Geoscience Reference
In-Depth Information
7.3 Understanding convection
Skewness is a measure of asymmetry in the distribution of vertical velocity perturbations.
Positive skewness at the surface suggests narrow intense updrafts from the surface and
broad downdrafts (fair weather, clear). Negative skewness suggests sharp, narrow
downdrafts and larger areas of weaker updraft, rather like “upside down” surface heating
driven by turbulence on a cloudy day. Skewness may be calculated using,
S = w
'3
/ (w
'2
)
3/2
Bother Doppler radar and Doppler lidar are capable of measuring vertical velocity and they
can measure skewness throughout the boundary layer. Knowing the skewness can help
understand the structure of convection (see for example Hogan et al., 2009).
Although weather radars are available around the edges of tropical rain forests, there have
as yet been only a few studies combining radar wind profilers and lidar (ceilometers) data
(Grimsdell and Angevine, 1998). Vila-Guerau de Arellano et al. (2009) studied the isoprene
fluxes in the tropical rain forest environment, but recommended the continued use of a
radar wind profiler or Doppler lidar. Pearson et al. (2010) used a Doppler lidar to measure
the diurnal cycle of the wind field in the tropical boundary layer, Sabah, Borneo.
8. Concluding remarks
Since radar and lidar provide measurements of backscatter and atmospheric motion based
upon different targets, it is clear that much useful complementary information on
atmospheric phenomena and processes can be obtained. Used together these instruments
provide a powerful mechanism by which to enhance our knowledge of the atmosphere and
develop improved forecasting procedures of a wide range of phenomena. Both technologies
offer instrumentation capable of continuous unattended operation.
9. References
Atlas, D. (ED) (1990)
Radar in Meteorology
, Am. Met. Soc., 806pp
Atlas, D., Srivastava, R.C. and Sekhon, A.S. (1973). Doppler radar characteristics of
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Rev. Geophys. Space Phys
., 2, pp 1-35
Bader, M.J., Forbes, G.S., Grant, J.R., Lilley, R.B.E. and Waters, A.J. (1995).
Images in Weather
Forecasting. A practical guide for interpreting satellite and radar imagery
, Cambridge
University Press, 499pp
Banta, R.M., Olivier, L.D., Holloway, E.T., Kropfli, R.A., Bartram, B.W., Cupp, R.E. and Post,
M.J. (1992). Smoke-column observations from two forest fires using Doppler lidar
and Doppler radar,
J. Appl. Met
., 31, 1328-1349
Barkwith, A. and Collier, C.G. (2011). Lidar observations of flow variability over complex
terrain,
Meteor.
Appl
., 18, 372-382
Bousquet, O. and Chong, M. (1998). A Multiple-Doppler Synthesis and Continuity
Adjustment Technique (MUSCAT) to recover wind components from Doppler
radar measurements,
J. Atmos. Ocean. Tech
., 15, pp 343-359
Bozier, K.E., Pearson, G.N. and Collier, C.G. (2007). Doppler lidar observations of Russian
forest fire plumes over Helsinki,
Weather
, 62, no 8, pp 203-208
