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Accurate tracking of tropical cyclones (TCs) can provide regions of interest for intelligent forecasting of TC tracks and intensity. There have been few studies on algorithms for automatic TC tracking. This study proposes an effective TC tracking method based on deep learning combined with infrared satellite images. The study first constructed a TC tracking dataset based on the infrared images of the China Fengyun-2D geostationary satellite covering six different TC intensity levels between 2009 and 2012. This included 47 complete cases (video sequences) of TCs from generation to extinction. Based on deep learning, the visual tracking algorithm SiamRPN was used as the model framework. Combining Bi-GRU and TC cloud spatiotemporal evolution characteristics to improve the performance of the SiamRPN network, the SiamTCNet target-tracking model was designed to track TCs automatically. Considering that the shape and scale of TC changes with time, a TC is regarded as a typical non-rigid object with obvious timing characteristics, so the first frame of a TC video sequence is combined with the satellite images of the first three frames of the current frame as inputs to the proposed SiamTCNet model, which then extracts the evolution of the TC's spatial structure and its bidirectional temporal change information. The experimental results show that the TC tracking of the proposed model is a significant improvement over the original SiamRPN model. [ABSTRACT FROM AUTHOR]

Several damaging tropical cyclones (TCs) have occurred recently over the South Indian Ocean (SIO) region, causing enormous social and economic losses. Yet, while many studies have examined SIO TC characteristics using observations and reanalysis, only a few have assessed these characteristics specifically for this region in climate models, and fewer have investigated their projections under climate change. Here we do this for a historical (1980–2010) and future (2020–2050) period, using multimodel simulations from the High Resolution Model Intercomparison Project, as well as examine biases in the historical period relative to a reanalysis (ERA5). The models have horizontal resolutions of 25–50 km, which has enabled an improved ability to represent tropical cyclones globally in previous studies. TempestExtremes software is employed to detect tropical storm and cyclone tracks. In cases where TempestExtremes cannot be applied due to a lack of requisite variables in a dataset, we instead examine extreme wind speeds in that dataset. For the historical period, we find considerable variation in model biases compared to ERA5, which itself exhibits realistic spatial patterns of tracks and their monthly distribution. Models do at least agree on positive biases in track frequency east of Madagascar and somewhat in the Mozambique Channel. However, the models and ERA5 only produce Category 3 tropical cyclones at best. Wind speeds for 25 km resolution models have much larger positive biases than for 50 km ones, suggesting the former can simulate even higher‐category tropical cyclones. Considerable intermodel variation is also found in track changes between the future and historical periods. No systematic intercategory pattern of change exists, and low signal‐to‐noise may obscure any such patterns in the limited timespan of available data. Thus, no meaningful conclusions can be drawn regarding changes in track intensity. Nevertheless, track frequency broadly decreases across models for the region, as does accumulated cyclone energy. An east‐to‐west shift in track location from east of Madagascar toward the Mozambique Channel is also implied by track frequency and wind speed changes. Our findings provide information to potentially improve storm resiliency in this vulnerable region. [ABSTRACT FROM AUTHOR]

Climate change is associated with the modification of the polar jet stream, cyclone tracks and corresponding circulation patterns over midlatitudes. To apply these synoptic changes on regional climate, the changing sensitivity of a specific location to different pressure centre regions must be studied. An automated, objective circulation pattern detection method was developed to investigate the evolution of cyclonic and anticyclonic influence at any specific point within the European domain. The algorithm was used to assign each location to influencing low‐ or high‐pressure centres within the domain. Pressure centre displacements and the frequency redistribution among different centres were studied for each location. The 180‐year (January 1836–December 2015) mean sea level pressure dataset on the European domain with a 0.703° × 0.702° spatial and daily temporal resolution was obtained from NOAA 20th Century Reanalysis project. The presented method can apply the continental‐scale changes to specific locations. A significant increase of anticyclonic influence was found in southern and central Europe, in line with the northward displacement of Atlantic cyclones. Hundred and eighty year (180‐year) change of the number of days with anticyclonic versus cyclonic influence was found to be between +10% and 15% in the Mediterranean and +2% and 10% in most of Europe. The increasing anticyclonic influence in central Europe was strongest in the spring and the winter and was most attributable to the eastern European anticyclone. Results highlight the importance of research on the dynamical climate response of anticyclonic pressure systems. [ABSTRACT FROM AUTHOR]

Spaceborne synthetic aperture radar (SAR) for measuring high winds is expected to reduce uncertainties in tropical cyclone (TC) intensity and structure estimation, yet the consistency of SAR observed winds equivalent to a 1-min sustained wind speed with the conventionally estimated 10-min maximum wind speed (Vmax10) remains to be assessed. This study compares SAR wind observations with western North Pacific best track estimates from the Japan Meteorological Agency (JMA) and the Joint Typhoon Warning Center (JTWC). Because SAR wind observations have a bias dependent on SAR incidence angle, a first order corrective term is proposed and used to correct SAR-derived maximum wind (SAR Vmax) tentatively. After this correction, conversion of SAR Vmax into SAR Vmax10 with Dvorak conversion tables revealed a mean difference between SAR Vmax10 and JMA Vmax10 (ΔVmax10) of -0.1 m s-1 and a mean absolute difference of 4.8 m s-1. ΔVmax10 is found to be correlated with current intensities and with subsequent intensity changes from the SAR observation time. Also, comparison of the JMA best track 50-kt wind radius (R50) with SAR wind speeds suggests that R50 is systematically underestimated. Aside from the SAR wind limitations, possible reasons for the observed discrepancies between SAR wind observations and best track estimates include biases in the Dvorak analysis and conventional surface wind products. Further accumulation of SAR wind observations with appropriate bias correction in the future is expected to contribute to a comprehensive evaluation and improvement of conventional Vmax estimation methods, which could also be useful to verify TC intensity forecasts. [ABSTRACT FROM AUTHOR]

To investigate the stratosphere-troposphere exchange (STE) process induced by the gravity waves (GWs) caused by Typhoon Molave (2020) in the upper troposphere and lower stratosphere, we analyzed the ERA5 reanalysis data provided by the European Centre for Medium-Range Weather Forecasts and the CMA Tropical Cyclone Best Track Dataset. We also adopted the mesoscale forecast model Weather Research and Forecasting model V4.3 for numerical simulation. Most of the previous studies were about typhoon-induced STE and typhoon-induced GWs, while our research focused on the STE caused by typhoon-induced gravity waves. Our analysis shows that most of the time, the gravity wave signal of Typhoon Molave appeared below the tropopause. It was stronger on the east side of the typhoon center (10°-20°N, 110°-120°E) than on the west side, suggesting an eastward tilted structure with height increase. When the GWs in the upper troposphere and lower stratosphere region on the west side of the typhoon center broke up, it produced strong turbulence, resulting in stratosphere-troposphere exchange. At this time, the average potential vorticity vertical flux increased with the average ozone mass mixing ratio. The gravity wave events and STE process simulated by the WRF model were basically consistent with the results of ERA5 reanalysis data, but the time of gravity wave breaking was different. This study indicates that after the breaking of the GWs induced by typhoons, turbulent mixing will also be generated, and thus the STE. [ABSTRACT FROM AUTHOR]

The Great Lakes Region (GLR) of North America is at the intersection of multiple extratropical cyclone (ETC) tracks, and the region's cold‐season climate is heavily influenced by the large temperature gradients and intense precipitation associated with these cyclones. The goal of this study is to understand how ETCs are changing within a warming climate. Historical GLR cyclone characteristics from 1959 to 2021 are examined using a storm tracking algorithm and the ERA‐5 atmospheric reanalysis. Of the 886 cyclones identified, half are the large long‐track cyclones that are typically included in ETC studies, and half are smaller short‐track cyclones that, while not always considered in ETC studies, still have an important impact on the GLR with significant precipitation trends. While all cyclones exhibit strong interannual variability, storm trajectories appear to be migrating northward and, most notably, the cyclones are becoming warmer and wetter at a rate faster than the background climate. Plain Language Summary: Winter weather patterns in the Great Lakes Region of the United States are driven by large‐scale weather systems known as "extratropical cyclones." Even with a warming climate, the Great Lakes Region experiences highly variable winters, with cold‐air outbreaks, extreme snowfall, and mild temperatures all possible from year to year. This study uses historical analyzed atmospheric data to identify and track extratropical cyclones that pass through the Great Lakes Region in order to understand how the cyclones have changed along with our changing climate. We find that the airmasses within the cyclones have increased in moisture and warmed up faster than the overall climate. We also find that the cyclone tracks have shifted northward, implying that the frequency of warm temperatures and heavy rainfall in the wintertime is increasing for the lower portion of the Great Lakes. Key Points: Cold season extratropical cyclones that pass through the Great Lakes region are shifting to more northern tracksThe number and intensity of Great Lakes cyclones do not show an increasing trendGreat Lakes extratropical cyclones are warming and are increasing in moisture at a rate faster than the background climate [ABSTRACT FROM AUTHOR]

This study introduces new insights into the climatology of South Atlantic (SAt) cyclones by employing a novel cyclone life cycle detection method, the CycloPhaser. Utilizing the minimum relative vorticity series and its derivative at the cyclone centre, the program effectively identifies distinct phases in the cyclone life cycle. Cyclone tracks are obtained through the analysis of relative vorticity at 850 hPa, using the ERA5 dataset. The study identified six main cyclone life cycle patterns from the analysis of 28,458 systems. The predominant cyclone type, accounting for approximately 60% of the analysed systems, exhibited a four‐phase configuration: incipient, intensification, mature and decay. Detailed statistics for each developmental phase and the overall life cycle are presented, offering valuable comparisons and new insights while corroborating previous research findings. Key genesis regions in the SAt are identified, along with track density maps that reveal distinct preferences in cyclone developmental cycle. The main outcome of this study is the demonstration that the automated classification procedure enables the analysis of cyclones' life cycles to be conducted promptly and with low computing costs, facilitating the comprehensive study of cyclone behaviour with high efficiency. [ABSTRACT FROM AUTHOR]

Extreme precipitation events induced by tropical cyclones have increased frequency and intensity, significantly impacting human socioeconomic activities and ecological environments. This study systematically examines the spatiotemporal characteristics of these events across Hainan Island and their influencing factors using GsMAP satellite precipitation data and tropical cyclone track data. The results indicate that while the frequency of typhoon events in Hainan decreased by 0.3 events decade−1 from 1949 to 2020, extreme precipitation events have increased significantly since 2000, especially in the eastern and central regions. Different typhoon tracks have distinct impacts on the island, with Track 1 (Northeastern track) and Track 2 (Central track) primarily affecting the western and central regions and Track 3 (Southern track) impacting the western region. The impact of typhoon precipitation on extreme events increased over time, being the greatest in the eastern region, followed by the central and western regions. Incorporating typhoon precipitation data shortened the recurrence interval of extreme precipitation in the central and eastern regions. Diurnal peaks occur in the early morning and late evening, primarily affecting coastal areas. Typhoon duration (CC_max = 0.850) and wind speed (CC_max = 0.369) positively correlated with extreme precipitation, while the pressure was negatively correlated. High sea surface temperature areas were closely associated with extreme precipitation events. The atmospheric circulation indices showed a significant negative correlation with extreme precipitation, particularly in the western and central regions. ENSO events, especially sea surface temperature changes in the Niño 1 + 2 region (−0.340 to −0.406), have significantly influenced typhoon precipitation characteristics. These findings can inform region-specific disaster prevention and mitigation strategies for Hainan Island. [ABSTRACT FROM AUTHOR]

In this study, a super cyclonic storm (Amphan) over Bay of Bengal was analyzed with the National Center for Medium-Range Weather Forecasting, Unified Model (NCUM) and National Center for Environmental Prediction (NCEP) global model data. Further, the weather research and forecasting (WRF) model has been utilized to simulate this super cyclonic storm with NCUM and NCEP data as initial and boundary conditions. The model is integrated, for every 12 h interval from 16 May 0000 UTC to 18 May 1200 UTC with horizontal resolution of 9 km and vertically at 34 levels. The results reveal that the track of the cyclone, central pressure of the cyclone, intensity of the cyclone, and landfall time of the cyclone are simulated by the model reasonably well in both the cases that are with NCUM-GFS and NCEP-GFS. However, the WRF simulations with NCEP data are closer to IMD's estimated track and intensity, when compared with the simulations with NCUM data. The relatively higher errors in simulations with NCUM could be due to the differences in the initial cyclone vortex against the observation in some initial conditions. [ABSTRACT FROM AUTHOR]

The interdecadal variability of tropical cyclone precipitation (TCP) over the western North Pacific (WNP) has not been thoroughly explored in previous studies. Here, we show that the TCP variations are modulated by both the Atlantic Multidecadal Oscillation (AMO) and Interdecadal Pacific Oscillation (IPO) as evidenced by reanalysis data and model experiments. A clustering analysis of tropical cyclone tracks shows that the AMO dominates a dipole pattern of TCP anomalies in the South China Sea and along the coastal eastern China. Meanwhile, the IPO dominates TCP over the southeastern WNP. Further analyses show that the AMO, particularly its extratropical component, affects TCP over the WNP by triggering an eastward‐propagating Rossby‐wave train, resulting in a pair of anomalous gyres over the WNP. Contrastly, the IPO modulates TCP by stimulating tropical circulation anomalies via the tropical pathway. These findings shed light on improving near‐term TCP forecast and its regional influence on East Asia. Plain Language Summary: Tropical cyclone precipitation (TCP) is a topic of great concern owing to its destructive nature. Here, we find that TCP over the western North Pacific (WNP) exhibits significantly interdecadal variability and regional characteristics, a departure from the previous studies focusing on the entire WNP. The Atlantic Multidecadal Oscillation (AMO) mainly causes a dipole response of decadal changes in TCP over the South China Sea and coastal eastern China. Moreover, the AMO primarily affects TCP over the sub‐regional WNP through the extra‐tropical pathway by exciting a Rossby wave train. On the other hand, the Interdecadal Pacific Oscillation (IPO) predominantly affects TCP over the southeastern part of the WNP through the tropical pathway, inducing significant circulation anomalies over the tropical WNP. These findings offer valuable insights for future research and forecasting of TCP. Key Points: The Atlantic Multidecadal Oscillation (AMO) contributes to the dipole pattern of interdecadal tropical cyclone precipitation (TCP) anomalies over the South China Sea and coastal eastern ChinaThe AMO affects TCP by triggering a westerly jet‐guided Rossby‐wave train via the extra‐tropical pathwayThe Interdecadal Pacific Oscillation dominates the TCP anomalies over the southeastern part of western North Pacific by modulating tropical circulation anomalies [ABSTRACT FROM AUTHOR]

AbstractIn order to grasp the influence of exhaust pipe size on cyclone flow fields and improve the recovery and utilization rate of coal powder in power plant, the flow characteristics and the separation capacity of the fine coal powder separator in power plant were investigated through numerical simulation. The Reynolds stress model was employed to model the turbulent flow. The Eulerian-Lagrangian computational procedure was used to predict particles tracking in the cyclone. The particle trajectories were simulated using the discrete random walk. Five cyclone types with different insertion lengths of the exhaust pipe at the same other geometric dimensions were considered. It was found that tangential velocity played a dominant role in the radial distribution of particles, which was mainly represented by Rankine vortex. When the insertion length of the exhaust pipe was increased from 1638 mm to 5100 mm, the grade efficiency of 5–12 μm particles was increased by 19%, and the pressure drop reduced by 8%. [ABSTRACT FROM AUTHOR]

The ECMWF's ensemble (ECEPS) predictions are documented for the lifecycles of six tropical cyclones (TCs) that formed during a long-lasting Rossby wave breaking event in the western North Pacific. All six TC tracks started between 20° N and 25° N, and between 136° E and 160° E. All five typhoons recurved north of 30° N, and the three typhoons that did not make landfall had long tracks to 50° N and beyond. The ECEPS weighted mean vector motion track forecasts from pre-formation onward are quite accurate, with track forecast spreads that are primarily related to initial position uncertainties. The ECEPS intensity forecasts have been validated relative to the Joint Typhoon Warning Center (JTWC) Working Best Track (WBT) intensities (when available). The key results for Tokage (11 W) were the ECEPS forecasts of the intensification to a peak intensity of 100 kt, and then a rapid decay as a cold-core cyclone. For Hinnamnor (12 W), the key result was the ECEPS intensity forecasts during the post-extratropical transition period when Hinnamnor was rapidly translating poleward through the Japan Sea. For Muifa (14 W), the key advantage of the ECEPS was that intensity guidance was provided for longer periods than the JTWC 5-day forecast. The most intriguing aspect of the ECEPS forecasts for post-Merbok (15 W) was its prediction of a transition to an intense, warm-core vortex after Merbok had moved beyond 50° N and was headed toward the Aleutian Islands. The most disappointing result was that the ECEPS over-predicted the slow intensification rate of Nanmadol (16 W) until the time-to-typhoon (T2TY), but then failed to predict the large rapid intensification (RI) following the T2TY. The tentative conclusion is that the ECEPS model's physics are not capable of predicting the inner-core spin-up rates when a small inner-core vortex is undergoing large RI. [ABSTRACT FROM AUTHOR]

In recent decades, there has been notable variability in the occurrence, frequency, and intensification of cyclones worldwide, and a significant trend has emerged in the northern Indian Ocean, notably the Arabian Sea. The recent cyclone, Biparjoy, occurred in June 2023 and stands out as a significant event among these occurrences. Through an extensive analysis utilizing available datasets (both in-situ and model-based) along the vertical trajectory of cyclone paths, it has been ascertained that the intensity of cyclones is influenced by the advection of warm saline (referred to as "Spicy") water originating north of the cyclone's track. This influx of high-saline waters occurs at various depths, inducing alterations in the vertical water column configuration. Notably, the diffusion of heat overtakes that of salt, resulting in the gradual dispersion of the saline water. These high salinity levels are prevalent at depths ranging from 0 to 300 m, with a pronounced concentration up to 100 m below the surface. The presence of these warm saline waters overlying the colder, fresher waters gives rise to a phenomenon known as "salt fingering" (45o < Tu < 90o), which establishes a stable surface layer predominantly controlled by temperature gradients. This stable surface layer, extending up to approximately 50 m in depth holding a substantial heat, fostering a positive feedback loop that amplifies cyclone intensity. As the spiciness of the Arabian Sea experiences an ascending trend in recent years, the continued extension of saline water advection bears intense implications for upper ocean heat content. This, in turn, creates a favorable environment for the formation and rapid intensification of cyclones. [ABSTRACT FROM AUTHOR]

We examined the seismic noise data collected from coastal and inland observatories in India, affected by the super cyclonic storm Amphan in the Indian Ocean, to understand the storm dynamics. Prominent disturbances in the 0.05–0.50 Hz frequency range were observed at the seismic stations, arising due to ocean‐continent interactions. The coastal stations displayed more pronounced ground motions contrary to the inland stations, with spindle‐shaped seismic wave envelopes intensifying as Amphan approached. The maximum ground displacements and energy occurred hours after the cyclone's eye, with maximum wind speed, moved away from the stations and not when it was close to the station. We observed significant variations in primary (0.05–0.10 Hz) and secondary microseism (0.10–0.50 Hz) energy during Amphan's directional changes. Secondary microseisms in short and long periods were found at 0.20–0.50 Hz and 0.10–0.20 Hz, respectively. Primary microseisms exhibited a simple pattern and were the weakest among the three energy bands. The CAL seismic station's seismic wave envelope showed an en‐echelon feature with increasing amplitude as Amphan approached, indicating the influence of ocean resonance and coastal wave reflection. This study demonstrates monitoring of the tropical cyclone paths based on seismic signatures obtained using microseisms recorded at seismic stations, a cost‐effective tool. Integrating these seismic signals with atmospheric observations in near real‐time would probably enable an effective monitoring of cyclones and timely issuance of their alerts. Key Points: Monitor the Super Cyclone Amphan in the Bay of Bengal using Coastal seismological ObservatoriesSecondary microseisms in the short and long periods were found at 0.20–0.50 Hz and 0.10–0.20 HzMicroseisms recorded at seismic stations can be potential source of information for cyclone tracking [ABSTRACT FROM AUTHOR]

In the Baltic Sea, sea level variations are often very pronounced. During the winter season, storm surges caused by strong extratropical cyclones (ETCs) can have major societal impacts on coastal cities. In this study, using reanalysis‐based cyclone tracks and in‐situ tide gauge records, we show that serial cyclone clustering (SCC) leads to higher sea levels in the Baltic Sea than situations where only one ETC passes the tide gauge. Consequently, almost half of extreme sea level events in the Baltic Sea are associated with cyclone clustering periods. For example, in Helsinki, 45% of the extreme sea level events coincided with SCC periods of three or more ETCs, while only 6% of the events coincided with a single ETC. Our study represents a significant advance in the understanding of the factors influencing sea level variations in the Baltic Sea. Plain Language Summary: Sea levels in the Baltic Sea can vary a lot. In winter, powerful storms can lead to storm surges that seriously affect coastal towns. Our research studied storm paths and sea level observations. We found that multiple consecutive storms within a week raise sea levels in the Baltic Sea more than when only one storm passes the same location. As a result, most of the high sea level events in the Baltic Sea are linked to periods when several storms pass the region. In Helsinki, for instance, almost half of all analyzed high sea level events co‐occurred with periods when at least three storms occurred, while only 6% of high sea level events occurred due to a single storm. Our analysis is a significant progression in understanding the reasons that impact the Baltic Sea water levels. Hence, these results could aid in preparing for possible coastal floods. Key Points: Serial cyclone clustering (SCC) commonly causes elevated sea levels in the Baltic SeaPeak storm surges during SCC are on average 2–2.7 times (25–48 cm) higher than during single cyclonesThe intensity of the cyclones within the SCC modulates the storm surge levels, with deeper cyclones more likely to induce higher surges [ABSTRACT FROM AUTHOR]

Madagascar has a harsh and stochastic climate because of regular natural disturbances. This history of regular cyclones has been hypothesised to have directed evolutionary changes to lemur behaviour and morphology that make them more resilient to sudden environmental change. These adaptations may include: small group sizes, high degrees of energy-conserving behaviours, generalist habitat use, small home ranges, small body size, and a limited number of frugivorous species. To date, however, no one has tested how variation in cyclone exposure across Madagascar is associated with variation in these resilience traits. In this study, we created a detailed cyclone impact map for Madagascar using Koppen-Geiger climate class, historical cyclone tracks, the Saffir Class of cyclone and hurricane intensity, and precipitation data. We also used existing literature to calculate a resilience score for 26 lemur species for which data existed on resilience traits. Our cyclone impact map was then overlaid on known geographic ranges of these species and compared to resilience score while controlling for phylogenetic non-independence and spatial autocorrelation. We found no association between cyclone impact in a lemur range and their resilience score. When assessing traits individually, however, we found that cyclone impact was positively associated with body size, suggesting that the more impacted a species is by cyclones the smaller they are. We also found cyclone impact to be negatively associated with frugivory, with species in higher impact zones eating more fruit. While unexpected, this could reflect an increased production in fruit in tree fall gaps following cyclones. While we did not find a pattern between cyclone impact and behavioural resilience in lemurs, we suggest a similar study at a global scale across all primates would allow for more taxonomic variation and reveal larger patterns key to understanding past and future vulnerability to natural disturbances in primates. [ABSTRACT FROM AUTHOR]

Four-dimensional COAMPS Dynamic Initialization (FCDI) analyses that include high-temporal- and high-spatial-resolution GOES-16 Atmospheric Motion Vector (AMV) datasets are utilized to understand and predict why pre-Bonnie (2022), designated as a Potential Tropical Cyclone (PTC 2), did not undergo rapid intensification (RI) while passing along the coast of Venezuela during late June 2022. A tropical cyclone lifecycle-prediction model based on the ECMWF ensemble indicated that no RI should be expected for the trifurcation southern cluster of tracks along the coast, similar to PTC 2, but would likely occur for two other track clusters farther offshore. Displaying the GOES-16 mesodomain AMVs in 50 mb layers illustrates the outflow burst domes associated with the PTC 2 circulation well. The FCDI analyses forced by thousands of AMVs every 15 min document the 13,910 m wind-mass field responses and the subsequent 540 m wind field adjustments in the PTC 2 circulation. The long-lasting outflow burst domes on both 28 June and 29 June were mainly to the north of PTC 2, and the 13,910 m FCDI analyses document conditions over the PTC 2 which were not favorable for an RI event. The 540 m FCDI analyses demonstrated that the intensity was likely less than 35 kt because of the PTC 2 interactions with land. The FCDI analyses and two model forecasts initialized from the FCDI analyses document how the PTC 2 moved offshore to become Tropical Storm Bonnie; however, they reveal another cyclonic circulation farther west along the Venezuelan coast that has some of the characteristics of a Caribbean False Alarm event. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) track forecasting provides essential guidance for coastal communities. However, track forecast errors still occur, highlighting the need for continued research into error sources. Piecewise potential vorticity (PV) inversion is used systematically to quantitatively diagnose errors in track forecasts in four models during the 2017 Atlantic hurricane season. The deep layer mean steering flow (DLMSF) provides a sufficient proxy for hurricane movement, and DLMSF errors are correlated with TC track errors. Analysis of track forecasts for Hurricanes Harvey, Irma, and Maria reveals that their track errors are attributed to steering errors caused by misrepresentations of specific pressure systems. Harvey's westward track error in the GFS resulted from zonal wind errors from the Continental High, while Irma's northward track error in the SHiELD gfsIC resulted from meridional wind errors in the Bermuda High and Continental High. Maria's southward track error in the IFS resulted from meridional wind errors in the Bermuda High and a misrepresentation of Jose to Maria's northwest. The mean absolute error of the DLMSF shows that the Bermuda High contributed the most to steering flow errors in the cases examined. Our results show that piecewise PV inversion can identify the sources of biases in TC track forecasts. The correction of these biases may lead to improved track forecasts. Quantitative diagnostics presented here provide useful information for future model development. Plain Language Summary: A tropical cyclone typically moves with the environmental wind, which is generated by several large‐scale pressure systems (e.g., Bermuda High, Continental High) in the atmosphere. Weather models can predict the path of tropical cyclones, but these forecasts have errors. Tropical cyclones often bring devastation along their path, so it is important to mitigate track errors to provide better warnings for impacted communities. Here, we use a diagnostic technique called "piecewise potential vorticity inversion" to understand how the environmental wind causes errors in tropical cyclone tracks. In three different examples of hurricane track forecasts, we show that errors in the predicted track are caused by errors in the environmental wind from specific pressure systems. By considering numerous cases, we can also identify model biases, or errors that are consistent throughout many forecasts. These errors are a result of errors in the models themselves. Overall, our results show that piecewise potential vorticity inversion is a useful diagnostic tool that has the potential to improve track forecasts through the identification of model biases. Key Points: Contributions to tropical cyclone movement from individual synoptic systems are quantified using piecewise potential vorticity inversionForecast errors of the deep layer mean steering flow are the main source of the track errors for Hurricanes Harvey, Irma, and Maria (2017)Forecast errors of the Bermuda High dominated the steering flow errors for the 2017 hurricane season [ABSTRACT FROM AUTHOR]

A global study of extreme value (1 in 100-year return period) tropical cyclone generated waves is conducted across all tropical cyclone basins. The study uses a 1000 year tropical cyclone synthetic track database to force a validated parametric wave model. The resulting distributions of extreme significant wave height show that values in the North Atlantic and Western Pacific basins are the largest globally. This is partly due to the relative intensities and frequencies of occurrence of storms in these basins but also because the typical velocities of forward movement of storms are larger and hence can sustain the generation of larger waves. These larger values of velocity of forward movement tend to occur at higher latitudes. As a result, in both of these basins the largest extreme waves occur at higher latitudes than the maximum tropical cyclone winds. In all other tropical cyclone basins, storms tend to propagate more east–west and hence the maximum values of extreme significant wave height and wind speed occur at comparable latitudes. [ABSTRACT FROM AUTHOR]

A global study of extreme value (1 in 100-year return period) tropical cyclone generated waves is conducted across all tropical cyclone basins. The study uses a 1000 year tropical cyclone synthetic track database to force a validated parametric wave model. The resulting distributions of extreme significant wave height show that values in the North Atlantic and Western Pacific basins are the largest globally. This is partly due to the relative intensities and frequencies of occurrence of storms in these basins but also because the typical velocities of forward movement of storms are larger and hence can sustain the generation of larger waves. These larger values of velocity of forward movement tend to occur at higher latitudes. As a result, in both of these basins the largest extreme waves occur at higher latitudes than the maximum tropical cyclone winds. In all other tropical cyclone basins, storms tend to propagate more east–west and hence the maximum values of extreme significant wave height and wind speed occur at comparable latitudes. [ABSTRACT FROM AUTHOR]

The role of large‐scale natural variability in influencing the distribution of midlatitude storm tracks and extratropical cyclones is well known. However, less understood is how compound natural variability—the concurrence of two modes of variability—influences the geospatial characteristics of extratropical cyclones. Here, we evaluate compound modes of Pacific‐North American variability—ENSO and PNA with the slow varying PDO—on extratropical cyclones over the North American region during 1950–2013, using an automated tracking algorithm applied to the Twentieth Century Reanalysis version 3 data. Seasonal analysis is conducted on spatial statistics including track, genesis and lysis density and some mean fields in varying compound phases. Spatial correlations of track density with seasonal mean of individual variability indices show that the PNA, ENSO and PDO enhance cyclone activity over Pacific storm track significantly while shifting the North American and Atlantic storm tracks in seasons, that is, equatorward in the positive phase. But the decadal variability of PDO moves the Atlantic storm track poleward, particularly in boreal winter. By comparing to the single Pacific variability impacts, compound Pacific variability results demonstrate the dominance of the higher‐frequency modes of ENSO and PNA on extratropical cyclone activity, while the slow varying PDO can modulate the ENSO‐ and PNA‐related cyclone variability. For example, during a negative PNA, cyclones over the North Atlantic become more (less) frequent during a positive (negative) PDO. The in‐phase modes of the PDO and ENSO influence Pacific storm track significantly, increasing (decreasing) cyclone frequency when both are positive (negative) in boreal winter. Results are linked to and used to explain the previous findings on how natural variability influences the occurrence of tornado outbreaks and high precipitation over the southeastern United States, Great Lakes, Ohio Valley and other US regions. Understanding compound variability modulations may be useful to predict extratropical cyclone frequency, development and decay regions seasonally and to study the physical mechanisms of extreme weather events. [ABSTRACT FROM AUTHOR]

In this study, we propose an easy and robust algorithm to identify and track extratropical cyclone events using 850 hPa relative vorticity data, gaussian filter and connected-component labeling technique, which recognize the cyclone as areas under a threshold. Before selecting the events, the algorithm can include essential characteristics that are good metrics of intensity, like minimum mean sea level pressure and maximum 10-m winds. We implemented the algorithm in the Southern Hemisphere, using a 41-year high resolution dataset. Sensitivity tests were performed to determine the best parameters for detection and tracking, such as degree of smoothing, thresholds of relative vorticity at 850 hPa and the minimum area within the threshold. Two case studies were used to assess the positive and negative points of the methodology. The results showed that it is efficient in obtaining the position of extratropical cyclones in their most intense stage, but it does not always perform well during cyclolysis. We compare the methodology using 1-h temporal resolution to that using a 6-hours temporal resolution, and their reproducibility regarding the literature. The extratropical cyclone climatology in the Southern Hemisphere is provided and discussed. The algorithm developed here can be applied to datasets with good spacial and temporal resolution, providing a better inventory of extratropical cyclones. [ABSTRACT FROM AUTHOR]

This paper highlights the quality and impact of the COSMIC-2 bending angle through an Observation System Experiment, using a hybrid Gridpoint Statistical Interpolation – 4 Dimensional Ensemble Variational analysis scheme. The data quality is assessed by comparing it with the model's background field. Analysis and background field that fit to temperature, humidity and wind components from radiosondes are verified, and the impact over model forecasts is evaluated using various metrics. There is an overall improvement, especially over the tropical region, where consistent and statistically significant positive impact is observed over the troposphere extending up through the lower to the middle stratosphere. A case study of the Super Cyclonic Storm AMPHAN showed minor improvement in central pressure and maximum 10 m wind speed. Reduction in the direct positional error of the cyclone track showed improvement in the analysis. [ABSTRACT FROM AUTHOR]

Extratropical cyclones influence midlatitude surface weather directly via precipitation and wind and indirectly via upscale feedbacks on the large‐scale flow. Biases in cyclone frequency and characteristics in medium‐range to subseasonal numerical weather prediction might therefore hinder exploitation of potential predictability on these timescales. We thus, for the first time, identify and track extratropical cyclones in 20 years (2000–2020) of subseasonal ensemble reforecasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF) in the Northern Hemisphere in all seasons. The reforecasts reproduce the climatology of cyclone frequency and life‐cycle characteristics qualitatively well up to six weeks ahead. However, there are significant regional biases in cyclone frequency, which can result from a complex combination of biases in cyclone genesis, size, location, lifetime, and propagation speed. Their magnitude is largest in summer, with the strongest regional deficit of cyclones of more than 30% in the North Atlantic, relatively large in spring, and smallest in winter and autumn. Moreover, the reforecast cyclones reach too‐high intensities during most seasons, although intensification rates are captured well. An overestimation of cyclone lifetime might partly but not exclusively explain this intensity bias. While the cyclone bias patterns often appear in lead‐time weeks 1 and 2, their magnitudes typically grow further at subseasonal lead times, in some cases up to weeks 5 and 6. Most of the dynamical sources of these biases thus likely appear in the early medium range, but sources on longer timescales probably contribute to the further increase of biases with lead time. Our study provides a useful basis to identify, better understand, and ultimately reduce biases in the large‐scale flow and in surface weather in subseasonal weather forecasts. Given the considerable biases during summer, when subseasonal predictions of precipitation and surface temperature will become increasingly important, this season deserves particular attention for future research. [ABSTRACT FROM AUTHOR]

We investigate the run-to-run consistency (jumpiness) of ensemble forecasts of tropical cyclone tracks from three global centers: ECMWF, the Met Office, and NCEP. We use a divergence function to quantify the change in cross-track position between consecutive ensemble forecasts initialized at 12-h intervals. Results for the 2019–21 North Atlantic hurricane season show that the jumpiness varied substantially between cases and centers, with no common cause across the different ensemble systems. Recent upgrades to the Met Office and NCEP ensembles reduced their overall jumpiness to match that of the ECMWF ensemble. The average divergence over the set of cases provides an objective measure of the expected change in cross-track position from one forecast to the next. For example, a user should expect on average that the ensemble mean position will change by around 80–90 km in the cross-track direction between a forecast for 120 h ahead and the updated forecast made 12 h later for the same valid time. This quantitative information can support users' decision-making, for example, in deciding whether to act now or wait for the next forecast. We did not find any link between jumpiness and skill, indicating that users should not rely on the consistency between successive forecasts as a measure of confidence. Instead, we suggest that users should use ensemble spread and probabilistic information to assess forecast uncertainty, and consider multimodel combinations to reduce the effects of jumpiness. Significance Statement: Forecasting the tracks of tropical cyclones is essential to mitigate their impacts on society. Numerical weather prediction models provide valuable guidance, but occasionally there is a large jump in the predicted track from one run to the next. This jumpiness complicates the creation and communication of consistent forecast advisories and early warnings. In this work we aim to better understand forecast jumpiness and we provide practical information to forecasters to help them better use the model guidance. We show that the jumpiest cases are different for different modeling centers, that recent model upgrades have reduced forecast jumpiness, and that there is not a strong link between jumpiness and forecast skill. [ABSTRACT FROM AUTHOR]

Hong Kong was under the direct hit of Severe Typhoon Koinu (2314) on 8 and 9 October 2023, necessitating the issuance of the Increasing Gale or Storm Signal, No. 9. Koinu was a very challenging case for TC forecasting and warning services due to its compact size and erratic movement over the northern part of the South China Sea. This paper reviews the difficulties and challenges of the forecasting aspect of the severe typhoon. The predicted tropical cyclone track and intensity from both conventional models and emerging artificial intelligence models are examined, as well as local wind and rainfall forecast. Experience in this case study showed that while deterministic global models only performed moderately and were not able to adequately support early warning, a regional model and AI models could more effectively support decision making for an operational tropical cyclone warning service. [ABSTRACT FROM AUTHOR]

Extratropical cyclones play an important role in shaping the weather and climate over vast regions across the globe. To effectively understand and manage the impact of these meteorological events, it is crucial to explore deeper into their structure and behaviour. Here, we have evaluated the characteristics and spatial distribution of extratropical cyclones that had their genesis in the Southern Ocean (SO) in the present climate, based on data from the HadGEM3‐GC3.1‐MM (HG3) global model and the ERA5 reanalysis. Both in winter and summer, cyclone tracks are spatially structured, forming a belt around the entire Antarctic continent, with greater concentration in the SO sector adjacent to the southern Pacific Ocean. The regions of the Somov, Amundsen and Bellingshausen seas exhibited a higher track density of moderate and strong cyclones, indicating a correlation with the strongest baroclinicity in these areas. In both datasets, the maximum occurrence of cyclogenesis was located over the regions of the Somov Sea and the Antarctic Peninsula, making these regions very important in the general configuration of cyclones along the SO. Despite underestimating the total number of cyclones along the SO, the HG3 model showed a good ability to represent the main patterns of cyclonic activity around the Antarctic continent, as well as its average behaviour. Knowing the characteristics and the behaviour of extratropical cyclones in such an important area for global climate is essential for understanding their role in weather and future climate resulting from climate change. [ABSTRACT FROM AUTHOR]

Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute resources to improve forecast accuracy but does not directly use historical weather data to improve the underlying model. Here, we introduce GraphCast, a machine learning–based method trained directly from reanalysis data. It predicts hundreds of weather variables for the next 10 days at 0.25° resolution globally in under 1 minute. GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclone tracking, atmospheric rivers, and extreme temperatures. GraphCast is a key advance in accurate and efficient weather forecasting and helps realize the promise of machine learning for modeling complex dynamical systems. [ABSTRACT FROM AUTHOR]

Baroclinic or extratropical cyclones (ETCs) transport heat and moisture to higher latitudes, making it fundamentally important to understand how their influence changes as Earth's climate evolves. A 2–8‐day Lanzcos bandpass filter is applied to European Center for Medium Range Weather Forecasting 5th Generation Reanalysis latent energy (LE) and kinetic energy (KE) data to assess how ETCs have changed from 1940 to 2020 relative to full‐scale changes in LE and KE. Full‐scale KE trends are more positive at high latitudes relative to mid‐latitudes, confirming several previous studies that ETCs have shifted poleward. LE increases have occurred globally, and trends in both full‐scale LE and KE are statistically significant in the southern high latitudes. The high relative fractional contribution of 2–8‐day LE wave power and trend clearly suggest that ETCs have an increasingly important role in poleward moisture transport but are not solely responsible for the observed statistically significant increases. Plain Language Summary: Understanding changes to mid‐ and high‐latitude storm locations is paramount to better predicting how Earth's climate has changed and may change going forward. This study extends several previous studies to focus on the role these storms have in transporting moisture poleward, and if changes in moisture transport over the last 80 years are significant. We find a significant increase in moisture across the southern midlatitudes, but this increase is not solely associated with significant increases in storm energy. No significant increases are found in the northern hemisphere. Key Points: Kinetic Energy trends from 1940 to 2020 confirm several existing studies showing a poleward shift in extratropical cyclone track locationsStatistically significant increases in kinetic and latent energy are found across southern hemisphere midlatitude cyclone track locationsCyclones at 2–8‐day time scales, however, are not solely responsible for these statistically significant increases [ABSTRACT FROM AUTHOR]

Extratropical cyclones (ETCs) are three-dimensional synoptic systems in the middle and high latitudes. Previous studies on ETC propagation have typically focused on cyclones identified at a single level. However, more recent studies have found that ETCs have diverse vertical structures and cyclones with different vertical extents always exhibit distinct characteristics and surface impacts. In this work, we study the movement of wintertime (December–February) extratropical cyclones by classifying North Pacific ETCs into deep cyclones, shallow low-level cyclones, and shallow upper-level cyclones, based on reanalysis data from 1979 to 2019. Applying a Lagrangian perspective, we track the cyclones at different vertical levels to investigate the different characteristics and mechanisms for the propagation of deep and shallow ETCs. A potential vorticity (PV) tendency analysis of cyclone-tracking composites reveals that, for deep cyclones, the diabatic heating at 850 hPa and the horizontal advection by the stationary flow at 500 hPa are the main contributors to the poleward movement. For shallow cyclones, the nonlinear advection terms play a dominant role in their meridional motion, advecting shallow low-level cyclones poleward but shallow upper-level cyclones equatorward. A piecewise PV inversion analysis suggests that the nonlinear advection by winds induced from upper-level PV anomalies is responsible for the different performance of nonlinear advection terms for shallow low-level and upper-level cyclones. These findings further our understanding of the mechanisms and variations of cyclone propagation. Significance Statement: Extratropical cyclones (ETCs) can be identified at different levels in the troposphere. These mobile low pressure cyclonic storms are the main sources of synoptic variability in the extratropics and often bring severe or even disastrous weather. Previous studies on ETC movement have typically been restricted to cyclones identified at a single level. Our study, by identifying ETCs at multiple levels, classifies cyclones into deep, shallow low-level, and shallow upper-level cyclones. For deep cyclones, their poleward movement is found to be a result of diabatic heating at lower levels and dominated by the stationary circulation at upper levels. For shallow cyclones, nonlinear advection determines whether they propagate poleward or equatorward. These findings further our understanding of the mechanisms of cyclone propagation and imply that the movement of deep and shallow cyclones may undergo different changes with different weather and climate impacts in the future, given the enhanced diabatic heating under global warming, which deserves further investigation. [ABSTRACT FROM AUTHOR]

Extreme precipitation events and associated flash floods caused by synoptic cyclonic systems profoundly impact society and the environment, particularly in arid regions. This study brings forward a satellite-reanalysis-based approach to quantify extreme precipitation characteristics over the Sinai Peninsula (SiP) in Egypt from a statistical–synoptic perspective for the period of 2001–2020. With a multi-statistical approach developed in this research, SiP's wet and dry periods are determined. Using satellite observations of precipitation and a set of derived precipitation indices, we characterize the spatiotemporal variations of extreme rainfall climatologies across the SiP. Then, using the reanalysis datasets, synoptic systems responsible for the occurrence of extreme precipitation events along with the major tracks of cyclones during the wet and dry periods are described. Our results indicate that trends and spatial patterns of the rainfall events across the region are inconsistent in time and space. The highest precipitation percentiles (∼20 mm per month), frequencies (∼15 d per month with rainfall ≥10 mm d -1), standard deviations (∼9 mm month per month), and monthly ratios (∼18 %) are estimated in the northern and northeastern parts of the region during the wet period, especially in early winter; also, a substantial below-average precipitation condition (drier trend) is clearly observed in most parts except for the south. Mediterranean cyclones accompanied by the Red Sea and Persian troughs are responsible for the majority of extreme rainfall events year-round. A remarkable spatial relationship is found between SiP's rainfall and the atmospheric variables of sea level pressure, wind direction, and vertical velocity. A cyclone-tracking analysis indicates that 125 cyclones (with rainfall ≥10 mm d -1) formed within, or transferred to, the Mediterranean basin and precipitated over the SiP during wet periods compared to 31 such cyclones during dry periods. It is estimated around 15 % of cyclones with sufficient rainfall >40 mm d -1 would be capable of leading to flash floods during the wet period. This study, therefore, sheds new light on the extreme precipitation characteristics over the SiP and its association with dominant synoptic-scale mechanisms over the eastern Mediterranean region. [ABSTRACT FROM AUTHOR]

This study evaluated the changes in cyclone track parameters, which include the maximum sustained wind speed (Vmax) and translation speed (Vt) during 2010–2019. It established the Vt and Vmax connection with the oceanic heat content for the upper 50-m, 100-m, and 150-m layers (UOHC50, UOHC100, and UOHC150) for cyclones that formed over the North Indian Ocean (NIO) basin. The study considered a total of 20 cyclone cases (10 each) for the Arabian Sea (AS) and Bay of Bengal (BoB) utilizing the India Meteorological Department (IMD) best-track data to establish a quantitative relationship connecting the UOHC with Vmax and Vt. The correlation of Vt is slightly positive with UOHC50 for NIO and moderately positive (+0.253) for the BoB; however, a proper correlation is lacking for AS region. Also, an inter-comparison was performed for UOHC50, UOHC100, and UOHC150 with Vmax. Maximum correlation of Vmax is obtained with UOHC100 for AS and UOHC50 for the BoB. Analysis was also performed with cyclone cases individually for the two periods 2010–2015 and 2016–2019. Results signify a phase reversal (positive to negative) for (Vmax-UOHC) correlation values from upper 50 to 100 m for BoB cyclones Phailin (2013) and Hudhud (2014). A similar negative (−0.182) to positive (+0.602) shift is observed for the recent BoB cyclone Fani (2019). The parameter Vmax for recent storms showed significant correlation with oceanic warming at 150 m depth for Titli (2018) (−0.869) and Fani (2019) (+0.735). Relationship between UOHC50 and Vt for AS cyclones is evident compared to BoB cyclones. Both the AS and BoB pre-monsoon cyclones viz; Sagar (2018), Vayu (2019), and Fani (2019) showed negative (Vt –UOHC50) correlation in spite of the overall positive correlation noticed for other cases. The above three pre-monsoon cyclones showed high (Vmax –UOHC) correlation values. Recent accelerated warming for the AS basin can further accelerate Vt over this region, with a higher probability of more rapidly intensifying storms. The proximal factors have collinearity in enhancing the coastal vulnerability over this region. [ABSTRACT FROM AUTHOR]

Tropical Cyclones (TCs) are counted among the most destructive phenomena that can be found in nature. Every year, globally an average of 90 TCs occur over tropical waters, and global warming is making them stronger and more destructive. The accurate localization and tracking of such phenomena have become a relevant and interesting area of research in weather and climate science. Traditionally, TCs have been identified in large climate data sets through the use of deterministic tracking schemes that rely on subjective thresholds. This study presents a Machine Learning (ML) ensemble approach for locating TCs center coordinates. The ensemble combines TCs center estimates of different ML models that agree about the presence of a TC in input data. ERA5 reanalysis data was used for model training and testing jointly with the International Best Track Archive for Climate Stewardship (IBTrACS) records. Compared to single models estimates, the ML ensemble approach was able to improve TCs localization in terms of Euclidean Distance with respect to the observed TCs locations from IBTrACS. Moreover, a hybrid tracking scheme was defined: starting from the individual TC center locations detected by the ML ensemble approach, a deterministic tracking algorithm was used for reconstructing TC trajectories. The hybrid tracking scheme was then compared with four deterministic trackers reported in literature, achieving a Probability of Detection and a False Alarm Rate of 71.49% and 23%, respectively, over 40 years of reanalysis data. Plain Language Summary: Every year an average of 90 Tropical Cyclones (TCs) occur globally, and this number is expected to rise due to global warming, which is also increasing the frequency and the intensity of such extremes. The localization and tracking of TCs have traditionally been addressed by means of deterministic tracking schemes. The present study introduces an ensemble approach based on Machine Learning (ML) that locates the TC center coordinates. Basically, the idea is to rely on several ML models accomplishing the same task—each with different training configurations—to integrate their results. The climate variables, used as predictor for training and testing of the models, were gathered from ERA5 reanalysis, while the historical TCs center positions, used as target, were retrieved from the International Best Track Archive for Climate Stewardship data set. The results showed the effectiveness of the proposed approach against the use of a single ML model. Moreover, starting from the individual TC center locations detected by the ML ensemble approach, a deterministic tracking scheme was used from literature to reconstruct TC trajectories. The proposed hybrid tracking algorithm was then compared with four deterministic trackers reported in literature, showing comparable skills. Key Points: An Ensemble Machine Learning approach for localizing Tropical Cyclone (TC) center using reanalysis and observation data is proposedThe ensemble approach was able to improve TC localization performance with respect to single model estimatesThe data‐driven localization approach was integrated with a deterministic tracking scheme and compared against other trackers in literature [ABSTRACT FROM AUTHOR]

Large-scale meteorological pattern (LSMP)–based analysis is used in a novel way to understand meteorological conditions before and during short-duration dry spells over the northeastern United States. These LSMPs are useful to assess models and select better-performing models for future projections. Dry-spell events are identified from histograms of consecutive dry days below a daily precipitation threshold. Events lasting 12 days or longer, which correspond to ∼10% of dry-spell events, are examined. The 500-hPa streamfunction anomaly fields for the first 12 days of each event are time averaged, and k-means clustering is applied to isolate the dry-spell-related LSMPs. The first cluster has a strong low pressure anomaly over the Atlantic Ocean, southeast of the region, and is more common in winter and spring. The second cluster has strong high pressure over east-central North America and is most common during autumn. Over the region, both clusters have negative specific humidity anomalies, negative integrated vapor transport from the north, and subsidence associated with a midlatitude jet stream dipole structure that reinforces upper-level convergence. Subsidence is supported by cold-air advection in the first cluster and the location on the east side of the lower-level high pressure in the second cluster. Extratropical cyclone storm tracks are generally shifted southward of the region during the dry spells. Individual events lie on a continuum between two distinct clusters. These clusters have similar local, but different remote, properties. Although dry spells occur with greater frequency during drought months, most dry spells occur during nondrought months. Significance Statement: This study examines the large-scale weather patterns and meteorological conditions associated with dry-spell events lasting at least 2 weeks while affecting the northeastern United States. A statistical approach groups events together on the basis of similar atmospheric features. We find two distinct sets of patterns that we call large-scale meteorological patterns. These patterns reduce moisture, foster localized sinking, and shift the storm track southward along the Atlantic seaboard, all of which reduce precipitation. Besides greater understanding, knowing the meteorological patterns during short-term dryness in the region provides an important tool to assess how well atmospheric models reproduce these specific patterns. More dry spells occur in nondrought months than in drought months, which means that dry spells can occur without preexisting drought conditions. [ABSTRACT FROM AUTHOR]

This study examines extratropical cyclone tracks, central pressure, and maximum intensification rates from a widely used automated cyclone tracking scheme and compares them with the manual tracking of five well-known North Atlantic cyclones whose histories are available in the refereed literature. The automated tracking scheme is applied to sea level pressure data from four different reanalyses of varying levels of sophistication to test the sensitivity of the results to input data resolution and quality. Further, we test the tracking scheme using lower-tropospheric vorticity obtained from the most recent reanalysis (ERA5) for four of these cyclone events. Substantial discrepancies in cyclone position, intensity, and maximum intensification rates exist between the manual tracking and the automated tracking and are not eliminated by using higher-resolution reanalyses or by "turning off" the spatial smoothing feature of the automated tracking scheme (needed to reduce spurious cyclone detections). The results point to a particular problem in detecting weaker and earlier stage cyclones and confirm findings from studies that have examined a broad range of cyclone tracking schemes for a range of reanalyses. Notably, this early cyclone stage often involves a smaller-scale secondary cyclogenesis or cyclone wave, which are detected by the automated scheme only after subsequent growth in the ensuing 6–12 h. It is known that these early stages are critical for a comprehensive understanding of rapid intensification events. A discussion of possible future solutions to this problem is presented. Significance Statement: Because of the availability of large modern datasets portraying sea level pressure across the globe, meteorologists have turned to automated detection and tracking of midlatitude cyclones. Detection and tracking are of interest since these storm systems play an important role in weather and climate and potential changes in their location, frequency, and intensity are of considerable societal interest given climate change. This paper compares the results obtained from one commonly used automated tracking method with tracks obtained by human analysts. We find substantial discrepancies in cyclone position, intensity, and intensification rates and that these differences are not eliminated by using improved analyses. A discussion of possible future solutions is presented. [ABSTRACT FROM AUTHOR]

The prediction of a tropical cyclone's trajectory is crucial for ensuring marine safety and promoting economic growth. Previous approaches to this task have been broadly categorized as either numerical or statistical methods, with the former being computationally expensive. Among the latter, multilayer perceptron (MLP)-based methods have been found to be simple but lacking in time series capabilities, while recurrent neural network (RNN)-based methods excel at processing time series data but do not integrate external information. Recent works have attempted to enhance prediction performance by simultaneously utilizing both time series and meteorological field data through feature fusion. However, these approaches have relatively simplistic methods for data fusion and do not fully explore the correlations between different modalities. To address these limitations, we propose a systematic solution called TC-TrajGRU for predicting tropical cyclone tracks. Our approach improves upon existing methods in two main ways. Firstly, we introduce a Spatial Alignment Feature Fusion (SAFF) module to address feature misalignment issues in different dimensions. Secondly, our Track-to-Velocity (T2V) module leverages time series differences to integrate external information. Our experiments demonstrate that our approach yields highly accurate predictions comparable to the official optimal forecast for a 12 h period. [ABSTRACT FROM AUTHOR]

The orthogonal conditional nonlinear optimal perturbations (O-CNOPs) approach for measuring initial uncertainties is applied to the Weather Research and Forecasting (WRF) Model to provide skillful forecasts of tropical cyclone (TC) tracks. The hindcasts for 10 TCs selected from 2005 to 2020 show that the ensembles generated by the O-CNOPs have a greater probability of capturing the true TC tracks, and the corresponding ensemble forecasts significantly outperform the forecasts made by the singular vectors, bred vectors, and random perturbations in terms of both deterministic and probabilistic skills. In particular, for two unusual TCs, Megi (2010) and Tembin (2012), the ensembles generated by the O-CNOPs successfully reproduce the sharp northward-turning track in the former and the counterclockwise loop track in the latter, while the ensembles generated by the other methods fail to do so. Moreover, additional attempts are performed on the real-time forecasts of TCs In-Fa (2021) and Hinnamnor (2022), and it is shown that O-CNOPs are very useful for improving the accuracy of real-time TC track forecasts. Therefore, O-CNOPs, together with the WRF Model, could provide a new platform for the ensemble forecasting of TC tracks with much higher skill. [ABSTRACT FROM AUTHOR]

This study investigated the structural and track change of cyclone Nepartak occurred over the western North Pacific in July 2021, a cyclone appeared at the surface level, using ERA5 data, focusing on the relationship with an upper‐tropospheric cutoff low (COL). The COL was shown to dominate the structural change of Nepartak which developed as a subtropical cyclone with a hybrid structure. As the surface cyclone approached the COL, it was overlapped by the COL and became a cyclone with a two‐storied structure, resulting in its abrupt track change. An analysis of the potential vorticity (PV) tendency showed that the movement of Nepartak toward the COL ceased because of the counter‐balance of the horizontal/vertical PV advection with the PV anomaly generated by diabatic heating, whereas the surface cyclone northward motion was primarily due to the horizontal PV advection associated with the COL. Nepartak is a case in which changes in the structure and track of a cyclone are controlled by a COL. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Assessing the capacity of numerical models to produce viable tropical cyclones, as well as assessing the climatological behavior of simulated tropical cyclones, requires an objective tracking method. These make use of parameter thresholds to determine whether a detected feature, such as a vorticity maximum or a warm core, is strong enough to indicate a tropical cyclone. The choice of parameter thresholds is generally subjective. This study proposes and assesses the parallel use of many threshold parameter combinations, combining a number of weaker and stronger values. The tracking algorithm succeeds in tracking tropical cyclones within the model data, beginning at their aggregation stage or shortly thereafter and ending when they interact strongly with extratropical flow and transition into extratropical cyclones or when their warm core decays. The sensitivity of accumulated cyclone energy to tracking errors is assessed. Tracking errors include the faulty initial detection and termination of valid tropical cyclones and systems falsely identified as tropical cyclones. They are found to not significantly impact the accumulated cyclone energy. Thus, the tracking algorithm produces an adequate estimate of the accumulated cyclone energy within the underlying data. [ABSTRACT FROM AUTHOR]

Tropical cyclones are the most devastating meteorological systems that affect the Southwestern Indian Ocean (SWIO) bordering nations. The understanding of their track and intensity evolution is essential for the characterization of the systems. The National Centers for Environmental Prediction–Climate Forecast System (CFS) dataset and the ECWMF fifth‐generation reanalysis (ERA5) are evaluated against best‐track data from the International Best‐Track Archive for Climate Stewardship (IBTrACS) from 1980 to 2019 cyclone seasons. The cyclones were tracked within the reanalyses using a tracking algorithm based on relative vorticity and identified using a spatiotemporal matching method. Climatology from IBTrACS showed significant negative (positive) trend in the total number (number of intense) of systems; however, the evaluation of trends has to be cautious because it may be limited due to uncertainties in the first decades of data. Results for reanalyses showed that both performed similarly although some peculiarities were noted. CFS presented a slightly higher probability of detection, related to its special treatment of tropical cyclone‐like vortex, being able to reproduce all systems between 2000 and 2019. For positions, the majority of the systems have average errors around 50–60 km and the errors generally decrease with the intensity of the systems in both reanalyses, with ERA5 being slightly better during the early stages, while CFS outperforms ERA5 for intensity beyond the IBTrACS's moderate tropical storm category. Intensity follows a reverse pattern of position, with reanalyses largely degrading systems' intensity as they intensify. CFS (ERA5) appeared to be slightly better in terms of maximum wind (minimum sea‐level pressure), representing stronger systems although both datasets were unable to reproduce wind intensities beyond the tropical cyclone category. All evaluated parameters showed improvements when the most recent data period (2000–2019) is considered, especially in ERA5, tailored to an improved quality of the observations that are used to generate best‐track data and also to nudge the reanalyses through data assimilation. Based on the reanalyses characteristics revealed here, the choice of the most suitable dataset to address a specific study within the SWIO region may be dependent on the desired parameters and period. [ABSTRACT FROM AUTHOR]

An objective detection and tracking algorithm based on relative vorticity at 850 hPa using National Centers for Environmental Prediction--National Center for Atmospheric Research (NCEP--NCAR) Reanalysis-1 data was applied to track cyclones in the Southern Hemisphere during austral winters from 1948 to 2017. The climatological characteristics of extratropical cyclones, including track density, frequency, intensity, lifetime, and their related variabilities, are discussed. The frequency and average lifetime of cyclones have substantially decreased. The average maximum intensity of cyclones has shown an increasing trend over the 70-yr study period. The cyclone track density shows a decreasing trend in lower latitudes, consistent with the region where the upper-troposphere zonal wind weakens. Baroclinicity can explain the increase in cyclone intensity: when a cyclone moves to higher latitudes and enters the region with greater baroclinicity, it strengthens. As there is no discernible increase in cyclogenesis in the medium latitudes (45°-70°S), but significantly less cyclogenesis in lower and higher latitudes, it is hypothesized that there is no clear poleward cyclogenesis shift over the Southern Hemisphere. [ABSTRACT FROM AUTHOR]

Tropical cyclones are the most devastating meteorological systems that affect the Southwestern Indian Ocean (SWIO) bordering nations. The understanding of their track and intensity evolution is essential for the characterization of the systems. The National Centers for Environmental Prediction–Climate Forecast System (CFS) dataset and the ECWMF fifth‐generation reanalysis (ERA5) are evaluated against best‐track data from the International Best‐Track Archive for Climate Stewardship (IBTrACS) from 1980 to 2019 cyclone seasons. The cyclones were tracked within the reanalyses using a tracking algorithm based on relative vorticity and identified using a spatiotemporal matching method. Climatology from IBTrACS showed significant negative (positive) trend in the total number (number of intense) of systems; however, the evaluation of trends has to be cautious because it may be limited due to uncertainties in the first decades of data. Results for reanalyses showed that both performed similarly although some peculiarities were noted. CFS presented a slightly higher probability of detection, related to its special treatment of tropical cyclone‐like vortex, being able to reproduce all systems between 2000 and 2019. For positions, the majority of the systems have average errors around 50–60 km and the errors generally decrease with the intensity of the systems in both reanalyses, with ERA5 being slightly better during the early stages, while CFS outperforms ERA5 for intensity beyond the IBTrACS's moderate tropical storm category. Intensity follows a reverse pattern of position, with reanalyses largely degrading systems' intensity as they intensify. CFS (ERA5) appeared to be slightly better in terms of maximum wind (minimum sea‐level pressure), representing stronger systems although both datasets were unable to reproduce wind intensities beyond the tropical cyclone category. All evaluated parameters showed improvements when the most recent data period (2000–2019) is considered, especially in ERA5, tailored to an improved quality of the observations that are used to generate best‐track data and also to nudge the reanalyses through data assimilation. Based on the reanalyses characteristics revealed here, the choice of the most suitable dataset to address a specific study within the SWIO region may be dependent on the desired parameters and period. [ABSTRACT FROM AUTHOR]

This study investigates the main climatological features of extreme precipitation (TCER) induced by tropical cyclones (TCs) affecting Guangxi (GX), South China using multiple datasets and a 99th percentile threshold during 1981–2020, with an emphasis on the rainfall diversities of different high-impact TC groups and their associated mechanisms. Results show that there are large regional differences and a seasonal imbalance in the climatological features of TCER in GX. In summer (fall), TCs with TCER events primarily move northward or eastward (northwestward or westward), namely, S-NWTCs and S-ETCs (F-WTCs and F-NWTCs). The rainfall centers exhibit asymmetrical features with S-NWTCs and F-NWTCs located in the northeast quadrant, but S-ETCs and F-WTCs in the southwest and northeast quadrants, respectively. Comparisons of atmospheric circulations and environmental factors indicate that the intense rainfall of F-WTCs is mainly attributed to the trough–TC interaction, which is accompanied by stronger upper-level westerly jet and cold air intrusion, thus increasing baroclinic energy and uplifting for the strongest rainfall among these four groups. This interaction is absent for other groups due to a greater South Asian high and western North Pacific subtropical high. Instead, the increased rainfall in S-NWTCs and F-NWTCs can mainly be attributed to the stronger low-level southwesterly jet, which, in combination with low-level warm advection and convergence induced by land–sea friction, promotes the release of latent heat through moisture condensation. S-ETCs differ from S-NWTCs and F-NWTCs in that moisture convergence is weaker due to the much-weakened TC circulation. [ABSTRACT FROM AUTHOR]

• The evaluation of the two DA methods, 4DVar and 4DEnVar, has been performed over the NIO. • The 4DEnVar DA method has shown significant results in TCs' track simulation compared to 4DVar. • The TC's rainfall, intensity, and other characteristics were simulated relatively closed from both DA techniques. In numerical weather prediction, the four-dimensional variational (4DVar) data assimilation (DA) is one of the most operative techniques to provide better initial and lateral boundary conditions and, thus, weather forecasts. However, the 4DVar DA system requires a high cost of running sequential iterations of linear and adjoint models. The four-dimensional ensemble variational (4DEnVar) DA is evolving over the 4DVar, which combines a climatological covariance model with covariances generated from an ensemble of forecasts to sample the current uncertainties. In this study, the weather research and forecasting model was used to evaluate the behaviour of the 4DVar, and 4DEnVar DA approaches in the simulation of two tropical cyclones, Titli and Lehar, over the Bay of Bengal basin of the North Indian Ocean by assimilating conventional and satellite radiances observations. An overall analysis indicates that the 4DEnVar simulations performed relatively better in cyclones' track prediction and more or less similar to the 4DVar for other cyclone-related characteristics. [ABSTRACT FROM AUTHOR]

Climate and extreme hydrometeorological studies are required to reduce risk and vulnerabilities. This study uses different cumulus and microphysics schemes in Weather Research and Forecasting (WRF) model to simulate heavy rainfall events and Cyclone Sidr in Bangladesh, where many extreme events occur. Results show that WRF can capture the cyclone track, intensity, and landfall position. In addition, regionalization and an ensemble method through Bayesian regression model (BRM) are used to improve WRF rainfall simulations. Although regionalization can improve results of the experiments with different schemes, BRM leads to the best performance. To consider uncertainties and evaluate hazards, a probabilistic framework and proper indices based on distributions are used. Spatiotemporal precipitation distributions are used to develop the flash flood index (FFI) to compare the intensity of heavy rainfall events. Results show a large FFI over the central and southeast divisions that indicates the areas prone to the flash flood hazards and high-level risk. We use standardized precipitation index (SPI) in 6- and 12-month time scales based on monthly precipitation of ACCESS-CM2 in 2015–2100 under two scenarios (SSP-126 and 585) to evaluate long-term precipitation changes and drought/flood tendency. In addition, the probability distributions of the precipitation and wind speed are used in reliability analysis of the infrastructure. Target reliability indices determine the proper design rainfall (105 mm d−1) and wind speed (60 m s−1) that leads to a safe design of infrastructure. This study provides an integrated analysis of extreme hydrometeorological events, which is crucial for sustainable adaptation and mitigation plans. [ABSTRACT FROM AUTHOR]

The formation of mid‐tropospheric cyclones (MTCs), responsible for a large portion of annual precipitation and extreme rainfall events over western India, is studied using an unsupervised machine learning algorithm and cyclone tracking. Both approaches reveal four dominant weather patterns that lead to the genesis of these synoptic systems. Specifically, re‐intensification of westward‐moving synoptic systems from the Bay of Bengal (type 1, 51%), in‐situ formation with a coexisting cyclonic system over the Bay of Bengal that precedes (type 2a, 31%) or follows (type 2b, 10%) genesis in the Arabian Sea, and finally in‐situ genesis within a northwestward‐propagating cyclonic anomaly from the south Bay of Bengal (type 2c, 8%). Thus, a large fraction of this region's rainy middle tropospheric synoptic systems form in association with cyclonic activity in the Bay of Bengal. The four variants identified also show a marked dependence on large‐scale environmental features. In particular, type 1 and type 2a MTC formation primarily occurs in phases 4 and 5, and type 2b and type 2c MTCs form mainly in phases 3 and 4 of the boreal summer intraseasonal oscillation. Further, though in‐situ formation with a Bay of Bengal cyclonic anomaly (types 2a and 2b) mostly occurs in June, downstream development is more likely in the core of the monsoon season. Out of all categories, type 2a is associated with the highest composite rain rate (60 mm·$$ \cdotp $$day −1$$ {}^{-1} $$) over western India and points towards the dynamic interaction between a low‐pressure system over the Bay of Bengal and the development of MTCs over western India and the northeast Arabian Sea. This classification, identification of precursors, connection with cyclonic activity over the Bay of Bengal, and dependence on a large‐scale environment provide an avenue for a better understanding of rain‐bearing MTCs over western India. [ABSTRACT FROM AUTHOR]

East coast cyclones (ECCs) provide an essential reprieve from dry periods across eastern Australia. They also deliver flood‐producing rains with significant economic, social and environmental impacts. Assessing and comparing the influence of different types of cyclones is hindered by an incomplete understanding of ECC typology, given their widely variable spatial and temporal characteristics. This study employs a track‐clustering method (probabilistic, curve‐aligned regression model) to identify key cyclonic pathways for ECCs from 1950 to 2019. Six spatially independent clusters were successfully distinguished and further sub‐classified (coastal, continental and tropical) based on their genesis location. The seasonality and long‐term variability, intensity (maximum Laplacian value ± 2 days) and event‐based rainfall were then evaluated for each cluster to quantify the impact of these lows on Australia. The highest quantity of land‐based rainfall per event is associated with the tropical cluster (Cluster 6), whereas widespread rainfall was also found to occur in the two continental clusters (clusters 4 and 5). Cyclone tracks orientated close to the coast (clusters 1, 2 and 3) were determined to be the least impactful in terms of rainfall and intensity, despite being the most common cyclone type. In terms of interannual variability, sea surface temperature anomalies suggest an increased cyclone frequency for clusters 1 (austral winter) and 4 (austral spring) during a central Pacific El Niño. Furthermore, cyclone incidence during IOD‐negative conditions was more pronounced in winter for clusters 1, 2, 3— and clusters 4 and 5 in spring. All cyclones also predominantly occurred in SAM‐positive conditions. However, winter ECCs for clusters 1 and 3 had a higher frequency in SAM‐negative. This new typology of ECCs via spatial clustering provides crucial insights into the systems that produce extreme rainfall across eastern Australia and should be used to inform future hazard management of cyclone events. [ABSTRACT FROM AUTHOR]

It is not fully understood how near-inertial kinetic energy (NIKE) is spatially distributed near Tsushima oceanic front (TOF) as a typhoon travels across the region. Underneath TOF, a year-round mooring covering a major part of water column was implemented in 2019. During summer, three massive typhoons (Krosa, Tapah, and Mitag) consecutively traversed the frontal area and delivered a substantial amount of NIKE into surface mixed layer. According to a mixed-layer slab model, NIKE was widely distributed near the cyclone's track. The mooring observation exhibited the vertical distribution and pathways of surface-generated NIKE in response to the successive typhoon events. According to the modal decomposition, first three modes mostly explain the NIKE's elevations following the typhoon events. According to ray-tracing experiments based on the internal-wave theory, large-scale near-inertial waves (NIWs) rapidly descend to a depth greater than 1000 m, while mesoscale NIWs slowly descend and rarely reached beyond the main pycnocline. Following the passage of Tapah, a profound energy mass was found nearly stationary at shallow depths coincident with vertical shear of geostrophic current. We infer that the descending rate of NIWs fell and then they were amplified through the energy conservation when the waves came from the north side of TOF. [ABSTRACT FROM AUTHOR]

Despite numerous studies, the impact of global warming on the tropical cyclone (TC) track and intensity by reasons of data inhomogeneity in remote sensing and large natural variability over a relatively short period of observation is still controversial. Three carbon-emission sensitivity experiments are conducted to investigate how TC track and intensity respond to changes in the oceanic and atmospheric environment under global warming. The results show a high sensitivity of the simulated TC track and intensity to global warming. On one hand, with increase in carbon emissions, the western Pacific subtropical high expands notably, increasing the poleward steering flow and eventually leading to a poleward shift of TC. On the other hand, the underlying sea-surface temperature and surface-entropy flux increase and, thus, favor the convections near the eyewall. Moreover, the TC structure becomes more upright, which is closely related to the larger pressure gradient near the eyewall. As a result, TC intensity increases with carbon emissions. However, this increase is notably smaller than the maximum potential intensity theory as the TC intensity can reach a threshold if carbon emission still increases in the future. The involved mechanisms on the changes of TC track and intensity are also revealed. [ABSTRACT FROM AUTHOR]