• Alishouse, J. C., Snyder S. A. , Vongsathorn J. , and Ferraro R. R. , 1990: Determination of oceanic total precipitable water from the SSM/I. IEEE Trans. Geosci. Remote Sens., 28 , 811816.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bishop, C. M., 1995: Neural Networks for Pattern Recognition. Oxford University Press, 481 pp.

  • Ferreira, F., and Amayenc P. , 1999: Impact of adjusting rain relations of rain profiling estimates from the TRMM precipitation. Preprints, 29th Conf. on Radar Meteorology, Montreal, Quebec, Canada, Amer. Meteor. Soc., 643–646.

    • Search Google Scholar
    • Export Citation
  • Gérard, E., and Eymard L. , 1998: Remote sensing of integrated cloud liquid water: Development of algorithms and quality control. Radio Sci., 33 , 433447.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guillou, C., English S. J. , Prigent C. , and Jones D. C. , 1996: Passive microwave airborne measurements of the sea surface response at 89 and 157 GHz. J. Geophys. Res., 101 ((C2),) 37753788.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hollinger, J. P., Pierce J. L. , and Poe G. A. , 1990: SSM/I instrument evaluation. IEEE Trans. Geosci. Remote Sens., 28 , 781789.

  • Hou, A. Y., Ledvina D. V. , da Silva A. M. , Zhang S. Q. , Joiner J. , Atlas R. M. , Huffman G. J. , and Kummerow C. D. , 2000: Assimilation of SSM/I-derived surface rainfall and total precipitable water for improving the GEOS analysis for climate studies. Mon. Wea. Rev., 128 , 509537.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jacobs, R. A., Jordan M. I. , Nowlan S. J. , and Hinton G. E. , 1991: Adaptive mixtures of local experts. Neural Comput., 3 , 7987.

  • Jordan, M. I., and Jacobs R. A. , 1995: Hierarchical mixtures of experts and EM algorithm. Neural Comput., 6 , 181214.

  • Jordan, M. I., and Xu L. , 1995: Convergence results for the Em approach to mixtures of experts, architectures. Neural Networks, 8 , 14091431.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jung, T., Ruprecht E. , and Wagner F. , 1998: Determination of cloud liquid water path over the oceans from Special Sensor Microwave/Imager (SSM/I) data using neural networks. J. Appl. Meteor., 37 , 832844.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Karstens, U., Simmer C. , and Ruprecht E. , 1994: Remote sensing of cloud liquid water. Meteor. Atmos. Phys., 54 , 157171.

  • Kummerow, C., Olson B. S. , and Giglio L. , 1996: A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors. IEEE Trans. Geosci. Remote Sens., 34 , 12131232.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C., Barnes W. , Kozu T. , Shiue J. , and Simpson J. , 1998: The Tropical Rainfall Measuring Mission (TRMM) sensor package. J. Atmos. Oceanic Technol., 15 , 809817.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C., and Coauthors. 2000: The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. J. Appl. Meteor., 39 , 19651982.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liebe, H. J., Hufford G. A. , and Cotton M. G. , 1993: Propagation modeling of moist air and suspended water/ice particles below 1000 GHz. Proc. AGARD 52d Specialists' Meeting of Panel on Electromagnetic Wave Propagation, Palma de Mallorca, Spain, AGARD, 3-1–3-10.

    • Search Google Scholar
    • Export Citation
  • Marécal, V., and Mahfouf J. F. , 2000: Variational retrieval of temperature and humidity profiles from TRMM precipitation data. Mon. Wea. Rev., 128 , 38533866.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marécal, V., Gérard E. , Mahfouf J. F. , and Bauer P. , 2001: The comparative impact of the assimilation of SSM/I and TMI brightness temperatures in the ECMWF 4D-Var system. Quart. J. Roy. Meteor. Soc., 127 , 573. 1123–1143.

    • Search Google Scholar
    • Export Citation
  • Moreau, E., Mallet C. , and Klapisz C. , 1999: Effects of aspherical ice and liquid hydrometeors on microwave brightness temperatures. Microwave Radiometry and Remote Sensing of the Earth's Surface and Atmosphere, P. Pampaloni and S. Paloscia, Eds., VSP, 291–298.

    • Search Google Scholar
    • Export Citation
  • Olson, W. S., Kummerow C. D. , Heymsfield G. M. , and Giglio L. , 1996: A method for combined passive-active microwave retrievals of clouds and precipitation profiles. J. Appl. Meteor., 35 , 17631789.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prigent, C., Sand A. , Klapisz C. , and Lemaitre Y. , 1994: Physical retrieval of liquid water contents in a North Atlantic cyclone using SSM/I data. Quart. J. Roy. Meteor. Soc., 120 , 11791207.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rumelhart, D. E., Hinton G. E. , and Williams R. J. , 1986: Parallel Distributed Processing. Vol. 1. MIT Press, 547 pp.

  • Thiria, S., Mejia C. , Badran F. , and Crepon M. , 1993: A neural network for modeling nonlinear transfer functions: Application for wind retrieval from spaceborne scatterometer data. J. Geophys. Res., 98 , 2282722841.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tiedtke, M., 1993: Representation of clouds in large-scale models. Mon. Wea. Rev., 121 , 30403061.

  • Ulaby, F. T., Moore R. K. , and Fung A. K. , 1981: Fundamentals and Radiometry. Vol. 1, Microwave Remote Sensing: Active and Passive, Artech House, 456 pp.

    • Search Google Scholar
    • Export Citation
  • Viltard, N., Kummerow C. , Olson W. S. , and Hong Y. , 2000: Combined use of the radar and the radiometer of TRMM to estimate the influence of drop size distribution on the rain retrieval. J. Appl. Meteor., 39 , 21032114.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weigend, A. S., Mangeas M. , and Srivastava A. N. , 1995: Nonlinear gated experts for time series: Discovering regimes and avoiding overfitting. Int. J. Neural Syst., 6 , 373399.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Atmospheric Liquid Water Retrieval Using a Gated Experts Neural Network

E. MoreauLaboratoire d'Océanographie Dynamique et de Climatologie, Paris, France. Centre d'Etude des Environnements Terrestre et Planetaires, Paris, France

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C. MalletCentre d'Etude des Environnements Terrestre et Planetaires, Paris, France

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S. ThiriaLaboratoire d'Océanographie Dynamique et de Climatologie, Paris, France

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B. MabbouxConservatoire National des Arts et Métiers, Paris, France

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F. BadranLaboratoire d'Océanographie Dynamique et de Climatologie, Paris, France. Conservatoire National des Arts et Métiers, Paris, France

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C. KlapiszCentre d'Etude des Environnements Terrestre et Planetaires, Paris, France

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Abstract

Gated experts (GE) neural networks have been developed in order to retrieve atmospheric liquid water content over ocean from radiometer data. Gated experts neural networks are statistical models, which can model any general class of function. This paper focuses on the case where the complex transfer functions can be split on different simpler functions in order to improve the accuracy. Two atmospheric quantities are considered: the integrated cloud liquid water (iclw) and the surface rain rate (RR). In the case of iclw, the GE neural network finds two modes, splitting the problem into low and high iclw values. The physical meaning of those modes is discussed. A comparison with a standard regression algorithm and a multilayer perceptron neural network is done on simulated data and an “indirect comparison” is done using Special Sensor Microwave Imager (SSM/I) data. In the case of RR, the focus is on the ability of GE neural networks to perform a classification between rainy and nonrainy situations. Tropical Rainfall Measuring Mission (TRMM) data are used for rain-rate validation: rain-rate retrieval from the GE algorithm applied to actual TRMM Microwave Imager (TMI) measurements are compared with collocated precipitation radar (PR) rain rate.

Corresponding author address: Dr. E. Moreau, CETP, 10-12 av. de l'Europe, 78140 Vélizy, France. Email: emmanuel.moreau@cetp.ipsl.fr

Abstract

Gated experts (GE) neural networks have been developed in order to retrieve atmospheric liquid water content over ocean from radiometer data. Gated experts neural networks are statistical models, which can model any general class of function. This paper focuses on the case where the complex transfer functions can be split on different simpler functions in order to improve the accuracy. Two atmospheric quantities are considered: the integrated cloud liquid water (iclw) and the surface rain rate (RR). In the case of iclw, the GE neural network finds two modes, splitting the problem into low and high iclw values. The physical meaning of those modes is discussed. A comparison with a standard regression algorithm and a multilayer perceptron neural network is done on simulated data and an “indirect comparison” is done using Special Sensor Microwave Imager (SSM/I) data. In the case of RR, the focus is on the ability of GE neural networks to perform a classification between rainy and nonrainy situations. Tropical Rainfall Measuring Mission (TRMM) data are used for rain-rate validation: rain-rate retrieval from the GE algorithm applied to actual TRMM Microwave Imager (TMI) measurements are compared with collocated precipitation radar (PR) rain rate.

Corresponding author address: Dr. E. Moreau, CETP, 10-12 av. de l'Europe, 78140 Vélizy, France. Email: emmanuel.moreau@cetp.ipsl.fr

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