Cyclone Trajectories Research Articles

Spatial Changes of Driving Parameters Affecting Cyclonic Activity over the North Indian Ocean from 1960 to 2020

This study undertakes a thorough analysis of the elements that influence the variability of tropical cyclones (TC) in the North Indian Ocean (NIO) from 1960 to 2020, with a specific focus on the periods before and after the monsoon season. The study utilizes historical satellite data to investigate the factors that impact the formation, strength, and trajectories of cyclones. The primary method for evaluating cyclone strength is by calculating the Accumulated Cyclone Energy (ACE). The study observes a decreasing trend in ACE levels during 1991–2005, which started increasing just after from 2006 to 2020. The Bay of Bengal (BoB) has a more uniform distribution of ACE in comparison to the Arabian Sea (AS), with higher average values and more variability over the Main Development Region (MDR), which is the area where cyclone development occurs most frequently. Cyclones of greater intensity generally occur following the monsoon season. Examination of storm paths reveals that cyclones with greater intensity frequently hit the northeastern and southeastern coastal regions of India. The study emphasizes notable discrepancies in parameters within the MDR, which impact cyclone strength and ACE values throughout various periods.

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.

The Westward Drift of Jupiter's Polar Cyclones Explained by a Center‐of‐Mass Approach

AbstractThe first orbits around Jupiter of the Juno spacecraft in 2016 revealed a symmetric structure of multiple cyclones that remained stable over the next 5 years. Trajectories of individual cyclones indicated a consistent westward circumpolar motion around both poles. In this paper, we propose an explanation for this tendency using the concept of beta‐drift and a “center‐of‐mass” approach. We suggest that the motion of these cyclones as a group can be represented by an equivalent sole cyclone, which is continuously pushed by beta‐drift poleward and westward, embodying the westward motion of the individual cyclones. We support our hypothesis with 2D model simulations and observational evidence, demonstrating this mechanism for the westward drift. This study joins consistently with previous studies that revealed how aspects of these cyclones result from vorticity‐gradient forces, shedding light on the physical nature of Jupiter's polar cyclones.

Possible Influence of Solar Cyclicity on Extratropical Cyclone Trajectories in the North Atlantic

In this work, we study long-term changes in the main directions of movement (storm tracks) of extratropical cyclones in the North Atlantic for the period 1873–2021, based on the data of the MSLP (Mean Sea Level Pressure) archives from the Climatic Research Unit and NCEP/DOE AMIP-II reanalysis. It was found that in the cold half of the year, the mean latitudes of storm tracks undergo oscillations with the periods ~80–100, ~40–45, and ~22 years, which indicates their possible association with solar cyclicity. Cyclone trajectories were found to shift northward at the minimum of the Gleissberg secular cycle (~1900–1930) and southward at its maximum (~1940–1960). The secular variations are the most pronounced in the western part of the North Atlantic, with the peak-to-peak amplitude reaching ~3–5°, and disappear at longitudes east of Greenwich. On the bidecadal timescale, cyclone tracks were found to shift noticeably to the north in even solar cycles and weakly to the south in odd ones. The most significant northward shifts (~1–2°) were detected during the descending phase and the minimum of the solar cycle (from the second to the sixth year after the solar maximum) in the eastern part of the North Atlantic (longitudes 30–10° W). The detected oscillations of cyclone trajectories may be caused by long-term changes in the intensity of the stratospheric polar vortex associated with galactic cosmic ray variations and geomagnetic activity.

Influence of Solar Activity and Galactic Cosmic Ray Variations on Trajectories of Extratropical Cyclones in the North Atlantic

Influence of Solar Activity and Galactic Cosmic Ray Variations on Trajectories of Extratropical Cyclones in the North Atlantic

Extratropical Cyclone Response to Projected Reductions in Snow Extent over the Great Plains

Extratropical cyclones develop in regions of enhanced baroclinicity and progress along climatological storm tracks. Numerous studies have noted an influence of terrestrial snow cover on atmospheric baroclinicity. However, these studies have less typically examined the role that continental snow cover extent and changes anticipated with anthropogenic climate change have on cyclones’ intensities, trajectories, and precipitation characteristics. Here, we examined how projected future poleward shifts in North American snow extent influence extratropical cyclones. We imposed 10th, 50th, and 90th percentile values of snow retreat between the late 20th and 21st centuries as projected by 14 Coupled Model Intercomparison Project Phase Five (CMIP5) models to alter snow extent underlying 15 historical cold-season cyclones that tracked over the North American Great Plains and were faithfully reproduced in control model cases, providing a comprehensive set of model runs to evaluate hypotheses. Simulations by the Advanced Research version of the Weather Research and Forecast Model (WRF-ARW) were initialized at four days prior to cyclogenesis. Cyclone trajectories moved on average poleward (μ = 27 +/− σ = 17 km) in response to reduced snow extent while the maximum sea-level pressure deepened (μ = −0.48 +/− σ = 0.8 hPa) with greater snow removed. A significant linear correlation was observed between the area of snow removed and mean trajectory deviation (r2 = 0.23), especially in mid-winter (r2 = 0.59), as well as a similar relationship for maximum change in sea-level pressure (r2 = 0.17). Across all simulations, 82% of the perturbed simulation cyclones decreased in average central sea-level pressure (SLP) compared to the corresponding control simulation. Near-surface wind speed increased, as did precipitation, in 86% of cases with a preferred phase change from the solid to liquid state due to warming, although the trends did not correlate with the snow retreat magnitude. Our results, consistent with prior studies noting some role for the enhanced baroclinity of the snow line in modulating storm track and intensity, provide a benchmark to evaluate future snow cover retreat impacts on mid-latitude weather systems.

“Cold-Dry” and “Cold-Wet” Events in the Late Holocene, Southern Russian Far East

Two late Holocene cold events were described for the Southern Russian Far East: 2800–2600 year BP and the Little Ice Age (LIA) (~1300–1850 CE). The synthesis is based on multi-proxy records on profile “the mainland (Primorye)-islands (Sakhalin-Kurils)”. Main archives are sediments of small lakes and peat bogs that recorded the high-resolution environmental changes. The temporal resolution of reconstructions here is up to 26–40 years. During the cold event of 2800–2600 year BP, the humidity decreased sharply, there were long-term dry seasons without strong floods, and buried soil formed on floodplains. The LIA was wet in the mainland and the Kurils, with frequent strong floods, but was drier in Sakhalin. The cooling was characterized by sharp temperature fluctuations where high moisture conditions alternated with short-term drier periods. The shift in geographical position and intensity of the main centers of atmospheric action caused a paleoclimatic interpretation of these events (Aleutian Low, Siberian and North Pacific Highs, the summer Okhotsk anticyclone and the Far East depression). Changes in the North Pacific oscillations played an important role in the alternation of cold-dry and cold-wet periods. Anomalies in the intensity of El Niño and the monsoon system led to changes in tropical and extratropical cyclone trajectories and cyclogenesis in general.

Cyclone trajectory and intensity prediction with uncertainty quantification using variational recurrent neural networks

Cyclone trajectory and intensity prediction with uncertainty quantification using variational recurrent neural networks

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.

Objective climatology and classification of the Mediterranean cyclones based on the ERA5 data set and the use of the results for the definition of seasons

An objective cyclone detection and tracking analysis is performed for the Mediterranean region with the use of 6-hourly (00, 06, 12, and 18 UTC) 1° × 1° mean sea-level pressure data obtained from the ERA5 database for the period 1950–2018. At first, the main cyclogenesis and high-density areas of cyclones are identified. Next, principal component analysis and cluster analysis are performed, classifying the detected cyclone trajectories into 12 clusters. In the following step, the application of the above methodology, this time on the intra-annual variations of the 12 cyclone clusters’ frequencies leads to the objective definition of four seasons, which generally correspond to the conventional ones, but they present differences in their limits and duration. The results of this method are also compared with the ones of two other methods for defining the seasons, which are based (i) on the long-term mean intra-annual variations of various meteorological parameters and (ii) on the intra-annual variations of the frequencies of defined weather types, revealing larger winter and shorter spring seasons. Finally, a composition of all three aforementioned approaches is attempted, leading to the following seasons: “winter” (November 16–March 25) which lasts more than 4 months, “spring” (March 26–June 11) with a duration of approximately 2.5 months, “summer” (June 12–September 12) which lasts about 3 months, and “autumn” (September 13–November 15) with a duration of about 2 months. The long-term changes of the above seasons’ characteristics are also examined.

The influence of recent and future climate change on spring Arctic cyclones

In recent decades, the Arctic has experienced rapid atmospheric warming and sea ice loss, with an ice-free Arctic projected by the end of this century. Cyclones are synoptic weather events that transport heat and moisture into the Arctic, and have complex impacts on sea ice, and the local and global climate. However, the effect of a changing climate on Arctic cyclone behavior remains poorly understood. This study uses high resolution (4 km), regional modeling techniques and downscaled global climate reconstructions and projections to examine how recent and future climatic changes alter cyclone behavior. Results suggest that recent climate change has not yet had an appreciable effect on Arctic cyclone characteristics. However, future sea ice loss and increasing surface temperatures drive large increases in the near-surface temperature gradient, sensible and latent heat fluxes, and convection during cyclones. The future climate can alter cyclone trajectories and increase and prolong intensity with greatly augmented wind speeds, temperatures, and precipitation. Such changes in cyclone characteristics could exacerbate sea ice loss and Arctic warming through positive feedbacks. The increasing extreme nature of these weather events has implications for local ecosystems, communities, and socio-economic activities.

PDO Modulation on the Relationship between ENSO and Typhoon Tracks

Abstract This study explores the role of the Pacific decadal oscillation (PDO) in modulating the relationship between El Niño–Southern Oscillation (ENSO) and typhoon tracks. Tropical cyclone (TC) trajectories in the western North Pacific (WNP) in 1950–2017 are clustered into seven clusters, including three recurved trajectories and four straight-moving tracks. These clusters are distinguished well by number of TCs, intensity, lifetime, genesis position/month, landing, and track. The sea surface temperature (SST) anomaly in the composite analysis and accumulated cyclone energy (ACE) of each cluster demonstrate that there are four clusters dominated by ENSO. The associated ENSO effects on these clusters are manifested by steering flow and vertical wind shear (VWS) in the composite differences between El Niño and La Niña years. However, such ENSO effects on TC quantity, genesis location, and track of these corresponding clusters are significantly enhanced during the PDO positive phases only for two clusters that are formed in the southeastern part of the WNP and undergo a long lifetime and track, because the PDO explains little local environmental variance where the other two clusters are located in the northern part of the WNP. This conclusion is also supported by TC track density analysis. The two leading modes of empirical orthogonal functions (EOF) analysis of TC track density are significantly correlated with ENSO. The enhancement of ENSO effects during the PDO positive phase exhibits by the second mode through local SST, VWS, and steering flow. Significance Statement Accurate prediction of tropical cyclone (TC) activity can help preparedness and therefore reduce the losses of life and property. Long-term track prediction relies on our understanding how TC tracks are associated with interannual and longer climate variability. This study uses historical data of 1950–2017 in the western North Pacific and reveals that only for two of four track clusters that are affected by El Niño–Southern Oscillation (ENSO), the associated ENSO effects are enhanced during the Pacific decadal oscillation positive phases because the oscillation has significant influence on vertical wind shear and steering flow where these two clusters are located. The findings enrich the mechanisms of TC track variabilities and will help improve long-term prediction of TC tracks.

Hybrid data-driven resilience assessment and enhancement of distribution system for cyclone susceptible zones

The sprawl of distribution system towards the need of smart grid, demands better sustenance and adaptation strategies to deal with high-impact low-frequency (HILF) events. One of the predominant causes of HILF events are natural calamities. Therefore, the resilience assessment of the distribution system is inevitable. The contributions majorly focuses on hybrid data driven approach using micro-phasor measurement unit (μ-PMU), for dynamic voltage, current phasors monitoring, and unmanned aerial vehicle (UAV) confirms structural vulnerability of nodes within network. Mesh grid approach, which analyses cyclone trajectory affecting the network, supplemented identification of most vulnerable part within network. However, priorities of vulnerable nodes are corroborated using complex network (CN) theory. This hybrid data driven approach and spatial parameters are used to estimate appropriate mitigation strategies against HILF scenarios. Hence, resilience analysis based on location parameters and dynamic network conditions are further analyzed based on degree of correlation of location dependent resilience with latitude, elevation, and probable water level. Based on susceptible vulnerable nodes, identification of optimum alleviation schemes is adopted and justified using resilience trapezoid. To validate efficacy of the proposed approach, the analysis is tested on IEEE 33-bus distribution network subjected to 5 cyclone prone geographical coordinates for 20 years cyclone data.

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.

Bioaerosols as Evidence of Atmospheric Circulation Anomalies over the Okhotsk Sea and Shantar Islands in the Late Glacial–Holocene

Allochthonous biofossil distribution in the blanket peat bog of Bolshoy Shantar Island was used to analyze atmospheric circulation anomalies in the north-western Okhotsk Sea over the last 12.6 ka. The main aim of this study was to determine periods of intensification of deep cyclones and extreme storms. The composition of bioaerosols is significantly influenced by atmospheric zonal and meridional transport anomalies associated with anomalies of the monsoon system of Northeast Asia, atmospheric fronts and cyclone trajectories. Marine diatoms enter the peatland from the sea during extreme storms and record the passage of sea cyclones in the autumn-winter, whereas the distribution of allochthonous pollen indicates the intensity of continental cyclones. We used Pinus pumila pollen as an indicator of heavy snowfalls and winter cyclone activity. Fifteen phases of extreme storms were identified. Changes in ice coverage also played an important role in bioaerosol emission. During cold periods, emissions of bioaerosols mainly occurred in the open sea, whereas during warm periods, emissions occurred near the coast. The recurrence and intensity of cyclones during the cold seasons depends on displacement of the Siberian High and Aleutian Low. Periods of continental cyclones intensified in spring-summer and coincided with periods of active winter cyclogenesis.

Cyclone Trajectory and Intensity Prediction with Uncertainty Quantification Using Variational Recurrent Neural Networks

Cyclone Trajectory and Intensity Prediction with Uncertainty Quantification Using Variational Recurrent Neural Networks