D-Matrix ocean rainfall algorithm

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.

Ferriday global rainfall algorithm

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 paper.

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.

Ferraro global rainfall algorithm

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.

Schluessel water vapor algorithm

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, pp. 2773.

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.

Goodberlet marine wind speed algorithm

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.

Weng cloud liqud water algorithm

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 J. Climate.

HIRS-12 Upper-tropospheric water vapor algorithm

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, pp. 427-438.

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.

GOES West Sea Surface Temperature algorithm

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.

Number of Observations

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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