Tropical Cyclone Trajectories Research Articles

Increasing WNP tropical cyclone-related extreme precipitation over East Asia during boreal summer associated with PDO shift

Over the past two decades, there has been a significant shift in tropical cyclone (TC) activity in the western North Pacific (WNP) basin during the boreal summer. Our analysis of data spanning from 1979 to 2021 reveals significant shifts in the WNP TC characteristics and rainfall pattern variation. To deepen our understanding of TC-related precipitation dynamics, we expressly address the difference between TC-related core precipitation (TCP) and remote precipitation (TRP). The results show that TRP significantly impacts the East Asian (EA) continent, especially on the Korean Peninsula. Notably, TCP exhibits decadal variability, with a pronounced negative correlation identified between it and the Pacific decadal oscillation (PDO) following a strong climate shift. This pivotal shift was marked by the PDO first transitioning to its negative phase in 1997, a notable change since 1979, resulting in a marked increase in TC-related extreme rainfall over the EA area. Concurrently, the rising sea surface temperatures (SSTs) over the WNP have intensified the western Pacific subtropical high (WPSH) circulation. The easterly steering flow associated with the WPSH then strengthened, leading to the continental migration of TC trajectories, thereby intensifying TC-related extreme precipitation.

Neural general circulation models for weather and climate.

General circulation models (GCMs) are the foundation of weather and climate prediction1,2. GCMs are physics-based simulators that combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. Recently, machine-learning models trained on reanalysis data have achieved comparable or better skill than GCMs for deterministic weather forecasting3,4. However, these models have not demonstrated improved ensemble forecasts, or shown sufficient stability for long-term weather and climate simulations. Here we present a GCM that combines a differentiable solver for atmospheric dynamics with machine-learning components and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best machine-learning and physics-based methods. NeuralGCM is competitive with machine-learning models for one- to ten-day forecasts, and with the European Centre for Medium-Range Weather Forecasts ensemble prediction for one- to fifteen-day forecasts. With prescribed sea surface temperature, NeuralGCM can accurately track climate metrics for multiple decades, and climate forecasts with 140-kilometre resolution show emergent phenomena such as realistic frequency and trajectories of tropical cyclones. For both weather and climate, our approach offers orders of magnitude computational savings over conventional GCMs, although our model does not extrapolate to substantially different future climates. Our results show that end-to-end deep learning is compatible with tasks performed by conventional GCMs and can enhance the large-scale physical simulations that are essential for understanding and predicting the Earth system.

An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based On Ensemble Weather Forecast Data

AbstractIn ensemble weather forecast, tropical cyclone (TC) tracks sometimes group together into trajectories parting away from each other. The goal of this study is to propose an objective method, based on a robust clustering approach, to detect such separation scenarios in the Japan Meteorological Agency Meso‐scale Ensemble Prediction System (MEPS) for three TCs: “Dolphin” (2020), “Nepartak” (2021), and “Meari” (2022). Taking advantage of the independence of the density‐based spatial clustering of applications with noise algorithm to the prior choice of the number of clusters, we first describe an objective way to calculate the aggregation distance, by searching the most frequent Euclidean distance between all the tracks. The clustering is then applied to the forecasted tracks, for each initialization time of MEPS (every 6 hr). Separation scenarios exist when the number of clusters is greater than one.

Changes to tropical cyclone trajectories in Southeast Asia under a warming climate

The impacts of tropical cyclones (TCs) on Southeast Asia’s coastlines are acute due to high population densities in low-lying coastal environments. However, the trajectories of TCs are uncertain in a warming climate. Here, we assess >64,000 simulated TCs from the nineteenth century to the end of the twenty-first century for both moderate- and high-emissions scenarios. Results suggest changes to TC trajectories in Southeast Asia, including: (1) poleward shifts in both genesis and peak intensification rates; (2) TC formation and fastest intensification closer to many coastlines; (3) increased likelihoods of TCs moving most slowly over mainland Southeast Asia; and (4) TC tracks persisting longer over land. In the cities of Hai Phong (Vietnam), Yangon (Myanmar), and Bangkok (Thailand), these variations result in future increases in both peak TC intensity and TC duration compared to historical TCs.

Tropical cyclone trajectory based on satellite remote sensing prediction and time attention mechanism ConvLSTM model

The accurate and timely prediction of tropical cyclones is of paramount importance in mitigating the impact of these catastrophic meteorological events. Presently, methods for predicting tropical cyclones based on satellite remote sensing images encounter notable challenges, including the inadequate extraction of three-dimensional spatial features and limitations in long-term forecasting. As a response to these challenges, this study introduces the Temporal Attention Mechanism ConvLSTM (TAM-CL) model, designed to conduct thorough spatiotemporal feature extraction on three-dimensional atmospheric reanalysis data of tropical cyclones. By leveraging ConvLSTM with three-dimensional convolution kernels, our model enhances the extraction of three-dimensional spatiotemporal features. Furthermore, an attention mechanism is integrated to bolster long-term prediction accuracy by emphasizing crucial temporal nodes. In the evaluation of tropical cyclone track and intensity forecasts across 24, 48, and 72 h, TAM-CL demonstrates a notable reduction in prediction errors, thereby underscoring its efficacy in forecasting both cyclone tracks and intensities. This contributes to an effective exploration of the application of deep networks in conjunction with atmospheric reanalysis data.

A context-aware hybrid deep learning model for the prediction of tropical cyclone trajectories

Tropical cyclones (TCs) are powerful natural disasters that lead to the loss of human lives and destruction of nature. Accurate prediction of the TC trajectory over a long period is crucial for timely warning and safe evacuation. Given the complexity of TCs and the geographical contexts that affects their movements, robust tools are required to process historical TC data and predict their locations. In this study, three deep learning models with flexible topology, namely, the multilayer perceptron (MLP) model, long short-term memory (LSTM) model, and a new data-driven model (MLP-LSTM) for TC trajectory prediction, were developed. They were evaluated using the North Atlantic Ocean TC dataset and contextual information such as wind speed, wind direction, and air pressure. The results showed that the hybrid MLP-LSTM model outperformed the MLP and LSTM models, especially when contextual information was considered. The average prediction distance errors for the next three hours were calculated as 52.73 km, 20.65 km, and 19.54 km for the MLP, LSTM, and MLP-LSTM models, respectively. The prediction enhancements for the next 24 h when contextual information was considered were 166 km, 203 km, and 208 km for the MLP, LSTM, and MLP-LSTM models, respectively.

Investigating the dynamics of point helical vortices on a rotating sphere to model tropical cyclones

A general exact weak solution to the non-linear equation of the conservation of the absolute vorticity in a thin layer of an incompressible medium on a rotating sphere is proposed. It takes into account the helicity of the point vortices and the non-uniformity of the depth of the layer. This is used to develop a model of the observed interactions of spiral atmospheric vortices. The fusion of two-point helical vortices (HVs) on the rotating sphere is considered. We also analyze the prognostic applicability of the dynamics of the HVs for modeling the abrupt changes observed in the trajectories of tropical cyclones and their landfall in comparison with the traditional approach. The analytical condition for chiral symmetry violation related to the direction of the movement of the center of a helical cyclone is obtained.

Tree dynamic response and survival in a category-5 tropical cyclone: The case of super typhoon Trami.

In the future with climate change, we expect more forest and tree damage due to the increasing strength and changing trajectories of tropical cyclones (TCs). However, to date, we have limited information to estimate likely damage levels, and nobody has ever measured exactly how forest trees behave mechanically during a TC. In 2018, a category-5 TC destroyed trees in our ongoing research plots, in which we were measuring tree movement and wind speed in two different tree spacing plots. We found damaged trees in only the wider spaced plot. Here, we present how trees dynamically respond to strong winds during a TC. Sustained strong winds obviously trigger the damage to trees and forests but inter-tree spacing is also a key factor because the level of support from neighboring trees modifies the effective “stiffness” against the wind both at the single tree and whole forest stand level.

Using a 3D convolutional neural network and gated recurrent unit for tropical cyclone track forecasting

The tropical cyclone (TC) track forecast is an essential task in meteorological operations. An accurate forecast should be based on a comprehensive understanding and description of TCs. A TC has a complex three-dimensional structure, and the surrounding atmosphere is the driving force for its development. Traditional forecasting methods performed relatively well for the TCs with stable moving speed and direction. However, the forecast accuracy still leaves some space to improve. In recent years, machine learning methods that can extract features from a large amount of historical data have been used in meteorological services and have shown excellent performance. To better forecast 6, 12, 18, and 24 h TC tracks in the Western North Pacific, a hybrid optimization model, combining the 3D convolutional neural network (3DCNN), gated recurrent unit (GRU), and smoothing algorithm is designed, which is called smoothed 3D-GRU. The 3DCNN is used to explore the potential relationship between environmental variables and TC movements at different pressure levels. The GRU is used to convert the TC track forecasting problem into a spatio-temporal sequence problem. The smoothing algorithm is used as a post-processing method to suppress unreasonable jumps of the model output. The mean spherical distances (MSDs) of the proposed smoothed 3D-GRU model at four prediction times are 27.89, 52.37, 79.16, and 112.05 km, which are lower than the comparative machine learning-based forecasting algorithms. Compared with the numerical prediction methods, the MSDs of the smoothed 3D-GRU model are lower in most situations. In general, the smoothed 3D-GRU model can provide reliable guidance for the TC trajectory prediction.

Near real-time predictions of tropical cyclone trajectory and intensity in the northwestern Pacific Ocean using echo state network

Near real-time predictions of tropical cyclone trajectory and intensity in the northwestern Pacific Ocean using echo state network

A Simple Trajectory Model for Climatological Study of Tropical Cyclones

AbstractThe establishment of a tropical cyclone (TC) trajectory model that can represent the basic physics and is practically advantageous considering both accuracy and computational cost is essential to the climatological studies of various global TC activities. In this study, a simple deterministic model is proposed based on a newly developed semiempirical formula for the beta drift under known conditions of the environmental steering flow. To verify the proposed model, all historical TC tracks in the western North Pacific and the North Atlantic Ocean basins during the period 1979–2018 are simulated and statistically compared with the relevant results derived from observed data. The proposed model is shown to well capture the spatial distribution patterns of the TC occurrence frequency in the two ocean basins. Prevailing TC tracks as well as the latitudinal distribution of the landfall TC number in the western North Pacific Ocean basin are also shown to agree better with the results derived from observed data, as compared to the existing models that took different strategies to include the effect of the beta drift. It is then concluded that the present model is advantageous in terms of not only the accuracy but also the capacity to accommodate the varying climate. It is thus believed that the proposed TC trajectory model has the potential to be used for assessing possible impacts of climate change on tropical cyclone activities.

Seasonal movement prediction of tropical cyclone over the North Indian Ocean by using atmospheric climate variables in statistical models

In this study, the contribution of atmospheric climate variables to the prediction skill of tropical cyclone (TC) activity over the North Indian Ocean (NIO) has been investigated. Statistical forecast models are developed through generalised linear model (GLM) and generalised additive model (GAM) for tropical cyclone trajectories over the NIO using atmospheric climate variables as predictors. TC from the Joint Typhoon Warning Centre and sets of climate predictor data were analysed for a period of 35-year (1979–2013). The velocity field is predicted by fitting GAM in each month and season. Hindcast validation method is applied to assess the reliability of the model. The skill of the GAM model is compared with GLM and found to be more successful in forecasting TC movement over the NIO region.

A case study of a phytoplankton bloom triggered by a tropical cyclone and cyclonic eddies.

Strong tropical cyclone (TC) Ockhi occurred in the southeastern Arabian Sea (AS) in 2017. Ockhi greatly changed the oceanic conditions and induced large variation in chlorophyll-a (Chl-a). The dynamic mechanisms of the long-term phytoplankton bloom after the passage of the TC were investigated in this study. Prominent surface ocean responses, e.g., decreasing temperature and salinity, were identified from Argo data by comparing the pre- and post-conditions of the TC. A phytoplankton bloom was observed in southeastern AS after the passage of TC Ockhi within the area of (11°N-14°N, 67°E-70°E) and lasted for seven days. Interestingly, there were two weaker cyclonic eddies, with an average vorticity of less than 0.14 s-1, on the TC trajectory from November 28 to December 2. As Ockhi approached, strong vertical mixing occurred on December 3, increasing the eddy vorticity to 0.26 s-1. After the passage of Ockhi, both eddies, with a two-day oscillation period, were substantially enhanced. Especially from December 11 to 16, the vorticity above 70 m was as high as 0.2 s-1 in the thermocline. Because of the high photosynthetically available radiation (PAR) and low precipitation, the enhanced cyclonic eddies induced upwelling for the entire thermocline for over ten days and uplifted nitrates into the mixed layer. This study offers new insights on the influence of eddies in regulating the impacts of typhoons on Chl-a, and the results can help evaluate typhoon-induced biological responses in the future.

Tropical Cyclone Track Forecasting Using Fused Deep Learning From Aligned Reanalysis Data.

The forecast of tropical cyclone trajectories is crucial for the protection of people and property. Although forecast dynamical models can provide high-precision short-term forecasts, they are computationally demanding, and current statistical forecasting models have much room for improvement given that the database of past hurricanes is constantly growing. Machine learning methods, that can capture non-linearities and complex relations, have only been scarcely tested for this application. We propose a neural network model fusing past trajectory data and reanalysis atmospheric images (wind and pressure 3D fields). We use a moving frame of reference that follows the storm center for the 24 h tracking forecast. The network is trained to estimate the longitude and latitude displacement of tropical cyclones and depressions from a large database from both hemispheres (more than 3,000 storms since 1979, sampled at a 6 h frequency). The advantage of the fused network is demonstrated and a comparison with current forecast models shows that deep learning methods could provide a valuable and complementary prediction. Moreover, our method can give a forecast for a new storm in a few seconds, which is an important asset for real-time forecasts compared to traditional forecasts.

Tropical cyclone landfalls south of the Tropic of Capricorn, southwest Indian Ocean

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 79:23-37 (2019) - DOI: https://doi.org/10.3354/cr01575 Tropical cyclone landfalls south of the Tropic of Capricorn, southwest Indian Ocean Micheal T. Pillay, Jennifer M. Fitchett* School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg 2050, South Africa *Corresponding author: jennifer.fitchett@wits.ac.za ABSTRACT: Common perception holds that tropical cyclones occur exclusively within the latitudinal range of 5° to 20°S. However, a few storms have been recorded poleward of this range each decade over the past half century. This study explores tropical cyclone occurrence south of the Tropic of Capricorn in the southwest Indian Ocean (SWIO; defined as 0°-30°S; African East coast up to 110°E). Using observational tropical cyclone and climate data, the small subset of storms making landfall in the region were examined in detail. The records indicate a decrease in storm frequency, while intensity has increased over the study period (1950-2015). Sea surface temperature (SST) reveals the interaction and influence of surface thermodynamics on tropical cyclone trajectory. Zonal wind at the 500 mb level indicates a modulation on track morphology following genesis, and may be responsible for recurving tracks. A decadal variability is observed for landfall location, which is currently in a low-amplitude southward component. SST is significantly related to storm intensity, while the Dipole Mean Index (DMI) and Southern Annular Mode (SAM) are related to and coincide with the highest landfall years. The Multivariate El Niño Southern Oscillation Index (MEI) is related to recurving track trajectories, while DMI and SAM may influence latitudinal and longitudinal track trajectories. These findings are important in understanding recent changes in tropical cyclone dynamics in the southwest Indian Ocean, particularly as the range of storm occurrence has expanded under climate warming. KEY WORDS: Tropical cyclone · Southwest Indian Ocean · Tropic of Capricorn · Climatology · Climate Variability Full text in pdf format PreviousNextCite this article as: Pillay MT, Fitchett JM (2019) Tropical cyclone landfalls south of the Tropic of Capricorn, southwest Indian Ocean. Clim Res 79:23-37. https://doi.org/10.3354/cr01575 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 79, No. 1. Online publication date: October 17, 2019 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2019 Inter-Research.

Tropical cyclones and the organization of mangrove forests: a review.

Many mangrove ecosystems are periodically exposed to high velocity winds and surge from tropical cyclones, and often recover with time and continue to provide numerous societal benefits in the wake of storm events. This review focuses on the drivers and disturbance mechanisms (visible and functional) that tropical cyclones of various intensities have on mangrove ecosystem properties around the world, as well as the potential ecosystem services role offered by mangroves along storm-ravaged coastlines. When viewed together, studies describe repeatable types of impact and a variety of responses of mangroves that make them ecologically resilient to high velocity winds, and which have served to advance the notion that mangroves are disturbance-adapted ecosystems. Studies have documented massive tree mortality and forest structural shifts as well as high variability of spatial effects associated with proximity and direction of the tropical cyclone trajectory that influence biogeochemical processes, recovery of individual trees, and forest regeneration and succession. Mangroves provide coastal protection through surge and wind suppression during tropical cyclones, and yet are able to overcome wind effects and often recover unless some alternative environmental stress is at play (e.g. hydrological alteration or sedimentation). Structural elements of mangroves are influenced by the legacies imposed by past tropical cyclone injury, which affect their current appearance, and presumably their function, at any point in time. However, much is yet to be discovered about the importance of the effects of tropical cyclones on these fascinating botanical ecosystems, including the role of storm-based sediment subsidies, and much more effort will be needed to predict future recovery patterns as the frequency and intensity of tropical cyclones potentially change.

The Tropical Cyclone Tracks and Formation over the Western Indian Ocean, And Impacts on the Iranian Southern Coasts

Cyclones are as one of the most dangerous meteorological phenomena of the tropical region that generate strong winds and heavy rainfall, impacting coastal regions. Behavior of tropical cyclone trajectories needs to be better understood in order to find predictable aspects of landfall potentially. This research aims to analyze the cyclone tracks statistically and then study of the associated meteorological effects of Gonu cyclone in June 2007 as an example. Using the cluster analysis (K-mean method) 5 principal clusters have been derived and spatial – temporal studies including the monthly variation of cyclone trajectories and their intensity and frequencies are performed. The 4th cluster indicated more spatial variability and expansion (4º-31ºN and 48.5º-78ºE). The second cluster showed the highest frequency with 349 events as well as the highest maximum intensity and standard deviation of 235.2km/h and 47.96km/h respectively. During 1-7 June 2007, the Gonu super cyclone traversed the Arabian Sea and reached the Iranian southern regions affecting the coastal infrastructures and communities. In this 7-day period the daily composite maps of different atmospheric levels showed that by intensifying of Gonu cyclone the axis of subtropical high pressures in the lower levels moved to the east and in the 500hp level they moved to the northward over the study region.

Trajectory Data Mining via Cluster Analyses for Tropical Cyclones That Affect the South China Sea

The equal division of tropical cyclone (TC) trajectory method, the mass moment of the TC trajectory method, and the mixed regression model method are clustering algorithms that use space and shape information from complete TC trajectories. In this article, these three clustering algorithms were applied in a TC trajectory clustering analysis to identify the TCs that affected the South China Sea (SCS) from 1949 to 2014. According to their spatial position and shape similarity, these TC trajectories were classified into five trajectory classes, including three westward straight-line movement trajectory clusters and two northward re-curving trajectory clusters. These clusters show different characteristics in their genesis position, heading, landfall location, TC intensity, lifetime and seasonality distribution. The clustering results indicate that these algorithms have different characteristics. The equal division of the trajectory method provides better clustering result generally. The approach is simple and direct, and trajectories in the same class were consistent in shape and heading. The regression mixture model algorithm has a solid theoretical mathematical foundation, and it can maintain good spatial consistency among trajectories in the class. The mass moment of the trajectory method shows overall consistency with the equal division of the trajectory method.

Contribution of Tropical Cyclones to Atmospheric Moisture Transport and Rainfall over East Asia

Abstract The coastal region of East Asia (EA) is one of the regions with the most frequent impacts from tropical cyclones (TCs). In this study, rainfall and moisture transports related to TCs are measured over EA, and the contribution of TCs to the regional water budget is compared with other contributors, especially the mean circulation of the EA summer monsoon (EASM). Based on ERA-Interim reanalysis (1979–2012), the trajectories of TCs are identified using an objective feature tracking method. Over 60% of TCs occur from July to October (JASO). During JASO, TC rainfall contributes 10%–30% of the monthly total rainfall over the coastal region of EA; this contribution is highest over the south/southeast coast of China in September. TCs make a larger contribution to daily extreme rainfall (above the 95th percentile): 50%–60% over the EA coast and as high as 70% over Taiwan Island. Compared with the mean EASM, TCs transport less moisture over EA. However, as the peak of the mean seasonal cycle of TCs lags two months behind that of the EASM, the moisture transported by TCs is an important source for the water budget over the EA region when the EASM withdraws. This moisture transport is largely performed by westward-moving TCs. These results improve understanding of the water cycle of EA and provide a useful test bed for evaluating and improving seasonal forecasts and coupled climate models.

Assessing sensitivities in algorithmic detection of tropical cyclones in climate data

AbstractThis study applies a sensitivity analysis (SA) technique (the Morris method, MM) to an automated Lagrangian tropical cyclone (TC) tracking algorithm used on gridded climate data. MM demonstrates the ability to screen for input parameters defining TCs (such as minimum intensity and lifetime) that contribute significantly to sensitivity in output metrics (such as storm count). The SA is performed by tracking TCs in four different reanalyses. Tracked TC trajectories are compared to a pointwise observational record. Results show that using thermally integrated metrics for isolating TC warm cores is superior to single‐temperature levels. Input thresholds defining TC vortex strength during tracking contribute the most variance in all output metrics. Integrated output metrics (such as accumulated cyclone energy) are less variable than “counting” metrics such as TC frequency. MM greatly reduces the computational requirements for tracker optimization, with tracked TCs demonstrating better hit and false alarm rates than previous studies.