An approach to deriving roughness length and zero-plane displacement height from satellite data, prototyped with BOREAS data

2000 ◽  
Vol 104 (2) ◽  
pp. 143-155 ◽  
Author(s):  
K Schaudt
2016 ◽  
Vol 9 (2) ◽  
pp. 546
Author(s):  
Thiago Lobão Cordeiro ◽  
Arcilan Trevenzoli Assireu ◽  
Ramon Moraes Freitas ◽  
Nandamudi Lankalapalli Vijaykumar ◽  
Reinaldo Roberto Rosa

A demanda para a produção de energia renovável e de baixo impacto ambiental cresce a cada ano e, com isso, há também o aumento do interesse em turbinas eólicas de pequena escala a serem instaladas em relevos complexos que inclui áreas onde montanhas afetam o padrão de vento, como em grandes sistemas aquáticos localizados em regiões de planaltos. A influência da complexidade do relevo e da intensidade de turbulência foi investigada pela aplicação do método de Análise por Padrões de Gradientes em um modelo digital de elevação e uma série de dados histórica da direção e velocidade do vento. Os resultados indicaram que os padrões de fluxos são extremamente complexos e variam significativamente dependendo da direção do fluxo em sentido contrário. Esta variabilidade também torna difícil definir um plano zero de deslocamento ou um comprimento de rugosidade para um determinado ponto de medição, o que compromete a utilização do modelo de extrapolação vertical do vento baseado no coeficiente de rugosidade fixo.      ABSTRACT As the demand for environmentally friendly energy production grows, there is also an increased interest in small scale wind turbines located in more complex relief that includes areas where mountains affect the wind pattern, as in large inland aquatic system localized close to hills. Influence of complex relief on the turbulence intensity was investigated by means of time series of the wind direction and speed and digital elevation model. The results indicated that the flow patterns are highly complex and vary significantly depending on the direction of the oncoming flow. This variability also makes it difficult to define a general zero plane displacement height or a roughness length for a certain measuring point. The resulting consequence for the usual one-dimensional wind profiles models are then pointed out. Keywords: GPA. Roughness of the relief. Wind power.   


2019 ◽  
Vol 16 (1) ◽  
pp. 0215
Author(s):  
Haraj Et al.

Roughness length is one of the key variables in micrometeorological studies and environmental studies in regards to describing development of cities and urban environments. By utilizing the three dimensions ultrasonic anemometer installed at Mustansiriyah university, we determined the rate of the height of the rough elements (trees, buildings and bridges) to the surrounding area of the university for a radius of 1 km. After this, we calculated the zero-plane displacement length of eight sections and calculated the length of surface roughness. The results proved that the ranges of the variables above are ZH (9.2-13.8) m, Zd (4.3-8.1) m and Zo (0.24-0.48) m.


Author(s):  
NAOYA SUZUKI ◽  
NAOTO EBUCHI ◽  
CHAO FANG ZHAO ◽  
TAKAHIRO OSAWA ◽  
TAKASHI MORIYAMA

The determination of wind friction velocity from satellite-derived wind data will take an important role of key factors for computation of C02 flux transfer. It is necessary for relation between wind speed and wind friction velocity to determine that of relation between nondimensional roughness length and wave age, included with all parameters (wind, wave). In this study, we proposed a new method to estimate u„, which is based on the new relationship between non-dimensional roughness and wave velocity, after considering fetch and wave directionality. Consequently, we obtained the new relationship between friction velocity and wind speed. Using this relationship, we estimated the wave frequency from two methods: 3 per 2 powers law (Toba, 1972) and WAM model (WAMDI, 1988). The results arc compared with the results estimated from Charnock formula (1955) and the above influence of wave effects on the wind stress is also discussed. A new relationship was established to determine CO. exchange coefficient based on whitecap model (Monahan and Spillane 1984), using U|0-u, relationship in North Pacific Ocean, satellite data of NOAA-AVHRR (SST) and DMSP-SSM-I (wind speed) in Oct., Nov., and Dec. 1991. The C02 exchange coefficient estimated by other models (Wanninkhof, 1992; Liss and Merlivat, 1986; Tans et al., 1990) are also compared with these results. The results show the importance of wave breaking effect. Key words: wind waves, friction velocity, C02 exchange coefficient, roughness length, wave age.


1997 ◽  
Vol 1 (1) ◽  
pp. 81-91 ◽  
Author(s):  
A. Verhoef ◽  
K. G. McNaughton ◽  
A. F. G. Jacobs

Abstract. Values of the momentum roughness length, z0, and displacement height, d, derived from wind profiles and momentum flux measurements, are selected from the literature for a variety of sparse canopies. These include savannah, tiger-bush and several row crops. A quality assessment of these data, conducted using criteria such as available fetch, height of wind speed measurement and homogeneity of the experimental site, reduced the initial total of fourteen sites to eight. These datapoints, combined with values carried forward from earlier studies on the parameterization of z0 and d, led to a maximum number of 16 and 24 datapoints available for d and z0, respectively. The data are compared with estimates of roughness length and displacement height as predicted from a detailed drag partition model, R92 (Raupach, 1992), and a simplified version of this model, R94 (Raupach, 1994). A key parameter in these models is the roughness density or frontal area index, λ. Both the comprehensive and the simplified model give accurate predictions of measured z0 and d values, but the optimal model coefficients are significantly different from the ones originally proposed in R92 and R94. The original model coefficients are based predominantly on measured aerodynamic parameters of relatively closed canopies and they were fitted `by eye'. In this paper, best-fit coefficients are found from a least squares minimization using the z0 and d values of selected good-quality data for sparse canopies and for the added, mainly closed canopies. According to a statistical analysis, based on the coefficient of determination (r2), the number of observations and the number of fitted model coefficients, the simplified model, R94, is deemed to be the most appropriate for future z0 and d predictions. A CR value of 0.35 and a cd1 value of about 20 are found to be appropriate for a large range of canopies varying in density from closed to very sparse. In this case, 99% of the total variance occurring in the d-data across 16 selected canopies can be explained, whereas the analogous value for the z0-data (24 datapoints available) is 81%. This makes the R94 model, with only two coefficients and its relatively simple equations, a useful universal tool for predicting z0 and d values for all kinds of canopies. For comparison, a similar fitting exercise is made using simple linear equations based on obstacle height only (e.g. Brutsaert, 1982) and another formula involving canopy height as well as roughness density (Lettau, 1969). The fitted Brutsaert equations explain 98% and 62% of the variance in the d and z0-data, respectively. Lettau's equation for prediction of z0 performs unsatisfactorily (r2 values <0, even after fitting of the coefficient) and so it is concluded that the drag partition model is definitely the most effective for prediction of the momentum roughness lengths for a wide rang of canopy densities.


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