Cyclonic Wind Research Articles

Trends of extreme waves around Hainan Island during typhoon processes

Extreme waves mostly occur during typhoon processes in China Seas. This study statistically shows the trends of extreme wave height over the past several decades around Hainan Island, based on the reconstruction of historical wave processes during the typhoon events from 1949 to 2022. Totally 807 events in the tropical cyclone (TC) best track datasets are numerically simulated by the widely used wave model SWAN. The driving wind fields are reconstructed as a combination of an empirical cyclone wind model with the reanalysis wind field data, in order to better simulate waves near cyclone centers with higher spatial resolution. Based on the reconstructed wave data, the spatial distribution of wave characteristics around Hainan Island has been provided. The Mann-Kendall test shows a descending trend of extreme wave height in the southeast side of Hainan Island, where the maximum descending rate could reach up to 5 cm/yr. The influence of the long-term trend on the design extreme wave height is also discussed. Comparing to the variation of the design extreme wave height with sampling period, the long-term trend is also a significant impact factor.

Emergence of decadal linkage between Western Australian coast and Western–central tropical Pacific

The impact of interbasin linkage on the weather/climate and ecosystems is significantly broader and profounder than that of only appearing in an individual basin. Here, we reveal that a decadal linkage of sea surface temperature (SST) has emerged between western Australian coast and western–central tropical Pacific since 1985, associated with continuous intensification of decadal variabilities (8–16 years). The rapid SST changes in both tropical Indian Ocean and Indo-Pacific warm pool in association to greenhouse gases and volcanoes are emerging factors resulting in enhanced decadal co-variabilities between these two regions since 1985. These SST changes induce enhanced convection variability over the Maritime Continent, leading to stronger easterlies in the western–central tropical Pacific during the warm phase off western Australian coast. The above changes bring about cooling in the western–central tropical Pacific and strengthened Leeuwin Current and anomalous cyclonic wind off western Australian coast, and ultimately resulting in enhanced coupling between these two regions. Our results suggest that enhanced decadal interbasin connections can offer further understanding of decadal changes under future warmer conditions.

Footprint of the air-sea momentum transfer saturation observed by ocean wave buoy network in extreme tropical cyclones

Tropical cyclones are one of the most destructive natural phenomena, causing tremendous coastal disasters worldwide. The maximum intensity of tropical cyclones is determined by momentum and heat transfer at the air-sea interface. Momentum transfer corresponds to the momentum loss of tropical cyclones and, consequently, to the underlying ocean's momentum gain causing extreme ocean waves and storm surges. Air-side observations of wind profiles and ocean-side observations of ocean subsurface currents show a slowdown of momentum transfer under high tropical cyclone wind speeds in the previous studies. However, there is still disagreement regarding the slowdown owing to lack of data. Here, we show momentum transfer under high wind speed conditions by observing ocean waves. Although ocean wave observations are highly spatially limited, we deployed a fleet of drifting ocean wave buoys covering the active area of tropical cyclones in the Western North Pacific. The buoy fleet captured extreme waves near the eye of the strongest category 5 tropical cyclone, indicating an ocean wave footprint of the momentum transfer saturation above surface wind speeds of 25 m/s. Our approach from ocean surface wave observation is a unique contribution to determination on air-sea momentum transfer slowdown under extreme wind speeds, which can compensate for the knowledge from conventional air and ocean-side observations. This finding advances tropical cyclones, extreme ocean waves, and storm surge modeling.

Enhancing Urban Resilience: Strategic Management and Action Plans for Cyclonic Events through Socially Constructed Risk Processes

Cities will face increasing challenges due to the impacts of global climate change, particularly in the form of cyclonic events, necessitating a deeper understanding and the establishment of effective response mechanisms at both institutional and citizen levels. In this research, we tested the efficiency of crowdsourcing in fostering participatory resilience and improving urban management. The main aim was to design novel and accurate proactive response strategies and mitigate the adverse effects of cyclonic wind events through volunteerism, citizen science, and urban science. To achieve this goal, as a case study, the municipality of Soledad, Colombia was used. This research employed a two-phase methodological approach: (i) initially evaluating the spatial distribution of emergency response resources, and (ii) developing a geo-referenced survey to map, systematize, and categorize data and outcomes. A total of three hundred and seventy-eight residents across five neighborhoods in Soledad, which have experienced a high frequency of atmospheric wind phenomena over the past two decades, were surveyed. The results indicate that the crowdsourcing mechanism effectively enhanced the empirical understanding of atmospheric wind events in Soledad, facilitating the establishment of a geo-referenced volunteer network for real-time responses. Additionally, this study shed light on previously undocumented challenges, in terms of reducing the number of people affected, and the actions that would lead to improved urban development to reduce the impacts of cyclonic events, emphasizing the significance of citizen science in the social construction of risk and disaster risk reduction (DDR) efforts.

Development of Indian summer monsoon precipitation biases in two seasonal forecasting systems and their response to large-scale drivers

Abstract. The Met Office Global Coupled Model (GC) and the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) are both widely used for predicting and simulating the Indian summer monsoon (ISM), and previous studies have demonstrated similarities in the biases in both systems at a range of timescales from weather forecasting to climate simulation. In this study, ISM biases are studied in seasonal forecasting setups of the two systems in order to provide insight into how they develop across timescales. Similarities are found in the development of the biases between the two systems, with an initial reduction in precipitation followed by a recovery associated with an increasingly cyclonic wind field to the north-east of India. However, this occurs on longer timescales in CFSv2, with a much stronger recovery followed by a second reduction associated with sea surface temperature (SST) biases so that the bias at longer lead times is of a similar magnitude to that in GC. In GC, the precipitation bias is almost fully developed within a lead time of just 8 d, suggesting that carrying out simulations with short time integrations may be sufficient for obtaining substantial insight into the biases in much longer simulations. The relationship between the precipitation and SST biases in GC seems to be more complex than in CFSv2 and differs between the early part of the monsoon season and the later part of the monsoon season. The relationship of the bias with large-scale drivers is also investigated, using the boreal summer intraseasonal oscillation (BSISO) index as a measure of whether the large-scale dynamics favour increasing, active, decreasing or break monsoon conditions. Both models simulate decreasing conditions the best and increasing conditions the worst, in agreement with previous studies and extending these previous results to include CFSv2 and multiple BSISO cycles.

Warm conveyor belt characteristics and impacts along the life cycle of extratropical cyclones: case studies and climatological analysis based on ERA5

Abstract. This study presents a systematic investigation of the characteristics and meteorological impacts of warm conveyor belts (WCBs). For this purpose, we compile a new WCB climatology (1980–2022) of trajectories calculated with the most recent reanalysis dataset ERA5 from the European Centre for Medium-Range Weather Forecasts (ECMWF). Based on this new climatology, two-dimensional masks are defined that represent the inflow, ascent, and outflow locations of WCBs. These masks are then used to objectively quantify the key characteristics (intensity, ascent rate, and ascent curvature) and meteorological impacts (precipitation and potential vorticity (PV) anomalies) of WCBs in order to (i) attribute them to different stages in the life cycle of the associated cyclones and to (ii) evaluate differences in the outflow of the cyclonic and anticyclonic branches. The approach was applied globally, but this study focuses on the North Atlantic, one of the regions where WCBs ascend most frequently. The method is first tested and illustrated through three case studies of well-documented cyclones, revealing both the similarities and the case-to-case variability in the evolution of the WCB characteristics and impacts. We then extend the analysis to about 5000 cyclones that occurred in winter between 1980–2022 in the North Atlantic. The case studies and the climatological analysis both show that WCBs are typically most intense (in terms of air mass transported, ascent rate, precipitation rate, and volume) during the intensification period of the associated cyclone. The northward displacement along the storm track and diabatic PV production lead to an increase in low-level PV in the region of WCB ascent during the cyclone life cycle. The negative PV anomaly at upper levels, associated with the WCB outflow, remains relatively constant. The investigation of the WCB branches reveals an increasing intensity of the cyclonic WCB branch with time, linked to the increasing strength of the cyclonic wind field around the cyclone. Due to a lower altitude, the outflow of the cyclonic WCB branch is associated with a weaker negative PV anomaly than the anticyclonic one, which ascends to higher altitudes. In summary, this study highlights the distinct evolution of WCB characteristics and impacts during the cyclone life cycle and the marked differences between the cyclonic and anticyclonic branches.

Assessing the influence of land use and land cover data on cyclonic winds and coastal inundation due to tropical cyclones: a case study for the east coast of India

Assessing the influence of land use and land cover data on cyclonic winds and coastal inundation due to tropical cyclones: a case study for the east coast of India

A Convolutional Neural Network for Tropical Cyclone Wind Structure Identification in Kilometer-Scale Forecasts

Abstract Detection and tracking of tropical cyclones (TCs) in numerical weather prediction model outputs is essential for many applications, such as forecast guidance and real-time monitoring of events. While this task has been automated in the 1990s with heuristic models, relying on a set of empirical rules and thresholds, the recent success of machine learning methods to detect objects in images opens new perspectives. This paper introduces and evaluates the capacity of a convolutional neural network based on the U-Net architecture to detect the TC wind structure, including maximum wind speed area and hurricane-force wind speed area, in the outputs of the convective-scale AROME model. A dataset of 400 AROME forecasts over the West Indies domain has been entirely hand-labeled by experts, following a rigorous process to reduce heterogeneities. The U-Net performs well on a wide variety of TC intensities and shapes, with an average intersection-over-union metric of around 0.8. Its performances, however, strongly depend on the TC strength, and the detection of weak cyclones is more challenging since their structure is less well defined. The U-Net also significantly outperforms an operational heuristic detection model, with a significant gain for weak TCs, while running much faster. In the last part, the capacity of the U-Net to generalize on slightly different data is demonstrated in the context of a domain change and a resolution increase. In both cases, the pretrained U-Net achieves similar performances as the original dataset.

Cyclonic wind loads and structural mitigation measures – vulnerability assessment of traditional housings in Odisha

Cyclonic wind loads and structural mitigation measures – vulnerability assessment of traditional housings in Odisha

Operational scheme for predicting tropical cyclone wind radius based on a statistical–dynamical approach and track pattern clustering

AbstractAn operational scheme for predicting the symmetric R30 and R50 of tropical cyclones (TCs) in the western North Pacific was developed using a statistical regression method and track pattern clustering (four clusters). The statistical–dynamical model employs multiple linear regressions of two to eight variables at each cluster and forecast lead time. The dependent variable for prediction was the change in the 5‐kt wind radius (R5)—a proxy of TC size—relative to the initial time. The performance of the model was compared for the training (2008–2016) and testing (2017–2018) periods. The effect of clustering on TC size prediction was evaluated by comparing the performance of the non‐clustering and clustering models. The clustering model improved the prediction of TC size by 3%–24% at all lead times during the training period, especially with a significant improvement of up to 43% in Cluster 2. In Cluster 2, because most TCs tend to develop strongly and continue to increase in size, it greatly reduced the variability in TC size through clustering, allowed for smarter predictor selection, and ultimately improved TC size prediction. In the real‐time R30 and R50 predictions for the 2017 and 2018 TCs, the error of the clustered model was 18%–19% less than that of the non‐clustered model. The analysis results revealed that the real‐time prediction errors of the current model increase when the TC tracks are difficult to classify into specific clusters, the predicted environments and TC tracks are inaccurate, and the size and intensity of a TC rapidly increase.

UQAM‐TCW: A Global Hybrid Tropical Cyclone Wind Model Based Upon Statistical and Coupled Climate Models

AbstractTropical cyclones (TCs) are among the most destructive natural hazards and yet, quantifying their financial impacts remains a significant methodological challenge. It is therefore of high societal value to synthetically simulate TC tracks and winds to assess potential impacts along with their probability distributions for example, land use planning and financial risk management. A common approach to generate TC tracks is to apply storm detection methodologies to climate model output, but such an approach is sensitive to the method and parameterization used and tends to underestimate intense TCs. We present a global TC model (the UQAM‐TCW model thereafter) that melds statistical modeling, to capture historical risk features, with a climate model large ensemble, to generate large samples of physically coherent TC seasons. Integrating statistical and physical methods, the model is probabilistic and consistent with the physics of how TCs develop. The model includes frequency and location of cyclogenesis, full trajectories with maximum sustained winds and the entire wind structure along each track for the six typical cyclogenesis basins from IBTrACS. Being an important driver of TCs globally, we also integrate ENSO effects in key components of the model. The global TC model thus belongs to a recent strand of literature that combines probabilistic and physical approaches to TC track generation. As an application of the model, we show global hazard maps for direct and indirect hits expressed in terms of return periods. The global TC model can be of interest to climate and environmental scientists, economists and financial risk managers.

Diurnal variation in short‐ and long‐duration precipitation within the Dabie Mountain region in Central China

AbstractDiurnal variation in regional precipitation in Central and East China has been broadly identified in previous study. This study focused on clarifying not only the differences in the diurnal variation concerning the local precipitation occurred in the Dabie Mountain (DBM) region and the regional precipitation occurred through the middle‐lower reaches of the Yangtze River Basin, but also the association mechanism between diurnal variation in local precipitation with differing duration and diurnal modulation of ambient circulations driven by large‐scale background synoptic force or local‐scale orographic force of the DBM. It was found that cases of short duration precipitation (SDP, duration <6 h) and cases of long duration precipitation (LDP, duration ≥6 h) exhibit vastly different patterns of diurnal variations. Further investigation revealed that the primary pattern of the ambient circulation responsible for SDP and LDP is the recycling transformation between a closed cyclonic circulation to a cyclonic wind shear, and the recycling movement of a closed cyclonic circulation in the lower troposphere respectively. Although the diurnal variation in LDP exhibited greater orographic regional differences between the west slope (WS) region and the east slope (ES) region in comparison with that of SDP, topographical sensitivity experiments of the DBM and detailed analysis of background synoptic systems associated with LDP indicated that this regional orographic difference shows closer association with eastward propagation of background synoptic systems than orographic effects of the DBM. Respective strengthening of upward vertical motion at different levels of the troposphere during diurnal variation in SDP and LDP cases provide substantial evidences to reconfirm the close association with orographic force in lower the troposphere for SDP cases and background synoptic force in middle troposphere for LDP cases. Further quantitative diagnosis also confirmed that the orographic effects of the DBM is conducive to cause marked differences in the diurnal variation in SDP cases between the WS region and the ES region via adjustment of the upslope wind over the former and the circumventing wind over the latter.

Global tropical cyclone extreme wave height climatology

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

A dataset of global tropical cyclone wind and surface wave measurements from buoy and satellite platforms

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

Estimating intraevent and interevent variability and spatial correlation of tropical cyclone wind fields and their use for the risk assessment of a portfolio of structures

Often the tropical cyclone (TC) wind hazard assessment requires the use of the TC wind field model. While theoretical models typically predict the observed wind field well, there can be a spatially varying residual correlation that impacts the damage assessment of communities or groups of structures. In this study, we focus on developing spatial correlation models and assessing the intraevent and interevent variability of the TC wind field using the H*Wind dataset and two widely used wind field models - the vertically averaged boundary layer slab model and the gradient wind field-based model. Our models and the statistics of the interevent and intraevent variability are integrated into a framework for evaluating the wind-induced damage of a portfolio of structures. The framework includes simulating TC tracks and wind fields, considering interevent and intraevent variabilities, and assessing peak linear elastic and nonlinear responses. Numerical examples illustrating the use of this framework are provided, indicating that realistic spatial correlation of the TC wind field needs to be considered to assess the correlation coefficient of the damage factor of a pair of spatially distributed structures and the probability distribution of the damage cost of a portfolio of structures.

Realistic tropical cyclone wind and pressure fields can be reconstructed from sparse data using deep learning

Tropical cyclones are responsible for large-scale loss of life and property1–4, motivating accurate risk assessment and forecasting. These objectives require accurate reconstructions of storms’ wind and pressure fields which assimilate real-time observations5–9, but current methods used for these reconstructions remain computationally expensive and limited10. Here, we show that a physics-informed neural network11,12 can be a promising and computationally efficient algorithm for tropical cyclone data assimilation. Using synthetic training data sparsely sampled from hurricanes simulated in a forecast model, a physics-informed neural network is able to reconstruct full realistic 2- and 3-dimensional wind and pressure fields which capture key features of the cyclone. We also demonstrate how a set of sparse, real-time observations, can be used to accurately reconstruct Hurricane Ida. Our results highlight how recent advances in deep learning can augment data assimilation schemes. The methods are also general and can be applied to other flow problems.

Evaluation of Parametric Tropical Cyclone Surface Winds over the Eastern Australian Region

Abstract Hazard studies based on thousands of synthetic tropical cyclone (TC) events require a validated model representation of the surface wind field. Here, we assess three different TC parametric vortex models with input from four along-track parameter studies of the TC size and shape, based on statistical formulation of the relationships to observed TC intensity, geographic location, and forward transition speed. The 12 model combinations are compared to in situ 10-min observed surface mean wind speeds for 10 TCs that made landfall over Queensland, Australia, which occurred over the period 2006–17. Empirical wind reduction factors to reduce gradient winds to the surface are recalculated for the more recent TCs at both offshore (ocean, small islands, reefs, and moorings) and onshore (land) locations. To improve the wind comparisons over ocean and land, a secondary reduction factor was developed based on an inland decay function. Pearson correlations for the unadjusted modeled peak wind speed from 118 instances of a TC passing a weather station sit between a range of 0.57 and 0.65 for the 12 model combinations. Using the secondary reduction factor based on the inland decay function increases the range of correlation to 0.74–0.81. Based on the assessment of the instances of peak surface wind speed correlations, bias, and root-mean-square error, along with the correlation 48 h around the peak, the top-ranked performing model combination for the region was an along-track parameter study with a double-vortex model, both previously tested for the South Pacific basin. Significance Statement When assessing tropical cyclone hazards, users are presented with several simplified parametric models to describe the surface wind field of tropical cyclones. These parametric models are used routinely for risk assessment of cyclonic winds, as well as for input to surge and wave models used in coastal hazard assessments. Differences between the models include the formulation of the parametric cyclone model, the way winds above the boundary layer are specified at the surface and along-track parameters that describe the cyclones’ size and shape. Of the 12 model combinations investigated in this study, the top-ranked performing model combination for the region was an along-track parameter equation with a double-vortex model, which were both tested previously for the South Pacific basin. Analysis is performed to show unadjusted modeled winds overestimate observed 10-min surface winds over the ocean by around 13% (median) and over land by around 73.9% (median), which is resolved in this study with a secondary empirical wind reduction factor. These findings will support future modeling of tropical cyclone winds for multiple applications, including regional risk assessment and coastal hazard studies.

Modeling Ocean Cooling Induced by Tropical Cyclone Wind Pump Using Explainable Machine Learning Framework

Modeling Ocean Cooling Induced by Tropical Cyclone Wind Pump Using Explainable Machine Learning Framework

Transfer Learning-Based Generative Adversarial Network Model for Tropical Cyclone Wind Speed Reconstruction From SAR Images

Transfer Learning-Based Generative Adversarial Network Model for Tropical Cyclone Wind Speed Reconstruction From SAR Images

Cyclonic Wind Speed Retrieval From SWIM Wave Spectrum Based on Machine Learning

Cyclonic Wind Speed Retrieval From SWIM Wave Spectrum Based on Machine Learning