2 Passive Microwave Remote Sensing of the Ocean
23
of its strong spectral signature and polarization signature (Wentz, 1997). Passive
microwave observations provide a direct estimate of the total absorption along the
sensor viewing path. At 18 and 37 GHz, clouds are semi-transparent allowing for
measurement of the total columnar absorption. The absorption is related to the total
amount of liquid water in the viewing path, after accounting for oxygen and water
vapor absorption.
Validation of columnar cloud liquid water is a difficult undertaking. The spatial variability of clouds makes comparisons between upward looking ground based
radiometers and the large footprint size of the downward looking satellite retrievals
problematic. The upward looking ground-based radiometers also have very limited geographic distribution, making meaningful validation over global conditions
impossible. Generally, validation is completed using a statistical histogram method
(Wentz, 1997).
2.5.4 Rain Rate
Rainfall is the key hydrological parameter, so much so that changes in the spatial
distribution of rainfall have led to the collapse of civilizations (Haug et al., 2003;
O’Conner and Kiker, 2004). Rain is one of the most difficult parameters to accurately retrieve using remote sensing because of its extreme variability in space and
time over a variety of scales. The most accurate and physically-based rain retrieval
techniques take advantage of the interactions between microwave radiation and
water, and both passive and active microwave remote sensing techniques can be
used to derive rain rates over both ocean and land.
PMW observations respond to the presence of rain in the instrument field-of-view
with two primary signals: an emission signal and a scattering signal (Petty, 1994).
The ocean surface is roughly 50% emissive, so it serves as a cold background around
150 K against which to observe rain. Since the ocean is an expansive flat surface, the
emission is strongly polarized. For typical incidence angles and clear skies, vertical
polarization brightness temperatures are larger than horizontal polarization brightness temperatures by as much as 100 K. The emission depends on the sea surface
temperature, salinity, and surface roughness.
Emission from small round rain and cloud drops is unpolarized, and the liquid
emission strongly decreases the polarization seen by the sensor. Heavy rain can
bring the difference between vertical and horizontal polarization brightness temperatures down to zero. The emission signal has a strong spectral signature that
increases with frequency – that is, higher microwave frequencies are more affected
by rain. The strength of the emission signal depends on the total amount of liquid
water below the freezing level, and this is related to the surface rain rate. The primary factors governing this relationship are: the height of the freezing level, the
relative portioning of cloud and rain water, and the horizontal inhomogeneity – the
beamfilling effect (Hilburn and Wentz, 2008; Wentz and Spencer, 1998). The scattering signal measures a decrease in brightness temperatures due to the presence of
ice above the freezing level (Spencer et al., 1989). Usually the scattering signal is
23
of its strong spectral signature and polarization signature (Wentz, 1997). Passive
microwave observations provide a direct estimate of the total absorption along the
sensor viewing path. At 18 and 37 GHz, clouds are semi-transparent allowing for
measurement of the total columnar absorption. The absorption is related to the total
amount of liquid water in the viewing path, after accounting for oxygen and water
vapor absorption.
Validation of columnar cloud liquid water is a difficult undertaking. The spatial variability of clouds makes comparisons between upward looking ground based
radiometers and the large footprint size of the downward looking satellite retrievals
problematic. The upward looking ground-based radiometers also have very limited geographic distribution, making meaningful validation over global conditions
impossible. Generally, validation is completed using a statistical histogram method
(Wentz, 1997).
2.5.4 Rain Rate
Rainfall is the key hydrological parameter, so much so that changes in the spatial
distribution of rainfall have led to the collapse of civilizations (Haug et al., 2003;
O’Conner and Kiker, 2004). Rain is one of the most difficult parameters to accurately retrieve using remote sensing because of its extreme variability in space and
time over a variety of scales. The most accurate and physically-based rain retrieval
techniques take advantage of the interactions between microwave radiation and
water, and both passive and active microwave remote sensing techniques can be
used to derive rain rates over both ocean and land.
PMW observations respond to the presence of rain in the instrument field-of-view
with two primary signals: an emission signal and a scattering signal (Petty, 1994).
The ocean surface is roughly 50% emissive, so it serves as a cold background around
150 K against which to observe rain. Since the ocean is an expansive flat surface, the
emission is strongly polarized. For typical incidence angles and clear skies, vertical
polarization brightness temperatures are larger than horizontal polarization brightness temperatures by as much as 100 K. The emission depends on the sea surface
temperature, salinity, and surface roughness.
Emission from small round rain and cloud drops is unpolarized, and the liquid
emission strongly decreases the polarization seen by the sensor. Heavy rain can
bring the difference between vertical and horizontal polarization brightness temperatures down to zero. The emission signal has a strong spectral signature that
increases with frequency – that is, higher microwave frequencies are more affected
by rain. The strength of the emission signal depends on the total amount of liquid
water below the freezing level, and this is related to the surface rain rate. The primary factors governing this relationship are: the height of the freezing level, the
relative portioning of cloud and rain water, and the horizontal inhomogeneity – the
beamfilling effect (Hilburn and Wentz, 2008; Wentz and Spencer, 1998). The scattering signal measures a decrease in brightness temperatures due to the presence of
ice above the freezing level (Spencer et al., 1989). Usually the scattering signal is