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

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

10.5194/esd-2017-10 ◽

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.

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BASIC DESIGN WIND SPEEDS BASED ON YEARLY MAXIMUM WIND SPEED RECORDS

Proceedings of the Japan Society of Civil Engineers ◽

10.2208/jscej1969.1981.23 ◽

1981 ◽

Vol 1981 (305) ◽

pp. 23-34 ◽

Cited By ~ 1

Author(s):

Yozo FUJINO ◽

Manabu ITO ◽

Toshio SAKAI

Keyword(s):

Wind Speed ◽

Maximum Wind Speed ◽

Basic Design ◽

Maximum Wind ◽

Wind Speeds ◽

Yearly Maximum

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A Common Wind Damage Pattern in Relation to the Classical Tornado

Bulletin of the American Meteorological Society ◽

10.1175/1520-0477-38.1.1.1 ◽

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.

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Intensity Models

Hurricane Climatology ◽

10.1093/oso/9780199827633.003.0012 ◽

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).

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Temporal and spatial variation of maximum wind speed days during the past 20 years in major cities of Xinjiang

10.1063/1.5034304 ◽

2018 ◽

Author(s):

Aliya Baidourela ◽

Zhen Jing ◽

Kahaer Zhayimu ◽

Adili Abulaiti ◽

Hakezi Ubuli

Keyword(s):

Wind Speed ◽

Spatial Variation ◽

Maximum Wind Speed ◽

Temporal And Spatial Variation ◽

Maximum Wind ◽

The Past ◽

Temporal And Spatial

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Application of SAR Data for Tropical Cyclone Intensity Parameters Retrieval and Symmetric Wind Field Model Development

Remote Sensing ◽

10.3390/rs13152902 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2902

Author(s):

Yuan Gao ◽

Jie Zhang ◽

Jian Sun ◽

Changlong Guan

Keyword(s):

Wind Speed ◽

Wind Field ◽

Maximum Wind Speed ◽

Wind Fields ◽

Maximum Wind ◽

Wind Field Model ◽

Wind Speeds ◽

Central Surface ◽

Intensity Parameters ◽

Wind Retrievals

The spaceborne synthetic aperture radar (SAR) is an effective tool to observe tropical cyclone (TC) wind fields at very high spatial resolutions. TC wind speeds can be retrieved from cross-polarization signals without wind direction inputs. This paper proposed methodologies to retrieve TC intensity parameters; for example, surface maximum wind speed, TC fullness (TCF) and central surface pressure from the European Space Agency Sentinel-1 Extra Wide swath mode cross-polarization data. First, the MS1A geophysical model function was modified from 6 to 69 m/s, based on three TC samples’ SAR images and the collocated National Oceanic and Atmospheric Administration stepped frequency microwave radiometer wind speed measurements. Second, we retrieved the wind fields and maximum wind speeds of 42 TC samples up to category 5 acquired in the last five years, using the modified MS1A model. Third, the TCF values and central surface pressures were calculated from the 1-km wind retrievals, according to the radial curve fitting of wind speeds and two hurricane wind-pressure models. Three intensity parameters were found to be dependent upon each other. Compared with the best-track data, the averaged bias, correlation coefficient (Cor) and root mean-square error (RMSE) of the SAR-retrieved maximum wind speeds were –3.91 m/s, 0.88 and 7.99 m/s respectively, showing a better result than the retrievals before modification. For central pressure, the averaged bias, Cor and RMSE were 1.17 mb, 0.77 and 21.29 mb and respectively, indicating the accuracy of the proposed methodology for pressure retrieval. Finally, a new symmetric TC wind field model was developed with the fitting function of the TCF values and maximum wind speeds, radial wind curve and the Rankine Vortex model. By this model, TC wind field can be simulated just using the maximum wind speed and the radius of maximum wind speed. Compared with wind retrievals, averaged absolute bias and averaged RMSE of all samples’ wind fields simulated by the new model were smaller than those of the Rankine Vortex model.

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