Search

• 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]

Ensemble-based sensitivity analysis (ESA) has been widely applied to identifying and investigating the sources of forecast uncertainty in tropical cyclone (TC) track. The standard ESA used in most preceding studies involves calculating the time-lagged covariance of ensemble perturbations by removing the ensemble mean. This method primarily focuses on the influence of initial errors. However, such studies ignore two critical dimensions of ESA. One is how ensemble sensitivity is influenced by the varying forecast performance across different ensemble prediction systems (EPSs). Secondly, the impact of model errors on forecast uncertainties remains unaddressed. An in-depth examination of these two aspects provides a more comprehensive understanding of the factors contributing to TC track uncertainties. This study employed the standard ESA to analyze and compare the sources of uncertainty in the track forecast of Typhoon In-fa (2021) in three representative operational EPSs. Our findings reveal that the EPS's specific performance in ensemble spread markedly influence ensemble sensitivity. We identified that variations in the shape and location of key synoptic systems, such as the western Pacific subtropical high and monsoon trough, across ensemble members were notably distinct. These variations played a significant role in shaping the uncertainty for In-fa's track forecast within each system. Furthermore, we introduced a modified ESA to better account for the influence of model errors on TC track uncertainties. The modified ESA, when applied to ensembles with substantial systematic deviations, predominantly reflects the impact of model errors on track forecast inaccuracies, offering a notably different perspective compared to the standard ESA. Ensemble-based sensitivity analysis (ESA) serves as an effective method for identifying the origins of forecast uncertainty in tropical cyclone (TC) tracks. Most preceding studies based on the standard ESA have predominantly concentrated on examining key physical processes within individual ensemble prediction system (EPS) and the impact of initial condition uncertainties. They often overlook the dependence of the ensemble sensitivity on the varying forecast performance of EPSs, as well as the impact of model errors on sensitivity assessment. This study revealed that the ensemble forecasts across different EPSs exhibit different performance in terms of ensemble spread in the shape and location of primary weather systems. This variability significantly contributes to the uncertainty in TC track forecasts through diverse processes. Furthermore, this research introduced a modified ESA, which effectively identifies the impact of model systematic deviations on TC track forecast errors. This approach along with the standard ESA provide a more comprehensive and nuanced analysis of the forecast error sensitivity associated with both initial condition and model related uncertainties. • The ensemble sensitivity of TC tracks in different operational EPSs varies with their ensemble spread. • The standard ESA only elucidates the impact of initial condition errors on TC track uncertainties. • The modified ESA was introduced to diagnose the impact of model errors on the uncertainties in TC track predictions. [ABSTRACT FROM AUTHOR]

Tropical cyclone tracks are one of the most critical factors in tropical cyclone forecasting, but there is an inherent uncertainty in their forecasts, but there are no relevant machine learning methods to carry out uncertainty studies. This study proposes an uncertainty forecasting system using machine learning models within a conformal forecasting framework, aiming to provide reliable forecast regions to improve the ability of decision makers to prepare for and respond to potential hazards. This study jointly examines the path forecasting performance of 10 major machine learning models and 10 major conformal forecasting methods through a comparative study. The research work models forecast timescales of 6, 12 and 24 h, and the study covers hurricanes from 1975 to 2021. The experimental results show that the deterministic forecast performance of the model is comparable to the skill of the operated benchmark model, demonstrating that the machine learning model possesses forecasting skill, while also providing tight uncertainty intervals about the path forecast. The method has high prediction accuracy and reliability and is expected to be widely used in the field of tropical cyclone track forecasting and risk communication in the future. • The first study using machine learning for tropical cyclone track uncertainty forecasting. • "Trajectory forecast ellipse" method proposed to characterise forecast area. • ML-based regional forecasting effective complement to operational methods. [ABSTRACT FROM AUTHOR]

Combination checkpoint blockade with anti-PD-1 and anti-CTLA-4 antibodies has shown promising efficacy in melanoma. However, the underlying mechanism in humans remains unclear. Here, we perform paired single-cell RNA and T cell receptor (TCR) sequencing across time in 36 patients with stage IV melanoma treated with anti-PD-1, anti-CTLA-4, or combination therapy. We develop the algorithm Cyclone to track temporal clonal dynamics and underlying cell states. Checkpoint blockade induces waves of clonal T cell responses that peak at distinct time points. Combination therapy results in greater magnitude of clonal responses at 6 and 9 weeks compared to single-agent therapies, including melanoma-specific CD8+ T cells and exhausted CD8+ T cell (T EX) clones. Focused analyses of T EX identify that anti-CTLA-4 induces robust expansion and proliferation of progenitor T EX , which synergizes with anti-PD-1 to reinvigorate T EX during combination therapy. These next generation immune profiling approaches can guide the selection of drugs, schedule, and dosing for novel combination strategies. [Display omitted] • A novel algorithm Cyclone uncovers patterns of clonal trajectories across time • Immune checkpoint blockade induces distinct waves of clonal responses after each dose • Melanoma-specific CD8+ T cell responses are present at 3 and 6 weeks after treatment • CTLA-4 blockade induces robust progenitor-exhausted CD8+ T cell responses Wang et al. analyze longitudinal blood from melanoma patients on checkpoint blockade using paired single-cell RNA and TCR sequencing and uncover waves of clonal responses that peak at different time points. By studying anti-CTLA-4, anti-PD-1, and in combination, they show that anti-CTLA-4 reinvigorates progenitor exhausted CD8+ T cells while anti-PD-1 drives their differentiation. [ABSTRACT FROM AUTHOR]

In this study, we analysed the influence of prevailing atmospheric oscillations at different scales on tropical cyclones (TC) movement over the Bay of Bengal. Composite analysis of dynamic and thermodynamic variables are investigated with and without TC during pre-monsoon and post-monsoon seasons. The composite features analysed at multi-scales clearly suggests that pre-monsoon TC track predominantly determine by a net interaction of mean climatology environment (CLM) and anomalies associated with sub-seasonal oscillations. Whereas, during post-monsoon CLM is critical in governing the storm track. Similarly, the study also signifies that CLM and sub-seasonal anomalies jointly provide guiding effect for various TC paths. • The study unveils the role of multi-scale atmospheric variability in governing cyclones (TC) track over the Bay of Bengal. • Pre-monsoon TC movement is determined by a net interaction of mean climatology environment (CLM), and sub-seasonal anomalies. • Meanwhile TC movement during post-monsoon relies on the CLM signals. • The unique distribution of flow anomalies induced at sub-seasonal scale led to the differences in the observed TC movement. • The inter-annual signals played quite insignificant role in deciding TC movement. [ABSTRACT FROM AUTHOR]

The perturbations in the ionosphere due to eight tropical cyclones (TCs), namely Iota, Haima, Harold, Willa, Amphan, Gaja, Vadrah and Bulbul, originated and grown in different oceanic basins, are investigated. Total Electron Content (TEC) data, from Global Positioning System (GPS) TEC receiver in operation at Agartala (AGT) or different International GNSS Service (IGS) stations near the cyclone landfall regions, are used in this study. Despite some differences, the ionosphere responds to all tropical cyclones in an almost similar manner. Though the geomagnetic conditions are quiet and there are no perturbations due to any other geophysical phenomena in the active cyclonic storm stage, in all the cases there is a fall in average vertical total electron content (VTEC) deviations below the monthly mean value either on the landfall day or on the following day or even on just previous day. Decrements in Vertical Total Electron Content are found higher for tropical cyclones over North Indian and South Pacific oceanic basins. Recoveries in vertical total electron content values are slower for cyclones over the North Atlantic and North West Pacific basins. Recoveries in vertical total electron content (VTEC) values are slow for tropical cyclones (TCs) over the North Atlantic and North West Pacific basins. But those over other basins are quick. The longer the track of a tropical cyclone (TC), the higher is the reduction in the vertical total electron content (VTEC) value. A negative correlation exists between the maximum sustained surface wind velocities and the total periods of different TCs and also the difference of lowest average differential VTECs with that on the previous day. The observed anomaly in ionospheric responses might be due to the combined effect of TC-inspired gravity waves, ejection of neutral particles from the terminator of a tropical cyclone (TC) and lightning electric fields. To explain the observed results convective activities during TC, with the help of outgoing long wave radiation (OLR) map, are also taken into account. This study provides the primary results regarding regional characteristics and hence a comparative idea for the responses of the ionosphere to different tropical cyclones (TCs) from different geographical positions on the globe, which needs further comprehensive investigation in future. • Fall in average VTEC deviations below the monthly mean occurred during the landfall or the following days of the TCs. • VTEC decrements are higher for TCs over North Indian and South Pacific oceanic basins. • Recoveries in VTEC values are slower for TCs from the North Atlantic and North West Pacific basins. • –Ve correlation exists between maximum sustained surface wind velocity with period of TCs and difference of lowest avg.dVTEC with that on previous day. • The regional characterizations are primary results that need more comprehensive study in the future. [ABSTRACT FROM AUTHOR]

An assessment of the performance of CYGNSS operational wind products is done against a multitude of reference datasets which include in-situ wind speed from Indian National Centre for Ocean Information Services (INCOIS) network of buoys (hereafter, INCOIS-buoys) in the Indian Ocean, SCATSAT-1 operational wind products and cyclone best track data from India Meteorological Department (IMD). The validation study is done for CYGNSS data version 2.1 for the period 17th March 2017 till 31st December 2017. The performance of the operational wind products is examined through a two-tier approach- first one covering low-to-moderate wind regimes (less than or equal to 15 m/s) where in-situ data from INCOIS-buoys and rain-free SCATSAT-1 products are used. While the former gives an idea of CYGNSS winds in the Indian Ocean basin, the latter can be treated as global reference dataset. In the second approach, high wind (greater than15 m/s) performance of CYGNSS winds are evaluated on a case study basis during some of the tropical cyclones (TC) of 2017. The first approach gives good accuracy of minimum variance estimator (MVE) wind speed products in terms of standard deviations of 1.85 m/s (INCOIS-buoys) and 2.07 m/s (SCATSAT-1). MVE is the best product amongst all the five different operational wind products viz. MVE, FDS-NBRCS, FDS-LES, YSLF-NBRCS, YSLF-LES of CYGNSS. Time series analyses of high wind performance during the TC- Mora and Ockhi show better sensitivity of young sea wind products especially YSLF-NBRCS compared to YSLF-LES. However, the fully developed wind product, FDS-NBRCS shows equally good sensitivity of wind speed. [ABSTRACT FROM AUTHOR]

Analyzing the spatial-temporal changes in tropical cyclone (TC) tracks in the east China coastal ocean (ECCO) to quantify the magnitude of poleward and landward migration of TCs is of significant importance for coastal disaster mitigation and planning due to its susceptibility to the impacts of TCs. In this study, the TCs that affected the ECCO from 1949 to 2022 are classified into three typical types of tracks using the k-means clustering method, mass moments, and track interpolation based on TC location, shape, and intensity information. Type 1 is a northwestward track, Type 2 is a northwest to northeast-turning track, and Type 3 is a northwest to northeast-turning offshore track. Type 1 tracks mainly make landfall in southern China, while Type 2 predominantly makes landfall in eastern China. Moreover, the proportion of Type 1 decreases while their landfall percentage increases over time, and the proportion of Type 2 tracks is increasing. The probability of TC effects on the eastern and northern parts of the ECCO is increasing, and the boundary where the TC center reaches after landfall is shifting landward. During the period from 1994 to 2022, there has been a significant migration in TC tracks, with the mean centroid of the TCs affecting the ECCO shifting westward by 0.66° in longitude and northward by 1.26° in latitude, which means the magnitude of the poleward shift is about twice that of the landward shift. This migration appears to have been pre-conditioned by a combined influence of a weakening westward steering flow, reduced vertical wind shear, and warmer sea surface temperature Our findings provide valuable insights into the longitudinal and latitudinal migration of TC tracks and have important implications for disaster prevention, mitigation planning, and the adjustment of crucial coastal protection zones in the ECCO and similar regions around the globe. • The TCs that affected the ECCO from 1949 to 2022 are classified into three typical types. • The poleward and landward migration of the TCs were significantly greater than the earlier period and poleward twice the landward. • The probability of TC effects on the eastern and northern parts of the ECCO is increasing, and the distance the TC center penetrates after landfall is shifting landward. [ABSTRACT FROM AUTHOR]

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

This work introduces CyTRACK (Cyclone TRACKing framework), a new open-source, comprehensive and user-friendly Python toolbox for detecting and tracking cyclones in model and reanalysis datasets. The kernel of CyTRACK is based on detecting critical cyclone centres in the mean sea level pressure field at a single time slice, which are then filtered following several threshold parameters. This paper also compares ten years of CyTRACK outputs forced with the ERA5 reanalysis against best-track archives and available cyclones track datasets. The results reveal that CyTRACK can capture the inter-annual (year to year) and intra annual (seasonal cycle) variability of cyclone frequency, life cycle characteristics and spatial distribution of track densities. Largest differences were observed in the annual and seasonal frequency. In summary, CyTRACK provides a user-friendly framework for sensitivity analysis of several free parameters used to perform the tracking, and it is useful for case or climatological studies of cyclone features. • CyTRACK is an open-source and user-friendly toolbox for tracking cyclones. • It can capture cyclone frequency, lifetime and spatial distribution. • It is useful for case or climatological studies of cyclone features. [ABSTRACT FROM AUTHOR]

[Display omitted] • Three new cyclone geometries are presented and numerically investigated namely: 1D2Dn, 1D3Dn and 2D2Dn. • RANS equation with the RSM turbulence model using Eulerian- Lagrangian two phase methods is solved. • To calculate the cyclone collection efficiency the Rosin-Rammler method is used. • The effect of reflection factor on the cyclone performance is investigated. In this paper, numerical simulation of flow field in three types of standard new design cyclone separators namely 1D2Dn, 1D3Dn and 2D2Dn are investigated. In these standard cyclones, the length of cylindrical top part of the body is equal to 1, 1 and 2 times of the body diameter, respectively; and the length of the cylindrical bottom part is 2, 3 and 2 times of the body diameter. The new design cyclone is based on the idea of improving cyclone collection efficiency and pressure drop by increasing the vortex length. The Eulerian-Lagrangian computational procedure is used to predict particles tracking in the cyclones. The velocity fluctuations are simulated using the Discrete Random Walk (DRW). Results show that among the three standard new design cyclones, cyclone 2D2Dn has the highest efficiency followed by 1D3Dn one with about only 2% lower efficiency. Cyclone 1D2Dn possesses the lowest efficiency among all. Similarly, the highest pressure drop occurs in cyclone 2D2Dn. Cyclones 1D3Dn and 1D2Dn followed 2D2Dn one with a marked difference of about 20%. In result section, the details of the flow field including velocity, pressure contours, turbulence, velocity vectors and particle trajectory will be presented. [ABSTRACT FROM AUTHOR]

[Display omitted] • In numerical simulations, five different wall temperature profiles are used. • The results of new design and conventional cyclones are compared. • RANS and energy equations with the RSM turbulence model using Eulerian- Lagrangian are solved. • To calculate the cyclone collection efficiency, the Rosin-Rammler method is used. In this paper, numerical study of flow field in the new design cyclones with five different wall temperature profiles are investigated. The new design cyclone is based on the idea of improving cyclone collection efficiency and pressure drop by increasing the vortex length. In this paper, the five wall temperature profiles are as follows: (A) cooling with uniform distribution, (B) without temperature change, (C) heating with uniform distribution, (D) incremental linear heating, (E) reduction linear heating. Results are compared in new design and conventional cyclones. The Reynolds averaged Navier–Stokes equations with Reynolds stress turbulence model (RSM) are solved. The Eulerian-Lagrangian computational procedure is used to predict particles tracking in the cyclones. The velocity fluctuations are simulated using the Discrete Random Walk (DRW). Results show that generally, heating the bottom zone of the cyclones can improve the collection efficiency and reduce the pressure drop while heating the top zone of the cyclones marginally affects the flow field. Moreover, cooling the cyclones reduces the efficiency and causes a higher pressure drop. Among five different wall temperature profiles, C and E profiles can increase the efficiency about 8% and profile C reduces the pressure drop by about 9%. The mentioned values in different conditions including particle diameter, flow rate, etc. can be different. [ABSTRACT FROM AUTHOR]

Eddies are known to play a crucial role in the sudden intensification of tropical cyclones. In this study, Sea Level Anomaly (SLA) data from satellite altimetry is utilized to investigate the role of eddies on tropical cyclones intensification in the North Indian Ocean (NIO) basin. SLA data obtained from Archiving Validation and Interpretation of Satellite Data in Oceanography (AVISO), Tropical Cyclone Heat Potential (TCHP) from National Remote Sensing Centre (NRSC) and cyclone intensity data from Indian Meteorological Department (IMD) have been utilized to analyse and understand the impact of TCHP and eddies on 60 tropical cyclones in the NIO spanning the years 2001–2018. Out of these 60 cyclones, 38 were formed in the Bay of Bengal (BoB) and 22 in the Arabian Sea (AS). From the analysis, it is found that nearly 40% cyclones are affected by Warm Core Eddy (WCE) generally intensifying them, 30% are influenced by Cold Core Eddy (CCE) that generally reduce cyclone intensities and the rest 30% are affected by TCHP variations (in the absence of eddies) in the NIO region during the study period. Cyclones which do not follow the established trend of intensifying due to WCEs or increasing TCHP; and diminishing owing to CCEs or TCHP decrease were found to be governed primarily by the Translational Speed of the cyclones. WCE are mostly present during the pre-monsoon season in the NIO (both BoB and AS). This analysis confirms that consideration of the underlying eddies in the cyclone track represented by SLA, TCHP variations, Translational Speed of cyclones and the ocean barrier layer are key factors that must be considered in forecasting cyclone intensity and track. [ABSTRACT FROM AUTHOR]

Tracking of atmospheric phenomena, such as Tropical Cyclones (TC) and Polar Mesocyclones (PMC), — is a frequently performed task in the climate sciences. It is a crucial part of any climatic or short-period study since the occurrence and characteristics of a phenomenon at some moment carries significantly less information compared to its evolution. In some cases of synoptic-scale phenomena, this problem has established well-developed solutions (e.g., for extratropical cyclones, ETC). However, in the majority of other cases, there are no reliable tracking algorithms at the moment. In this study, we present the generic framework for the tracking of atmospheric phenomena of an arbitrary kind in the source data of nearly arbitrary origin. The core entity of our approach is the function of dissimilarity of representations of a phenomenon in two subsequent data frames which is learned under supervision of an artificial neural network. As an implementation of the proposed approach, we present the results of the tracking of Tropical Cyclones and Polar Mesocyclones. We demonstrate the success of our approach in terms of formal metric MOTA (Multiple Object Tracking Accuracy) and the empirical distributions of key lifecycle characteristics of TC and PMC. [ABSTRACT FROM AUTHOR]

The lack of observational data in the northern part of the Bay of Bengal (BoB) makes it challenging to investigate the upper oceanic responses to tropical cyclones. To overcome this challenge, a high-resolution Regional Ocean Model (ROMS) is set up with horizontal resolution of 0.03° × 0.03° and 50 vertical layers for the northern BoB, with the daily output of the First Institute of Oceanography surface wave-tide-circulation coupled ocean model (FIO-COM) as the boundary condition, and hourly ERA5 data as atmospheric forcings. This regional model is systematically validated and then utilized to reconstruct the upper ocean response due to the super cyclonic storm Amphan over the BoB from May 16 to 20, 2020. The upper ocean responses to Amphan in the northern BoB is well reconstructed with this regional model. On the right side of the cyclone track, sea surface temperature (SST) cooling (4 °C) and increased sea surface salinity (0.5 psu) are well reproduced. The primary oceanic triggering forces intensified the cyclone through the extraordinarily high SST (>31 °C) and deep isothermal layer depth. Tropical cyclone heat potential was high (over 100Kj cm−2) during the early stages of the cyclone, which aided the transformation of a depression into a super cyclonic storm. Vertical entrainment and horizontal advection had a major influence in the pronounced cooling within the mixed layer. • Upper ocean responses to the super cyclonic storm Amphan were simulated • ROMS model was systematically validated with available datasets • ROMS model successfully simulated the SST cooling and increased sea surface salinity on the right side of the cyclone track • Horizontal advection and vertical entrainment dominantly reduced the mixed layer temperature. [ABSTRACT FROM AUTHOR]

A tropical cyclone (TC) full-track model based on geographically weighted regression (GWR) is developed to probabilistically generate TCs from genesis to lysis over the western North Pacific basin for TC hazard assessment. The full-track model includes genesis, movement, and intensity components. Probabilistic models based on auto-regression and copula-based joint distribution functions are established to propagate TCs and simulate intensity evolution. Unlike previous studies where the model parameters of TC full-track models were estimated for non-overlapping grid subregions over an ocean basin, the current full-track model treats the model parameter values as spatially continuous over the whole ocean basin and guarantees the balance between model complexity and goodness-of-fit by conducting cross-validation. To compare the performances of the proposed GWR-based full-track model and its grid-based counterpart, 1000-year synthetic TCs are simulated using the two full-track models individually, from which the statistics of TC key parameters along the Chinese mainland coastline are estimated including annual occurrence rate, central pressure difference, translation speed and heading. The results from the GWR-based model generally match the results estimated from historical TC track data, while the accuracy of the grid-based model is rather poor. Finally, the GWR-based full-track model is combined with a widely used parametric TC wind field model to assess TC wind hazard in the coastal regions of the Chinese mainland. • A full-track tropical cyclone model is developed for wind hazard assessment in China. • Geographically weighted regression-based method is developed to estimate spatially variant model parameters. • Cross-validation is incorporated into parameter estimation to balance model complexity and goodness-of-fit. • The proposed full-track model is compared with a widely used full-track model and historical cyclone track data. [ABSTRACT FROM AUTHOR]

A novel tropical cyclone surge potential index (TCSPI) for estimating tropical cyclone (TC) induced potential peak surge levels near the landfall locations for the entire coast of the Bay of Bengal (BoB) is presented. The proposed TCSPI incorporates parameters that are often readily available in real-time for BoB: maximum sustained wind speed (V max), the radius of given wind (R), translation speed (S), approach angle (θ), coastal geometry (a, b), and bathymetry information (L 30). The inclusion of approach angle and associated coastal geometry information led to improvements of the TCSPI to other existing surge indices. A retrospective analysis of TCSPI using the Indian Meteorological Department (IMD) and Joint Typhoon Warning Center (JTWC) tropical cyclone (TC) best track data from 1990 to 2021 for BoB suggests that the index captures historical events reasonably well. In particular, it explains up to ∼90% of observed surge variance and up to ∼92% of those peak surge values obtained from physics-based numerical simulation using the Advanced Circulation (ADCIRC) model. Further, we utilized renowned regression-based supervised machine learning (ML) models on small data samples. The Linear Regression (LR), Regression Tree (TR), Support Vector Machine (SVM), Ensembles of Trees (EnTR), and Gaussian Process Regression (GPR) models are incorporated that can rapidly predict peak surge height across an extensive coastal region of BoB, using the same input parameter information (i.e., V max , R, S, L 30 , and θ) that have been considered in the TCSPI at the landfall time frame. Finally, the observed peak surge values associated with historical TC events in the BoB are compared with those estimated by TCSPI, ADCIRC, and ML models. The errors associated with TCSPI are comparatively less. The consideration of approach angle information in the TCSPI has improved the accuracy of the overall prediction of peak surge height associated with historical TCs by up to 17% and 19% for observed and ADCIRC-simulated peak surges, respectively. The development of such a prediction methodology can reduce the numerical computation requirements to improve the surge hazard maps, and hence, proper implementation based on surge risk is possible. • tA novel Tropical Cyclone Potential Surge Index (TCSPI) for the prediction of peak surge height in the Bay of Bengal (BoB). • The incorporation of approach angle and coastal geometry information improved TCSPI in comparison to existing surge indices. • A rapid prediction of peak surge height using regression-based supervised machine learning (ML) models along the BoB coast. • Observed Peak surge values are compared with TCSPI, ADCIRC, and ML model estimates for historical TC events in the BoB. [ABSTRACT FROM AUTHOR]

To meet the demand for unmanned observation of tropical cyclones and to improve the tracking efficiency of multiple autonomous underwater vehicles (Multi-AUV), a forecast path correction model is proposed in this paper. Four types of points, which are mainly treated as model computational objects, are defined for abstracting the specific tropical cyclone (TC) tracking process. Necessary methodologies and a simplified model are harnessed and applied to Multi-AUV, facilitating the determination of points essential for solving the computations of the model. A correction algorithm is designed, serving as a guide for the tracking trajectory of Multi-AUV while concurrently ameliorating the errors inherent in conventional TC forecast data generation. The algorithm is proved to be effective. Finally, a simulation process is constructed to verify the effectiveness of the model, and the results conform well to the theory. This model furnishes a comprehensive blueprint for the realization of unmanned real-time observation of TCs. • Proposing a forecast path correction model for autonomous underwater vehicles. • Improving the accuracy of tracking the tropical cyclones. • Using current data and multilateration to determine the optimal center of tropical cyclones. • Reducing the position errors of traditional forecasting method. [ABSTRACT FROM AUTHOR]

This paper aims to improve the prediction accuracy of Tropical Cyclone Tracks (TCTs) over the South China Sea (SCS) with 24 h lead time. The model proposed in this paper is a regularized extreme learning machine (ELM) ensemble using bagging. The method which turns the original problem into quadratic programming (QP) problem is proposed in this paper to solve lasso and elastic net problem in ELM. The forecast error of TCTs data set is the distance between real position and forecast position. Compared with the stepwise regression method widely used in TCTs, 8.26 km accuracy improvement is obtained by our model based on the dataset with 70/1680 testing/training records. By contrast, the improvement using this model is 16.49 km based on a smaller dataset with 30/720 testing/training records. Results show that the regularized ELM bagging has a general better generalization capacity on TCTs data set. [ABSTRACT FROM AUTHOR]

The D-region ionospheric disturbances due to the tropical cyclone Fani over the Indian Ocean have been analysed using Very Low Frequency (VLF) radio communication signals from three transmitters (VTX, NWC and JJI) received at two low latitude stations (Kolkata-CUB and Cooch Behar-CHB). The cyclone Fani formed from a depression on 26th April, 2019 over the Bay of Bengal (Northeastern part of the Indian Ocean) and turned into an extremely severe cyclone with maximum 1-min sustained winds of 250 km/h on 2 May, 2019 which made landfall on 3 May, 2019. Out of six propagation paths, five propagation paths, except the JJI-CHB which was far away from the cyclone track, showed strong perturbations beyond 3 σ level compared to unperturbed signals. Consistent good correlations of VLF signal perturbations with the wind speed and cyclone pressure have been seen for both the receiving stations. Computations of radio signal perturbations at CUB and CHB using the Long Wave Propagation Capability (LWPC) code revealed a Gaussian perturbation in the D-region ionosphere. Analysis of atmospheric temperature at different layers from the NASA's TIMED satellite revealed a cooling effect near the tropopause and warming effects near the stratopause and upper mesosphere regions on 3 May, 2019. This study shows that the cyclone Fani perturbed the whole atmosphere, from troposphere to ionosphere and the VLF waves responded to the disturbances in the conductivity profiles of the lower ionosphere. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) is a highly destructive natural disaster, whose impact is closely correlated to its track and intensity. Thus, it is crucial to obtain the information on TC track and intensity both timely and accurately for early warning and mitigation of related disasters. While there are numerous prediction methods, deep learning (DL) technology has demonstrated exceptional potential in recent years. However, studies on TC prediction via DL techniques are still insufficient, and in particular there is a lack of efforts toward the dependence of prediction results upon some key parameters involved in the prediction strategy. To this end, this paper present a comprehensive study on TC prediction via four mainstream DL techniques, i.e., Long Short-Term Memory (LSTM) network, Convolutional LSTM (CovnLSTM) network, Temporal Convolutional Network (TCN), and the recently developed Transformer model. The performance of different DL techniques is examined, with a highlight on the effects of three setting parameters involved in the prediction process on the model performance, i.e., length of input records N in , interval between input records Δ T , and lead time of the prediction N out. Results reveal that TCN performs best from the perspectives of prediction accuracy, running efficiency and stability, followed by ConvLSTM; whilst LSTM and Transformer fail to provide competitive predictions, although Transformer showcases notable training efficiency. In reference to the prediction strategy, increasing Δ T and N out can consistently enlarge prediction errors across all models, while the model performance can be optimized by setting the value of N in in a range of 3–6. It is interesting to find that the overall performance of all models can be improved noticeably by using records with a finer Δ T in the scenarios with the same lead time but with varied N out. Meanwhile, TCN can be further improved by extending N out from N out = m to N out = n (n > m) and using the m -th step prediction in the scenario with N out = n as the output in the scenario with N out = m. The presented results provide useful insights for reasonably utilizing DL models to deal with prediction issues, which are applicable not only for TCs but also for other objectives that are widely concerned in wind engineering, e.g., wind speed/pressure and structural responses. • Explored 4 mainstream deep learning models for short-term forecasting of typhoon track and intensity. • Examined the dependence of model performance on 3 key parameters involved in prediction strategy. • TCN outperforms others from various perspectives, while predictions via Transformer and LSTM suffer from larger errors. • Model performance can be improved by using appropriate strategy even based on the same database. [ABSTRACT FROM AUTHOR]

Extreme floods are anticipated to become more frequent in a future warmer climate. However, the long-term alterations in flood patterns across different regions of Europe remain unclear. In this study, we present a 15,000-year record of extreme floods in the Duero River, located in the southwestern Atlantic region. We analysed slackwater flood sediments, quantified the discharge and timing of individual flood beds over millennial time scales, and identified their potential climate influences. The composite record includes at least 62 floods grouped into ten flood-rich periods (with an average duration of 230 years). A high-frequency phase of moderate-magnitude floods (>10 events) occurred at ∼11.6–11.5 ka, following a period of reduced flood activity during the Younger Dryas. Similar clusters of Early Holocene floods (10.8–10.3 ka, 9.5 ka) coincided with or preceded meltwater pulses from the North Atlantic. The absence of palaeoflood records with discharges exceeding 6100 m3/s during the Mid-Holocene suggests a decline in winter hydro-meteorological extremes. High flood magnitudes were recorded during transition periods toward cooler and wetter conditions at 4.4, 2.3, 0.5, and 0.11 ka with discharges ranging from 7600 to 10,000 m3/s. These periods were interpreted as indicative of a southward shift in cyclone tracks in Europe driven by negative phases of the North Atlantic Oscillation (NAO). Conversely, flood magnitudes decreased during past warmer climate conditions (1.7 ka, 0.9 ka, and the present), although flood frequency remained high. The current decline in flood frequency reflects an increase in flood regulation due to dams, but it is also consistent with the prevailing positive trend in the NAO observed over the last 40 years. • 15-ka record of extreme floods in one of the largest river catchments in S Europe. • Early Holocene flood-rich periods overlap frequencies of meltwater pulses. • Floods decrease during Mid-Holocene indicating a positive mode of NAO-like circulation. • Late Holocene flood clusters are associated with negative NAO and strong Siberian High. • Flood magnitude declined during past warm periods including the present time. [ABSTRACT FROM AUTHOR]

• Persistent tropical cyclones have deployed impact on environment and human life. • Learning approaches have been utilized and spatial features from large datasets. • Meteorological factors and the cyclone tracks a single-dimensional convolution. • Machine learning techniques is utilized to solve diverse needs and various. • Finally, the comparative analysis is carried out the proposed technique and various. Tropical cyclones have a devastating impact on both human life and the natural environment, and accurate prediction of their tracks is critical for minimizing their negative effects. Traditional approaches to track tropical cyclones have several limitations, including low prediction accuracy and efficiency. With the accumulation of vast amounts of investigating and meteorological data, deep learning techniques have become increasingly popular for addressing the temporal and spatial features of tropical cyclones. This article proposes a three-step approach for predicting the tracks of tropical cyclones. The first step involves preprocessing various meteorological factors and cyclone tracks. The second step involves feature selection to minimize input variables and eliminate redundant data, enhancing efficiency. In the prediction process, the chosen meteorological factors and cyclone tracks are fed into a single-dimensional convolution neural network autoencoder, along with the independent recurrent neural network-based Student Psychology Optimization Algorithm and Harris Hawk optimization algorithm. The proposed method, called single-dimensional CAE-IRNN based SPH2, is designed to predict tropical cyclones accurately and efficiently. The technique is then compared with other existing methods to determine its efficiency. The comparison performance is implemented to evaluate the effectiveness of the proposed approach. Overall, this article highlights the importance of deep learning approaches for predicting tropical cyclones' tracks accurately and efficiently. The proposed three-step approach and the single-dimensional CAE-IRNN based SPH2 method contribute to enhancing the accuracy and efficiency of tropical cyclone prediction, which can help reduce their negative impact on human life and the natural environment. [ABSTRACT FROM AUTHOR]

Abstract Bangladesh coast is recognized as one of the hotspots of several coastal hazards like coastal flood, sea level rise, increased tropical cyclone occurrence etc. To understand the problem followed by necessary coastal management recommendation, an integrated coastal vulnerability index i.e., Composite Vulnerability Index (CVI) was developed for the coastal region applying geospatial technique. Three sub-indices, namely, Coastal Characteristics Vulnerability sub-index (CCVI), Coastal Forcing Vulnerability Sub-index (CFVI) and Socio-economic vulnerability sub-index (SEVI) were separately estimated and combined to determine the CVI. Since, the classical set theory of traditional vulnerability classification is inadequate to express the natural variability of vulnerability at continuous scale, this study offered a framework of vulnerability representation using fuzzy set theory. The approach was effective to handle the uncertainty of large scale vulnerability analysis with the normalization of featured factors applying three fuzzy membership functions i.e., Fuzzy-Large, Fuzzy-Small and Fuzzy-Linear in geospatial analysis. The analysis was carried out at 10 m cell level and classified with five vulnerability classes i.e., very high, high, moderate, low and very low. The analyses show that the Chittagong coast and Feni district are very highly vulnerable. High tide amplitude (>4 m), cyclone track density and mild coastal slope are responsible for the high vulnerability of the Chittagong coast. On the other hand, Feni district shows the highest SEVI which is resulted from high population density and socio-economic conditions. Presence of mangrove forest results a reduced CFVI which significantly decrease the vulnerability of Pirojpur, Satkhira, Khulna and Bagerhat district. Based on the responsible vulnerability index, necessary coastal protection measures are recommended for the coast. It is expected that the comprehensive vulnerability map with CVI can be a key element for coastal management of Bangladesh. Furthermore, the classification of vulnerability under different class will help to develop necessary coastal protection measures according to the resiliency of the coastal community. Highlights • Fuzzy logic based geospatial normalization technique was applied for coastal vulnerability analysis. • Factors related to Coastal forcing to assess the vulnerability were considered for a long period of 20–40 years data. • Socio-economic vulnerability was considered in overall vulnerability assessment. • Coastal vulnerability outcomes are obtained at finer resolution of 10 m. [ABSTRACT FROM AUTHOR]

Abstract Proxy-based reconstructions of the Asian Summer Monsoon exhibit variability over multiple scales, from inter-annual to centennial. The relationship between East Asia Summer Monsoon (EASM) variability and the El Niño-Southern Oscillation (ENSO) over the last millennium remains unclear and a focus of current research. This study investigates this relationship using observations, reanalysis data, proxy-based reconstructions, and a control simulation using the Community Earth System Model (CESM). The results derived from both the observation/reanalysis/proxy-based reconstruction and the simulation show consistently that the relationship between EASM strength and Niño3.4 SST is divergent. The observation/reanalysis data illustrate strong negative correlations between Niño3.4 SST and EASM strength at an annual scale in the 1920s-1930s and 1980s-1990s, with weak correlations during other periods. The proxy-based reconstruction demonstrates strong negative correlations at inter-decadal scales from the 1360s to the 1530s, with either weak correlations or positive correlations during other periods. Moreover, the simulations reveal positive correlations in some periods and negative or weak correlations in other periods. The simulations demonstrate that the divergent relationship may be derived from multi-decadal variations in Pacific SST caused by internal variability in the climate system, likely due to the Inter-decadal Pacific Oscillation (IPO). The EASM variation is usually in (out of) phase with Niño3.4 SST during the positive (negative) phase of the IPO. In the positive phase of the IPO, there is a tripolar pattern expressed in a cyclone-anticyclone-cyclone arrangement at pressure level of 850 hPa from the tropics to mid-latitudes over the northwest Pacific. Warming of the Niño3.4 strengthens the tropical cyclone by weakening the trade winds and subsequently strengthening the subtropical anticyclone, leading to a strong Meiyu front, which suggests a strong EASM. However, in the negative phase of the IPO, a huge cyclone occupies the northwest Pacific. Warming of the Niño3.4 strengthens the cyclone by weakening the trade winds, which leads to a weak Meiyu front, which suggests a weak EASM. These results may be helpful in further understanding the mechanisms through which the ENSO and IPO impact the atmospheric circulations in the Asian Pacific, especially regarding the long-term variations in EASM at inter-annual to inter-decadal scales. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are an important water source in many regions around the world, replenishing local dams, waterways and groundwater systems. Three diverse precipitation datasets were tested for dissimilarities in their rainfall characteristics via a new, freely available rainfall tracking toolbox for MATLAB and GNU Octave users: 1) the ERA5 global reanalysis, 2) the Global Historical Climatology Network (GHCN)-Daily station dataset and 3) the regional SILO (Scientific Information for Land Owners) database. Although SILO only covers Australia, its relatively high resolution (0.05°) provides advantages for studies in that region. To test the differences in precipitation datasets, six episodes (eight individual TC events) in all major basins affected by TCs have been selected. These include two instances in which consecutive TCs severely impacted the same region (TCs Idai and Kenneth in south-eastern Africa during March/April 2019 – and hurricanes Eta and Iota in Central America in November 2020). Precipitation for TC episodes was explored through event totals and the proportional contribution to water years within each dataset. Each precipitation dataset demonstrated its inherent advantages and drawbacks, highlighting the benefits of using more than one source to thoroughly evaluate an individual event. These attributes – coupled with the associated impacts of cyclonic events – reinforce the importance of developing tools that can aid in managing TC-related rainfall and flooding potential. [Display omitted] • rainfall_tracker : free toolbox for MATLAB and GNU Octave to extract rain properties. • Six tropical cyclone events from all major basins were assessed for inconsistencies. • Up to 63% of annual rainfall accumulation was attributed to one tropical cyclone. • The ERA5, GHCN-Daily and SILO precipitation datasets were statistically different. • Uncertainty in applicable precipitation period of station data may misalign tracks. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) can develop as a result of the tropical transition (TT) process, which occurs when an extratropical cyclone (EC) begins to exhibit tropical characteristics, forming a TC. In this study, four TT processes that lead to a hurricane structure [Delta (2005), Ophelia (2017), Leslie (2018), and Theta (2020)] are evaluated using two high-resolution numerical models (WRF and HARMONIE-AROME). Both tracks and intensities of the cyclones are assessed by comparing the simulated minimum sea level pressure and maximum wind speed to an observational dataset. Moreover, a spatial verification is performed by comparing the MSG-SEVIRI brightness temperature (BT) and accumulated precipitation (IMERG) to the corresponding simulations accomplished by both models. Analyzing the track results, the WRF model, on average, outstands HARMONIE-AROME. However, it is the HARMONIE-AROME model that performs better than WRF when reproducing the intensity of these cyclones. Concerning the BT spatial validation, HARMONIE-AROME slightly outperformed WRF when reproducing the cyclone's structure but failed when simulating the BT amplitude. Besides, both models achieved a nearly perfect cyclone location. In terms of accumulated precipitation results, the HARMONIE-AROME model overestimates the larger structures while underestimating the smaller ones, whereas the WRF model underestimates the bigger structures, being poorly located by both models. Although it is difficult to establish which numerical model performs better, the overall results show an outstanding of the HARMONIE-AROME model over the WRF model when simulating TT processes. • Performance of HARMONIE-AROME and WRF-ARW in four Tropical Transitions. • Track and intensity cyclone and spatial verification are evaluated. • HURDAT, MSG-SEVIRI and GPM-IMERG are used as observations. • Object-based method used to evaluate Brightness Temperature and accumulated precipitation. [ABSTRACT FROM AUTHOR]

The epiphenomena of tropical cyclones (TCs) such as landslides, storm surges, and floods cause the largest loss of life and property in the Philippines. In order to improve the disaster risk management efforts of the country, it is necessary to evaluate the return periods (RPs) or chance of occurrence (CoC) of TCs. Hence, this study generally aimed to investigate the relationship of the RPs/CoC, sea surface temperature (SST) anomaly associated with the Interdecadal Pacific Oscillation (IPO), and the cost of socio-economic damages and the number of deaths caused by TCs. The Weibull parametric models and the Pareto principle were utilised to achieve this overarching objective. Using the maximum sustained wind speed ( v ), forty-five (45) years of TC data (1970–2014) within the Philippines Area of Responsibility (PAR) were analysed by applying the stationary and non-stationary stochastic modelling techniques. The stationary Weibull probability density function revealed that TCs Rita (1978), Dot (1985) and Haiyan (2013) occupy the wind speed region of 61 ≥  v  ≤ 64 m/s with a probability of 0.4% for any given year. On the other hand, the analysis of the cumulative distribution function revealed a 60% probability of TCs for the cumulative years with a maximum sustained wind speed of at most 38 m/s. This indicates the central estimate of the wind speed from 1970 to 2014 with TCs Ruby (1988) and Vicki (1998) as the observed cases. Furthermore, the probability values on the annual CoC maps depict the indicative positions of TCs, either singly, co-shared or cross-shared, that made landfall (or not) in the Philippines. Results from the non-stationary stochastic modelling revealed that the low probability values on the decadal CoC maps indicate the locations where extreme TC events are likely to occur within PAR; hence, showing the areas in the country that are more at-risk. The relationship of SST anomaly and CoC values disclosed that the TCs are intensified in the northern Philippines and south of West Philippines Sea during the positive(+) phase and the negative(-) phase of the IPO, respectively. Finally, the Pareto analysis revealed that 80% of the TC-related damage cost and the number of deaths are shared by three (3) different stationary and non-stationary RPs with TCs Ike (1984), Nina (1987), Fengshen (2008), Mike (1990), Parma (2009), and Haiyan (2013) as the observed extreme events. In the absence of accurate or updated cyclone risk models, the communities that are highly vulnerable to TCs can use the stationary and non-stationary stochastic CoC models as an early warning tool for disaster preparedeness. Ultimately, the results of this study can provide significant insights to support the Philippines in their pursuit of improving cyclone resilience programs. [ABSTRACT FROM AUTHOR]

The tropical cyclones (TCs) trajectories are mainly controlled by the large-scale parameters related with the steering flow. We used the HURDAT2 database from 1980 to 2018 for grouping the tracks of TCs formed in the North Atlantic (NATL) main development region into straight moving (SM), recurving landfall (RCL) and recurving ocean (RCO). Based on this classification, we investigated the changes in the moisture sources' contributions to the precipitation along the TCs trajectories for each track category by applying a Lagrangian moisture source diagnostic method to the air parcels pathways from the FLEXPART model. The highest moisture contribution occurred within 3-5° from the average TCs trajectories. The moisture supplied from the Gulf of Mexico represented 19.1% of humidity gained by SM but was negligible for recurving TCs. Likewise, the Caribbean Sea contributed 31.8% for SM, and its humidity support notably decreased for recurving storms to 0.9–8.3%. In addition, the moisture uptake from the tropical NATL was similar for all track types. The western NATL increased the moisture supply from 15.3% for SM to 31.1% for RCO and 42.6% for RCL, while the eastern subtropical NATL provided 3.1% of moisture to SM, 12.5% to RCL and 45.4% to RCO. It was also notable the moisture support from the terrestrial source southeastern United States (5.3%) for RCL tracks. Furthermore, we found that El Niño–Southern Oscillation, the North Atlantic Oscillation, the Atlantic Multidecadal Oscillation and the Atlantic Meridional Mode influence the moisture contributions variability from sources for each track type. • Tropical cyclones gained the highest amount of moisture from sources within 3–5° from their pathways. • The moisture source contributions varied accordingly to tropical cyclones trajectories. • Climate modes modulated the moisture uptake for the precipitation of tropical cyclones accordingly to their tracks. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) is one of the most destructive natural disasters, and hence it is very crucial to predict attributes of TC accurately. In this paper, a transformer-based method for short-term prediction of the track and intensity of TCs, including the central latitude, the central longitude, the minimum sea level pressure and the maximum sustained wind speed, in the Northwest Pacific Basin is proposed. The data set used in this study ranging from 1980 to 2021 was released by China Meteorological Administration (CMA). A data preprocessing step including feature augmentation is adopted, which can make full use of the historical information of TCs. The transformer networks can model the complex dynamic relationships between the time series data and dig the relationship between different attributes based on the multi-head self-attention mechanism. The transformer-based method proposed in this study exhibits excellent performance in the prediction task. Meanwhile, compared to the traditional recurrent neural network models including gated recurrent units model and long short-term memory model, the transformer model has a better performance in predicting all attributes. This method provides a practical guidance of using advanced AI-based approaches to predict TC attributes. • A transformer-based model for TC track and intensity forecasting is proposed. • The long-distance and short-distance dependencies of input sequence can be captured based on the multi-head self-attention mechanism. • A new preprocessing method is proposed and applied in input sequence for the use of historic data effectively. • The performance of the proposed model is better than those of GRU and LSTM-based models. [ABSTRACT FROM AUTHOR]

Tropical cyclone's activities can affect the ozone pollution characteristics to a certain extent over the Pearl River Delta (PRD) region based on different track types of tropical cyclones. In this study, we utilize the Finite Mixture Model algorithm to classify tropical cyclones which land on the South China coastal area between 2009 and 2018 into 6 track's types, and those types are land on the PRD region directly (A1 type), the east of the PRD region (B1 and B2 types) and the west of the PRD region (C1, C2 and C3 types) respectively. Combined with observation data, the results indicate that the A1 type, B1 type and B2 type tropical cyclones can increase the ozone concentration about 44.9% ∼ 65.3% in the PRD region before landfall accompanied with stagnant meteorological conditions usually, such as high temperature (>27 °C), low humidity (<80%) or weak wind (<3 m/s). And it should be emphasized that, due to the northward or northeastward deflection of the tracks, the ozone concentration in the PRD region does not decrease obviously (<10%) on the landing-day under B1 and B2 types, and ozone pollution maybe persistent longer than other types. By simulating typical cases of each track's type within WRF-Chem model coupled with process analysis method, and the results indicate that tropical cyclone's peripheral subsidence airflow is more obvious over the PRD region under A1, B1 and B2 cases before landfall, and it maybe an external condition which can limit the development of boundary layer and vertical diffusion of ozone, rather than transporting ozone downward from the upper atmosphere to the near surface level. In addition, based on process analysis result, the chemical reaction with strong radiation condition maybe the essential cause of ozone production and pollution, and the physical process can cause the ozone consumption. • Quantifying the influence of tropical cyclones tracks on ozone pollution. • northward deflection could lead to persistent pollution. • Strong radiation before landing is the essential cause of ozone production. [ABSTRACT FROM AUTHOR]

The recent severe cyclonic storm (SCS) Shaheen was a rare and unique Tropical Cyclone (TC) formed on September 30, 2021, on the Saurashtra coast off Gujrat, from a depression remnant of another cyclone Gulab off the Bay of Bengal. Under the influence of warm waters, this unique storm travelled westwards, skirting Pakistan's Makran coast before making landfall on northern Oman on October 4, 2021. The present study attempts to understand cyclone Shaheen's track, intensity, and synoptic features with the GPU-based WRF-ARW (v-3.8.1) on the google cloud platform (GCP). A 9 km resolution model domain covering the cyclone's track is initiated with an 18 UTC cycle of the Global Forecasting System (GFS) data. The model skilfully captured Shaheen's track and intensity up to 24 h of lead time which was in close agreement with the India Meteorological Department (IMD) estimates. The simulated track showed an overall track error of approximately 73 km compared to IMD. However, the surface wind stress was in close agreement with IMD. The spatial-temporal analysis of the simulated maximum surface wind speed showed the formation of the distinct eye of the cyclone moving away from the Indian coast towards the Gulf of Oman. Results indicated that Shaheen had a strong low-level (850 hPa) relative vorticity of approximately 193.56 10-5s-1 on 2nd October during the matured severe cyclonic stage, which got reduced to 77.88 × 10-5s-1 on 4th October as the system weakened. Comparison of the simulated synoptic parameters of the storm with reanalysis datasets ERA-5 and NGFS datasets indicated that the model simulated a stronger TC. The cyclone system showed significant relative humidity of about 80–90% with the well-delineated eyewall throughout various stages. The study shows the GPU-based WRF model's reliability with the current set of parameterizations for investigating a unique TC, Shaheen, on a public cloud infrastructure. • GPU based WRF-ARW model is simulated with GFS forecast data to simulate the trak, intensity and synoptic parameters of tropical cyclone Shaheen. • Simulated track, intensity is compared with observed estimates from IMD. • Compared to surface wind estimates of satellite data and reanalysis datasets, NGFS winds show a closer match to WRF. • Considering the synoptic parameters of the cyclone, the model can skilfully capture the rapid intensification and cyclogenesis. [ABSTRACT FROM AUTHOR]

The global UK Met office Unified Model (UM) is currently operational at National Centre for Medium Range Weather Forecasting (NCMRWF), the global model named as NCUM. An inter-comparison of two different versions of NCUM has been carried out for simulating the track and intensity of Tropical Cyclones (TCs), which formed over the Bay of Bengal (BoB). For this purpose, two series of numerical experiments named as NCUM25 (New Dynamical core with NCUM N512 resolution) and NCUM17 (ENDGame core with NCUM N768 resolution and upgraded physics and data assimilation scheme) are carried out with seven different initial conditions (ICs) for two TCs. The results suggested that the location, intensity, and vertical structure of the TCs are reasonably well predicted by the NCUM17 over the NCUM25. The Direct Position Error (DPE) and landfall error of TCs are reduced in the NCUM17 in comparison to the NCUM25 for all initial conditions. The mean DPEs and intensity error are reduced by 21–41% and 18–21% in NCUM17 over NCUM25 in both the cases respectively. Improvements in mean landfall position errors are shown to range from 43 to 65% in the NCUM17 as compared to the NCUM25. The mean statistical skill scores for rainfall are considerably improved in NCUM17. [ABSTRACT FROM AUTHOR]

Large volume changes (LVCs) and major Baltic inflows (MBIs) are essential processes for the water exchange and renewal of the stagnant water in the Baltic Sea deep basins. These strong inflows are known to be forced by persistent westerly wind conditions. In this study, MBIs are considered as subset of LVCs transporting with the large water volume a big amount of highly saline and oxygenated water into the Baltic Sea. Since the early 1980s the frequency of MBIs has dropped drastically from 5 to 7 events to only one inflow per decade, and long lasting periods without MBIs became the usual state. Only in January 1993, 2003 and December 2014 MBIs occurred that were able to interrupt the stagnation periods in the deep basins of the Baltic Sea. However, in spite of the decreasing frequency of MBIs, there is no obvious decrease of LVCs. The Landsort sea level is known to reflect the mean sea level of the Baltic Sea very well, and hence LVCs have been calculated for the period 1887–2015 filtering daily time series of Landsort sea surface elevation anomalies. The cases with local minimum and maximum difference resulting in at least 60 km 3 of water volume change excluding the volume change due to runoff have been chosen for a closer study (1948–2013) of characteristic pathways of deep cyclones. The average duration of LVCs is about 40 days. During this time, 5–6 deep cyclones move along characteristic storm tracks. Furthermore, MBIs are characterized by even higher cyclonic activity compared to average LVCs. We obtained four main routes of deep cyclones which were associated with LVCs, but also with the climatology. One is approaching from the west at about 56–60°N, passing the northern North Sea, northern Denmark, Sweden and the Island of Gotland. A second broad corridor of frequent cyclone pathways enters the study area north of Scotland between 60 and 66°N turning north-eastwards along the northern coast of Scandinavia. This branch bifurcates into smaller routes. One at about 62°N passing Oslo, southern Sweden and entering the central Baltic Sea, and another less frequent one at about 65°N, crossing Scandinavia south-eastwards passing the Sea of Bothnia and entering Finland. The conditions for LVCs to happen are temporal clustering of deep cyclones in certain trajectory corridors. We also found an increasing linear trend of the number of deep cyclones for the period 1950–2010. [ABSTRACT FROM AUTHOR]

The high population density along the coastal stretch of India necessitates a real-time storm surge warning system. Keeping this in view, the Earth System Science Organization (ESSO) - Indian National Centre for Ocean Information Services (INCOIS) initiated the Storm Surge Early Warning System (SSEWS) for Indian coasts using the ADCIRC (Advanced Circulation) model. ADCIRC is a finite-element based, depth-integrated shallow water model that can be used to model storm surges and for other coastal applications. This warning system utilizes the automated Decision Support System (DSS) based on Geographic Information System (GIS) and database technology. Wind and pressure fields are generated using the Jelesnianski and Taylor dynamic wind model which make use of track forecasts from IMD. While DSS was initially tested for the very severe cyclonic storm ‘Phailin’ (October, 2013) in experimental mode, it was used for the first time to provide real-time storm surge and inundation forecasts during cyclone ‘Hudhud’ (October, 2014). In this paper, we highlight the performance of SSEWS during this event while comparing with observations. Forecasts from SSEWS were found to be quite promising and proving its capability. [ABSTRACT FROM AUTHOR]

Cyclone track forecasting is a critical climate science problem involving time-series prediction of cyclone location and intensity. Machine learning methods have shown much promise in this domain, especially deep learning methods such as recurrent neural networks (RNNs) However, these methods generally make single-point predictions with little focus on uncertainty quantification. Although Markov Chain Monte Carlo (MCMC) methods have often been used for quantifying uncertainty in neural network predictions, these methods are computationally expensive. Variational Inference (VI) is an alternative to MCMC sampling that approximates the posterior distribution of parameters by minimizing a KL-divergence loss between the estimate and the true posterior. In this paper, we present variational RNNs for cyclone track and intensity prediction in four different regions across the globe. We utilise simple RNNs and long short-term memory (LSTM) RNNs and use the energy score (ES) to evaluate multivariate probabilistic predictions. The results show that variational RNNs provide a good approximation with uncertainty quantification when compared to conventional RNNs while maintaining prediction accuracy. • Cyclone trajectory and intensity forecast is a multivariate time-series prediction problem. • RNN and LSTM models are excellent for modelling temporal correlations in the time-series. • We use variational inference to train and quantify uncertainty in RNN and LSTM models. • We use the energy score to assess the performance of probabilistic models. • We find that variational inference-based RNNs provide a good approximation of their deterministic counterparts. [ABSTRACT FROM AUTHOR]

The geographical location of Bangladesh makes it quite vulnerable to natural disasters. Flooding is one of those disasters. The coastal area of Bangladesh has higher vulnerability to flooding which is important to assess for planning and development. The study focuses on the coastal flood susceptibility through a holistic approach by combining three machine learning algorithms, namely Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K Nearest Neighbor (KNN), with GIS techniques. Nine flood conditioning factors (Aspect, Slope, Elevation, Mean Sea Level, Wind, Distance from coast, Distance from river, Distance from cyclone and Geology) were considered in this study. These variables are selected by reviewing site-specific available literature and experts opinions. Relevant data were collected from several sources, and these data were analyzed in various software. The study also incorporates an analysis utilizing the best-fitted model among the three algorithms from a humanistic perspective. After obtaining results from the models, the output went under accuracy assessment to justify whether the model yielded accurate results or not. A comparative analysis to understand the suitability of these three algorithms is presented in the study, along with the delineation of five categories of flood susceptible classes of the study area. The Random Forest model was the most accurate, with an accuracy of 86.7 percent, followed by XGBoost with an accuracy of 86.3 percent and KNN with an accuracy of 85.5 percent. The Random Forest (RF) model was selected for further analysis as it yielded better accuracy incorporating the Upazila and population data. Further analysis shows that the district of Barguna, Bhola, and Patuakhali had the highest number of very high flood susceptible Upazilas among the 19 coastal districts in the study area. Around 10.63% of the study area's population, which is 4,214,084 people approximately, live in the Very High Susceptibility Zone. The results derived from the study, particularly from the humanistic perspective, are quite significant as policymakers better understand the overall situation and take necessary actions to make those areas and the communities residing in those areas more resilient and adaptive to this kind of disaster. • Assessment of Flood Susceptibility using three machine learning methods namely Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN). • Among them Random Forest method shows better accuracy. • Distance from the coast, Distance from Cyclone Tracks cyclone, distance from rivers have higher influence on susceptibility of coastal flooding. • 10% of the coastal areas of Bangladesh falls in high susceptible flood zones. [ABSTRACT FROM AUTHOR]

Extratropical cyclones (ETCs) are one of the primary weather systems affecting China. However, atmospheric environmental effects generated under the control of ETCs are rarely reported. In this study, we investigate air quality in Beijing from the perspective of ETCs. The ETCs affecting Beijing from 2014 to 2021 are tracked using the 850 hPa relative vorticity cyclone tracking algorithm. We classify the identified ETCs by cyclogenesis region, which includes 228 northern cyclones (NCs) and 41 southern cyclones (SCs). Generally, NCs are more likely to cause air pollution. For 27.4% and 16.3% of the days under the control of NCs, PM 2.5 and PM 10 pollution occur, respectively. These are mainly associated with high northwest wind and enhanced sinking motion. SCs are conducive to good or moderate air quality. The PM 2.5 /PM 10 pollution days account for 20.0%/5.0% of total days under the control of SCs, mainly due to enhanced rising motion and increased precipitation. Nevertheless, the influence of NCs/SCs on air quality shows significant seasonal differences. NCs negatively impact air quality in most seasons, except in summer. SCs tend to reduce the probability of air pollution in summer and autumn. Higher chances of air pollution associated with SCs occur in spring and winter. For a typical NC case that causes a dust storm, dust backflow occurs after the NC moves away, resulting in secondary pollution. A typical SC case generates different atmospheric environmental effects owing to the changes in its location. These results highlight the importance of ETCs for air quality in Beijing. • The air quality was investigated from the perspective of extratropical cyclones. • The important influence of extratropical cyclones on air quality was identified. • Different atmosphere environmental effects of extratropical cyclones were observed. [ABSTRACT FROM AUTHOR]

This study inter-compares extratropical cyclone activity in the following nine reanalysis datasets: the ERA-20C Reanalysis (ERA20C), the Twentieth Century Reanalysis, version 2c (20CR), the Japanese 55-year Reanalysis (JRA55), the Modern Era Retrospective-analysis for Research and Applications (MERRA), the NCEP Climate Forecast System Reanalysis (CFSR), the ERA-Interim Reanalysis (ERAint), the ERA40 Reanalysis, the NCEP–NCAR Reanalysis (NCEP1), and the NCEP-DOE Reanalysis (NCEP2). The inter-comparison is based on cyclones identified using an objective cyclone tracking algorithm. In general, reanalyses of higher horizontal resolutions show higher cyclone counts, with MERRA and 20CR showing the highest and lowest mean counts of all-cyclones, respectively. However, MERRA shows the highest mean intensity (i.e., geostrophic winds) of all-cyclones, and CFSR the lowest, although MERRA and CFSR share a similar horizontal resolution. MERRA is most different from the other datasets, showing many more cyclones of shallow-medium core pressures and much higher counts of cyclones of strong intensity than the others, while CFSR shows many more cyclones of moderate intensity than the others. MERRA cyclones tend to have weaker surface winds but stronger geostrophic winds than the corresponding CFSR cyclones. The track-to-track agreement between the datasets is better for moderate-deep cyclones than for shallow ones, better in the NH than in the SH, and better in winter than in summer in both hemispheres. There is more similarity in temporal trends and variability than in specific cyclone counts and intensity, and more similarity in deep-cyclone (core pressure ≤ 980 hPa) statistics than in all-cyclone statistics. In particular, all the four datasets that cover the period from 1958 to 2010 agree well in terms of trend direction and interannual variability in hemispheric counts of deep-cyclones, showing a general increase in both hemispheres over the past half century, although the magnitude of increase varies notably from dataset to dataset. The agreement in trends of deep-cyclone counts is generally better in winter than in summer, and better in the NH than in the SH, with nearly perfect agreement for the counts of NH winter deep-cyclones. However, the nine datasets do not agree well in terms of trend and interannual variability in the mean intensity of deep cyclones, especially in summer and in SH winter. The temporal homogeneity of cyclone statistics in each dataset was also analyzed. The results show that ERAint, NCEP2, MERRA, ERA40, and CFSR are homogeneous for the NH, and that ERAint and NCEP2 are also homogeneous for the SH. However, large inhomogeneities were found in the other datasets, especially in the earlier period. Most of the identified inhomogeneities coincide with changes in the quantity and/or types of assimilated observations. These inhomogeneities contribute notably to the differences between the datasets, which are larger in the earlier period than in the recent decades. Better trend agreements between these datasets are seen after the inhomogeneities are accounted for. It is critically important to identify and account for temporal inhomogeneities when using these datasets to analyze trends. [ABSTRACT FROM AUTHOR]

A link between solar wind magnetic sector boundary (heliospheric current sheet) crossings by the Earth and the upper-level tropospheric vorticity was discovered in the 1970s. These results have been later confirmed but the proposed mechanisms remain controversial. Extratropical-cyclone tracks obtained from two meteorological reanalysis datasets are used in superposed epoch analysis of time series of solar wind plasma parameters and green coronal emission line intensity. The time series are keyed to times of maximum growth of explosively developing extratropical cyclones in the winter season. The new statistical evidence corroborates the previously published results (Prikryl et al., 2009). This evidence shows that explosive extratropical cyclones tend to occur after arrivals of solar wind disturbances such as high-speed solar wind streams from coronal holes when large amplitude magneto-hydrodynamic waves couple to the magnetosphere-ionosphere system. These MHD waves modulate Joule heating and/or Lorentz forcing of the high-latitude thermosphere generating medium-scale atmospheric gravity waves that propagate energy upward and downward from auroral zone through the atmosphere. At the tropospheric level, in spite of significantly reduced amplitudes, these gravity waves can provide a lift of unstable air to release the moist symmetric instability thus initiating slantwise convection and forming cloud/precipitation bands. The release of latent heat is known to provide energy for rapid development and intensification of extratropical cyclones. [ABSTRACT FROM AUTHOR]

Extreme waves forced by extreme winds, such as tropical cyclones, pose a great threat to ocean and coastal structures. Therefore, the study on the influence range of tropical cyclones on ocean waves is crucial in the engineering community. In this study, the comparative advantages of different wind input and dissipation schemes available in the third-generation wave model, WAVEWATCH III, are explored in most of the China Seas, where tropical cyclones frequently occur, including the Yellow Sea, East China Sea and northern South China Sea. Compared with buoy observations, the effects of 6 parameterization schemes of input and dissipation source terms on the simulated tropical cyclone-generated waves show that the simulation with the parameterization ST6-4.18 performs well, especially during strong wind. Analyzing the simulated wave fields forced by 90 tropical cyclones, the influence ranges of tropical cyclones on ocean waves are derived, performing along the left and right sides of the tropical cyclone track. The resulting formulas can be used to screen out the tropical cyclone-generated wave for studying the extreme wave and predict the influence of tropical cyclones on waves in the engineering area before the tropical cyclone approaches. • Six parameterization schemes for input and dissipation source terms in WW3 are compared in most of the China Seas. • The influences (intensity and range) of tropical cyclones on ocean waves are analyzed. • Influence ranges of tropical cyclones on waves are estimated on the left and right sides of tropical cyclone tracks. [ABSTRACT FROM AUTHOR]

Coastal Bangladesh faces an increasing number of challenges including cyclones, tidal surges, floods, drought, saline water intrusion, waterlogging and land subsidence, which pose substantial threats to the livelihoods of the coastal inhabitants. In addition to these threats, profound social and land-use changes are complicating the livelihoods of resource users in the region, including the introduction of aquaculture and increasing competition for ground and surface water sources. The government of Bangladesh has targeted this region for investment with irrigation expansion. This paper uses a sustainable livelihood lens to understand the role of investments in water management and irrigation in driving and shaping livelihood changes and transitions over the past ten years and offers recommendations for investments. We find that while water infrastructure development has greatly enhanced the role of agriculture in coastal livelihoods over the last 10 years, further development of irrigation infrastructure should only be prioritized after issues of water governance and inequity across agricultural and aquacultural livelihoods are addressed. [ABSTRACT FROM AUTHOR]

Darwin Harbour, Northern Territory, Australia, is a semi diurnal macro-tidal embayment that is situated in a cyclone prone area. The tidal variations range up to 8 m with a mean tidal range of 3.7 m. The coastal area consists of mangrove fringes, sandy beaches, tidal flats, rocky shore platforms and coastal cliffs. The main morphological changes are movement of the sandbars and erosion of beaches and coastal cliffs. Sea level rise due to climate change and more intense cyclones and storm surges may exacerbate these processes with detrimental impacts on the coast and the adjacent city, particularly when occurring at high tide. To assist with coastal erosion management, a greater understanding of morphological changes is required. A two-dimensional depth averaged finite-element hydrodynamic model (RMA-2), coupled with a sediment transport model (RMA-11) from Resource Modelling Associates, have been used to deduce the sources and spatial patterns of sand erosion and deposition in the harbour. Geochemical analysis is also used to characterize the sand source(s). This paper presents hydrodynamic simulations focusing on culturally and recreationally significant beaches in Fannie Bay. Simulations indicate that the Cullen Bay sandbar is an indirect sand source replenishing Fannie Bay beaches. Respective geochemical results also show similar Rare Earth Element contents of the sand in the area. Considering the fast pace of development in and around Darwin Harbour, this study is essential in providing a fundamental understanding of coastal processes and to assist coastal and shoreline management in a tropical estuary. [ABSTRACT FROM AUTHOR]

The tropical cyclone (TC) full track simulation method pioneered by Vickery has been widely used in the estimation of hurricane/typhoon wind hazards. The linear auto-regression used in the Vickery model may be inadequate to capture the complex patterns of storm tracks in the western North Pacific Ocean, which accommodates almost one-third of the world's annual TCs. In this study, a novel TC simulation method is developed based on the random forests method (RF) in conjunction with cluster analysis of TC tracks. Specifically, TC movement models with probabilistic termination classifiers, and the TC intensity model are newly developed based on the RF technique for typhoon simulation in the western North Pacific. Historical typhoon tracks are classified into four clusters in light of their geographical characteristics for facilitating the development of the TC movement model corresponding to each cluster. Thousand-year typhoon simulations for 47 coastal sites of China are performed by the proposed RF-based full track simulation method, which achieves more accurate results in reproducing historical statistics of key typhoon parameters than those of the original Vickery full track simulation approach. • A novel typhoon full track simulation method is developed by using random forests (RF) algorithms. • With the aid of cluster analysis, four RF-based movement models with termination classifiers, are developed and validated. • One RF-based tropical cyclone intensity model is also developed and evaluated by several intensity metrics. • The proposed RF-based full track simulation shows a well accuracy in reproducing the historical statistical characteristics. [ABSTRACT FROM AUTHOR]

Due to the increasing trend of tropical cyclone occurrence in the recent decades, the necessity of developing models for forecasting and hindcasting these atmospheric-oceanic hazards, becomes more apparent. One of the uncertainties about the tropical cyclones is the wind field accuracy of modeling oceanic waves and flows. In this paper, six different wind fields have been examined for hindcasting the wave field of cyclone Gonu within the areas of the Arabian Sea and the Gulf of Oman. The wind field of ECMWF (ERA-Interim) and Climate Forecast System Reanalysis (CFSR) data, that obtained via weather research forecasting (WRF) mesoscale modeling and three wind fields achieved via Holland, Rankin, Young and Sobey (S&Y) parametric models have been investigated as well. The wave field modeling results have been compared with the third-generation SWAN model in two stations within the area of Chabahar Bay. The wind field results obtained via the reanalysis models and WRF one indicate an appropriate estimation of the wave characteristics and the maximum wave height at the measurement stations. In order to better evaluate the results of different wind fields, the maximum wave height maps have been studied in the simulation time period. The results indicated that the maximum wave height values obtained from the reanalysis data do not match the track and development direction of the cyclone Gonu. The mismatch between storm development track and maximum wave height in WRF data can also be observed. The maximum significant wave height obtained via the parametric wind fields is very compatible with the cyclone Gonu track. Examining the maximum wave heights obtained via different wind fields, along with comparing the wave characteristics at the measurement stations, highlights the effect of different wind fields in hindcasting the wave characteristics, especially on the northern coasts of the Gulf of Oman. Examination of the maximum wave heights obtained from different wind fields has been investigated in this study along with comparison of wave characteristics at measurement stations. Results indicated that the optimal approach for simulating the tropical cyclone's wave field is to use a hybrid one. Therefore, a hybrid wind field has been developed which includes the wind characteristics of the parametric model in the areas near the storm source and the WRF meso scale model in the outer zone. The hybrid wind field has led to an improvement in the characteristics of waves within deep and coastal waters. • The effect of different wind field models for hindcasting the wave field of a tropical cyclone is evaluated. • A hybrid wind field including WRF model and Holland parametric model is developed. • Forecasting wave characteristics in deep and coastal water is improved by using the hybrid wind field. • Applying the effects of tropical cyclone simplifies long-term hindcasting of wave field. [ABSTRACT FROM AUTHOR]

As one of the most destructive natural disasters, Tropical Cyclone (TC) can cause severe casualties and economic losses almost every year in TC-prone areas. For the prevention and reduction of TC-induced disasters in wind engineering, it is of vital importance to forecast TC's intensity and track both accurately and efficiently. Although great achievements have been made in this field, it remains a challenge to balance accuracy and efficiency during the forecast. In recent years, deep learning techniques have gained fast development and demonstrated huge application potential in various fields. This article presents a study on the short-term prediction of TCs in Northwest Pacific basin via the newly developed Convolutional Long and Short Term Memory (ConvLSTM) network which is able to extract both time-related correlations (involved in the sequence of a single parameter) and parameter-related correlations (among different parameters) from input information of different feature parameters. Results demonstrate that the proposed ConvLSTM model has good performance in terms of prediction accuracy and working stability. Through comparison, it is also suggested the proposed model works better than the standard LSTM model. Meanwhile, unlike most dynamic methods which require expensive computational resources, the proposed model can be operated conveniently and economically. Thus, it can be readily applied for engineering practices. • Presented a ConvLSTM model for short-term forecast of TCs. • It's able to consider both time- and parameter-related correlations among input records. • Comparison analysis demonstrates ConvLSTM model works better than LSTM model. • Results are compared with predictions from other methods (ADT, ATCF, ECMWF). [ABSTRACT FROM AUTHOR]

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

Although the performance of the high-resolution global ocean forecasting system, which is based on First Institute of Oceanography surface wave-tide-circulation coupled ocean model (FIO-COM) has been demonstrated to have reasonable accuracy in global context, especially in tropical oceans and some special cases, the assessment needs to be extended in different parts of the global oceans. As such, given the significance of a regional Ocean Forecasting System (OFS) in the world's prominent freshwater dominated basin, the northern Bay of Bengal, a groundwork is crucial for the modeling community. Thus, performance of FIO-COM analysis and forecasting results is assessed by utilizing available moored buoy, satellite, and reanalysis datasets. The present study compares the thermohaline structure related variables (e.g., temperature, salinity) from FIO-COM with aforementioned datasets on a seasonal and daily basis. We focused on evaluating the FIO-COM outputs during a very severe cyclonic storm named Titli that formed in October, 2018. The root-mean-square errors (RMSEs) for sea surface temperature (SST) ranges from 0.47 to 0.71 °C, and for sea surface salinity from 0.62 to 0.83 psu; while vertical profile RMSE ranges from 0.27 to 1.0 °C for temperature, and 0.22 to 0.56 psu for salinity between FIO-COM products and observations. Daily variations in different thermohaline structure related variables at two RAMA positions are also well captured by the FIO-COM outputs. Both model and satellite data show pronounced SST cooling (approximately 2.0–2.5 °C) and increased sea surface salinity (~ 1 psu) on the right side of the cyclone track. High SST, TCHP and deep isothermal layer depth were the main oceanic triggering forces to intensify the cyclone Titli. • To evaluate the upper ocean response and forecasting ability of FIO-COM during the severe cyclonic storm 'Titli' • FIO-COM can capture pronounce SST cooling and increased sea surface salinity on the right side of the cyclone track • FIO-COM has reasonably simulated the seasonal responses of temperature, salinity, thermocline depth and mixed layer [ABSTRACT FROM AUTHOR]

The wave climate along the Indian coast is mostly influenced by the dynamics of the Indian Ocean in the south, Bay of Bengal (BoB) in the east and the Arabian Sea in the west. The BoB is the region of frequent cyclonic occurrence. In the futuristic climate change scenario, it is found that the intensity of cyclones in the BoB is getting increased (Reddy et al., 2021). The cyclonic wind induced wave climate could make a substantial impact on coastal infrastructures. The severe cyclonic storm, Vardah which made landfall in 2016 has been chosen as a test case for the present study. The behaviour of Vardah cyclone under different Representative Concentration Pathway (RCP) scenarios of RC4.5, RCP6.0, RCP8.5 in the Near-Future (2035) and Far-Future (2075) categories has been considered. The wave climate has been simulated using the WAM (The WAMDI group, 1988). A coarse model covering the entire Indian Ocean with a grid resolution of 0.25° × 0.25° and a nested BoB domain with a finer grid of 2.5 km × 2.5 km have been setup. The surface wind above 10 m height which is obtained from the WRF model, is forced into the WAM to predict the wave climate. The variation in the wave characteristics has been analysed and discussed in detail with a focus on the future climate scenarios. The significant wave height (H s) obtained for the far-future category of RCP6.0 and RCP8.5 is found to be higher of the order of 6–8%. However, RCP4.5 has an insignificant influence on the wave climate. • The projection of cyclone Vardah for the Far-Future categories of RCP6.0 and RCP8.5 shows an increase of 6–8% in the significant wave height than the present scenario. • RCP4.5 has insignificant influence on the wave climate. • The maximum H s occurs to the north of the cyclone track. • After landfall, the high energy spreads northward of the coast. [ABSTRACT FROM AUTHOR]

This paper proposed a probability forecast methodology for the storm surges induced by tropical cyclones, including a probability forecast method of tropical cyclone tracks and a probability forecast scheme of storm surge levels. The probability forecast method of tropical cyclone tracks is developed based on the Error-Estimation Ensemble (EEE) method. Empirical distributions of the positions of the tropical cyclone centers around the EEE-forecasted ones are gained and used to make probability forecast of tropical cyclone tracks. It performs well in hindcasting the tropical cyclones that moved in the 24-h warning line of China in 2018. The probability forecast of storm surge levels is based on the probability forecast of tropical cyclone tracks. Under a given target fall-in probability, five possible tracks are used to drive the storm surge model and yields five forecasts of storm surge levels, the envelope of which is taken as the possible variation range of water level under the target fall-in probability. Using the probability forecast method, six launches of the probability forecast of three typical storm surges that affects the Changjiang Estuary are conduced, and the performance of the probability forecast method is assessed via a comparison between the target and realistic fall-in probabilities. • A probability forecast methodology for the storm surges induced by tropical cyclones is proposed. • The probability forecast method of the tropical cyclone tracks is developed based on the Error-Estimation Ensemble method. • The probability forecast scheme of storm surges is developed based on the probability forecast of tropical cyclone tracks. • The performance of the probability forecast method is assessed and discussed. [ABSTRACT FROM AUTHOR]