Estimation of monthly mean air‐sea temperature difference from satellite observations using genetic algorithm

Abstract

We present a new method to determine the monthly mean air sea temperature difference (ΔT = SST − Ta) from satellite observations. The satellite observed parameters viz., vertically integrated water vapour (W), sea surface temperature (SST) and wind speed (U) are used to derive ΔT. Genetic Algorithm (GA) is used to find the optimum relations between the input (W, SST, U) and output (ΔT) parameters. The input data consist of 6 years (January 1988–December 1993) of monthly averages of water vapour and wind speed from SSM/I (Special Sensor Microwave Imager), and sea surface temperature data from AVHRR (Advanced Very High Resolution Radiometer). Surface Marine Data (based on COADS observations) of SST and Ta (air temperature) are used to develop and evaluate the non‐linear empirical relationship. Largest seasonal dependent differences between ΔT derived from the new method and in‐situ data are found over the western boundary currents (such as the Kuroshio and Gulf Stream). After removal of the systematic biases, ΔT can be determined with accuracy of 0.40 ± 0.11°C.

Rapid changes in the seasonal sea level cycle along the US Gulf coast from the late 20th century

“…However, referencing ST to 700 m did not change the results and for deeper layers the profiles are too sparse. Oceanic heat advection, either horizontal or vertical, can lead to differences between T and SST and it is well known that these variables differ considerably in regions where strong currents prevail [e.g., Singh et al ., ]. Either way, examining the mechanisms behind the observed differences between T, SST, and ST in detail is beyond the scope of this study.…”

Estimation of Atmospheric Motion Vectors from Kalpana-1 Imagers

“…A very short description of the GA is given in appendix B. A number of studies have been reported using GA for the prediction of space-time variability of the sea surface temperature (Alvarez et al 2000), estimation of surface heat fluxes (Singh et al 2006), and monthly mean air-sea temperature differences (Singh et al 2005) from satellite observations. In this study, an attempt has been made to use this empirical approach to determine the height of the cloud tracers.…”

“…The GA is one of the best techniques (Szpiro 1997;Alvarez et al 2000;Singh et al 2006) to determine the optimum relationship between the independent and dependent parameters. The genetic algorithm is programmed to approximate the equation, in symbolic form, that best describes the relationship between independent and dependent parameters.…”

Effective medium approximation model of sea foam layer microwave emissivity of a vertical profile

“…From Figure 7, it is indicated that the instability (or stability) of the air-sea boundary condition ( t < 0 or t > 0) can reduce (or enhance) the brightness temperature of the sea surface that is covered by a foam layer when we consider the air-sea temperature in the EMA model. Here, we should note that, under higher wind speed, although the sea foam layer and the sea surface roughness are major factors affecting sea surface brightness temperature, the effects of air-sea temperature difference on the foam coverage and the brightness temperature have been reported (Reul and Chapron 2003;Singh, Kishtawal, and Joshi 2005). In natural ocean conditions, because the foam temperature is different from the sea and air temperatures, the temperature differences should be involved in a foam emissive model.…”