A Global All-weather Sea Surface Temperature Data Product from AMSR2

Sea surface temperature (SST) is fundamental to the understanding of air-sea interaction and the role it plays in climate variability. It is pivotal to the energy and moisture exchange between oceans and the atmosphere, and it influences the development of extreme wind events such as tropical cyclones (TCs) [1][2].

The era of satellite-based SST measurements commenced in the 1970s with the advent of infrared (IR) radiometers on board geostationary and polar-orbiting platforms operated by the National Oceanic and Atmospheric Administration (NOAA) [3]. While IR sensors offer measurements with relatively high spatial resolution, their inability to penetrate clouds and aerosols limits the utility of their measurements. To address this limitation, microwave radiometers emerged as a powerful tool, providing researchers with a clear view of the ocean surface, except for rain. This enabled the production of reliable time series of sea surface parameters and offered unprecedented data on both short- and long-term temporal scales with near-global coverage to be used in various climate studies [e.g. 4, 5].

Under rain-free conditions, microwave emission from the ocean surface can be exploited to infer accurate surface parameters (e.g. SST) from the measured microwave brightness temperatures (Tbs). In contrast, the retrieval process is much more challenging in the presence of precipitation. The existence of rain within the microwave radiometer instantaneous field of view (IFOV) augments the values of Tbs and results in erroneous retrievals that are usually flagged. Thus, SST retrievals from standard retrieval algorithms [6] suffer from considerable gaps for rainy scenes especially the areas of tropical convection such as TCs.

In this study, a new global all-weather SST retrieval algorithm, hereafter referred to as GAWSST, will be presented in attempt to mitigate rain contamination of microwave radiometer measurements for the purpose of retrieving SST. GAWSST is statistical based and relies on empirical quantities that are immune to rain contamination while maintain high sensitivity to SST. Meissner and Wentz [7] have shown that the linear combination of the 37-GHz H- and V-pol Tbs significantly reduces the effects of the atmospheric absorption and scattering. Later, this method was refined by Soisuvarn et al. [8] using Tbs at 10.65, 18.7, and 36.7 GHz to show that a linear combination of Tbs, expressed as “” (or AV-H for short), reduces the impact of the atmospheric upwelling and reflected downwelling components of Tbs, thereby making the AV-H measurements almost independent of atmospheric transmittance components, such as water vapor and CLW.

In this study, we will further extend the concept of AV-H , and apply it to Tbs obtained from the Advanced Microwave Scanning Radiometer-2 (AMSR2), to retrieve global all-weather SSTs. Calibrated AMSR2 Tbs [9] from 6, 10, and 18 GHz horizontal (H-pol) and vertical (V-pol) channels will be linearly combined using the AV-H formulation to generate empirical quantities for the corresponding channels. These empirical quantities will then be used as the independent variables for a multi-stage linear regression to retrieve SSTs that are less susceptible to rain.

References:

[1] M. DeMaria, and J. Kaplan, “Sea surface temperature and the maximum intensity of Atlantic tropical cyclones,” J. Climate, vol. 7, pp. 1324 – 13334, 1994

[2] K. A. Emanuel, “Thermodynamic Control of Hurricane Intensity,” Nature, vol. 401, pp. 665-669, 1999.

[3] E. P. McClain, W. G. Pichel, and C. C. Walton, “Comparative Performance of AVHRR-based Multichannel Sea Surface Temperatures,” J. Geophys. Res., vol. 90, pp. 11587-11601, 1985.

[4] N. C. Gordy, “Remote sensing of atmospheric water content from satellites using microwave radiometry,” IEEE Trans. Antennas Propagat., vol. AP-24, pp. 155–162, 1976.

[5] T. T. Wilheit and A. T. C. Chang, “An algorithm for retrieval of ocean surface and atmospheric parameters from the observations of the scanning multichannel microwave radiometer,” Radio Sci., vol. 15, pp.525–544, 1980.

[6] S. O. Alsweiss, Z. Jelenak, and P. S. Chang, “Remote sensing of sea surface temperature using AMSR-2 measurements,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 10, no. 9, pp. 3948–3954, Sep. 2017.

[7] T. Meissner and F. Wentz, “An updated analysis of the ocean surface wind direction signal in passive microwave brightness temperature,” IEEE Trans. Geosci. Remote Sens., vol. 40, no. 6, pp. 1230–1240, Jun. 2002.

[8] S. Soisuvarn, Z. Jelenak, and W. L. Jones, “An ocean surface wind vector model function for a spaceborne microwave radiometer,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 10, pp. 3119–3130, Oct. 2007.

[9] S. O. Alsweiss, Z. Jelenak, P. S. Chang, J. D. Park, and P. Meyers, “Intercalibration results of the advanced microwave scanning radiometer-2 over ocean,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 9, pp. 4230–4238, Sep. 2015.