Geoscience Reference
In-Depth Information
Ideally, thermal stratification of air should be analyzed from the virtual poten-
tial temperature (
0.609 q )), where q is specific humidity) in order
to include the effects of the vertical moisture distribution on the atmospheric sta-
bility. Unfortunately, no active remote-sensing device for the determination of
high-resolution moisture profiles is available. Therefore, the acoustic potential tem-
perature (
θ v
= θ
(1
+
0.513 q )), which actually is the temperature that is delivered
by a RASS, is often used as a substitute. This is sufficient for cold and dry envi-
ronments, but somewhat underestimates the virtual potential temperature in humid
and warm environments. In case of larger vertical moisture gradients and small
vertical temperature gradients, this can lead to a switch in stability from stable to
unstable (especially so in marine boundary layers where relative humidity typically
decreases with height (see, e.g., Edson et al. 2004 )) or vice versa. The following two
subchapters give two examples where RASS has been used for MLH determination.
θ a = θ
(1
+
Combined Deployment of Two Different RASS
Engelbart and Bange ( 2002 ) have analyzed the possible advantages of the deploy-
ment of two RASS instruments, a SODAR-RASS (i.e. a SODAR with an elec-
tromagnetic extension) and a high-UHF WPR-RASS (i.e. a wind profiler with an
additional sound source), to derive boundary layer parameters. With these instru-
ments, in principle, MLH can either be determined from the temperature profiles or
from the electromagnetic backscatter intensity. The latter depends on temperature
and moisture fluctuations in the atmosphere. The derivation of MLH from the tem-
perature profile requires a good vertical resolution of the profile, which is mainly
available only from the SODAR-RASS. But even if the inversion layer at the top of
the boundary layer is thick enough, due to the high attenuation of sound waves in the
atmosphere, also the 1290 MHz-WPR-RASS used by Engelbart and Bange ( 2002 )
can measure the temperature profile only up to about 1 km. Therefore, in the case
of a deeper CBL, MLH was determined from a secondary maximum of the elec-
tromagnetic backscatter intensity, which marks the occurrence of the entrainment
zone at the CBL top. Thus, with this instrument combination the whole diurnal
cycle of MHL is ideally monitored by interpreting the temperature profile from
the SODAR-RASS at night-time and by analyzing the electromagnetic backscatter
intensity profile from the WPR-RASS during daytime.
Further Algorithms Using a RASS
Hennemuth and Kirtzel ( 2008 ) have recently developed a method that uses data
from a SODAR-RASS and surface heat flux data. MLH is primarily detected from
the acoustic backscatter intensity received by the SODAR part of the SODAR-
RASS and verified from the temperature profile obtained from the RASS part of
the instrument. Surface heat flux data and statistical evaluations complement this
rather complicated scheme. The surface heat flux is used to identify situations
with unstable stratification. In this respect this observable takes over an analogous
role as the
σ w in the EARE algorithm (see section “Enhanced Acoustic Received
Echo Method”). The results have been tested against radiosonde soundings. The
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