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Fig. B.1 Principal sketch of vertical profiles of some important variables within the well-mixed
daytime atmospheric boundary layer (left) and the more stable nocturnal surface layer and the
residual layer (lower and middle layer in the right-hand frame) and above in the free troposphere
radiosondes. Evaluation of radiosonde data gives quite reliable data in most cases.
The great disadvantage of radiosondes is the missing temporal continuity.
Therefore, remote sensing methods are preferable although (with the exception of
RASS which directly detect temperature profiles) they only give an indirect
detection of the mixing height. A first rather complete overview of methods to
determine the MLH from in situ measurements and surface-based remote sensing
has been given by Seibert et al.
(2000)
. Since then considerable development has
taken place, especially with regard to the usage of surface-based remote sensing
methods [see the review paper by Emeis et al. (
2008
) and the monograph by Emeis
(
2011
)]. This Appendix will mainly follow these sources.
Newly developed optical methods for MLH detection illustrate this recent
progress. Seibert et al.
(2000)
still classified LIDAR methods as expensive, not
eye-save, with a high lowest range gate, limited range resolution, and sometimes
subject to ambiguous interpretation. This has changed drastically in the last
10 years when better and smaller LIDARs have been built and ceilometers have
been discovered to be a nearly ideal boundary layer sounding instrument. Progress
has been made in the field of acoustic sounding as well. Similarly, algorithms for
the determination of MLH from vertical profiles of the acoustic backscatter
intensity as described in Beyrich (
1997
) and Seibert et al.
(2000)
have been
enhanced by using further variables available from SODAR measurements such as
the wind speed and the variance of the vertical velocity component
(Asimakopoulos et al.
2004
; Emeis and Türk
2004
). Such enhancements had
been named as possible methods in Beyrich (
1995
) and Seibert et al.
(2000)
but
obviously no example was available at that time.
A variety of different algorithms have been developed by which the MLH is
derived from ground-based remote sensing data (see Table
B.1
for a short
overview). We will mainly concentrate on acoustic and optical remote sensing
because electro-magnetic remote sensing with wind profilers has too high lowest
range gates for a good coverage of shallow MLH. The disadvantage of a too high
lowest range gate can sometimes partly be circumvented by slantwise profiling or
conical scanning if the assumption of horizontal homogeneity can be made.
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