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producing values of blockage ranging mostly between 30% and 80%. On the other hand, the
1.3º elevation beam blockage values are mostly below 20% and for some targets are always
null (no blockage at all) except for the most super refractive situations.
In Fig. 12, target MNT, shows moderate (around 40%) to low (10%) rate of beam blockage,
respectively (similar results were obtained for LML). On the other hand, we found that the
most distant target, MNY, intercepted the radar beam mostly between 8% and 14%. The
range of variations in the beam blockage observed in the above mentioned histograms
oscillates from 8% (LML) and 10% (MNT) to 18% (MNY). From the cumulative probability
plots obtained it may be noted that MNT and LML show single classes representing more
than 50% while a more smoothed distribution is found for MNY.
Fig. 12. Simulated beam blockage frequency and cumulative probability distributions (left)
and the corresponding correction histograms (right) for 1º antenna elevation for target MNT.
The corresponding correction histogram is also shown. Should the beam blockage correction
have been a continuous function, where for a particular value of blockage a different
correction factor would be applied, then the spread of the beam blockage histograms would
have been reflected in the spread of the correction histograms. However, this is not the case
for the particular type of correction considered where only four different correction values
are possible depending on the beam blockage. Therefore, a big variability in the beam
blockage occurrence does not necessarily produce the same variability in the blockage
correction. An additional conclusion of this analysis (Bech et al. 2003) was that errors in
beam blockage corrections derived from propagation variability were comparable to
antenna pointing errors of 0.1º, which is a typical value for operational systems. This
confirms the need for hardware calibration control and monitoring, particularly if
quantitative precipitation estimates are required.
4.5 Improved quantitative precipitation estimates
The methodology proposed in the previous section to simulate the radar beam blockage by
topography has been implemented to derive correction factors which were applied to improve
precipitation estimates. For example Fornasiero et al. (2006b) performed corrections in
different events, calculating specific corrections assuming both standard and non-standard
propagation conditions and finding some improvement with the corrections. In Bech et al.
(2007b, 2010a) results reported were carried out in the framework of the COST-731 action
(Rossa et al. 2010) using the so-called BPM model (which implements the blockage function
presented above. Larger data sets were considered for blockage corrections under standard
