In this study solar elevation angle is shown to have a dominant impact on the net radiation due to tropical cyclone (TC) clouds. As solar elevation angle increases, net cooling effects from TC clouds dominate over net warming effects. From 2001 to 2020, the radiative effect of TC clouds remained stable. However, because of the strong dependency on solar elevation angle, future changes in seasonal occurrence could affect this contribution.

The indirect health risks of tropical cyclones (TCs), the costliest climate extremes, remain unclear, with cardiovascular diseases (CVDs) being a major contributor. We applied two-stage time-series analysis to 6.54 million CVD hospitalizations across six countries/territories (Canada, New Zealand, South Korea, Taiwan, Thailand, and Vietnam) between 2000 and 2019 to quantify the long-term risks and burden of CVD hospitalizations following TCs. Hospitalization risks for cause-specific CVDs consistently increased following TC exposure, generally peaking around 2 months postexposure and dissipating by 6 months. Overall, each additional TC day was associated with a 13% (95% confidence interval, 7 to 19%) increase in CVD hospitalizations in 6 months following TCs. Particularly high TC-attributable burden was found for ischemic heart diseases and stroke, especially among males, individuals aged 20 to 59 years, and those with higher levels of socioeconomic deprivation. The TC-attributable proportions of CVD hospitalizations showed decreasing trends in less deprived populations and increasing trends in more deprived populations from 2000 to 2019.

In earlier work we have demonstrated that TC clouds are, on average, net cooling in that they increase the upwelling radiation. This is due to a larger increase in daytime short wave reflectivity compared with the corresponding decrease in long wave emission that happens at night. While the trend computed over 20 years was for TC clouds to be cooling, there was approximately a 3:1 ratio between individual storms that were net cooling and net warming. In that earlier work, we noted that late-season storms tended to be preferentially warming. Here we demonstrate that this effect is dominated by local solar angle (which is highly correlated with seasonality) and over TC cloud extent.

We demonstrate that a convolutional neural network (CNN) based on the U-Net architecture can be used to create a cloud mask data set that accurately identifies the clouds associated with tropical cyclones (TCs). The CNN can be trained using a single year of cloud masks produced by an earlier first-principles algorithm, and the results are insensitive to the specific year of training data used. These masks were originally created in order to compute the upwelling radiation due to TC clouds, and we show that the predicted masks result in both pixel areas and radiation calculations that are nearly identical to those computed using the earlier masks.

AbstractObservations and climate projections suggest a larger increase in tropical cyclone (TC)‐induced rainfall than that can be explained by the Clausius‐Clapeyron relationship of 7% increase in vapor content for each 1°C degree rise in temperature. However, these studies using diverse data sources and methods over various periods show inconsistencies regarding the location of this increase ‐ whether in the TC inner core or outer regions ‐ and offer differing explanations for the reported trends. This study uses the Pseudo‐global warming methodology on simulations of 117 western North Pacific TCs making landfall in Southeast Asia to investigate changes in TC rainfall structure by the end of the century under the SSP2‐4.5 and SSP3‐7.0 scenarios. Specifically, it tests the sensitivity of changing trends to various analysis methods used in previous studies and identifies the underlying physical mechanisms driving these changes. The findings indicate an amplified increase in rainfall in the TC inner core across all future scenarios, along with potentially decreased rainfall in the outer region under certain future climate conditions. Among TC categories, Supertyphoons exhibit the most significant increased rainfall across future states. Changes in TC primary and secondary circulations, TC structure, and the convergence of heat and moisture are the main factors shaping future rainfall patterns, outweighing the effects of changes in atmospheric and convective stability.

Tropical Cyclone Debbie (2017) made landfall near Airlie Beach on 28 March 2017 causing 14 fatalities and an estimated $2.67 billion (U.S. dollars) economic loss and was ranked as the most dangerous cyclone to hit Australia since TC Tracy in 1974. In addition to the extreme flooding as TC Debbie moved onshore and down the east coast of Australia, it intensified rapidly just offshore from category 2 to category 4 on the Australian TC intensity scale in under 18 h prior to making landfall. A high-resolution WRF simulation is used to analyze the inner-core structure and evolution during the offshore intensification period. Two stages are identified: a slow intensification (SI) stage characterized by an asymmetric eyewall contraction and a rapid intensification (RI) stage characterized by three eyewall breakdown and redevelopment events. Each round of breakdown and reestablishment brings high potential vorticity and equivalent potential temperature air back into the eyewall, reinvigorating eyewall convection activity and driving intensification.

BACKGROUND:More intense cyclones are expected in the future as a result of climate change. A comprehensive review is urgently needed to summarize and update the evidence on the health effects of cyclones. OBJECTIVES:We aimed to provide a systematic review with meta-analysis of current evidence on the risks of all reported health outcomes related to cyclones and to identify research gaps and make recommendations for further research. METHODS:We systematically searched five electronic databases (MEDLINE, Embase, PubMed, Scopus, and Web of Science) for relevant studies in English published before 21 December 2022. Following the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines, we developed inclusion criteria, screened the literature, and included epidemiological studies with a quantitative risk assessment of any mortality or morbidity-related outcomes associated with cyclone exposures. We extracted key data and assessed study quality for these studies and applied meta-analyses to quantify the overall effect estimate and the heterogeneity of comparable studies. RESULTS:In total, 71 studies from eight countries (the United States, China, India, Japan, the Philippines, South Korea, Australia, Brazil), mostly the United States, were included in the review. These studies investigated the all-cause and cause-specific mortality, as well as morbidity related to injury, cardiovascular diseases (CVDs), respiratory diseases, infectious diseases, mental disorders, adverse birth outcomes, cancer, diabetes, and other outcomes (e.g., suicide rates, gender-based violence). Studies mostly included only one high-amplitude cyclone (cyclones with a Saffir-Simpson category of 4 or 5, i.e., Hurricanes Katrina or Sandy) and focused on mental disorders morbidity and all-cause mortality and hospitalizations. Consistently elevated risks of overall mental health morbidity, post-traumatic stress disorder (PTSD), as well as all-cause mortality or hospitalizations, were found to be associated with cyclones. However, the results for other outcomes were generally mixed or limited. A statistically significant overall relative risk of 1.09 [95% confidence interval (CI): 1.04, 1.13], 1.18 (95% CI: 1.12, 1.25), 1.15 (95% CI: 1.13, 1.18), 1.26 (95% CI: 1.05, 1.50) was observed for all-cause mortality, all-cause hospitalizations, respiratory disease, and chronic obstructive pulmonary disease hospitalizations, respectively, after cyclone exposures, whereas no statistically significant risks were identified for diabetes mortality, heart disease mortality, and preterm birth. High between-study heterogeneity was observed. CONCLUSIONS:There is generally consistent evidence supporting the notion that high-amplitude cyclones could significantly increase risks of mental disorders, especially for PTSD, as well as mortality and hospitalizations, but the evidence for other health outcomes, such as chronic diseases (e.g., CVDs, cancer, diabetes), and adverse birth outcomes remains limited or inconsistent. More studies with rigorous exposure assessment, of larger spatial and temporal scales, and using advanced modeling strategy are warranted in the future, especially for those small cyclone-prone countries or regions with low and middle incomes. https://doi.org/10.1289/EHP12158.

The net effect on the upwelling radiation caused by tropical cyclone clouds is calculated over a 20-year global data set, and the corresponding contribution to the earth energy balance is analyzed. Tropical cyclone clouds are shown on average to increase the upwelling radiation at the top of the atmosphere compared with the background non-tropical-cyclone-cloud climatology. This increase in upwelling radiation provides an overall cooling effect on the climate system because the increased reflected shortwave radiation (cooling) outweighs the decreased emitted longwave radiation (warming). While the effect neglects the (likely considerable) contribution due to tropical cyclone drying, the amount of cooling by clouds alone represents a considerable fraction of the excess warming energy in the climate system. Thus, any future change in tropical cyclone activity has the potential to impact the overall energy balance if it substantially alters this total. The seasonal and geographic distribution of warming and cooling effects, and the diurnal dynamics that impact whether any particular cyclone is net cooling or net warming are discussed in this study.

The Ganges–Brahmaputra–Meghna Delta (GBMD) located in the head of the Bay of Bengal is regularly affected by severe tropical cyclones frequently. The GBMD covers the Bangladesh coast, which is one of the most vulnerable areas in the world due to cyclone-induced storm surges. More than 30% of the total country’s population lives on the Bangladesh coast. Hence, it is crucial to understand the underlying processes that modulate the storm surge height in the GBMD. A barotropic numerical 3D model setup is established by using Delft3D and SWAN to investigate a cyclone-induced storm surge event. The model is calibrated and validated for Cyclone Sidr in 2007 and applied to six idealized cyclonic scenarios. Numerical experiments with different coupling configurations are performed to distinguish the contribution of wind, tides, waves, and wave–current interactions (WCI) on the storm surge height. Results show that the wind-driven setup is the dominant contributor to the storm surge height during cyclonic events. Based on the tidal phase and wind direction, the interaction between tide and wind can increase or decrease the magnitude of the storm surge height. Finally, considering the wind-driven wave may increase the surge height up to 0.3 m along the coastline through a strong wave setup.

The North Indian Ocean is a region with a high coastal population and a low-lying delta, making it a high-risk region for tropical cyclone impacts. A 30-year period from 1989–2018 has been used to examine the TC landfalling exposure in the North Indian Ocean and its changes by considering 30 years of IBTrACs data, ERA5 atmospheric data, and 20 years of TRMM and DAV data. A total of 185 TCs made landfall in the NIO during the 30-year period with the majority of the TCs making landfall during the pre- and post-monsoon seasons. Rainfall associated with landfalling TCs decreased in the last 10 years of analysis (2009–2018) compared to the first 10 years of available data from 1999–2008. During the monsoon, TC activity is relatively lower compared to the post-monsoon periods, even though higher accumulated TC-associated rainfall typically occurs during the monsoon period, particularly along the eastern coastlines of the Arabian Sea and the Bay of Bengal. The TC winds impact most of the Bay of Bengal coastline, including Sri Lanka. The spatial distribution of landfalling TCs changes with the season, with most of the landfalling activity occurring during the pre- and post-monsoon periods. Interestingly, more recent TC activity has shifted to the northeast India and Bangladesh coasts, suggesting that these regions may be more vulnerable to TC impacts in the future.

In this study, the landfalling tropical cyclone (TC) exposure in Southeast Asia for a 50‐year period from 1970 to 2019 is investigated relative to the total western North Pacific (WNP) climatology taking disparities in historical records into account. Long‐term trends in landfalling TCs are analyzed and intercompared among the Regional Specialized Meteorological Center of Tokyo (TOKYO), China Meteorological Administration (CMA), Hong Kong Observatory (HKO), and Joint Typhoon Warning Center (JTWC) best track datasets. Interannual and intra‐seasonal variations are further examined by sub‐region and nation using JTWC records. Approximately half of the WNP TCs make landfall in Southeast Asia representing over 75% of the total WNP landfalls in all datasets. Over the study period, there is a slight upward trend in landfalling TC frequency in both the WNP and Southeast Asia in the JTWC dataset, while the number of landfall events has decreased in the CMA and TOKYO datasets. A consistent northward shift in landfalling locations over the 50‐year period is found in all datasets such that landfalls have decreased in the Philippines, Vietnam, but increased in some South China areas. The TOKYO dataset alone suggests that landfalling TCs in South China have slowed down over the study period, which would increase rainfall and wind risks in their path if substantiated. Less TC landfalls occur in El Niño years with landfalling locations shifting northwestward over the Asian mainland, while landfalls are higher and more distributed in La Niña, and highest in Neutral years.

In this Pseudo-global Warming study, potential future changes in the Southeast Asia tropical cyclone (TC) exposure climatology are quantified. One hundred and seventeen landfalling TCs in the last 20 years are simulated with their current climate conditions and also with the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble perturbed conditions under the SSP2-4.5 and SSP3-7.0 climate change scenarios. Our simulations suggest that landfalling TCs are projected to be 8% more intense at landfall, 2.8% faster and have smaller sizes by the end of the 21st century under the SSP3-7.0 scenario. In addition, TC landfall locations shift northward with tracks extending further inland toward Laos and Thailand. In particular, TC exposures, wind and rainfall impacts significantly increase in the northern Philippines, Taiwan, southwestern coast of China, and northern Vietnam; and significantly decrease in the southern areas of Southeast Asia and the southeastern coast of China.

We present a framework to quantify the radiation from tropical cyclones (TCs) in shortwave (SW, wavelength smaller than 3 micron) and longwave (LW, wavelength larger than 3 micron) portions of the electromagnetic spectrum. The framework includes two stages: segmentation of TC clouds and calculation of the radiation effects attributable to TC clouds. The segmentation task is accomplished by an algorithm which takes a time series of brightness temperature images of TCs and uses image processing techniques to acquire segmentation for each image in a semisupervised manner. The radiation is calculated by combining the segmentation results with the cloud and earth's radiant energy system dataset via a coordinate-matching scheme due to their difference in resolution. The framework was implemented to analyze the net contribution of TCs to the upwelling radiation in 2016 and in summer months between 2015 and 2019 at regional and global scales. Results show that both the magnitude and the variability of radiation contribution by TCs are of an order of magnitude that could have a significant effect on the overall earth's energy balance.

Historical simulations of models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) are evaluated over 10 Australian regions for their performance in simulating extreme temperatures, among which three models with initial‐condition large ensembles (LEs) are used to estimate the effects of internal variability. Based on two observational data sets, the Australian Water Availability Project (AWAP) and the Berkeley Earth Surface Temperatures (BEST), we first analyze the models' abilities in simulating the probability distributions of daily maximum and minimum temperature (TX and TN), followed by the spatial patterns and temporal variations of the extreme indices, as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). Overall, the CMIP6 models are comparable to CMIP5, with modest improvements shown in CMIP6. Compared to CMIP5, the CMIP6 ensemble tends to have narrower interquartile model ranges for some cold extremes, as well as narrower ensemble ranges in temporal trends for most indices. Over southeast, tropical, and southern regions, both CMIP ensembles generally exhibit relatively large deficiencies in simulating temperature extremes. We also confirm that internal variability can affect the trends of the extremes and there is uncertainty in representing the irreducible variability among different LEs in CMIP6. Furthermore, the evaluation based on Perkins' skill score (PSS) and root‐mean‐square error (RMSE) in the three LEs does not directly correlate with the ranges of the trends for extreme temperatures. The findings of this study are useful in informing and interpreting future projections of temperature‐related extremes over Australia.

The deviation angle variance (DAV) is a parameter that characterizes the level of organization of a cloud cluster compared with a perfectly axisymmetric tropical cyclone (TC) using satellite infrared (IR) imagery, and can be used to estimate the intensity of the TC. In this study, the DAV technique is further used to analyze the relationship between satellite imagery and TC future intensity over the North Atlantic basin. The results show that the DAV of the TC changes ahead of the TC intensity change, and this can be used to predict short-term TC intensity. The DAV-IR 24-h forecast is close to the National Hurricane Center (NHC) 24-h forecast, and the bias is lower than NHC and other methods during weakening periods. Furthermore, an improved TC intensity forecast is obtained by incorporating all four satellite bands. Using SST and TC latitude as the other two predictors in a linear regression model, the RMSE and MAE of the DAV 24-h forecast are 13.7 and 10.9 kt (1 kt ≈ 0.51 m s−1), respectively, and the skill space of the DAV is about 5.5% relative to the Statistical Hurricane Intensity Forecast model with inland decay (Decay-SHIFOR) during TC weakening periods. Considering the DAV is an independent intensity technique, it could potentially add value as a member of the suite of operational intensity forecast techniques, especially during TC weakening periods.

The appropriate design of infrastructure in tropical cyclone (TC) prone regions requires an understanding of the hazard risk profile underpinned by an accurate, homogenous long-term TC dataset. The existing Australian region TC archive, or ‘best track’ (BT), suffers from inhomogeneities and an incomplete long-term record of key TC parameters. This study assesses mostly satellite-based objective techniques for 1981–2016, the period of a geostationary satellite imagery dataset corrected for navigation and calibration issues. The satellite-based estimates of Australian-region TCs suffer from a general degradation in the 1981–1988 period owing to lower quality and availability of satellite imagery.The quality of the objective techniques for both intensity and structure is compared to the reference BT 2003–2016 estimates. For intensity the Advanced Dvorak Technique algorithm corresponds well with the BT 2003–2016, when the algorithm can use passive microwave data (PMW) as an input. For the period prior to 2003 when PMW data is unavailable, the intensity algorithm has a low bias. Systematic corrections were made to the non-PMW objective estimates to produce an extended (1989–2016) homogeneous dataset of maximum wind that has sufficient accuracy to be considered for use where a larger homogeneous sample size is valued over a shorter more accurate period of record. An associated record of central pressure using the Courtney-Knaff-Zehr wind pressure relationship was created.For size estimates, three techniques were investigated: the Deviation Angle Variance and the ‘Knaff’ techniques (IR-based), while the ‘Lok’ technique used model information (ECMWF reanalysis dataset and TC vortex specification from ACCESS-TC). However, results lacked sufficient skill to enable extension of the reliable period of record. The availability of scatterometer data makes the BT 2003–2016 dataset the most reliable and accurate. Recommendations regarding the best data source for each parameter for different periods of the record are summarised.

Tropical Cyclone Oswald (2013) is considered to be one of the highest-impact storms to make landfall in northern Australia even though it only reached a maximum category 1 intensity on the Australian category scale. After making landfall on the west coast of Cape York Peninsula, Oswald turned southward, and persisted for more than 7 days moving parallel to the coastline as far south as 308S. As one of the wettest tropical cyclones (TCs) in Australian history, the favorable configurations of a lower-latitude active monsoon trough and two consecutive midlatitude trough-jet systems generally contributed to the maintenance of the Oswald circulation over land and prolonged rainfall. As a result, Oswald produced widespread heavy rainfall along the east coast with three maximum centers near Weipa, Townsville, and Rockhampton, respectively. Using high-resolution WRF simulations, the mechanisms associated with TC Oswald's rainfall are analyzed. The results show that the rainfall involved different rainfall mechanisms at each stage. The land-sea surface friction contrast, the vertical wind shear, and monsoon trough were mostly responsible for the intensity and location for the first heavy rainfall center on the Cape York Peninsula. The second torrential rainfall near Townsville was primarily a result of the local topography and land-sea frictional convergence in a conditionally unstable monsoonal environment with frictional convergence due to TC motion modulating some offshore rainfall. The third rainfall area was largely dominated by persistent high vertical wind shear forcing, favorable large-scale quasigeostrophic dynamic lifting from two midlatitude trough-jet systems, and mesoscale frontogenesis lifting.

Based on 12 years (2004-2015) of GOES-East satellite infrared (IR) imagery over the North Atlantic basin, the diurnal cycle of the tropical cyclone (TC) Deviation Angle Variation (DAV) value is analysed, and a backward 24-hr time average DAV is selected to filter the noise in the DAV-TC intensity estimation. The effect of satellite observation angle on the DAV-TC intensity estimation is analysed in theory, in a case study, and in the longer-term statistics. Based on these results, an improvement to the DAV-TC intensity estimation is presented and evaluated in this study. The results show that, after taking the observation angle into account, the new DAV-TC intensity estimation is shown to produce smaller errors and higher correlations than the previous versions, especially for major hurricanes.

A case of an extreme rainfall event in Canberra, the capital city of Australia, on 24–25 February 2018 is investigated. Canberra received more than the long‐term statistical February total rainfall within 6 hr on that day, causing overwhelming flash flooding in the city. In contrast to many coastal cities or tropical regions, Canberra is located at the boundary between the tropics and extratropics and is usually dry and warm in February. The diagnostic analysis shows that this relatively rare brief heavy rainfall was the joint effects of lower‐level warm, moist conveyor belts, the remnants of tropical cyclone Kelvin, surface cold fronts, and a midlatitude trough at middle to upper levels. The lower‐level northeast and west conveyor belts of water vapor contributed to the accumulation of moisture and created favorable conditional instability over Canberra by transporting tropical moist and warm air from Coral Sea and from the Kelvin remnant to Canberra prior to and during the rainfall period. Enhanced uplift was triggered by the arrival of a cold front in the warm, moist unstable air. The rainfall reached a peak 13.6 mm in 30 min soon after a low‐level frontal updraft coupled with middle‐ to upper‐level dynamical uplift ahead of the midlatitude trough. After the passage of the upper‐level trough, the dry, cold air in the rear of the trough stabilized and dried the atmosphere above Canberra. The rainfall decreased and ended rapidly.