Editorial Type:
Article
Article Type:
Research Article

Combined IR–Microwave Satellite Retrieval of Temperature and Dewpoint Profiles Using Artificial Neural Networks

Robert J. Kuligowski National Environmental Satellite, Data, and Information Service, Camp Springs, Maryland

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Ana P. Barros Division of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts

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Print Publication:
01 Nov 2001
DOI:
https://doi.org/10.1175/1520-0450(2001)040<2051:CIMSRO>2.0.CO;2
Page(s):
2051–2067
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Abstract

Radiance measurements from satellites offer the opportunity to retrieve atmospheric variables at much higher spatial resolution than is presently afforded by in situ measurements (e.g., radiosondes). However, the accuracy of these retrievals is crucial to their usefulness, and the ill-posed nature of the problem precludes a straightforward solution. A number of retrieval approaches have been investigated, including empirical techniques, coupling with numerical weather prediction models, and data analysis techniques such as regression. In this paper, artificial neural networks are used to retrieve vertical temperature and dewpoint profiles from infrared and microwave brightness temperatures from a polar-orbiting satellite. This approach allows retrievals to be performed even in cloudy conditions—a limitation of infrared-only retrievals. In a direct comparison of this technique with results from the operational Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) retrievals, it was found that the neural-network temperature retrievals had larger errors than the ATOVS retrievals (though generally smaller than the first guess used in the ATOVS retrievals) but that the dewpoint retrievals showed consistent improvement over the comparable ATOVS retrievals.

Corresponding author address: Ana P. Barros, 118 Pierce Hall, 29 Oxford St., Cambridge, MA 02138. barros@deas.harvard.edu

Abstract

Radiance measurements from satellites offer the opportunity to retrieve atmospheric variables at much higher spatial resolution than is presently afforded by in situ measurements (e.g., radiosondes). However, the accuracy of these retrievals is crucial to their usefulness, and the ill-posed nature of the problem precludes a straightforward solution. A number of retrieval approaches have been investigated, including empirical techniques, coupling with numerical weather prediction models, and data analysis techniques such as regression. In this paper, artificial neural networks are used to retrieve vertical temperature and dewpoint profiles from infrared and microwave brightness temperatures from a polar-orbiting satellite. This approach allows retrievals to be performed even in cloudy conditions—a limitation of infrared-only retrievals. In a direct comparison of this technique with results from the operational Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) retrievals, it was found that the neural-network temperature retrievals had larger errors than the ATOVS retrievals (though generally smaller than the first guess used in the ATOVS retrievals) but that the dewpoint retrievals showed consistent improvement over the comparable ATOVS retrievals.

Corresponding author address: Ana P. Barros, 118 Pierce Hall, 29 Oxford St., Cambridge, MA 02138. barros@deas.harvard.edu

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Journal of Applied Meteorology and Climatology

  • Barros, A. P. and D. P. Lettenmaier. 1993. Dynamic modeling of the spatial distribution of precipitation in remote mountainous areas. Mon. Wea. Rev 121:1195–1214.

    • Search Google Scholar
    • Export Citation

  • Barros, A. P. and D. P. Lettenmaier. 1994. Introduction of a very simple evaporative cooling scheme into a dynamic model of precipitation efficiency in mountainous regions. Mon. Wea. Rev 122:2777–2783.

    • Search Google Scholar
    • Export Citation

  • Butler, C. T., R. V. Z. Meredith, and A. P. Stogryn. 1996. Retrieving atmospheric temperature parameters from DMSP SSM/T-1 data with a neural network. J. Geophys. Res 101:7075–7083.

    • Search Google Scholar
    • Export Citation

  • Caberra-Mercader, C. R. and D. H. Staelin. 1995. Passive microwave relative humidity retrievals using feedforward neural networks. IEEE Trans. Geosci. Remote Sens 33:1324–1328.

    • Search Google Scholar
    • Export Citation

  • Caudill, M. and C. Butler. 1992. Understanding Neural Networks, Vol. 1: Basic Networks. MIT Press, 309 pp.

  • Cheng, B. and D. M. Titterington. 1994. Neural networks: A review from a statistical perspective. Stat. Sci 9:2–54.

  • Chevallier, F., F. Chéruy, N. A. Scott, and A. Chédin. 1998. A neural network approach for a fast and accurate computation of a longwave radiative budget. J. Appl. Meteor 37:1385–1397.

    • Search Google Scholar
    • Export Citation

  • Fletcher, D. and E. Goss. 1993. Forecasting with neural networks: An application using bankruptcy data. Inf. Manage 24:159–167.

  • Gardner, M. W. and S. R. Dorling. 1998. Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos. Environ 32:2627–2636.

    • Search Google Scholar
    • Export Citation

  • Hassibi, B. and T. Kailath. 1995. H∞ optimal training algorithms and their relation to backpropagation. Advances in Neural Information Processing Systems, G. Tesauro et al., Eds., Vol. 7, MIT Press, 191–199.

    • Search Google Scholar
    • Export Citation

  • Hassibi, B., A. H. Sayed, and T. Kailath. 1994. LMS and backpropagation are minimax filters. Theoretical Advances In Neural Computation and Learning, V. Roychowdhury et al., Eds., Kluwer, 424–449.

    • Search Google Scholar
    • Export Citation

  • Hecht-Nielsen, R. 1989. Theory of the backpropagation neural network. Proc. Int. Joint Conf. on Neural Networks, 1989: IJCNN-89, Washington, DC, IEEE, I593–I606.

    • Search Google Scholar
    • Export Citation

  • Heckley, W. A., G. Kelly, and M. Tiedke. 1990. On the use of satellite-derived heating rates for data assimilation within the Tropics. Mon. Wea. Rev 118:1743–1757.

    • Search Google Scholar
    • Export Citation

  • Hoskins, B. J., M. E. McIntyre, and A. W. Robertson. 1985. On the use and significance of isentropic potential vorticity maps. Quart. J. Roy. Meteor. Soc 111:877–946.

    • Search Google Scholar
    • Export Citation

  • Isaacs, R. G., R. N. Hoffman, and L. D. Kaplan. 1986. Satellite remote sensing of meteorological parameters for global numerical weather prediction. Rev. Geophys 24:701–743.

    • Search Google Scholar
    • Export Citation

  • Jacobs, R. A. 1988. Increased rates of convergence through learning rate adaption. Neural Networks 1:295–307.

  • Jung, T., E. Ruprecht, and F. Wagner. 1998. Determination of cloud liquid water path over the oceans from Special Sensor Microwave/Imager (SSM/I) data using neural networks. J. Appl. Meteor 37:832–844.

    • Search Google Scholar
    • Export Citation

  • Karnin, E. D. 1990. A simple procedure for pruning back-propagation trained neural networks. IEEE. Trans. Neural Networks 1:239–242.

  • Klein, W. H. 1983. Objective specification of monthly mean surface temperature from mean 700 mb heights in winter. Mon. Wea. Rev 111:674–691.

    • Search Google Scholar
    • Export Citation

  • Krasnopolsky, V. M., L. C. Breaker, and W. H. Gemmill. 1995. A neural network as a nonlinear transfer function model for retrieving surface wind speeds from the Special Sensor Microwave Imager. J. Geophys. Res 100:11033–11045.

    • Search Google Scholar
    • Export Citation

  • Krishnamurti, T. N. and H. S. Bedi. 1996. A brief review of physical initialization. Meteor. Atmos. Phys 60:137–142.

  • Krishnamurti, T. N., J. Xue, H. S. Bedi, K. Ingles, and D. Oosterhof. 1991. Physical initialization for numerical weather prediction over the Tropics. Tellus 43AB:53–81.

    • Search Google Scholar
    • Export Citation

  • Krishnamurti, T. N., H. S. Bedi, and K. Ingles. 1993. Physical initialization using SSM/I rain rates. Tellus 45A:247–269.

  • Krishnamurti, T. N., G. D. Rohaly, and H. S. Bedi. 1994. On the improvement of precipitation forecast skill from physical initialization. Tellus 46A:598–614.

    • Search Google Scholar
    • Export Citation

  • Kuligowski, R. J. and A. P. Barros. 1998a. Experiments in short-term precipitation forecasting using artificial neural networks. Mon. Wea. Rev 126:470–482.

    • Search Google Scholar
    • Export Citation

  • Kuligowski, R. J. and A. P. Barros. 1998b. Using artificial neural networks to estimate missing rainfall data. J. Amer. Water. Resour. Assoc 34:1437–1447.

    • Search Google Scholar
    • Export Citation

  • Kuligowski, R. J. and A. P. Barros. 1999. High-resolution short-term quantitative precipitation forecasting in mountainous regions using a nested model. J. Geophys. Res 104:31553–31564.

    • Search Google Scholar
    • Export Citation

  • Lipton, A. E. 1993. Cloud shading retrieval and assimilation in a satellite–model coupled mesoscale analysis system. Mon. Wea. Rev 121:3062–3081.

    • Search Google Scholar
    • Export Citation

  • Lipton, A. E. and T. H. Vonder Haar. 1990a. Mesoscale analysis by numerical modeling coupled with sounding retrieval from satellites. Mon. Wea. Rev 118:1308–1329.

    • Search Google Scholar
    • Export Citation

  • Lipton, A. E. and T. H. Vonder Haar. 1990b. Preconvective mesoscale analysis over irregular terrain with a satellite-model coupled system. Mon. Wea. Rev 118:1330–1358.

    • Search Google Scholar
    • Export Citation

  • Lipton, A. E., G. D. Modica, S. T. Heckman, and A. J. Jackson. 1995. Satellite-model coupled analysis of convective potential in Florida with VAS water vapor and surface temperature data. Mon. Wea. Rev 123:3292–3304.

    • Search Google Scholar
    • Export Citation

  • Liu, Q. and C. J. E. Schuurmans. 1990. The correlation of tropospheric and stratospheric temperatures and its effect on the detection of climate changes. Geophys. Res. Lett 17:1085–1088.

    • Search Google Scholar
    • Export Citation

  • Menzel, W. P., F. C. Holt, T. J. Schmit, R. M. Aune, A. J. Schreiner, G. S. Wade, and D. G. Gray. 1998. Application of GOES-8/9 soundings to weather forecasting and nowcasting. Bull. Amer. Meteor. Soc 79:2059–2077.

    • Search Google Scholar
    • Export Citation

  • Oh, S-H. 1997. Improving the error backpropagation algorithm with a modified error function. IEEE Trans. Neural Networks 8:799–803.

  • Oh, S-H. and S-Y. Lee. 1999. A new error function at hidden layers for fast training of multilayer perceptrons. IEEE Trans. Neural Networks 10:960–964.

    • Search Google Scholar
    • Export Citation

  • Ponnapalli, P. V. S., K. C. Ho, and M. Thomson. 1999. A formal selection and pruning algorithm for feedforward artificial neural network optimization. IEEE Trans. Neural Networks 10:964–968.

    • Search Google Scholar
    • Export Citation

  • Porto, V. W., D. B. Fogel, and L. J. Fogel. 1995. Alternative neural network training methods. IEEE Expert 10:16–22.

  • Puri, K. and M. J. Miller. 1990. The use of satellite data in the specification of convective heating for diabatic initialization and moisture adjustment in numerical weather prediction models. Mon. Wea. Rev 118:67–93.

    • Search Google Scholar
    • Export Citation

  • Puri, K. and N. E. Davidson. 1992. The use of infrared satellite cloud imagery as proxy data for moisture and diabatic heating in data assimilation. Mon. Wea. Rev 120:2329–2341.

    • Search Google Scholar
    • Export Citation

  • Reale, A., M. Chalfant, and L. Wilson. 1999. Scientific status of NOAA Advanced-TOVS sounding products. Tech. Proc. 10th Int. TOVS Study Conf. Boulder, CO, Int. TOVS Working Group, IAMAP, 437–446.

    • Search Google Scholar
    • Export Citation

  • Ripley, B. D. 1994. Neural networks and related methods for classification. J. Roy. Stat. Soc 56:409–456.

  • Schmidlin, F. J. and A. Ivanov. 1998. Radiosonde relative humidity sensor performance: The WMO intercomparison—Sept. 1995. Preprints, 10th Symp. on Meoeorological Observations and Instrumentation, Phoenix, AZ, Amer. Meteor. Soc., 68–71.

    • Search Google Scholar
    • Export Citation

  • Smith, W. L., H. M. Woolf, and W. J. Jacob. 1970. A regression method for obtaining real-time temperature and geopotential height profiles from satellite spectrometer measurements and its application to Nimbus 3 “SIRS” observations. Mon. Wea. Rev 98:582–603.

    • Search Google Scholar
    • Export Citation

  • Stogryn, A. P., C. T. Butler, and T. J. Bartolac. 1994. Ocean surface wind retrievals from Special Sensor Microwave Imager data with neural networks. J. Geophys. Res 99:981–984.

    • Search Google Scholar
    • Export Citation

  • Turpeinen, O. M. 1990. Diabatic initialization of the Canadian Regional Finite-Element (RFE) model using satellite data. Part II: Sensitivity to humidity enhancement, latent-heating profile and rain rates. Mon. Wea. Rev 118:1396–1407.

    • Search Google Scholar
    • Export Citation

  • Turpeinen, O. M., L. Garand, R. Benoit, and M. Roch. 1990. Diabatic initialization of the Canadian Regional Finite-Element (RFE) model using satellite data. Part I: Methodology and application to a winter storm. Mon. Wea. Rev 118:1381–1395.

    • Search Google Scholar
    • Export Citation

  • van Ooyen, A. and B. Nienhuis. 1992. Improving the convergence of the back-propagation algorithm. Neural Networks 5:465–471.

  • Vogl, T. P., J. K. Mangis, A. K. Rigler, W. T. Zink, and D. L. Alkon. 1988. Accelerating the convergence of the back-propagation method. Biol. Cybern 59:257–263.

    • Search Google Scholar
    • Export Citation

  • Watrous, R. L. 1987. Learning algorithms for connectionist networks: applied gradient methods of nonlinear optimization. Proc. IEEE First Int. Conf. on Neural Networks, San Diego, CA, IEEE, 619–627.

    • Search Google Scholar
    • Export Citation

  • Wessels, L. F. A. and E. Barnard. 1992. Avoiding false local minima by proper initialization of connections. IEEE Trans. Neural Networks 3:899–905.

    • Search Google Scholar
    • Export Citation

  • Wright, B. J. and B. W. Golding. 1990. The interactive mesoscale initialization. Meteor. Mag 119:234–244.

  • Wu, X., G. R. Diak, C. M. Hayden, and J. A. Young. 1995. Short-range precipitation forecasts using assimilation of simulated satellite water vapor profiles and column cloud liquid water amounts. Mon. Wea. Rev 123:347–365.

    • Search Google Scholar
    • Export Citation
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