Clamp Calibration Notes - extracted from:
Renfrew, I. A. and P. S. Anderson, 2002: The surface climatology of an
ordinary katabatic wind regime in Coats Land, Antarctica, Tellus, 54A,
463-484.
Halley is a meteorological observing station making 3-hourly synoptic
observations and daily radiosonde launches. Here we make use of the operational
cloud observations, plus hourly pressure and wind data. The winds are
measured at 4 m by a cup-vane anenometer, which is checked daily for rime
build up. The wind data tabulated here are corrected to 3 m using a neutral
logarithmic wind profile, and assuming a roughness length of 1?10-4 m
(King and Anderson, 1994). In addition, a number of research instruments
have been sited at Halley for use in boundary-layer meteorology experiments.
Here we use temperature and humidity measurements at 2 and 4 m, from Vaisala
HMP35A sensors housed in R. M. Young force-ventilated radiation shields.
King and Anderson (1994) discuss instrumentation at Halley in more detail.
At the Coats Land sites an AWS records hourly station pressure; air temperature
and humidity at two heights (nominally 1 and 2.5 m); and winds at one
height (nominally 3 m). The temperature and humidity data are from Vaisala
HMP35D sensors housed in a modified R. M. Young naturally-ventilated shield,
where an internal solar-powered fan assists ventilation during periods
of strong insolation. The HMP35 range of instruments employed at Halley
and the AWS sites use 1/30 DIN platinum resistance thermometers and solid
state capacitive humidity sensors. The wind data are from an R. M. Young
propeller-vane anenometer. It was found that this design is less susceptible
to becoming frozen into position. The AWS pressure and wind data are laboratory
calibrated prior to deployment and the pressure sensors are checked annually
on site against a Vaisala PA11 digital barometer, which itself is calibrated
annually by the UK Met Office calibration laboratory. The temperature
data are calibrated in two stages: firstly using a series of precision
resistors, which establish a linear correction to the temperatures registered
by the AWS, and secondly by making a uniform adjustment of the lower (1
m) temperatures at each AWS, using an offset determined by averaging all
the data with very high wind speeds (greater than 15 m s-1) and assuming
the atmosphere is well-mixed by mechanical turbulence under such conditions.
The second calibration step involves corrections of only ~0.1 oC and is
carried out to remove discontinuities in the time series and to obtain
more reliable surface heat flux estimates. The temperature calibrations
are implemented separately for each AWS and each year. The relative humidity
data are post-calibrated for each sensor and each year following the method
of Anderson (1994). This makes use of the fact that over a snow-covered
surface the atmosphere is saturated with respect to (w.r.t.) ice much
of the time (e.g. King and Anderson, 1999). The capacitive sensor acts
as a nucleation site when the atmosphere is supersaturated, and thus a
well-defined relative humidity versus temperature upper bound can be obtained
by curve-fitting to the data. This method is extremely robust and circumvents
the on-site calibration problems inherent in using capacitive humidity
devices to give reliable sub-saturated humidity measurements.
Surface sensible and latent heat fluxes have been calculated using the
temperature and humidity measurements at two heights and following a profile
bulk-flux methodology. In this case we use a surface roughness length
of 1x10-4 m, and use the limited-value flux-profile relations of King
et al. (1996). Although it is robust, the profile method is extremely
sensitive to small changes in temperature or humidity difference, which
means unphysically large fluxes can be calculated (e.g. Stearns and Weidner,
1993).To try and eliminate these erroneous fluxes the temperature and
humidity difference data are neglected if t1 - t0 > 10 oC on the stable
side, and t1 - t0 < -0.5 oC on the unstable side. The unstable side
threshold is smaller as grossly unstable conditions do not occur at Halley
or further inland (King and Anderson, 1994). To give an idea of the uncertainty
in the calculated fluxes a sensitivity study was carried out, with the
following varied: the threshold for 'bad' data on the unstable side was
changed to -0.2 oC and -1.0 oC, the roughness length was changed to 0.5x10-4
and 1.2x10-4 m (King and Anderson, 1994; King et al., 1996), and the instrument
heights were changed by -0.5 and +1.0 m (the instrument heights are only
known exactly when the site is visited). The accumulated (i.e. worst case)
differences for these changes are used to define a range of uncertainty,
as shown in Table 5, for each season and site. The ranges are relatively
large in the summer, and all year at C1 (where earlier instrumentation
was used). To give a quick impression of the uncertainty, where the magnitude
of the mean flux is larger than the range the mean is printed in bold.
In other words, the bold estimates are more reliable. To concentrate just
on these values: Qs is always negative, that is a flux of heat into the
snow surface, with the largest magnitude at C2, the windiest site, followed
by C4, C3 and then Halley. The mean latent heat fluxes are an order of
magnitude smaller than the sensible heat fluxes and have greater uncertainty.
To focus on Ql in JJA, there appears to be a change of sign from positive
at C3 (e.g. due to sublimation) to negative at C2 and on the ice shelf
(e.g. due to freezing).
The perturbation pressure (p') is the deviation from a mean pressure
for that month at that site. Thus large differences in p' between the
sites indicate that the pressure distribution is anomalous from the mean
pressure distribution. Perturbations from a monthly mean are used to nullify
the seasonal shifts of mass observed over the Antarctic, a by product
of the polar location and elevation of the continent (Parish and Bromwich,
1998). It was found that interpreted carefully, using p' was a useful
indicator of the mesoscale pressure gradient, better than calculating
a mean sea-level pressure where the problems of reducing to sea level
without a temperature profile are well known (e.g. King and Turner, 1997).
However one should bear in mind that p' does not tell us anything about
the 'background' pressure distribution that will exist due to the differential
heating between the continent and the surrounding ocean for example.
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