How is roughness related to wind shear

In this current effort, an attempt has been made to list roughness as a function of five categories, that is, natural surfaces, including seasonal variations, agricultural lands, urban roughness, effective roughness, and land-use categories.

View the full text of this report. Accession Number: ADA Title: Surface Roughness Lengths. Concrete runways in airports are in roughness class 0. The same applies to the flat, open landscape to the left which has been grazed by sheep. The proper definition of roughness classes and roughness lengths may be found in the Reference Manual. The term roughness length is really the distance above ground level where the wind speed theoretically should be zero.

This graph was plotted with the wind speed calculator on the next page. The fact that the wind profile is twisted towards a lower speed as we move closer to ground level, is usually called wind shear. Wind shear may also be important when designing wind turbines. The term roughness length is really the distance above ground level where the wind speed theoretically should be zero.

Wind Shear. This graph was plotted with the wind speed calculator on the next page. The fact that the wind profile is twisted towards a lower speed as we move closer to ground level, is usually called wind shear.

Wind shear may also be important when designing wind turbines. If you consider a wind turbine with a hub height of 40 metres and a rotor diameter of 40 metres, you will notice that the wind is blowing at 9. Figures 3 , 4 , and 5 show power difference when using the two values for Nyamecheo, Kisii University, and Ikobe stations, respectively, for the six-month period. It is evident from all the stations that topography plays a critical role in determining energy discrepancy at any site.

According to Saif et al, , these discrepancies must be adopted when performing prefeasibility studies of wind energy production assessment. On average, air density of Kisii region based on this research has been calculated and found to be equal to 1. Wind shear exponent and roughness parameters depended on wind speed and air density.

Just like wind speeds, air density also varies with height. The results show that air density decreases with height due to the vertical drop of air pressure in higher altitudes. Accurate prediction of wind speed to different hub heights is vital for any wind energy investor. For one to invest in wind energy, availability of adequate wind profiles is necessary to enable making an informed choice on whether to invest on or not and on the selection of the best machine that can match the wind profiles of the given site.

In profiling, surface roughness parameter is mandatory since it acts as the reference point for extrapolation purposes. In this research, the average roughness parameter for two months has been calculated and recorded in Table 3.

How this parameter varies from hour to hour is presented in Figure 6. The overall mean for Nyamecheo, Kisii University, and Ikobe stations has been calculated and found to be 3. Diurnal variation of surface roughness parameter at the selected sites for the six months. Investigation about the variation of the roughness parameter has been done by various researchers and a table providing the roughness parameter lengths for different terrain descriptions summarized [ 8 ].

Kisii region is hilly and receives rain all year round, something which makes the place bushy and with many trees.

Based on this, the region can be categorized under terrain description of many hills, hedges, and few buildings with a roughness parameter of 0. But when this value is compared with practical values obtained at the site, there is a huge difference. This gap in the findings can be argued based on many contributing unique factors at the site. Kisii region is highly populated, something which makes the place have so many buildings congested at the site.

The soils of the site are fertile and famers practice small scale crop farming all year round and trees at site grow tall due to enough rain and good soils.

Also the place is invested with small hills which are very close. These factors when added up together give a good reason as to why the values obtained practically contradict those obtained by the other researchers on variation of this factor based on terrains. This implies that, as much as there is already established theoretical roughness parameter values to help categorize regions when assessing wind power potentials, practical research of the roughness parameter needs to be done before setting up a wind firm.

As it can be seen from Figure 6 , variation of for all stations showed a pattern where maximum values were obtained during the night stable hours with minimum values obtained during day hours like in wind shear distribution. Surface roughness parameter of a locale is dependent on surface cover characteristics. In determining surface roughness parameter, the survey of the terrain is necessary. Variation characteristics of this parameter at a site have a major influence on wind regime at the surface layer.

Kisii region is invested with complex topographical features like hills and bushes something which causes a considerable variation of wind speeds across the region. The vertical distribution of wind speeds as indicated in Figures 7 , 8 , and 9 is a function of surface roughness parameter and wind shear exponent based on atmospheric stability at the site.

Figures 7 , 8 , and 9 show hourly variation of wind speeds at the sites for the six months. From the figures, it is evident that wind speeds at the site have an inverse variation with surface roughness parameter. During night stable hours, when the wind speed values at both heights of 10m and 13m are at their minimum values, roughness parameters values are at their peak. During day stable hours, when wind speed values are at their maximum, surface roughness parameter values are at their minimum.

This variation is vital for predicting long term variation of one of these parameters at the site given long term data of the other parameter. The information on variation of wind speed with surface roughness parameter is of critical importance especially when identifying preferred shapes, orientations, and optimizing the layout of wind turbine within a wind farm.

The graphs demonstrate a smooth and predictable diurnal wind speed distribution patterns with high wind speeds prevailing from approximately hours to around hours for stations of Ikobe and Nyamecheo with Kisii showing a different pattern with prevailing winds appearing approximately between the hrs and hrs.

This implies that, for the stations of Ikobe and Nyamecheo, the wind speed is high during the mid-day approaching evenings when the temperature is high. While on the other hand, wind speed is high after midnight approaching down for the Kisii station when the temperatures are low and are in reducing trend. Kisii at the prevailing wind recorded high wind speeds as compared to Ikobe and Nyamecheo.

These variations are directly linked to differential heating up of the surface depending on the nature of the terrain hence surface roughness parameter. Therefore for effective installation of wind power station at any site, practical information on variation of wind power and surface roughness is mandatory.

Correlation between wind speed and surface roughness parameter has also been done. As is depicted in Figures 10 , 11 , and 12 , wind speed has, in average, a strong negative correlation with roughness parameter. Using the measure, correlate, and predict method, the wind speeds and surface roughness parameter of the stations have been predicted and equations of prediction given. The shear-velocity trend analysis has been presented in the figures and models developed as per station which can be used to explain the variability of one the variables given the long term data of the other variable.

The shear-velocity trend of Nyamecheo station gives a very strong coefficient of correlation of which implies that the model is very reliable for future forecasts of either of the variables given data of the other. Kisii university on the other hand gives a strong coefficient of correlation of which implies that the model can be relied on for future forecasts though with lesser reliability when compared to that of Nyamecheo station.

Ikobe station has a strong correlation coefficient of. Figure 13 shows hourly variation of surface roughness parameter and wind speeds at the sites for the six months under study. From the figures, it is clear that roughness parameter and wind speeds at the site have an inverse variation. It is demonstrated from stations of Nyamecheo and Ikobe that, during night stable hours, when the wind speed values at the heights of 10m are at their minimum values, roughness parameters values are at their peak.

Conversely, Kisii University, despite of lying within the same region, demonstrates the opposite behavior. In this site, the value of roughness parameter increases during the day and decreases as the atmosphere begins to separate into layers during the nocturnal hours. Table 4 gives a summary record of statistically computed estimations of normal wind speed, variance, standard deviation, wind power, wind direction, shape factor, and scale factor parameters of the Kisii locale for the period of 6 months that were under study.

As it can be indicated from the table, the most astounding average wind speed is 3. Likewise from the table, it can be noticed that the estimations of shape factor range from 1. The shape factor k indicates how peaked the wind conveyance is at the sites.