Comparative performance of AVHRR‐based multichannel sea surface temperatures

Abstract

A brief outline of the basic concepts of cloud filtering and atmospheric attenuation corrections used in the Multi‐channel Sea Surface Temperature (MCSST) method is given. The operational MCSST procedures and products are described in detail. The comparative performance of AVHRR‐based MCSST'S is discussed via the use of the results of the JPL Satellite‐Derived Sea Surface Temperature workshops. For the four data periods there is surprisingly good correspondence in the sign and location of the major monthly mean SST anomaly features derived from MCSST's and those from a screened set of ship‐based SST's. With the partial exception of the one data period severely affected in some areas by volcanic aerosol from El Chichon eruptions, global statistical measures of the MCSST anomalies relative to the the ship data are as follows: biases, 0.3–0.4°C (MCSST lower than ship); standard deviations, 0.5–0.6°C; and cross‐correlations, +0.3 to +0.7. A refined technique in use with NOAA 9 data in 1985 has yielded consistent biases and rms differences near −0.1°C and 0.5°C, respectively.

Eddy activities of the surface layer in the western North Pacific detected by satellite altimeter and radiometer

“…The correlation function of the signal was chosen as the Gaussian shape with amplitude of 1°C considering the rms amplitude of the signal. The precision of this data set is unknown, but McClain et al (1985) compared Multi-Channel SST (though it is different from the present data set) with ship-derived SST and found rms error of 0.5-0.6 °C. Therefore, the correlation function of the error was set as the white noise with amplitude of 0.5°C.…”

Upwelling in the Taiwan Strait during the summer monsoon detected by satellite and shipboard measurements

“…Coefficients of the MCSST algorithm were calculated over a 5-month period and centered over each month by a leastsquare fit to SST buoys (Vazquez, Perry, & Kilpatrick, 1998). Global statistical measure of the MCSST data relative to drifting buoys show consistent biases (buoy minus satellite) and root mean square error of À 0.1 and 0.5-0.7 jC, respectively (McClain, Pichel, & Walton, 1985). All AVHRR SST images covering the period of July 1 to October 30, 1998 on the study area were processed and checked.…”

Determination of the bulk temperature from NOAA/AVHRR satellite data in a midlatitude lake

“…The emissivity of the water body is assumed to be 1 and scattering is neglected (Schlüssel, 1995). For a small wavelength interval a linear approximation of the Planck function can be used for determining the temperature (McClain et al, 1985;Barton, 1995):…”

“…So far, there exist several standard algorithms to derive sea surface temperature from NOAA/AVHRR data. They are based on the split-window technique that corrects for atmospheric effects using different channels in the thermal infrared wavelength range (Anding and Kauth, 1970;Deschamps and Phulpin, 1980;McMillin and Crosby, 1984;McClain et al, 1985;Yokoyama and Tanba, 1991;Barton, 1995;Schlüssel 1995Schlüssel , 1996. These algorithms have been developed on a global scale.…”