Geoscience Reference
In-Depth Information
Apart from the above-mentioned non-precipitation errors, meteorologically related
factors influence precipitation estimation from weather radar measurements.
Attenuation by hydrometeors, which depends on precipitation phase (rain, snow,
melting snow, graupel or hail), intensity, and radar wavelength, particularly C and X-
band, may cause the strong underestimation in precipitation, especially in case of hail.
Another source of error is
Z-R
relation which expresses the dependence of precipitation
intensity
R
on radar reflectivity
Z
. This empirical formula is influenced by drop size
distribution, which varies for different precipitation phases, intensities, and types of
precipitation: convective or non-convective (Šálek et al., 2004). The melting layer located
at the altitude where ice melts to rain additionally introduces uncertainty into
precipitation estimation. Since water is much more conductive than ice, a thin layer of
water covering melting snowflakes causes strong overestimation in radar reflectivity.
This effect is known as the bright band (Battan, 1973; Goltz et al., 2006). Moreover the
non-uniform vertical profile of precipitation leads to problems with the estimation of
surface precipitation from radar measurement (e.g. Franco et al., 2002; Germann & Joss,
2004; Einfalt & Michaelides, 2008), and these vertical profiles may strongly vary in space
and time (Zawadzki, 2006).
Dual-polarization radars have the potential to provide additional information to overcome
many of the uncertainties in contrast to situation when only the conventional reflectivity
Z
and Doppler information is available (Illingworth, 2004).
3. Methods for data quality characterization
3.1 Introduction
Characterization of the radar data quality is necessary to describe uncertainty in the data
taking into account potential errors that can be quantified as well as the ones that can be
estimated only qualitatively. Generally, values of many detailed “physical” quality descriptors
are not readable for end users, so the following quality metrics are used as more suitable:
total error level, i.e. measured value ± standard deviation expressed as measured
physical quantity (radar reflectivity in dBZ, precipitation in mm h
-1
, etc.),
quality flag taking discrete value, in the simplest form 0 or 1 that means “bad” or
”excellent” data,
quality index as unitless quantity related to the data errors, which is expressed by
numbers e.g. from 0 to 1.
Many national meteorological services provide quality information in form of flags to
indicate where radar data is burdened with specific errors and if it is corrected by dedicated
algorithms (Michelson et al., 2005; Norman et al., 2010). The flags are expressed as discrete
numbers.
The quality index (
QI
) is a measure of data quality that gives a more detailed characteristic
than a flag, providing quantitative assessment, for instance using numbers in a range from 0
(for bad data) to some value (e.g. 1, 100, or 255 for excellent data). The quality index concept
is operationally applied to surface precipitation data in some national meteorological
services (see review in Einfalt et al., 2010).
