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At least 253 records · Page 14

Climatic change by cloudiness linked to the spatial variability of sea surface temperatures

An active role in modifying the earth's climate is suggested for low cloudiness over the circumarctic oceans. Such cloudiness, linked to the spatial differences in ocean surface temperatures, was studied. The temporal variations from year to year of ocean temperature patterns can be pronounced and therefore, the low cloudiness over this region should also show strong temporal variations, affecting the albedo of the earth and therefore the climate. Photographs are included.

Otterman, J.↗

Comments on Navy/NRL requirements for sea surface temperature and surface wind measurements on Seasat-A

SEASAT instrumentation payload requirements to provide satellite data for the Navy fleet operational fog prediction program include: (1) some form of C-band microwave radiometer capability; (2) a scanning antenna with a 40-km Instanteneous Field of View (IFOV) for the C-band channel; (3) a narrow band and high resolution IR scanning radiometer for cloud free areas; and (4) a capability for measuring surface winds of 3 to 50 m/sec at + or - 10% accuracy and 50 to 100 km spatial resolution.

Ruskin, R. E.↗

Sea surface temperature of the coastal zones of France

The results of an investigation to map the various thermal gradients in the coastal zones of France are presented. Paricular emphasis is given to the natural phenomena and man made thermal effluents. It is shown that a close correlation exist between wind speed direction and the offshore width of the effluent.

Deschamps, P. Y.↗

Some effects of topography, soil moisture, and sea-surface temperature on continental precipitation as computed with the GISS coarse mesh climate model

The effects of terrain elevation, soil moisture, and zonal variations in sea/surface temperature on the mean daily precipitation rates over Australia, Africa, and South America in January were evaluated. It is suggested that evaporation of soil moisture may either increase or decrease the model generated precipitation, depending on the surface albedo. It was found that a flat, dry continent model best simulates the January rainfall over Australia and South America, while over Africa the simulation is improved by the inclusion of surface physics, specifically soil moisture and albedo variations.

Spar, J.↗

Infrared remote sensing of sea surface temperature

The surface temperature TS of the ocean and solid earth can be derived from the radiance data measured in the 3.7 micron transparent region between 2700 and 2500 per cm as well as from the 11 micron water vapor continuum between 960 and 775 per cm. The effects on the accuracy of the recovered values of TS of surface emissivity, reflection of solar radiation and variations in the concentration of water vapor in the atmosphere are different in the two bands. In this paper, the accuracy of the surface temperature derived from each of these two transparent spectral regions is discussed. The possibility of determining the difference between the air temperature at the surface and the true skin surface temperature is also considered.

Chahine, M. T.↗

Sea surface temperatures from VAS MSI data

The results of the SST intercomparison workshop series are the first examination of monthly mean SSTs derived from MSI data provided by the VAS instrument on the GOES series satellites. The most significant change in deriving the SST's was the use of the three window channel algorithm in the processing of the July data as opposed to the use of only the two window channel algorithm for the March data. In addition, further satellite/buoy matches indicated that the triple window channel algorithm showed a smaller standard deviation than the two window channel algorithm and was less sensitive to the effects of volcanic aerosol contamination and low level inversion conditions. This is due to the smaller brightness temperature attenuation by aerosols and water vapor at 3.9 microns than at 11.0 and 12.6 microns. Thus, the decision was made to use the best product (i.e., the three window channel aglorithm) for processing the July data.

Bates, J. J.↗