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| Titel |
A model-based approach to adjust microwave observations for operational applications: results of a campaign at Munich Airport in winter 2011/2012 |
| VerfasserIn |
J. Güldner |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 6, no. 10 ; Nr. 6, no. 10 (2013-10-29), S.2879-2891 |
| Datensatznummer |
250085095
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| Publikation (Nr.) |
 copernicus.org/amt-6-2879-2013.pdf |
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| Zusammenfassung |
In the frame of the project "LuFo iPort VIS" which focuses on the
implementation of a site-specific visibility forecast, a field campaign was
organised to offer detailed information to a numerical fog model. As part of
additional observing activities, a 22-channel microwave radiometer profiler
(MWRP) was operating at the Munich Airport site in Germany from October 2011
to February 2012 in order to provide vertical temperature and humidity
profiles as well as cloud liquid water information. Independently from the
model-related aims of the campaign, the MWRP observations were used to study
their capabilities to work in operational meteorological networks. Over the
past decade a growing quantity of MWRP has been introduced and a user
community (MWRnet) was established to encourage activities directed at the
set up of an operational network. On that account, the comparability of
observations from different network sites plays a fundamental role for any
applications in climatology and numerical weather forecast.
In practice, however, systematic temperature and humidity differences (bias)
between MWRP retrievals and co-located radiosonde profiles were observed and
reported by several authors. This bias can be caused by instrumental offsets
and by the absorption model used in the retrieval algorithms as well as by
applying a non-representative training data set. At the Lindenberg
observatory, besides a neural network provided by the manufacturer, a
measurement-based regression method was developed to reduce the bias. These
regression operators are calculated on the basis of coincident radiosonde
observations and MWRP brightness temperature (TB) measurements. However, MWRP
applications in a network require comparable results at just any site, even
if no radiosondes are available.
The motivation of this work is directed to a verification of the suitability
of the operational local forecast model COSMO-EU of the Deutscher
Wetterdienst (DWD) for the calculation of model-based regression operators in
order to provide unbiased vertical profiles during the campaign at Munich
Airport. The results of this algorithm and the retrievals of a neural
network, specially developed for the site, are compared with radiosondes from
Oberschleißheim located about 10 km apart from the MWRP site.
Outstanding deviations for the lowest levels between 50 and 100 m are
discussed. Analogously to the airport experiment, a model-based regression
operator was calculated for Lindenberg and compared with both radiosondes and
operational results of observation-based methods.
The bias of the retrievals could be considerably reduced and the accuracy,
which has been assessed for the airport site, is quite similar to those of
the operational radiometer site at Lindenberg above 1 km height. Additional
investigations are made to determine the length of the training period
necessary for generating best estimates. Thereby three months have proven to
be adequate. The results of the study show that on the basis of numerical
weather prediction (NWP) model data, available everywhere at any time, the
model-based regression method is capable of providing comparable results at a
multitude of sites. Furthermore, the approach offers auspicious conditions
for automation and continuous updating. |
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