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Multiscale Hydrologic Remote Sensing: Perspectives and Applications
that uses passive MW rainfall observations from low-orbiting satellites to adjust the
IR-based rainfall estimates at the resolution of geostationary satellites.
With respect to the use of information, both pixel- and window-based approaches
utilize limited attributes of the cloud patches. Rain rates retrieved from these
methods tend to be nonunique and may be insufficient to identify the relationships
between cloud types and surface rain rates (Hong et al. 2004). Cloud patch–based
approaches, on the other hand, attempt to include more information from the cloud
images and are likely to provide a more reliable rainfall retrieval system than the
pixel-based approaches. Therefore, successful characterization of cloud patch–based
imagery information can be one step toward better estimation of rainfall.
15.2.3 Mw-BaSed Rainfall eStiMation
Kummerow et al. (2007) provided a review of the satellite MW-based rainfall estimation algorithms that have evolved steadily from the early Electronically Scanning
Microwave Radiometer in the 1970s to the current SSM/I, AMSU, and TRMM
Microwave Imager and Aqua AMSR-E sensors. These algorithms can be roughly
categorized into three classes: (1) the “emission”-type algorithms (Wilheit et al.
1991; Berg and Chase 1992; Chang et al. 1999), which use low-frequency channels
to detect the increased radiances due to rain over radiometrically cold oceans, (2) the
“scattering” algorithms (Spencer et al. 1983; Grody 1991; Ferraro and Marks 1995),
which correlate rainfall to radiance depressions caused by ice scattering present in
many precipitating clouds, and (3) the “multichannel inversion”–type algorithms
(Olson 1989; Mugnai et al. 1993; Kummerow and Giglio 1994; Smith et al. 1994;
Petty 1994; Bauer et al. 2001; Kummerow et al. 2001), which seek to invert the entire
radiance vector simultaneously.
Among these algorithms, the algorithms developed by Wilheit et al. (1991) and
Kummerow et al. (2001) are used operationally for the TMI as well as the AMSR-E,
while the algorithms developed by Wilheit et al. (1991) and Ferraro and Marks (1995)
are used with SSM/I in the GPCP over ocean and land, respectively. More recently
PMW observations from the AMSU-B instrument have been converted to precipitation estimates at the National Environmental Satellite Data and Information Service
(NESDIS) with operational versions supported by the algorithm developed by Weng
et al. (2003). It is most recently described by Ferraro et al. (2005). In each case,
algorithms have been optimized for the corresponding satellite sensor. Therefore,
algorithm intercomparison efforts initially aimed at identifying the “best” algorithm
for all sensors have not made much headway, as each algorithm appears to have
strengths and weaknesses related to specific applications.
Kummerow et al. (2007) also proposed the next advancement in global precipitation monitoring, which is the Global Precipitation Measurement (GPM) mission. Of
utmost importance, GPM is a transparent, parametric, and unified algorithm that
ensures uniform rainfall products across all MW sensors from all satellite platforms.
A mission of GPM’s scope requires the international community to participate in the
algorithm development, refinement, and error characterization. A generalized parametric framework will avoid the impasse of cross-evaluation of previous MW algorithms that are designed for specific radiometers with defined frequencies, viewing
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
that uses passive MW rainfall observations from low-orbiting satellites to adjust the
IR-based rainfall estimates at the resolution of geostationary satellites.
With respect to the use of information, both pixel- and window-based approaches
utilize limited attributes of the cloud patches. Rain rates retrieved from these
methods tend to be nonunique and may be insufficient to identify the relationships
between cloud types and surface rain rates (Hong et al. 2004). Cloud patch–based
approaches, on the other hand, attempt to include more information from the cloud
images and are likely to provide a more reliable rainfall retrieval system than the
pixel-based approaches. Therefore, successful characterization of cloud patch–based
imagery information can be one step toward better estimation of rainfall.
15.2.3 Mw-BaSed Rainfall eStiMation
Kummerow et al. (2007) provided a review of the satellite MW-based rainfall estimation algorithms that have evolved steadily from the early Electronically Scanning
Microwave Radiometer in the 1970s to the current SSM/I, AMSU, and TRMM
Microwave Imager and Aqua AMSR-E sensors. These algorithms can be roughly
categorized into three classes: (1) the “emission”-type algorithms (Wilheit et al.
1991; Berg and Chase 1992; Chang et al. 1999), which use low-frequency channels
to detect the increased radiances due to rain over radiometrically cold oceans, (2) the
“scattering” algorithms (Spencer et al. 1983; Grody 1991; Ferraro and Marks 1995),
which correlate rainfall to radiance depressions caused by ice scattering present in
many precipitating clouds, and (3) the “multichannel inversion”–type algorithms
(Olson 1989; Mugnai et al. 1993; Kummerow and Giglio 1994; Smith et al. 1994;
Petty 1994; Bauer et al. 2001; Kummerow et al. 2001), which seek to invert the entire
radiance vector simultaneously.
Among these algorithms, the algorithms developed by Wilheit et al. (1991) and
Kummerow et al. (2001) are used operationally for the TMI as well as the AMSR-E,
while the algorithms developed by Wilheit et al. (1991) and Ferraro and Marks (1995)
are used with SSM/I in the GPCP over ocean and land, respectively. More recently
PMW observations from the AMSU-B instrument have been converted to precipitation estimates at the National Environmental Satellite Data and Information Service
(NESDIS) with operational versions supported by the algorithm developed by Weng
et al. (2003). It is most recently described by Ferraro et al. (2005). In each case,
algorithms have been optimized for the corresponding satellite sensor. Therefore,
algorithm intercomparison efforts initially aimed at identifying the “best” algorithm
for all sensors have not made much headway, as each algorithm appears to have
strengths and weaknesses related to specific applications.
Kummerow et al. (2007) also proposed the next advancement in global precipitation monitoring, which is the Global Precipitation Measurement (GPM) mission. Of
utmost importance, GPM is a transparent, parametric, and unified algorithm that
ensures uniform rainfall products across all MW sensors from all satellite platforms.
A mission of GPM’s scope requires the international community to participate in the
algorithm development, refinement, and error characterization. A generalized parametric framework will avoid the impasse of cross-evaluation of previous MW algorithms that are designed for specific radiometers with defined frequencies, viewing