How consistent is the satellite derived SST–LHF relationship in comparison with observed values?

Abstract

Time series data for sea surface temperature (moored buoy), wind speed, air temperature, sea level pressure, relative humidity, short wave radiation and rainfall were collected close to the Lakshadweep islands for five months from July 2000 to cover two seasons, namely summer monsoon and autumn. Day and night passes of TMI data for the same period were analysed to compare with the observed values. Daily mean values were then generated from both satellite‐derived as well as observed parameters and daily latent heat flux (LHF) values computed using the advanced COARE‐3.0 version of the model. In concurrence with earlier studies, the observed LHF–SST relationship was inverse as the SST during this season seldom fell below 27°C. On the contrary, the satellite derived LHF–SST relationship exhibited a direct correlation. It is also observed that the satellite underestimation of SST increases linearly on either side of a threshold value of 28.5°C. Although the SST over the eastern Arabian Sea was generally above 27°C, the satellite underestimation often produced SSTs less than 27°C, thereby supporting a linear relationship with LHF, as suggested by Zhang and McPhaden. Similarly for SSTs higher than 28°C, the satellite underestimation prevented a further decrease of LHF (to sustain the linear relationship) by virtue of the inverse relationship for SSTs higher than 28°C. The overestimation of SST and wind speed in the satellite scenario generates a virtual enhancement of LHF values without cooling the sea surface. The linear relationship between SST and LHF is thus nothing but a virtual display of the observed inverse SST–LHF relationship.

Acknowledgments

We thank the Director, National Institute of Oceanography, Goa and the Director, Space Application Center, Ahmedabad for the facilities and funds to set up the Autonomous Weather Station at Lakshadweep Island. The efforts of Dr Ismail Koya, Deputy Director, Science and Technology, Kavarathi, in collecting surface meteorological data are gratefully acknowledged. We also thank Dr Frank Wentz and his office ‘Remote Sensing Systems’ for providing SSMI and TMI products. This is NIO contribution number 4288.