scholarly journals A statistics and physics-based tropical cyclone full track model for catastrophe risk modeling

2022 ◽  
Author(s):  
Yu Chen ◽  
Pingzhi Fang ◽  
Jian Yang ◽  
Chen Liu ◽  
Anyu Zhang ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Storm Surges ◽  
Generation Model ◽  
Three Dimension ◽  
Risk Modeling ◽  
Seasonal Characteristics ◽  
Global Environmental ◽  
Proposed Model ◽  
Drift Theory ◽  
Comprehensive Test

Catastrophe (CAT) risk modeling of perils such as typhoon and earthquake has become a prevailing practice in the insurance and reinsurance industry. The event generation model is the key component of the CAT modeling. In this paper, a physics-based tropical cyclone (TC) full track model is introduced to model typhoons events in the western North Pacific basin. At the same time, a comprehensive test of the model is presented from the perspective of CAT risk modeling for insurance and reinsurance applications. The full track model includes the genesis, track, intensity, and landing models. Driven by the global environmental circulations, the model employs the advection and beta drift theory in atmospheric dynamics to model the track of typhoons. The proposed model is novel in the way of modeling the genesis of TCs with three-dimension kernel distributions in space and time. This enables the simulation of seasonal characteristics of TCs. By generating 10,000-year TC events, we comprehensively test the model from the standpoint of CAT insurance and reinsurance applications. The typhoon hazard model and the generated events can serve as the inputs for assessing the typhoon risk and insured loss caused by winds, rains, floods, and storm surges.

Estimation of Global Synthetic Tropical Cyclone Hazard Probabilities using the STORM dataset

2020 ◽  
Author(s):  
Nadia Bloemendaal ◽  
Ivan Haigh ◽  
Hans de Moel ◽  
Sanne Muis ◽  
Jeroen Aerts
Keyword(s):  
Tropical Cyclone ◽  
Spatial Information ◽  
Risk Mitigation ◽  
Global Scale ◽  
Generation Model ◽  
Risk Modeling ◽  
Climate Conditions ◽  
Wind Speeds ◽  
South Pacific Ocean ◽  
Novel Approach

<p>Tropical cyclones (TCs), also referred to as hurricanes or typhoons, are amongst the deadliest and costliest natural disasters, affecting people, economies and the environment in coastal areas around the globe when they make landfall. In 2017, Hurricanes Harvey, Irma and Maria entered the top-5 costliest Atlantic hurricanes ever recorded, with combined losses estimated at $220 billion. Therefore, to minimize future loss of life and property and to aid risk mitigation efforts, it is crucial to perform accurate TC risk assessments in low-lying coastal regions. Calculating TC risk at a global scale, however, has proven to be difficult, given the limited temporal and spatial information on landfalling TCs around much of the global coastline.</p><p>In this research, we present a novel approach to calculate TC risk under present and future climate conditions on a global scale, using the newly developed Synthetic Tropical cyclOne geneRation Model (STORM). For this, we extract 38 years of historical data from the International Best-Track Archive for Climate Stewardship (IBTrACS). This dataset is used as input for the STORM algorithm to statistically extend this dataset from 38 years to 10,000 years of TC activity. Validation shows that the STORM dataset preserves the TC statistics as found on the original IBTrACS dataset. The STORM dataset is then used to calculate global-scale return periods of TC-induced wind speeds at 0.1°resolution. This return period dataset can then be used to assess the low probabilities of extreme events all around the globe. Moreover, we demonstrate the application of this dataset for TC risk modeling on small islands in e.g. the Caribbean or in the South Pacific Ocean.</p>

scholarly journals Generation of a global synthetic tropical cyclone hazard dataset using STORM

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Nadia Bloemendaal ◽  
Ivan D. Haigh ◽  
Hans de Moel ◽  
Sanne Muis ◽  
Reindert J. Haarsma ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Spatial Information ◽  
Historical Data ◽  
Global Scale ◽  
Generation Model ◽  
Risk Modeling ◽  
The Past ◽  
Original Dataset ◽  
Temporal And Spatial ◽  
Atlantic Hurricanes

Abstract Over the past few decades, the world has seen substantial tropical cyclone (TC) damages, with the 2017 Hurricanes Harvey, Irma and Maria entering the top-5 costliest Atlantic hurricanes ever. Calculating TC risk at a global scale, however, has proven difficult given the limited temporal and spatial information on TCs across much of the global coastline. Here, we present a novel database on TC characteristics on a global scale using a newly developed synthetic resampling algorithm we call STORM (Synthetic Tropical cyclOne geneRation Model). STORM can be applied to any meteorological dataset to statistically resample and model TC tracks and intensities. We apply STORM to extracted TCs from 38 years of historical data from IBTrACS to statistically extend this dataset to 10,000 years of TC activity. We show that STORM preserves the TC statistics as found in the original dataset. The STORM dataset can be used for TC hazard assessments and risk modeling in TC-prone regions.

scholarly journals A Simple Trajectory Model for Climatological Study of Tropical Cyclones

2020 ◽  
Vol 33 (18) ◽  
pp. 7777-7786
Author(s):  
Kaiyue Shan ◽  
Xiping Yu
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Western North Pacific ◽  
Deterministic Model ◽  
North Atlantic Ocean ◽  
North Pacific Ocean ◽  
Computational Cost ◽  
Ocean Basin ◽  
Trajectory Model ◽  
Proposed Model

AbstractThe establishment of a tropical cyclone (TC) trajectory model that can represent the basic physics and is practically advantageous considering both accuracy and computational cost is essential to the climatological studies of various global TC activities. In this study, a simple deterministic model is proposed based on a newly developed semiempirical formula for the beta drift under known conditions of the environmental steering flow. To verify the proposed model, all historical TC tracks in the western North Pacific and the North Atlantic Ocean basins during the period 1979–2018 are simulated and statistically compared with the relevant results derived from observed data. The proposed model is shown to well capture the spatial distribution patterns of the TC occurrence frequency in the two ocean basins. Prevailing TC tracks as well as the latitudinal distribution of the landfall TC number in the western North Pacific Ocean basin are also shown to agree better with the results derived from observed data, as compared to the existing models that took different strategies to include the effect of the beta drift. It is then concluded that the present model is advantageous in terms of not only the accuracy but also the capacity to accommodate the varying climate. It is thus believed that the proposed TC trajectory model has the potential to be used for assessing possible impacts of climate change on tropical cyclone activities.

scholarly journals Probabilistic Generation Model of Solar Irradiance for Grid Connected Photovoltaic Systems Using Weibull Distribution

2020 ◽  
Vol 12 (6) ◽  
pp. 2241 ◽  
Author(s):  
Muhammad Umar Afzaal ◽  
Intisar Ali Sajjad ◽  
Ahmed Bilal Awan ◽  
Kashif Nisar Paracha ◽  
Muhammad Faisal Nadeem Khan ◽  
...  
Keyword(s):  
Weibull Distribution ◽  
Solar Irradiance ◽  
Goodness Of Fit ◽  
Pattern Generation ◽  
Generalized Regression Neural Network ◽  
Generation Model ◽  
Moving Window ◽  
Renewable Energy Resources ◽  
Proposed Model

Around the world, countries are integrating photovoltaic generating systems to the grid to support climate change initiatives. However, solar power generation is highly uncertain due to variations in solar irradiance level during different hours of the day. Inaccurate modelling of this variability can lead to non-optimal dispatch of system resources. Therefore, accurate characterization of solar irradiance patterns is essential for effective management of renewable energy resources in an electrical power grid. In this paper, the Weibull distribution based probabilistic model is presented for characterization of solar irradiance patterns. Firstly, Weibull distribution is utilized to model inter-temporal variations associated with reference solar irradiance data through moving window averaging technique, and then the proposed model is used for irradiance pattern generation. To achieve continuity of discrete Weibull distribution parameters calculated at different steps of moving window, Generalized Regression Neural Network (GRNN) is employed. Goodness of Fit (GOF) techniques are used to calculate the error between mean and standard deviation of generated and reference patterns. The comparison of GOF results with the literature shows that the proposed model has improved performance. The presented model can be used for power system planning studies where the uncertainty of different resources such as generation, load, network, etc., needs to be considered for their better management.

scholarly journals Substorm associated radar auroral surges: a statistical study and possible generation model

1998 ◽  
Vol 16 (4) ◽  
pp. 441-449 ◽  
Author(s):  
B. A. Shand ◽  
M. Lester ◽  
T. K. Yeoman
Keyword(s):  
Local Time ◽  
Statistical Investigation ◽  
Generation Model ◽  
Plasma Convection ◽  
Ion Drifts ◽  
System A ◽  
Proposed Model ◽  
Comprehensive Examination ◽  
E Region ◽  
Maximum Occurrence

Abstract. Substorm-associated radar auroral surges (SARAS) are a short lived (15–90 minutes) and spatially localised (~5° of latitude) perturbation of the plasma convection pattern observed within the auroral E-region. The understanding of such phenomena has important ramifications for the investigation of the larger scale plasma convection and ultimately the coupling of the solar wind, magnetosphere and ionosphere system. A statistical investigation is undertaken of SARAS, observed by the Sweden And Britain Radar Experiment (SABRE), in order to provide a more extensive examination of the local time occurrence and propagation characteristics of the events. The statistical analysis has determined a local time occurrence of observations between 1420 MLT and 2200 MLT with a maximum occurrence centred around 1700 MLT. The propagation velocity of the SARAS feature through the SABRE field of view was found to be predominately L-shell aligned with a velocity centred around 1750 m s–1 and within the range 500 m s–1 and 3500 m s–1. This comprehensive examination of the SARAS provides the opportunity to discuss, qualitatively, a possible generation mechanism for SARAS based on a proposed model for the production of a similar phenomenon referred to as sub-auroral ion drifts (SAIDs). The results of the comparison suggests that SARAS may result from a similar geophysical mechanism to that which produces SAID events, but probably occurs at a different time in the evolution of the event.Key words. Substorms · Auroral surges · Plasma con-vection · Sub-auroral ion drifts

scholarly journals Commonsense Knowledge Aware Conversation Generation with Graph Attention

2018 ◽  
Author(s):  
Hao Zhou ◽  
Tom Young ◽  
Minlie Huang ◽  
Haizhou Zhao ◽  
Jingfang Xu ◽  
...  
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Semantic Information ◽  
Attention Mechanism ◽  
Generation Model ◽  
Dynamic Graph ◽  
Commonsense Knowledge ◽  
Word Generation ◽  
Proposed Model ◽  
Knowledge Graphs

Commonsense knowledge is vital to many natural language processing tasks. In this paper, we present a novel open-domain conversation generation model to demonstrate how large-scale commonsense knowledge can facilitate language understanding and generation. Given a user post, the model retrieves relevant knowledge graphs from a knowledge base and then encodes the graphs with a static graph attention mechanism, which augments the semantic information of the post and thus supports better understanding of the post. Then, during word generation, the model attentively reads the retrieved knowledge graphs and the knowledge triples within each graph to facilitate better generation through a dynamic graph attention mechanism. This is the first attempt that uses large-scale commonsense knowledge in conversation generation. Furthermore, unlike existing models that use knowledge triples (entities) separately and independently, our model treats each knowledge graph as a whole, which encodes more structured, connected semantic information in the graphs. Experiments show that the proposed model can generate more appropriate and informative responses than state-of-the-art baselines. 

scholarly journals EXTREME WATER LEVEL EXCEEDANCE PROBABILITIES AROUND AUSTRALIA

2012 ◽  
Vol 1 (33) ◽  
pp. 53
Author(s):  
Leigh MacPherson ◽  
Ivan David Haigh ◽  
Matthew Mason ◽  
Sarath Wijeratne ◽  
Charitha Pattiaratchi ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Water Level ◽  
Return Period ◽  
Meteorological Data ◽  
Storm Surges ◽  
Value Theory ◽  
Water Levels ◽  
Sea Levels ◽  
Extreme Water Level ◽  
Extreme Water Levels

The potential impacts of extreme water level events on our coasts are increasing as populations grow and sea levels rise. To better prepare for the future, coastal engineers and managers need accurate estimates of average exceedance probabilities for extreme water levels. In this paper, we estimate present day probabilities of extreme water levels around the entire coastline of Australia. Tides and storm surges generated by extra-tropical storms were included by creating a 61-year (1949-2009) hindcast of water levels using a high resolution depth averaged hydrodynamic model driven with meteorological data from a global reanalysis. Tropical cyclone-induced surges were included through numerical modelling of a database of synthetic tropical cyclones equivalent to 10,000 years of cyclone activity around Australia. Predicted water level data was analysed using extreme value theory to construct return period curves for both the water level hindcast and synthetic tropical cyclone modelling. These return period curves were then combined by taking the highest water level at each return period.

scholarly journals Study on Storm Surge Using Parametric Model with Geographical Characteristics

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2251
Author(s):  
Yeon-joong Kim ◽  
Tea-woo Kim ◽  
Jong-sung Yoon
Keyword(s):  
Storm Surge ◽  
Wind Field ◽  
Global Climate ◽  
Grid Point ◽  
Storm Surges ◽  
Parametric Model ◽  
Hazard Map ◽  
Proposed Model ◽  
Pass Through ◽  
Geographical Characteristics

The coastal area of Japan has been damaged yearly by storm surges and flooding disasters in the past, including those associated with typhoons. In addition, the scale of damage is increasing rapidly due to the changing global climate and environment. As disasters due to storm surges become increasingly unpredictable, more measures should be taken to prevent serious damage and casualties. The Japanese government published a hazard map manual in 2015 and obligates the creation of a hazard map based on a parametric model as a measure to reduce high-scale storm surges. Parametric model (typhoon model) accounting for the topographical influences of the surroundings is essential for calculating the wind field of a typhoon. In particular, it is necessary to calculate the wind field using a parametric model in order to simulate a virtual typhoon (the largest typhoon) and to improve the reproducibility. Therefore, in this study, the aim was to establish a hazard map by assuming storm surges of the largest scale and to propose a parametric model that considers the changing shape of typhoons due to topography. The main objectives of this study were to analyze the characteristics of typhoons due to pass through Japan, to develop a parametric model using a combination of Holland’s and Myers’s models that is appropriate for the largest scale of typhoon, and to analyze the parameters of Holland’s model using grid point values (GPVs). Finally, we aimed to propose a method that considers the changing shape of typhoons due to topography. The modeling outcomes of tide levels and storm surge heights show that the reproduced results obtained by the analysis method proposed in this study are more accurate than those obtained using GPVs. In addition, the reproducibility of the proposed model was evaluated showing the high and excellent reproducibility of storm surge height according to the geographic characteristics.

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