provide some new insights into the motions of nearby typhoons embedded in the monsoon circulation.

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Chia-Ying Lee, Suzana J. Camargo, Adam H. Sobel, and Michael K. Tippett

Abstract

Tropical cyclone (TC) activity is examined using the Columbia Hazard model (CHAZ), a statistical–dynamical downscaling system, with environmental conditions taken from simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) for both the historical period and a future scenario under the representative concentration pathway 8.5. Projections of individual global and basin TC frequency depend sensitively on the choice of moisture variable used in the tropical genesis cyclone index (TCGI) component of CHAZ. Simulations using column relative humidity show an increasing trend in the future, while those using saturation deficit show a decreasing trend, although both give similar results in the historical period. While the projected annual TC frequency is also sensitive to the choice of model used to provide the environmental conditions, the choice of humidity variable in the TCGI is more important. Changes in TC frequency directly affect the projected TCs’ tracks and the frequencies of strong storms on both basin and regional scales. This leads to large uncertainty in assessing regional and local storm hazards. The uncertainty here is fundamental and epistemic in nature. Increases in the fraction of major TCs, rapid intensification rate, and decreases in forward speed are insensitive to TC frequency, however. The present results are also consistent with prior studies in indicating that those TC events that do occur will, on average, be more destructive in the future because of the robustly projected increases in intensity.

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Chia-Ying Lee, Michael K. Tippett, Adam H. Sobel, and Suzana J. Camargo

Abstract

An autoregressive model is developed to simulate the climatological distribution of global tropical cyclone (TC) intensity. The model consists of two components: a regression-based deterministic component that advances the TC intensity in time and depends on the storm state and surrounding large-scale environment and a stochastic forcing. Potential intensity, deep-layer mean vertical shear, and midlevel relative humidity are the environmental variables included in the deterministic component. Given a storm track and its environment, the model is initialized and then iterated along the track. Model performance is evaluated by its ability to represent the observed global and basin distributions of TC intensity as well as lifetime maximum intensity (LMI). The deterministic model alone captures the spatial features of the climatological TC intensity distribution but with intensities that remain below 100 kt (1 kt ≈ 0.51 m s−1). Addition of white (uncorrelated in time) stochastic forcing reduces this bias by improving the simulated intensification rates and the frequency of major storms. The model simulates a realistic range of intensities, but the frequency of major storms remains too low in some basins.

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