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to 2006. For all stations, MR gave results with highest accuracy (Tab. 1). In all cases, the
coefficient of determination (CoD) ranged between 96 and 99% for temperature and 92 and
96% for relative humidity, respectively. For the 13 MSs, the mean deviation for temperature
was less than 0.1°C and for relative humidity less than 0.6% as calculated with MR. The
absolute maximum and minimum for temperature was less than 4.7°C and for relative
humidity less than 32.6%. The data also were tested for significance between calculated and
measured data using a t-test. The test indicated that for all stations the differences between
the calculated and measured values were random. The MR method gave plausible results,
so it was chosen to interpolate the meteorological data to be used as input for the forecasting
models.
3.3 Spatial precipitation data
16 radar stations are run by the German meteorological service to record precipitation all
over Germany. These stations do not measure the amount of precipitation at ground level
but the signal reflected from the rain drops in the atmosphere. These measurements at first
only allowed calculation of an unspecific 'precipitation intensity', a shortcoming. With the
system RADOLAN intensity is now calibrated online with data from a comprehensive
network of ombrometers, using complex mathematic algorithms. As a result the amount of
precipitation can be provided in a spatial resolution of 1 km² (Bartels, 2006). These calibrated
amounts of precipitation based on radar measured rainfall intensities are referred to as
“radar data” in the following. The validation of precipitation data took place in intensely
used agricultural areas, joining the radar grid with stations of the meteorological network.
In this way, it was possible to relate each station to a grid cell.
The radar derived precipitation at the station's grid cell and the actually measured data
formed the basis for the statistical verification. Since rain events differ throughout the year,
two representative months (May and August 2007) were selected to analyse uniform rainfalls
in spring as well as convective rainfall events in summer. This resulted in a validation dataset
of 1488 hours for each MS. Depending on the region, the number of MSs ranged from 9 to 29.
In addition, the influence of the distance between radar station and MSs was analysed.
Furthermore, a leaf wetness simulation model used by ZEPP (Racca, 2001, unpublished) was
run on data from both methods of precipitation measurement and the results were
compared.
The parameters for the amount of precipitation, number of hours with precipitation and
calculated leaf wetness showed high correlations between radar values and measured data.
The maximum of the hourly deviation of the amount of precipitation was 0.06 mm. In hours
with rainfall the deviation was slightly higher (0.36 mm). No correlation could be detected
for the distance between radar stations and MSs. For hourly rainfall pattern, a correlation of
91.4% between stations and validation areas was measured. The best correlations were
obtained for the leaf wetness model for which values > 99.9% were achieved.
The results clearly show that the use of radar data as an input parameter in disease forecast
models is valid. By adding data of temperature and relative humidity with high spatial
resolution, an optimal basis for plot-specific forecasts has been established. Moreover, this
system allows the exact detection of local convective rainfall events, which at the moment
often remain undetected using individual weather stations. Significant improvements of the
spatial forecasting by plant disease simulation models can be expected from the use of radar
data.
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