A systemic analysis of typhoon risk across China

Xu, Xinliang; Sun, Duixiong; Guo, Tengjiao

doi:10.1007/s11069-015-1586-0

Cited by 24 publications

(16 citation statements)

References 31 publications

(25 reference statements)

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“…TCs are one of the most serious types of natural hazards and cause severe damage in China each year (Xu et al, ). However, TC forecasting in China using NWP models lags behind other advanced operational centres, e.g.…”

Section: Introductionmentioning

confidence: 99%

AGRAPES‐based mesoscale ensemble prediction system for tropical cyclone forecasting: configuration and performance

Zhang¹

2018

Quart J Royal Meteoro Soc

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To improve short-range tropical cyclone (TC) forecasting in China from both deterministic and probabilistic standpoints, a mesoscale ensemble prediction system (TREPS) based on the Global/Regional Assimilation and Prediction System (GRAPES) is constructed as a planned operational system. TREPS comprises one unperturbed and 30 perturbed forecasts ranging up to 60 h at 0.09 • horizontal resolution. Downscaling perturbations from the European Centre for Medium-range Forecasts' (ECMWF) ensemble prediction system (ENS) are used to generate initial perturbations by blending them with balanced random perturbations implemented around the TC and to generate perturbations for lateral boundary conditions. The surface temperature around the TC is also perturbed, with a combination of multiphysics and stochastically perturbed parametrization tendencies considered to represent uncertainties in the model physics. These perturbation methods contribute a full-area, multi-scale and multi-variable mutual complement to increasing forecast perturbations.TREPS is evaluated for 19 TCs making landfall in China during 2014-2016. For deterministic guidance, TREPS significantly improves TC forecasting for intensity, rainfall and wind compared to ECMWF deterministic and ensemble forecasts. TREPS demonstrates significant advantages over its deterministic forecasts in predicting track, intensity, (extremely) heavy rainfall and wind. Furthermore, TREPS effectively reduces the degree of underdispersion in intensity forecasting compared to ENS. For probabilistic forecasting, TREPS outperforms ENS in accurately predicting extremely heavy rainfall and (extremely) strong wind owing to its better discriminating ability, but performs worse in predicting modest wind, light and heavy rainfall. Two post-processing techniques -optimal member and optimal probability -to improve, respectively, deterministic and probabilistic guidance of TREPS in rainfall and wind forecasting are proposed. The former is shown to be effective only for light rainfall, while the latter demonstrates consistent (partial) improvements in rainfall (wind). The superiorities and inferiorities of TREPS, as well as possible reasons for these, are intuitively illustrated in a case-study for Rammasun.

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Section: Introductionmentioning

confidence: 99%

AGRAPES‐based mesoscale ensemble prediction system for tropical cyclone forecasting: configuration and performance

Zhang¹

2018

Quart J Royal Meteoro Soc

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“…These indexes were used to construct different disaster assessment models (Liang and Fan, 1999;Lei et al, 2009;Wang et al, 2010). Xu et al (2015) comprehensively assessed the impact of typhoons across China using the geographical information system. The future direction of tropical cyclone risk management is quantitative risk models (Chen et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Risk zoning of typhoon disasters in Zhejiang Province, China

Ren

Zhu

2018

Nat. Hazards Earth Syst. Sci.

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Abstract. In this paper, typhoon simply means tropical cyclone. As risk is future probability of hazard events, when estimated future probability is the same as historical probability for a specific period, we can understand risk by learning from past events. Based on precipitation and wind data over the mainland of China during 1980–2014 and disaster and social data at the county level in Zhejiang Province from 2004 to 2012, a study on risk zoning of typhoon disasters (typhoon disasters in this paper refer to affected population or direct economic losses caused by typhoons in Zhejiang Province) is carried out. Firstly, characteristics of typhoon disasters and factors causing typhoon disasters are analyzed. Secondly, an intensity index of factors causing typhoon disasters and a population vulnerability index are developed. Thirdly, combining the two indexes, a comprehensive risk index for typhoon disasters is obtained and used to zone areas of risk. The above analyses show that southeastern Zhejiang is the area most affected by typhoon disasters. The annual probability of the occurrence of typhoon rainstorms >50 mm decreases from the southeast coast to inland areas, with a maximum in the boundary region between Fujian and Zhejiang, which has the highest risk of rainstorms. Southeastern Zhejiang and the boundary region between Zhejiang and Fujian provinces and the Hangzhou Bay area are most frequently affected by extreme typhoon winds and have the highest risk of wind damage. The population of southwestern Zhejiang is the most vulnerable to typhoons as a result of the relatively undeveloped economy, mountainous terrain and the high risk of geological disasters in this region. Vulnerability is lower in the cities due to better disaster prevention and reduction strategies and a more highly educated population. The southeast coastal areas face the highest risk of typhoon disasters, especially in the boundary region between Taizhou and Wenzhou cities. Although the inland mountainous areas are not directly affected by typhoons, they are in the medium-risk category for vulnerability.

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“…Evolution law: The evolution logic of spatiotemporal, sequential, and causal relationships exists between different typhoon states [54][55][56]. By analyzing and combining these relationships, it reveals the general trend of typhoon events, such as the evolution of the typhoon track and the evolution of the typhoon intensity.…”

mentioning

confidence: 99%

TKRM: A Formal Knowledge Representation Method for Typhoon Events

Zhang

Shi

et al. 2020

Sustainability

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Typhoon events can cause serious environmental damage and economic losses. Understanding the development of typhoon events will provide valuable knowledge for disaster prevention and mitigation. In the age of big data, the sharp contrast between the sudden increase of mass information and the lack of a knowledge appreciation mechanism appears. There is an urgent need to promote the transformation of information services to knowledge services in the field of hazard management. Knowledge representation, as a strategy for symbolizing and formalizing knowledge, affects knowledge acquisition, storage, management, and application, and is the basis and prerequisite for the implementation of knowledge services. Based on the evolution law of typhoon events and human cognitive habits, a formal knowledge representation method for typhoon events (TKRM) is proposed in this paper. First, by analyzing the evolution characteristics of typhoon events, the TKRM framework with three layers consisting of “event–process–state” was constructed, which was used to describe the knowledge composition and relationship of the different granularity of typhoon events. Second, the formal representation of the TKRM framework was formed by using a finite state machine (FSM) as a reference, taking time and location as the basic conditions, and extending the hierarchical and parallel representation mechanism. Finally, the rationality and practical value of the TKRM were verified using a case study.

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A systemic analysis of typhoon risk across China

Cited by 24 publications

References 31 publications

AGRAPES‐based mesoscale ensemble prediction system for tropical cyclone forecasting: configuration and performance

AGRAPES‐based mesoscale ensemble prediction system for tropical cyclone forecasting: configuration and performance

Risk zoning of typhoon disasters in Zhejiang Province, China

TKRM: A Formal Knowledge Representation Method for Typhoon Events

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