Geoscience Reference
In-Depth Information
5. Quality control algorithms for surface precipitation products
Corrections of 2-D radar data should constitute consecutive stages in radar data
processing in order to get the best final radar products. These corrections include
algorithms related to specific needs of the given product. The particular quality factors
employed for calculating quality indices for 3-D data which also influence quality of 2-D
data are not described here.
Many algorithms for surface precipitation field estimation from weather radar
measurements applied in operational practice (e.g. Michelson et al., 2005) are described in
this Section. For precipitation accumulation a different group of quality factors is applied.
More common quality control algorithms employed in the practice are listed in Table 2.
Task
Correction algorithm
Quality factor
QC QI
Z-R
relationship
estimation
Changeable
Z-R
relationship used
-
x
Bright band (melting
layer) effect correction
VPR-based correction
Presence of melting layer
x
x
Data extrapolation onto
the Earth surface
Height of the lowest
radar beam
VPR-based correction
x
x
Magnitude of the
enhancement
Orographic enhancement
Physical model
x
x
Adjustment with rain
gauge data
Correction using rain
gauge data
Radar precipitation - rain
gauge differences
x
x
Temporal continuity of
data (number of the
products)
For accumulation:
number of rate data
-
x
For accumulation:
averaged
QI
for rate data
Quality of included data
(averaged
QI
)
-
x
Table 2. Quality control algorithms (correction and characterization) for 2-D surface
precipitation data (in order of implementation into the chain).
5.1 Estimation of
Z-R
relationship
b
Za
) variability is one of the most significant error sources in
precipitation estimation. Each hydrometeor contributes to the precipitation intensity
roughly to 3.7
th
power of its diameter, thus assumption on the drop size distribution is
needed as the integral intensity is measured. Nowadays, for a single polarization radar it is
a common practice to apply a single (usually Marshall and Palmer formula
The
Z-R
relationship (
1.6
) or
seasonally-dependent
Z-R
relationship. However, use of a fixed
Z-R
relation can lead to
significant errors in the precipitation estimation, as it depends on precipitation type
(stratiform or convective), its kind (rain, snow, hail), etc. There are approaches that use
tuned
Z-R
relationships for different meteorological situations. It requires the different
types of precipitation to be identified on the basis of dedicated algorithms, which is easier if
disdrometer measurements are available (Tenório et al., 2010).
Z
200
R
