This algorithm is a modified version of the original prelaunch ocean rainfall
algorithm developed by Hughes called the D-Matrix algorithm. The original
climate codes for various seasons and latitudes have been replaced with a
weighting scheme based on empirical data. Details on the algorithm and its
implementation are given in the following references.
Hollinger, J., R. Lo, and G. Poe, 1987: Special Sensor Microwave/Imager User's
Guide, Naval Research Laboratory, Washington D.C.
Berg, W. and R. Chase, 1992: Determination of mean rainfall from the special
sensor microwave/imager (SSM/I) using a mixed lognormal distribution, J. Atm.
Oceanic Tech., Vol 9, pp. 129-141.
This algorithm is based on a fairly simple combination of the SSM/I channels
utilizing all four SSM/I frequences as well as dual polarization information.
Different algorithms are utilized over land and ocean with the ocean algorithm
using both liquid water emission and ice scattering information while the
land algorithm uses a simple ice scattering retrieval technique. The algorithm
was developed by James Ferriday and is discussed in detail in the following
Ferriday, J. G. and S. K. Avery, 1994: Passive microwave remote sensing of
rainfall with SSM/I: Algorithm development and implementation, J. Appl.
Meteor., Vol 33, pp. 1587-1596.
This algorithm is less sensitive to liquid water and more sensitive to ice
particles aloft. It is also based on a simple combination of the SSM/I
channels utilizing all four SSM/I frequences as well as dual polarization
information. Different algorithms are utilized over land and ocean and a
number of tests are performed to screen out sea ice over the ocean, and snow
cover, desert, and semi-arid signatures over land which produce a scattering
signal similar to precipitation. The algorithm was developed by Ralph Ferraro
and Gerald Marks and is discussed in detail in the following paper.
Ferraro, R.R. and G.F. Marks, 1995: The development of SSM/I rain-rate retrieval
algorithms using ground-based radar measurements, J. Atmos. Oceanici Technol.,
Vol 12, pp. 755.
Estimates of the total column integrated water vapor amount are estimated using
information from the 22.235 GHz SSM/I channel, which is centered on a weak water
vapor line. Integrated water vapor is the simplest and most accurate product
to retrieve from the SSM/I and is therefore very useful for quantitative
comparisons with model results or other data sets.
Schluessel, P., and W. J. Emery, 1990: Atmospheric water vapour over oceans
from SSM/I measurements, Int. J. of Remote Sensing, Vol 11, pp. 753.
Schulz, J., P. Schluessel, and H. Grassl, 1993: Water vapour in the atmospheric
boundary layer over oceans from SSM/I measurements, Int. J. Rem. Sens., Vol 14,
Bauer, P. and P. Schluessel, 1993: Rainfall, total water, ice water, and water
vapor over sea from polarized microwave simulations and special sensor
microwave/imager data, J. Geophys. Res., Vol 98, pp. 20,737.
This is a revised version of the original D-Matrix SSM/I marine surface wind
speed algorithm. The accuracy requirement for the algorithm is to obtain
surface wind speed values within +/-2 m/sec for wind speeds within the range
from 3 to 25 m/sec. The wind speed values are converted to a reference level
of 19.5 meters above the ocean surface. Note that the surface wind speed
cannot be retrieved when rainfall is present.
Goodberlet, M. A., C. T. Swift, and J. C. Wilkerson, 1989: Remote sensing of
surface winds with the special sensor microwave/imager, J. Geophys. Res.,
Vol 94, pp. 14,547-14,555.
Goodberlet, M. A., C. T. Swift, and J. C. Wilkerson, 1990: Ocean surface wind
speed measurements of the special sensor microwave/imager (SSM/I), IEEE Trans.
Geoscience Rem. Sens., Vol 28, pp. 823-828.
This is an updated version of the operational cloud liquid water path
retrieval algorithm. Because of significant improvements in this algorithm
from the original, cloud liquid water path estimates prior to 1995 made
with that algorithm have been deleted.
Weng, F. and N. C. Grody, 1994: Retrieval of cloud liquid water using the
special sensor microwave imager, J. Geophys. Res., Vol 99, pp. 25,535.
Weng, F., N. C. Grody, R. R. Ferraro, A. Basist, and D. Forsyth, 1996: Cloud
liquid water climatology from the special sensor microwave imager, submitted to
Anomalies of upper tropospheric water vapor produced from HIRS channel 12
brightness temperatures have been produced for a 16 year period. The
resulting anomalies are from cloud-cleared scenes. In regions with a moist
upper troposphere the weighting function for this channel peaks around
200-300 mb, while for drying regions the peak moves down to around 300-400
mb. Details of the retrieval and discussion of the 16 year climatology are
given in the following publications.
Bates, J. J., X. Wu, and D. L. Jackson, 1996: Interannual variability of
upper-tropospheric water vapor band brightness temperature, J. Climate, Vol 9,
Wu, X., J. J. Bates, and S. J. S. Khalsa, 1993: A climatology of the water
vapor band brightness temperatures from NOAA operational satellites, J. Climate,
Vol 6, pp. 1282-1300.
The SST values are computed using a split-window multi-channel sea surface
temperature (MCSST) equation with coefficients for GOES-9 derived at CDC.
The coefficients were empirically derived using cloud-free satellite
radiances matched to buoy SST measurements obtained in 1997 and 1998.
The algorithm was derived for use both during the day and night. The
algorithm is currently being revised for proper use with the GOES-10.
When the revisions are complete, we will provide the new algorithm here.
The number of observations images indicate the total number of pixel
observations included within the 0.5 by 0.5 degree lat/lon bin. The individual
pixel observations are approximately 25 km apart so for a bin at the equator
there are between 5 and 10 pixels with each bin for a given overpass. The
total number of observations is then a function of the number of pixels within
a 0.5 by 0.5 degree bin, the number of overpasses per observation period by a
given satellite, and the number of satellites. Some regions such as coastlines
and areas with sea ice have fewer observations because those observations are
masked out and not counted in the resulting satellite estimate.
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Last modified: Mon Aug 26 11:01:45 1996