ta QUA lity control of
When using meteorological data (from different data providers), it is advisable to check the data with a uniform method in order to get a more homogeneous data set, irrespective of e.g. state borders: The meteorological stations of MAP are operated by several dozens of institutions. Therefore, a certain variety of measuring and processing procedures as well as of quality control efforts has to be expected.
For this reason MAP-NWS (- National Weather Services) started to support DAQUAMAP. It is a project with the aim to assess the performance of the meteorological measurements for the stations of all the participating institutions in the MAP area in order to get a more homogeneous data set for the whole Alpine region. For this purpose a mathematical method of quality control has been developed and continually improved during the last years at the Department for Meteorology and Geophysics (University of Vienna):
For reference see:
Steinacker Reinhold, Christian Häberli and Wolfgang Pöttschacher, 2000:
"A transparent method for the analysis and quality evaluation
of irregularly distributed and noisy observational data", Monthly Weather Review, Vol. 128, No. 7, pp. 2303-2316.
Abstract: click here
The basic advantages of this method are that
no first guess or (prognostic) model field and
no a priori knowledge about structure or weighting functions
are necessary .
At the moment the method is applied to scalar quantities (mean sea level pressure, potential temperature, humidity) of GTS stations (< 750 msl) in a two-dimensional domain. As soon as there is enough data at the MDC the method will be applied for the surface data of the MAP-database.
3. Results of DAQUAMAP can give some hints as to
Remarks on Graphics
- Attention: The "sign" in the plots has been changed. That means that we are not talking about "correction proposals" any longer but about deviations.
- the percentage of gross errors (PGE);
- possible systematic errors (e.g. bias)
- representativity of the stations with respect to the scale which can be resolved
by the local/ regional station density.
- sensor problems
The plots are only meant to visualise time series.
Remarks on Statistics
- state borders are strongly simplified (rough guide)
- dashed line (_ _ _ _): median
- dotted lines (......): three times the interquartile range (+/-).
Deviations may be caused by different reasons. If it persists and does not show any correlation with the diurnal or seasonal cycle or with a
synoptic forcing, it may be caused by sensor problems (e.g. miscalibration). If there is some dependency on the diurnal or seasonal cycle or if we can assume
some correlation with the location of the station then its representativity for the region may lead to a deviation in the DAQUAMAP investigations. E.g. if a station is
situated near a lake, it will probably measure higher humidity than the surrounding stations. The distinction between these cases enforces some experience.
- How to interpret the statistic of the DAQUAMAP results click here
- data is not always complete! - compare "n" in statistic of each individual station for each time step (optimum is 365 for the year 1999)
For Data Providers
The 1999 results in detail for ...
sorry, only for registered WGROUND members - you will be asked for your DAQUAMAP password
In case you are a WGROUND-member without DAQUAMAP-account,
please contact Inga Groehn, eMail: firstname.lastname@example.org
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