Meteorological Disasters Research Articles

An Efficient Lossless Compression Algorithm for Maritime Safety Information Using Byte Encoding Network

The short message function of the BeiDou satellite system, due to its strong concurrent processing capabilities, can quickly and accurately send information to the target location in emergency situations. However, because of data redundancy and limitations on message length, a single piece of information often requires multiple transmissions through BeiDou short messages to be completed, resulting in limited transmission capacity. To improve the transmission capacity of information, it is necessary to compress the information during transmission using BeiDou’s short message communication function for maritime safety information. This paper proposes a Byte Encoding-enhanced Prediction by Partial Matching, variant D (BPPMd) algorithm that is particularly suitable for transmitting maritime safety information. Combined with a maritime safety information encoding algorithm (ME), it further improves compression efficiency, optimizes byte space, reduces information redundancy, and ensures the accuracy of the information. In this study, we constructed a maritime safety information dataset that includes three categories of information: meteorological warnings, navigation warnings, and disaster warnings. Experimental results show that the proposed algorithm is particularly suitable for compressing the maritime safety information dataset and outperforms other benchmark algorithms. Therefore, this study indicates that the proposed lossless compression method can be a feasible and effective solution for BeiDou short message communication.

Identification and threshold analysis of strong winds and heavy rain disaster factors based on frequent-pattern mining

Meteorological disasters pose a significant challenge to megacities, and quantitatively identifying the influence of meteorological conditions on disasters is an urgent issue. Compared to other traditional datasets, disaster reporting data offers significant informational value for urban meteorological risk response and assessment due to its high credibility and timeliness. This study is based on meteorological disaster reporting data for Shanghai over the past 10 years and focuses on the identification and threshold analysis of meteorological disaster risk factors for disaster-affected entities, offering a novel perspective on analyzing disaster-causing risk under the combined influence of meteorological conditions. The findings suggest that disaster reporting data can be used for meteorological risk assessment. There were noticeable differences in the disaster thresholds for affected entities in central urban areas (city center) versus suburban areas (suburbs). The combined effects of strong winds and heavy rain were often significant contributors to the occurrence of disasters. Furthermore, several distinct disaster characteristics were identified through frequent pattern (FP)-growth mining. The results of this study provide technical support for early warning and disaster management under specific weather conditions by constructing a quantitative framework for understanding the impact of hazardous weather.

Temperature and domestic trade: City evidence from China

Climate change poses an enormous global challenge, particularly for human economic activities. Previous literature has paid limited attention to the impact of temperature change on domestic trade. To examine the nexus between temperature and domestic trade, we employ panel data from 285 Chinese cities from 2000 to 2021 and a spatial econometric model. First, we reveal significant spatial correlation between domestic trade between cities. More importantly, a city's domestic trade is found to be significantly influenced by temperature changes in neighbouring cities. Specifically, the significantly negative spillover effect of temperature changes on domestic trade is consistent with the significantly negative direct effect, suggesting an overall negative total effect. Temperature changes also have a cumulative impact on domestic trade. Labour productivity, population migration and meteorological disasters are the mechanisms through which temperature changes influence Chinese cities’ domestic trade. Furthermore, the negative impact of temperature changes is more pronounced in the retail sector and in cities with low air–conditioning penetration. We provide city–level evidence of the relationship between climate change and economic activities from a new perspective of domestic trade, highlight the influential channels and confirm the role of temperature change in impeding the prosperity of domestic trade.

Spatial Analysis and Risk Assessment of Meteorological Disasters Affecting Cotton Cultivation in Xinjiang: A Comprehensive Model Approach

A systematic understanding of the spatial distribution of meteorological disasters that affect cotton growth, such as rainstorms, gales, and hail, is important for reducing plant losses and promoting sustainable development. Our study aimed to evaluate the risk of meteorological disasters during cotton growth and analyze their spatial distribution and driving factors. A risk assessment model for major meteorological disasters during cotton cultivation in Xinjiang was established by integrating entropy weight methods and an analytic hierarchy process. A cotton meteorological disaster risk assessment index system, including the vulnerability of disaster-bearing bodies, hazards of disaster-causing factors, and exposure of disaster-bearing bodies, was constructed using Google Earth Engine. We determined the comprehensive risk levels of major meteorological disasters for cotton in various regions of Xinjiang. Research shows that the selection of indicators is very important, and crop risk assessment with a clear disaster-bearing body can make the results more accurate. It is necessary to consider the risk assessment of multiple disaster species for meteorological disaster risk assessment. The results revealed spatial differences in the meteorological disaster risk for cotton in 2020. The very high and high risks for cotton accounted for 42% of the cotton planting area, mainly distributed in Karamay, Tacheng, Kashgar, Changjizhou, Kezhou, and Ilizhou. Consequently, this study provides a scientific basis for cotton cultivation in Xinjiang, China.

Successive Cyclone Idai Disaster and Covid-19 Pandemic Negatively Affected Tourist Arrivals in Chimanimani National Park, Zimbabwe

Zimbabwe suffered a devastating meteorological disaster when Cyclone Idai affected the southeast part of the country in March 2019. Barely a year after the cyclone event, the coronavirus disease 2019 pandemic emerged. This study explored how consecutive Cyclone Idai and Covid-19 affected tourist arrivals in Chimanimani National Park (CNP), Zimbabwe through; (i). Determining trend in tourist arrivals between 2010 and 2022, (ii). Establishing how Cyclone Idai and Covid-19 affected tourist arrivals and, (iii). Determining groups of tourist mostly affected by the consecutive Cyclone Idai and Covid-19 pandemic in CNP, Zimbabwe. Analysis of printed and electronic literature and tourists arrival records (2010-2022) from the Zimparks Chimanimani tourist office was done. There was a significant difference in number of tourist arrivals recorded before and after Cyclone Idai and Covid-19 incidence (p-value – 0.004). Tourist arrivals declined beginning 2019 to 2021. Local adult arrivals decreased from an average of 3566.75 to 1222 representing 34.26% while international adults’ arrival per annum decreased from 375.75 to 101.75 representing 27.08% during the same period. Tropical Cyclone Idai left trail of destruction on road networks, while Covid-19 resulted in travel restrictions and prohibited gatherings. Future researches may quantify in monetary value, total losses incurred in CNP as a result of consecutive Cyclone Idai and Covid-19 pandemic.

Design and Development of a Quality Evaluation and Monitoring System for the Short-term Weather Forecast in Gansu Province

In order to further improve the development of the meteorological observation quality management system in Gansu Province, this article takes the organic integration of quality management and meteorological observation as the concept, and combines practical business applications to develop a scoring model to quantitatively evaluate the quality of short-term warning signals and observation data transmission in Gansu Province. At the same time, a visual model is developed to achieve a personalized monitoring and analysis system for the entire process of “monitoring warning service”. System users can monitor the actual situation of various meteorological disasters, analyze disasters, and assess risks in the system, and display and apply decision information, thereby improving the scientific and standardized level of meteorological observation business management.

Analyzing the effectiveness of the interagency coordination on environmental risk communication to the Rohingya refugees in Bangladesh

AbstractRefugees often face structural inequities, a disproportionate share of environmental challenges, and receive inadequate risk information. In most cases, the host country, United Nations, and other humanitarian organizations provide critical weather and climate information and disaster risk management support. This is equally true for the Rohingya refugee in Bangladesh, who fled their home country Myanmar as a response to political violence. To provide critical humanitarian services to the Rohingya population, the in‐country United Nations agencies formed the Inter‐Sector Coordination Group (ISCG). This paper investigates the effectiveness of the ISCG's strategies for communicating monsoon risks in the Rohingya context. Particularly, it measures ISCG communication effectiveness by both the increased formalization of ISCG communication and the number of Rohingya refugees affected during the monsoon seasons in the years 2021–2022. The paper highlights the importance of the ISCG's existence in this humanitarian context as an effective coordinating institution. The ISCG provides impoartant environmental risk information to partner organizations that benefit the Rohingya's overall physical well‐being. Even though the geographical focus of this paper is on the Rohingya populations in Cox's Bazar, the discussions and findings have important policy implications in other parts of the world facing similar social, environmental, political, and humanitarian challenges.

Machine Learning Investigation of Downburst-Prone Environments in Canada

Abstract Thunderstorms are recognized as one of the most disastrous weather threats in Canada because of their power to cause substantial damage to human-made structures and even result in fatalities. It is therefore essential for operational forecasting to diagnose thunderstorms that generate damaging downdrafts of negatively buoyant air, known as downbursts. This study develops several machine learning models to identify environments supportive of downbursts in Canada. The models are trained and evaluated using 38 convective parameters calculated based on ERA5 reanalysis vertical profiles prior to thunderstorms with (306 cases) and without (19 132 cases) downbursts across Canada. Various resampling techniques are implemented to adjust data imbalance. An increase in the performance of the random forest (RF) model is observed when the support vector machine synthetic minority oversampling technique is utilized. The RF model outperforms other tested models, as indicated by model performance metrics and calibration. Several model interpretability methods highlight that the RF model has learned physical trends and patterns from the input variables. Moreover, the thermodynamic parameters are deemed to have higher impacts on the model outcomes compared to parcel, kinematic, and composite variables. For example, a considerable rise in the downburst probability is detected with an increase in cold pool strength. This study serves as one of the earliest attempts toward the fledgling field of machine learning applications in weather forecasting systems in Canada. The findings suggest that the developed model has the potential to enhance the effectiveness of issuing severe thunderstorm warnings in Canada, although further assessment with operational meteorologists is needed to validate its practical application. Significance Statement Severe thunderstorms demand particular attention in forecasting as their outflow can pose a serious threat to both structures and human life. This study uses machine learning techniques to predict whether or not a thunderstorm generates a damaging outflow in Canada. Atmospheric conditions that could trigger a severe thunderstorm are identified and discussed. Results show that the models have the potential to assist forecasters in better analyzing and predicting thunderstorms that generate destructive winds. Consequently, taking advantage of promising machine learning tools can yield more reliable forecasts of damaging thunderstorms, thereby mitigating the economic and societal burdens of these storms on Canadian communities.

Precipitation prediction based on variational mode decomposition combined with the crested porcupine optimization algorithm for long short-term memory model

Accurate precipitation prediction is very important for meteorological disaster prevention, water resources management, and agricultural decision making. To improve the accuracy of precipitation prediction, a hybrid model based on variational mode decomposition (VMD), crested porcupine optimization algorithm (CPO), and long short-term memory model (LSTM) is proposed in this paper. The model first uses VMD to decompose the precipitation time series into intrinsic mode functions of different frequencies to capture the multi-scale characteristics of precipitation data. Then, the CPO algorithm is used to optimize LSTM adaptive parameters to improve the global search ability and robustness of the model. Finally, the decomposed precipitation component is input into the LSTM network to learn the spatiotemporal dependence relationship and improve the ability of long-term prediction. The experimental results show that compared with the traditional LSTM model, CPO-LSTM model, and VMD-LSTM model, the hybrid model achieves better performance in many evaluation indices and effectively improves the accuracy of precipitation prediction. The application of the model can provide an effective tool for the fields of meteorology and water resources management, as well as provide new ideas for related research.

Study on Optimization of Placement Method for Transmission Line Monitoring Devices Based on Probability of Meteorological Disasters

Abstract To ensure the reliability and safety of the power grid system, it is of great significance to carry out electric meteorological monitoring for transmission channels. In this paper, a method for optimizing the placement of transmission line monitoring devices based on the probability of meteorological disasters is proposed. By considering the climatic characteristics near the transmission line, the probability of occurrence of adverse meteorological faults for each transmission tower in the transmission line is analyzed and calculated by using existing meteorological and GIS elevation data. Meanwhile, the historical failure data and voltage levels of transmission lines are combined to achieve the optimization of the installation position of monitoring devices in transmission channels. This method was applied to a 220 kV transmission line for practical analysis, achieving dense monitoring in areas with high failure rates and sparse monitoring in areas with low failure rates.

Evaluation of the real-time El Niño forecasts by the climate network approach between 2011 and present

El Niño episodes are part of the El Niño-Southern Oscillation (ENSO), which is the strongest driver of interannual climate variability, and can trigger extreme weather events and disasters in various parts of the globe. Previously we have described a network approach that allows to forecast El Niño events about 1 year ahead. Here we evaluate the real-time forecasts of this approach between 2011 and 2022. We find that the approach correctly predicted (in 2013 and 2017) the onset of both El Niño periods (2014-2016 and 2018-2019) and generated only 1 false alarm in 2019. In June 2022, the approach correctly forecasted the onset of an El Niño event in 2023. For determining the p-value of the 12 real-time forecasts, we consider 2 null hypotheses: (a) random guessing where we assume that El Niño onsets occur randomly, and (b) correlated guessing where we assume that in the year an El Niño ends, no new El Niño will start. We find pa≅0.005\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p_a\\cong 0.005$$\\end{document} and pb≅0.015\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p_b\\cong 0.015$$\\end{document}, this way rejecting both the null hypotheses that the same quality of the forecast can be obtained by chance. We also discuss how the network algorithm can be further improved by systematically reducing the number of false alarms. For 2024, the method indicates the absence of a new El Niño event.

STOCK MARKET RESPONSE TO METEOROLOGICAL DISASTER RISK: FROM LIQUIDITY PERSPECTIVE

This study investigates the impact of meteorological disaster risk perception on stock liquidity of Chinese listed companies by constructing meteorological disaster risk perception indicators through the Baidu index. Empirical evidence reveals that an increase in meteorological disaster risk perception decreases investors’ risk appetite and increases investors’ loss aversion, which in turn negatively affects stock liquidity. In addition, the increase in meteorological disaster risk perception also shrinks stock trading volume by reducing investor sentiment. The negative effect of meteorological disaster risk perception on stock liquidity is more pronounced in summer and winter and for firms located in water-rich areas. The findings of this study provide empirical evidence from investors’ perspective for understanding the impact of meteorological disasters on stock market liquidity.

Analysis of the Multi-Dimensional Characteristics of City Weather Forecast Page Views and the Spatiotemporal Characteristics of Meteorological Disaster Warnings in China

In order to provide insights into how various page views are influenced by public engagement with weather information and to shed light on the patterns of warning issuance across different seasons and regions, this study analyzes the multi-dimensional characteristics of city weather forecast page views and the spatiotemporal characteristics of early warning information in China, from 1 March 2020 to 31 August 2023. This is achieved by utilizing the daily page views of city weather forecasts and meteorological warning data, comparing the public’s attention to weather during holidays versus regular days, assessing the public’s attention to weather under different meteorological warning levels, and performing statistical analysis of the spatiotemporal scale of meteorological disasters. Our analysis shows that compared to weekends and holidays, the public pays more attention to the weather on weekdays, and the difference between weekdays and national statutory holidays is more significant. Due to the widespread impact of heat waves, typhoons, severe convective weather, and geological disasters caused by heavy rainfall, public awareness and participation in flood season weather forecasting have significantly increased. Under red alerts, flash floods, typhoons, and geological risks are the primary concerns. Orange alerts predominantly feature flash floods, rainstorms, typhoons, snowstorms, and cold waves, while sandstorms attract the most attention during yellow alerts. Droughts, however, receive relatively less attention regardless of the warning level. Seasonal patterns in the issuance of meteorological warnings reveal a peak in summer, particularly with typhoons and rainstorms being the main concerns in July, followed by high temperatures and additional typhoon warnings in August. Heavy sea surface wind warnings exhibit a strong seasonal trend, with the majority issued during the winter months. Regionally, southern China experiences the highest frequency of severe convection weather warnings, with provinces such as Jiangxi, Guangxi, and Hunan being the most affected.

Research on Port Risk Assessment Based on Various Meteorological Disasters

Within the framework of economic globalisation, ports serve as critical junctures in international trade and play a vital role. However, as infrastructure is closely linked to the natural environment, ports are highly susceptible to the impacts of meteorological disasters. Therefore, a comprehensive assessment of the risks posed by meteorological hazards to ports, establishing corresponding early warning mechanisms, and adopting reasonable response and recovery strategies, is paramount in ensuring the safe operation of ports and maintaining the stability of international trade. This study has comprehensively analysed historical data and identified the pre-established loss stratification system, improving the theoretical construct of “expected loss”. Additionally, this research has innovatively integrated the idea of preventative factors aligned with risk indicators. A quantitative algorithm was used to factor in the preventative factors within the computational procedure, deriving the weights pertinent to each risk indicator. This research aimed to reduce the subjectivity inherent in the weighting assignment process through such an approach, thereby enhancing disaster risk assessment’s scientific rigour and reliability. Moreover, it underscores the critical role of adaptive urban planning in enhancing the resilience of crucial economic nodes like ports, thereby contributing to the broader objectives of sustainable urban development.

Non-cholera Vibrio spp. invasive infections in the summer following May 2023 flood disaster in Romagna, Italy: a case series.

Non-cholera Vibrio spp. includes ubiquitous organisms living in aquatic environments. Their occurrence is associated with global warming and meteorological disasters. In May 2023 the Romagna region, Italy, was affected by severe floods. In the following 15weeks we observed 5 patients with invasive infections caused by V. vulnificus (3/5) and V. harveyi (2/5). All patients (median age 77years) had medical comorbidities and shared exposure to seawater. Two patients needed surgery; 2 died. In conclusion, we observed an increased burden of Vibrio spp. invasive infections after May 2023 floods, affecting old patients with predisposing medical conditions.

A quantum group decision model for meteorological disaster emergency response based on D-S evidence theory and Choquet integral

In addressing complex and dynamic meteorological disaster decision-making environment, the traditional multi-attribute group decision-making domain model is often unable to effectively deal with the correlation between attributes and the mutual influence of group opinions. To overcome this challenge, this paper proposes a novel quantum framework for group decision-making, which is used to deal with the emergency situation of meteorological disaster. The model initially characterizes attribute correlations using 2-additive Choquet integrals and employs Dempster-Shafer evidence theory to both integrate information and ascertain attribute weights, and decision makers' weights are calculated based on grey relative correlation. On this basis, a quantum-like Bayesian network is developed to capture the interference among decision-makers' opinions. The alternatives are ranked by quantum probabilities computed based on Bayesian principle. Finally, a case study on meteorological disaster emergency scenario assessment is conducted to validate the proposed model's effectiveness and superiority. Additionally, its stability and practicality are confirmed through sensitivity analysis and comparative analysis.

Navigating Insurance Challenges: A Decision Tree Model for Insurance Pricing and Risk Strategy

Amid escalating natural disasters intensified by climate change, accurately assessing risks and determining insurance rates in disaster-prone areas present significant challenges to the insurance industry, impacting the underwriting decisions and strategies correspondingly. This article employs a frequency-loss degree framework to quantify meteorological disaster risk as the Annual Average Loss (AAL), which was classified into three categories (severe, moderate, and mild) further. Utilizing a Poisson Distribution to process extreme event data and integrating it with data from typical years into a comprehensive prediction model, this approach facilitates enhanced risk assessment and premium estimation. A decision tree model was further established to explore insurance institution’s underwriting decisions and risk strategies on the basis of monetary benefits and mental utility respectively, highlighted by case studies in Australia and the Philippines. The findings underscore the necessity for insurers to incorporate advanced decision-making tools taking utility factors into account, to navigate the complexities of risk and pricing in vulnerable areas efficiently. A tendency was observed among insurers to prefer conservative strategies in medium-risk areas, like the Philippines, over aggressive underwriting in high-risk zones, such as Western Australia. This suggests further research into expanding the model's applicability and delving into the evolving influence of climate change on the insurance industry.

Making The Best Investment Strategy for Insurance Companies Under The Background of Extreme Weather

In the face of the negative impacts of the frequent occurrence of extreme weather disasters around the world, we have developed natural disaster risk prediction and analysis models for insurance companies to maximize their benefits. This paper first selects six risk indicators, such as flood, cold wave, drought and other disasters, and combines the analytic hierarchy process (AHP) and grey prediction model (GM) to calculate the impact weights of six kinds of natural disasters, and predicts the probability of future occurrence, so as to realize the comprehensive assessment of natural disaster risk. This paper also analyzes the effect of the owner's intention to buy insurance on the insurance strategy through the function model, and solves the optimal insurance strategy through the technique for order preferenceby similarity to ideal solution (TOPSIS) and neural network. We define the ratio of the total profit of natural disaster insurance to the total cost as the decision coefficient K. When K is greater than 35%, the insurance company chooses to write the policy, and when K is greater than 5% and less than 35%, the insurance company chooses to bear the risk. Finally, this paper collected the data of Florida and Kyushu, Japan. Through the insurance model, it calculated that the total profit of natural disaster insurance in Florida was 635.8 million US dollars, while that in Kyushu was -61.51 million US dollars. Therefore, insurance companies should invest in Florida.

Different data-driven prediction of global ionospheric TEC using deep learning methods

Ionospheric Total Electron Content (TEC) is a crucial parameter for monitoring the ionosphere and space weather disasters. Its accurate prediction is vital for precise applications of Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS). This study proposes a novel method for ionospheric TEC prediction that considers multiple TEC-related factors. We present the random forest method and the Autoformer deep learning with multilayer perceptron (Autoformer-MLP) to predict the global TEC by incorporating the geomagnetic and solar activity parameters. Two schemes with different ionospheric data, i.e., spherical harmonic coefficients (SHC) and vertical TEC (VTEC), are performed by Autoformer-MLP. Ionospheric products from 2009 to 2019 (Cycle 24), obtained from the Center for Orbit Determination in Europe (CODE), are collected to test and evaluate the proposed method. Experimental results demonstrate that the root mean square errors (RMSEs) of predicted global ionospheric maps (GIMs) using the SHC scheme are 3.79 and 1.39 TECU in 2015 and 2019, respectively, and those are 3.55 and 1.28 TECU for the VTEC scheme. Moreover, the RMSEs of the prediction results are 0.5 and 0.2 TECU lower than that of CODE's 1-day predicted global ionospheric map (GIM) product (C1PG) during high and low solar activity years, respectively. These analyses indicate that the proposed random forest and Autoformer-MLP deep learning methods exhibit high accuracy and stability for data-driven prediction of global ionospheric TEC in various scenarios.

Using point-of-care strategies to prepare island communities for global warming, rising oceans, and weather disasters

Using point-of-care strategies to prepare island communities for global warming, rising oceans, and weather disasters