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Multiscale Hydrologic Remote Sensing: Perspectives and Applications
are being collected by the international constellation of GEO satellites. The Climate
Prediction Center (CPC) of the National Oceanic and Atmospheric Administration
(NOAA)/National Weather Service merges the international complement of GEO-IR
data into half-hourly 4 km × 4 km equivalent latitude–longitude grids (hereafter the
“CPC merged IR”; Janowiak et al. 2001). The IR brightness temperatures (T b ) are corrected for zenith angle viewing effects and intersatellite calibration differences. The
suite of geostationary satellites is able to continuously monitor the earth, providing
data every 15 min in the operational mode.
Compared with the GEO satellite observations, passive microwave (PMW) data
collected from LEO satellites have a more direct relationship to the hydrometeors
that result in surface precipitation; however, they suffer from low temporal sampling resolution. These LEO satellites include the Tropical Rainfall Measuring
Mission (TRMM) Microwave Imager (TMI), Special Sensor Microwave Imager
(SSM/I) on Defense Meteorological Satellite Program (DMSP) satellites, Advanced
Microwave Scanning Radiometer-Earth Observing System (AMSR-E) on Aqua, and
the Advanced Microwave Sounding Unit-B (AMSU-B) on the NOAA satellite series.
Even merging all of the available PMW observations within every 3-h time window,
there are still significant coverage gaps, only representative of about 80% of the
earth’s surface in the latitude band 50°N–S.
15.2.2 iR-BaSed Rainfall eStiMation algoRithMS
IR methods were among the first to arrive, historically, in the remote sensing of
rainfall since the late 1970s (Arkin and Meisner 1987). Because satellites measure
rainfall as an integral of space at a point in time, the sampling frequency (4 × 4 km 2
and 30-min sampling interval) of IR-based rainfall estimation algorithms offer the
unique advantages of both extensive global coverage as well as relatively high temporal sampling rates. The majority of algorithms attempt to correlate the surface rain
rate with IR cloud-top T b using the information extracted from IR imagery. The algorithms developed to date may be classified into three groups, depending on the level
of information extracted from the IR cloud images. They include cloud pixel–based,
cloud window–based, and cloud patch–based. Several examples of these algorithms
may clarify this classification further.
15.2.2.1 Cloud Pixel–Based Algorithms
The Geostationary Operational Environment Satellite (GOES) Precipitation Index
(GPI) developed by Arkin and his colleagues (Arkin and Meisner 1987) is a cloud pixel
based algorithm that assigns a constant conditional rain rate of 3 mm/h to pixels with
a cloud-top temperature lower than 235 K, and zero rain rates otherwise over a 2.5° ×
2.5° area. The GPI is essentially an area–time integral approach to rainfall estimation
at a large scale.
Another pixel-based algorithm is the auto estimator algorithm, which utilizes a
power-law function to fit the IR–RR relationship. Pixel rainfall values are further
adjusted using the hydroestimator (HE) algorithm (Scofield and Kuligowski 2003),
which takes into account several correction factors, such as relative humidity and
precipitable water. This method is computationally inexpensive but is subjective
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
are being collected by the international constellation of GEO satellites. The Climate
Prediction Center (CPC) of the National Oceanic and Atmospheric Administration
(NOAA)/National Weather Service merges the international complement of GEO-IR
data into half-hourly 4 km × 4 km equivalent latitude–longitude grids (hereafter the
“CPC merged IR”; Janowiak et al. 2001). The IR brightness temperatures (T b ) are corrected for zenith angle viewing effects and intersatellite calibration differences. The
suite of geostationary satellites is able to continuously monitor the earth, providing
data every 15 min in the operational mode.
Compared with the GEO satellite observations, passive microwave (PMW) data
collected from LEO satellites have a more direct relationship to the hydrometeors
that result in surface precipitation; however, they suffer from low temporal sampling resolution. These LEO satellites include the Tropical Rainfall Measuring
Mission (TRMM) Microwave Imager (TMI), Special Sensor Microwave Imager
(SSM/I) on Defense Meteorological Satellite Program (DMSP) satellites, Advanced
Microwave Scanning Radiometer-Earth Observing System (AMSR-E) on Aqua, and
the Advanced Microwave Sounding Unit-B (AMSU-B) on the NOAA satellite series.
Even merging all of the available PMW observations within every 3-h time window,
there are still significant coverage gaps, only representative of about 80% of the
earth’s surface in the latitude band 50°N–S.
15.2.2 iR-BaSed Rainfall eStiMation algoRithMS
IR methods were among the first to arrive, historically, in the remote sensing of
rainfall since the late 1970s (Arkin and Meisner 1987). Because satellites measure
rainfall as an integral of space at a point in time, the sampling frequency (4 × 4 km 2
and 30-min sampling interval) of IR-based rainfall estimation algorithms offer the
unique advantages of both extensive global coverage as well as relatively high temporal sampling rates. The majority of algorithms attempt to correlate the surface rain
rate with IR cloud-top T b using the information extracted from IR imagery. The algorithms developed to date may be classified into three groups, depending on the level
of information extracted from the IR cloud images. They include cloud pixel–based,
cloud window–based, and cloud patch–based. Several examples of these algorithms
may clarify this classification further.
15.2.2.1 Cloud Pixel–Based Algorithms
The Geostationary Operational Environment Satellite (GOES) Precipitation Index
(GPI) developed by Arkin and his colleagues (Arkin and Meisner 1987) is a cloud pixel
based algorithm that assigns a constant conditional rain rate of 3 mm/h to pixels with
a cloud-top temperature lower than 235 K, and zero rain rates otherwise over a 2.5° ×
2.5° area. The GPI is essentially an area–time integral approach to rainfall estimation
at a large scale.
Another pixel-based algorithm is the auto estimator algorithm, which utilizes a
power-law function to fit the IR–RR relationship. Pixel rainfall values are further
adjusted using the hydroestimator (HE) algorithm (Scofield and Kuligowski 2003),
which takes into account several correction factors, such as relative humidity and
precipitable water. This method is computationally inexpensive but is subjective