International Best Track Archive For Climate Stewardship Research Articles

Projections of tropical cyclones over the north Indian Ocean using different tracking schemes under CMIP5 models

This research compares two different methods of tracing cyclones in the North Indian Ocean (NIO)- (i) Commonwealth Scientific and Industrial Research Organisation (CSIRO) Direct Detection (CDD) and Okubo-Weiss-Zeta parameter (OWZ) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) model data. Many CMIP5 models are evaluated against TC observations from the International Best Track Archive for Climate Stewardship (IBTrACS) and a statistical Generalised Additive Model for climate change projections in the past (1970–2000). Estimates of TCs' potential future occurrence in the NIO are evaluated using CMIP5 models (2070–2100). When compared to historical tracks, the geographic distribution of TCs generated by both detection techniques is consistent with what would be expected, and the frequency of TCs in the models is, with a few exceptions, consistent with observations. Generally, the OWZ plan results in more TCs per unit time than the CDD scheme. Though there are significant differences between the two tracking techniques, a small number of models have TC counts that are virtually similar. Compared to the CDD plan, the OWZ scheme generally has higher performance in the NIO area.

Spatiotemporal changes in hurricane-force wind risk assessment in the Yucatan Peninsula, Mexico

Tropical cyclones are one of the most dangerous natural phenomena. These extreme events involve various hazards, such as strong winds, severe precipitation, storm surge, flooding, and landslides. In Mexico, tropical cyclones are the most frequent natural threats and have a high cost to affected populations. This research aimed to characterise the spatial and temporal changes in risk associated with hurricane winds on the Yucatan Peninsula. This effort included a comprehensive analysis of three integral risk components (hazard, vulnerability, and exposure) for three distinct time intervals (1950–2000, 1950–2010, and 1950–2020). This analytical process was executed utilising a fine-resolution hexagonal grid. Hazard was estimated by calculating the probabilities of occurrence of winds related to various hurricane categories after estimating wind fields from the International Best Track Archive for Climate Stewardship (IBTrACS) data with a parametric model. Vulnerability was approximated by constructing indicators with sociodemographic data from the National Population and Housing Census issued by Mexico’s National Institute of Statistics and Geography. With these indicators, a factor analysis was performed, and a weighted index was constructed. Finally, exposure was estimated from population density. Each of these indices was aggregated at the hexagonal level, allowing the calculation of the risk associated with hurricane-force wind. The results showed high-risk levels associated with high-hazard levels, e.g. in coastal areas such as the Riviera Maya. Similarly, high-risk levels are related to high marginalisation, i.e. vulnerability, in the northeastern zone of the Yucatan Peninsula. The increased frequency of tropical cyclones combined with high population densities has recently led to higher risk levels in this region of Mexico.

Machine Learning-Based Estimation of Tropical Cyclone Intensity from Advanced Technology Microwave Sounder Using a U-Net Algorithm

A U-Net algorithm was used to retrieve surface pressure and wind speed over the ocean within tropical cyclones (TCs) and their neighboring areas using NOAA-20 Advanced Technology Microwave Sounder (ATMS) reprocessed Sensor Data Record (SDR) brightness temperatures (TBs) and geolocation information. For TC locations, International Best Track Archive for Climate Stewardship (IBTrACS) data have been used over the North Atlantic Ocean and West Pacific Ocean between 2018 and 2021. The European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) surface pressure and wind speed were employed as reference labels. Preliminary results demonstrated that the visualizations for wind speed and pressure matched the prediction and ERA5 location. The residual biases and standard deviations between the predicted and reference labels were about 0.15 m/s and 1.95 m/s, respectively, for wind speed and 0.48 hPa and 2.67 hPa, respectively, for surface pressure, after applying cloud screening for each ATMS pixel. This indicates that the U-Net model is effective for surface wind speed and surface pressure estimates over general ocean conditions.

Exploring the Complex Dynamics of Tropical Cyclone Activity in the Southern Indian Ocean: A Multidecade Analysis

This study analyzes tropical cyclones (TCs) in the Southern Indian Ocean from 1970 to 2022, utilizing data from the International Best Track Archive for Climate Stewardship (IBTrACS) and Sea Surface Temperature (SST) data from ERA-5. Trends are assessed with Sen's slope method. Significant TC shifts are evident. Categories 1-2 TCs have substantially reduced in number, duration, and speed, indicating an overall decline in activity. Conversely, more powerful TCs (categories 3-5) have intensified, driven by rising SST. This intensification is associated with alarming increases in the Power Dissipation Index (PDI) and Accumulated Cyclone Energy (ACE), reflecting heightened destructive potential. TCs predominantly occur during DJFM, influenced by elevated SST compared to MJJASO. Climate drivers, including El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Madden-Julian Oscillation (MJO), significantly affect TC characteristics. Elevated TC activity coincides with La Niña, neutral ENSO phases, and positive IOD events. At the same time, MJO influences are nuanced, leading to a slight TC activity decline, potentially due to data limitations in specific MJO phases. This study offers vital climatological insights into evolving TC risks in the Southern Indian Ocean, aiding agencies and decision-makers in understanding TC-related risks in the region.

An Adaptive Learning Approach for Tropical Cyclone Intensity Correction

Tropical cyclones (TCs) are dangerous weather events; accurate monitoring and forecasting can provide significant early warning to reduce loss of life and property. However, the study of tropical cyclone intensity remains challenging, both in terms of theory and forecasting. ERA5 reanalysis is a benchmark data set for tropical cyclone studies, yet the maximum wind speed error is very large (68 kts) and is still 19 kts after simple linear correction, even in the better sampled North Atlantic. Here, we develop an adaptive learning approach to correct the intensity in the ERA5 reanalysis, by optimising the inputs to overcome the problems caused by the poor data quality and updating the features to improve the generalisability of the deep learning-based model. Specifically, we use understanding of TC properties to increase the representativeness of the inputs so that the general features can be learned with deep neural networks in the sample space, and then use domain adaptation to update the general features from the known domain with historical storms to the specific features for the unknown domain of new storms. This approach can reduce the error to only 6 kts which is within the uncertainty of the best track data in the international best track archive for climate stewardship (IBTrACS) in the North Atlantic. The method may have wide applicability, such as when extending it to the correction of intensity estimation from satellite imagery and intensity prediction from dynamical models.

An Ensemble Machine Learning Approach for Tropical Cyclone Localization and Tracking From ERA5 Reanalysis Data

AbstractTropical Cyclones (TCs) are counted among the most destructive phenomena that can be found in nature. Every year, globally an average of 90 TCs occur over tropical waters, and global warming is making them stronger and more destructive. The accurate localization and tracking of such phenomena have become a relevant and interesting area of research in weather and climate science. Traditionally, TCs have been identified in large climate data sets through the use of deterministic tracking schemes that rely on subjective thresholds. This study presents a Machine Learning (ML) ensemble approach for locating TCs center coordinates. The ensemble combines TCs center estimates of different ML models that agree about the presence of a TC in input data. ERA5 reanalysis data was used for model training and testing jointly with the International Best Track Archive for Climate Stewardship (IBTrACS) records. Compared to single models estimates, the ML ensemble approach was able to improve TCs localization in terms of Euclidean Distance with respect to the observed TCs locations from IBTrACS. Moreover, a hybrid tracking scheme was defined: starting from the individual TC center locations detected by the ML ensemble approach, a deterministic tracking algorithm was used for reconstructing TC trajectories. The hybrid tracking scheme was then compared with four deterministic trackers reported in literature, achieving a Probability of Detection and a False Alarm Rate of 71.49% and 23%, respectively, over 40 years of reanalysis data.

Wave climate characteristics and effects of tropical cyclones on high wave occurrences in Indonesian waters: Strengthening sea transportation safety management

Tropical cyclones can cause bad weather and high waves that can endanger maritime activities, especially in the waters of Indonesia, which is a maritime country. Understanding ocean wave climate characteristics and the effect of Tropical Cyclones (TCs) on high wave occurrences is important, particularly to ensure the safety of fisheries activities in Indonesian seas. We aim to do so by focusing on Indonesia's 11 Fisheries Management Areas (IFMAs) using descriptive and inferential statistics. Wind and wave data were taken from the European Center for Medium-Range Weather Forecast (ECMWF), namely ECMWF Reanalysis v5 (ERA5) within the range of 42 years (1979–2020), whereas the TC data were from the International Best Track Archive for Climate Stewardship (IBTrACS). We found that significant wave heights (SWHs) in the inner seas are strongly related to monsoonal wind fields. The maximum SWH occurred in northern Indonesian seas during December–February (DJF), while occurring in southern and inner seas between June–August (JJA). The highest extreme SWH and probability of exceedance (PE) for SWH >2 m were found in IFMAs facing the open ocean, such as Malacca Strait, Western Sumatra waters, Southern Java waters, Natuna Sea, Sulawesi Sea, and Northern Papua waters. We found that TCs activity in adjacent Indonesian waters could increase anomalous SWH occurrences. Positive SWH anomalies ranged from 0.1 to 0.4 m and varied depending on TC location and season. TC that forms in the following areas increase the PE for SWH >2 m: (1) Bay of Bengal: Western Sumatra waters (March–May, MAM) around 20%; (2) Western Pacific Ocean: Northern Papua waters (DJF) around 5%; (3) Northeastern Indian Ocean: Western Sumatra waters (DJF) about 3%; and (4) Western and Northern Australian waters: Southern Java waters (DJF) about 20%. The results of this study can serve as an early assessment for investigating wave climate characteristics and identifying the areas in Indonesian waters that are most susceptible to the impact of TCs, particularly in IFMAs. These findings hold significant value for managers and policy authorities, such as the Ministry of Transportation and the Ministry of Marine Affairs and Fisheries of the Republic of Indonesia. They can utilize this information to develop measures aimed at reducing vulnerabilities among marine resource users, specifically fishers, and improving fishing vessel safety. Additionally, these findings can be utilized to enhance advance warnings and forecasts, enabling marine managers to implement strategies that minimize the impact of extreme events and increase resilience in the face of ocean and coastal disasters.

Observed Surface Wind Field Structure of Severe Tropical Cyclones and Associated Precipitation

Using the International Best Track Archive for Climate Stewardship (IBTrACS) dataset, this study assessed the surface wind fields from high spatial resolution Synthetic Aperture Radar (SAR) observations, the fifth generation ECMWF reanalysis for the global climate and weather (ERA5) data and the Tropical Cyclone Winds and Inflow Angle Asymmetry (TCIAA) wind model. The results showed that SAR data are sufficient to reveal the surface wind field near a TC center and can accurately describe TC intensity and size under severe TC conditions. Then, a new, improved statistical wind structure model was set up using ERA5 data alone based on the assessment. In addition, the warm sea surface (SST > 26.5 °C) produced stronger TC wind fields and heavier precipitation. When the SST was higher (lower), the heavy rainfall was located on the left (right) side of the TC track and the strong positive correlation between wind speed and precipitation increased as the SST decreased.

Tropical cyclones position and intensity in the Southwest Indian Ocean as represented by CFS and ERA5 atmospheric reanalysis datasets

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

Further Discussion on Temperature Field During Storms and Thermal Score in Typhoon Actives and Damages of Typhoons

Background: As we know, tropical cyclones or hurricanes (TCs) and typhoons are one of the natural disasters causing considerable damage to our life, economy and society. Objective: This study goal is to explore damages of typhoons and the temperature field during storms operating in the East Sea under the influence of cold air over time. Methodology: To determine the activities of the cold surge, the study analyzes the evolution of the 24-hour sea level barometric value in the region of 20-250N; 105-1150E. This is the area that is often affected first when KKL operates in East Asia in general and Vietnam in particular. Then, cold surge is considered to affect the area when the 24-hour transformer has a value greater than 1hPa. Results: Studies showed that the effects were investigated from 1991 to 2011 based on archived data from the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP-NCAR) and the number of typhoons were sourced from the International Best Track Archive for Climate Stewardship (IBTrACS). Conclusion: research showed that most typhoons occurred in August and September, which was related to high temperature in the summer season and the southwest monsoon in the area. In Vietnam, Although Typhoons has dissipated, the rain after typhoon No. 4 has left 3 people dead and missing, more than 7,000 houses were flooded, tens of thousands of hectares of agriculture and fisheries were damaged.

Single-Pass Tropical Cyclone Detector and Scene-Classified Wind Speed Retrieval Model for Spaceborne GNSS Reflectometry

Spaceborne global navigation satellite system reflectometry has been used to retrieve wind speed, but few studies have directly detected tropical cyclones from spaceborne GNSS-R observations. Moreover, it is challenging to solve the multivalued dependence of the spaceborne observable on wind speed to unambiguously and accurately retrieve wind speed in cyclone conditions. This paper presents a single-pass cyclone detector and coarse estimation approach of the tropical cyclone position using a spaceborne GNSS-R full delay-Doppler map (FDDM). When the specular point passes through a tropical cyclone, the FDDM asymmetry experiences an abnormal change. From the FDDM asymmetry sequence along the specular point trajectory, two subsequence features, including the slope and extremum difference, are defined, from which the tropical cyclone detector is proposed. The results from the simulation and the Cyclone GNSS (CYGNSS) data both indicate a good detection performance for tropical cyclones. The location corresponding to the peak detector is considered as a coarse estimation of the tropical cyclone position. The test result from the CYGNSS data shows that the mean error between the detected cyclone center and the International Best Track Archive for Climate Stewardship (IBTrACS) cyclone center is approximately 124.50 km. From the detector of tropical cyclones, a scene-classified wind speed retrieval model is proposed. The simulated and experimental results show that a better retrieval performance can be obtained at high wind speed (> 30 m/s) using the scene-classified model. Reductions of 10 m/s and 4 m/s in the root mean square errors (RMSEs) are obtained for the simulation and CYGNSS data, respectively. This work is meaningful for directly detecting tropical cyclones and retrieving high wind speed data using spaceborne GNSS-R in real time.

Evaluation of Three Genesis Potential Indices for Tropical Cyclogenesis in the Arabian Sea: Two Case Studies Using WRF and ERA5

Abstract Tropical cyclones (TCs) generated over the Arabian Sea can cause significant damage to infrastructure, human lives, landfall, and property near inshore and maritime trade route areas. A key to successful prediction of TCs is a skillful prediction of potential cyclogenesis locations. This study focuses on evaluating three genesis potential indices (GPIs) derived from a global reanalysis (ERA5) and dynamically downscaling using a regional model (WRF) for two TC cases: Gonu in 2007 and Kyarr in 2019, selected by analyzing the accumulated cyclone energy trend from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset over the period of 1981–2019. The two TCs belong to category 4 and above on the Saffir–Simpson scale. To test the sensitivity of downscaling to cumulus parameterizations, two WRF experiments were conducted using the Kain–Fritsch and New Tiedke cumulus schemes, respectively. The calculated genesis locations with help of the three GPIs were compared with IBTrACS. The results show that 1) all indices have reasonable skills in reproducing genesis locations, although their performances differ somewhat; 2) the dynamic downscaling with two WRF experiments added value to the study by comparing two numerical schemes for estimating genesis locations; and 3) WRF with the New Tiedke and Kain–Fritsch schemes showed good skill in reproducing the spatial distribution of the most relevant dynamical parameters. The pattern correlations are well correlated with environmental parameters of untransformed GPI and higher correlations with binary logarithmic transformed GPI. The applicability to other cyclones is also tested (e.g., TC Nilofar in 2014) with encouraging results. This study demonstrates the usefulness of GPIs for forecasting TC genesis in the region. Significance Statement The trend analysis of accumulated cyclone energy (ACE) over the Arabian Sea (AS) shows an increase over the period of 1981–2019 with the highest ACE values for 2019. The genesis potential indices (GPI) show strong ability for use as a forecasting tool for tropical cyclone genesis, and hence, are helpful for providing a reference for future studies. WRF experiments were able to reproduce the GPI with slight differences from the observations and ERA5. WRF schemes show good performance in reproducing key meteorological fields. The analysis of the GPI and WRF schemes shows the potential to be implemented for maritime forecasts of the tropical cyclones in the region. This study will be helpful scientifically and strategically with a significant impact on socioeconomic activities in the region.

Tropical Cyclone Wind Field Reconstruction and Validation Using Measurements from SFMR and SMAP Radiometer

Accurate information on tropical cyclone position, intensity, and structure is critical for storm surge prediction. Atmospheric reanalysis datasets can provide gridded, full coverage, long-term and multi-parameter atmospheric fields for the research on the impact of tropical cyclones on the upper ocean, which effectively makes up for the uneven temporal and spatial distribution of satellite remote sensing and in situ data. However, the reanalysis data cannot accurately describe characteristic parameters of tropical cyclones, especially in high wind conditions. In this paper, the performance of the tropical cyclone representation in ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation) is investigated and analyzed with respect to IBTrACS (International Best Track Archive for Climate Stewardship) during the period 2018–2020. Comparisons demonstrate that ERA5 winds significantly underestimate the maximum wind speed during the tropical cyclones (>30 m/s) compared to those provided by IBTrACS. An effective wind reconstruction method is examined to enhance tropical cyclone intensity representation in reanalysis data in 94 cases of 31 tropical cyclones 2018–2020. The reconstructed wind speeds are in good agreement with the SFMR (Stepped Frequency Microwave Radiometer) measured data and SMAP (Soil Moisture Active Passive) L-band radiometer remotely sensed measurements. The proposed wind reconstruction method can effectively improve the accuracy of the tropical cyclone representation in ERA5, and will benefit from the establishment of remote sensing satellite retrieval model and the forcing fields of the ocean model.

F2F-NN: A Field-to-Field Wind Speed Retrieval Method of Microwave Radiometer Data Based on Deep Learning

In this paper, we present a method for retrieving sea surface wind speed (SSWS) from Fengyun-3D (FY-3D) microwave radiation imager (MWRI) data. In contrast to the conventional point-to-point (P2P) retrieval methods, we propose a field-to-field (F2F) SSWS retrieval method based on the basic framework of a Convolutional Neural Network (CNN). Considering the spatial continuity and consistency characteristics of wind fields within a certain range, we construct the model based on the basic framework of CNN, which is suitable for retrieving various wind speed intervals, and then synchronously obtaining the smooth and continuous wind field. The retrieval results show that: (1) Comparing the retrieval results with the label data, the root-mean-square error (RMSE) of wind speed is about 0.26 m/s, the F2F-NN model is highly efficient in training and has a strong fitting ability to label data. Comparing the retrieval results with the buoys (NDBC and TAO) data, the RMSE of F2F-NN wind speed is less than 0.91 m/s, the retrieval accuracy is better than the wind field products involved in the comparison. (2) In the hurricane (Sam) area, the F2F-NN model greatly improves the accuracy of wind speed in the FY-3D wind field. Comparing five wind field products with the Stepped-Frequency Microwave Radiometer (SFMR) data, the overall accuracy of the F2F-NN wind data is the highest. Comparing the five wind field products with the International Best Track Archive for Climate Stewardship (IBTrACS) data, the F2F-NN wind field is superior to the other products in terms of maximum wind speed and maximum wind speed radius. The structure of the wind field retrieved by F2F-NN is complete and accurate, and the wind speed changes smoothly and continuously.

Predicting Indian Ocean Cyclone Parameters Using an Artificial Intelligence Technique

Precise prediction of a cyclone track with wind speed, pressure, landfall point, and the time of crossing the land are essential for disaster management and mitigation, including evacuation processes. In this paper, we use an artificial neural network (ANN) approach to estimate the cyclone parameters. For this purpose, these parameters are obtained from the International Best Track Archive for Climate Stewardship (IBTrACS), from the National Oceanic and Atmospheric Administration (NOAA). Since ANN benefits from a large number of data points, each cyclone track is divided into different segments. We use past information to predict the geophysical parameters of a cyclone. The predicted values are compared with the observations.

A Machine-Learning Based Tool for Diagnosing Inland Tropical Cyclone Maintenance or Intensification Events

Tropical Cyclone Maintenance and Intensification (TCMI) is a generalized definition of tropical cyclones that strengthen or maintain intensity inland while maintaining tropical characteristics. Herein, a novel methodology, using a machine learning method was created to examine the tropical cyclone record to improve climatological representation of such cases. Using the International Best Track Archive for Climate Stewardship (IBTrACS) dataset, individual times of inland tropical cyclones were classified into TCMI and non-TCMI (weakening) events. The MERRA-2 dataset was applied to develop a prototypical machine-learning model to help diagnose future TCMI events. A list of possible TCMI storms for case studies in future analyses is provided. Two of these storms were examined for attributes characteristic of the Brown Ocean Effect, a hypothesized mechanism for TCMI centered on warm, moist soils. It was revealed that variables that were important at the time of storm arrival were important the prior day, which indicates that a TCMI event is a reaction to the environment. Moreover, the variables that were finally selected show a heavy emphasis on land-surface processes. This supports the idea that the accurate representation of the land surface state is critical to the accurate diagnosis of TCMI.

Minor impacts of major volcanic eruptions on hurricanes in dynamically-downscaled last millennium simulations

The effects of volcanic eruptions on hurricane statistics are examined using two long simulations from the Community Earth System Model (CESM) Last Millennium Ensemble (LME). The first is an unforced control simulation, wherein all boundary conditions were held constant at their 850 CE values (LMEcontrol). The second is a “fully forced” simulation with time evolving radiative changes from volcanic, solar, and land use changes from 850 CE through present (LMEforced). Large tropical volcanic eruptions produce the greatest change in radiative forcing during this time period, which comprise the focus of this study. The Weather Research and Forecasting (WRF) model is used to dynamically downscale 150 control years of LMEcontrol and an additional 84 years of LMEforced for all mid-latitude volcanic eruptions between 1100 and 1850 CE. This time period was selected based on computational considerations. For each eruption, 2 years are dynamically downscaled. 23 of these volcanic eruptions are in the Northern Hemisphere and 19 are in the Southern Hemisphere. The effectiveness of the downscaling methodology is examined by applying the same downscaling approach to historical ERA-I reanalysis data and comparing the downscaled storm tracks and intensities to the International Best Track Archive for Climate Stewardship (IBTrACS) database. Hurricane statistics are then computed from both the downscaled control and downscaled forced LME simulations. Results suggest moderate effects on hurricanes from the average of all northern hemisphere eruptions, with the largest effects being from the volcanoes with the most aerosol forcing. More specifically, reductions in hurricane frequency, intensity, and lifetime following northern hemisphere eruptions are apparent. Strong evidence is also shown for correlation between eruption strength and changes in these diagnostics. The aggregate effect from both northern and southern hemisphere eruptions is minor. While reductions in frequency, intensity, and lifetime from northern hemisphere eruptions occur, the opposite effect is observed from southern hemisphere eruptions.

Influence of major typhoons on ocean, atmosphere and air quality of Northwest Pacific during August 2020

Tropical storms (TSs)/Typhoons (TYs) in the Northwest Pacific (NWP) region are one of the most devastating natural hazards, which cause large-scale impacts on human life and infrastructure. In August 2020, a total of 9 tropical storms were identified by International Best Track Archive for Climate Stewardship (IBTrACS) in the NWP region. These TSs/TYs made their landfall over the coastal parts of China, North Korea, and South Korea, while none of them made their landfall over Taiwan. These conditions were unique in recent years and induced drought conditions in Taiwan during 2020. We have carried out a three-dimensional analysis of oceanic, atmospheric, and meteorological parameters for the evaluation of changes associated with typhoons during August 2020. The model, satellite, and ground observations data have been used for the assessment of the impact of these TS/TYs on the ocean, atmosphere, and air quality. The rise in ocean temperature (1 °C–2 °C) was observed even at the depth of 100 m. Strong upwelling of the ocean water in the NWP originated a change in the mixed layer depth and also has affected the salinity in South China, East China, and the Philippines Sea. The strong convective forces during the storm conditions produced a prominent rise in CO and Ozone concentration. These typhoons also affected the air quality of Taiwan during August 2020. The transboundary air pollutants triggered the enhancement in particulate matter (PM1.0 and PM2.5) and surface ozone over Taiwan, which resulted in major health hazards to a large population.

Skew Surge and Storm Tides of Tropical Cyclones in the Delaware and Chesapeake Bays for 1980–2019

Coastal flooding poses the greatest threat to human life and is often the most common source of damage from coastal storms. From 1980 to 2020, the top 6, and 17 of the top 25, costliest natural disasters in the U.S. were caused by coastal storms, most of these tropical systems. The Delaware and Chesapeake Bays, two of the largest and most densely populated estuaries in the U.S. located in the Mid-Atlantic coastal region, have been significantly impacted by strong tropical cyclones in recent decades, notably Hurricanes Isabel (2003), Irene (2011), and Sandy (2012). Current scenarios of future climate project an increase in major hurricanes and the continued rise of sea levels, amplifying coastal flooding threat. We look at all North Atlantic tropical cyclones (TC) in the International Best Track Archive for Climate Stewardship (IBTrACS) database that came within 750 km of the Delmarva Peninsula from 1980 to 2019. For each TC, skew surge and storm tide are computed at 12 NOAA tide gauges throughout the two bays. Spatial variability of the detrended and normalized skew surge is investigated through cross-correlations, regional storm rankings, and comparison to storm tracks. We find Hurricanes Sandy (2012) and Isabel (2003) had the largest surge impact on the Delaware and Chesapeake Bay, respectively. Surge response to TCs in upper and lower bay regions are more similar across bays than to the opposing region in their own bay. TCs that impacted lower bay more than upper bay regions tended to stay offshore east of Delmarva, whereas TCs that impacted upper bay regions tended to stay to the west of Delmarva. Although tropical cyclones are multi-hazard weather events, there continues to be a need to improve storm surge forecasting and implement strategies to minimize the damage of coastal flooding. Results from this analysis can provide insight on the potential regional impacts of coastal flooding from tropical cyclones in the Mid-Atlantic.

Rapidly intensified, long duration North Indian Ocean tropical cyclones: Mesoscale downscaling and validation

High-resolution (6 km × 6 km) reanalyses (HRR) have been generated using the Advanced Research Weather Research and Forecasting model (WRF-ARW) and three-dimensional variational-Ensemble Kalman Filter (3DEnKF). The six-hourly cyclic downscaling methodology has been used to develop HRR for simulating the life cycle of three rapidly intensified long-life tropical cyclones (TCs) viz. Fani, Ockhi, and Luban over the North Indian Ocean (NIO). The available surface and upper-air observations, radiance data, and scatterometer/radiometer wind data have been assimilated. The simulated maximum surface wind (intensity), minimum sea level pressure (MSLP), and cyclone tracks have been compared to the International Best Track Archive For Climate Stewardship (IBTrACS) dataset. During the maximum intensity stage, the model successfully simulated the maximum surface wind variation and drop of MSLP. The surface winds validated using the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) buoys observations showed a better correlation at the cyclonic stage and severe cyclonic stage compared to the very severe cyclonic stage. Comparing wind distribution at 850 hPa to the 5th Generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5) showed that the analysis successfully captured more intense TC stages. The significant wind structure features in the surface, lower, middle, and upper tropospheric observed by Scatsat-1 and INSAT3D/3DR satellites have been simulated by analysis. The rapid intensifications (RI) of considered TCs were successfully simulated in the analysis. The simulated and the Global Precipitation Measurement (GPM) accumulated rainfall distribution was found to be collocated, especially over heavy rainfall regions.