Characteristics of the Wind - Wind Turbine Technology: Fundamental Concepts of Wind Turbine Engineering - page 479

-- the most suitable format for wind power assessment;

-- the longest record;

-- the least change in anemometer elevation and exposure;

-- the most frequent daily observations.

The same screening criteria should be applied when assessing areas with a high density of
meteorological stations.
Two other types of wind data that may be of value are coastal marine data ( i.e. ship
observations and offshore “fixed” stations) and upper-air data. In coastal regions where
very few land stations with good exposure are available, the marine data can be a very
useful supplement. Upper-air wind data are useful in estimating the wind resource on
mountain summits and ridge crests, where existing surface station data are sparse. While
a strong correlation exists between mountain-top and free-air speeds [Wahl 1966], there is
currently no universal procedure for reliably estimating the wind energy potential over
mountainous areas. In particular, a procedure which applies to one season may not apply
to the others.
Time scales involved in wind resource analysis include annual, seasonal, monthly, and
(to a lesser extent) diurnal. Annual mean values are generally based on an average of the
one- or three-hour observations of wind speed, and a complete calendar year of data is
needed. Data from stations with less than 24 one-hour observations (or 8 three-hour obser-
vations) per day should be used only as a last resort when calculating annual mean wind
speeds. For purposes of calculating seasonal mean wind speeds, the months in each of the
four seasons in the northern hemisphere are generally divided as follows:

-- Winter: December, January, and February

-- Spring: March, April, and May

-- Summer: June, July, and August

-- Autumn: September, October, and November

Procedures for calculating wind power density from various types of wind data records
are described by Elliott [1979] and Wegley et al. [1980], including adjusting the data for
differences in elevation and accounting for differences in air density. Quite often in
assessing a stations's seasonal and annual mean wind speeds, a visual examination of the
data provides a rough but fast and inexpensive means of making a preliminary estimate of
its wind power class. A subjective estimate of this type depends on the skill and experience
of the observer, but it often provides the only timely information on the wind resource in
many areas.

Qualitative Indicators of the Wind Resource

In many remote areas wind data may be sparse or non-existent, and evaluation of the
wind resource may have to rely on qualitative rather than quantitative methods. For
example, there are topographic/meteorologic indicators of both high and low wind power
classes. The following are some indicators of a potentially high wind power class:

-- gaps, passes, and gorges in areas of frequent strong pressure gradients;
-- long valleys extending down from mountain ranges;
-- plains and plateaus at high elevations;
-- plains and valleys with persistent downslope winds associated with strong
pressure gradients;