Landscape-level variation in forest response to hurricane disturbance across a storm track

2008 ◽  
Vol 38 (12) ◽  
pp. 2942-2950 ◽  
Author(s):  
Posy E. Busby ◽  
Glenn Motzkin ◽  
Emery R. Boose
Keyword(s):  
Wind Speed ◽  
New England ◽  
Storm Track ◽  
Wind Damage ◽  
Forest Damage ◽  
Growth Responses ◽  
Minimal Growth ◽  
Wind Speeds ◽  
Hurricane Wind ◽  
Study Sites

Hurricane wind speeds at a given site are related to the intensity of the storm and the distance and direction from the storm center. As a result, forest damage is expected to vary predictably with respect to location relative to the storm track. To determine whether patterns of forest response along the track of a major hurricane in coastal New England were consistent with the expected patterns of wind damage, we investigated tree growth responses to the storm in several study sites that are similar with respect to site conditions, vegetation, and disturbance history. Growth responses to a severe hurricane in 1944 varied predictably among study sites with respect to distance from the storm track. Sites closest to the storm track experienced lesser wind damage and exhibited minimal growth responses, whereas sites farther east of the storm track and closer to the area of maximum estimated wind speed were characterized by greater wind damage and growth changes. Variation in estimated wind speed among our study sites (5–10 m/s) is not much greater than anticipated increases in hurricane intensity predicted under future climate scenarios (3–7 m/s). Thus, our results suggest that the magnitude of anticipated increases in wind speeds associated with Atlantic hurricanes may be sufficient to cause changes in forest response.

scholarly journals Topographic Speed-Up Effects and Observed Roof Damage on Bermuda following Hurricane Fabian (2003)

2013 ◽  
Vol 28 (1) ◽  
pp. 159-174 ◽  
Author(s):  
Craig Miller ◽  
Michael Gibbons ◽  
Kyle Beatty ◽  
Auguste Boissonnade
Keyword(s):  
Surface Roughness ◽  
Wind Speed ◽  
Surface Wind ◽  
Open Water ◽  
Clear Trend ◽  
Wind Speeds ◽  
Research Division ◽  
Hurricane Wind ◽  
Layer Flow ◽  
Observed Damage

Abstract In this study the impacts of the topography of Bermuda on the damage patterns observed following the passage of Hurricane Fabian over the island on 5 September 2003 are considered. Using a linearized model of atmospheric boundary layer flow over low-slope topography that also incorporates a model for changes of surface roughness, sets of directionally dependent wind speed adjustment factors were calculated for the island of Bermuda. These factors were then used in combination with a time-stepping model for the open water wind field of Hurricane Fabian derived from the Hurricane Research Division Real-Time Hurricane Wind Analysis System (H*Wind) surface wind analyses to calculate the maximum 1-min mean wind speed at locations across the island for the following conditions: open water, roughness changes only, and topography and roughness changes combined. Comparison of the modeled 1-min mean wind speeds and directions with observations from a site on the southeast coast of Bermuda showed good agreement between the two sets of values. Maximum open water wind speeds across the entire island showed very little variation and were of category 2 strength on the Saffir–Simpson scale. While the effects of surface roughness changes on the modeled wind speeds showed very little correlation with the observed damage, the effect of the underlying topography led to maximum modeled wind speeds of category 4 strength being reached in highly localized areas on the island. Furthermore, the observed damage was found to be very well correlated with these regions of topographically enhanced wind speeds, with a very clear trend of increasing damage with increasing wind speeds.

Estimating Enhanced Fujita Scale Levels Based on Forest Damage Severity

2017 ◽  
Vol 32 (1) ◽  
pp. 243-252 ◽  
Author(s):  
Christopher M. Godfrey ◽  
Chris J. Peterson
Keyword(s):  
Wind Speed ◽  
Rural Areas ◽  
Geographic Location ◽  
Forest Damage ◽  
Great Smoky Mountains ◽  
Wind Speeds ◽  
Tree Resistance ◽  
Resistance Model ◽  
Scale Levels ◽  
Resampling Procedure

Abstract Enhanced Fujita (EF) scale estimates following tornadoes remain challenging in rural areas with few traditional damage indicators. In some cases, such as the 27 April 2011 tornadoes that passed through mostly inaccessible terrain in the Great Smoky Mountains National Park and the Chattahoochee National Forest in the southeastern United States, traditional ground-based tornado damage surveys are nearly impossible. This work presents a novel method to infer EF-scale categories in forests using levels of tree damage and a coupled wind and tree resistance model. High-resolution aerial imagery allows detailed analyses based on a field of nearly half a million trees labeled with their geographic location and fall direction. Ground surveys also provide details on the composition of tree species and tree diameters within each tornado track. A statistical resampling procedure randomly draws a sample of trees from this database of observed trees. The coupled wind and tree resistance model determines the percentage of trees in that sample that fall for a given wind speed. By repeating this procedure, each wind speed value corresponds with a distribution of treefall percentages in the sampled plots. Comparing these results with the observed treefall percentage in small subplots along the entire tornado track allows estimation of the most probable wind speed associated with each subplot. Maps of estimated EF-scale levels reveal the relationship between complex terrain and wind speeds and show the variability of the intensity of each tornado along both tracks. This approach may lead to methods for the straightforward estimation of EF-scale categories in remote or inaccessible locations.

Measured Metocean Characteristics of Gulf of Mexico Hurricanes

2015 ◽  
Author(s):  
George Z. Forristall ◽  
Jason McConochie
Keyword(s):  
Gulf Of Mexico ◽  
Wave Height ◽  
Storm Track ◽  
Significant Wave ◽  
Wind Speeds ◽  
Hurricane Wind ◽  
Wave Parameters ◽  
Wave Heights ◽  
Significant Wave Heights ◽  
Buoy Data

A wealth of Gulf of Mexico hurricane wind and wave data has been measured in recent years. We have constructed a database that combines HURDAT storm track information with NDBC buoy data for the years 1978–2010. HURDAT contains 141 storms for that period of which 67 had measured significant wave heights greater than 5 m. Industry measurements in Hurricanes Camille, Lili, Ivan, Katrina, Rita, Gustav and Ike have been added to the buoy data. We have used this data base to study the relationships between wind and wave parameters in hurricanes. Specifically, we have calculated regressions and equal probability contours for significant wave height and peak spectral periods, first and second moment periods, wave height and Jonswap gamma values, wind speeds and wave heights, and wave and wind directions. All of these calculations have been done for azimuthal quadrants of the storm and radial distances near and far from the storm center.

A Common Wind Damage Pattern in Relation to the Classical Tornado

1957 ◽  
Vol 38 (1.1) ◽  
pp. 1-5
Author(s):  
George W. Reynolds
Keyword(s):  
Wind Speed ◽  
Wind Pressure ◽  
Wind Damage ◽  
Total Force ◽  
Total Speed ◽  
Damage Pattern ◽  
Damage Area ◽  
Maximum Wind ◽  
Wind Speeds ◽  
The Right

A wind damage pattern in which no major damage has been accomplished by winds blowing toward the direction from which the storm came can result from a classical tornado if the speed of translation is high enough relative to the maximum wind speed. A distinct rotary damage pattern indicates that the speed of translation is a less important component of the total speed. One might consider a wind damage pattern in which there is,An increase in damage intensity from right to left,No major damage accomplished to the left of the line of most extreme damage, andEvidence indicating that the damage in the major damage area was accomplished by winds blowing with the direction of translation of the storm, to be a point in favor of cyclonic rotation. If the build-up were from left to right, with the break-off to the right of the line of most extreme damage, the point would be in favor of anti-cyclonic rotation. The wind speed-wind pressure relationship is discussed briefly, and tables and graphs showing the wind speeds and pressures at points on opposite sides of a tornado, for assigned values of the speeds of rotation and translation, are included. A table estimating the total force on a wall resulting from assumed wind speeds is also presented.

Intensity Models

2013 ◽  
Author(s):  
James B. Elsner ◽  
Thomas H. Jagger
Keyword(s):  
Wind Speed ◽  
Maximum Wind Speed ◽  
Cumulative Distribution ◽  
Dade County ◽  
Data Set ◽  
Maximum Wind ◽  
Wind Speeds ◽  
The North ◽  
Hurricane Wind ◽  
Wind Speed Distribution

Strong hurricanes, such as Camille in 1969, Andrew in 1992, and Katrina in 2005, cause catastrophic damage. It is important to have an estimate of when the next big one will occur. You also want to know what influences the strongest hurricanes and whether they are getting stronger as the earth warms. This chapter shows you how to model hurricane intensity. The data are basinwide lifetime highest intensities for individual tropical cyclones over the North Atlantic and county-level hurricane wind intervals. We begin by considering trends using the method of quantile regression and then examine extreme-value models for estimating return periods. We also look at modeling cyclone winds when the values are given by category, and use Miami-Dade County as an example. Here you consider cyclones above tropical storm intensity (≥ 17 m s−1) during the period 1967–2010, inclusive. The period is long enough to see changes but not too long that it includes intensity estimates before satellite observations. We use “intensity” and “strength” synonymously to mean the fastest wind inside the cyclone. Consider the set of events defined by the location and wind speed at which a tropical cyclone first reaches its lifetime maximum intensity (see Chapter 5). The data are in the file LMI.txt. Import and list the values in 10 columns of the first 6 rows of the data frame by typing . . . > LMI.df = read.table("LMI.txt", header=TRUE) > round(head(LMI.df)[c(1, 5:9, 12, 16)], 1). . . The data set is described in Chapter 6. Here your interest is the smoothed intensity estimate at the time of lifetime maximum (WmaxS). First, convert the wind speeds from the operational units of knots to the SI units of meter per second. . . . > LMI.df$WmaxS = LMI.df$WmaxS * .5144 . . . Next, determine the quartiles (0.25 and 0.75 quantiles) of the wind speed distribution. The quartiles divide the cumulative distribution function (CDF) into three equal-sized subsets. . . . > quantile(LMI.df$WmaxS, c(.25, .75)) 25% 75% 25.5 46.0 . . . You find that 25 percent of the cyclones have a lifetime maximum wind speed less than 26 m s−1 and 75 percent have a maximum wind speed less than 46ms−1, so that 50 percent of all cyclones have a maximum wind speed between 26 and 46 m s−1 (interquartile range–IQR).

scholarly journals Tropical Cyclone Wind Speed Retrieval from Dual-Polarization Sentinel-1 EW Mode Products

2020 ◽  
Vol 37 (9) ◽  
pp. 1713-1724
Author(s):  
Yuan Gao ◽  
Changlong Guan ◽  
Jian Sun ◽  
Lian Xie
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Noise Removal ◽  
Cross Polarization ◽  
High Wind ◽  
Dual Polarization ◽  
High Wind Speed ◽  
Wind Speeds ◽  
Hurricane Wind ◽  
The Cross

AbstractRecent studies indicate that the cross-polarization synthetic aperture radar (SAR) images have the ability of retrieving high wind speed on ocean surface without wind direction input. This study presents a new approach for tropical cyclone (TC) wind speed retrieval utilizing thermal-noise-removed Sentinel-1 dual-polarization (VV + VH) Extra-Wide Swath (EW) Mode products. Based on 20 images of 9 TCs observed in the 2016 and 2018 and SAR-collocated European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) data and the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division’s (HRD) Real-time Hurricane Wind Analysis System (H*Wind) data, a subswath-based geophysical model function (GMF) Sentinel-1 EW Mode Wind Speed Retrieval Model after Noise Removal (S1EW.NR) is developed and validated statistically. TC wind speed is retrieved by using the proposed GMF and the C-band model 5.N (CMOD5.N). The results show that the wind speeds retrieved by the S1EW.NR model are in good agreement with wind references up to 31 m s−1. The correlation coefficient, bias, and standard deviation between the retrieval results and reference wind speeds are 0.74, −0.11, and 3.54 m s−1, respectively. Comparison of the wind speeds retrieved from both channels suggests that the cross-polarized signal is more suitable for high–wind speed retrieval, indicating the promising capability of cross-polarization SAR for TC monitoring.

scholarly journals Estimation of the high-resolution variability of extreme wind speeds for a better management of wind damage risks to forest-based bioeconomy

2017 ◽  
Author(s):  
Ari K. Venäläinen ◽  
Mikko O. Laapas ◽  
Pentti I. Pirinen ◽  
Matti Horttanainen ◽  
Reijo Hyvönen ◽  
...  
Keyword(s):  
Wind Speed ◽  
Spatial Resolution ◽  
Maximum Wind Speed ◽  
Wind Damage ◽  
Return Level ◽  
Maximum Wind ◽  
Wind Speeds ◽  
Coarse Spatial Resolution ◽  
Elevation Data ◽  
Topographic Variation

Abstract. The bioeconomy has an increasing role to play in climate change mitigation and the sustainable development of national economies. In a forested country, such as Finland, over 50 % of its current bioeconomy relies on the sustainable management and utilization of forest resources. Wind storms are a major risk that forests are exposed to and high spatial resolution analysis of the most vulnerable locations can produce risk assessment of forest management planning. Coarse spatial resolution estimates of the return levels of maximum wind speed based, e.g., on reanalysed meteorological data or climate scenarios can be downscaled to forest stand levels with the help of land cover and terrain elevation data. In this paper, we examine the feasibility of the wind multiplier approach for downscaling of maximum wind speed, using 20 meter spatial resolution CORINE-land use dataset and high resolution digital elevation data. A coarse spatial resolution estimate of the 10-year return level of maximum wind speed was obtained from the ERA-Interim reanalysed data. These data were downscaled to 26 meteorological station locations to represent very diverse environments: Open Baltic Sea islands, agricultural land, forested areas, and Northern Finland treeless fells. Applying a comparison, the downscaled 10-year return levels explained 77 % of the observed variation among the stations examined. In addition, the spatial variation of wind multiplier downscaled 10-year return level wind was compared with the WAsP- model simulated wind. The heterogeneous test area was situated in Northern Finland, and it was found that the major features of the spatial variation were similar, but in the details, there were relatively large differences. However, for areas representing a typical Finnish forested landscape with no major topographic variation, both of the methods produced very similar results. Further fine-tuning of wind multipliers could improve the downscaling for the locations with large topographic variation. However, the current results already indicate that the wind multiplier method offers a pragmatic and computationally feasible tool for identifying at a high spatial resolution those locations having the highest forest wind damage risks. It can also be used to provide the necessary wind climate information for wind damage risk model calculations, thus making it possible to estimate the probability of predicted threshold wind speeds for wind damage and consequently the probability (and amount) of wind damage for certain forest stand configurations.

scholarly journals Scatterometer hurricane wind speed retrievals using cross polarization

2013 ◽  
Vol 6 (4) ◽  
pp. 7945-7984 ◽  
Author(s):  
G.-J. van Zadelhoff ◽  
A. Stoffelen ◽  
P. W. Vachon ◽  
J. Wolfe ◽  
J. Horstmann ◽  
...  
Keyword(s):  
Wind Speed ◽  
Weather Prediction ◽  
Data Sets ◽  
Data Set ◽  
Wind Speeds ◽  
Geophysical Model ◽  
Hurricane Wind ◽  
Geophysical Model Function ◽  
Sar Imagery ◽  
Nwp Model

Abstract. Hurricane-force wind speeds can have a large societal impact and in this paper microwave C-band cross-polarized (VH) signals are investigated to assess if they can be used to derive extreme wind speed conditions. European satellite scatterometers have excellent hurricane penetration capability at C-band, but the vertically (VV) polarized signals become insensitive above 25 m s−1. VV and VH polarized backscatter signals from RADARSAT-2 SAR imagery acquired during severe hurricane events were compared to collocated SFMR wind measurements acquired by NOAA's hurricane-hunter aircraft. From this data set a Geophysical Model Function (GMF) at strong-to-extreme/severe wind speeds (i.e. 20 m s−1 < U10 < 45 m s−1) is derived. Within this wind speed regime, cross-polarized data showed no distinguishable loss of sensitivity and as such, cross-polarized data can be considered a good candidate for the retrieval of strong-to-severe wind speeds from satellite instruments. The upper limit of 45 m s−1 is defined by the currently available collocated data. The validity of the derived relationship between wind speed and VH has been evaluated by comparing the cross polarized signals to two independent wind speed datasets, i.e. short-range ECMWF Numerical Weather Prediction (NWP) model forecast winds and the NOAA best estimate one-minute maximum sustained winds. Analysis of the three comparison data sets confirm that cross-polarized signals from satellites will enable the retrieval of strong-to-severe wind speeds where VV or horizontal (HH) polarization data has saturated. The VH backscatter increases exponentially with respect to wind speed (linear against VH [dB]) and a near real time assessment of maximum sustained wind speed is possible using VH measurements. VH measurements thus would be an extremely valuable complement on next-generation scatterometers for Hurricane forecast warnings and hurricane model initialization.

The Impact of Assimilating SSM/I and QuikSCAT Satellite Winds on Hurricane Isidore Simulations

2007 ◽  
Vol 135 (2) ◽  
pp. 549-566 ◽  
Author(s):  
Shu-Hua Chen
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Mesoscale Model ◽  
Three Dimensional ◽  
Storm Track ◽  
Initial Position ◽  
Wind Speeds ◽  
The Impact ◽  
Quikscat Winds

Abstract Three observational datasets of Hurricane Isidore (in 2002) were analyzed and compared: the Special Sensor Microwave Imager (SSM/I), the Quick Scatterometer (QuikSCAT) winds, and dropsonde winds. SSM/I and QuikSCAT winds were on average about 1.9 and 0.3 m s−1 stronger, respectively, than dropsonde winds. With more than 20 000 points of data, SSM/I wind speed was about 2.2 m s−1 stronger than QuikSCAT. Comparison of the wind direction observed by QuikSCAT with those from the dropsondes showed that the quality of QuikSCAT data is good. The effect of assimilating SSM/I wind speeds and/or QuikSCAT wind vectors for the analysis of Hurricane Isidore was assessed using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and its three-dimensional variational data assimilation system. For the Hurricane Isidore case study, it was found that the assimilation of either satellite winds strengthened the cyclonic circulation in the analysis. However, the increment of the QuikSCAT wind analysis is more complicated than that from the SSM/I analysis due to the correction of the storm location, a positive result from the assimilation of wind vectors. The increase in low-level wind speeds enhanced the air–sea interaction processes and improved the simulated intensity for Isidore. In addition, the storm structure was better simulated. Assimilation of QuikSCAT wind vectors clearly improved simulation of the storm track, in particular during the later period of the simulation, but lack of information about the wind direction from SSM/I data prevented it from having much of an effect. Assessing the assimilation of QuikSCAT wind speed versus wind vector data confirmed this hypothesis. The track improvement partially resulted from the relocation of the storm’s initial position after assimilation of the wind vectors. For this case study, it was found that the assimilation of SSM/I or QuikSCAT data had the greatest impact on the Hurricane Isidore simulation during the first 2 days.

scholarly journals Estimation of the high-spatial-resolution variability in extreme wind speeds for forestry applications

2017 ◽  
Vol 8 (3) ◽  
pp. 529-545 ◽  
Author(s):  
Ari Venäläinen ◽  
Mikko Laapas ◽  
Pentti Pirinen ◽  
Matti Horttanainen ◽  
Reijo Hyvönen ◽  
...  
Keyword(s):  
Wind Speed ◽  
Spatial Variation ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Maximum Wind Speed ◽  
Wind Damage ◽  
Return Level ◽  
Mapping Technique ◽  
Maximum Wind ◽  
Wind Speeds

Abstract. The bioeconomy has an increasing role to play in climate change mitigation and the sustainable development of national economies. In Finland, a forested country, over 50 % of the current bioeconomy relies on the sustainable management and utilization of forest resources. Wind storms are a major risk that forests are exposed to and high-spatial-resolution analysis of the most vulnerable locations can produce risk assessment of forest management planning. In this paper, we examine the feasibility of the wind multiplier approach for downscaling of maximum wind speed, using 20 m spatial resolution CORINE land-use dataset and high-resolution digital elevation data. A coarse spatial resolution estimate of the 10-year return level of maximum wind speed was obtained from the ERA-Interim reanalyzed data. Using a geospatial re-mapping technique the data were downscaled to 26 meteorological station locations to represent very diverse environments. Applying a comparison, we find that the downscaled 10-year return levels represent 66 % of the observed variation among the stations examined. In addition, the spatial variation in wind-multiplier-downscaled 10-year return level wind was compared with the WAsP model-simulated wind. The heterogeneous test area was situated in northern Finland, and it was found that the major features of the spatial variation were similar, but in some locations, there were relatively large differences. The results indicate that the wind multiplier method offers a pragmatic and computationally feasible tool for identifying at a high spatial resolution those locations with the highest forest wind damage risks. It can also be used to provide the necessary wind climate information for wind damage risk model calculations, thus making it possible to estimate the probability of predicted threshold wind speeds for wind damage and consequently the probability (and amount) of wind damage for certain forest stand configurations.

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