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

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.

scholarly journals Estimating the Key Parameter of a Tropical Cyclone Wind Field Model over the Northwest Pacific Ocean: A Comparison between Neural Networks and Statistical Models

2021 ◽  
Vol 13 (14) ◽  
pp. 2653
Author(s):  
Ziyao Sun ◽  
Biao Zhang ◽  
Jie Tang
Keyword(s):  
Wind Speed ◽  
Statistical Models ◽  
Pressure Difference ◽  
Wind Field ◽  
Field Model ◽  
Wind Pressure ◽  
Maximum Wind Speed ◽  
Maximum Wind ◽  
Wind Field Model ◽  
Key Parameter

Estimation of maximum wind speed associated with tropical cyclones (TCs) is crucial to evaluate potential wind destruction. The Holland B parameter is the key parameter of TC parametric wind field models. It plays an essential role in describing the radial distribution characteristics of a TC wind field and has been widely used in TC disaster risk evaluation. In this study, a backpropagation neural network (BPNN) is developed to estimate the Holland B parameter (Bs) in TC surface wind field model. The inputs of the BPNN include different combinations of TC minimum center pressure difference (Δp), latitude, radius of maximum wind speed, translation speed and intensity change rate from the best-track data of the Joint Typhoon Warning Center (JTWC). We find that the BPNN exhibits the best performance when only inputting TC central pressure difference. The Bs estimated from BPNN are compared with those calculated from previous statistical models. Results indicate that the proposed BPNN can describe well the nonlinear relation between Bs and Δp. It is also found that the combination of BPNN and Holland’s wind pressure model can significantly improve the maximum wind speed underestimation and overestimation of the two existing wind pressure models (AH77 and KZ07) for super typhoons.

Quantification of NOX Emissions Based on Car MAX-DOAS Measurements over Beijing and Impacts of Wind Field

2020 ◽  
Author(s):  
Xinghong Cheng
Keyword(s):  
Spatial Distribution ◽  
Wind Speed ◽  
Wind Field ◽  
Meteorological Condition ◽  
Emission Rates ◽  
Wind Fields ◽  
Vertical Column ◽  
Wind Speeds ◽  
South Central ◽  
The Mean

<p>We carried out 14 days of Car MAX-DOAS experiments on the 6th Ring Rd of Beijing in January, September and October, 2014. The tropospheric vertical column densities (VCD) of NO<sub>2</sub> are retrieved and used to estimate the emissions of NO<sub>x</sub>. The offline LAPS-WRF-CMAQ model system is used to simulate wind fields by assimilation of observational data and calculate the NO<sub>2</sub> to NO<sub>x</sub> concentration ratios. The NO<sub>X</sub> emissions in Beijing for different seasons derived from Car MAX-DOAS measurements are compared with the multi-resolution emission inventory in China for 2012 (MEIC 2012), and impacts of wind field on estimated emissions and its uncertainties are also investigated. Results show that the NO<sub>2</sub> VCD is higher in January than other two months and it is typically larger at the southern parts of the 6th Ring Road than the northern parts of it. Wind field has obvious impacts on the spatial distribution of NO<sub>2</sub> VCD, and the mean NO<sub>2</sub> VCD with south wind at most sampling points along the 6th Ring Rd is higher than north wind. The journey-to-journey variation pattern of estimated NO<sub>X</sub> emissions rates (E<sub>NOX</sub>) is consistent with that of the NO<sub>2</sub> VCD, and E<sub>NOX </sub>is mainly determined by the NO2 VCD. In addition, the journey-to-journey E<sub>NOX</sub> in the same month is different and it is affected by wind speed, the ratio of NO<sub>2</sub> and NOx concentration and the decay rate of NO<sub>X</sub> from the emission sources to measured positions under different meteorological condition. The E<sub>NOX</sub> ranges between 6.46×10<sup>25</sup> and 50.05×10<sup>25</sup> molec s<sup>-1</sup>. The averaged E<sub>NOX</sub> during every journey in January, September and October are respectively 35.87×10<sup>25</sup>, 20.34×10<sup>25</sup>, 8.96×10<sup>25</sup> molec s<sup>-1</sup>. The estimated E<sub>NOX</sub> after removing the simulated error of wind speed and observed deviation of NO<sub>2</sub> VCD are found to be mostly closer to the MEIC 2012, but sometimes E<sub>NOX </sub>is lower or higher and it indicates that the MEIC 2012 might be overestimate or underestimate the true emissions. The estimated E<sub>NOX</sub> on January 27 and September 19 are obviously higher than other journeys in the same month because the mean NO<sub>2</sub> VCD and Leighton ratio during these two periods are larger, and corresponding wind speeds are smaller. Additionally, because south wind may affect the spatial distribution of mean NO<sub>2</sub> VCD in Beijing which is downwind of south-central regions of Hebei province with high source emission rates, the uncertainty of the estimated E<sub>NOX</sub> with south wind will be increased.</p>

Wind tunnel tests on the characteristics of wind fields over a simplified gorge

2016 ◽  
Vol 20 (10) ◽  
pp. 1599-1611 ◽  
Author(s):  
Peng Hu ◽  
Yongle Li ◽  
Yan Han ◽  
CS Cai ◽  
Guoji Xu
Keyword(s):  
Wind Speed ◽  
Wind Tunnel ◽  
Wind Direction ◽  
Wind Field ◽  
Small Range ◽  
Wind Fields ◽  
Wind Speeds ◽  
Mean Wind Speed ◽  
Wind Characteristics ◽  
The Mean

Characteristics of wind fields over the gorge or valley terrains are becoming more and more important to the structural wind engineering. However, the studies on this topic are very limited. To obtain the fundamental characteristics information about the wind fields over a typical gorge terrain, a V-shaped simplified gorge, which was abstracted from some real deep-cutting gorges where long-span bridges usually straddle, was introduced in the present wind tunnel studies. Then, the wind characteristics including the mean wind speed, turbulence intensity, integral length scale, and the wind power spectrum over the simplified gorge were studied in a simulated atmospheric boundary layer. Furthermore, the effects of the oncoming wind field type and oncoming wind direction on these wind characteristics were also investigated. The results show that compared with the oncoming wind, the wind speeds at the gorge center become larger, but the turbulence intensities and the longitudinal integral length scales become smaller. Generally, the wind fields over the gorge terrain can be approximately divided into two layers, that is, the gorge inner layer and the gorge outer layer. The different oncoming wind field types have remarkable effects on the mean wind speed ratios near the ground. When the angle between the oncoming wind and the axis of the gorge is in a certain small range, such as smaller than 10°, the wind fields are very close to those associated with the wind direction of 0°. However, when the angle is in a larger range, such as larger than 20°, the wind fields in the gorge will significantly change. The research conclusions can provide some references for civil engineering practices regarding the characteristics of wind fields over the real gorge terrains.

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 A Hurricane Boundary Layer and Wind Field Model for Use in Engineering Applications

2009 ◽  
Vol 48 (2) ◽  
pp. 381-405 ◽  
Author(s):  
Peter J. Vickery ◽  
Dhiraj Wadhera ◽  
Mark D. Powell ◽  
Yingzhao Chen
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Drag Coefficient ◽  
Wind Field ◽  
Field Model ◽  
Radial Dependence ◽  
Surface Winds ◽  
Wind Field Model ◽  
Wind Speeds ◽  
Hurricane Boundary Layer

Abstract This article examines the radial dependence of the height of the maximum wind speed in a hurricane, which is found to lower with increasing inertial stability (which in turn depends on increasing wind speed and decreasing radius) near the eyewall. The leveling off, or limiting value, of the marine drag coefficient in high winds is also examined. The drag coefficient, given similar wind speeds, is smaller for smaller-radii storms; enhanced sea spray by short or breaking waves is speculated as a cause. A fitting technique of dropsonde wind profiles is used to model the shape of the vertical profile of mean horizontal wind speeds in the hurricane boundary layer, using only the magnitude and radius of the “gradient” wind. The method slightly underestimates the surface winds in small but intense storms, but errors are less than 5% near the surface. The fit is then applied to a slab layer hurricane wind field model, and combined with a boundary layer transition model to estimate surface winds over both marine and land surfaces.

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.

Analysis of Boundary Layer Wind Field Statistical Characteristics and Reference Wind Pressure in Wenzhou District

2011 ◽  
Vol 368-373 ◽  
pp. 1424-1430
Author(s):  
Jian Jia Wu ◽  
Wen Hai Shi
Keyword(s):  
Wind Speed ◽  
Wind Field ◽  
Wind Pressure ◽  
Maximum Wind Speed ◽  
Statistical Characteristics ◽  
Annual Maximum ◽  
Maximum Wind ◽  
Wind Speed Maximum ◽  
Mean Wind Speed ◽  
Extreme Wind Speed

Based on large amount of meteorological wind field records observed in Wenzhou district, this paper analyzed the annual maximum wind speed (maximum 10 minute mean wind speed), annual extreme wind speed (maximum 3 seconds mean wind speed), reference wind pressure and wind field characteristics of typhoon in Wenzhou. The results shows that the annual maximum wind speed have a decreased trend on the whole in different areas of Wenzhou, and the trend in coastal area is more obvious than that in inland areas; the annual maximum wind speed in different areas of Wenzhou is unsteady and the typhoons have great effect on it; the value of reference wind pressure in Dongtou is greater than the value given by the design load code of China (GB50009-2001, 2002), but the values of other areas are less than the value of Code. Based on the wind field of three typhoon records, some significant results about the variation and routine characteristics of typhoon are also discussed.

scholarly journals Comparative assessment of validity of gradient wind models for a translating tropical cyclone

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Yuzuru Eguchi ◽  
Yasuo Hattori ◽  
Mitsuharu Nomura
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Wind Field ◽  
Comparative Assessment ◽  
Maximum Wind Speed ◽  
Observation Data ◽  
High Wind ◽  
Maximum Wind ◽  
Gradient Wind ◽  
The Difference

AbstractAccurate and conservative evaluations of the gradient wind in the free atmosphere are needed to account for high-wind hazards when designing wind resistance for critical infrastructure. This paper compared the validity of three existing gradient wind models to select an appropriate evaluation model, which enables us to accurately compute the asymmetric gradient wind field of a translating tropical cyclone under the condition of a symmetric pressure distribution and a constant translation velocity. The validity of the three models was assessed by evaluating the residuals in momentum conservation equations for the gradient wind under a specific tropical cyclone condition. The magnitude of the residuals was considered to be the measure of error in the gradient wind derived from each model. The results showed that the most frequently used model yielded the largest magnitude of residuals with the lowest maximum wind speed among the three models. The wind characteristics of the three models were validated using archived observation data of hurricanes. The physical reason for the difference in maximum wind speed among the three models was explained by the difference in the streamline feature of the gradient wind field. It was also revealed that the differences in maximum wind speed and magnitude of residuals became more pronounced as the translation speed and the intensity of a tropical cyclone increased. The comparative assessment of the three gradient wind models allowed us to identify the best model for use in conservative wind-resistant design and high-wind risk estimates.

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.

Sign in / Sign up

Export Citation Format

Share Document