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Arctic cyclones, as significant weather systems at high latitudes, are often accompanied by strong winds or heavy precipitation, leading to prolonged and more destructive disasters. In this study, we identified summer (JJAS) Arctic cyclones in the ERA5 reanalysis dataset from 1979 to 2022 using a machine-learning cyclone identification and tracking algorithm. We selected 40 sets of the strongest cyclones on the northern edge of Eurasia (NEE), one of the most identifiable Arctic Frontal Zones, over the past 44 years to assess the role of tropopause polar vortices (TPVs) in intense summer cyclones by using the Weather Research and Forecasting model (WRF). Our sensitivity experiments show that removing the horizontal temperature gradient of the lower stratosphere significantly reduces cyclone intensity, particularly for TPVs-matched cyclones, increasing by 7.4 hPa at their maximum intensity moment (2.8 hPa for TPVs-unmatched cyclones). TPVs-matched cyclones typically exhibit a lower tropopause and an "upper warm-lower cold" thermodynamic structure. High potential vorticity (PV) induced by the downward intrusion of TPVs due to the thermal structure closely links with the intensified development of these cyclones, leading to prolonged lifetimes. On the other hand, TPVs-unmatched cyclones display a distinct frontal structure in the lower troposphere but are not sensitive to changes in the upper-level horizontal temperature gradient. This may help to highlight the role of TPVs downward intrusion on TPVs-matched intense cyclones on NEE. [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]

Based on the 6‐hourly tropical cyclone best track and global reanalysis data, statistical analyses and a machine learning approach are used to identify/quantify factors that affect the relative weakening rate (RWR) of landfalling TCs (LTCs) over China mainland during 1980–2020. Results show that the enhanced RWR of LTC events usually occurs when LTCs move into regions with large environmental vertical wind shear (VWS), large surface roughness (SURR), high land surface soil temperature (SOILT), low surface latent heat flux (SLHF), and with relatively faster translational speeds (SPD). The SPD and SURR are dominant factors determining the RWR of LTCs over China mainland, contributing about 20% and 18.5% to the RWR of LTCs. VWS is also a key factor affecting RWR of LTCs with mid‐level VWS contributing 17.8% to RWR of LTCs and low‐ and deep‐level VWS contributing about 12.9% and 11.2%, respectively. Furthermore, factors affecting the LTC weakening rate in south and north China are different. In north China, the VWS at different levels are all highly correlated with LTC RWR after landfall, whereas the influence of mid‐layer VWS shows significant correlation with LTC RWR in south China. In addition, surface characteristics, including SURR, SLHF, and SOILT, have significant correlation with LTC RWR in south China. But the relationships between surface characteristics and LTC RWR in north China are not statistically significant. It is worth noting that although the correlation between DIV200 and LTC RWR is insignificant for the whole China mainland, it presents highly negative and positive correlations in south and north China, respectively. Plain Language Summary: In this study, statistical analyses and a machine learning approach (XGBoost) are used to identify and quantify factors affecting the relative weakening rate (RWR) of landfalling tropical cyclones (LTCs) in China mainland. Results show that the large environmental vertical wind shear (VWS, no matter in the deep‐layer or in the lower‐layer), large surface roughness (SURR), high land surface soil temperature (SOILT), low surface latent heat flux (SLHF), and faster translational speed (SPD) are key factors, which enhanced the weakening rate of LTC over China mainland. Among them, the SPD and SURR play the most important role in the LTC weakening rate over China mainland. Furthermore, factors affecting LTC weakening rate in south and north are explored. In south China, LTC weakening rate highly correlated with the surface conditions (including SLHF, SOILT, and SURR), upper‐level divergence (DIV200), and SPD. However, the main factors affecting LTC weakening rate over North China are dynamical variables, including VWS, DIV200 and SPD. Although the correlation between DIV200 and LTC RWR is insignificant for the whole China mainland, it presents highly negative and positive correlations in south and north China, respectively. Key Points: A machine learning approach is used to quantify factors affecting the weakening rate of landfalling TCsThe translation speed of TCs and the surface roughness contribute most to the weakening rate of LTCs over China mainlandThe effect of upper‐level divergence to the weakening rate of LTCs is opposite over south and north China [ABSTRACT FROM AUTHOR]

Extratropical cyclones (ETCs) are the focus of many open questions, spanning from weather to climate and from local to global scales. The relationship between moisture uptake and precipitation over different mesoscale ETC structures remains poorly understood. The availability of moisture parameters to the scientific community through tracer dispersion models benefits the representation of the processes involving moisture, the dynamics of ETCs and the understanding of the associated meteorological fields. In this study, we present a database for North Atlantic ETC tracks and a Lagrangian-Derived Moisture Uptake Dataset, both of which are derived from dynamically downscaled ERA5 reanalysis data. We provide moisture parameters such as the total moisture uptake and its vertical distribution by layer, and shapefiles of ETC structures, to facilitate further studies in this area. Our data have been thoroughly validated to ensure that the storm tracks and their characteristics are accurately represented in the model without distortion. Additionally, we conduct a series of experiments to demonstrate the rigour and quality of the methods employed to generate the data. [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]

The impact of assimilating in situ temperature, moisture and wind observations from a WindBorne Systems balloon, with long duration and adjustable ballast, is evaluated using a case study from the 2022 THINICE field campaign. A case is selected wherein the WindBorne balloon directly sampled a jet streak associated with a tropopause polar vortex (TPV). The observed TPV merged with another TPV at the same time as a downstream Arctic cyclone (AC) redeveloped eastward. The case is used to better understand the role of the observed TPV in the evolution of the downstream AC. The assimilation of the WindBorne observations improves the forecast track and amplitude of the TPV during the ∼1 day forecast period that the TPV can be tracked as a distinct feature. The root mean square error of temperature at 350 hPa is improved by the WindBorne assimilation throughout the 2.5 day forecast period. The surface cyclone track forecast is improved by the WindBorne assimilation during the period of eastward redevelopment of the surface cyclone, and the sea level pressure RMSE in the surrounding region is reduced during the first ∼1.5 days of forecast lead time. Results demonstrate the capability of the long‐duration controllable WindBorne balloons to improve the analysis of the TPV associated with the jet streak, leading to improved forecast of both tropopause‐level and surface pressure features. Additionally, the importance of observing the upstream TPV amplitude for improving forecasts of the process of TPV merging and its impact on the evolution and longevity of the mature AC is confirmed. Plain Language Summary: During a recent field campaign, WindBorne Systems deployed novel weather balloons that remain in the atmosphere for weeks at a time, adjusting their altitude to follow winds towards features of interest. These balloons provide an opportunity to study the impact of the merging of two upper‐level disturbances on the performance of an Arctic cyclone (AC) forecast. WindBorne observations of one of the two merged disturbances allowed for an improved forecast of the eastward redevelopment of the AC. These experiments confirm the importance of observing such features for AC prediction and the importance of the merging process in the maintenance and evolution of long‐lived Arctic cyclones. Key Points: WindBorne balloons can improve in situ sampling of small scale features critical for Arctic predictabilityTropopause polar vortex (TPV)‐related jet streak is important for predictability of subsequent TPV merging and Arctic cyclone (AC) redevelopmentMerging of TPVs without associated surface cyclone should be included in conceptual models of AC longevity [ABSTRACT FROM AUTHOR]

Polynyas play a critical role in the formation of Antarctic Bottom Water and the enhancement of polar primary productivity. Accurate and exhaustive identification of Antarctic polynyas is fundamental to advancing in-depth research. However, due to methodological limitations, previous studies paid more attention to frequent polynyas and infrequent polynyas have not been investigated much despite that they could be vulnerable to climate change. Inspired by a cyclone tracking algorithm, we develop a novel method to overcome challenges identifying all types of polynyas satisfying spatiotemporal criteria and tracing their daily evolution, extracting from an extensive amount of sea ice concentration data. Based on it, we establish a dataset called "Daily Edge of Each Polynya in Antarctica" (DEEP-AA). Validation against remote sensing and ship-based observations confirms DEEP-AA's reliability. Compared to existing maps, the DEEP-AA identifies a threefold number of polynyas and reveals the seasonal area recovery of infrequent polynyas is earlier than frequent ones. [ABSTRACT FROM AUTHOR]

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]

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]

In this study, we investigate the climatology and trend of explosive cyclones (ECs) over the Southern Ocean (50oS–70oS) during 1980–2020 by combining a method that is most suited for identifying and tracking cyclones in the Southern Ocean and a latest climate reanalysis. On average, approximately 50 ECs are generated annually over the Southern Ocean, with a significant increasing trend of 2.3 per decade during the studying period. This increasing trend is dominated by the trend of strong ECs, particularly in autumn. We analyze the dynamical and thermodynamical effects associated with multiple deepened strong ECs in autumn over an identified key region in the southern Pacific Ocean sector (155oW–170oW, 50oS–65oS), where the density of the initiation of ECs shows the largest increasing trend in autumn. The composite analysis reveals the general patterns and duration of the effects on the atmosphere, ocean, and sea ice associated with multiple ECs in the southern Pacific Ocean. The results indicate that the deepened strong ECs are associated with significant changes in meridional winds, downward longwave radiation, and sensible and latent heat fluxes. These changes lead to cold sea surface temperature anomalies in the northeast of the key region, reaching a maximum 5–7 days after the EC deepening, and the increased sea ice cover south of the key region, peaking 4–5 days after the EC deepening. Highlights: Explosive cyclones (ECs) over the Southern Ocean (50oS–70oS) have a significant increasing trend of 2.3 per decade during 1980–2020. The increasing trend is dominated by the trend of strong ECs, especially in autumn. The anomalies of sea surface temperature and sea ice cover can sustain about 2 weeks following the generation of strong ECs. [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]

Improving tropical cyclone (TC) rainfall prediction is vital as climate change has led to an increase in TC rainfall rates. Enhanced reliability in predicting TC tracks has paved the way for statistical methodologies to make use of them in estimating current TC rainfall, achieved by identifying similar past TC tracks and obtaining their corresponding rainfall data. While the Fuzzy C Means (FCM) clustering algorithm is widely used, it has limitations stemming from its clustering‐centric design, hindering its ability to pinpoint the most appropriate similar TCs. Our study introduces the Sinkhorn distance, a novel similarity metric that measures the cost of transforming one set of data to another, for assessing TC similarity in rainfall prediction. Our findings indicate that utilizing Sinkhorn distance enhances the accuracy of TC rainfall predictions across the Western North Pacific region. When compared to the conventional approach using FCM, our Sinkhorn distance‐based methodology yields slightly better yet statistically significant results. The improvement is due to better identification of similar TCs, characterized by closer proximity of similar TC tracks to the target TC track, facilitated by Sinkhorn distance. This underscores how minor differences in TC track can alter rainfall distribution, emphasizing the critical importance of accurate track prediction in rainfall prediction and the need to reconsider how we categorize TCs together, which can have implications for climate and atmospheric sciences. Collectively, the inclusion of Sinkhorn distance stands as a valuable addition to our toolkit for discerning similar TC tracks, thus elevating the accuracy of TC rainfall predictions. Plain Language Summary: We need to get better at predicting how much rain tropical cyclones will bring because climate change is making them bring more rain. We have gotten better at predicting their paths, which lets us use a method that looks at similar past storms to guess how much rain the current one will bring. The usual method, called Fuzzy C Means (FCM), has some problems because it focuses too much on grouping storms. Our study introduces Sinkhorn distance, a measure of moving a cyclone to the path of another cyclone, as a new way of measuring how similar storms are. We found that using the Sinkhorn distance makes our predictions of tropical cyclone rainfall in the Western North Pacific slightly more accurate, primarily because it better identifies similar cyclone paths, thus highlighting the sensitivity of rainfall to minor changes in cyclone tracks. Compared to the usual FCM method, the Sinkhorn distance generally gives better results. So, including the Sinkhorn distance in our toolkit helps us find storms that are more similar, which makes our predictions of rainfall more accurate. This study is an important step forward in making more accurate prediction for tropical cyclone rainfall in the Western North Pacific region. Key Points: Proposed in this study is the use of Sinkhorn distance as a novel measure of tropical cyclone (TC) similarity in rainfall predictionSinkhorn distance statistically significantly improves TC rainfall prediction accuracy, serving as an alternative similarity measureResults highlight minor differences in TC tracks can alter rainfall, due to the interaction between TC and land topography [ABSTRACT FROM AUTHOR]

The Great Arctic Cyclone 2012 (AC12) is used to understand the role of initial condition errors in the predictability at 2–3‐day forecast range of a high‐impact summer Arctic Cyclone (AC). Ensemble sensitivity analysis (ESA) is first performed to identify potentially sensitive regions of the cyclone evolution using an ensemble baseline forecast with conventional in situ observations assimilated. A pseudo‐observation method is then introduced to investigate impacts of hypothetical observations in these sensitive but unobserved regions. In the baseline experiments with in situ observations assimilated, the forecasted AC12 reaches its peak intensity 18 hr earlier than in the verifying Global Forecast System Analysis (GFS‐ANL) and the cyclone track is biased toward the southwest. Using ESA, the time of peak intensity and the cyclone track error are identified to be sensitive to the upstream trough, downstream ridge, and the tropopause polar vortex (TPV) to the northeast (NE TPV) of the AC12. These features were not observed by the in situ observation networks. To examine the impact of the observation gaps, pseudo‐observations drawn from GFS‐ANL are assimilated. Pseudo‐observations sample the three features separately to study the impact of the initial condition error on the predictability of AC12. The cyclone peak intensity timing error and track error are greatly reduced when the initial condition error is reduced near the NE TPV. A southward expansion of the NE TPV and the corresponding southward shifting low‐level front lead the forecasted AC12 to progress to the east, which better agrees with the verifying GFS‐ANL. Plain Language Summary: Accurate prediction of strong Arctic cyclones is critical to the scientific and economical activities in the Arctic. However, the sparse observation networks in the Arctic pose challenges to the data assimilation system to constrain the initial conditions in various features of the Arctic Cyclones. This paper used a case study approach to investigate the impact of initial condition errors in different features on the development of the 2012 Great Arctic Cyclone. Synoptic features that are sensitive to the track and timing of the cyclone were first identified using ensemble sensitivity analysis. Assimilating in situ conventional observations did not sufficiently constrain the initial conditions in the sensitive features. Hypothetical pseudo‐observations from an external analysis system were then designed and assimilated to study how each sensitive feature affects the prediction of the physical processes of cyclone development. The results showed that more observations inside the TPV to the northeast of the cyclone brought the most significant forecast improvements at a 2‐day forecast lead time. Key Points: Ensemble sensitivity analysis reveals track and deepening trend forecast biases of Arctic Cyclone 2012 are sensitive to unobserved synoptic scale featuresPseudo‐observation experiments show the northeast tropopause polar vortex (TPV) is the most impactful feature among sensitive featuresSouthward expansion of the northeast TPV impacts the cyclone evolution by shifting the low‐level front southward [ABSTRACT FROM AUTHOR]

Cyclones tracking northward across the Bay of Bengal represent a significant threat to life and safety when they make landfall. Surface moorings of the Ocean Moored Network for the Northern Indian Ocean in the Bay of Bengal have been deployed to provide real-time observations in support of improved alerts and predictions of cyclones. Engineering goals are both to develop robust surface moorings that survive and to field meteorological and oceanographic instruments that reliably provide data in real time. While the mooring design and reliability were previously reported, the focus here is on the motivations for installing the instrumentation, and on experience gained during the passage of cyclone Amphan in May 2020, examining the sensor as well as mooring performance. The paper presents reasons for fielding the chosen sensors, merits, and shortcomings during the effort to observe Amphan, and recommendations for how to better address the challenges of providing real-time observations of cyclones in the Bay of Bengal. [ABSTRACT FROM AUTHOR]

The coastal zones are one of the most productive ecosystems in the world, rich in resources, supporting livelihoods and driving economies. The thickly populated coastal stretches of Kerala are grappling with multiple threats in the form of coastal hazards such as sea level rise, high waves, coastal erosion, and coastal flooding. To address these threats, a comprehensive multi-risk approach is adopted based on the Fifth and Sixth Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC), in which risk is conceived as a function of hazard, vulnerability, and exposure. The study assesses and map the risks associated with coastal hazards on the central coast of Kerala by developing a composite disaster risk index that integrates physical, and socioeconomic variables specific to the study area. Under the hazard component, indicators such as sea level rise, precipitation intensity, shoreline change rate, proximity to cyclone tracks, storm surge height, and frequency of sea surges are considered. Vulnerability indicators encompass population density and land use/land cover, while exposure indicators include elevation, slope, mean significant wave height, and drainage density. The risk assessment indicates varying levels of risk across the study area, with 2% coming under very high risk zone, 6% under high risk zone, 21% under medium risk zone, 43% under low risk, and 28% under very low risk. These findings help coastal planners and managers with effective disaster management and adaptation strategies. [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]

Ockhi was a devastating tropical cyclone that struck parts of Sri Lanka, India, and the Maldives in November 2017. The storm began as a low-pressure system over the Bay of Bengal and rapidly intensified into a very severe cyclonic storm, causing havoc along its path. Interestingly, Ockhi was a rare storm to have rapid intensification over the Arabian Sea basin during its cyclogenesis stage. In this analysis, we examine the factors that influenced Ockhi's genesis and rapid intensification. It is known that the warm core eddies have a potential role in the rapid intensification of tropical cyclones. However, in our study, it was observed that no warm core eddies were found along the track of tropical cyclone Ockhi during its rapid intensification. Thus, additional atmospheric and oceanic features that contribute to the development and intensification of tropical cyclones have been taken into account. A very high tropical cyclone heat potential of 60–90 kJ cm−2, an exceptionally high sea surface temperature anomaly of above 1–1.5 °C, and a high moist static energy of 340 × 103 J kg−1 or greater were observed to be persisting over the region where the intensification has taken place. The brightness temperature was < 200 K, indicating strong and very deep convective activity over the region of intensification. Along with favourable ocean and atmospheric features such as high tropical cyclone heat potential, sea surface temperature, and moist static energy, the presence of deep convection persisting for a prolonged time can eventually intensify the system. [ABSTRACT FROM AUTHOR]

The risk posed by Arctic cyclones to ships has seldom been quantified due to the lack of publicly available historical Arctic ship track data. This study investigates Arctic ship tracks derived from automatic identification system (AIS) transponders from September 2009 to December 2016. These are analysed with historical synoptic-scale cyclone tracks derived from ERA-5 reanalysis data and reports of past Arctic shipping incidents. We determine the number of ship tracks that intersected with intense Arctic cyclones tracks and how many of these intersections resulted in a reported shipping incident. The number of ships operating in the Arctic has increased year-on-year from 2010 to 2016. The highest density of ships occurs year-round in the Barents Sea. Trans-Arctic shipping transits via the Northern Sea Route and the Northwest Passage are limited to summer and autumn months, when sea ice extent has retreated sufficiently from the coastlines. Ship track density along these trans-Arctic routes is far less than the thousands of ships travelling in the Barents Sea year-round. Between 2010 and 2016, 158 Arctic shipping incidents were reported, but only 6 % of these reported incidents occurred following the passage of an intense Arctic cyclone. Arctic cyclones with significant wave heights greater than 6 m are found to frequently intersect ships, but only 0.1 % of these intersections resulted in a reported shipping incident. Results from this study indicate that ships are frequently impacted by Arctic cyclones, but cyclones were not a dominant cause of reported Arctic shipping incidents between 2010 and 2016. This suggests that ships are resilient to the rough sea conditions that past Arctic cyclones have caused, therefore mitigating and reducing risk. [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]

In this article we present a method for numerical simulations of extreme sea levels using synthetic low-pressure systems as atmospheric forcing. Our simulations can be considered to be estimates of the high sea levels that may be reached when a low-pressure system of high intensity and optimal track passes the studied region. We test the method using sites located along the Baltic Sea coast and simulate synthetic cyclones with various tracks. To model the effects of the cyclone properties on sea level, we simulate internal Baltic Sea water level variations with a numerical two-dimensional hydrodynamic model, forced by an ensemble of time-dependent wind and air-pressure fields from synthetic cyclones. The storm surges caused by the synthetic cyclones come on top of the mean water level of the Baltic Sea, for which we used a fixed upper estimate of 100 cm. We find high extremes in the northern Bothnian Bay and in the eastern Gulf of Finland, where the sea level extreme due to the synthetic cyclone reaches up to 3.5 m. In the event that the mean water level of the Baltic Sea has a maximal value (1 m) during the cyclone, the highest sea levels of 4.5 m could thus be reached. We find our method to be suitable for use in further studies of sea level extremes. [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]

Consecutive Argo float profiles are used to observe upper ocean changes resulting from the passage of tropical cyclones (TCs) in the South Pacific between 2001 and 2023. Cross‐sectional composites of the ocean response are produced by normalizing the distance between the profile and cyclone track by the 34‐ knot wind radius (R34) of each TC to better resolve the average changes in water properties. Surface cooling in the initial mixed layer (ML) extends from the center to about twice R34 and is stronger to the left of the TC track. Subsurface warming at the base of the ML is observed between 0.4 and 2 R34 on both sides of the track. Strong cooling is observed in a distinct core directly under the TC path, reaching temperature changes of over − 1.6°C between 70 and 450 m depth. Significant upwelling of over 40 m displacement is observed within ±0.05 R34, extending to 1,000 m depth. Surface cooling (and subsurface warming at the base of the ML) are positively correlated with wind stress on the ocean from the TC, quantified by the Local Wind Power Dissipation (PDL), and negatively correlated with the energy required to destratify the upper ocean, defined by a Cooling Inhibition index (CI). Subsurface warming is common with low CI but is not observed with high CI. We demonstrate an improved method to analyze Argo float data for measuring TC‐upper ocean interaction in remote oceans and the usefulness of PDL and CI in calibrating the TC‐ocean interaction. [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]

The North Indian Ocean tropical cyclones (TCs) are devastating multi-hazard disasters with associated gale wind, torrential rainfall and storm-surge which pose severe threat to humankind and responsible for huge economic loss and livelihood. Predictions of track, intensity and structure along with the track uncertainty are very important to produce forecast and warning for the populace and disaster managers. India Meteorological Department issues operational track forecasts and associated uncertainty as a cone of uncertainty (CoU) based on the climatological track errors computed from past operational forecasts. Due to large variation between different TCs, instead of static, dynamical CoU (DYN-COU) based on dynamical Ensemble Prediction Systems is more sophisticated way to provide track uncertainty. In this study, the utility of different CoU including (i) Climatological CoU (CLM-COU) (ii) CoU from dynamical EPS using different methods and (iii) Hybrid CoU (utilising CLM-COU and DYN-COU) are analysed with respect to various characteristics of 13 TCs formed during 2019–2020. The result suggests that the CLM-COU is more skilful during the first 36 h of forecast whereas DYN-COU based on weights assigned to different members of EPS performs better during longer lead time. Basin-wise, CLM-COU performs better over the Arabian Sea for all the forecast hours whereas in case of Bay of Bengal during first 18 h and DYN-COU shows better skill with lead time longer than 18 h. For recurving TCs, CLM-COU provides better uncertainty forecast information up to 96-h forecast lead time as compared to DYN-COU based forecast uncertainty information. The skill of DYN-COU-based forecast uncertainty information is also dependent on the operational track forecast performance, which has more errors for recurving TCs as compared to straight moving TCs. [ABSTRACT FROM AUTHOR]

Most tropical cyclone (TC) forecasting centers have implemented a probabilistic circle to represent track uncertainty at a specified lead time. Recent studies suggest that probability ellipses constructed from ensemble prediction systems can convey the anisotropy of track predictability. In this study, a new probability ellipse model is developed to interpret the extent of forward speed and heading uncertainties in ensemble forecasts by selecting an equal proportion of members in the along‐ and cross‐track directions. This method is validated using the 2019–2021 western North Pacific (WNP) TC track forecasts from the ensemble predictions of the European Centre for Medium‐Range Weather Forecasts, the United States National Centers for Environmental Prediction, and the Korea Meteorological Administration. When the proportion of ensemble members in the ellipse is set to 70%, more than one‐half (50.0%–73.6%) of the forecasts, depending on the lead time, indicate reduced area compared with that of the circle. The mean areas of the probability ellipses are 4.9%, 7.0%, 10.0%, and 11.5% smaller than those of the circle in 48‐, 72‐, 96‐, and 120‐hr forecasts, respectively. The forward speed shows greater uncertainty than the heading, as evidenced by the along‐track radii being larger than the cross‐track counterpart in ∼60% of the samples, regardless of the lead time. In addition, the regional distribution of the along‐track/cross‐track ratio in the probability ellipses can explain the dominant direction of the track error in a particular location. The proposed probability ellipse shows potential for application in operational TC track predictions. Plain Language Summary: Operational tropical cyclone (TC) forecasting centers usually represent the uncertainty of a TC track forecast with a circle, namely the probabilistic circle. In this paper, the circle is generalized to a probability ellipse directly using the output of ensemble prediction systems. The key to the new ellipse is that an equal proportion of ensemble members are selected in the along‐ and cross‐track directions to determine the two elliptical radii. This probability ellipse is smaller in size than the circle when representing the same level of uncertainty. The eccentric property of the probability ellipse allows for easy interpretation of the direction with larger ensemble‐forecast uncertainty. Such results can aid in operational TC track predictions and subsequent implementation of preventive measures. Key Points: The proposed probability ellipse explicitly shows the uncertainty of ensemble forecast tracks in the along‐ and cross‐track directionsThe probability ellipse is capable of explaining the dominant direction of error in track forecastsRegional distributions of uncertainty and error resemble each other in terms of magnitude and direction [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]

Large-scale meteorologic events (e.g. cyclones), referred to as synoptic events, strongly influence weather predictability but still cannot be fully characterised in the Arctic region because of the sparse coverage of measurements. Due to the fact that atmospheric dynamics in the lower stratosphere and troposphere influence the ozone field, an approach to analyse these events further is the use of space-borne measurements of ozone vertical distributions and total columns. In this study we investigate the link between cyclones and changes in stratospheric ozone by using a combination of unique measurements during the MOSAiC ship expedition, ozone profile and total column observations by satellite instruments (OMPS-LP, TROPOMI), and ERA5 reanalysis data. Three special cases during the MOSAiC expedition were selected and classified for the analysis. They consist of one 'normal' cyclone, where a low surface pressure coincides with a minimum in tropopause height, and two 'untypical' cyclones, where this is not observed. The influence of cyclone events on ozone in the upper-troposphere lower-stratosphere (UTLS) region was investigated, using the fact that both are correlated with tropopause height changes. The negative correlation between tropopause height from ERA5 and ozone columns was investigated in the Arctic region for the three-month period from June to August 2020. This was done using total ozone columns and subcolumns from TROPOMI, OMPS-LP and MOSAiC ozonesonde data. The greatest influence of tropopause height changes on ozone contour levels occurs at an altitude between 10 and 20 km. Moreover, the lowering of the 250 ppb ozonopause (about 11 km altitude) below 9 km was used to identify and track cyclones using OMPS-LP ozone observations. The potential of this approach was demonstrated in two case studies where the boundaries of cyclones could be determined using ozone observations. The results of this study can help improve our understanding of the relationship between cyclones, tropopause height, and ozone in the Arctic and demonstrate the usability of satellite ozone data for investigating cyclones in the Arctic. [ABSTRACT FROM AUTHOR]

Tropical Cyclones (TCs) cause significant socio-economic damages to the US and Caribbean coastal regions annually, making it important to understand TC risk at the local-to-regional scales. However, the short length of the observed record and the substantial computational expense associated with high-resolution climate models make it difficult to assess TC risk using either approach. To overcome these challenges, we developed a database of synthetic TCs using the Risk Analysis Framework for Tropical Cyclones (RAFT). The database includes 40,000 synthetic TC tracks, along-track intensities and storm-induced precipitation. TC tracks generated in RAFT are in reasonable agreement with the observed spatial distribution of TC tracks and basin-scale TC statistics. Specifically along the coast, spatial variations in TC crossing probability and extreme winds upon landfall are well-reproduced by RAFT with R-squared values of 0.81 and 0.73, respectively. In summary, the synthetic TC database constructed with RAFT provides a reasonable pathway for the robust assessment of North Atlantic TC wind and rainfall risks. [ABSTRACT FROM AUTHOR]

There are now a range of potential data sources for wind and surface wave conditions within tropical cyclones. These sources include: in situ buoy data and remote sensing data from satellite altimeters, scatterometers, and radiometers. In addition, data providing estimates of tropical cyclone tracks and wind field parameters are available from best track archives. The present dataset brings together this information in a single archive, providing the available data for each tropical cyclone from each of the data sources in a single file. The data consists of observations in a total of 2927 global tropical cyclones over the period from 1985 to 2017. Global statistics of the observations are provided, along with data on the geographic distribution of tropical cyclones within the database. [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]

A tropical cyclone dataset is an important data source for tropical cyclone disaster research, and the evaluation of its applicability is a necessary prerequisite. The Global Synthetic Tropical Cyclone Hazard (GSTCH) dataset is a dataset of global tropical cyclone activity for 10,000 years from 2018, and has become accepted as a major data source for the study of global tropical cyclone hazards. On the basis of the authoritative Tropical Cyclone Best Track (TCBT) dataset proposed by the China Meteorological Administration, this study evaluated the applicability of the GSTCH dataset in relation to two regions: the Northwest Pacific and China's coastal provinces. For the Northwest Pacific, the results show no significant differences in the means and standard deviations of landfall wind speed, landfall pressure, and annual occurrence number between the two datasets at the 95% confidence level. They also show the cumulative distributions of central minimum pressure and central maximum wind speed along the track passed the Kolmogorov–Smirnov (K-S) test at the 95% confidence level, thereby verifying that the GSTCH dataset is consistent with the TCBT dataset at sea-area scale. For China's coastal provinces, the results show that the means or standard deviations of tropical cyclone characteristics between the two datasets were not significantly different in provinces other than Guangdong and Hainan, and further analysis revealed that the cumulative distributions of the tropical cyclone characteristics in Guangdong and Hainan provinces passed the K-S test at the 95% confidence level, thereby verifying that the GSTCH dataset is consistent with the TCBT dataset at province scale. The applicability evaluation revealed that no significant differences exist between most of the tropical cyclone characteristics in the TCBT and GSTCH datasets, and that the GSTCH dataset is an available and reliable data source for tropical cyclone hazard studies in China's coastal areas. [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]

Historical tropical cyclone (TC) best track databases are used as the basis to develop the stochastic TC track models, which are essential for TC hazard assessment. However, different databases may report different numbers of TCs or inconsistent parameters for the same TC event. The impact of such inconsistencies on the developed stochastic models and estimated TC wind hazards has not been systematically quantified. In this study, we investigated the impact of using different best track databases on statistical characteristics such as TC occurrence rate, translation velocity, translation direction, and central pressure difference for landfalling TC events along the coastline of mainland China. The comparison of the TC track variables for landfalling TCs showed notable differences in statistics among different databases. We developed stochastic beta-advection TC track models for TC events originating from the western North Pacific basin using five TC best track databases provided by different agencies or organizations. We used the developed TC track models to assess TC wind hazards for sites in the southeast coastal regions of mainland China. We presented a quantitative comparison of the T-year return period values of the annual maximum TC wind speed, vA-T, estimated by using the developed stochastic track models as well as using the historical tracks directly. The results indicated that the estimated vA-T for T = 50 and 500 years for some sites can be sensitive to the TC track database used to develop the stochastic track model. [ABSTRACT FROM AUTHOR]