Next-Generation Tropical Cyclone Model

Tropical cyclone (TC) intensity is controlled by both environmental factors and its internal structure. This paper investigates the influence of inner core features derived from satellite data on TC intensity changes and explores effective methods to improve TC intensity forecast accuracy. We construct models for forecasting TC intensity over the next 5 days using the light gradient boosting machine (LGBM) algorithm in the northwestern Pacific. The models integrate climatological and persistence predictors, large-scale environmental variables at the initial time, inner core features extracted from satellite data, and variables related to the forecasted TC location. The models are trained on TC data from 2005 to 2019, tested using best track data from 2020 to 2022 and real-time data in 2023. Results show that the inner core features derived from satellite data are helpful for intensity forecasts within 36 h, and variables related to the forecasted location are helpful for improving TC intensity forecast accuracy in most cases, especially for the forecasts beyond 48 h. Compared with the National Centers for Environmental Prediction (NCEP) and European Centre for Medium-Range Weather Forecasts (ECMWF) models, our models demonstrate 4%–48% and 19%–70% decreases in mean absolute errors (MAEs) in real-time intensity forecasts. The most important predictors identified include potential future intensity change, intensity changes during the previous 12 h, vertical wind shear, and sea–land ratio. The inner core feature extracted from satellite data is also among the top 10 important variables for short-time intensity forecasts. Significance Statement This study advances tropical cyclone (TC) intensity forecasting by integrating climatological and persistence predictors, large-scale environmental variables at the initial time, satellite-derived inner core features, and variables related to the forecasted TC location into the light gradient boosting machine (LGBM) models. Our models significantly improve forecast accuracy, reducing mean absolute errors by 4%–48% compared with the National Centers for Environmental Prediction (NCEP) and 19%–70% compared with the European Centre for Medium-Range Weather Forecasts (ECMWF). Results denote that the inner core features improve the intensity prediction in the short time, and variables related to the forecasted TC location improve the intensity forecasts in most cases, especially for the forecasts beyond 48 h. This study provides valuable insights for enhancing operational TC intensity forecasting.

: The long-term goal of this project is to improve the prediction of tropical cyclone (TC) genesis, structure, and intensity changes through improved representation of 3-dimensional TC structure in operational weather forecast models. The accurate prediction of TC genesis, structure, and intensity changes is critical to Navy missions and civilian activities in coastal areas. Significant gains have been made in TC track prediction over the past decades. However, genesis and intensity forecasts have shown very little progress during the same time period. A primary contributing factor to our lack of skill in predicting TC genesis and intensity is an inadequate representation of initial axisymmetric and asymmetric TC dynamic and thermodynamic structures. TC initialization is a universal problem in nearly all current operational weather forecast systems, as indicated in a recent HFIP workshop. By conducting both idealized Observation System Simulation Experiments (OSSE) and real-case TC forecast experiments, we intend to tackle the weaknesses in current operational weather forecast models. We propose a combined TC dynamic initialization-3DVar data assimilation approach. The key component of this new scheme is a TC dynamic initialization (TCDI) package. The TCDI package includes three steps. The first step is the decomposition of the TC vortex from its environmental field. The second step is a dynamic initialization with the full nonlinear dynamics and physics, which forces the first guess field toward the observed central minimum pressure, given realistic asymmetric heating profiles. The third step is to add the newly generated TC field into the environmental field and use it in the subsequent 3DVar assimilation. After the TCDI is completed, the 3Dvar assimilation follows. A preliminary OSSE study was conducted and the results show that this new approach greatly improves both TC symmetric and asymmetric dynamic and thermodynamic patterns.

The relationship between sea surface temperature (SST) and tropical cyclone (TC) intensity change exhibits a strong dependence on the current TC intensity. Using western North Pacific TC observations from 1982 to 2018, a threshold SST (TSST) is identified as the SST required to maintain TC intensity. TSST increases with TC intensity, with TCs intensifying and weakening when SST is higher and lower than TSST, respectively. Across the dataset, mean TC intensity change is proportional to the difference between SST and TSST. This study also formulates an equation to quantify TC intensity change using SST and current TC intensity, which replicates 99.46% of the mean observed TC intensity changes. This equation could serve as an alternative to the linear regression‐based relationship between SST and TC intensity change that is widely used in statistical‐dynamical intensity models, thereby improving intensity forecasts.

A tropical cyclone (TC) vortex is an immense, coherent, organized-convective system. Beneath this large-scale organization, is a litany of azimuthally asymmetric convective motions that exist on a spectrum of scales. These asymmetries are especially dominant during periods when the vortex undergoes critical transitions in its intensity and structure. However, the precise nature of influence of the organization of asymmetries on TC intensity change remains an enigma. The inherent difficulty in predicting their behavior is because asymmetries may arise due to different external or intrinsic sources and occur at different spatial and temporal scales while several complex mechanisms act near-simultaneously to dictate their evolution in time. As a result, multiple pathways are possible for a TC vortex that is influenced by these asymmetries. Our preliminary investigations using numerical models made it apparent that there wasn't a single, unifying way to address this problem. In this thesis, I outline multiple novel techniques of diagnosing and predicting which of the many pathways are likely for a TC vortex that is influenced by azimuthal asymmetries. First, using three-dimensional numerical simulations of a pair of sheared and non-sheared vortices, I demonstrate the diagnostic potential of the juxtaposition in the azimuthal phasing of: (i) the asymmetrically distributed vertical eddy flux of moist-entropy across the top of the boundary layer, and the radial eddy flux of moist-entropy within the boundary layer; and (ii) eddy relative vorticity, eddy moist-entropy, and vertical velocity throughout the depth of the vortex. Second, I introduce an energetics-based diagnostic framework that computes the energy transactions occurring at asymmetries across various length-scales in the wavenumber domain. By applying it to select cases, this thesis uncovers the relative importance of all the energy pathways that support or disrupt the growth of asymmetries within the vortex. Contrary to the traditional explanations of convective aggregation/disaggregation and axi/asymmetrization through barotropic mean-eddy transactions, my thesis reveals that the growth or disruption of asymmetries are predominantly due to (i) the baroclinic conversion from available potential to kinetic energy at individual scales of asymmetries and (ii) the transactions of kinetic energy across asymmetries of different length scales. Finally, this thesis introduces two further diagnostic frameworks targeted at tackling the problem of real-time forecasting of TC rapid intensity changes. The first is an empirical framework which examines symmetric and asymmetric convection and other state variables within the vortex, and in the environment across a suite of TCs and identifies a set of `important' variables that are significantly different during time periods that precede a rapid intensification as opposed to a rapid weakening. My framework then ranks the variables identified based on how significantly they influence a rapid intensity change in a TC and the amplification factor of any associated variability. We recommend that future observational, and consequent TC modeling and data assimilation efforts prioritize the highest ranked variables identified here. The second is a stochastic model wherein a scale-specific stochastic term is added to the equations describing the energy transactions within the TC vortex. By simulating a stochastic forcing that may arise from any scale, I compute the probability of the vortex transitioning into a rapidly intensifying or a rapidly weakening configuration across an ensemble of scenarios. In summary, this thesis introduces and applies a variety of diagnostic techniques that help determine the impact of azimuthal asymmetries on TC intensity evolution.

For typhoon warning centers, effective forecasting of tropical cyclone intensity is always required. The major difficulties and challenges in forecasting tropical cyclone intensity are the complex physical mechanism and the structure of tropical cyclones. The interaction between the tropical cyclone and its environment is also a complex process. In this paper, a model based on support vector machines is developed to yield the 12, 24, 36, 48, 72 h forecasts of tropical cyclone intensity. Furthermore, the forecasts resulting from the proposed model are compared with those from the Joint Typhoon Warning Center. Cross-validation tests are also applied to evaluate the accuracy and the robustness of the proposed model. The results confirm that the proposed model can provide accurate forecasts of tropical cyclone intensity, especially for a long lead-time. When the sample events are classified into five categories according to the Saffir-Simpson scale, the forecasts resulting from the proposed model have the best performance for events in categories 4 and 5. In addition, when a typhoon turns northward, although the water temperature drops rapidly, the proposed model still performs well. In conclusion, the proposed model is useful to improve the forecasts of tropical cyclones intensity.

Diabatic Heating and Convective Asymmetries During Rapid Intensity Changes of Tropical Cyclones Over North Indian Ocean

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.

Using primitive equations with adiabatic, non‐friction non‐environmental steering, the dynamical equilibrium features of tropical cyclones (TC) are investigated. The parameter, dI/dt, was obtained and its dynamical balance formulation describing TC intensity change. The results of the we discussed the characteristics of TC motion and intensity change in barotropic and baroclinic model showed that the stationary motion of TC possess “equivalent‐geostrophic equilibrium” and isolines of potential vorticity and E(E = φ + (u2 + v2)/2) coincide with streamlines. The TC intensity will be weakened by “ventilation flow” in “beta (β) gyres” and “shear gyres” from the positive (negative) wind vertical shear at high‐ (low‐) level. The positive (negative) vorticity vertical convection of high‐ (low‐) level strengthens TC intensity. Generally, the TC center has the tendency to drift poleward and there is a gradient (ascendent) direction of vorticity vertical convection at high‐ (low‐) level. The TC motion is connected with the asymmetry structure of vertical shear and vertical motion, the “γ effect” (γ = ∂β/∂y) that is one of the dynamical mechanisms of “ventilation flow” steering TC center motion.

This study examines the potential limit in predicting tropical cyclone (TC) intensity under idealized conditions. Using the phase-space reconstruction method for TC intensity time series obtained from the CM1 idealized simulations, it is found that CM1 axisymmetric dynamics contain low-dimensional chaos at the maximum intensity equilibrium. Examination of several attractor invariants including the largest Lyapunov exponent, the Sugihara–May correlation, and the correlation dimension captures a consistent range of the chaotic attractor dimension between 4 and 5 for TC intensity at the maximum intensity equilibrium. In addition, the intensity error doubling time estimated from the largest Lyapunov exponent is roughly 1–3 h, which accords with the decay time obtained from the Sugihara–May correlation. Furthermore, the findings in this study reveal a relatively short TC intensity predictability limit for CM1, which is ∼3–9 h based on the maximum tangential wind but noticeably longer for the minimum central pressure (∼12–18 h) after reaching the mature stage. So long as the traditional metrics for TC intensity such as the maximum surface wind or the minimum central pressure is used for intensity forecast, our results support that TC intensity forecast errors will not be reduced indefinitely in any model, even in the absence of all model and observational errors. As such, the future improvement of TC intensity forecast should be based on different metrics beyond the absolute intensity errors that are currently used in real-time intensity verification. Significance Statement Using the phase-space reconstruction method for tropical cyclone (TC) intensity time series obtained from idealized axisymmetric simulations, we show that TC axisymmetric dynamics in CM1 possesses low-dimensional chaos at the maximum intensity equilibrium. This low-dimensional dynamics explains the long tradition of representing TC intensity by a few measures as in the current practice. The chaotic property of CM1 axisymmetric dynamics also suggests a relatively short predictability range for TC intensity at the maximum intensity equilibrium. The potential existence of low-dimensional chaos for TC intensity in CM1 idealized simulations as found in this study supports the use of different intensity verification metrics beyond the traditional absolute intensity errors currently used in operational model evaluation.

An updated climatology of Atlantic basin tropical cyclone (TC) intensity change in the presence of upper tropospheric trough forcing is presented. To control for changes in the background thermodynamic environment, a methodology that normalizes intensity change by the potential intensity of the TC is used to more narrowly focus on the effect of troughs compared to previous studies. Relative to the full sample of Atlantic TCs, troughs are a negative influence on intensification: trough interaction cases are 4% less likely to intensify and 5% more likely to weaken. Troughs are especially detrimental compared to TCs without trough forcing: trough interaction cases are 14% less likely to intensify and 13% more likely to weaken. Additionally, eddy flux convergence of angular momentum, previously shown to positively affect TC intensity change, is shown to be a weak predictor of intensity change compared to vertical wind shear, which is enhanced during a trough interaction.

The study of the impact of global warming on tropical cyclone (TC) intensity is subject to uncertainty in historical datasets, especially in the western North Pacific (WNP) basin, where conflicting results have been found with the TC datasets archived in different organizations. In this study the basinwide TC intensity in the WNP basin is derived dynamically with a TC intensity model, based on the track data from the Joint Typhoon Warning Center (JTWC), the Regional Specialized Meteorological Center (RSMC) of Tokyo, and the Shanghai Typhoon Institute (STI) of the China Meteorological Administration. The dynamically derived TC intensity is compared to the three datasets and used to investigate trends in TC intensity. The associated contributions of changes in SST, vertical wind shear, and prevailing tracks are also examined. The evolution of the basinwide TC intensity in the JTWC best-track dataset can be generally reproduced over the period 1975–2007. Dynamically derived data based on the JTWC, RSMC, and STI track datasets all show an increasing trend in the peak intensity and frequency of intense typhoons, mainly because of the combined effect of changes in SST and vertical wind shear. This study suggests that the increasing intensity trend in the JTWC dataset is real, but that it may be overestimated. In contrast, the TC intensity trends in the RSMC and STI intensity datasets are dynamically inconsistent. Numerical simulations also suggest that the frequency of intense typhoons is more sensitive to changes in SST and vertical wind shear than the peak and average intensities defined in previous studies.

Much attention has been given to the impact of environmental wind shear in the 850–200-hPa layer on tropical cyclones (TCs). However, even with the same magnitude of shear, helicity in this layer can vary significantly. A new parameter is presented, the tropical cyclone–relative environmental helicity (TCREH). Positive TCREH leads to a tilted storm that enhances local storm-scale helicity in regions of convection within the TC. This enhanced local-scale helicity potentially allows for more robust and longer-lasting convection, which is more effective at generating latent heat and subsequent TC intensification. TC vertical tilt is often attributed to wind shear. Different values of helicity modulate this tilt and certain tilt configurations are more favorable for development or intensification than others, suggesting that mean positive environmental helicity is more favorable for development and intensification than mean negative helicity. Idealized modeling simulations demonstrate the impact of environmental helicity on TC development and intensification. Results show that wind profiles with the same 850–200-hPa wind shear but different values of helicity lead to different rates of development. TCREH also is computed from the Interim ECMWF Re-Analysis (1979–2011) and Global Forecast System analyses (2004–11) to determine if a significant signal exists between TCREH and TC intensification. Mean annular helicity is averaged over various time periods and correlated with the TC intensity change during those periods. Results suggest a weak but statistically significant correlation between environmental helicity and TC intensity change with positive helicity being more favorable for intensification.

Recent studies have shown that assimilating enhanced satellite-derived atmospheric motion vectors (AMVs) has improved mesoscale forecast of tropical cyclones (TC) track and intensity. The authors conduct data-denial experiments to understand where the TC analyses and forecasts benefit the most from the enhanced AMV information using an ensemble Kalman filter and the Weather Research and Forecasting Model. The Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin provides enhanced AMV datasets with higher density and temporal resolution using shorter-interval image triplets for the duration of Typhoon Sinlaku and Hurricane Ike (both 2008). These AMV datasets are then spatially and vertically subsetted to create six parallel cycled assimilation-forecast experiments for each TC: all AMVs; AMVs withheld between 100 and 350 hPa (upper layer), between 350 and 700 hPa (middle layer), and between 700 and 999 hPa (lower layer); and only AMVs within (interior) and outside (exterior) 1000-km radius of the TC center. All AMV subsets are found to be useful in some capacity. The interior and upper-layer AMVs are particularly crucial for improving initial TC position, intensity, and the three-dimensional wind structure along with their forecasts. Compared with denying interior or exterior AMVs, withholding AMVs in different tropospheric layers had less impact on TC intensity and size forecasts. The ensemble forecast is less certain (larger spread) in providing accurate TC track, intensity, and size when upper-layer AMVs or interior AMVs are withheld. This information could be useful to potential targeting scenarios, such as activating and focusing satellite rapid-scan operations, and decisions regarding observing system assessments and deployments.

This study examines the dependence of tropical cyclone (TC) intensity forecast errors on track forecast errors in the Coupled Ocean–Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC) model. Using real-time forecasts and retrospective experiments during 2015–18, verification of TC intensity errors conditioned on different 5-day track error thresholds shows that reducing the 5-day track errors by 50%–70% can help reduce the absolute intensity errors by 18%–20% in the 2018 version of the COAMPS-TC model. Such impacts of track errors on the TC intensity errors are most persistent at 4–5-day lead times in all three major ocean basins, indicating a significant control of global models on the forecast skill of the COAMPS-TC model. It is of interest to find, however, that lowering the 5-day track errors below 80 n mi (1 n mi = 1.852 km) does not reduce TC absolute intensity errors further. Instead, the 4–5-day intensity errors appear to be saturated at around 10–12 kt (1 kt ≈ 0.51 m s−1) for cases with small track errors, thus suggesting the existence of some inherent intensity errors in regional models. Additional idealized simulations under a perfect model scenario reveal that the COAMPS-TC model possesses an intrinsic intensity variation at the TC mature stage in the range of 4–5 kt, regardless of the large-scale environment. Such intrinsic intensity variability in the COAMPS-TC model highlights the importance of potential chaotic TC dynamics, rather than model deficiencies, in determining TC intensity errors at 4–5-day lead times. These results suggest a fundamental limit in the improvement of TC intensity forecasts by numerical models that one should consider in future model development and evaluation.

Climatological characteristics of simulated intense tropical cyclones (TCs) in the western North Pacific were explored with a 20-km-mesh atmospheric general circulation model (AGCM20) and a 5-km-mesh regional atmospheric nonhydrostatic model (ANHM5). From the AGCM20 climate runs, 34 intense TCs with a minimum central pressure (MCP) less than or equal to 900 hPa were sampled. Downscaling experiments were conducted with the ANHM5 for each intense TC simulated by the AGCM20. Only 23 developed into TCs with MCP ≤ 900 hPa. Most of the best-track TCs with an MCP ≤ 900 hPa underwent rapid intensification (RI) and attained maximum intensities south of 25°N. The AGCM20 simulated a similar number of intense TCs as the best-track datasets. However, the intense AGCM20 TCs tended to intensify longer and more gradually; only half of them underwent RI. The prolonged gradual intensification resulted in significant northward shifts of the location of maximum intensity compared with the location derived from two best-track datasets. The inner-core structure of AGCM20 TCs exhibited weak and shallow eyewall updrafts with maxima below an altitude of 6 km, while downscaling experiments revealed that most of the intense ANHM5 TCs underwent RI with deep and intense eyewall updrafts and attained their maximum intensity at lower latitudes. The altitudes of updraft maxima simulated by the AGCM20 descended rapidly during the phase of greatest intensification as midlevel warming markedly developed. The change in major processes responsible for precipitation in AGCM20 TCs before and after maximum intensification suggests close relationships between the large-scale cloud scheme and midlevel warming and prolonged gradual intensification.