Cyclone Intensity Research Articles

TC-SINDy: Improving physics-based deterministic tropical cyclone track and intensity model via data-driven sparse identification of Nonlinear Dynamics

Physics-based deterministic tropical cyclone (TC) simulation models play an important role in TC risk analysis, for their unique capability to represent physical principles and incorporate climate change impacts. However, establishing effective deterministic models can be challenging. In this paper, we present a simple yet powerful framework that distills physics-based TC models from historical TC data. By treating TC evolutions as dynamical systems, we adaptively discover the TC track and intensity governing equations from historical TC and environmental data via the Sparse Identification of Nonlinear Dynamics (SINDy) approach. Our proposed TC-SINDy approach is able to identify parsimonious, interpretable yet effective physics-based TC models. Compared to alternative physics-based deterministic models, our models match historical records much better in terms of the intensity evolution, prevailing tracks, and key parameter statistics across the coastlines of the Western North Pacific (WNP) basin and the North Atlantic (NA) basin. Our proposed models were finally coupled with a widely used parametric wind field, and the obtained probabilistic distribution of the surface wind also matched the historical histograms. Overall, the proposed method presents a general data-driven framework for discovering physics-based TC models, enabling reliable effective TC simulations while reserving the capacity to accommodate the changing climate.

Long-term assessment of resilience of avian assemblages following a major hurricane

Strong tropical cyclones directly devastate forest habitats and indirectly affect forest-dependent animals. Many forecast models suggest cyclone intensity and size will increase as a result of climate change, and cyclone frequency also may increase. Short-term effects of strong cyclones on vertebrate assemblages are well documented, but we know little about longer-term effects. From 1997 to 2013 we monitored avian assemblages at tropical forest in southern Belize, a forest that was largely destroyed by category 4 Hurricane Iris in October 2001. We found little change in recruitment or species richness, but evenness dropped markedly the first two sampling efforts after the hurricane and species turnover (β diversity) did not stabilize until 6–9 years afterward. Body condition dropped immediately after the hurricane passed but recovered quickly. That population size of resident tropical, but not Neotropical migrant, bird species dropped across our 17-year study highlights the potential urgency of altered cyclonic activity: on average, a hurricane strikes Belize once per decade, yet an increase in frequency, to say nothing of destructive power, could serve to erode extant species associations, lead to local extirpation of forest-dependent species, and create novel, transitory assemblages whose chief characteristic is instability.

A flexible tropical cyclone vortex initialization technique for GFDL SHiELD

Tropical cyclone (TC) intensity forecasting poses challenges due to complex dynamical processes and data inadequacies during model initialization. This paper describes efforts to improve TC intensity prediction in the Geophysical Fluid Dynamics Laboratory (GFDL) System for High-resolution prediction on Earth-to-Local Domains (SHiELD) model by implementing a Vortex Initialization (VI) technique. The GFDL SHiELD model, relying on the Global Forecast System (GFS) analysis for initialization, faces deficiencies in initial TC structure and intensity. The VI method involves adjusting the TC vortex inherited from the GFS analysis and merging it back into the environment at the observed location, enhancing the analyzed representation of storm structure. We made modifications to the VI package implemented in the operational Hurricane Analysis and Forecast System, including handling initial condition data, reducing input domain size, and improving storm intensity enhancement. Experiments using the T-SHiELD configuration demonstrate that using VI significantly improves the representation of initial TC intensity and size, enhancing TC predictions, particularly in storm intensity and outer wind forecasts within the first 48 h.

Estimating the Intensity of Tropical Cyclones from Spiral Signatures Acquired by Spaceborne SAR

Accurate estimates of tropical cyclone (TC) intensity are important for improving forecasts as well as studying ocean dynamics during such extreme events. Since most cyclone life occurs over the open ocean, remote sensing techniques play an important role in obtaining the necessary data. The possibility of using the configuration of spiral signatures of mature tropical cyclones (TCs) observed in synthetic aperture radar (SAR) images to estimate the maximum wind speed of a TC is considered. This study assessed the intensity of 14 TCs in the Atlantic and Pacific Oceans using radar images obtained by the Radarsat Hurricane Application Project. TC intensity was estimated using the hyperbolic-logarithmic approximation of TC spiral signatures (HLS approximation). Additionally, the edges of the spiral signatures were partially fitted using a logarithmic spiral to improve the reliability of the HLS approximation. For the first time, a physical model of changing the crossing angle of the logarithmic portion of the edges was proposed and tested on SAR images of the TC. HLS maximum wind speed estimates were compared with Best Track estimates. The results showed the closeness of both estimates with a correlation of 0.95 and a standard deviation of 2.9 m s−1. The results indicate the possibility of using the HLS approximation to estimate the intensity of mature TCs from SAR data.

Footprint of the air-sea momentum transfer saturation observed by ocean wave buoy network in extreme tropical cyclones

Tropical cyclones are one of the most destructive natural phenomena, causing tremendous coastal disasters worldwide. The maximum intensity of tropical cyclones is determined by momentum and heat transfer at the air-sea interface. Momentum transfer corresponds to the momentum loss of tropical cyclones and, consequently, to the underlying ocean's momentum gain causing extreme ocean waves and storm surges. Air-side observations of wind profiles and ocean-side observations of ocean subsurface currents show a slowdown of momentum transfer under high tropical cyclone wind speeds in the previous studies. However, there is still disagreement regarding the slowdown owing to lack of data. Here, we show momentum transfer under high wind speed conditions by observing ocean waves. Although ocean wave observations are highly spatially limited, we deployed a fleet of drifting ocean wave buoys covering the active area of tropical cyclones in the Western North Pacific. The buoy fleet captured extreme waves near the eye of the strongest category 5 tropical cyclone, indicating an ocean wave footprint of the momentum transfer saturation above surface wind speeds of 25 m/s. Our approach from ocean surface wave observation is a unique contribution to determination on air-sea momentum transfer slowdown under extreme wind speeds, which can compensate for the knowledge from conventional air and ocean-side observations. This finding advances tropical cyclones, extreme ocean waves, and storm surge modeling.

Impact Of Global Warming on Tropical Cyclones in The Northwest Pacific Ocean

Tropical cyclones are meteorological phenomena that can affect mid to low latitudes around the world. This Paper systematically summarizes and reviews the main characteristics, potential influencing factors, and influencing mechanisms of tropical cyclones in Northwestern Pacific, and summarizes and analyzes their changing trends in the context of global change. Global warming will affect the generation and development of tropical cyclones from multiple dimensions, with the most intuitive being the surface temperature of seawater. Through the analysis of global surface temperature and tropical cyclones in the Northwest Pacific, it is shown that as global climate warms, the frequency of tropical cyclones in the Northwest Pacific decreases, the extreme central pressure decreases, and the extreme maximum central wind speed increases, there is no significant trend in the average minimum pressure and average maximum wind speed of tropical cyclones. In other words, the extreme intensity of tropical cyclones strengthens; In the 2010s, this relationship was particularly prominent.

Enhancing tropical cyclone intensity forecasting with explainable deep learning integrating satellite observations and numerical model outputs

Enhancing tropical cyclone intensity forecasting with explainable deep learning integrating satellite observations and numerical model outputs

Modelling additive extremile regression by iteratively penalized least asymmetric weighted squares and gradient descent boosting

Quantile regression has emerged as one of the standard tools for regression analysis that enables a proper assessment of the complete conditional distribution of responses. This article considers a valuable alternative class to quantiles, called extremiles. Extremiles bear much better than quantiles the burden of representing an alert risk measure to the magnitude of infrequent catastrophic losses. Additive regression model has concentrated on making the regression structure more flexible by including nonlinear effects of continuous covariates and interaction effects. As a consequence, additive extremile regression based on minimizing an asymmetrically weighted sum of squared residuals is introduced. Different estimation procedures are presented including iteratively penalized least asymmetric weighted squares and gradient descent boosting. The properties of these procedures are investigated in a simulation study and an analysis of tropical cyclone intensity of the North Atlantic which reveals the function of variable selection by modelling additive extremile regression simultaneously.

Cyclone Intensity Estimation Using Deep Learning

Cyclone Intensity Estimation based on Deep Learning focuses on cyclone intensity estimation, enhancing early warning systems and empowering decision-makers to take proactive measures to safeguard vulnerable communities and infrastructure, which aims to minimize the devastating impacts of cyclones worldwide. The challenge in cyclone intensity prediction persists due to the complex and dynamic nature of these storms. Traditional methods fall short of capturing rapid changes. This research project leverages the power of deep learning to enhance the accuracy of cyclone intensity prediction by utilizing both satellite images and grayscale representations as input datasets. It involves preprocessing and feature extraction from satellite images captured during cyclonic events. Convolutional Neural Networks (CNN) are employed to automatically learn and extract relevant patterns from these complex datasets. In parallel, grayscale images derived from the original satellite images are utilized to capture essential structure information that contributes to cyclone intensity. The fusion of information from both modalities is achieved through a novel deep learning patterns that go beyond the capabilities of traditional intensity estimation methods, and architecture, fostering a comprehensive understanding of cyclonic patterns that go beyond the capabilities of traditional intensity estimation methods. A new approach seeks to automate cyclone estimation, streamlining timelines and increasing efficiency by merging deep learning with hurricane-focused satellite data. This approach might result in more precise forecasts, Reducing the amount of casualties and property damage in prone areas.

Damage to tropical forests caused by cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics.

Each year, an average of 45 tropical cyclones affect coastal areas and potentially impact forests. The proportion of the most intense cyclones has increased over the past four decades and is predicted to continue to do so. Yet, it remains uncertain how topographical exposure and tree characteristics can mediate the damage caused by increasing wind speed. Here, we compiled empirical data on the damage caused by 11 cyclones occurring over the past 40 years, from 74 forest plots representing tropical regions worldwide, encompassing field data for 22,176 trees and 815 species. We reconstructed the wind structure of those tropical cyclones to estimate the maximum sustained wind speed (MSW) and wind direction at the studied plots. Then, we used a causal inference framework combined with Bayesian generalised linear mixed models to understand and quantify the causal effects of MSW, topographical exposure to wind (EXP), tree size (DBH) and species wood density (ρ) on the proportion of damaged trees at the community level, and on the probability of snapping or uprooting at the tree level. The probability of snapping or uprooting at the tree level and, hence, the proportion of damaged trees at the community level, increased with increasing MSW, and with increasing EXP accentuating the damaging effects of cyclones, in particular at higher wind speeds. Higher ρ decreased the probability of snapping and to a lesser extent of uprooting. Larger trees tended to have lower probabilities of snapping but increased probabilities of uprooting. Importantly, the effect of ρ decreasing the probabilities of snapping was more marked for smaller than larger trees and was further accentuated at higher MSW. Our work emphasises how local topography, tree size and species wood density together mediate cyclone damage to tropical forests, facilitating better predictions of the impacts of such disturbances in an increasingly windier world.

Predicting the Intensity of Tropical Cyclones over the Western North Pacific Using a Dual-Branch Spatiotemporal Attention Convolutional Network

Abstract This paper proposes a spatiotemporal attention convolutional network (STAC-Pred) that leverages deep learning techniques to model the spatiotemporal features of tropical cyclones (TCs) and enable real-time prediction of their intensity. The proposed model employs dual branches to concurrently extract and integrate features from intensity heatmaps and satellite cloud imagery. Additionally, a residual attention (RA) module is integrated into the three-channel cloud imagery convolution process to automatically respond to high wind speed regions. TC’s longitude, latitude, and radius of winds are injected into the multi-timepoint prediction model to assist in the prediction task. Furthermore, a rolling mechanism (RM) is employed to smooth the fluctuation of losses, achieving accurate prediction of TC intensity. We use several TC records to evaluate and validate the universality and effectiveness of the model. The results indicate that STAC-Pred achieves satisfactory performance. Specifically, the STAC-Pred model improves prediction performance by 47.69% and 28.26% compared to the baseline (official institutions) at 3- and 6-h intervals, respectively. Significance Statement Tropical cyclones are one of the most deadly and damaging natural disasters in coastal areas worldwide. Early prediction can significantly reduce casualties and property losses. This study innovatively conducts dimensionality augmentation on one-dimensional intensity numerical sequences and proposes a new network model for rolling forecast of their future intensity. The proposed prototype model (not yet incorporating any atmospheric conditions) shows promising results for 3- and 6-h advance forecasts, providing valuable guidance for forecasters regarding real-time operational predictions of short-term tropical cyclone intensity.

On the Local Available Potential Energy Perspective of Baroclinic Wave Development

Abstract Extratropical cyclones convert available potential energy (APE) to kinetic energy. However, our current understanding of APE conversion on synoptic scales is limited, as the well-established Lorenz APE framework is only applicable in a global, volume-integrated sense. Here, we employ a recently developed local APE framework to investigate APE and its tendencies in a highly idealized, dispersive baroclinic wave, which leads to the formation of a primary and a downstream cyclone. By utilizing a Lagrangian approach, we demonstrate that locally the downstream cyclone not only consumes APE but also generates it. Initially, APE is transported from both poleward and equatorward reservoirs into the baroclinic zone, where it is then consumed by the vertical displacement of air parcels associated with the developing cyclone. To a lesser extent, APE is also created within the cyclone when air parcels overshoot their reference state; i.e., air colder than its reference state is lifted and air warmer than its reference state is lowered. The volume integral of the APE tendency is dominated by slow vertical displacements of large air masses, whereas the dry intrusion (DI) and warm conveyor belt (WCB) of the cyclone are responsible for the largest local APE tendencies. Diabatic effects within the DI and WCB contribute to the generation of APE in regions where it is consumed adiabatically, thereby enhancing baroclinic conversion in situ. Our findings provide a comprehensive and mechanistic understanding of the local APE tendency on synoptic scales within an idealized setting and complement existing frameworks explaining the energetics of cyclone intensification.

A Global Increase in Nearshore Tropical Cyclone Intensification

AbstractTropical Cyclones (TCs) inflict substantial coastal damages, making it pertinent to understand changing storm characteristics in the important nearshore region. Past work examined several aspects of TCs relevant for impacts in coastal regions. However, few studies explored nearshore storm intensification and its response to climate change at the global scale. Here, we address this using a suite of observations and numerical model simulations. Over the historical period 1979–2020, observations reveal a global mean TC intensification rate increase of about 3 kt per 24‐hr in regions close to the coast. Analysis of the observed large‐scale environment shows that stronger decreases in vertical wind shear and larger increases in relative humidity relative to the open oceans are responsible. Further, high‐resolution climate model simulations suggest that nearshore TC intensification will continue to rise under global warming. Idealized numerical experiments with an intermediate complexity model reveal that decreasing shear near coastlines, driven by amplified warming in the upper troposphere and changes in heating patterns, is the major pathway for these projected increases in nearshore TC intensification.

Cyclone Intensity Estimation System using Satellite Images

Tropical cyclones are big storms that can cause a lot of damage. Cyclone intensity estimation plays a vital role in disaster preparedness, response, and mitigation strategies. This paper introduces a novel approach to estimating cyclone intensity using satellite images through a Convolutional Neural Network (CNN) model. Unlike previous methods, we employ advanced techniques such as histogram analysis for feature extraction and adaptive thresholding for image segmentation using mean, Gaussian, and Otsu methods. The model also predicts potential coverage distance. Additionally, we present a user-friendly visualization portal, a pioneering effort in this field, which displays the deep learning output along with contextual information for end-users

On the effects of the timing of an intense cyclone on summertime sea-ice evolution in the Arctic

Abstract This study investigates the impacts of the timing of an extreme cyclone that occurred in August 2012 on the sea-ice volume evolution based on the Arctic Ice Ocean Prediction System (ArcIOPS). By applying a novel cyclone removal algorithm to the atmospheric forcing during 4–12 August 2012, we superimpose the derived cyclone component onto the atmospheric forcing one month later or earlier. This study finds that although the extreme cyclone leads to strong sea-ice volume loss in all runs, large divergence occurs in sea-ice melting mechanism in response to various timing of the cyclone. The extreme cyclone occurred in August, when enhanced ice volume loss is attributed to ice bottom melt primarily and ice surface melt secondarily. If the cyclone occurs one month earlier, ice surface melt dominates ice volume loss, and earlier appearance of open water within the ice zone initiates positive ice-albedo feedback, leading to a long lasting of the cyclone-induced impacts for approximately one month, and eventually a lower September ice volume. In contrast, if the cyclone occurs one month later, ice bottom melt entirely dominates ice volume loss, and the air-open water heat flux in the ice zone tends to offset ice volume loss.

Improvement of Global Forecast of Tropical Cyclone Intensity by Spray Heat Flux and Surface Roughness

AbstractIn global forecasting systems, tropical cyclone (TC) intensity is usually underestimated, which is one of the major challenges for TC forecasting. One possible reason is the deficiency of physical parameterizations associated with ocean surface waves. To improve the TC intensity forecast, effects of two powerful wave‐related processes, the spray‐mediated heat flux and surface roughness, are considered in a global coupled ocean‐atmosphere‐wave system (CFSv2.0‐WW3). Serial 24 hr forecasting experiments for all TCs from May to October 2019 are conducted, and comparisons are made against the International Best Track TC data. The results show that for strong TCs the underestimation of TC intensity in CFSv2.0‐WW3 is significantly improved by the modified surface roughness parameterization and the accelerated spray heat flux parameterization based on Gaussian Quadrature. For major hurricanes with initial wind speeds above 30 m/s, the error of TC maximum wind speed decreases by about 34.4%. To understand the associated dynamic and thermodynamic processes, a series of 120 hr simulations for major hurricane Hagibis is conducted. The effect of spray‐mediated heat flux accounts for 91.7% of the TC intensity improvement. The increased heat fluxes offset the negative contributions of vertical advection and ventilation, leading to enhanced entropy. The low‐level pressure is then decreased, resulting in enhanced convergence of absolute vorticity, which overpowers the negative effects of frictional dissipation and vertical momentum advection. The study indicates that the application of these two wave‐related parameterizations can potentially improve the global forecast and reanalysis of TC intensity.

Seasonal patterns and interannual variations of atmospheric circulation over the Bering Sea in 1995–2022

Mean patterns of atmospheric circulation over the Bering Sea in 4 seasons: winter (January-March), spring (April-June), summer (July-September) and autumn (OctoberDecember) are described using the author’s typification of synoptic situations. Frequency of all 6 types of synoptic situations is calculated and the predominant types are determined, by season. Mean values of the meridional and zonal indices of atmospheric circulation and the number and intensity of cyclones in the Bering Sea area are calculated for each season in the period 1995–2022. Cyclonic activity and direction and intensity of general wind transfer, particularly intensity of winter and summer monsoons, are considered separately for the western and eastern parts of the Bering Sea. Interannual variations of all these parameters are traced. For all seasons, the wind transfer over the entire area is determined by cyclonic activity in the western Bering Sea: the higher activity leads to weaker monsoon in winter (northeasterly) and summer (southwesterly) but strengthening in spring (southeasterly) and autumn (northwesterly). Since the middle 1990s, autumns and winters in the Bering Sea became warmer (with the warmest period in the late 2010s when southeasterlies prevailed in winter), but springs became colder (southeastern wind transfer in early 2000s changed to the northeastern one in late 2010s), with no definite tendency for summer, when the meridional index of atmospheric circulation was rather stable and the zonal index had a negative trend (weakening of westerlies). Trends and cycles of oceanographic conditions in the northwestern Bering Sea generally coincided with the changes in wind transfer in any season.

Numerical Study of Effects of Warm Ocean Eddies on Tropical Cyclones Intensity in Northwest Pacific

This study investigates the impact of warm core eddies (WCEs) on the ocean response and intensity of tropical cyclones (TCs) in the Northwest Pacific, focusing on three typhoons in 2018: Jebi, Trami, and Kong-rey. The research uses the Hurricane Weather Research and Forecast (HWRF) model coupled with the MPIPOM-TC ocean model. Idealized WCEs are embedded into the ocean model ahead of each TC. The impacts of WCEs are evaluated by comparing simulations with and without their presence. Uncoupled experiments with the fixed sea surface temperature (SST) serve as a reference for TC maximum potential intensity. To quantitatively assess the impact of WCEs on the SST, enthalpy fluxes, and TC intensity, a Maximum WCE Potential Index (MWPI) is introduced. Our findings indicate that for a WCE with a 200 km radius, the potential to reduce SST cooling ranges from 34 to 37%, while the potential to increase enthalpy fluxes varies between 25 and 39%. The influence of WCEs on TC intensity, as measured by minimum pressure, shows a larger variation from 27% to 48%, depending on the oceanic and atmospheric environmental conditions in each storm. Additional experiments reveal the sensitivity of the MWPI to WCE size, with TC Trami showing less sensitivity due to its slower translational speed. This study underscores the significant role of oceanic thermal conditions, particularly WCEs, in modulating TC intensity.

Improvement in Forecasting Short-Term Tropical Cyclone Intensity Change and Their Rapid Intensification Using Deep Learning

Abstract Forecasting the intensity of a tropical cyclone (TC) remains challenging, particularly when it undergoes rapid changes in intensity. This study aims to develop a convolutional neural network (CNN) for 24-h forecasts of the TC intensity changes and their rapid intensifications over the western Pacific. The CNN model, the DeepTC, is trained using a unique loss function, an amplitude focal loss, to better capture large intensity changes, such as those during rapid intensification (RI) events. We showed that the DeepTC outperforms operational forecasts, with a lower mean absolute error (8.9%–10.2%) and a higher coefficient of determination (31.7%–35%). In addition, the DeepTC exhibits a substantially better skill at capturing RI events than operational forecasts. To understand the superior performance of the DeepTC in RI forecasts, we conduct an occlusion sensitivity analysis to quantify the relative importance of each predictor. Results revealed that scalar quantities such as latitude, previous intensity change, initial intensity, and vertical wind shear play critical roles in successful RI prediction. Additionally, the DeepTC utilizes the three-dimensional distribution of relative humidity to distinguish RI cases from non-RI cases, with higher dry–moist moisture gradients in the mid-to-low troposphere and steeper radial moisture gradients in the upper troposphere showed during RI events. These relationships between the identified key variables and intensity change were successfully simulated by the DeepTC, implying that the relationship is physically reasonable. Our study demonstrates that the DeepTC can be a powerful tool for improving RI understanding and enhancing the reliability of TC intensity forecasts.

Why Was the Weakening of Typhoon In-fa (2021) to the East of Taiwan Island Not Forecasted in the GFS?

Abstract Typhoon In-fa (2021) experienced a weakening process on 22–23 July in a large-scale environment favorable for tropical cyclone (TC) intensification. All operational forecasts and the Global Forecast System (GFS) forecasts predicted a continuous intensification, which deviated significantly from the observation. The analysis of the GFS analysis product shows a coalescence process of Typhoon In-fa with an intraseasonal monsoon gyre during the period, resulting in an increased outer size of In-fa and well-organized convection to the east, which prevented transporting the mass and moisture into the inner-core area of In-fa, thus leading to the weakening. Nevertheless, this essential coalescence process was not captured in the GFS forecasts due to the poor prediction of the monsoon gyre. The analysis shows that the forecasted monsoon gyre on 20–22 July had an eastward location at 72- and 96-h lead times and a weaker intensity and outer circulation at 24- and 48-h lead times, leading to the forecasted TC always moving in its north and west, in agreement with numerical simulation results that the monsoon gyre with a weaker outer circulation is not conducive to the coalescence. Thus, the deep convection to the east of In-fa preventing the inward transportation of mass and moisture did not develop in the GFS forecasts. As a result, the GFS forecasted that In-fa would continue intensifying in a favorable environment on 22–23 July. The findings of this study would prompt forecasters to pay attention to the prediction of the monsoon gyre and its influence on the TC intensity in forecast products available to them.