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| Titel |
Intercomparison of methods for the estimation of displacement height and roughness length from single-level eddy covariance data |
| VerfasserIn |
Alexander Graf, Anneke van de Boer, Dirk Schüttemeyer, Arnold Moene, Harry Vereecken |
| Konferenz |
EGU General Assembly 2013
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 15 (2013) |
| Datensatznummer |
250080166
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| Zusammenfassung |
| The displacement height d and roughness length z0 are parameters of the logarithmic wind
profile and as such these are characteristics of the surface, that are required in a multitude of
meteorological modeling applications. Classically, both parameters are estimated from
multi-level measurements of wind speed over a terrain sufficiently homogeneous to avoid
footprint-induced differences between the levels. As a rule-of thumb, d of a dense, uniform
crop or forest canopy is 2/3 to 3/4 of the canopy height h, and z0 about 10% of canopy height
in absence of any d. However, the uncertainty of this rule-of-thumb becomes larger if the
surface of interest is not "dense and uniform", in which case a site-specific determination is
required again.
By means of the eddy covariance method, alternative possibilities to determine z0 and d
have become available. Various authors report robust results if either several levels of sonic
anemometer measurements, or one such level combined with a classic wind profile is used to
introduce direct knowledge on the friction velocity into the estimation procedure. At the same
time, however, the eddy covariance method to measure various fluxes has superseded the
profile method, leaving many current stations without a wind speed profile with enough levels
sufficiently far above the canopy to enable the classic estimation of z0 and d. From
single-level eddy covariance measurements at one point in time, only one parameter can be
estimated, usually z0 while d is assumed to be known. Even so, results tend to
scatter considerably. However, it has been pointed out, that the use of multiple
points in time providing different stability conditions can enable the estimation
of both parameters, if they are assumed constant over the time period regarded.
These methods either rely on flux-variance similarity (Weaver 1990 and others
following), or on the integrated universal function for momentum (Martano 2000 and
others following). In both cases, iterations over the range of possible d values are
necessary.
We extended this set of methods by a non-iterative, regression based approach.
Only a stability range of data is used in which the universal function is known to be
approximately linear. Then, various types of multiple linear regression can be used to
relate the terms of the logarithmic wind profile equation to each other, and derive
z0 and d from the regression parameters. Two examples each of the two existing
iterative approaches, and the new non-iterative one are compared to each other and to
plausibility limits in three different agricultural crops. The study contains periods of
growth as well as of constant crop height, also allowing for an examination of the
relations between z0, d, and canopy height. Results indicate that estimated z0 values,
even in absence of prescribed d values, are fairly robust, plausible and consistent
across all methods. The largest deviations are produced by the two flux-variance
similarity based methods. Estimates of d, in contrast, can be subject to implausible
deviations with all methods, even after quality-filtering of input data. Again, the largest
deviations occur with flux-variance similarity based methods. Ensemble averaging
between all methods can reduce this problem, offering a potentially useful way
of estimating d at more complex sites where the rule-of-thumb cannot be applied
easily.
Martano P (2000): Estimation of surface roughness length and displacement
height from single-level sonic anemometer data. Journal of Applied Meteorology
39:708-715.
Weaver HL (1990): Temperature and Humidity flux-variance relations determined by
one-dimensional eddy correlation. Boundary-Layer Meteorology 53:77-91. |
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