Editorial Type:
Article
Article Type:
Research Article

Estimating Longwave Net Radiation at Sea Surface from the Special Sensor Microwave/Imager (SSM/I)

Quanhua Liu Institute for Marine Sciences, Kiel, Germany

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Clemens Simmer Institute for Marine Sciences, Kiel, Germany

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Eberhard Ruprecht Institute for Marine Sciences, Kiel, Germany

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Print Publication:
01 Jul 1997
DOI:
https://doi.org/10.1175/1520-0450(1997)036<0919:ELNRAS>2.0.CO;2
Page(s):
919–930
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Abstract

A neural network is used to calculate the longwave net radiation (Lnet) at the sea surface from measurements of the Special Sensor Microwave/Imager (SSM/I). The neural network applied in this study is able to account largely for the nonlinearity between Lnet and the satellite-measured brightness temperatures (TB). The algorithm can be applied for instantaneous measurements over oceanic regions with the area extent of satellite passive microwave observations (30–60 km in diameter). Comparing with a linear regression method the neural network reduces the standard error for Lnet from 17 to 5 W m−2 when applied to model results. For clear-sky cases, a good agreement with an error of less than 5 W m−2 for Lnet between calculations from SSM/I observations and pyrgeometer measurements on the German research vessel Poseidon during the International Cirrus Experiment (ICE) 1989 is obtained. For cloudy cases, the comparison is problematic due to the inhomogenities of clouds and the low and different spatial resolutions of the SSM/I data. Global monthly mean values of Lnet for October 1989 are computed and compared to other sources. Differences are observed among the climatological values from previous studies by H.-J. Isemer and L. Hasse, the climatological values from R. Lindau and L. Hasse, the values of W. L. Darnell et al., and results from this study. Some structures of Lnet are similar for results from W. L. Darnell et al. and the present authors. The differences between both results are generally less than 15 W m−2. Over the North Atlantic Ocean the authors found a poleward increase for Lnet, which is contrary to the results of H.-J. Isemer and L. Hasse.

Corresponding author address: Dr. Quanhua Liu, Institute for Marine Sciences, Düsternbrooker Weg 20, 24105 Kiel, Germany.

Abstract

A neural network is used to calculate the longwave net radiation (Lnet) at the sea surface from measurements of the Special Sensor Microwave/Imager (SSM/I). The neural network applied in this study is able to account largely for the nonlinearity between Lnet and the satellite-measured brightness temperatures (TB). The algorithm can be applied for instantaneous measurements over oceanic regions with the area extent of satellite passive microwave observations (30–60 km in diameter). Comparing with a linear regression method the neural network reduces the standard error for Lnet from 17 to 5 W m−2 when applied to model results. For clear-sky cases, a good agreement with an error of less than 5 W m−2 for Lnet between calculations from SSM/I observations and pyrgeometer measurements on the German research vessel Poseidon during the International Cirrus Experiment (ICE) 1989 is obtained. For cloudy cases, the comparison is problematic due to the inhomogenities of clouds and the low and different spatial resolutions of the SSM/I data. Global monthly mean values of Lnet for October 1989 are computed and compared to other sources. Differences are observed among the climatological values from previous studies by H.-J. Isemer and L. Hasse, the climatological values from R. Lindau and L. Hasse, the values of W. L. Darnell et al., and results from this study. Some structures of Lnet are similar for results from W. L. Darnell et al. and the present authors. The differences between both results are generally less than 15 W m−2. Over the North Atlantic Ocean the authors found a poleward increase for Lnet, which is contrary to the results of H.-J. Isemer and L. Hasse.

Corresponding author address: Dr. Quanhua Liu, Institute for Marine Sciences, Düsternbrooker Weg 20, 24105 Kiel, Germany.

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Journal of Applied Meteorology and Climatology

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