scholarly journals Tropical cyclone damage assessment of distributed infrastructure systems under spatially correlated wind speeds

2021 ◽  
Vol 91 ◽  
pp. 102080
Author(s):  
Diqi Zeng ◽  
Hao Zhang ◽  
Quanwang Li ◽  
Bruce R. Ellingwood
Keyword(s):  
Tropical Cyclone ◽  
Damage Assessment ◽  
Infrastructure Systems ◽  
Spatially Correlated ◽  
Wind Speeds ◽  
Distributed Infrastructure

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).

scholarly journals Impact of Satellite Observations on the Tropical Cyclone Track Forecasts of the Navy Operational Global Atmospheric Prediction System

2009 ◽  
Vol 137 (1) ◽  
pp. 41-50 ◽  
Author(s):  
James S. Goerss
Keyword(s):  
Tropical Cyclone ◽  
Precipitable Water ◽  
Satellite Observation ◽  
Satellite Observations ◽  
Prediction System ◽  
Wind Speeds ◽  
The North ◽  
Percent Improvement ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact

Abstract The tropical cyclone (TC) track forecasts of the Navy Operational Global Atmospheric Prediction System (NOGAPS) were evaluated for a number of data assimilation experiments conducted using observational data from two periods: 4 July–31 October 2005 and 1 August–30 September 2006. The experiments were designed to illustrate the impact of different types of satellite observations on the NOGAPS TC track forecasts. The satellite observations assimilated in these experiments consisted of feature-track winds from geostationary and polar-orbiting satellites, Special Sensor Microwave Imager (SSM/I) total column precipitable water and wind speeds, Advanced Microwave Sounding Unit-A (AMSU-A) radiances, and Quick Scatterometer (QuikSCAT) and European Remote Sensing Satellite-2 (ERS-2) scatterometer winds. There were some differences between the results from basin to basin and from year to year, but the combined results for the 2005 and 2006 test periods for the North Pacific and Atlantic Ocean basins indicated that the assimilation of the feature-track winds from the geostationary satellites had the most impact, ranging from 7% to 24% improvement in NOGAPS TC track forecasts. This impact was statistically significant at all forecast lengths. The impact of the assimilation of SSM/I precipitable water was consistently positive and statistically significant at all forecast lengths. The improvements resulting from the assimilation of AMSU-A radiances were also consistently positive and significant at most forecast lengths. There were no significant improvements/degradations from the assimilation of the other satellite observation types [e.g., Moderate Resolution Imaging Spectroradiometer (MODIS) winds, SSM/I wind speeds, and scatterometer winds]. The assimilation of all satellite observations resulted in a gain in skill of roughly 12 h for the NOGAPS 48- and 72-h TC track forecasts and a gain in skill of roughly 24 h for the 96- and 120-h forecasts. The percent improvement in these forecasts ranged from almost 20% at 24 h to over 40% at 120 h.

scholarly journals Air–Sea Momentum Fluxes during Tropical Cyclone Olwyn

2019 ◽  
Vol 49 (6) ◽  
pp. 1369-1379 ◽  
Author(s):  
Joey J. Voermans ◽  
Henrique Rapizo ◽  
Hongyu Ma ◽  
Fangli Qiao ◽  
Alexander V. Babanin
Keyword(s):  
Tropical Cyclone ◽  
Wind Stress ◽  
Momentum Flux ◽  
Turbulent Stress ◽  
High Wind ◽  
Wind Speeds ◽  
Induced Stress ◽  
Induced Stresses ◽  
Turbulent Momentum ◽  
Wave Induced

AbstractObservations of wind stress during extreme winds are required to improve predictability of tropical cyclone track and intensity. A common method to approximate the wind stress is by measuring the turbulent momentum flux directly. However, during high wind speeds, wave heights are typically of the same order of magnitude as instrument heights, and thus, turbulent momentum flux observations alone are insufficient to estimate wind stresses in tropical cyclones, as wave-induced stresses contribute to the wind stress at the height of measurements. In this study, wind stress observations during the near passage of Tropical Cyclone Olwyn are presented through measurements of the mean wind speed and turbulent momentum flux at 8.8 and 14.8 m above the ocean surface. The high sampling frequency of the water surface displacement (up to 2.5 Hz) allowed for estimations of the wave-induced stresses by parameterizing the wave input source function. During high wind speeds, our results show that the discrepancy between the wind stress and the turbulent stress can be attributed to the wave-induced stress. It is observed that for > 1 m s−1, the wave-induced stress contributes to 63% and 47% of the wind stress at 8.8 and 14.8 m above the ocean surface, respectively. Thus, measurements of wind stresses based on turbulent stresses alone underestimate wind stresses during high wind speed conditions. We show that this discrepancy can be solved for through a simple predictive model of the wave-induced stress using only observations of the turbulent stress and significant wave height.

scholarly journals Gust Factors and Turbulence Intensities for the Tropical Cyclone Environment

2009 ◽  
Vol 48 (3) ◽  
pp. 534-552 ◽  
Author(s):  
Bo Yu ◽  
Arindam Gan Chowdhury
Keyword(s):  
Tropical Cyclone ◽  
Surface Wind ◽  
Monitoring Program ◽  
Near Surface ◽  
Wind Speeds ◽  
Flat Terrain ◽  
Short Period ◽  
Terrain Roughness ◽  
American Society ◽  
Gust Factors

Abstract Gust factors are used to convert peak wind speeds averaged over a relatively short period (e.g., 3 s) to mean wind speeds averaged over a relatively long reference period (e.g., 1 h) or vice versa. Such conversions are needed for engineering, climatological, or forecasting purposes. In this paper, gust factors in tropical cyclone (TC) winds are estimated from Florida Coastal Monitoring Program (FCMP) observations of near-surface TC wind speeds representative of flow over the sea surface and over open flat terrain in coastal areas. Comparisons are made with gust factors in extratropical winds over open flat terrain that are available in the literature. According to the results of the study, for gust durations of less than 20 s, the Durst model underestimates, and the Krayer–Marshall model overestimates, gust factors of TC winds over surfaces with roughness specified in the American Society of Civil Engineers (ASCE) 7 Standard Commentary as typical of open terrain. Consideration should be given to these findings when updating the gust factors provided in the ASCE 7 Standard Commentary. The study also compares gust factors in TC winds obtained from FCMP data with gust factors in extratropical winds obtained from near-surface wind data collected at eight Automated Surface Observing System (ASOS) stations and concludes that, depending upon terrain roughness, gust factors in TC winds can be higher by about 10%–15% than gust factors in extratropical winds. The study also presents FCMP-based estimates of turbulence intensities and their variability and shows that turbulence intensities in TC winds increase as the terrain roughness increases. The longitudinal turbulence intensity can vary from storm to storm and can exceed its typical value by as much as 20%. It is recommended that future TC wind measurement campaigns obtain temperature data usable for stratification estimation purposes, as well as information on waves and storm surge upwind of the anemometer towers.

scholarly journals A global historical data set of tropical cyclone exposure (TCE-DAT)

2017 ◽  
Author(s):  
Tobias Geiger ◽  
Katja Frieler ◽  
David N. Bresch
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
The United States ◽  
Long Term Effects ◽  
Wind Fields ◽  
Damage Functions ◽  
Data Set ◽  
Wind Field Model ◽  
Wind Speeds ◽  
The Difference

Abstract. Tropical cyclones pose a major risk to societies worldwide with about 22 million directly-affected people and damages of $29 billion on average per year over the last 20 years. While data on observed cyclones tracks (location of the center) and wind speeds is publically available these data sets do not contain information about the spatial extent of the storm and people or assets exposed. Here, we apply a simplified wind field model to estimate the areas exposed to wind speeds above 34, 64, and 96 knots. Based on available spatially-explicit data on population densities and Gross Domestic Product (GDP) we estimate 1) the number of people and 2) the sum of assets exposed to wind speeds above these thresholds accounting for temporal changes in historical distribution of population and assets (TCE-hist) and assuming fixed 2015 patterns (TCE-2015). The associated country-event level exposure data (TCE-DAT) covers the period 1950 to 2015 and is freely available at http://doi.org/10.5880/pik.2017.005. It is considered key information to 1) assess the contribution of climatological versus socio-economic drivers of changes in exposure to tropical cyclones, 2) estimate changes in vulnerability from the difference in exposure and reported damages and calibrate associated damage functions, and 3) build improved exposure-based predictors to estimate higher-level societal impacts such as long-term effects on GDP, employment, or migration. We validate the adequateness of our methodology by comparing our exposure estimate to estimated exposure obtained from reported wind fields available since 1988 for the United States. We expect that the free availability of the underlying model and TCE-DAT will make research on tropical cyclone risks more accessible to non-experts and stakeholders.

scholarly journals pyPI (v1.3): Tropical Cyclone Potential Intensity Calculations in Python

2020 ◽  
Author(s):  
Daniel M. Gilford
Keyword(s):  
Tropical Cyclone ◽  
Heat Engine ◽  
Maximum Speed ◽  
Speed Limit ◽  
Tropical Cyclone Intensity ◽  
Cyclone Intensity ◽  
Wind Speeds ◽  
Intensity Calculations ◽  
First Time ◽  
Oceanographic Conditions

Abstract. Potential intensity (PI) is the maximum speed limit of a tropical cyclone found by modeling the storm as a thermal heat engine. Because there are significant correlations between PI and actual storm wind speeds, PI is a useful diagnostic for evaluating or predicting tropical cyclone intensity climatology and variability. Previous studies have calculated PI given a set of atmospheric and oceanographic conditions, but although a PI algorithm – originally developed by Kerry Emanuel – is in widespread use, it remains under-documented. The Tropical Cyclone Potential Intensity Calculations in Python (pyPI, v1.3) package develops the PI algorithm in Python, and for the first time details the full background and algorithm (line-by-line) used to compute tropical cyclone potential intensity constrained by thermodynamics. The pyPI package (1) provides a freely available, flexible, validated Python PI algorithm, (2) carefully documents the PI algorithm and its Python implementation, and (3) demonstrates and encourages the use of PI theory in tropical cyclone analyses. Validation shows pyPI output is nearly identical to the previous potential intensity computation, but is an improvement on the algorithm's consistency and handling of missing data. Example calculations with reanalyses data demonstrate pyPI's usefulness in climatological and meteorological research. Planned future improvements will improve on pyPI's assumptions, flexibility, and range of applications and tropical cyclone thermodynamic calculations.

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