24
C.L. Gentemann et al.
used over a warm background, and is especially useful over land. The relationship
of the scattering signal to surface rain rate is less direct than it is for the emission
signal.
The relationship between the emission signal and the rain rate is strongly nonlinear. Since rain is horizontally inhomogeneous over satellite footprints (which may
range in diameter from 6 to 56 km), the measurement represents an average over
the satellite footprint. Averaging a highly variable observable quantity, when the
observable quantity is nonlinearly related to the desired quantity, results in a bias
in the desired quantity. This is the beamfilling effect, and it causes rain rates to be
underestimated by PMW radiances.
Different sensors have systematically different spatial resolutions and the probability distribution function of liquid water in the footprint changes systematically
with the size of the footprint. For example, an infinitely small satellite footprint
would model the variability of liquid in the footprint with a delta function, whereas
a satellite footprint the size of the Earth models that variability with the global rain
probability distribution function – typically taken to be a mixed log-normal distribution. Fortunately, real satellite footprints do not vary that much. The spatial
resolution of SSM/I rain retrievals is nominally 32 km, and the spatial resolution
of AMSR rain retrievals is nominally 12 km. This means that SSM/I rain retrievals
require a larger beamfilling correction than AMSR rain retrievals, because SSM/I
retrievals have more spatial averaging.
Hilburn and Wentz (2008) developed a new beamfilling correction by simulating lower resolution SSM/I data with higher resolution AMSR data. Rain retrievals
were computed from the simulated SSM/I data at several resolutions and compared
to the AMSR rain retrievals at the highest possible resolution to deduce how the
variability of liquid water changes systematically with footprint size. When the
new correction was applied to satellite data, rain rates agreed to within 3% (after
removing sampling biases due to the different local times-of-day for each satellite). New inter-calibrated rain rate retrievals have been successfully used to close
the water cycle (Wentz et al., 2007), show excellent agreement with rain gauges on
ocean buoys (Bowman et al., 2009), and correlate well with the TRMM Precipitation
Radar (Cecil and Wingo, 2009).
2.5.5 Sea Ice
PMW retrievals of sea ice form one of the most important climate data records in
existence. The time series of sea ice, from 1979 – present, has provided measurements of ice concentration and classification of sea ice types (multiyear or first-year
ice) on a daily basis. The PMW sea ice retrievals are vital because of their ability to see through clouds. Large ice shelf breakup events, such as the Larsen Ice
shelf breakup, have been witnessed and monitored using PMW retrievals. Sea ice
is important to the global climate as it acts to regulate heat, moisture, and salinity
in the polar ocean. The recent increase in summer Arctic sea ice acts as a positive
feedback for global warming by changing the albedo.
C.L. Gentemann et al.
used over a warm background, and is especially useful over land. The relationship
of the scattering signal to surface rain rate is less direct than it is for the emission
signal.
The relationship between the emission signal and the rain rate is strongly nonlinear. Since rain is horizontally inhomogeneous over satellite footprints (which may
range in diameter from 6 to 56 km), the measurement represents an average over
the satellite footprint. Averaging a highly variable observable quantity, when the
observable quantity is nonlinearly related to the desired quantity, results in a bias
in the desired quantity. This is the beamfilling effect, and it causes rain rates to be
underestimated by PMW radiances.
Different sensors have systematically different spatial resolutions and the probability distribution function of liquid water in the footprint changes systematically
with the size of the footprint. For example, an infinitely small satellite footprint
would model the variability of liquid in the footprint with a delta function, whereas
a satellite footprint the size of the Earth models that variability with the global rain
probability distribution function – typically taken to be a mixed log-normal distribution. Fortunately, real satellite footprints do not vary that much. The spatial
resolution of SSM/I rain retrievals is nominally 32 km, and the spatial resolution
of AMSR rain retrievals is nominally 12 km. This means that SSM/I rain retrievals
require a larger beamfilling correction than AMSR rain retrievals, because SSM/I
retrievals have more spatial averaging.
Hilburn and Wentz (2008) developed a new beamfilling correction by simulating lower resolution SSM/I data with higher resolution AMSR data. Rain retrievals
were computed from the simulated SSM/I data at several resolutions and compared
to the AMSR rain retrievals at the highest possible resolution to deduce how the
variability of liquid water changes systematically with footprint size. When the
new correction was applied to satellite data, rain rates agreed to within 3% (after
removing sampling biases due to the different local times-of-day for each satellite). New inter-calibrated rain rate retrievals have been successfully used to close
the water cycle (Wentz et al., 2007), show excellent agreement with rain gauges on
ocean buoys (Bowman et al., 2009), and correlate well with the TRMM Precipitation
Radar (Cecil and Wingo, 2009).
2.5.5 Sea Ice
PMW retrievals of sea ice form one of the most important climate data records in
existence. The time series of sea ice, from 1979 – present, has provided measurements of ice concentration and classification of sea ice types (multiyear or first-year
ice) on a daily basis. The PMW sea ice retrievals are vital because of their ability to see through clouds. Large ice shelf breakup events, such as the Larsen Ice
shelf breakup, have been witnessed and monitored using PMW retrievals. Sea ice
is important to the global climate as it acts to regulate heat, moisture, and salinity
in the polar ocean. The recent increase in summer Arctic sea ice acts as a positive
feedback for global warming by changing the albedo.