An approximate model for the microwave brightness temperature of the sea

Guissard, A.; Sobieski, Piotr

doi:10.1080/01431168708954802

Cited by 56 publications

(26 citation statements)

References 22 publications

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“…Rudimentary considerations were applied in order to estimate the influence of sea surface roughness on the spread of data. The two-scale wind-induced roughness calculations by Guillou et al [24], [25] (for 89 GHz and wind velocity of 7 m/s) were compared to brightness temperatures calculated for smooth water surfaces. The results indicate that the wind induced changes in the T-2 signals at 40.5° nadir angle and 7 m/s are about +11 K, at 20° about +16 K, and at 0° about +17 K. The corresponding shifts for M/I are about +25 K in the horizontal channel and -11 K in the vertical channel.…”

Section: Discussionmentioning

confidence: 99%

Comparison between cross-track and conical scanning microwave window channels near 90 GHz

Wessel

Boucher

1998

IEEE Trans. Geosci. Remote Sensing

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Section: Discussionmentioning

confidence: 99%

Comparison between cross-track and conical scanning microwave window channels near 90 GHz

Wessel

Boucher

1998

IEEE Trans. Geosci. Remote Sensing

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“…The ocean is characterized by lower emissivity values but also by higher emissivity polarization differences. Several ocean emissivity models have been developed (Rosenkranz and Staelin 1972;Wentz 1975;Guissard and Sobieski 1987;Prigent and Abba 1990;Guillou et al 1998; and English 2000, among many others) that are accurate enough for use in atmospheric applications. However, the uncertainties of these models should not be ignored.…”

Section: Introductionmentioning

confidence: 99%

Microwave land emissivity and skin temperature for AMSU‐A and ‐B assimilation over land

Karbou

Gérard

Rabier

2006

Quart J Royal Meteoro Soc

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SUMMARY AMSU-A and -B measurements are still not extensively used over land surfaces for atmospheric applications. Recent studies have shown that it should now be possible to take advantage of the information content of these instruments provided land emissivity and skin temperature estimates are improved. This paper reports on comparisons between three land-surface schemes using the Météo-France four-dimensional variational (4D-Var) assimilation system. Firstly, a monthly mean estimated land emissivity atlas using AMSU data is used. A second land-surface scheme based on direct emissivity calculations is developed to obtain dynamically emissivity values. The third approach is based on the first one with the addition of a dynamic skin temperature estimation based on one AMSU-A or AMSU-B window channel. The land-surface schemes described above have been implemented within the 4D-Var system and their results have been compared with those of the operational surface scheme (which uses emissivity models). All land schemes have been evaluated by examining the performances of the observation operator for sounding channels prior to the assimilation. With dynamically varying emissivities and/or skin temperatures or with averaged emissivities, the simulations are clearly improved compared with the operational model and many more data pass the quality-control check.

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“…An analysis at a resolution of 0.5 degree is taken for the first day of each month at 12:00UTC. From those twelve analyses, brightness temperatures and backscattering coefficient are simulated for the Jason-1/JMR configuration using the UCL radiative transfer model [1]: brightness temperatures are simulated at 18.7, 23.8 and 34 GHz and the altimeter backscattering coefficient at Ku band. Then 20% percent of this database is used as the learning database.…”

Section: A Learning and Test Databasesmentioning

confidence: 99%

Comparison of retrieval algorithms for the wet tropospheric path delay

Thao

Obligis

Picard

et al. 2014

2014 13th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad)

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This paper provides a comparative analysis of statistical algorithms for the retrieval of the wet tropospheric correction from microwave radiometers in the context of altimetry missions. The algorithms are based on the algorithms used in Envisat and Jason-1 missions. The objective of this comparison is two-folds: 1)To find which regression method is better suited for the retrieval between the neural network algorithm and the log linear regression. 2)To tackle the problem of variable selection, i.e. to find the best set of variables to include as inputs in order to reduce the retrieval error. In particular, we want to determine whether the lack of a radiometer third channel at 18GHz can be compensated by the altimeter backscattering coefficient.Several configurations of algorithms, including those used in the operational processing of altimetry missions such as JASON-1 or ENVISAT, are built and compared on the same learning and test databases to determine which retrieval strategy is more appropriate. The database is composed of atmospheric and oceanographic conditions taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses and the brightness temperatures are simulated using a radiative transfer model. The importance of each input for the different algorithms is analyzed and the performances of the different algorithms are assessed in terms of error (bias and standard deviation) but also in terms of geographical distribution of the errors and correlation with other environmental variables. The results are then validated on Jason-2 radiometer measurements.Our results show that, in terms of variable selection, better results were obtained when the brightness temperature at 18 GHz was used instead of the backscattering coefficient. Moreover, better estimations of the wet tropospheric path delay were obtained with neural networks.Keywords-wet tropospheric path delay; water vapor; retrieval algorithms; artificial neural networks.

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An approximate model for the microwave brightness temperature of the sea

Cited by 56 publications

References 22 publications

Comparison between cross-track and conical scanning microwave window channels near 90 GHz

Comparison between cross-track and conical scanning microwave window channels near 90 GHz

Microwave land emissivity and skin temperature for AMSU‐A and ‐B assimilation over land

Comparison of retrieval algorithms for the wet tropospheric path delay

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