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 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.

scholarly journals Temporal variations in the wind and wave climate at a location in the eastern Arabian Sea based on ERA-Interim reanalysis data

2014 ◽  
Vol 14 (5) ◽  
pp. 1371-1381 ◽  
Author(s):  
P. R. Shanas ◽  
V. Sanil Kumar
Keyword(s):  
Wind Speed ◽  
Arabian Sea ◽  
Temporal Variations ◽  
Reanalysis Data ◽  
Maximum Wind Speed ◽  
Annual Maximum ◽  
Upward Trend ◽  
Monsoon Period ◽  
Maximum Wind ◽  
Eastern Arabian Sea

Abstract. Temporal variations in wind speed and significant wave height (SWH) at a location in the eastern Arabian Sea are studied using ERA-Interim reanalysis data from 1979 to 2012. A shallow water location is selected for the study since measured buoy data are available close to the location for comparison with the reanalysis data. The annual mean wind speed shows a statistically significant decreasing trend of 1.5 cm s−1 year−1, whereas a statistically insignificant increasing trend of 3.6 cm s−1 year−1 is observed for annual maximum wind speed due to the local events that altered the trend in annual maximum wind speed. Weakening of SWH during one of the peak monsoon months (August) is identified from the monthly analysis of SWH, which shows a higher upward trend in SWH during the southwest monsoon period, with an exception during August. The annual mean SWH shows a slight upward trend (0.012 cm year−1), whereas a larger upward trend (1.4 cm year−1) is observed for annual maximum SWH. Both identified trends are statistically insignificant. The influence of tropical cyclone activity is also studied and it is found that the maximum SWH and wind speed during 1996 are directly related to the cyclonic event.

scholarly journals Comments on “Reexamination of Tropical Cyclone Wind–Pressure Relationship”

2008 ◽  
Vol 23 (4) ◽  
pp. 758-761 ◽  
Author(s):  
Shyamnath Veerasamy
Keyword(s):  
Wind Speed ◽  
Tropical Cyclones ◽  
North Atlantic ◽  
Wind Pressure ◽  
Maximum Wind Speed ◽  
Central Pressure ◽  
Southwest Indian Ocean ◽  
Maximum Wind ◽  
The North ◽  
The North Atlantic

Abstract In their study on the wind–pressure relationship (WPR) that exists in tropical cyclones, Knaff and Zehr presented results of the use of the Dvorak Atlantic WPR for estimating central pressure and maximum wind speed of tropical cyclones. These show some fairly large departures of estimated central pressure and maximum surface winds from observed values. Based on a study carried out in the southwest Indian Ocean (SWIO), it is believed that improvements in the use of the Dvorak WPR can be achieved by using the size of a closed isobar (it is the 1004-hPa closed isobar in the SWIO) to determine whether to use the North Atlantic (NA), the western North Pacific (WNP), or a mean of the NA and WNP Dvorak WPR for estimating central pressure and maximum wind speed in tropical cyclones.

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 Decision tree-based detection of blowing snow events in the European Alps

2021 ◽  
Vol 25 (7) ◽  
pp. 3783-3804
Author(s):  
Zhipeng Xie ◽  
Weiqiang Ma ◽  
Yaoming Ma ◽  
Zeyong Hu ◽  
Genhou Sun ◽  
...  
Keyword(s):  
Wind Speed ◽  
Decision Tree ◽  
Maximum Wind Speed ◽  
Optimal Size ◽  
European Alps ◽  
Blowing Snow ◽  
Spatiotemporal Resolution ◽  
Maximum Wind ◽  
Wind Speed Maximum

Abstract. Blowing snow processes are crucial in shaping the strongly heterogeneous spatiotemporal distribution of snow and in regulating subsequent snowpack evolution in mountainous terrain. Although empirical formulae and constant threshold wind speeds have been widely used to estimate the occurrence of blowing snow in regions with sparse observations, the scarcity of in situ observations in mountainous regions contrasts with the demands of models for reliable observations at high spatiotemporal resolution. Therefore, these methods struggle to accurately capture the high local variability of blowing snow. This study investigated the potential capability of the decision tree model (DTM) to detect blowing snow in the European Alps. The DTMs were constructed based on routine meteorological observations (mean wind speed, maximum wind speed, air temperature and relative humidity) and snow measurements (including in situ snow depth observations and satellite-derived products). Twenty repetitions of a random sub-sampling validation test with an optimal size ratio (0.8) between the training and validation subsets were applied to train and assess the DTMs. Results show that the maximum wind speed contributes most to the classification accuracy, and the inclusion of more predictor variables improves the overall accuracy. However, the spatiotemporal transferability of the DTM might be limited if the divergent distribution of wind speed exists between stations. Although both the site-specific DTMs and site-independent DTM show great ability in detecting blowing snow occurrence and are superior to commonly used empirical parameterizations, specific assessment indicators varied between stations and surface conditions. Events for which blowing snow and snowfall occurred simultaneously were detected the most reliably. Although models failed to fully reproduce the high frequency of local blowing snow events, they have been demonstrated to be a promising approach requiring limited meteorological variables and have the potential to scale to multiple stations across different regions.

scholarly journals Decision tree-based detection of blowing snow events in the European Alps

2021 ◽  
Author(s):  
Zhipeng Xie ◽  
Weiqiang Ma ◽  
Yaoming Ma ◽  
Zeyong Hu ◽  
Genhou Sun ◽  
...  
Keyword(s):  
Wind Speed ◽  
Decision Tree ◽  
Maximum Wind Speed ◽  
Optimal Size ◽  
European Alps ◽  
Blowing Snow ◽  
Spatiotemporal Resolution ◽  
Maximum Wind ◽  
Mountainous Regions ◽  
Wind Speed Maximum

Abstract. Blowing snow processes are crucial in shaping the strongly heterogeneous spatiotemporal distribution of snow, and in regulating subsequent snowpack evolution in mountainous terrain. Although empirical formulae and a constant threshold wind speed have been widely used to estimate the occurrence of blowing snow in regions with sparse observations, the scarcity of in-situ observations in mountainous regions contrasts with the demands of models for reliable observations at high spatiotemporal resolution. Therefore, these methods struggle to accurately capture the high local variability of blowing snow. This study investigated the potential capability of the decision tree model (DTM) to detect blowing snow in the European Alps. The DTMs were constructed based on routine meteorological observations (mean wind speed, maximum wind speed, air temperature and relative humidity). Twenty repetitions of random sub-sampling validation test with an optimal size ratio (0.8) between the training and validation subset were applied to train and assess the DTMs. Results show that the maximum wind speed contributes most to the classification accuracy, and the inclusion of more predictor variables improves the overall accuracy. However, the spatiotemporal transferability of the DTM might be limited if divergent distributions exist between stations. Although both the site-specific DTMs and site-independent DTM show strong performance for accurately detecting blowing snow, specific assessment indicators varied between stations and surface conditions. Events for which blowing snow and snowfall occurred simultaneously were detected the most reliably. Although models failed to fully reproduce the high frequency of local blowing snow events, they have been demonstrated a promising approach requiring limited meteorological variables and have the potential to scale to multiple stations across different regions.

Sign in / Sign up

Export Citation Format

Share Document