Maximum Sustained Wind Research Articles

Python-based Prediction of Rapid Intensification from MIMIC-TC Ensemble (PRIME)

Rapid intensification (RI), as defined by the National Hurricane Center (NHC), is an increase in the maximum sustained winds of a tropical cyclone (TC) of at least 30 knots (~34-35 mph) within a 24-hour period. Intensity forecasting is one of the most difficult aspects of TC analysis forecasting, with RI prediction being one of the most challenging issues. Predicting intensity and RI is critical for emergency responses, including evacuation and disaster prevention. Deep learning (DL) and its application in TC analysis holds much potential. Morphed Integrated Microwave Imagery at the Cooperative Institute for Meteorological Satellite Studies (MIMIC) is a product that synthesizes “morphed” images of TCs. MIMIC-TC is a product that uses 85-92 GHz microwave imagery to create the images. Using the Python programming language, a DL convolutional neural network (CNN) ensemble was developed as a proof-of-concept for prediction of RI, known as the Prediction of Rapid Intensification from MIMIC-TC Ensemble (PRIME). Six members comprise PRIME, split into three 10 and 20 epoch models. Each model has either 2, 3, or 4 convolutional layers. A MIMIC-TC dataset was created using available North Atlantic Basin (NATL) storms from 2019 and 2020, and a total of 1508 images were used for training the models. After running the Ensemble on all available storms from 2019 and 2020, it appeared all models were overfit, and subsequently gave inaccurate classifications. The average percentage of correct classifications of “No RI” (nRI) was 30%, and the average percentage of correct classifications of “Possible RI” (pRI) was 27%.

Hurricane Laura (2020): A Comparison of Drop Size Distribution Moments Using Ground and Radar Remote Sensing Retrieval Methods

AbstractHurricane Laura was the strongest hurricane to make landfall in Louisiana since 1969 with maximum sustained winds of 130 knots. One University of Oklahoma Shared Atmospheric Mobile and Teaching Polarmetric Radar (SR1‐P), and four portable in situ precipitation stations (PIPSs) equipped with parsivel disdrometers were spatially and temporally collocated with two NASA Global Precipitation Measurement Mission Dual‐frequency Precipitation Radar overpasses. The combined retrieval methods were able to quantify and compare drop size distribution moments and radar‐inferred precipitation processes before, during, and after the storm center made landfall. It was found that the magnitude of collision‐coalescence dominant precipitation decreased from before to after landfall. Further, the presence of a bright‐band becomes more evident across all percentiles in the post‐landfall overpass, indicating an increase in stratiform precipitation compared to convective precipitation after Laura moved inland. The PIPS showed an increase in mean drop size from 1.0 mm before landfall to as high as 4.0 mm in the eyewall, while decreasing to below 1.0 mm as Laura continued to move inland with a decrease in maximum echo top height of 0.5–1.0 km. Last, the Dual‐frequency Precipitation Radar (DPR) algorithm overestimated the normalized intercept parameter by 0.5–1.0 m−3 mm−1 compared to the PIPS implying differences in measured drop number concentration, potentially due to differences in measurement footprint or assumptions in the DPR retrieval algorithm. These findings can potentially be used to improve the DPR particle size distribution algorithm in tropical cyclones.

The Sensitivity of Initial Condition and Horizontal Resolution on Simulation of Tropical Cyclone Amphan over the Bay of Bengal using WRF-ARW Model

Simulation of Super Cyclonic Storm (SuCS) Amphan (2020) has been carried out as a case study to analyze the sensitivity of Initial Condition (IC) and Horizontal Resolution (HR) on the intensity, track, and landfall predictions of the Tropical Cyclones (TCs). The results suggest that the IC and HR have a significant impact on TC simulations. Diminishing the HR in simulation results in a comparatively higher severity of the system. The simulation with a reduced lead time and a comparatively smaller HR has forecasted the Minimum Central Pressure (MCP) distribution reasonably well. In comparison to the observations, the reduced lead time model run with higher resolution has forecasted the Maximum Sustained Wind Speed (MSWS) distribution precisely. According to the statistical analysis, the continual reduction of lead time in simulation with the HR of 27 km has simulated better track and landfall positions than other resolutions. Thus, the combination of reduced lead time and higher resolution in simulation may be considered for the proper track and landfall forecasting. Dhaka Univ. J. Sci. 69(3): 202-211, 2022 (June)

Effect of scale interaction on the development of typhoon intensity in summer and autumn

<p>本研究利用綜觀尺度擾動(SSE)動能方程式，定量分析尺度交互作用對颱風強度發展的影響。研究結果發現，強烈颱風(C3-C5等級，最大強度≥96 knots)的移動速度(發展時間)較弱颱風(C1-C2等級，最大強度64~95 knots)慢(長)，增強率較大。分析強颱移動速度較慢的原因，發現弱颱風與強烈颱風主要受到大尺度副高環流場的導引而移動。然而，強颱伴隨的駛流場較弱，其可能與副高環流場減弱及季風槽和季內震盪(ISO)氣旋式環流的增強有關。此外，強(弱)颱往西北方向(往西以及往北轉向)移動。而強颱增強率較強的原因為其東南-西北向的移動路徑，經過西北太平洋海溫最高、對流層頂溫度最低、垂直風切場最弱，以及ISO擾動動能最大的區域，有利強颱的強度發展。</p> <p>SSE動能方程式的研究結果顯示，在颱風從生成增強至最大強度的過程中，正壓能量轉換(CK項)與斜壓能量轉換(CE項)均是颱風強度增強的能量來源。CK項中的CKS-M 和CKS-ISO兩項均有正貢獻，即季節平均環流與ISO均傳送能量給颱風發展。然而，在颱風發展後期，強颱與弱颱CKS-ISO差異大，強颱自ISO 獲得較多能量。CKS-ISO差異主要來源為CKS-ISO中的-(〖u_s^’ v〗_s^’ (∂u_I^’)/∂y) ̅ 項，該項與強颱伴隨ISO氣旋式環流(-(∂u_I^’)/∂y>0)的增強及強颱 〖u_s^’ v〗_s^’向北傳送動量較多有關。因此，當ISO與颱風增強，強颱將自ISO 獲得更多能量。此外，因強颱與伴隨強颱的ISO氣旋式環流移速較慢，在颱風發展後期，強颱與ISO氣旋式環流仍位在暖洋面上。而ISO氣旋式環流南側西南氣流所提供的水氣，亦有利強颱的潛熱釋放，將強颱可用位能轉換成更多的強颱動能。此正回饋效應，使強颱得到較多能量而強度較強。因此，尺度交互作用是颱風強度發展的重要機制。</p> <p> </p><p>This study quantitatively analyzed the influence of scale interaction on typhoon intensity by using the synoptic-scale eddy (SSE) kinetic energy equation. Our research showed that speed of movement, development time, and intensification rate of intense typhoons (categories 3–5 with maximum sustained wind speeds greater than 96 knots) are slower, longer, and larger, respectively, than those of weak typhoons (categories 1–2 with maximum sustained wind speeds between 64 and 95 knots). By analyzing the reasons for the slower speed of movement of intense typhoons, we discovered that weak and intense typhoons are mainly steered by large-scale subtropical high circulation during the intensification process. However, the steering flow of intense typhoons are much weaker which may result from the weakened subtropical high circulation, the enhanced monsoon trough and strengthened intraseasonal oscillation (ISO) cyclonic circulation. In addition, intense typhoons were steered northwestward, while weak typhoons moved westward or northward recurving. The northwestward propagation of intense typhoons experienced the highest sea surface temperature (SST), lowest tropopause temperature, weakest vertical wind shear, and largest ISO kinetic energy region over the western North Pacific (WNP). These large-scale environments were favorable for the growth (a larger intensification rate) of intense typhoons.</p> <p>Further diagnosis of the SSE kinetic energy budget suggested that the enhancement of typhoon intensity is contributed by both barotropic (CK) and baroclinic (CE) energy conversions during the intensification process. The positive contributions of CKS-M and CKS-ISO in the CK term indicate that both seasonal mean circulation and ISO flow provide energy to typhoons. However, intense typhoons gain more eddy kinetic energy from the ISO flow during the late period of typhoon development. This CKS-ISO difference is dominated by the -(〖u_s^’ v〗_s^’ (∂u_I^’)/∂y) ̅ term associated with the strengthened ISO cyclonic circulation (-(∂u_I^’)/∂y>0) and the greater momentum transport (〖u_s^’ v〗_s^’) of intense typhoons. Thus, as ISO and typhoons intensified, intense typhoons gain more energy from ISO. In addition, both the intense typhoons and their accompanying ISO cyclonic circulation are still located over the warm ocean due to their slower speed of movement. The moisture provided by the southwesterly flows at the southern flank of this ISO cyclonic circulation was also favorable for the latent heat release of intense typhoons and converted more typhoon available potential energy into typhoon kinetic energy. This positive feedback causes intense typhoons to receive more energy and further results in more intensification of intense typhoons. This research indicates that scale interaction plays an important role in the development of typhoon intensity.</p> <p> </p>

Predictive skill comparative assessment of WRF 4DVar and 3DVar data assimilation: An Indian Ocean tropical cyclone case study

Tropical cyclones (TCs) predictions are crucial and of significant scientific and socio-economic interest. To improve the TC's simulation, this study aimed to use the Weather Research and Forecasting (WRF) model to assimilate satellite radiances and surface and upper-air conventional observations cyclically at the intervals of 6 h. The predictive skill of WRF with 4D variational (4DVar) and 3D variational (3DVar) data assimilation (DA) systems are investigated to examine the added value of 4DVar considering four cyclones namely Vardah, Titli, Bulbul, and Yaas over the North Indian Ocean (NIO). Each analysis was subjected to a short-range free forecast, and the estimates obtained from the 4DVar DA showed more realistic TC characteristics in terms of structure, intensity, rainfall, and track. The simulation of mean sea-level pressure and maximum sustained wind has demonstrated significant improvements in the 4DVar run for all the cases. Furthermore, the vector displacement error in cyclone Vardah was reduced by 54%–57% in the 4DVar simulation, whereas this improvement was found to be 9%–18% in the case of Titli. More or less similar results were found for Bulbul and Yaas also. Simulation of rainfall also showed marked improvement from the 4DVar scheme. Thus, the performance of the WRF model integrated with the 4DVar DA method enhanced the simulations of TC intensity, tracks, and structures over the NIO.

Influence of the ocean on tropical cyclone intensity using a high resolution coupled atmosphere–ocean model: A case study of very severe cyclonic storm Ockhi over the North Indian Ocean

AbstractA very severe cyclonic storm (VSCS), Ockhi was conceived in the Bay of Bengal and moved over the Arabian Sea undergoing rapid intensification (RI) in the early stages of its life cycle. Ockhi is simulated using the high‐resolution coupled Hybrid Coordinate Ocean Model and the Hurricane Weather Research Forecast model. The study suggests that sea surface temperature warmer than the seasonal climatological mean with the availability of a large tropical cyclone heat potential (TCHP) has acted as a potential cause for RI when the TC moved over that region. We have conducted experiments with (CP) and without (UCP) ocean coupling. The predicted tracks of UCP and CP experiments agree well with the India Meteorological Department's best track data. However, CP showed a significant improvement in intensity prediction with 61 and 19% for mean sea level pressure and maximum sustained surface wind at 10 m respectively in comparison to UCP. The presence of thermal and haline stratification in the subsurface affects surface cooling. The subsurface turbulent heat flux calculated for the high‐TCHP region under the TC suggests that the upward transport of heat, which increases the enthalpy flux, becomes necessary to bring about RI. The primary circulation, secondary circulation, inflow, thermal structure, moisture distribution and mass flux within the TC environment during the 24‐hr RI period are analyzed to investigate the evolution of TC characteristics for both UCP and CP experiments. The analyses suggest that the UCP experiments show higher values of intensity as compared to CP experiments. Further, a more organized evolution of updrafts is present in the CP simulation, maybe due to the formation of a stable boundary layer. The study shows the significant influence of coupling on surface enthalpy controlling boundary layer characteristics and thus ultimately producing a more organized TC.

Forecasting pressure drop and maximum sustained wind speed associated with cyclonic systems over Bay of Bengal with neuro-computing

The current research anticipates developing a model based on adaptive neuro-computation to foresee the minimum pressure drop (PD) at the centre as well as the maximum sustained wind speed (MSWS) accompanying with cyclonic systems over Bay of Bengal (BOB). The cyclonic systems taken in this work contain systems of different ranges starting from deep depression to extreme severe cyclones. For predicting PD and MSWS, suitable predictors have been sorted using factor analysis and it is observed that low-level vorticity (LLV), mid-tropospheric relative humidity (MRH) and vertical wind velocity at 850, 500 and 200 hPa pressure levels are appropriate parameters to create input matrix of neural network (NN). The adaptive NN representations are skilled with the data from 1990 to 2015 to estimate the PD as well as MSWS over BOB for 47 cyclonic systems. The outcome divulges that the multi-layer perceptron (MLP) NN model delivers decent precision at 6- and 30-h lead time in foretelling the PD. But the lowest error has been found at 6-h lead time in forecasting the central PD during mature stage of cyclonic systems. The result also illustrates that the MLP model is the utmost capable in forecasting the MSWS during mature stage of cyclonic structures with the lowest prediction error at 60-h lead time. The model results were validated and compared with the operational forecast by IMD for the 10 cyclonic systems from 2016 to 2019.

A Review of Historical Changes of Tropical and Extra-Tropical Cyclones: A Comparative Analysis of the United States, Europe, and Asia.

Tropical cyclones are highly destructive weather systems, especially in coastal areas. Tropical cyclones with maximum sustained winds exceeding 74 mph (≈119 kph) are classified as typhoons in the Northwest Pacific, whilst the term ‘hurricanes’ applies to other regions. This study aims to investigate the general characteristics of the most devastating and catastrophic tropical cyclones in the USA Europe, and Asia. To achieve the study objectives, the three most devastating typical tropical cyclones in each region were selected. The tropical cyclones were examined based on various features, such as the number of deaths, minimum pressure, highest wind speed, total financial losses, and frequency per year. In contrast to Europe and Asia, the USA has recorded the highest number of catastrophic tropical cyclones. The damage induced by hurricanes Katrina, Harvey, and Maria in the USA totalled approximately USD USD 380 billion. In addition, the present research highlights the demand to improve the public attitude and behaviour toward the impact of climate change along with the enhancement of climate change alleviation strategies. The number of intense tropical cyclones is expected to rise, and the tropical cyclone-related precipitation rate is expected to increase in warmer-climate areas. Stakeholders and industrial practitioners may use the research findings to design resilience and adaptation plans in the face of tropical cyclones, allowing them to assess the effects of climate change on tropical cyclone incidents from an academic humanitarian logistics viewpoint in the forthcoming years.

Tropical Cyclone Impact and Forest Resilience in the Southwestern Pacific

Tropical cyclones (TCs) can have profound effects on the dynamics of forest vegetation that need to be better understood. Here, we analysed changes in forest vegetation induced by TCs using the normalized difference vegetation index (NDVI). We used an accurate historical database of TC tracks and intensities, together with the Willoughby cyclone model to reconstruct the 2D surface wind speed structure of TCs and analyse how TCs affect forest vegetation. We used segmented linear models to identify significant breakpoints in the relationship between the reconstructed maximum sustained wind speed (Wmax) and the observed changes in NDVI. We tested the hypothesis that the rate of change in damage caused by TCs to forest and recovery time would increase according to Wmax thresholds as defined in the widely used Saffir–Simpson hurricane wind scale (SSHWS). We showed that the most significant breakpoint was located at 50 m/s. This breakpoint corresponds to the transition between categories 2 and 3 TCs in the SSHWS. Below this breakpoint, damages caused to forest vegetation and the time needed to recover from these damages were negligable. We found a second breakpoint, with a sharp increase in damages for winds >75 m/s. This suggested that extremely intense tropical cyclones, which might be more frequent in the future, can cause extreme damages to forest vegetation. Nevertheless, we found high variation in the observed damages and time needed to recover for a given Wmax. Further studies are needed to integrate other factors that might affect the exposure and resistance to TCs as well as forests’ capacity to recover from these disturbances.

Tropical cyclone track and intensity prediction skill of GFS model over NIO during 2019 & 2020

The Tropical Cyclone (TC) track prediction using different NWP models and its verification is the critical task to provide prior knowledge about the model errors, which is beneficial for giving the model guidance-based real-time cyclone warning advisories. This study has attempted to verify the Global Forecast System (GFS) model forecasted tropical cyclone track and intensity over the North Indian Ocean (NIO) for the years 2019 and 2020. GFS is one of the operational models in the India Meteorological Department (IMD), which provides the medium-range weather forecast up to 10 days. The forecasted tracks from the GFS forecast are obtained using a vortex tracker developed by Geophysical Fluid Dynamics Laboratory (GFDL). A total of 13 tropical cyclones formed over the North Indian Ocean, eight during 2019 and five in 2020 have been considered in this study. The accuracy of the model predicted tracks and intensity is verified for five days forecasts (120 h) at 6-h intervals; the track errors are verified in terms of Direct Position Error (DPE), Along Track Error (ATE) and Cross-Track Error (CTE). The annual mean DPE over NIO during 2019 (51–331 km) is lower than 2020 (82–359 km), and the DPE is less than 150 km up to 66 h during 2019 and 48 h during 2020. The positive ATE (76–332 km) indicates the predicted track movement is faster than the observed track during both years. The positive CTE values for most forecast lead times suggest that the predicted track is towards the right side of the observed track during both years. The cyclone Intensity forecast for the maximum sustained wind speed (MaxWS) and central mean sea level pressure (MSLP) are verified in terms of mean error (ME) and root mean square error (RMSE). The errors are lead time independent. However, most of the time model under-predicted the cyclone intensity during both years. Finally, there is a significant variance in track and intensity errors from the cyclone to cyclone and Bay of Bengal basin to the Arabian Sea basin.

Characterization of Tropical Cyclone Intensity Using the HY-2B Scatterometer Wind Data

The estimation of tropical cyclone (TC) intensity using Ku-band scatterometer data is challenging due to rain perturbation and signal saturation in the radar backscatter measurements. In this paper, an alternative approach to directly taking the maximum scatterometer-derived wind speed is proposed to assess the TC intensity. First, the TC center location is identified based on the unique characteristics of wind stress divergence/curl near the TC core. Then the radial extent of 17-m/s winds (i.e., R17) is calculated using the wind field data from the Haiyang-2B (HY-2B) scatterometer (HSCAT). The feasibility of HSCAT wind radii in determining TC intensity is evaluated using the maximum sustained wind speed (MSW) in the China Meteorological Administration best-track database. It shows that the HSCAT R17 value generally better correlates with the best-track MSW than the HSCAT maximum wind speed, therefore indicating the potential of using the HSCAT data to improve the TC nowcasting capabilities.

Impact of highest maximum sustained wind speed and its duration on storm surges and hydrodynamics along Krishna–Godavari coast

The storm surge and hydrodynamics along the Krishna–Godavari (K–G) basin are examined based on numerical experiments designed from assessing the landfalling cyclones in Bay of Bengal (BoB) over the past 38 years with respect to its highest maximum sustained wind speed and its duration. The model is validated with the observed water levels at the tide gauge stations at Visakhapatnam during 2013 Helen and 2014 Hudhud. Effect of gradual and rapid intensification of cyclones on the peak water levels and depth average currents are examined and the vulnerable locations are identified. The duration of intensification of a rapidly intensifying cyclone over the continental shelf contributed to about 10–18% increase in the peak water levels, whereas for the gradually intensifying cyclone the effect is trivial. The inclusion of the wave-setup increased the peak water levels up to 39% compared to those without wave-setup. In the deep water region, only rapidly intensifying cyclones affected the peak MWEs. Intensification over the continental slope region significantly increases the currents along the shelf region and coast. The effect on peak maximum depth averaged current extends up to 400 km from the landfall location. Thus, it is necessary to consider the effect of various combinations of the highest cyclone intensity and duration of intensification for identifying the worst scenarios for impact assessment of coastal processes and sediment transport. The study is quite useful in improving the storm surge prediction, in preparedness, risk evaluation, and vulnerability assessment of the coastal regions in the present changing climate.

Southwest Pacific tropical cyclone development classification utilizing machine learning and synoptic composites

AbstractThis study evaluates the ability of machine learning algorithms to classify tropical depressions (TDs) and tropical storms (TSs) in the western region of the southwest Pacific Ocean (SWPO). Decision rules are generated to predict the environment required for a depression to fully develop into a mature storm, and the most influential predictors in the classification decision are ranked. TD and TS are discriminated based on a maximum sustained wind speed threshold (≥17 ms−1). Various aerosol, thermodynamic, and dynamic parameters are extracted closest to the initiation point of each non‐developing and developing sample. The covariates associated with each labelled sample are used to train a decision tree and random forest model. Results using a testing dataset suggest the random forest approach more accurately distinguishes between non‐developing and developing samples. The classification accuracy of the decision tree and random forest are 72% and 91%, respectively. Random forest outperformed the decision tree by providing higher accuracy in test data. The most important variables for binary classification are sea salt aerosol optical depth (AOD), 1,000 mb relative humidity, and sea surface temperature. AOD is a quantitative estimate of the aerosols presents in the air through the extinction of a ray of light as it passes through the atmosphere. Mean composite maps constructed in an unsupervised manner have been created for the most important variables identified by the random forest classifier during TD and TS events to highlight the difference in geophysical and aerosol variables' climatology during the two different classifications. This work will advance the risk management strategies for northeastern Australia and other SWPO basin islands to control their tropical cyclone related losses through prioritizing forecasting variables that are the strongest predictors of the strengthening of tropical depressions into tropical cyclones.

Investigation of Machine Learning Using Satellite-Based Advanced Dvorak Technique Analysis Parameters to Estimate Tropical Cyclone Intensity

AbstractSeveral simple and computationally inexpensive machine learning models are explored that can use advanced Dvorak technique (ADT)-retrieved features of tropical cyclones (TCs) from satellite imagery to provide improved maximum sustained surface wind speed (MSW) estimates. ADT (version 9.0) TC analysis parameters and operational TC forecast center best track datasets from 2005 to 2016 are used to train and validate the various models over all TC basins globally and select the best among them. Two independent test sets of TC cases from 2017 to 2018 are used to evaluate the intensity estimates produced by the final selected model called the “artificial intelligence (AI)” enhanced advanced Dvorak technique (AiDT). The 2017–18 MSW results demonstrate a global RMSE of 7.7 and 8.2 kt (1 kt ≈ 0.51 m s−1), respectively. Basin-specific MSW RMSEs of 8.4, 6.8, 7.3, 8.0, and 7.5 kt were obtained with the 2017 dataset in the North Atlantic, east/central Pacific, northwest Pacific, South Pacific/south Indian, and north Indian Ocean basins, respectively, with MSW RMSE values of 8.9, 6.7, 7.1, 10.4, and 7.7 obtained with the 2018 dataset. These represent a 30% and 23% improvement over the corresponding ADT RMSE for the 2017–18 datasets, respectively, with the AiDT error reduction significant to 99% in both sets. The AiDT model represents a notable improvement over the ADT performance and also compares favorably to more computationally expensive and complex machine learning models that interrogate satellite images directly while still preserving the operational familiarity of the ADT.

Long short-term memory (LSTM)-based wind speed prediction during a typhoon for bridge traffic control

A short-term wind speed prediction framework is proposed for bridge traffic control under strong winds. The framework mainly focuses on improving the prediction accuracy for the timeframe of traffic control during a typhoon. Two concepts are newly proposed to achieve the goal: 1) hybrid modeling of wind speed at the bridge; and, 2) the adoption of a time-shifted data correction (TSDC) method. First, the hybrid modeling considers two available data types, one from a structural health monitoring system of the bridge and the other from the regional specialized meteorological center (RSMC). The training features of a long short-term memory (LSTM) approach are chosen based on the maximum sustained winds of a typhoon. Second, the TSDC method accounts for a time-delay phenomenon between the maximum wind speed at the bridge deck and the maxima or minima of the selected features. The Mean Absolute Error (MAE)-based grid search method determines the preferable combinations of two parameters: input data length and the time-shifted length of the training data. As a numerical example, typhoons from 2020 are used as test data to demonstrate the improvement in prediction performance via the use of hybrid modeling and the TSDC method.

Transport of Water Vapor from Tropical Cyclones to the Upper Troposphere

This paper investigates the influence of tropical cyclones on water vapor concentrations in the upper atmosphere above these storms. We use independent data sets of tropical storm intensity, water vapor and lightning activity to investigate this relationship. Water vapor in the upper troposphere is a key greenhouse gas, with direct impacts on surface temperatures. Both the amount and altitude of water vapor impact the radiative balance and the greenhouse effect of the atmosphere. The water vapor enters the upper troposphere through deep convective storms, often associated with lightning activity. The intensity of the lightning activity represents the intensity of the convection in these storms, and hence the amount of water vapor transported aloft. In this paper, we investigate the role of tropical cyclones on the contribution of water vapor to the upper atmosphere moistening. Tropical cyclones are the largest most intense storms on Earth and can last for up to two weeks at a time. There is also evidence that the intensity of tropical cyclones is increasing, and will continue to increase, due to global warming. In this study we find that the maximum moistening of the upper atmosphere occurs at the 200 hPa level (~12 km altitude), with a lag of 1–2 days after the maximum sustained winds in the tropical cyclone. While the water vapor peaks after the maximum of the storm intensity, the lightning activity peaks before the maximum intensity of the storms, as shown previously. We show here that the absolute amount of water vapor in the upper troposphere above tropical storms increases linearly with the intensity of the storms. For every 10 hPa decrease in the minimum pressure of tropical storms, the specific humidity increases around 0.2 g/kg at the 200 hPa level.

Survival of western Gulf Coast Mottled Ducks (Anas fulvigula) in the path of a Category 4 hurricane.

Tropical cyclones are the most powerful storms on earth, causing catastrophic damage to human lives and infrastructure. Hurricanes also cause wildlife mortality when they make landfall, but the severity of these effects is difficult to quantify because data collection is either logistically impossible or deprioritized in the wake of human tragedy. On August 27, 2020, Hurricane Laura made landfall in southwestern Louisiana with maximum sustained winds of 241 kph (150 mph), making it one of the most powerful storms to strike the mainland United States. Hurricane Laura passed directly over the core breeding range of the western Gulf Coast population of Mottled Duck (Anas fulvigula), during a time when many adult birds were undergoing a simultaneous wing feather molt and were flightless. We used GPS‐GSM telemetry data to evaluate survival rates of adult female Mottled Ducks in late summer 2020 (bracketing August 27 by one month on either side) relative to the same period in 2018 and 2019. Mortality was lower in 2018 (12 out of 29; 41%) and 2019 (8 out of 28; 29%) than in 2020 (12 out of 18; 67%), and 7 out of 12 mortalities documented in 2020 occurred when Hurricane Laura made landfall. Survival analyses in program MARK confirmed lower survival probability in 2020, but there was overlap in 85% confidence intervals in all years. This single storm resulted in the death of ~40% of all marked birds in our sample, suggesting that hurricanes have the potential to influence population demographics. In addition, Hurricane Laura resulted in widespread habitat loss and degradation that has reduced available nesting habitat in 2021, and possibly for years to come. The acute and chronic effects of hurricanes may exacerbate Mottled Duck population declines, which may worsen in the face of increasingly frequent and more severe tropical storms.

Understanding the post‐monsoon tropical cyclone variability and trend over the Bay of Bengal during the satellite era

AbstractAn effort is made to investigate the tropical cyclone (TC) variability over the Bay of Bengal in the warming climate scenario during the satellite era (1979–2018). In this study, the authors investigate interannual variations in the overall capacity of the Bay of Bengal (BoB) basin in the post‐monsoon season to produce either intense or moderate TCs. Globally accepted and widely used metrics accumulated cyclone energy (ACE) and power dissipation index (PDI) are used to examine the energy and damage generated by TCs. A statistical change‐point analysis is conducted to detect the shift in the tropical cyclone frequency and ACE during the study period, indicating the year 1999 as a change point. The results show the increasing trend in ACE in the BoB during the post‐monsoon season with a statistical significance of 95%. On the contrary, the overall frequency is almost unchanged but has shown an increasing trend in the last decade of the study period. Additionally, atmospheric and ocean factors influencing the TC intensity are investigated by applying principal component analysis for three categories of TCs, viz. the very severe cyclonic storm, severe cyclonic storm, and cyclonic storm. Implementation of principal component analysis and correlations of maximum sustained wind speed (MSW2; intensity) with relative vorticity, minimum sea‐level pressure (MSLP), sea‐surface temperature (SST), specific rainwater content, and vertical wind shear revealed relative vorticity as the most dominating parameter amongst all governing factors which determine the final intensity of the BoB TCs. On the contrary, SST shows the different nature of the relationship with different TC categories.

Tropical cyclone characteristics associated with extreme precipitation in the northern Philippines

AbstractThe Philippines is exposed to tropical cyclones (TCs) throughout the year due to its location in the western North Pacific. While these TCs provide much‐needed precipitation for the country's hydrological cycle, extreme precipitation from TCs may also cause damaging hazards such as floods and landslides. This study examines the relationship between TC extreme precipitation and TC characteristics, including movement speed, intensity and season, for westward‐moving TCs crossing Luzon, northern Philippines. We measure extreme precipitation by the weighted precipitation exceedance (WPE), calculated against a 95th percentile threshold, which considers both the magnitude and spatial extent of TC‐related extreme precipitation. WPE has a significant, moderate positive relationship with TC intensity with a non‐significant, weak negative relationship with movement speed. When TCs are classified by intensity 1 day before landfall (or pre‐landfall), Typhoons (1‐min maximum sustained wind speed ≥64 knots) tend to yield higher WPE than non‐Typhoons (<64 knots). On the other hand, when TCs are classified by pre‐landfall speed, slow TCs (movement speed <11.38 knots) tend to yield higher WPE than fast TCs (movement speed ≥11.38 knots). However, the relationship between pre‐landfall TC intensity and WPE is more pronounced during June–September while there is no significant difference between the WPE of the southwest monsoon (June–September) and northeast monsoon (October–December) seasons. These results suggest that it is important to consider the pre‐landfall cyclone movement speed, intensity and season to anticipate extreme precipitation of incoming TCs. A decision table considering these factors is devised to aid in TC extreme precipitation forecasting.

The Response and Feedback of Ocean Mesoscale Eddies to Four Sequential Typhoons in 2014 Based on Multiple Satellite Observations and Argo Floats

Four sequential tropical cyclones generated and developed in the Northwest Pacific Ocean (NWP) in 2014, which had significant impacts on the oceanic environment and coastal regions. Based on a substantial dataset of multiple-satellite observations, Argo profiles, and reanalysis data, we comprehensively investigated the interactions between the oceanic environment and sequential tropical cyclones. Super typhoon Neoguri (2014) was the first typhoon-passing studied area, with the maximum sustained wind speed of 140 kts, causing strong cold wake along the track. The location of the strongest cold wake was consistent with the pre-existing cyclonic eddy (CE), in which the average sea surface temperature (SST) cooling exceeded −5 °C. Subsequently, three tropical cyclones passed the ocean environment left by Neoguri, namely, the category 2 typhoon Matmo (2014), the tropical cyclone Nakri (2014) and the category 5 typhoon Halong (2014), which caused completely different subsequent responses. In the CE, due to the fact that the ocean stratification was strongly destroyed by Neoguri and difficult to recover, even the weak Nakri could cause a secondary response, but the secondary SST cooling would be overridden by the first response and thus could cause no more serious ocean disasters. If the subsequent typhoon was super typhoon Halong, it could cause an extreme secondary SST cooling, exceeding −8 °C, due to the deep upwelling, exceeding 700 m, surpassing the record of the maximum cooling caused by the first typhoon. In the anti-cyclonic eddy (AE), since the first typhoon Neoguri caused strong seawater mixing, it was difficult for the subsequent weak typhoons to mix the deeper, colder and saltier water into the surface, thus inhibiting secondary SST cooling, and even the super typhoon Halong would only cause as much SST cooling as the first typhoon. Therefore, the ocean responses to sequential typhoons depended on not only TCs intensity, but also TCs track order and ocean mesoscale eddies. In turn, the cold wake caused by the first typhoon, Neoguri, induced different feedback effects on different subsequent typhoons.