Information Technology Reference
In-Depth Information
A gap can also be filled by copying the data of a selected station into the gap.
Small gaps which are not bigger than three hours can be filled automatically by executing a
linear interpolation.
Big gaps which are bigger than three hours and not bigger than 15 days can be filled with
the data of the best adjacent station. The best adjacent station is found by calculating the
correlation coefficient. The data of the station with the best correlation coefficient are then
merged into the gap by vertical moving and horizontal rotating so that the tangential points
at the beginning and the end fit exactly. This is done by graphical support.
2.3 Plausibility checking
Plausibility checking is a very important feature in AgmedaWin because if the
meteorological data provided for the prognosis models are wrong the models will give
wrong results.
In AgmedaWin two kinds of plausibility checking are possible.
The first kind is the internal plausibility checking. With this method data of a single station
can be checked. Several checking algorithms can be defined and adjusted in AgmedaWin.
Examples for checking algorithms:
checking of lower and upper limits
several algorithms for checking the dynamic of the data (e.g.: 12 equal values of air
temperature in series would be marked as implausible)
comparing a value with its previous and following value
When values are found to be implausible by the checking routines they are marked with the
plausibility sign “*”. The weather administrator has to decide what to do with the marked
values. Either he would check if the sensor still works correctly or he could define that a
value is plausible although the plausibility checking had marked it as implausible by
changing the plausibility sign manually.
The second kind of plausibility checking is the external plausibility checking. With this
method the data of adjacent stations can be compared. At first groups of stations in a
subregion with similar climatical conditions are defined. Then the deviations of the daily
mean values are calculated and shown in a cross table. Deviations which exceed a defined
limit are marked. The external plausibility checking can be a help to detect defect sensors.
2.4 Representation of meteorological data
In AgmedaWin meteorological data can be represented in different ways.
The data of all stations can be shown on a map after selecting the sensor, the aggregation
(hourly, daily or monthly values) and the date and time.
Data of all sensors of one station can be represented in table form.
Also the values of selected stations and sensors can be represented in a diagram. The user
has several possibilities to adjust and configure the representation by selecting or
unselecting sensors, by highlighting single sensors or changing the colours. Also it is
possible to zoom and scroll in the diagram and to print and save it (fig. 2).
2.5 Evaluation of meteorological data
The following evaluations are available in AgmedaWin:
Sum analysis: Output of the sum of all values of a chosen sensor and station depending on a
specified lower and upper limit.
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