A Simple Model for Mesoscale Effects of Topography on Surface Winds

A three-dimensional model for wind prediction over rough terrain has been developed for practical use. It is a compromise between hydrodynamic and objective wind models. The proposed model includes:(1) a statistical model to predict the wind velocity and potential temperature at anemometer height at observing stations, (2) the drainage wind model expressed by Prandtl's analytic solution for the slope wind, (3) the Businger-Dyer surface-layer formulation which considers the surface energy budget and (4) the model for three-dimensional boundary-layer solutions to the stationary flow. In this model, mass consistency is guaranteed by using flow fields that satisfy the continuity equation. Model predictions show good agreement with the observations.

Section: Drainage Wind Componentmentioning

confidence: 99%

Simulation of three-dimensional wind flow over complex terrain in the atmospheric boundary layer

Lee

Kau

1984

“…This has been verified in another application of the model to Juan de Fuca and Georgia Straits in British Columbia (Danard, 1977). In that study, the same overall temperature and pressure differences between land and water were developed for various values of 7 and the total iterating time was varied from 50 min to 10 h. The changes from initial to final winds tended to level off for iterating times greater than lOOmin, i.e., T, -100 min in that case.…”

Section: Equation (3) One Setsmentioning

confidence: 99%

“…The model thus provides the field of surface winds in the area under consideration. The predominantly terrain-influenced aspects of the model have been discussed elsewhere by Danard (1977).…”

Section: Introductionmentioning

confidence: 99%

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A model for computing small-scale wind variations over a water surface

Venkatesh

Danard

1978

Based on the primitive equations, a one-level niodel is described for computing surface winds under the meso-scale influences of orography, friction and heating. The effects of atmospheric stability and land-water contrasts are examined in detail. Results of testing the model with data collected during the International Field Year for the Great Lakes (IFYGL) in 1972 are given. The model is able to simulate land and lake breezes as well as meso-scale effects due to gradients in surface water temperature. Compared to simple methods of computing surface winds, use of the model reduces the vector error in estimates of surface winds by about 1.25 m s oh the average.

“…The surface winds for this scale of prediction are obtained on a 25 km grid (see Danard, 1977) using data from synoptic charts routinely prepared by the Canadian Meteorological Centre, Montreal, Canada. According to our analysis and experience (Neralla et al, 1977) these techniques of generating surface winds are better suited for ice drift studies in the Canadian Arctic than the procedure suggested by Feldman et al…”

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confidence: 99%

On estimating the surface wind speed over drifting pack ice from surface weather charts

Khandekar

Neralla

1982

A recent publication in this Journal (Feldman et al., 1979) on estimating the surface wind speed over drifting pack ice from surface weather charts has prompted us to offer the following comments :(1) Feldman et al. have used linear regression equations based on the studies of Hasse and Wagner (197 1) and Hasse (1974a, b) to estimate surface wind speed U(at 10 m level) in terms of the geostrophic wind speed G; these authors, however, do not mention the surface wind direction. Without appropriate determination of the deviation of the surface wind direction from the geostrophic wind (cross-isobar angle), a knowledge of surface wind speed alone is not sufficient in studying pack ice movement or the movement of isolated floes. The cross-isobar angle, which is a function of surface Rossby number and atmospheric stability, can vary from 15 to 25 ' for typical stability values observed over high latitude ocean areas (see Haltiner and Martin, 1957). The classical study of Campbell (1965) which Feldman et al. refer to, obtains mean wind stress field by constructing a field of surface Rossby number over the polar ocean and applying Blackadar's (1962) results ; thus Campbell appropriately takes into account surface wind speed as well as direction.(2) Feldman et al. mention using data of Smith (1971, 1973) and Smith et al. (1970) for calculating the regression of U on G and G on U as shown in their Figure 2; they have omitted the set of data at site 1 because it gave a negative correlation coefficient of -0.58 between U and G. This negative value of the correlation coefficient appeared doubtful to us and accordingly we carefully examined the data of Smith et al. (1970) collected over the Gulf of St. Lawrence. Using all 9 observations in Table I of Smith et al. (1970), we obtained a linear correlation coefficient (between U and G) of + 0.95 which appears reasonable because the geostrophic wind speed and the surface wind speed at 10 m level should be positively correlated. Feldman et al. appear to have used only 5 of the 9 observations to obtain a correlation coefficient of -0.58. It is our contention that this negative correlation coefficient may not be due to widely spaced isobars as speculated in the paper.(3) For estimating surface wind speed U over the Arctic Ocean, Feldman et al. propose the use of regression Equations (2), (3) and (4) of their paper; these regression