Swarm intelligence and neural nets in forecasting the maximum sustained wind speed along the track of tropical cyclones over Bay of Bengal

Tropical cyclones are well-known extreme weather and the cause of considerable damages, injuries and loss of life. The assessment of the maximum sustained wind speed along the track of the tropical cyclones is very important for estimating the strength of the cyclones. The swarm intelligence in the form of ant colony optimization (ACO) technique is introduced in this study to compute the pheromone deposition along the track of tropical cyclones followed by neural nets to forecast the maximum sustained wind speed of the cyclones occurring over the Bay of Bengal of North Indian Ocean. The ACO is a nonlinear problem-based meta-heuristic optimization method for finding approximate solutions to discrete optimization problems and simulates the decision-making processes of ant colony similar to other adaptive learning techniques. The method has shown its application potential in various fields including the prediction of monsoon rainfall. In this study, the amount of pheromone deposition during the successive stages of the cyclones has been estimated. A range of minimum central pressure (MCP), central pressure drop (PD), maximum sustained wind speed (MSWS) and intensity (T-No) associated with the cyclones of Bay of Bengal are utilized to form the input matrix of the neural nets. The neural nets are trained to forecast the maximum sustained wind speed along the track of the tropical cyclones over Bay of Bengal. The result reveals that the errors in forecasting the MSWS along the track of tropical cyclones with 6, 12, 18 and 24 h lead time are 2.6, 2.9, 3.1 and 4.8, respectively. The result is compared with the existing dynamical, statistical and adaptive models to evaluate the skill of the present model. The result is well validated with observation.     

A Comparative Research on Climatic Characteristics of Jianghuai Cyclones in Rainfall Season between Warming and Cooling Stages

By EOF, Power spectrum and nonlinear mapping methods, the temperature time series of May to July in Jianghuai river basins from 1948-2014 (67 years) were objectively divided into several climate stages. The time series were divided into three climate stages with similar lengths: the stage with its air temperature trend going downwards stage (1948-1970); the stage with its air temperature trend going upwards (1971-1994) and the stage with its air temperature trend going gentle upwards (1995-2014). Their climate characteristics can be quantified by a special climate index. Then, the statistic characteristics of Jianghuai cyclones in the three climate stages were compared with the cooling or warming trends. The results showed that characteristic evolutions of the frequency of yearly average of Jianhuai cyclones, the cyclones with their life cycles in the area longer than 48h, the cyclones with three types of thickness, shallow and bottom shapes classified according to the cyclone height, the cyclones with three kinds of zonal, meridional, and circular trajectory, responded to the temperature changing trends in the three climate stages. The cooling trends of the stage temperature restrained the activity of Jianghuai cyclones, and the warming trends of the stage temperature promoted the cyclone’s development, maintaining, activity, and also led cyclone moving towards to north further. The composite analysis of cyclone structures showed that the evolution characteristics in the center intensity, temperature grads, the maxima velocity of jet of the cyclones, and the intensity, length and position of the vapor flux passage of the Jianghuai cyclones, also the intensity, thickness, the positions of the warm and cold air masses within the cyclones, all responded to the trend change of stage temperature including cooling, warming and gentle warming. The results show clearly that temperature cooling reduces the intensity of cyclone structures and restrains the activity of cyclones in the temperatures trend down stage. The temperature warming enhances the stable development and strengthening of cyclone structure,and maintains the activity of cyclones in the temperature warming stages.     

Mitigation of Flooding and Cyclone Hazard in Orissa, India

Storm surges generated by the strong tangential wind stressesand normal atmospheric pressure gradients at the sea surface due to tropical cyclones (TC'S)have been studied with the goal of detecting any significant and systematic changes due to climatechange. Cyclone and storm surge data for the 19th and 20th centuries for the Bay of Bengalcoast of the state of Orissa in India are available to varying degrees of quality and detail,the data being more scientific since the advent of the India Meteorological Department in 1875.Based on more precise data for the period 1971 to 2000, statistical projections have been madeon the probable intensities of tropical cyclones for various return periods. The super cyclone ofOctober 29, 1999 (SC1999) appears to have a return period of about 50 years. The cyclones of1831, 1885 and possibly the one in 1895 could have been super cyclones. During the 19th century,there were 72 flooding events associated with cyclones, whereas in the 20th century therewere only 56 events. There was no observational evidence to suggest that there was an increaseeither in the frequency or intensity of cyclones or storm surges on the coast of Orissa. However,the impact of cyclones and surges is on the increase due to increase of population and coastalinfrastructure.     

Climatology of landfalling tropical cyclones in Bangladesh 1877–2003

Bangladesh is highly susceptible to tropical cyclones. Unfortunately, there is a dearth of climatological studies on the tropical cyclones of Bangladesh. The Global Tropical Cyclone Climatic Atlas (GTCCA) lists historical storm track information for all the seven tropical cyclone ocean basins including the North Indian Ocean. Using GIS, tropical cyclones that made landfall in Bangladesh during 1877–2003 are identified and examined from the climatological perspective. For the convenience of study, the coast of Bangladesh is divided into five segments and comparisons are made among the coastal segments in terms of cyclone landfall and vulnerability. There is a large variability in the year-to-year occurrence of landfalling tropical cyclones in Bangladesh. Most of the tropical cyclones (70%) hit in the months of May–June and October–November generally show the well-known pattern of pre- and post-monsoon cyclone seasons in that region.     

SPH-based simulation of flow process of a landslide at Hongao landfill in China

The cyclones over Bay of Bengal (BoB) have varied socio-economic impacts and meteorological importance. There are considerable uncertainties in predicting the track and intensity of cyclonic systems in the BoB. The present study examines the cyclogenesis characteristics over the BoB and addresses the regional impacts and their importance in terms of intensification of cyclones. An analysis of cyclone track data from 1971–2013 reveals that the cyclones generated in Andaman Sea (a regional sea of BoB) and propagating through central BoB sustain maximum life time. Furthermore, within the BoB, the cyclones originated from Andaman Sea are the most intensified and characterized by highest cyclogenesis potential index. Interestingly, we have found that higher value of mid-tropospheric relative humidity over Andaman Sea during the cyclone period is enhancing the cyclone’s intensity. Climatologically also the Andaman Sea is dominated by higher values of mid-tropospheric relative humidity compared to other regions of BoB. There is no significant distinction between Andaman Sea and rest of the BoB for other meteorological and oceanic parameters that supports cyclogenesis. Climatologically dominant east–west asymmetry in mid-tropospheric relative humidity is enhancing the intensity of cyclones from Andaman Sea. The results will be helpful in understanding the processes of cyclone intensification and useful in the statistical and dynamical prediction of cyclones.     

Tropical cyclones over north Indian Ocean during El-Niño Modoki years

Tropical cyclones are the most hazardous weather systems, which form over warm ocean waters. The frequencies of tropical cyclones show variabilities over all the oceanic basins, during the El-Niño and El-Niño Modoki years. Recent studies have shown significant impact of air–sea interaction processes like El-Niño and El-Niño Modoki on the cyclone activity over different ocean basins. The results suggest in most cases, El-Niño events suppress the formation of cyclones over various basins. A recent study indicated that concurrent occurrence of El-Niño Modoki and positive Indian Ocean dipole (IOD) events can generate more cyclones over north-west Pacific. We propose to study the impact of El-Niño Modoki events on the formation of tropical cyclones over north Indian Ocean (NIO). Our present study suggests that the cyclogenesis over the NIO is a complex phenomenon, as it is influenced by several coupled ocean atmospheric phenomena such as El-Niño, El-Niño Modki, IOD and Madden–Julian oscillation.     

Intensity of tropical cyclones during pre- and post-monsoon seasons in relation to accumulated tropical cyclone heat potential over Bay of Bengal

The aim of the present study is to understand the impact of oceanic heat potential in relation to the intensity of tropical cyclones (TC) in the Bay of Bengal during the pre-monsoon (April–May) and post-monsoon (October–November) cyclones for the period 2006–2010. To accomplish this, the two-layer gravity model (TLGM) is employed to estimate daily tropical cyclone heat potential (TCHP) utilizing satellite altimeter data, satellite sea surface temperature (SST), and a high-resolution comprehensive ocean atlas developed for Indian Ocean, subsequently validated with in situ ARGO profiles. Accumulated TCHP (ATCHP) is estimated from genesis to the maximum intensity of cyclone in terms of minimum central pressure along their track of all the cyclones for the study period using TLGM generated TCHP and six-hourly National Centre for Environmental Prediction Climate Forecast System Reanalysis data. Similarly, accumulated sea surface heat content (ASSHC) is estimated using satellite SST. In this study, the relationship between ATCHP and ASSHC with the central pressure (CP) which is a function of TC intensity is developed. Results reveal a distinct relationship between ATCHP and CP during both the seasons. Interestingly, it is seen that requirement of higher ATCHP during pre-monsoon cyclones is required to attain higher intensity compared to post-monsoon cyclones. It is mainly attributed to the presence of thick barrier layer (BL) resulting in higher enthalpy fluxes during post-monsoon period, where as such BL is non-existent during pre-monsoon period.     

China’s tropical cyclone disaster risk source analysis based on the gray density clustering

In recent years, tropical cyclones on the Pacific Northwest have decreased. We cannot infer that tropical cyclones impact China have reduced, because the Pacific Northwest is not homogeneous, and the variation characteristics of tropical cyclones in different sea areas are not clear. This paper uses gray relational density clustering algorithm to cluster tropical cyclone data sets between 1949 and 2008, according to the generated position of tropical cyclones, generated density and the possibility of landing. The Pacific Northwest is divided into different sea areas. Then, we analyze the risk of tropical cyclones generated in these sea areas. The results show that the probability of tropical cyclones landing generated in some sea areas is very high, reached 74 %, but the probability of tropical cyclones landing generated in other sea areas is only 2 %. Tropical cyclones generated in some sea areas are more likely to develop into typhoons, strong typhoons and so on, but the intensity of tropical cyclones generated in other sea areas is lower, there is little risk for China. Finally, according to the climate change stage trends, we divide the period 1949–2008 into three stages and analyze the tropical cyclone risk of each sea areas.     

Intensity forecast of tropical cyclones over North Indian Ocean using multilayer perceptron model: skill and performance verification

The coastal regions of India are profoundly affected by tropical cyclones during both pre- and post-monsoon seasons with enormous loss of life and property leading to natural disasters. The endeavour of the present study is to forecast the intensity of the tropical cyclones that prevail over Arabian Sea and Bay of Bengal of North Indian Ocean (NIO). A multilayer perceptron (MLP) model is developed for the purpose and compared the forecast through MLP model with other neural network and statistical models to assess the forecast skill and performances of MLP model. The central pressure, maximum sustained surface wind speed, pressure drop, total ozone column and sea surface temperature are taken to form the input matrix of the models. The target output is the intensity of the tropical cyclones as per the T??number. The result of the study reveals that the forecast error with MLP model is minimum (4.70?%) whereas the forecast error with radial basis function network (RBFN) is observed to be 14.62?%. The prediction with statistical multiple linear regression and ordinary linear regression are observed to be 9.15 and 9.8?%, respectively. The models provide the forecast beyond 72?h taking care of the change in intensity at every 3-h interval. The performance of MLP model is tested for severe and very severe cyclonic storms like Mala (2006), Sidr (2007), Nargis (2008), Aila (2009), Laila (2010) and Phet (2010). The forecast errors with MLP model for the said cyclones are also observed to be considerably less. Thus, MLP model in forecasting the intensity of tropical cyclones over NIOs may thus be considered to be an alternative of the conventional operational forecast models.     

A Study on the Impact of Parameterization of Physical Processes on Prediction of Tropical Cyclones over the Bay of Bengal With NCAR/PSU Mesoscale Model

Prediction of the track and intensity of tropical cyclones is one of the most challenging problems in numerical weather prediction (NWP). The chief objective of this study is to investigate the performance of different cumulus convection and planetary boundary layer (PBL) parameterization schemes in the simulation of tropical cyclones over the Bay of Bengal. For this purpose, two severe cyclonic storms are simulated with two PBL and four convection schemes using non-hydrostatic version of MM5 modeling system. Several important model simulated fields including sea level pressure, horizontal wind and precipitation are compared with the corresponding verification analysis/observation. The track of the cyclones in the simulation and analysis are compared with the best-fit track provided by India Meteorological Department (IMD). The Hong-Pan PBL scheme (as implemented in NCAR Medium Range Forecast (MRF) model) in combination with Grell (or Betts-Miller) cumulus convection scheme is found to perform better than the other combinations of schemes used in this study. Though it is expected that radiative processes may not have pronounced effect in short-range forecasts, an attempt is made to calibrate the model with respect to the two radiation parameterization schemes used in the study. And the results indicate that radiation parameterization has noticeable impact on the simulation of tropical cyclones.     

Prediction of severe tropical cyclones over the Bay of Bengal during 2007–2010 using high-resolution mesoscale model

In this paper, the performance of a high-resolution mesoscale model for the prediction of severe tropical cyclones over the Bay of Bengal during 2007?C2010 (Sidr, Nargis, Aila, and Laila) is discussed. The advanced Weather Research Forecast (WRF) modeling system (ARW core) is used with a combination of Yonsei University PBL schemes, Kain-Fritsch cumulus parameterization, and Ferrier cloud microphysics schemes for the simulations. The initial and boundary conditions for the simulations are derived from global operational analysis and forecast products of the National Center for Environmental Prediction-Global Forecast System (NCEP-GFS) available at 1°lon/lat resolution. The simulation results of the extreme weather parameters such as heavy rainfall, strong wind and track of those four severe cyclones, are critically evaluated and discussed by comparing with the Joint Typhoon Warning Center (JTWC) estimated values. The simulations of the cyclones reveal that the cyclone track, intensity, and time of landfall are reasonably well simulated by the model. The mean track error at the time of landfall of the cyclone is 98?km, in which the minimum error was found to be for the cyclone Nargis (22?km) and maximum error for the cyclone Laila (304?km). The landfall time of all the cyclones is also fairly simulated by the model. The distribution and intensity of rainfall are well simulated by the model as well and were comparable with the TRMM estimates.     

A statistical cyclone intensity prediction (SCIP) model for the Bay of Bengal

A statistical model for predicting the intensity of tropical cyclones in the Bay of Bengal has been proposed. The model is developed applying multiple linear regression technique. The model parameters are determined from the database of 62 cyclones that developed over the Bay of Bengal during the period 1981–2000. The parameters selected as predictors are: initial storm intensity, intensity changes during past 12 hours, storm motion speed, initial storm latitude position, vertical wind shear averaged along the storm track, vorticity at 850 hPa, Divergence at 200 hPa and sea surface temperature (SST). When the model is tested with the dependent samples of 62 cyclones, the forecast skill of the model for forecasts up to 72 hours is found to be reasonably good. The average absolute errors (AAE) are less than 10 knots for forecasts up to 36 hours and maximum forecast error of order 14 knots occurs at 60 hours and 72 hours. When the model is tested with the independent samples of 15 cyclones (during 2000 to 2007), the AAE is found to be less than 13 knots (ranging from 5.1 to 12.5 knots) for forecast up to 72 hours. The model is found to be superior to the empirical model proposed by Roy Bhowmik et al (2007) for the Bay of Bengal.     

A parametric Markov renewal model for predicting tropical cyclones in Bangladesh

In this paper, we consider a Markov renewal process (MRP) to model tropical cyclones occurred in Bangladesh during 1877–2009. The model takes into account both the occurrence history and some physical constraints to capture the main physical characteristics of the storm surge process. We assume that the sequence of cyclones constitutes a Markov chain, and sojourn times follow a Weibull distribution. The parameters of the Weibull MRP jointly with transition probabilities are estimated using the maximum likelihood method. The model shows a good fit with the real events, and probabilities of occurrence of different types of cyclones are calculated for various lengths of time interval using the model. Stationary probabilities and mean recurrence times are also calculated. A brief comparison with a Poisson model and a marked Poisson model has also been demonstrated.     

Tropical Cyclones and Their Transformation into Extratropical: Estimates of the Half-Century Trends

Doklady Earth Sciences - Results from an analysis of the frequency of tropical cyclones, the probability of their transformation into extratropical cyclones, and their trends in different oceanic...     

The economic impact of future increase in tropical cyclones in Japan

This article estimates the non-first-order economic loss in Japan due to a future increase in tropical cyclones. One possible effect of global warming could be the increase in intensity of tropical cyclones. Using historical storm tracks between the years 1978 and 2007 and altering their intensities due to this potential increase in their intensity, this paper calculates the future potential regional GDP loss in a certain area that is affected by tropical cyclones. Most of the literature is concerned with physical damage and the loss of lives due to tropical cyclones. However, there are additional economic costs when sustained wind speeds are higher than 30 knots (55.56 km/h), a level that generally will lead to a precautionary cessation of many human activities. Using a Monte Carlo simulation, the paper calculates the potential economic costs for the year 2085 under a climate change scenario with a linear one-per cent yearly increase in CO₂. Using a spatial distribution of economic activity in Japan, it is possible to forecast which parts of the country are likely to experience the highest loss risk.     

Probabilistic assessment of tropical cyclones’ extreme wind speed in the Bay of Bengal: implications for future cyclonic hazard

Natural Hazards - Tropical cyclones (TCs) of the Bay of Bengal (BoB) cause catastrophic loss over the coasts at the time of landfall in India, Bangladesh, Myanmar and Sri Lanka. To strengthen...     

The influence of tropical cyclones upon the Earth’s crust

Doklady Earth Sciences - This paper discusses the influence of tropical cyclones (TCs) upon the Earth’s crust beneath the ocean bottom, which is associated with rapid variations of pressure...     

中国热带气旋暴雨洪水的分布和水文特性

描述了由热带气旋形成和影响的暴雨及其洪水的地域分布,给出了区划;讨论了热带气旋暴雨洪水的水文特性,并与非热带气旋暴雨洪水作了比较。     

Tropical cyclone prediction over Bay of Bengal: a comparison of the performance of NCEP operational HWRF, NCAR ARW, and MM5 models

Much progress has been made in the area of tropical cyclone prediction using high-resolution mesoscale models based on community models developed at National Centers for Environmental Predication (NCEP) and National Center for Atmospheric Research (NCAR). While most of these model research and development activities are focused on predicting hurricanes in the Atlantic and Eastern Pacific domains, there has been much interest in using these models for tropical cyclone prediction in the North Indian Ocean region, particularly for Bay of Bengal storms that are known historically causing severe damage to life and property. In this study, the advanced operational hurricane modeling system developed at NCEP, known as the Hurricane Weather Research and Forecast (HWRF) model, is used to simulate two recent Bay of Bengal tropical cyclones??Nargis of November 2007 and Sidr of April 2008. The advanced NCEP operational vortex initialization procedure is adapted for simulating these Bay of Bengal tropical cyclones. Two additional regional models, the NCAR Advanced Research WRF and NCAR/Penn State University Mesoscale Model version 5 (MM5) are also used in simulating these storms. Results from these experiments highlight the superior performance of HWRF model over other models in predicting the Bay of Bengal cyclones. These results also suggest the need for a sophisticated vortex initialization procedure in conjunction with a model designed exclusively for tropical cyclone prediction for operational considerations.     

Numerical storm surge model for India and Pakistan

The northeastern sector of the Arabian Sea, which covers the Gujarat coast of India and western coast of Pakistan, is a region vulnerable to extreme sea levels associated with tropical cyclones (TCs). Although the frequency of tropical cyclones in the Arabian Sea is not high, the coastal regions of India and Pakistan suffer in terms of loss of life and property caused by the surges. In view of this a location-specific fine resolution model is developed for the Gujarat coast of India and adjoining Pakistan coast. The east–west and north–south grid distance is about 3.0 km. Using this model, numerical experiments are carried out to simulate the surges generated by 1999 and 2001 cyclones which struck the Pakistan coast. The model computed surges are in agreement with the available observational estimates.