CALCULATION OF VERTICAL WIND SPEED PROFILE WITH VIDEO SONDE AND CLOUD MICROPHYSICAL ANALYSIS

2019 ◽  
Vol 75 (2) ◽  
pp. I_1177-I_1182
Author(s):  
Satoru OISHI ◽  
Naoki HAYASHI ◽  
Mariko OGAWA ◽  
Yoshiyuki KAJIKAWA ◽  
Eiichi NAKAKITA
Keyword(s):  
Wind Speed ◽  
Vertical Wind ◽  
Speed Profile ◽  
Wind Speed Profile ◽  
Vertical Wind Speed

scholarly journals An empirical-analytical model of the vertical wind speed profile above and within an Amazon forest site

2015 ◽  
Vol 23 (1) ◽  
pp. 158-164 ◽  
Author(s):  
Cledenilson Mendonça de Souza ◽  
Cléo Quaresma Dias-Júnior ◽  
Júlio Tóta ◽  
Leonardo Deane de Abreu Sá
Keyword(s):  
Wind Speed ◽  
Analytical Model ◽  
Vertical Wind ◽  
Forest Site ◽  
Amazon Forest ◽  
Speed Profile ◽  
Wind Speed Profile ◽  
Vertical Wind Speed

Estimation of sector roughness lengths and the effect on prediction of the vertical wind speed profile

1993 ◽  
Vol 66 (1-2) ◽  
pp. 19-47 ◽  
Author(s):  
R. J. Barthelmie ◽  
J. P. Palutikof ◽  
T. D. Davies
Keyword(s):  
Wind Speed ◽  
Vertical Wind ◽  
Speed Profile ◽  
Wind Speed Profile ◽  
Roughness Lengths ◽  
Vertical Wind Speed

Vertical wind speed extrapolation: Modelling using a response surface methodology (RSM) based on unconventional designs

2021 ◽  
pp. 0309524X2110463
Author(s):  
Feriel Adli ◽  
Nawel Cheggaga ◽  
Farouk Hannane ◽  
Leila Ouzeri
Keyword(s):  
Response Surface Methodology ◽  
Wind Speed ◽  
Response Surface ◽  
Vertical Wind ◽  
Industrial Processes ◽  
Wind Speed Profile ◽  
Wind Speeds ◽  
Vertical Wind Speed ◽  
Mathematical Techniques ◽  
Rsm Model

The main objective of this paper is to develop a predictive model of vertical wind speed profile. Response surface methodology (RSM) is used for this purpose. RSM is a set of statistical and mathematical techniques useful for the development, improvement and optimisation of processes. It is mainly used in industrial processes and is successfully applied in this paper to model the wind speed at the hub height of the wind turbine. An unconventional model is adopted due to the nature of the input parameters which cannot be controlled or modified. The model validation indicators, namely correlation coefficient ([Formula: see text]) and root mean square error (RMSE = 1.02), give excellent results when comparing predicted and measured wind speeds. For the same data, the RSM model gives a better RMSE compared to the conventional power law and the artificial neural network.

Evaluation of micro physical products of GPM/DPR with X-band radar network data

2021 ◽  
Author(s):  
Nobuhiro Takahashi ◽  
Takeharu Kouketsu
Keyword(s):  
Wind Speed ◽  
Three Dimensional ◽  
Vertical Wind ◽  
Network Data ◽  
Dual Frequency ◽  
Convective Storm ◽  
Radar Rainfall ◽  
Radar Network ◽  
X Band ◽  
Vertical Wind Speed

<p>One of the major characteristics of dual-frequency precipitation radar (DPR) onboard Global Precipitation Measurement (GPM) core satellite, is estimation of cloud physical properties of precipitation such as drop size distribution (DSD), existence of hail/graupel particles and possibly the mixed phase region above freezing height.  In this study, ground-based X-band radar network data are utilized for evaluate the cloud physical products from GPM/DPR.  The X-band radar network, composed of 39 X-band dual polarimetric radars developed by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) of Japan, called XRAIN[1] is utilized for the evaluation.  The XRAIN radar completes volume scan up to the elevation angle of 20 degrees in 5 minutes.  By using multiple radars, three dimensional wind field is estimated by using the dual-Doppler analysis technique.　In this analysis DSD parameter from DPR (which is called epsilon in DPR product) and dual frequency ratio (DFR) that correlate well median diameter of DSD are compared with ZDR and KDP from XRAIN data.  The vertical wind data from XRAIN is utilized to characterize the Z of DPR. The case on August 27, 2018, on which GPM satellite flew over a hail producing convective storm around Tokyo, is analyzed.  Comparison of three dimensional structure of the storm between KuPR (Ku-band radar of DPR) and XRAIN from multiple radar observations shows that both observations are quite similar each other except for the KuPR observation show rather larger volume because of the larger footprint size.  At the rain region (below freezing height), the DSD parameter of DPR (epsilon) and DFR correlate well with ZDR and KDP from XRAIN, respectively.  This result indicates the DPR algorithm works well to estimate the DSD information of rain.  The comparison of Z with vertical wind speed indicates that the higher Z is characterized as higher variance of vertical wind speed. Above the freezing height, the relationship between both observations are complicated.  This result indicates that the various types of precipitation particles not only solid particles but also liquid/mixed phase particle can exist in the severe convective storm.  The hydrometeor type classification from XRAIN by using the method by Kouketsu et al. (2015) [2] confirms that the various types of precipitation exist in this case.</p><p>References</p><p>[1] Tsuchiya, S., M. Kawasaki, H. Godo, 2015: Improvement of the radar rainfall accuracy of XRAIN by modifying of rainfall attenuation correction and compositing radar rainfall, Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), 2015, Volume 71, Issue 4, pp. I_457-I_462 (in Japanese with English abstract).</p><p>[2] Kouketsu, T., Uyeda, H., Ohigashi, T., Oue, M., Takeuchi, H., Shinoda, T., Tsuboki, K., Kubo, M., and Muramoto, K., 2015: A Hydrometeor Classification Method for X-Band Polarimetric Radar: Construction and Validation Focusing on Solid Hydrometeors under Moist Environments, Journal of Atmospheric and Oceanic Technology, 32(11), 2052-2074.</p>

scholarly journals The interaction between drifting snow and atmospheric turbulence

1998 ◽  
Vol 26 ◽  
pp. 167-173 ◽  
Author(s):  
Richard Bintanja
Keyword(s):  
Surface Layer ◽  
Wind Speed ◽  
Atmospheric Surface Layer ◽  
Order Approximation ◽  
Atmospheric Stability ◽  
Blowing Snow ◽  
Speed Profile ◽  
Stability Parameters ◽  
Wind Speed Profile ◽  
First Order

This paper presents a modelling study of the influence of suspended snow on turbulence in the atmospheric surface layer. Turbulence is diminished in drifting and blowing snow, since part of the turbulent energy is used to keep the particles in suspension. This decrease in turbulence directly affects the vertical turbulent fluxes of momentum and snow particles (and other scalars), and can effectively be simulated by introducing an appropriate Richardson number to account for the stability effects of the stably stratified air-snow mixture. We use a one-dimensional model of the atmospheric surface layer in which the Reynolds stress and turbulent suspended snow flux are parameterized in terms of their mean vertical gradients (first-order closure). The model calculates steady-state vertical profiles of mean wind speed, suspended snow mass in 16 size classes and stability parameters. Using the model, the influence of snowdrifting on the wind-speed profile is quantified for various values of the initial friction Velocity (which determines the steepness of the initial wind-speed profile). It will be demonstrated why the roughness length appears to increase when snowdrifting occurs. Finally, we present a parameterization of the effects of snowdrifting on atmospheric stability which can be used in data analyses as a first-order approximation.

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