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To forecast the path of tropic cyclones by using a non‐linear statistical forecasting technique – the method of successive analogy.

Non‐linear statistical forecasting models can describe the non‐linear relationship between the factors and the forecasting objects and the real atmospheric movement more accurately, so they usually have stronger forecasting capability. In practice, however, it is shown that the relationships between predictors and predictands sometimes are so complex that it is very difficult or even impossible to establish the kind of non‐linear mathematical model. Therefore, it is an important topic for atmospheric science to solve non‐linear prediction problem of atmospheric systems by using the non‐function model approach.

The objective and quantitative prediction of tropical cyclone moving path can be given by using the method of successive analogy, a non‐linear forecasting technique, and calculating the similarity parameters between the grayscale field and the height field.

Further experiments are needed to verify this technique.

A very useful technique for solving non‐linear problem.

Illustrates the new technique of solving non‐linear statistic problem and its application.

Tropical storms Urduja and Vinta battered the Philippines in December 2017. Despite advances in disaster risk reduction efforts of the country, the twin December storms caused numerous deaths in the Visayas and Mindanao regions. Analysis of these events shows that alerts raised during the Pre-Disaster Risk Assessment (PDRA) for both storms were largely ineffective because they were too broad and general calling for forced evacuations in too many provinces. Repeated multiple and general warnings that usually do not end up in floods or landslides, desensitize people and result in the cry-wolf effect where communities do not respond with urgency when needed. It was unlike the previous execution of PDRA from 2014 to early 2017 by the National Disaster Risk Reduction and Management Council (NDRRMC), which averted mass loss of lives in many severely impacted areas because of hazard-specific, area-focused and time-bound warnings. PDRA must reinstate specific calls, where mayors of communities are informed by phone hours in advance of imminent danger to prompt and ensure immediate action. Mainstreaming Climate Change Adaptation and Disaster Risk Reduction information using probabilistic (multi-scenario) hazard maps is also necessary for an effective early warning system to elicit appropriate response from the community. The paper aims to discuss these issues.

Methods of early warning through the PDRA of the National Disaster Mitigation and Management Council (NDRRMC) of the Philippines during tropical storm Urduja and Typhoon Vinta were assessed in this study and compared to the previous PDRA system from 2014 to early 2017.

It was found out that the numerous casualties were due to inadequate warning issued during the approach of the tropical cyclones. During an impending hazard, warnings must be accurate, reliable, understandable and timely. Despite the availability of maps that identified safe zones for different communities, warnings raised during the PDRA for both tropical cyclones were deemed too general calling for evacuations of whole provinces. As such, not all communities were evacuated in a timely manner because of failure in the key elements of an effective early warning system.

To avoid future disasters from happening, it is recommended that the PDRA reinstate its hazards-specific, area-focused and time-bound warnings. Similarly, to increase the resilience of communities, more work on mainstreaming of Climate Change Adaptation and Disaster Risk and Vulnerability Reduction systems for communities must be done as well. Learning from the lessons of these previous disasters will enable communities, their leaders and every stakeholder, not to repeat the same mistakes in the future.

The coastal zone of Bangladesh that is in the front line of the battle against climate change faced over 200 natural disasters in the past 40 years, and most of the disasters were cyclones. The inevitable cyclone shelter (CS), the backbone of disaster management (DM), provides short-term safety for the disaster victims in Bangladesh. This study aims to explore the community-based limitations and sustainable development features of CSs including the gender issues.

A questionnaire survey was carried out among 230 community people to identify the requirements and sustainable development features of CSs. A field visit was carried out in 23 CSs to capture its existing facilities. Key informant interviews were conducted in the office of Upazila Engineers to strengthen survey data.

This research found that the plan of CSs, quality of construction, capacity, facilities, entrance and exit, space allocation, management and policy were not capable enough to fulfill the needs and requirements of the community people. Due to lack of separate facilities, women and girls avoided shelters for fear of sexual and mental harassment in CSs, as they had experiences in the earlier events of cyclones. Insufficient facilities discourage community from using the shelters.

Women and girls were shy to share their experience in CSs. The historical data were limited in the study area. To the best of the authors’ knowledge, this research presents the actual community-based outcome. During CCRIP training program, the authors met 3,625 community people, and participatory discussions were made to explore the participants’ experiences and perceptions about the sustainable development of CSs.

South-Asian coastal zones are prone to natural, quasi-natural hazard and disasters, where shelters are required for protecting lives of community people during such disasters such as cyclones, storm surges, and floods. Therefore, this study can help in making sustainable development decisions in terms of constructing shelters in disaster-prone countries like Bangladesh.

The outcomes of this investigation are useful for uplifting psychosocial status to protect lives during disasters such as cyclones, storm surges and floods and increase accessibility to shelters, and users will consider CSs as a social asset. In turn, the acceptability of CSs into community level are expected to be increased for combating against cyclones, storm surges, and floods.

This study introduces the bottom-up approach that refers to the community-based decision-making to identify the limitations and sustainable improvement of CSs. This research contributes to bridging the gaps between decision-makers and users of CSs. From the authors’ field experience, it can be said that this is the first fieldwork regarding the objectives.

The purpose of this paper is to develop an algorithm, using PCA‐based neural network, to retrieve the vertical rainfall structure in a precipitating atmosphere. The algorithm is powered by a rigorous solution to the plane parallel radiative transfer equation for the atmosphere with thermodynamically consistent vertical profiles of humidity, temperature and cloud structures, together with “measured” vertical profiles of the rain structure derived from a radar.

The raining atmosphere is considered to be a plane parallel, radiatively participating medium. The atmospheric thermodynamic profiles such as pressure, temperature and relative humidity along with wind speed at sea surface and cloud parameters corresponding to Nargis, a category 4 tropical cyclone that made its landfall on May 2, 2008 at the Republic of Myanmar, are obtained by solving the flux form of Euler's equations in three‐dimensional form. The state‐of‐the‐art community software Weather Research and Forecasting has been used for solving the set of equations. The three‐dimensional rain profiles for the same cyclone at the same instant of time are obtained from National Aeronautics and Space Administration's space borne Tropical Rainfall Measuring Mission's precipitation radar over collocated pixels. An in‐house Micro‐Tropiques code is used to perform radiative transfer simulations for frequencies corresponding to a typical space borne radiometer, and hence to generate the database which is later used for training the neural network. The back propagation‐based neural network is optimized with reduced number of parameters using principal component analysis (PCA).

The results show that neural network is capable of retrieving the vertical rainfall structure with a correlation coefficient of over 0.99. Further, reducing the ill‐posedness in retrieving 56 parameters from just nine measurements using PCA has improved the root mean square error in the retrievals at reduced computational time.

The paper shows that combining numerically generated atmospheric profiles together with radar measurements to serve as input to a radiative transfer model brings in the much‐required synergy between numerical weather prediction, radar measurements and radiative transfer. This strategy can be gainfully used in satellite meteorology. Using principal components to reduce the ill‐posedness, thereby increasing the robustness in retrieving vertical rain structure, has been attempted for the first time. A well‐trained network can be used as one possible option for an operational algorithm for the proposed Indian climate research satellite Megha‐Tropiques, due to be launched in early 2011.

The simulated scenarios can be used to compute risk premiums per risk class in the portfolio. These can then be used to adjust the policy premiums by accounting for storm risk.

A complete model to analyse and characterise future losses of the property portfolio of an insurance company due to hurricanes is proposed. The model is calibrated by using the loss data of the Fidelidade insurance company property portfolio resulting from Hurricane Leslie, which hit the centre of continental Portugal in October, 2018.

Several scenarios are simulated and risk maps are constructed. The risk map of the company depends on its portfolio, especially its exposure, and provides a Hurricane risk management tool for the insurance company.

A statistical model is considered, in which weather data is not required. The authors reconstruct the behaviour of storms through the registered claims and respective losses.

This paper aims to examine tweet posts regarding Typhoon Washi to contend the usefulness of social media and big data as an aid of post-disaster management. Through topic modelling and content analysis, this study examines the priorities of the victims expressed in Twitter and how the priorities changed over a year.

Social media, particularly Twitter, was where the data gathered. Using big data technology, the gathered data were processed and analysed according to the objectives of the study. Topic modelling was used in clustering words from different topics. Clustered words were then used for content analysis in determining the needs of the victims. Word frequency count was also used in determining what words were repeatedly used during the course period. To validate the gathered data online, government documents were requested and concerned government agencies were also interviewed.

Findings of this study argue that housing and relief goods have been the top priorities of the victims. Victims are seeking relief goods, especially when they are in evacuation centres. Also, the lack of legal basis hinders government officials from integrating social media information unto policymaking.

This study only reports Twitter posts containing keywords either, Sendong, SendongPH, Washi or TyphoonWashi. The keywords were determined based on the words that trended after Typhoon Washi struck.

For social media and big data to be adoptable and efficacious, supporting and facilitating conditions are necessary. Structural, technical and financial support, as well as legal framework, should be in place. Maintaining and sustaining positive attitude towards it should be taken care of.

Although many studies have been conducted on the usefulness of social media in times of disaster, many of these focused on the use of social media as medium that can efficiently spread information, and little has been done on how the government can use both social media and big data in collecting and analysing the needs of the victims. This study fills those gaps in social big data literature.

This paper aims to understand the vulnerability of community livelihoods (human, social, financial, natural and physical assets) at a coastal environment in southern Mozambique, considering the level of exposure, sensitivity and adaptive capacity to climate change.

The study adopted the sustainable livelihoods approach. Data were collected through distribution of a structured questionnaire to 476 randomly selected households at the municipality of Inhambane. The questionnaire assessed all capital assets, covering 14 indicators and 43 sub-indicators of vulnerability, derived from published literature.

Results indicate that overall community vulnerability is largely derived from the vulnerability of physical, financial and social capitals, illustrated by declared food shortage, low nutrition levels, weak social networks, high level of biomass utilization and lack of financial resources due to unemployment. These aspects largely influence the noticed reduced adaptive capacity of surveyed households.

The study identified the need to improve the overall process of natural resources appropriation and utilization and the improvement of the governance capacity at the local targeting infrastructure, community structure and networks and capacity building that might enhance community livelihoods in changing scenarios.

The study is a contribution to the overall understanding of how livelihoods are exposed to climate change and variability in coastal settings.

Insurance‐linked securities can benefit both issuers and investors; they supply insurance and reinsurance companies with additional risk capital at reasonable prices (with little or no credit risk), and supply excess returns to investors that are uncorrelated with the returns of other financial assets. This article explains the terminology of insurance and reinsurance, the structure of insurance‐linked securities, and provides an overview of major transactions. First, there is a discussion of how stochastic catastrophe modeling has been applied to assess the risk of natural catastrophes, including the reliability and validation of the risk models. Second, the authors compare the risk‐adjusted returns of recent securitizations on the basis of relative value. Compared with high‐yield bonds, catastrophe (“CAT”) bonds have wide spreads and very attractive Sharpe ratios. In fact, the risk‐adjusted returns on CAT bonds dominate high‐yield bonds. Furthermore, since natural catastrophe risk is essentially uncorrelated with market risk, high expected excess returns make CAT bonds high‐alpha assets. The authors illustrate this point and show that a relatively small allocation of insurance‐linked securities within a fixed income portfolio can enhance the expected return and simultaneously decrease risk, without significantly changing the skewness and kurtosis of the return distribution.

The small-scale artisanal fishers in coastal Bangladesh are comparatively more vulnerable to climate risks than any other communities in Bangladesh. Based on practicality, this paper aims to explain the local level climate change perception, its impact and adaptation strategies of the fisher in southeast coastal villages in Bangladesh.

To achieve the above objective, this study used structural, semi-structured interviews and focus group discussion in two coastal communities, namely, at Salimpur in the Sitakund coast and Sarikait Sandwip Island, Bangladesh. It reviews and applies secondary data sources to compare and contrast the findings presented in this study.

Results show that the fishers perceived an increase in temperature, frequency of tropical cyclones and an increase in sea level. They also perceived a decrease in monsoon rainfall. Such changes impact the decreasing amount of fish in the Bay of Bengal and the fishers’ livelihood options. Analysing seasonal calendar of fishing, findings suggest that fishers’ well-being is highly associated with the amount of fish yield, rather than climatic stress, certain non-climatic factors (such as the governmental rules, less profit, bank erosion and commercial fishing) also affected their livelihood. The major adaptation strategies undertaken include, but are not limited to, installation of tube well or rainwater harvesting plant for safe drinking water, raising plinth of the house to cope with inundation and use of solar panel/biogas for electricity.

Despite experiencing social stress and extreme climatic events and disasters, the majority of the fishing community expressed that they would not change their profession in future. The research suggests implementing risk reduction strategies in the coastal region of Bangladesh that supports the small-scale fishers to sustain their livelihood despite climate change consequences.

To minimize risk of coastal communities arising from cyclones, several risk mitigation initiatives have been taken in countries. Cyclone shelters have proven to be an important critical infrastructure in saving lives from cyclones. A large number of coastal critical infrastructure in the form of multi-purpose cyclone shelters (MPCS) are built to provide safe shelter during disasters. Often observed, such critical infrastructures are non-operational during the normal period, which makes them difficult to use during any disaster. Efforts have been made to keep these infrastructures in working condition. This research paper aims to bring together various management practices adopted for the MPCS in the South-Asian region with a focus on Bangladesh, and India. It also suggests ways to improve these practices for sustainable management of the MPCS.

India and Bangladesh are the most vulnerable countries in the South Asian region. As per the Global Climate Index, India and Bangladesh come in the list of “in extreme risk” countries in the world and are vulnerable to several natural hazards, especially climate-induced hydrometeorological hazards. India has a vast coastline and out of 7,516 km of coastline, a large extent, i.e. 5,700 km is prone to cyclones and that keeps 40% of the population vulnerable living within 100 km of the coastline. On the other hand, Bangladesh has a coastline of 580 km, which is equally vulnerable to tropical cyclones. Safeguarding communities from impending coastal risk through coastal cyclone shelters are of prime concern. This paper uses a qualitative approach to analyze secondary data, and literature in the field of critical infrastructure, sustainability, cyclone shelter, and management practice for cyclone shelters.

To provide sustainability and community ownership of the MPCS, various service plans are adopted in different countries. This paper provides insights on service and sustainability efforts made for the proper functioning of the MPCS in India and Bangladesh. It also provides insight into the roles played by different institutions involved in maintaining the MPCSs.

The research reiterates understanding of the cyclone shelter management from different geographic locations in the South Asian region. Various gaps identified in shelter management practices are discussed in the paper and key recommendations are proposed for better management of cyclone shelters.