Search Results (862)

Tropical cyclones (TCs) form over warm ocean surfaces and are driven by complex air–sea interactions, posing significant challenges to their forecasting. Accurate parameterization of physical processes is crucial for enhancing the precision of TC predictions. In this study, we employed the Weather Research and Forecasting model coupled with the Simulating Waves Nearshore (WRF-SWAN) model to forecast Typhoon Doksuri (2023), which exhibited a secondary intensification process in the South China Sea (SCS). We also investigated its sensitivity to various atmospheric physical parameterization schemes (PPS). The findings indicate that improvements in microphysical and cumulus convection parameterizations have significantly enhanced the prediction accuracy of Typhoon Doksuri’s trajectory and intensity. The simulation of sea surface heat flux is primarily influenced by the microphysical scheme, while the cumulus convection scheme substantially affects the representation of the typhoon core’s size and shape. Variations in the wind field induce differences in wave height, potentially reaching up to 2–3 m at any given moment. This study provides valuable insights into the effective selection of physical parameterizations for improving typhoon forecasts. Full article

As extreme forms of tropical cyclones (TCs), typhoons pose significant threats to both human society and the natural environment. To better understand and predict their behavior, scientists have relied on numerical simulations. Current typhoon modeling primarily falls into two categories: (1) complex simulations based on fluid dynamics and thermodynamics, and (2) empirical parameterized models. Most comparative studies on these models have focused on wind speed below 50 m/s, with fewer studies addressing high wind speed (above 50 m/s). In this study, we design and compare four different simulation approaches to model two super typhoons: Typhoon Surigae (2102) and Typhoon Nepartak (1601). These approaches include: (1) The Weather Research and Forecasting (WRF) model simulation driven by NCEP Final Operational Global Analysis data (FNL), (2) WRF simulation driven by the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5), (3) the empirical parameterized Holland model, and (4) the empirical parameterized Jelesnianski model. The simulated wind fields were compared with the measured wind data from The Soil Moisture Active Passive (SMAP) platform, and the resulting wind fields were then used as inputs for the Simulating WAves Nearshore (SWAN) model to simulate typhoon-induced waves. Our findings are as follows: (1) for high wind speeds, the performance of the empirical models surpasses that of the WRF simulations; (2) using more accurate driving wind data improves the WRF model’s performance in simulating typhoon wind speeds, and WRF simulations excel in representing wind fields in the outer regions of the typhoon; (3) careful adjustment of the maximum wind speed radius parameter is essential for improving the accuracy of the empirical models. Full article

Extremely severe cyclonic storms over the North Indian Ocean increased by approximately 10% during the past 30 years. The climatological characteristics of tropical cyclones for 38 years were assessed over the Bay of Bengal (BoB). A total of 24 ESCSs formed over the BoB, having their genesis in the southeast BoB, and the intensity and duration of these storms have increased in recent times. The Advanced Research version of the Weather Research and Forecasting (ARW) model is utilized to simulate the five extremely severe cyclonic storms (ESCSs) over the BoB during the past two decades using the Indian Monsoon Data Assimilation and Analysis (IMDAA) data. The initial and lateral boundary conditions are derived from the IMDAA datasets with a horizontal resolution of 0.12° × 0.12°. Five ESCSs from the past two decades were considered: Sidr 2007, Phailin 2013, Hudhud 2014, Fani 2019, and Amphan 2020. The model was integrated up to 96 h using double-nested domains of 12 km and 4 km. Model performance was evaluated using the 4 km results, compared with the available observational datasets, including the best-fit data from the India Meteorological Department (IMD), the Tropical Rainfall Measuring Mission (TRMM) satellite, and the Doppler Weather Radar (DWR). The results indicated that IMDAA provided accurate forecasts for Fani, Hudhud, and Phailin regarding the track, intensity, and mean sea level pressure, aligning well with the IMD observational datasets. Statistical evaluation was performed to estimate the model skills using Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), the Probability of Detection (POD), the Brier Score, and the Critical Successive Index (CSI). The calculated mean absolute maximum sustained wind speed errors ranged from 8.4 m/s to 10.6 m/s from day 1 to day 4, while mean track errors ranged from 100 km to 496 km for a day. The results highlighted the prediction of rainfall, maximum reflectivity, and the associated structure of the storms. The predicted 24 h accumulated rainfall is well captured by the model with a high POD (96% for the range of 35.6–64.4 mm/day) and a good correlation (65–97%) for the majority of storms. Similarly, the Brier Score showed a value of 0.01, indicating the high performance of the model forecast for maximum surface winds. The Critical Successive Index was 0.6, indicating the moderate model performance in the prediction of tracks. It is evident from the statistical analysis that the performance of the model is good in forecasting storm structure, intensity and rainfall. However, the IMDAA data have certain limitations in predicting the tracks due to inadequate representation of the large-scale circulations, necessitating improvement. Full article

Sediment transport in Hangzhou Bay and the adjacent Changjiang Estuary is extremely complex due to the bathymetry and hydrodynamic conditions in this region. Using the particle tracing method based on the ROMS model, three-dimensional (3D) passive particle transport in Hangzhou Bay and the Changjiang Estuary was simulated. Ocean temperature, salinity, and circulation patterns before and during Severe Tropical Storm Ampil (2018) were reproduced by the model. The circulation in Hangzhou Bay is significantly influenced by the passing of the storm with an enhanced southeastward surface current. The along-front current offshore of the Changjiang Estuary, accompanied by the Changjiang River plume, is weakened by strong mixing under the storm. The transport of passive particles before and during the storm was also simulated based on the current fields of the model. The results show that the passing of the tropical storm enhances mass exchange in Hangzhou Bay by the storm-induced southeast circulation, while particle transport near the Changjiang Estuary decreases as the estuarine plume is weakened by the intense mixing of strong winds of the storm. Full article

Global Navigation Satellite System (GNSS) Radio Occultation (RO) and GNSS Reflectometry (GNSS-R) are the two major spaceborne GNSS remote sensing (GNSS-RS) techniques, providing observations of atmospheric profiles and the Earth’s surface. With the rapid development of GNSS-RS techniques and spaceborne missions, many experiments and studies were conducted to assimilate those observational data into numerical weather-prediction models for tropical cyclone (TC) forecasts. GNSS RO data, known for its high precision and all-weather observation capability, is particularly effective in forecasting mid-to-upper atmospheric levels. GNSS-R, on the other hand, plays a significant role in improving TC track and intensity predictions by observing ocean surface winds under high precipitation in the inner core of TCs. Different methods were developed to assimilate these remote sensing data. This review summarizes the results of assimilation studies using GNSS-RS data for TC forecasting. It concludes that assimilating GNSS RO data mainly enhances the prediction of precipitation and humidity, while assimilating GNSS-R data improves forecasts of the TC track and intensity. In the future, it is promising to combine GNSS RO and GNSS-R data for joint retrieval and assimilation, exploring better effects for TC forecasting. Full article

Using in situ microstructure observations from 2010 to 2018, this study assesses the applicability of turbulent mixing parameterization schemes in the northwestern South China Sea (NSCS) and improves the MG model proposed by MacKinnon and Gregg in 2003 using machine learning methods. The results show that the estimation error of the MG model is still more than one order of magnitude in the NSCS. Also, the importance of parameters obtained from machine learning indicates that the normalized depth (D) is one of the most relevant parameters to the turbulent kinetic energy dissipation rate ε. Therefore, in this study, D is introduced into the MG model to obtain an improved MG model (IMG). The IMG model has an average correlation (r) between the estimated and observed log₁₀ε of 0.79, which is at least 49% higher than the MG model, and an average root mean square error (RMSE) of 0.25, which is at least 42% lower than that of the MG model. The IMG model accurately estimates the multi-year turbulent mixing observed in the NSCS, including before and after tropical cyclone passages. This provides a new perspective to study the physical principles and spatial and temporal distribution of turbulent mixing. Full article

Tropical cyclones (TCs) are natural hazards causing extensive damage to society, infrastructure, and the natural environment. Due to the multi-hazardous nature of TCs, comprehensive risk assessments are essential to understanding how to better prepare for potential impacts. This study develops an integrated methodology for TC multi-hazard risk assessment that utilises the following individual assessments of key TC risk components: a variable enhanced bathtub model (VeBTM) for storm surge-driven hazards, a random forest (RF) machine learning model for rainfall-induced flooding, and indicator-based indices for exposure and vulnerability assessments. To evaluate the methodology, the regions affected by TC Debbie (2017) for Queensland and TC Winston (2016) for Fiji’s main island of Viti Levu were used as proof-of-concept case studies. The results showed that areas with the highest risk of TC impacts were close to waterbodies, such as at the coastline and along riverine areas. For the Queensland study region, coastal populated areas showed levels of “high”, “very high”, and “extreme” risk, specifically in Bowen and East Mackay, driven by the social and infrastructural domains of TC risk components. For Viti Levu, areas classified with an “extreme” risk to TCs are primarily areas that experienced coastal inundation, with Lautoka and Vuda found to be especially at risk to TCs. Additionally, the Fiji case study was validated using post-disaster damage data, and a statistically significant correlation of 0.40 between TC Winston-attributed damage and each tikina’s overall risk was identified. Ultimately, this study serves as a prospective framework for assessing TC risk, capable of producing results that can assist decision-makers in developing targeted TC risk management and resilience strategies for disaster risk reduction. Full article

This study focuses on the relationship between hotel sites (current and planned) and exposure to hurricanes in Cuba. The hypothesis focused on demonstrating that Cuban tourist areas have differing degrees of exposure to tropical cyclones according to the month. The results indicate that although the whole Cuban archipelago is exposed to hurricanes, the tourist regions in Cuba’s western provinces have a greater chance of being affected than those located in the center, east, and south. The tourism development that will take place by 2030 in Cuba includes significant hotel expansion in coastal areas that are highly exposed to hurricanes. Information on the risk of hurricanes in tourist regions provides information that could enable the creation of the organizational conditions needed to tackle these phenomena in the short to medium term and to refine spatial and tourism planning approaches in the long term. Full article

The anthropic causes of climate change are well known, but the influence of climate change on society needs to be better estimated. This study estimates the impact of climate change on energy consumption on small tropical islands using monthly temperatures and energy production/consumption statistics during the last decades. Here, we show, using energy, meteorological, demographic, and economic datasets, as well as statistical correlations, that energy consumption is sensitive to (i) cyclonic activity and (ii) temperature warming. On small tropical islands, increased electricity consumption correlates with temperatures rising above 26 °C in relation to air conditioner electricity consumption. On La Réunion Island, a +1 °C increase is expected to cause an electricity production of 1.5 MWh/inhabitant per year, representing a growth of 3.2%. Considering that non-renewable sources are primarily used to produce electricity, this feedback contributed significantly (i.e., 2000 to 4000 TWh) to the greenhouse gas increase caused by climate warming over the last decades on tropical islands. Demographic and wealth variations, as well as socio-economic crises, also have a significant impact on energy consumption (2 kWh for 1000 inhabitants, 0.008 GWh/inhabitant growth for a 10,000 GDP/inhabitant growth, and a 0.2 GWh/inhabitant decrease during COVID-19, for annual consumption, respectively) and must be taken into account for decadal variation analysis. The relationship between climate change and energy consumption in tropical areas should be better integrated into climatic scenarios to adapt building isolation and energy production. Full article

The retrieval of global significant wave height (SWH) data is crucial for maritime navigation, aquaculture safety, and oceanographic research. Leveraging the high temporal resolution and spatial coverage of Cyclone Global Navigation Satellite System (CYGNSS) data, machine learning models have shown promise in SWH retrieval. However, existing models struggle with accuracy under high-SWH conditions and discard a significant number of such observations due to low quality, which limits their effectiveness in global SWH retrieval, particularly for monitoring tropical cyclone (TC) events. To address this, this study proposes a daily global SWH retrieval framework through the enhanced eXtreme Gradient Boosting model (XGBoost-SC), which incorporates Cumulative Distribution Function (CDF) matching to introduce prior distribution information and reduce errors for SWH values exceeding 3 m. An enhanced loss function is employed to improve accuracy and mitigate the distribution bias in low-SWH retrieval induced by CDF matching. The results were tested over one million sample points and validated against the European Centre for Medium-Range Weather Forecasts (ECMWF) SWH product. With the help of CDF matching, XGBoost-SC outperformed all models, significantly reducing RMSE and bias while improving the retrieval capability for high SWHs. For SWH values between 3–6 m, the RMSE and bias were 0.94 m and −0.44 m, and for values above 6 m, they were 2.79 m and −2.0 m. The enhanced performance of XGBoost-SC for large SWHs was further confirmed in TC conditions over the Western North Pacific and in the Western Atlantic Ocean. This study provides a reference for large-scale SWH retrieval, particularly under TC conditions. Full article

This work identifies and analyses, from a synoptic point of view, episodes of torrential rainfall (equal to or greater than 200 mm in a single day) that occurred in the Canary Islands between 1950 and 2020. For this purpose, all daily rainfall series available in different databases were used, with a final selection, after applying various filters for the detection of errors, of 88 days on which 200 mm was exceeded. Subsequently, the isobaric configurations at the surface and at 500 hPa were analysed by applying the following two classification methods: the automatic one of Jenkinson and Collinson (1977) and the subjective one of Jorge Olcina (1994). Most of the selected days (63.4%) corresponded to high-altitude isolated depressions (known by their initials in Spanish as DANAs), as well as troughs showing the advection of polar air of different origins (36.5%). According to the Jenkinson and Collinson classification, half of the days were classified as cyclonic or hybrid cyclonic and 37.5% as pure advective or directional (37.5%), with five days classified as undetermined. On only one day, 23 November 1954, was a tropical disturbance observed, with cloud fronts moving from the south of the Canary Islands along the west coast of Africa. Full article

Since the beginning of the satellite era, only three tropical cyclones have been recorded over the South Atlantic Ocean. To investigate the potential occurrence of such systems since the 1900s, ERA20C, a centennial reanalysis, was utilised. This study first evaluates the performance of ERA20C in reproducing the climatology of all cyclone types over the southwestern South Atlantic Ocean by comparing it with a modern reanalysis (ERA5) for the period 1979–2010. Despite its simpler construction, ERA20C is able to reproduce key climatological features, such as frequency, location, seasonality, intensity, and thermal structure of cyclones similar to ERA5. Then, the Cyclone Phase Space (CPS) methodology was applied to determine the thermal structure at each time step for every cyclone between 1900 and 2010 in ERA20C. The cyclones were then categorised into different types (extratropical, subtropical, and tropical), and systems exhibiting a warm core at their initial time step were classified as tropical cyclogenesis. Between 1900 and 2010, 96 cases of tropical cyclogenesis were identified over the South Atlantic. Additionally, throughout the lifetime of all cyclones, a total of 1838 time steps exhibited a tropical structure, indicating that cyclones can acquire a warm core at different stages of their lifecycle. The coasts of southeastern and southern sectors of northeast Brazil emerged as the most favourable for cyclones with tropical structures during their lifecycle. The findings of this study highlight the occurrence of tropical cyclones in the South Atlantic prior to the satellite era, providing a foundation for future research into the physical mechanisms that enabled these events. Full article

Climate change is driving regime shifts across ecosystems, exposing species to novel challenges of extreme weather, altered disturbances, food web disruptions, and habitat loss. For disturbance-dependent species like the endangered piping plover (Charadrius melodus), these shifts present both opportunities and risks. While most piping plover populations show net growth following storm-driven habitat creation, similar gains have not been documented in the Eastern Canadian breeding unit. In September 2022, post-tropical cyclone Fiona caused record coastal changes in this region, prompting our study of population and nesting responses within the central subunit of Prince Edward Island (PEI). Using satellite imagery and machine learning tools, we mapped storm-induced change in open sand habitat on PEI and compared nest outcomes across habitat conditions from 2020 to 2023. Open sand areas increased by 9–12 months post-storm, primarily through landward beach expansion. However, the following breeding season showed no change in abundance, minimal use of new habitats, and mixed nest success. Across study years, backshore zones, pure sand habitats, and sandspits/sandbars had lower apparent nest success, while washover zones, sparsely vegetated areas, and wider beaches had higher success. Following PTC Fiona, nest success on terminal spits declined sharply, dropping from 45–55% of nests hatched in pre-storm years to just 5%, partly due to increased flooding. This suggests reduced suitability, possibly from storm-induced changes to beach elevation or slope. Further analyses incorporating geomorphological and ecological data are needed to determine whether the availability of suitable habitat is limiting population growth. These findings highlight the importance of conserving and replicating critical habitat features to support piping plover recovery in vulnerable areas. Full article

This study aims to analyze the impact of sea-level rise and tropical cyclones on mangroves in the context of global climate change from 1993 to 2023, and to explore the development status, co-operative relationships and future trends in this research field. In order to analyze future research directions for mangroves in the context of climate, this study also provides an important basis and reference for the development of research related to the mitigation of natural disasters. Using CNKI and the Web of Science as data sources, this study employs the bibliometric tool CiteSpace 6.3 R1 to conduct a quantitative and visual analysis of the research field. The research findings indicate the following: (1) The volume of publications in this field has been increasing year by year; especially since 2010, the rate of increase has accelerated, indicating an increased academic interest in this area. (2) From the authorship maps of the two data sources, it can be observed that the collaboration network is dense, indicating the existence of co-operative relationships among researchers. (3) From the analysis of the keywords, it is evident that, with the rise of artificial intelligence, the focus of keywords has gradually shifted from traditional mangrove mechanism research and ecosystem studies to research on mangroves that integrates big data, artificial intelligence, and high-resolution remote sensing data. (4) As time has progressed, areas of research interest have been shifting from the study of disturbances and damage to mangrove vegetation to the study of mangrove resilience and vulnerability in the context of natural disasters, their carbon sequestration capabilities, and their protective functions against wind and waves. The use of remote sensing technology for the monitoring and conservation of mangroves has emerged as a key area of focus for future research. In future research, there will be a focus on the adaptive capacity of mangroves to varying degrees of sea-level rise and the increasing frequency of tropical cyclones, as well as on what measures can be taken to enhance the resilience of mangrove ecosystems. Quantitative and visual analysis of the development trends in this field can provide a reference for the construction of a disaster monitoring platform for mangroves affected by sea-level rise and tropical cyclones, and can aid the development of research aimed at mitigating the impacts of natural disasters. Furthermore, the integration of remote sensing technology and ecological models can facilitate more detailed research, offering more effective tools and strategies for the conservation and management of mangroves. This approach also provides a reference point for developing a monitoring platform for mangrove disasters associated with sea-level rise and the impact of tropical cyclones. Full article

This study uses the Finite-Volume Community Ocean Model (FVCOM) to simulate the hydrodynamic processes during typhoon “Saola”. The simulation results closely match observed data. Typhoon “Saola” was a major system in the Pacific typhoon season, highlighting the complexity and uncertainty of tropical cyclone dynamics. By analyzing historical sea surface temperature data and the typhoon’s trajectory, the three-dimensional response of sea temperature during typhoon “Saola” was explored. The key findings are as follows: 1. Typhoon passage affects both coastal and deep-sea warming and cooling. Temperature changes are more pronounced near the coast, with the highest warming and cooling occurring within five days after the typhoon. In deep-sea areas, the highest warming occurs within five days, while the lowest cooling occurs within two days. 2. The nearshore water layers respond quickly to the typhoon, while the deep-sea water layers primarily respond in the middle depths, with a delayed effect. 3. In coastal shallow waters, the response is intense, with the maximum temperature increase and decrease occurring near the bottom, reaching 5.26 °C and −5.17 °C, respectively. In deep-sea areas, the response is weaker, with the maximum temperature change occurring near the surface: an increase of 0.49 °C and a decrease of −0.98 °C. The deepest response in coastal waters reaches about 80 m, while in the deep-sea area, it only reaches 50 m due to the thicker mixed layer. Full article