2 Passive Microwave Remote Sensing of the Ocean
21
Atmospheric brightness temperatures and transmittance are computed from these
scenes and noise, commensurate with measurement error which depends on spatial
resolution, is added. The noise-added simulated brightness temperatures along with
the known environmental scene are used to generate multiple linear regression coefficients. Algorithm testing is undertaken by repeating the process using the withheld
scenes.
2.5 Geophysical Retrievals
2.5.1 Wind Speed
Ocean surface winds are crucial to transferring heat, gases, energy and momentum
between the atmosphere and ocean. Winds also determine the large scale ocean circulation and transport, power global weather patterns, and play a key role in marine
ecosystems. Hurricanes, typhoons, and mid-latitude winter storms all contain high
wind speeds that threaten international shipping and the lives and property of people
along the coasts. Ocean surface winds change rapidly in both time and space and
satellite sampling and accuracy make these observations the most useful wind data
available for research and forecasting over the global oceans.
Surface wind speeds (at 10 m height, without directions) are routinely estimated from passive microwave radiometers (SSM/I, AMSR-E, TMI, SSMIS) on
a spatial scale of roughly 25 km. Wind speeds in the range of 0–30 m/s are simultaneously retrieved along with SST, water vapor, cloud liquid water and rain rates
using an algorithm that exploits the polarization signature of wind induced sea surface emissivity (Wentz, 1997). Radiometer winds are quite accurate under typical
ocean conditions when no rain is present, however when even a little rain exists, the
wind speeds are unusable. Validations of radiometer winds in rain-free conditions
have been performed. Comparisons with ocean buoy and weather model winds show
root-mean-square differences near (model winds) or less than 1 m/s (buoy winds)
in rain-free conditions (Mears et al., 2001; Meissner et al., 2001). Since 1996, there
have been three or more radiometers in polar orbits simultaneously, resulting in good
spatial and temporal sampling, yielding over 95% Earth ocean surface coverage in
a given day.
WindSat is a passive fully-polarimetric microwave radiometer designed to measure ocean surface vector winds. It has been found to have wind accuracies close to
that of scatterometers for winds between 6 and 20 m/s, with significant wind direction uncertainty below 6 m/s (Bettenhausen et al., 2006). WindSat vector winds have
been poor in rainy conditions until recently when a new WindSat algorithm has been
developed that improves WindSat winds even in rain (Meissner and Wentz, 2009).
The quality of these new winds is similar to QuikScat in all but very heavy rain and
very low winds. Excellent agreement (to within 0.5 m/s) is found between passive
radiometer wind speeds, polarimetric radiometer wind vectors and scatterometer
vector winds despite the different measuring methods of each instrument (Wentz
21
Atmospheric brightness temperatures and transmittance are computed from these
scenes and noise, commensurate with measurement error which depends on spatial
resolution, is added. The noise-added simulated brightness temperatures along with
the known environmental scene are used to generate multiple linear regression coefficients. Algorithm testing is undertaken by repeating the process using the withheld
scenes.
2.5 Geophysical Retrievals
2.5.1 Wind Speed
Ocean surface winds are crucial to transferring heat, gases, energy and momentum
between the atmosphere and ocean. Winds also determine the large scale ocean circulation and transport, power global weather patterns, and play a key role in marine
ecosystems. Hurricanes, typhoons, and mid-latitude winter storms all contain high
wind speeds that threaten international shipping and the lives and property of people
along the coasts. Ocean surface winds change rapidly in both time and space and
satellite sampling and accuracy make these observations the most useful wind data
available for research and forecasting over the global oceans.
Surface wind speeds (at 10 m height, without directions) are routinely estimated from passive microwave radiometers (SSM/I, AMSR-E, TMI, SSMIS) on
a spatial scale of roughly 25 km. Wind speeds in the range of 0–30 m/s are simultaneously retrieved along with SST, water vapor, cloud liquid water and rain rates
using an algorithm that exploits the polarization signature of wind induced sea surface emissivity (Wentz, 1997). Radiometer winds are quite accurate under typical
ocean conditions when no rain is present, however when even a little rain exists, the
wind speeds are unusable. Validations of radiometer winds in rain-free conditions
have been performed. Comparisons with ocean buoy and weather model winds show
root-mean-square differences near (model winds) or less than 1 m/s (buoy winds)
in rain-free conditions (Mears et al., 2001; Meissner et al., 2001). Since 1996, there
have been three or more radiometers in polar orbits simultaneously, resulting in good
spatial and temporal sampling, yielding over 95% Earth ocean surface coverage in
a given day.
WindSat is a passive fully-polarimetric microwave radiometer designed to measure ocean surface vector winds. It has been found to have wind accuracies close to
that of scatterometers for winds between 6 and 20 m/s, with significant wind direction uncertainty below 6 m/s (Bettenhausen et al., 2006). WindSat vector winds have
been poor in rainy conditions until recently when a new WindSat algorithm has been
developed that improves WindSat winds even in rain (Meissner and Wentz, 2009).
The quality of these new winds is similar to QuikScat in all but very heavy rain and
very low winds. Excellent agreement (to within 0.5 m/s) is found between passive
radiometer wind speeds, polarimetric radiometer wind vectors and scatterometer
vector winds despite the different measuring methods of each instrument (Wentz