A summary of results from the first NIMBUS 7 SMMR observations

Abstract

Over open oceans these calculations have provided values for sea surface temperatures, near-surface winds, atmospheric water vapor in a column, and rainfall rates. In polar regions, sea ice concentration, multiyear ice fraction, and radiating temperatures have been obtained. Finally, the extent and water equivalence of snow cover over terrain have been calculated. These parameters have been compared with in situ measurements of the same geophysical parameters, where available, and the results of these comparisons are described. The self-consistency of the global displays of all the parameters is discussed along with the plans for archiving them for subsequent research purposes. A description of the SMMR calibration and data processing scheme is also given.

Passive microwave observations of inundation area and the area/stage relation in the Amazon River floodplain

“…Satellite observations of 37 GHz emission are available for December 1978 to August 1987 from the SMMR instrument operated on board the Nimbus-7 satellite (Gloersen et al 1984, Fu et al 1988. Similar observations are available from the Special Sensor Microwave/Imager (SSM/I) instruments, which began operation in July 1987 and continue operation now as part of the Defense Meteorological Satellite Program ( Hollinger et al 1990).…”

Seasat—A 25-year legacy of success

“…A summary of the early results from the Seasat SMMR is provided by Lipes et al (1979), and from the Nimbus-7 SMMR, it was provided by Gloersen et al (1984). A compendium of later papers, and references to other research incorporating some key contributions of the SMMR to global change studies, was provided in the atlas of satellite observations edited by Gurney et al (1993).…”

In-Situ Measurements for Validation of Microwave Data: Problems, Accuracies

“…The effects at the three lowest frequencies of the SSM/I are equivalent to those at the three highest frequencies of the SMMR's. Algorithms based on physical considerations as well as algorithms based on multiple regression equations have been used to solve for a desired parameter, while eliminating influences from others (e.g., Staelin, et al, 1976;Wilheit, et aI., 1980;Bernstein and Morris, 1983;Pandey, 1983;Wentz, 1983;Gloersen, et al, 1984;Spencer, 1986;Wentz, et al, 1986;Hollinger, et al, 1987;Olson, 1987).…”