Geoscience Reference
In-Depth Information
prediction model: wind speed, wind direction, relative humidity, temperature, as well as the
topography of the region (Alpert & Shafir, 1989). Magnitude of such correction can be taken
to determine related quality index.
5.5 Adjustment with rain gauge data
Weather radar-based precipitation may differ from “ground truth”, which can be locally
estimated from rain gauge measurements, especially in close vicinity of the gauge. It is
assumed that rain gauge measures precipitation exactly as its correction can be calculated
(Førland et al., 1996), whereas radar provides information about space distribution. The idea
is to use rain gauge information to improve radar data, as so called adjustment. The
following solutions are proposed (Gjertsen et al., 2004):
Mean field correction is a simple method to make the radar measurements unbiased.
The correction factor is calculated from comparison of the averaged radar observations
over the whole considered area, and the analogical averaged rain gauge measurements.
The mean field bias can be calculated from historical data set or dynamic time-window.
The last method allows to take into consideration variability in precipitation
characteristics with time, but the time-period of the dynamic window cannot be too
short due to requirement of data representativeness.
Other methods of radar precipitation correction employ the distance to radar site
L
as
the predictor apart from rain gauge information. Correction factor
C
can be expressed
as e.g. polynomial relationship in form proposed by Michelson et al. (2000):
2
C L bLc
(10)
where
a
,
b
, and
c
are the empirically estimated parameters of the equation.
More advanced methods based on multiple regression involve more predictors which
play significant role in precipitation estimation. Especially in mountainous terrain the
distance to radar site turned out not sufficient because of strong influence of beam
blockage and shielding. Additional predictors can be height of the lowest radar beam,
height above sea level, etc.
Quality index related to the adjustment with rain gauge data can be determined from
magnitude of the correction.
5.6 Quality factors for precipitation accumulation
The following quality factors for precipitation accumulation can be considered:
Number of precipitation rate products.
Accumulated precipitation field is composed from a
certain number of discrete radar measurements. The number of precipitation rate
products included into the given precipitation accumulation can be used to calculate a
related quality index. Lack of one or more products during the accumulation period
results in a significant decrease of quality. Moreover lack of the products one after the
other results in much lower quality.
Averaged quality index from precipitation rate products
is computed as a mean from all
values
of quality indices for precipitation rates (e.g. maximally seven for 10-minute time
resolution and 1-hour period of accumulation) that are aggregated into the accumulation.
