Implications of the OEN mixture model of the mean wind vector for the generation of synthetic timeseries and for the assessment of extremes

2021 ◽  
Vol 208 ◽  
pp. 104424
Author(s):  
Nicholas J. Cook
Keyword(s):  
Mixture Model ◽  
Wind Vector ◽  
The Mean

The Use of Large Scale Parameters in Small Scale Diffusion Studies

1957 ◽  
Vol 38 (1.1) ◽  
pp. 6-12 ◽  
Author(s):  
William G. Tank
Keyword(s):  
Large Scale ◽  
Meteorological Parameters ◽  
Surface Wind ◽  
Small Scale ◽  
Relative Importance ◽  
Wind Vector ◽  
Scale Parameters ◽  
Diffusion Studies ◽  
The Mean ◽  
Low Levels

A method is set forth whereby gaseous diffusion in the low levels of the atmosphere can be calculated by Roberts' diffusion equation (modified to consider instantaneous volume sources) using only large scale synoptic parameters that are readily obtainable from the surface analysis and pibal reports. The three pertinent meteorological parameters utilized are: (1) the mean surface wind, (2) the angle between the surface wind vector and the surface isobars, and (3) the height of the gradient level. Theoretical and observed dosage values are compared by means of dosage isopleth diagrams. Results show that the method yields quite satisfactory results, with regard to both dosage magnitude and distribution. The assumptions necessary for the application of the method and its limitations are mentioned and their relative importance discussed.

scholarly journals Indigenuous Vocabulary Reformulation for Continuousyorùbá Speech Recognition In M-Commerce Using Acoustic Nudging-Based Gaussian Mixture Model

2021 ◽  
Author(s):  
Kehinde Lydia Ajayi ◽  
Victor Azeta ◽  
Isaac Odun-Ayo ◽  
Ambrose Azeta ◽  
Ajayi Peter Taiwo ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Error Rate ◽  
System Performance ◽  
Recognition Rate ◽  
Gaussian Mixture ◽  
Computer Applications ◽  
Word Error Rate ◽  
The Mean

Abstract One of the current research areas is speech recognition by aiding in the recognition of speech signals through computer applications. In this research paper, Acoustic Nudging, (AN) Model is used in re-formulating the persistence automatic speech recognition (ASR) errors that involves user’s acoustic irrational behavior which alters speech recognition accuracy. GMM helped in addressing low-resourced attribute of Yorùbá language to achieve better accuracy and system performance. From the simulated results given, it is observed that proposed Acoustic Nudging-based Gaussian Mixture Model (ANGM) improves accuracy and system performance which is evaluated based on Word Recognition Rate (WRR) and Word Error Rate (WER)given by validation accuracy, testing accuracy, and training accuracy. The evaluation results for the mean WRR accuracy achieved for the ANGM model is 95.277% and the mean Word Error Rate (WER) is 4.723%when compared to existing models. This approach thereby reduce error rate by 1.1%, 0.5%, 0.8%, 0.3%, and 1.4% when compared with other models. Therefore this work was able to discover a foundation for advancing current understanding of under-resourced languages and at the same time, development of accurate and precise model for speech recognition.

Automatic Air Navigation

1945 ◽  
Vol 17 (3) ◽  
pp. 64-69
Author(s):  
F.H. Scrimshaw ◽  
J.A. Wells
Keyword(s):  
Dead Reckoning ◽  
Basic Method ◽  
Wind Vector ◽  
True Airspeed ◽  
The Mean

THE basic method of air navigation is deduced reckoning or simply dead reckoning. The method comprises the maintenance of an air pilot, which is made by calculating true airspeed and hence air distance run and then plotting this along the aircraft's heading from some initial ground fix. Subsequent ground positions may then be deduced by laying off the wind vector from the air position. As an example (Fig. 1) suppose an aircraft flics for one hour on a true heading of 060 deg. starting from an initial ground position A. If the true airspeed is 180 knots the air position will be at B, and if the mean wind over the flight is 45 knots from 340 deg. true then the ground position (by D.R.) corresponding to an air position at B would be at C. Now if the aircraft flics for the next hour on a true heading of 085 deg. and the mean wind over this hour is 30 knots from 310 deg. true, the air position with respect to A would be at D and the ground position at F. If a new air plot had been started at C then the air position, at the end of the second hour, would be at E and the ground position (by D.R.) again at F.

scholarly journals Relationship between horizontal wind velocity and normalized surface cross section using data from the HIWRAP dual-frequency airborne radar

2020 ◽  
Author(s):  
R. Meneghini ◽  
L. Liao ◽  
G.M. Heymsfield
Keyword(s):  
Cross Section ◽  
Transfer Functions ◽  
Correlation Coefficients ◽  
Horizontal Wind ◽  
Airborne Radar ◽  
Wind Vector ◽  
Dual Frequency ◽  
Near Surface ◽  
The Mean ◽  
Ku Band

AbstractThe HIWRAP dual-frequency conically-scanning airborne radar provides estimates of the range-profiled mean Doppler and backscattered power from the precipitation and surface. A VAD (velocity azimuth display) analysis yields near-surface estimates of the mean horizontal wind vector, vh, in cases where precipitation is present throughout the scan. From the surface return, the normalized radar cross section (NRCS) is obtained which, by a method previously described, can be corrected for path attenuation.Comparisons between vh and the attenuation-corrected NRCS are used to derive transfer functions that provide estimates of the wind vector from the NRCS data under both rain and rain-free conditions. A reasonably robust transfer function is found by using the mean NRCS, 〈NRCS〉, over the scan along with a filtering of the data based on a Fourier series analysis of vh and the NRCS.The approach gives good correlation coefficients between vh and 〈NRCS〉 at Ku-band at incidence angles of 300 and 400. The correlation degrades if the Ka-band data are used rather that the Ku-band.

Anemometry

2001 ◽  
Author(s):  
Fred V. Brock ◽  
Scott J. Richardson
Keyword(s):  
Wind Speed ◽  
Wind Velocity ◽  
Elevation Angle ◽  
Vertical Component ◽  
Wind Vector ◽  
Wind Speeds ◽  
North East ◽  
Orthogonal Components ◽  
The Mean ◽  
Design Type

The function of an anemometer (sometimes with a wind vane) is to measure some or all components of the wind velocity vector. It is common to express the wind as a two-dimensional horizontal vector since the vertical component of the wind speed is usually small near the earth’s surface. In some cases, the vertical component is important and then we think of the wind vector as being three-dimensional. The vector can be written as orthogonal components (u, v, and sometimes w] where each component is the wind speed component blowing in the North, East, or vertically up direction. Alternatively, the vector can be written as a speed and a direction. In the horizontal case, the wind direction is the direction from which the wind is blowing measured in degrees clockwise from North. The wind vector can be expressed in three dimensions as the speed, direction in the horizontal plane as above, and the elevation angle. Standard units for wind speed (a scalar component of the velocity) are m s-1 and knots (nautical miles per hour). Some conversion factors are shown in table 7-1. Wind velocity is turbulent; that is, it is subject to variations in speed, direction, and period. The wind vector can be described in terms of mean flow and gustiness or variation about the mean. The WMO standard defines the mean as the average over 10 minutes. The ideal wind-measuring instrument would respond to the slightest breeze yet be rugged enough to withstand hurricane-force winds, respond to rapidly changing turbulent fluctuations, have a linear output, and exhibit simple dynamic performance characteristics. It is difficult to build sensors that will continue to respond to wind speeds as they approach zero or will survive as wind speeds become very large. Thus a variety of wind sensor designs and, even within a design type, a spectrum of implementations have evolved to meet our needs.

Short-Term Convective Mode Evolution along Synoptic Boundaries

2010 ◽  
Vol 25 (5) ◽  
pp. 1430-1446 ◽  
Author(s):  
Greg L. Dial ◽  
Jonathan P. Racy ◽  
Richard L. Thompson
Keyword(s):  
Residence Time ◽  
Linear Growth ◽  
Normal Component ◽  
Dominant Mode ◽  
Cloud Layer ◽  
Vertical Shear ◽  
Wind Vector ◽  
Short Term ◽  
The Mean ◽  
Convective Mode

Abstract This paper investigates the relationships between short-term convective mode evolution, the orientations of vertical shear and mean wind vectors with respect to the initiating synoptic boundary, the motion of the boundary, and the role of forcing for ascent. The dominant mode of storms (linear, mixed mode, and discrete) was noted 3 h after convective initiation along cold fronts, drylines, or prefrontal troughs. Various shear and mean wind vector orientations relative to the boundary were calculated near the time of initiation. Results indicate a statistical correlation between storm mode at 3 h, the normal components of cloud-layer and deep-layer shear vectors, the boundary-relative mean cloud-layer wind vector, and the type of initiating boundary. Thunderstorms, most of which were initially discrete, tended to evolve more quickly into lines or mixed modes when the normal components of the shear vectors and boundary-relative mean cloud-layer wind vectors were small. There was a tendency for storms to remain discrete for larger normal shear and mean wind components. Smaller normal components of mean cloud-layer wind were associated with a greater likelihood that storms would remain within the zone of linear forcing along the boundary for longer time periods, thereby increasing the potential for upscale linear growth. The residence time of storms along the boundary is also dependent on the speed of the boundary. It was found that the boundary-relative normal component of the mean cloud-layer wind better discriminates between mode types than does simply the ground-relative normal component. The influence of mesoscale forcing for ascent and type of boundary on mode evolution was also investigated. As expected, it was found that the magnitude and nature of the forcing play a role in how storms evolve. For instance, strong linear low-level convergence often contributes to rapid upscale linear growth, especially if the boundary motion relative to the mean cloud-layer wind prevents storms from moving away from the boundary shortly after initiation. In summary, results from this study indicate that, for storms initiated along a synoptic boundary, convective mode evolution is modulated primarily by the residence time of storms within the zone of linear forcing, the nature and magnitude of linear forcing, and secondarily by the normal component of the cloud-layer shear.

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