Tropical Cyclone Counts Research Articles

Effects of Solar Variability on Tropical Cyclone Activity

AbstractThe current study explores the relationship between solar variability and tropical cyclone (TC) activity using sunspot number (SSN) and TC best‐track data as respective proxies. We have considered six regions of the globe, for example, EP: Eastern Pacific, NA: North Atlantic, NI: North Indian, SI: South Indian, SP: South Pacific, and WP: Western Pacific. The results show strong anti‐correlation between yearly TC activity and yearly SSN while considering their 11‐year moving averages. This behavior is consistent for TC counts as well as accumulated cyclone energy. However, this is true only for the North Atlantic region. Overall, when we consider all regions together, more TCs (in terms of counts) are observed during lower solar activity periods (SSN < 50) as compared to higher solar activity conditions (SSN > 100). However, the yearly rates remain more or less similar. On the other hand, extreme TC events with a maximum wind speed of 137 knots and higher (category 5) are most likely to occur during the declining phase of a solar cycle and least likely to occur during the ascending phase or the maximum phase. Although solar activity levels are similar during the declining and ascending phases, the yearly occurrence rate is nearly double in the declining phase (1.123) as compared to that in the ascending phase (0.625).

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.

Tropical cyclone genesis prediction based on support vector machine considering effects of multiple meteorological parameters

Most coastal regions worldwide face great challenges from tropical cyclones (TCs). Accurate prediction of TC genesis is critical for conducting risk assessments and disaster mitigation. Compared to conventional statistical models, this study introduces a machine-learning-based approach utilizing a support vector machine (SVM) to predict TC genesis. Historical TC genesis data from the Japan Meteorological Agency (JMA), China Meteorological Administration (CMA), and Joint Typhoon Warning Center (JTWC) are utilized. Five meteorological parameters are extracted from climate reanalysis datasets: absolute vorticity, relative humidity, vertical velocity relative sea surface temperature, and vertical wind shear. A high-dimensional mapping between meteorological parameters and TC genesis labels is performed before classification training. The effects of proportions of positive/negative samples and training/testing sets on model performance are examined, aiming to identify the optimal solution. Different meteorological parameter combinations tested to achieve the highest classification accuracy. Temporal and spatial outcomes of TC predictions are compared with historical data to verify model accuracy. The proposed approach enables simultaneous determination of TC genesis locations and counts, significantly enhancing computational efficiency. It also uses meteorological parameters more comprehensively considering the underlying physical mechanisms. In future climate change scenarios, forecasted meteorological data can be directly incorporated to derive TC genesis patterns.

Seasonal forecast of tropical cyclones in the Southwest Pacific Ocean

AbstractPredictions of tropical cyclone (TC) activity have been a topic of recurrent interest and research in the past. Here we utilize reanalysis datasets of sea surface temperature (SST) and mixed layer depth (MLD) to build a statistical seasonal forecasting model that produces outlooks of expected TC counts in the region of the Southwest Pacific (SWP). Nevertheless, the model applicability can be extended to other regions and basins. A novel TC predictor index is developed at the daily scale and used to obtain an objective classification of synoptic weather patterns. This classification has been performed by clustering the daily index predictor fields, previously transformed into principal components, using a K‐mean algorithm. As a result, 49 daily weather types (DWTs) are presented which inform about the mean representative features and spatial patterns of both predictor and predictand variables. Thus, statistical relationships between TC activity and nonlinear combinations of predictor variables are found to assign daily rates of expected TCs. The cluster‐based model is calibrated from 1982 to 2019 and validated by recent TC season observations, demonstrating the operational application using ensembles of long‐term predictions in the Southwest Pacific. Results have shown which synoptic types of SST and MLD are favourable to cyclogenesis and activity, with additional information related to concurrent sea level pressure and precipitation synoptic patterns, as well as seasonal and interannual climate variability.

An Open‐Source, Physics‐Based, Tropical Cyclone Downscaling Model With Intensity‐Dependent Steering

AbstractAn open‐source, physics‐based tropical cyclone (TC) downscaling model is developed, in order to generate a large climatology of TCs. The model is composed of three primary components: (a) a random seeding process that determines genesis, (b) an intensity‐dependent beta‐advection model that determines the track, and (c) a non‐linear differential equation set that determines the intensification rate. The model is entirely forced by the large‐scale environment. Downscaling ERA5 reanalysis data shows that the model is generally able to reproduce observed TC climatology, such as the global seasonal cycle, genesis locations, track density, and lifetime maximum intensity distributions. Inter‐annual variability in TC count and power‐dissipation is also well captured, on both basin‐wide and global scales. Regional TC hazard estimated by this model is also analyzed using return period maps and curves. In particular, the model is able to reasonably capture the observed return period curves of landfall intensity in various sub‐basins around the globe. The incorporation of an intensity‐dependent steering flow is shown to lead to regionally dependent changes in power dissipation and return periods. Advantages and disadvantages of this model, compared to other downscaling models, are also discussed.

The influence of solar variability on Atlantic basin tropical cyclones: a 10-year study

The Sun is a dynamic celestial body that changes as the universe progresses through space and time. The Sun is by far the most important driving force of the climate system. This paper aims to study the impact of solar variability on our planet’s extreme weather, namely tropical cyclones in the Atlantic basin. From the year 2010 to 2020, we have analyzed the trend of Thermosphere Climate Index (TCI) values, Tropical Cyclone (TC) counts, and its maximum wind speeds. The r value presented by the Pearson’s correlation coefficient confirms a strong anti-correlation relationship of -0.719 between the TCI value and TC count. Meanwhile, the observed cyclone wind speed shows a moderately strong negative correlation with TCI values, with an r value of -0.520. The seasonal fluctuations in TCs are believed to be caused by a shift in thermal energy within the thermosphere.

Neutrophil to Lymphocyte Ratio in Hospitalized COVID 19 Patients – A Study in a Tertiary Care Covid Centre in Eastern India

Background Corona Virus Disease -2019 (COVID-19) has jeopardised human life globally for last more than one year due to its high infectivity and tendency to develop sudden deterioration of cases by complicated pathophysiology. Some cost effective markers are necessary to predict severity so that timely appropriate management can be given. Neutrophil to lymphocyte ratio (NLR) in blood is such a common parameter that has been previously used to predict severity in various conditions like cardiovascular diseases and sepsis. Our objective was to estimate total White blood cell count (TC) and NLR in hospitalised COVID-19 patients and to find out their role to predict severity. Methods This observational cross sectional study was done on hospitalized COVID-19 adult patients where patients were categorized into moderate and severe cases as per guideline of Govt. of India. TC and Differential count were estimated by automated cell counter and NLR was compared in these two groups by unpaired t test to find out their significance. Results Out of total 175 cases, 49(28%) were categorized as severe while rest 126 (72%) patients were in moderate category of disease. The mean ± standard deviations of TC (X103/µl) and NLR for moderate disease were 8.85±4.60 and 5.57±6.80 respectively while those for severe disease were 12.78±6.54 and 12.99±12.21 respectively. Both the parameters have statistically significant difference between two groups (p <0.001). Conclusion TC and NLR were significantly higher in severe cases compared to moderate cases and hence they can be utilised to triage COVID-19 cases at an early stage.

Evolution of Tropical Cyclone Properties Across the Development Cycle of the GISS-E3 Global Climate Model.

The next‐generation global climate model from the NASA Goddard Institute for Space Studies, GISS‐E3, contains many improvements to resolution and physics that allow for improved representation of tropical cyclones (TCs) in the model. This study examines the properties of TCs in two different versions of E3 at different points in its development cycle, run for 20 years at 0.5° resolution, and compares these TCs with observations, the previous generation GISS model, E2, and other climate models. E3 shares many TC biases common to global climate models, such as having too few tropical cyclones, but is much improved from E2. E3 produces strong enough TCs that observation‐based wind speed thresholds can now be used to detect and track them, and some storms now reach hurricane intensity; neither of these was true of E2. Model development between the first and second versions of E3 further increased the number and intensity of TCs and reduced TC count biases globally and in most regions. One‐year sensitivity tests to changes in various microphysical and dynamical tuning parameters are also examined. Increasing the entrainment rate for the more strongly entraining plume in the convection scheme increases the number of TCs (though also affecting other climate variables, and in some cases increasing biases). Variations in divergence damping did not have a strong effect on simulated TC properties, contrary to expectations based on previous studies. Overall, the improvements in E3 make it more credible for studies of TC activity and its relationship to climate.

Resolving Long‐Term Variations in North Atlantic Tropical Cyclone Activity Using a Pseudo Proxy Paleotempestology Network Approach

AbstractPaleohurricane reconstructions extend the observational record of tropical cyclones (TCs) back thousands of years. However, these records are subject to biases–capturing only close‐moving intense storms at varying resolutions. We devise two pseudo proxy networks drawing from the full suite of published paleohurricane studies in the North Atlantic. We run synthetic storms forced with two global climate model simulations of the past millennium through each pseudo network to assess the theoretical skill of paleohurricane proxies at capturing low‐frequency variability in North Atlantic basin‐wide and intrabasin TCs. We find that basin‐wide and paleohurricane compiled TC counts are significantly correlated with one another for the past millennium on annual to multi‐decadal timescales, but compilation skill is limited by proxy temporal resolution. Current paleohurricane proxy networks predominantly capture storms moving in the Caribbean/Gulf of Mexico. Increasing the quantity of paleohurricane records from the North American coastline substantially improves reconstruction skill.

A Machine Learning Based Ensemble Forecasting Optimization Algorithm for Preseason Prediction of Atlantic Hurricane Activity

In this study, nine different statistical models are constructed using different combinations of predictors, including models with and without projected predictors. Multiple machine learning (ML) techniques are employed to optimize the ensemble predictions by selecting the top performing ensemble members and determining the weights for each ensemble member. The ML-Optimized Ensemble (ML-OE) forecasts are evaluated against the Simple-Averaging Ensemble (SAE) forecasts. The results show that for the response variables that are predicted with significant skill by individual ensemble members and SAE, such as Atlantic tropical cyclone counts, the performance of SAE is comparable to the best ML-OE results. However, for response variables that are poorly modeled by individual ensemble members, such as Atlantic and Gulf of Mexico major hurricane counts, ML-OE predictions often show higher skill score than individual model forecasts and the SAE predictions. However, neither SAE nor ML-OE was able to improve the forecasts of the response variables when all models show consistent bias. The results also show that increasing the number of ensemble members does not necessarily lead to better ensemble forecasts. The best ensemble forecasts are from the optimally combined subset of models.

Modulation of North Pacific and North Atlantic Tropical Cyclones by Tropical Transbasin Variability and ENSO during May–October

AbstractThis study highlights the distinct modulation of May–October tropical cyclones (TCs) in the western North Pacific (WNP), eastern North Pacific (ENP), and North Atlantic (NATL) Ocean basins by tropical transbasin variability (TBV) and ENSO. The pure TBV significantly modulates total TC counts in all three basins, with more TCs in the WNP and ENP and fewer TCs in the NATL during warm TBV years and fewer TCs in the WNP and ENP and more TCs in the NATL during cold TBV years. By contrast, the pure ENSO signal shows no impact on total TC count over any of the three basins. These results are consistent with changes in large-scale factors associated with TBV and ENSO. Low-level relative vorticity (VOR) is an important driver of WNP TC genesis frequency, with broad agreement between the observed spatial distribution of TC genesis and TBV/ENSO-associated VOR anomalies. TBV significantly affects ENP TC frequency as a result of changes in basinwide vertical wind shear and sea surface temperatures, whereas the modulation in TC frequency by ENSO is primarily caused by a north–south dipole modulation of large-scale atmospheric and oceanic factors. The pure TBV-related low-level VOR changes appear to be the most important factor modulating NATL TC frequency. Changes in large-scale factors compare well with the budget of synoptic-scale eddy kinetic energy. Possible physical processes associated with pure TBV and pure ENSO that modulate TC frequency are further discussed. This study contributes to the understanding of TC interannual variability and could thus be helpful for seasonal TC forecasting.

Efficacy of commercial overhead washing and waxing systems on the microbiological quality of fresh peaches

Overhead spray washing and waxing systems (WWS) are used commercially to reduce the risk of microbial contamination and improve the quality of fresh produce during packing. This study evaluated the microbiological quality of overhead spray water and spent peach wash water, as well as fresh peaches before and after they pass the WWS. Pre- and post-washed/waxed peach samples (n = 192) and overhead spray water and spent peach wash water samples (n = 54) were collected several times over the course of a processing day in three packing facilities located in the state of Georgia. Populations of total aerobes (TA), yeasts and molds (YM), and coliforms (TC) and the presence of thermotolerant coliforms (TTC) and enterococci (EC) were measured in collected samples. The average TA and TC counts and the incidences of TTC and EC were significantly higher (P < 0.05) on peach samples collected after the WWS compared to those collected before the WWS. Counts and incidences of TA, YM, and TC in spent peach wash water were significantly higher than in the overhead spray water where neither TTC nor EC was detected. Results suggest that the commercial washing and waxing systems had little effect in improving the microbiological quality of fresh peaches.

U.S. Tropical Cyclone Activity in the 2030s Based on Projected Changes in Tropical Sea Surface Temperature

AbstractWe use a statistical tropical cyclone (TC) model, the North Atlantic Stochastic Hurricane Model (NASHM), in combination with sea surface temperature (SST) projections from climate models, to estimate regional changes in U.S. TC activity into the 2030s. NASHM is trained on historical variations in TC characteristics with two SST indices: global–tropical mean SST and the difference between tropical North Atlantic Ocean (NA) SST and the rest of the global tropics, often referred to as “relative SST.” Testing confirms the model’s ability to reproduce historical U.S. TC activity as well as to make skillful predictions. When NASHM is driven by SST projections into the 2030s, overall NA annual TC counts increase, and the fractional increase is the greatest at the highest wind intensities. However, an eastward anomaly in mean TC tracks and an eastward shift in TC formation region result in a geographically varied signal in U.S. coastal activity. Florida’s Gulf Coast is projected to see significant increases in TC activity relative to the long-term historical mean, and these increases are fractionally greatest at the highest intensities. By contrast, the northwestern U.S. Gulf Coast and the U.S. East Coast will see little change.

WASH infrastructure and practices in primary health care clinics in the rural Vhembe District municipality in South Africa

BackgroundSouth Africa has unique and diverse social and economic factors that have an impact on the provision of basic water, sanitation, hygiene and waste management infrastructure and practices at health care facilities in ensuring patient safety and prevent the spread of diseases.MethodsThe aim of this study was to evaluate water, sanitation and hygiene access and standards at 50 government owned public health care clinics in the rural region of the Vhembe district of South Africa during 2016/2017, using self-observation, an observation checklist, record reviews and interviews with clinic managers. Water quality from all available water sources on the clinic compound was analysed for Total coliform and E. coli counts using the Colilert Quanti-tray/2000 system. The prevalence of pathogenic diarrhea causing E. coli strains was established using multiplex-Polymerase Chain Reaction.ResultsThe health care clinics in the Vhembe District generally complied with the basic WASH services guidelines according to the World Health Organisation. Although 80% of the clinics used borehole water which is classified as an improved water source, microbiological assessment showed that 38% inside taps and 64% outside taps from the clinic compounds had TC counts higher than guideline limits for safe drinking. Similarly, EC counts above the guideline limit for safe drinking water were detected in 17% inside taps and 32% outside taps from the clinic compounds. Pathogenic EAEC, EPEC, ETEC and EHEC strains were isolated in the collected water samples. Although improved sanitation infrastructures were present in most of the clinics, the sanitary conditions of these toilets were not up to standard. Waste systems were not adequately managed. A total of 90% of the clinics had hand washing basins, while only 61% of the clinics had soap present and only 64% of the clinics had adequate signs and posters reminding the staff, care givers and patients to wash their hands.ConclusionsVarious WASH aspects within the primary health care system in South Africa needs to be improved and corrected. A more rigorous system that is inclusive of all role players in the WASH sectors, with regular monitoring and training sessions, should be used.

Evaluation of Physicochemical and Biological Parameters on the Water Quality of ShilabatiRiver, West Bengal, India

ABSTRACT Anthropogenic activities affect the rivers and the entire basin in different dimensions. All the river basins including Ganga and Damodar are polluted from unplanned anthropogenic activities. This study is an attempt to access the quality of Shilabati River by computing water quality and pollution index using eleven parameters pH, COD, BOD, DO, TDS, Total Hardness, Total Alkalinity, Phosphate, Chloride, Nitrate, and Turbidity measured at eighteen different sites. The BOD and TC counts are found to be 2.45 mg/l and 9334 MPN/100 ml; which reveal that the river water falls under the moderately polluted category of Central Pollution Control Board. The high phosphate concentration suggests a heavy load of anthropogenic and industrial wastes and contributes to the rise in COD and BOD. The high phosphate concentrations also account for the eutrophic water. The physicochemical parameters fall mostly under the E category of Central Pollution Control Board. The Water Quality Index, Comprehensive Pollution Index, and Eutrophication index values show that the river water quality is poor and seriously polluted indicating that the water is not suitable for drinking, domestic purpose, and also for healthy survival of aquatic life and needs urgent management.

Increasing frequency in off-season tropical cyclones and its relation to climate variability and change

Abstract. This article analyzes the relationship between off-season tropical cyclone (TC) frequency and climate variability and change for the Pacific Ocean and Atlantic Ocean basins. TC track data were used to extract the off-season storms for the 1900–2019 period. TC counts were aggregated by decade, and the number of storms for the first 6 decades (presatellite era) was adjusted. Mann–Kendall nonparametric tests were used to identify trends in decadal TC counts and multiple linear regression (MLR) models were used to test if climatic variability or climate change factors explained the trends in off-season storms. MLR stepwise procedures were implemented to identify the climate variability and change factors that explained most of the variability in off-season TC frequency. A total of 713 TCs were identified as occurring earlier or later than their peak seasons, most during the month of May and in the West Pacific and South Pacific basins. The East Pacific (EP), North Atlantic (NA) and West Pacific (WP) basins exhibit significant increasing trends in decadal off-season TC frequency. MLR results show that trends in sea surface temperature, global mean surface temperature and cloud cover explain most of the increasing trend in decadal off-season TC counts in the EP, NA and WP basins. Stepwise MLR results also identified climate change variables as the dominant forces behind increasing trends in off-season TC decadal counts, yet they also showed that climate variability factors like El Niño–Southern Oscillation, the Atlantic Multidecadal Oscillation and the Interdecadal Pacific Oscillation also account for a portion of the variability.

Dynamic wavelet correlation analysis for multivariate climate time series

The wavelet local multiple correlation (WLMC) is introduced for the first time in the study of climate dynamics inferred from multivariate climate time series. To exemplify the use of WLMC with real climate data, we analyse Last Millennium (LM) relationships among several large-scale reconstructed climate variables characterizing North Atlantic: i.e. sea surface temperatures (SST) from the tropical cyclone main developmental region (MDR), the El Niño-Southern Oscillation (ENSO), the North Atlantic Multidecadal Oscillation (AMO), and tropical cyclone counts (TC). We examine the former three large-scale variables because they are known to influence North Atlantic tropical cyclone activity and because their underlying drivers are still under investigation. WLMC results obtained for these multivariate climate time series suggest that: (1) MDRSST and AMO show the highest correlation with each other and with respect to the TC record over the last millennium, and: (2) MDRSST is the dominant climate variable that explains TC temporal variability. WLMC results confirm that this method is able to capture the most fundamental information contained in multivariate climate time series and is suitable to investigate correlation among climate time series in a multivariate context.

Using Indicators of ENSO, IOD, and SAM to Improve Lead Time and Accuracy of Tropical Cyclone Outlooks for Australia

AbstractCatastrophic impacts associated with tropical cyclone (TC) activity mean that the accurate and timely provision of TC outlooks are important to people, places, and numerous sectors in Australia and beyond. In this study, we apply a Poisson regression statistical framework to predict TC counts in the Australian region (AR; 5°–40°S, 90°–160°E) and its four subregions. We test 10 unique covariate models, each using different representations of the influence of El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), and southern annular mode (SAM) and use an automated covariate selection algorithm to select the optimum combination of predictors. The performance of preseason TC count outlooks generated between April and October for the AR TC season (November–April) and in-season TC count outlooks generated between November and January for the remaining AR TC season are tested. Results demonstrate that skillful TC count outlooks can be generated in April (i.e., 7 months prior to the start of the AR TC season), with Pearson correlation coefficient values betweenr= 0.59 and 0.78 and covariates explaining between 35% and 60% of the variance in TC counts. The dependence of models on indices representing Indian Ocean sea surface temperature highlights the importance of the Indian Ocean for TC occurrence in this region. Importantly, generating rolling monthly preseason and in-season outlooks for the AR TC season enables the continuous refinement of expected TC counts in a given season.

Comparative study of single versus multiple gallstone disease in KGH, Visakhapatnam

Objective: To evaluate the differences in the presentation, their operative implications and difficulties of single stone versus multiple gall stone disease.Methods: This is a prospective study of a minimum of 60 patients with gall stone disease (Group 1-single, Group 2-multiple) from January 2019 to December 2019 in KGH, Visakhapatnam.Results: More common in females, 40-50 years symptoms and complications, TC count difficult cholecystectomies were more in multiple gall stone pts.Conclusion: Multiple gall stones have led to more severe symptoms and complications and difficult surgery. should be motivated for early surgery even in incidental finding and should not opt for conservative management.

Immune escape mutations in HIV-1 controllers in the Brazilian Amazon region

BackgroundHuman immunodeficiency virus (HIV-1) infection is characterized by high viral replication and a decrease in CD4+ T cells (CD4+TC), resulting in AIDS, which can lead to death. In elite controllers and viremia controllers, viral replication is naturally controlled, with maintenance of CD4+TC levels without the use of antiretroviral therapy (ART).MethodsThe aim of the present study was to describe virological and immunological risk factors among HIV-1-infected individuals according to characteristics of progression to AIDS. The sample included 30 treatment-naive patients classified into three groups based on infection duration (> 6 years), CD4+TC count and viral load: (i) 2 elite controllers (ECs), (ii) 7 viremia controllers (VCs) and (iii) 21 nonviremia controllers (NVCs). Nested PCR was employed to amplify the virus genome, which was later sequenced using the Ion PGM platform for subtyping and analysis of immune escape mutations.ResultsViral samples were classified as HIV-1 subtypes B and F. Greater selection pressure on mutations was observed in the group of viremia controllers, with a higher frequency of immunological escape mutations in the genes investigated, including two new mutations in gag. The viral sequences of viremia controllers and nonviremia controllers did not differ significantly regarding the presence of immune escape mutations.ConclusionThe results suggest that progression to AIDS is not dependent on a single variable but rather on a set of characteristics and pressures exerted by virus biology and interactions with immunogenetic host factors.