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 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.

scholarly journals A satellite and reanalysis view of cloud organization, thermodynamic, and dynamic variability within the subtropical marine boundary layer

2017 ◽  
Author(s):  
Brian H. Kahn ◽  
Georgios Matheou ◽  
Qing Yue ◽  
Thomas Fauchez ◽  
Eric J. Fetzer ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Spatial Organization ◽  
Marine Boundary Layer ◽  
Global Scale ◽  
Data Sets ◽  
Wind Speeds ◽  
Novel Approach ◽  
Process Understanding ◽  
Static Energy ◽  
Visible Reflectance

Abstract. The global-scale patterns and covariances of subtropical marine boundary layer (MBL) cloud fraction and spatial organization with atmospheric thermodynamic and dynamic fields remain poorly understood. We describe a novel approach that leverages coincident NASA A-train and the Modern Era Retrospective-Analysis for Research and Applications (MERRA) data to quantify the relationships in the subtropical MBL derived at the native pixel and grid resolution. Four subtropical oceanic regions that capture transitions from closed-cell stratocumulus to open-cell trade cumulus are investigated. We define stratocumulus and cumulus regimes based exclusively from infrared-based thermodynamic phase. Visible reflectances are normally distributed within stratocumulus and are increasingly skewed away from the coast where disorganized cumulus dominates. Increases in MBL depth, wind speed and effective radius (re), and reductions in 700–1000 hPa moist static energy differences and 700 and 850 hPa vertical velocity, correspond with increases in reflectance skewness. We posit that a more robust representation of the cloudy MBL is obtained using visible reflectance rather than retrievals of optical thickness that are limited to a smaller subset of cumulus. An increase in re within shallow cumulus is strongly related to higher MBL wind speeds that further correspond to increased precipitation occurrence according to CloudSat. Our results are consistent with surface-based observations and suggest that the combination of A-train and MERRA data sets have potential to add global context to our process understanding of the subtropical cumulus-dominated MBL.

scholarly journals Estimation of global tropical cyclone wind speed probabilities using the STORM dataset

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Nadia Bloemendaal ◽  
Hans de Moel ◽  
Sanne Muis ◽  
Ivan D. Haigh ◽  
Jeroen C. J. H. Aerts
Keyword(s):  
Wind Speed ◽  
Extreme Value Distribution ◽  
Global Scale ◽  
Risk Assessments ◽  
Return Periods ◽  
Wind Model ◽  
Climate Conditions ◽  
Wind Speeds ◽  
The Impact ◽  
Good Agreement

Abstract Tropical cyclones (TC) are one of the deadliest and costliest natural disasters. To mitigate the impact of such disasters, it is essential to know extreme exceedance probabilities, also known as return periods, of TC hazards. In this paper, we demonstrate the use of the STORM dataset, containing synthetic TCs equivalent of 10,000 years under present-day climate conditions, for the calculation of TC wind speed return periods. The temporal length of the STORM dataset allows us to empirically calculate return periods up to 10,000 years without fitting an extreme value distribution. We show that fitting a distribution typically results in higher wind speeds compared to their empirically derived counterparts, especially for return periods exceeding 100-yr. By applying a parametric wind model to the TC tracks, we derive return periods at 10 km resolution in TC-prone regions. The return periods are validated against observations and previous studies, and show a good agreement. The accompanying global-scale wind speed return period dataset is publicly available and can be used for high-resolution TC risk assessments.

VIDEO-SHOT TRANSITION DETECTION: A SPATIO-TEMPORAL SALIENCY APPROACH

2010 ◽  
Vol 24 (08) ◽  
pp. 1295-1309 ◽  
Author(s):  
XIAN WU ◽  
JIANHUANG LAI ◽  
PONG C. YUEN
Keyword(s):  
Error Probability ◽  
Spatial Information ◽  
Saliency Map ◽  
Detection Error ◽  
Novel Approach ◽  
Video Shot ◽  
Recent Method ◽  
Temporal And Spatial ◽  
Spatio Temporal ◽  
Shot Transitions

This paper proposes a novel approach for video-shot transition detection using spatio-temporal saliency. Both temporal and spatial information are combined to generate a saliency map, and features are available based on the change of saliency. Considering the context of shot changes, a statistical detector is constructed to determine all types of shot transitions by the minimization of the detection-error probability simultaneously under the same framework. The evaluation performed on videos of various content types demonstrates that the proposed approach outperforms a more recent method and two publicly available systems, namely VideoAnnex and VCM.

Climate Conditions at Perennially Ice-Covered Lake Untersee, East Antarctica

2015 ◽  
Vol 54 (7) ◽  
pp. 1393-1412 ◽  
Author(s):  
Dale T. Andersen ◽  
Christopher P. McKay ◽  
Victor Lagun
Keyword(s):  
Wind Speed ◽  
Austral Summer ◽  
Solar Flux ◽  
East Antarctica ◽  
Daily Maximum ◽  
Mean Annual Temperature ◽  
Secondary Source ◽  
Climate Conditions ◽  
Wind Speeds ◽  
Strong Winds

AbstractIn November 2008 an automated meteorological station was established at Lake Untersee in East Antarctica, producing a 5-yr data record of meteorological conditions at the lake. This dataset includes five austral summer seasons composed of December, January, and February (DJF). The average solar flux at Lake Untersee for the four years with complete solar flux data is 99.2 ± 0.6 W m−2. The mean annual temperature at Lake Untersee was determined to be −10.6° ± 0.6°C. The annual degree-days above freezing for the five years were 9.7, 37.7, 22.4, 7.0, and 48.8, respectively, with summer (DJF) accounting for virtually all of this. For these five summers the average DJF temperatures were −3.5°, −1.9°, −2.2°, −2.6°, and −2.5°C. The maximum (minimum) temperatures were +5.3°, +7.6°, +5.7°, +4.4°, and +9.0°C (−13.8°, −12.8°, −12.9°, −13.5°, and −12.1°C). The average of the wind speed recorded was 5.4 m s−1, the maximum was 35.7 m s−1, and the average daily maximum was 15 m s−1. The wind speed was higher in the winter, averaging 6.4 m s−1. Summer winds averaged 4.7 m s−1. The dominant wind direction for strong winds is from the south for all seasons, with a secondary source of strong winds in the summer from the east-northeast. Relative humidity averages 37%; however, high values will occur with an average period of ~10 days, providing a strong indicator of the quasi-periodic passage of storms across the site. Low summer temperatures and high wind speeds create conditions at the surface of the lake ice resulting in sublimation rather than melting as the main mass-loss process.

scholarly journals The substructure of extremely hot summers in the Northern Hemisphere

2019 ◽  
Author(s):  
Matthias Röthlisberger ◽  
Michael Sprenger ◽  
Emmanouil Flaounas ◽  
Urs Beyerle ◽  
Heini Wernli
Keyword(s):  
Northern Hemisphere ◽  
Western Europe ◽  
Climate Model ◽  
High Arctic ◽  
Large Variability ◽  
Statistical Characterization ◽  
Climate Conditions ◽  
Rossby Wave Breaking ◽  
Novel Approach ◽  
Grid Points

Abstract. In the last decades, extremely hot summers (hereafter extreme summers) have challenged societies worldwide through their adverse ecological, economic and public health effects. In this study, extreme summers are identified at all grid points in the Northern Hemisphere in the upper tail of the July–August (JJA) seasonal mean 2-meter temperature (T2m) distribution, separately in ERA-Interim reanalyses and in 700 simulated years with the Community Earth System Model (CESM) large ensemble for present-day climate conditions. A novel approach is introduced to characterize the substructure of extreme summers, i.e., to elucidate whether an extreme summer is mainly the result of the warmest days being anomalously hot, or of the coldest days being anomalously mild, or of a general shift towards warmer temperatures on all days of the season. Such a statistical characterization can be obtained from considering so-called rank day anomalies for each extreme summer, that is, by sorting the 92 daily mean T2m values of an extreme summer and by calculating, for every rank, the deviation from the climatological mean rank value of T2m. Applying this method in the entire Northern Hemisphere reveals spatially strongly varying extreme summer substructures, which agree remarkably well in the reanalysis and climate model data sets. For example, in eastern India the hottest 30 days of an extreme summer contribute more than 70 % to the total extreme summer T2m anomaly, while the colder days are close to climatology. In the high Arctic, however, extreme summers occur when the coldest 30 days are substantially warmer than climatology. Furthermore, in roughly half of the Northern Hemisphere land area, the coldest third of summer days contribute more to extreme summers than the hottest third, which highlights that milder than normal coldest summer days are a key ingredient of many extreme summers. In certain regions, e.g., over western Europe and western Russia, the substructure of different extreme summers shows large variability and no common characteristic substructure emerges. Furthermore, we show that the typical extreme summer substructure in a certain region is directly related to the region’s overall T2m rank day variability pattern. This indicates that in regions where the warmest summer days vary particularly strongly from one year to the other, these warmest days are also particularly anomalous in extreme summers (and analogously for regions where variability is largest for the coldest days). Finally, for three selected regions, thermodynamic and dynamical causes of extreme summer substructures are briefly discussed, indicating that, for instance, the onset of monsoons, physical boundaries like the sea ice edge, or the frequency of occurrence of Rossby wave breaking, strongly determine the substructure of extreme summers in certain regions.

Analysis of Idealized Tropical Cyclone Simulations Using the Weather Research and Forecasting Model: Sensitivity to Turbulence Parameterization and Grid Spacing

2009 ◽  
Vol 137 (2) ◽  
pp. 745-765 ◽  
Author(s):  
Kevin A. Hill ◽  
Gary M. Lackmann
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Maximum Intensity ◽  
Weather Research And Forecasting ◽  
Grid Spacing ◽  
Exchange Coefficient ◽  
Model Sensitivity ◽  
Wind Speeds ◽  
Horizontal Grid ◽  
Weather Research

Abstract The Weather Research and Forecasting Advanced Research Model (WRF-ARW) was used to perform idealized tropical cyclone (TC) simulations, with domains of 36-, 12-, and 4-km horizontal grid spacing. Tests were conducted to determine the sensitivity of TC intensity to the available surface layer (SL) and planetary boundary layer (PBL) parameterizations, including the Yonsei University (YSU) and Mellor–Yamada–Janjic (MYJ) schemes, and to horizontal grid spacing. Simulations were run until a quasi-steady TC intensity was attained. Differences in minimum central pressure (Pmin) of up to 35 hPa and maximum 10-m wind (V10max) differences of up to 30 m s−1 were present between a convection-resolving nested domain with 4-km grid spacing and a parent domain with cumulus parameterization and 36-km grid spacing. Simulations using 4-km grid spacing are the most intense, with the maximum intensity falling close to empirical estimates of maximum TC intensity. Sensitivity to SL and PBL parameterization also exists, most notably in simulations with 4-km grid spacing, where the maximum intensity varied by up to ∼10 m s−1 (V10max) or ∼13 hPa (Pmin). Values of surface latent heat flux (LHFLX) are larger in MYJ than in YSU at the same wind speeds, and the differences increase with wind speed, approaching 1000 W m−2 at wind speeds in excess of 55 m s−1. This difference was traced to a larger exchange coefficient for moisture, CQ, in the MYJ scheme. The exchange coefficients for sensible heat (Cθ) and momentum (CD) varied by <7% between the SL schemes at the same wind speeds. The ratio Cθ/CD varied by <5% between the schemes, whereas CQ/CD was up to 100% larger in MYJ, and the latter is theorized to contribute to the differences in simulated maximum intensity. Differences in PBL scheme mixing also likely played a role in the model sensitivity. Observations of the exchange coefficients, published elsewhere and limited to wind speeds <30 m s−1, suggest that CQ is too large in the MYJ SL scheme, whereas YSU incorporates values more consistent with observations. The exchange coefficient for momentum increases linearly with wind speed in both schemes, whereas observations suggest that the value of CD becomes quasi-steady beyond some critical wind speed (∼30 m s−1).

Modelling Thousands of Fractures Using Analytic Elements

2021 ◽  
Author(s):  
Erik Toller ◽  
Otto Strack
Keyword(s):  
Exact Solution ◽  
Plane Strain ◽  
Numerical Models ◽  
Element Model ◽  
Global Scale ◽  
Hydraulic Fractures ◽  
Infinite Domain ◽  
Novel Approach ◽  
Analytic Elements ◽  
Kolosov Functions

<p>Understanding and modelling hydraulic fractures and fracture networks have a fundamental role in mapping the mechanical behaviour of rocks. A problem arises in the discontinuous behaviour of the fractures and how to accurately and efficiently model this. We present a novel approach for modelling many cracks randomly using analytic elements placed under plane strain conditions in an elastic medium. The analytic elements allow us to model the assembly computationally efficiently and up to machine precision. The crack element is the first step in the development of a model suitable for investigating the effect of fissures on tunnels in rock. The model can be used to validate numerical models and more.The solution for a single hydraulic pressurized crack in an infinite domain in plane strain was initially developed by Griffith (1921). We demonstrate that it is possible, by using series expansions in terms of complex variables, based on the Muskhelisvili-Kolosov functions, to generalize this solution to the case of an assembly of non-intersecting pressurized cracks. The solution consists of infinite series for each element Strack & Toller (2020). The expressions for the displacements and stress tensor components approach the exact solution, as the number of terms in the series approaches infinity.We present the case where two cracks approach each other orthogonally to less than 1/2000th of the cracks length. We show the effect of increasing the number of terms in the expansion and how this influences the precision, demonstrating that the result approaches the exact solution. We also present a case with 10,000 cracks; the coefficients are determined using an iterative solver. By using analytic elements, we can both present the corresponding stress and deformations field for the global scale and for small scales in the close proximity of individual cracks.ReferencesGriffith, A. A. (1921). The phenomena of rupture and flow in solids. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 221(582-593):163–198.Strack, O. D. L. and Toller, E. A. L. (2020). An analytic element model for highly fractured elastic media, manuscript submitted for publication in International Journal for Numerical and Analytical Methods in Geomechanics.</p>

Learning from nature: favoring small lahars formation for hazard mitigation

2021 ◽  
Author(s):  
Valeria Lupiano ◽  
Claudia Calidonna ◽  
Paolo Catelan ◽  
Francesco Chidichimo ◽  
Gino Mirocle Crisci ◽  
...  
Keyword(s):  
Cellular Automata ◽  
Risk Mitigation ◽  
Low Cost ◽  
Pyroclastic Flows ◽  
Risk Modeling ◽  
Security Measures ◽  
Rainfall Events ◽  
Volcanic Deposit ◽  
Volcanic Deposits ◽  
And Mathematics

<p>Lahars represent one of the world destructive natural phenomena as number of casualties (Manville et al., 2013). Lahars originate as mixtures of water and volcanic deposits frequently by heavy rainfalls; they are erosive floods capable of increase in volume along its path to more than 10 times their initial size, moving up to 100 km/h in steeply sloping as far as an extreme distance of hundreds of kilometers.</p><p>Beside tools of early warning, security measures have been adopted in volcanic territory, by constructing retaining dams and embankments in key positions for containing and deviating possible lahars (Leung et al., 2003). This solution could involve a strong environmental impact both for the works and the continuous accumulation of volcanic deposits, such that equilibrium conditions could lack far, triggering more disastrous events.</p><p>The growing frequency of lahars in the Vascún Valley area, Tungurahua Volcano Ecuador, maybe for the climatic change, has recently produced smaller (shorter accumulation periods) and therefore less dangerous events.</p><p>Momentary ponds form along rivers in volcanic areas, when they become usually blocked by landslides of volcanic deposits, which are originated by pyroclastic flows and lahars. The most frequent cause of a breakout of such natural ponds is the overflow of water across the newly formed dam and subsequent erosion and rapid downcutting into the loose rock debris.</p><p>Dam collapse can occur by sliding of the volcanic deposit or by its overturning. By eroding the blockage and flowing out river channel downstream, the initial surge of water will incorporate a dangerous volume of sediments. This produces lahars with possible devastating effects for settlements in their path (Leung et al., 2003).</p><p>The use of simulation tools (from the cellular automata model LLUNPIY) and field data (including necessary subsoil survey) permit to individuate points, where dams by backfills, easy to collapse, can produce momentary ponds.</p><p>Small temporary dams with similar (but controlled) behavior of above mentioned dams can be designed and built at low cost by local backfills in order to allow the outflow of streams produced by regular rainfall events. This result is achieved by properly dimensioning a discharge channel at the dam base (Lupiano et al., 2020).</p><p>So small lahars can be triggered for minor rainfall events, lahar detachments can be anticipated for major events, avoiding simultaneous confluence with other lahars (Lupiano et al., 2020).</p><p><strong>REFERENCES</strong></p><p>Leung, MF, Santos, JR, Haimes, YY (2003). Risk modeling, assessment, and management of lahar flow threat. Risk Analysis, 23(6), 1323-1335.</p><p>Lupiano, V., Chidichimo, F., Machado, G., Catelan, P., Molina, L., Calidonna, C.R., Straface, S., Crisci, G. M., And Di Gregorio, S. (2020) - From examination of natural events to a proposal for risk mitigation of lahars by a cellular-automata methodology: a case study for Vascún valley, Ecuador. Nat. Hazards Earth Syst. Sci., 20, 1–20, 2020.</p><p>Manville, V., Major, J.J. and Fagents, S.A. (2013). Modeling lahar behavior and hazards. in Fagents, SA, Gregg, TKP, and Lopes, RMC (eds.) Modeling Volcanic Processes: The Physics and Mathematics of Volcanism. Cambridge: Cambridge University Press, pp. 300–330.</p>

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