Hydrological modelling of the Zambezi catchment for ... - TU Delft

HYDROLOGICAL MODEL INPUT DATA
3.1.1 Microwave-Infrared Rainfall Algorithm (MIRA)
Due to large demand from meteorologists, climatologists and hydrologists on accurate and
high-resolution rainfall estimates on a daily base, the MIRA [10] algorithm was developed
(Todd et al, 2001). Previous algorithms, which determine the rainfall by remote sensing,
usually have a spatial resolution of 2.5° and a monthly time scale. Some algorithms have a
better spatial resolution, but this mostly means that the temporal resolution is bad and vice
versa. The MIRA algorithm avoids this constraint by combining the input of the different
satellite sources. The MIRA algorithm uses two sources:
• Passive Microwave (PMW) and
• Infrared (IR)
The PMW is a low orbiting sensor that can provide accurate rainfall estimates, but suffer from
poor temporal sampling. Conversely, the IR sensor provides high temporal data, but gives
only a very weak signal and an indirect relationship with the surface rain.
GOES Precipitation Index
The most common algorithm to derive rainfall estimates out of the IR-sensor is the GOES
Precipitation Index (GPI) (Arkin and Meisner, 1987). For large grid cells the IR-sensor
measures the temperature of the cloud top and next the rainfall is calculated by:
R = Fc
× G× T
Equation 3.1
Where:
R = rainfall per cell [mm]
F c = fractional coverage of each cell by cloud colder than 235 K [%]
G = GPI coefficient equal to 3.0 [mm/h]
T = number of hours in the integration period [h]
Atlas and Bell (1992) showed that the GPI (Equation 3.1) is essentially an area-time-integral
(ATI) approach. This ATI approach (Doneaud et al., 1984) has been used to estimate the
storm rainfall volume from the area and duration of the storm measured by radar. As the GPI
uses cold cloud area as a surrogate for rain, Atlas and Bell (1992) showed that G can be
defined as:
−1
⎛A ⎞
G = R ⎜ c ⎟ c ⎜A ⎟
⎝ r ⎠
Where:
Equation 3.2
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