Analysis of the Relationship between Banded Orographic Convection and Atmospheric Properties Using Factorial Discriminant Analysis and Neural Networks

A. Godart Laboratoire d’étude des Transferts en Hydrologie et Environnement, Université de Grenoble (CNRS, UJF, IRD, INPG), Grenoble, France

Search for other papers by A. Godart in
Current site
Google Scholar
PubMed
Close
,
E. Leblois Cemagref, UR Hydrologie–Hydraulique, Lyon, France

Search for other papers by E. Leblois in
Current site
Google Scholar
PubMed
Close
,
S. Anquetin Laboratoire d’étude des Transferts en Hydrologie et Environnement, Université de Grenoble (CNRS, UJF, IRD, INPG), Grenoble, France

Search for other papers by S. Anquetin in
Current site
Google Scholar
PubMed
Close
, and
N. Freychet Laboratoire des Ecoulements Géophysiques et Industriels, Université de Grenoble (UJF, CNRS, INPG), Grenoble, France

Search for other papers by N. Freychet in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The relationship between banded orographic convection and atmospheric properties is investigated for a region in the south of France where the associated rainfall events are thought to represent a significant portion of the hydrologic input. The purpose is to develop a method capable of producing an extensive database of banded orographic convection rainfall events from atmospheric sounding data for this region where insufficient rain gauge data and little or no suitable radar or satellite data are available. Two statistical methods—discriminant factorial analysis (DFA) and neural networks (NNs)—are used to determine 16 so-called elaborated nonlinear variables that best identify rainfall events related to banded orographic convection from atmospheric soundings. The approach takes rainfall information into account indirectly because it “learns” from the results of a previous study that explored meteorological and available rainfall databases, even if incomplete. The new variables include wind shear, low-level moisture fluxes, and gradients of the potential temperature in the lower layers of the atmosphere, and they were used to create an extensive database of banded orographic convection events from the archive of atmospheric soundings. Results of numerical simulations using the nonhydrostatic mesoscale (Méso-NH) meteorological model validate this approach and offer interesting perspectives for the understanding of the physical processes associated with banded orographic convection. DFA proves to be useful to determine the most discriminant factors with a physical meaning. Neural networks provide better results, but they do not allow for physical interpretation. The best solution is therefore to use the two methods together.

Corresponding author address: Sandrine Anquetin, LTHE, BP 53 X, 38041 Grenoble CEDEX 09, France. Email: sandrine.anquetin@hmg.inpg.fr

Abstract

The relationship between banded orographic convection and atmospheric properties is investigated for a region in the south of France where the associated rainfall events are thought to represent a significant portion of the hydrologic input. The purpose is to develop a method capable of producing an extensive database of banded orographic convection rainfall events from atmospheric sounding data for this region where insufficient rain gauge data and little or no suitable radar or satellite data are available. Two statistical methods—discriminant factorial analysis (DFA) and neural networks (NNs)—are used to determine 16 so-called elaborated nonlinear variables that best identify rainfall events related to banded orographic convection from atmospheric soundings. The approach takes rainfall information into account indirectly because it “learns” from the results of a previous study that explored meteorological and available rainfall databases, even if incomplete. The new variables include wind shear, low-level moisture fluxes, and gradients of the potential temperature in the lower layers of the atmosphere, and they were used to create an extensive database of banded orographic convection events from the archive of atmospheric soundings. Results of numerical simulations using the nonhydrostatic mesoscale (Méso-NH) meteorological model validate this approach and offer interesting perspectives for the understanding of the physical processes associated with banded orographic convection. DFA proves to be useful to determine the most discriminant factors with a physical meaning. Neural networks provide better results, but they do not allow for physical interpretation. The best solution is therefore to use the two methods together.

Corresponding author address: Sandrine Anquetin, LTHE, BP 53 X, 38041 Grenoble CEDEX 09, France. Email: sandrine.anquetin@hmg.inpg.fr

Save
  • Amari, S., N. Murata, K-R. Muller, M. Finke, and H. H. Yang, 1997: Asymptotic statistical theory of overtraining and cross-validation. IEEE Trans. Neural Networks, 8 , 985996.

    • Search Google Scholar
    • Export Citation
  • Andrieu, H., and J-D. Creutin, 1995: Identification of vertical profiles of radar reflectivity for hydrological applications using an inverse method. Part 1: Formulation. J. Appl. Meteor., 34 , 225239.

    • Search Google Scholar
    • Export Citation
  • Anquetin, S., F. Minsicloux, J-D. Creutin, and S. Cosma, 2003: Numerical simulation of orographic rainbands. J. Geophys. Res., 108 , 8386. doi:10.1029/2002JD001593.

    • Search Google Scholar
    • Export Citation
  • Anquetin, S., and Coauthors, cited. 2008: HyMeX: Shallow orographic convection contribution to the water resources in Mediterranean: Proposition of an observation device within the framework of HyMEx. [Available online at http://ltheln21.hmg.inpg.fr/PagePerso/anquetin/Hymex.html].

    • Search Google Scholar
    • Export Citation
  • Bankert, R., 1994: Cloud classification of AVHRR imagery in maritime regions using a probabilistic neural network. J. Appl. Meteor., 33 , 909918.

    • Search Google Scholar
    • Export Citation
  • Banta, R. M., 1990: The role of mountain flows in making clouds. Atmospheric Processes over Complex Terrain, Meteor. Monogr., No. 45, Amer. Meteor. Soc., 173–228.

    • Search Google Scholar
    • Export Citation
  • Barros, A. P., and D. P. Lettenmaier, 1994: Dynamic modeling of orographically induced precipitation. Rev. Geophys., 32 , 265284.

  • Bennani, Y., 2006: Apprentissage Connexionniste. Hermès, 361 pp.

  • Bishop, C., 1995: Neural Networks for Pattern Recognition. Clarendon Press, 482 pp.

  • Bois, P., C. Obled, M-F. Saintignon, and H. Mailloux, 1997: Atlas Expérimental des Risques de Pluies Intenses: Cévennes-Vivarais. CNRS, 24 pp.

    • Search Google Scholar
    • Export Citation
  • Bontron, G., and C. Obled, 2005: A probabilistic adaptation of meteorological model outputs to hydrological forecasting. Houille Blanche, 1 , 2328.

    • Search Google Scholar
    • Export Citation
  • Bougeault, P., and P. Lacarrère, 1989: Parameterization of orography-induced turbulence in a mesobeta-scale model. Mon. Wea. Rev., 117 , 18721890.

    • Search Google Scholar
    • Export Citation
  • Cacoullos, T., 1973: Discriminant Analysis and Applications. Academic Press, 434 pp.

  • Caniaux, G., J-L. Redelsperger, and J-P. Lafore, 1994: A numerical study of the stratiform region of a fast-moving squall line. Part I. General description and water and heat budgets. J. Atmos. Sci., 51 , 20462074.

    • Search Google Scholar
    • Export Citation
  • Caudill, M., 1991: Neural network training tips and techniques. AI Expert, 6 , 5661.

  • Cheng, B., and D. Titterington, 1994: Neural networks: A review from a statistical perspective. Stat. Sci., 9 , 254.

  • Colle, B. A., 2004: Sensitivity of orographic precipitation to changing ambient conditions and terrain geometries: An idealized modeling perspective. J. Atmos. Sci., 61 , 588606.

    • Search Google Scholar
    • Export Citation
  • Cosma, S., E. Richard, and F. Miniscloux, 2002: The role of small-scale orographic features in the spatial distribution of precipitation. Quart. J. Roy. Meteor. Soc., 128 , 7592.

    • Search Google Scholar
    • Export Citation
  • David Shepard Associates, 1990: The New Direct Marketing: How to Implement a Profit-Driven Database Marketing Strategy. Dow Jones–Irwin, 535 pp.

    • Search Google Scholar
    • Export Citation
  • Doolittle, M., 1888: Association ratios. Bull. Philos. Soc. Wash., 7 , 122127.

  • Doswell III, C. A., R. Davies-Jones, and D. L. Keller, 1990: On summary measures of skill in rare event forecasting based on contingency tables. Wea. Forecasting, 5 , 576585.

    • Search Google Scholar
    • Export Citation
  • Elsner, J., and A. Tsonis, 1992: Nonlinear prediction, chaos, and noise. Bull. Amer. Meteor. Soc., 73 , 4960.

  • Emanuel, K. A., 1994: Atmospheric Convection. Oxford University Press, 580 pp.

  • Fletcher, D., and E. Goss, 1993: Forecasting with neural networks: An application using bankruptcy data. Inf. Manage., 24 , 159167.

  • Flood, I., and N. Kartam, 1994: Neural networks in civil engineering. I: Principles and understanding. J. Comput. Civ. Eng., 8 , 131148.

    • Search Google Scholar
    • Export Citation
  • Frei, C., and C. Schär, 1998: A precipitation climatology of the Alps from high-resolution raingauge observations. Int. J. Climatol., 18 , 873900.

    • Search Google Scholar
    • Export Citation
  • Fuhrer, O., and C. Schär, 2007: Dynamics of orographically triggered banded convection in sheared moist orographic flows. J. Atmos. Sci., 64 , 35423561.

    • Search Google Scholar
    • Export Citation
  • Gardner, M., and S. Dorling, 1998: Artificial neural networks: A review of applications in the atmospheric sciences. Atmos. Environ., 32 , 26272636.

    • Search Google Scholar
    • Export Citation
  • Gardner, M., and S. Dorling, 1999: Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London. Atmos. Environ., 33 , 709719.

    • Search Google Scholar
    • Export Citation
  • Ghosh, S., P. Sen, and U. De, 2004: Classification of thunderstorm and non-thunderstorm days in Calcutta (India) on the basis of linear discriminant analysis. Atmósphera, 17 , 112.

    • Search Google Scholar
    • Export Citation
  • Godart, A., 2009: Les précipitations orographiques organisées en bandes dans la région Cévennes-Vivarais. Caractérisation et contribution au régime pluviométrique. Ph.D. thesis, University Joseph Fourier–Grenoble, 336 pp.

  • Godart, A., S. Anquetin, and E. Leblois, 2009: Rainfall regimes associated with banded convection in the Cévennes-Vivarais area. Meteor. Atmos. Phys., 103 , 2534. doi:10.1007/s00703-008-0326-3.

    • Search Google Scholar
    • Export Citation
  • Gysi, H., 1998: Orographic influence on the distribution of accumulated rainfall with different wind directions. Atmos. Res., 47–48 , 615633.

    • Search Google Scholar
    • Export Citation
  • Houze, R., W. Schmid, R. Fovell, and H. H. Schiesser, 1993: Hailstorms in Switzerland: Left movers, right movers, and false hooks. Mon. Wea. Rev., 121 , 33453370.

    • Search Google Scholar
    • Export Citation
  • Kirshbaum, D. J., and D. R. Durran, 2004: Factors governing cellular convection in orographic precipitation. J. Atmos. Sci., 61 , 682698.

    • Search Google Scholar
    • Export Citation
  • Kirshbaum, D. J., and D. R. Durran, 2005a: Atmospheric factors governing banded orographic convection. J. Atmos. Sci., 62 , 37583774.

  • Kirshbaum, D. J., and D. R. Durran, 2005b: Observations and modelling of banded orographic convection. J. Atmos. Sci., 62 , 14631479.

  • Kirshbaum, D. J., G. H. Bryan, R. Rotunno, and D. R. Durran, 2007a: The spacing of orographic rainbands triggered by small-scale topography. J. Atmos. Sci., 64 , 42224245.

    • Search Google Scholar
    • Export Citation
  • Kirshbaum, D. J., G. H. Bryan, R. Rotunno, and D. R. Durran, 2007b: The triggering of orographic rainbands by small-scale topography. J. Atmos. Sci., 64 , 15301549.

    • Search Google Scholar
    • Export Citation
  • Kuligowski, R. J., and A. P. Barros, 1998: Experiments in short-term precipitation forecasting using artificial neural networks. Mon. Wea. Rev., 126 , 470482.

    • Search Google Scholar
    • Export Citation
  • Lafore, J. P., and Coauthors, 1998: The Meso-NH Atmospheric Simulation System. Part I: Adiabatic formulation and control simulation. Ann. Geophys., 16 , 90109.

    • Search Google Scholar
    • Export Citation
  • Lau, C., and B. Widrow, 1990: Special issue on neural networks. Proc. IEEE, 78 , 14111414.

  • Leroy, D., 2007: Développement d’un modèle de nuage tridimensionnel à microphysique détaillée - Application à la simulation de cas de convection profonde. Ph.D. thesis, University Blaise Pascal, 214 pp.

  • Lin, Y. L., S. Chiao, T. A. Wang, M. L. Kaplan, and R. P. Weglarz, 2001: Some common ingredients for heavy orographic rainfall. Wea. Forecasting, 16 , 633660.

    • Search Google Scholar
    • Export Citation
  • Maier, H., and G. Dandy, 2000: Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications. Environ. Modell. Software, 15 , 101124.

    • Search Google Scholar
    • Export Citation
  • Manzato, A., 2005: The use of sounding-derived indices for a neural network short-term thunderstorm forecast. Wea. Forecasting, 20 , 896917.

    • Search Google Scholar
    • Export Citation
  • Marzban, C., 2000: A neural network for tornado diagnosis. Neural Comput. Appl., 9 , 133141.

  • Marzban, C., and G. Stumpf, 1996: A neural network for tornado prediction based on Doppler radar–derived attributes. J. Appl. Meteor., 35 , 617626.

    • Search Google Scholar
    • Export Citation
  • Marzban, C., and A. Witt, 2001: A Bayesian neural network for severe-hail size prediction. Wea. Forecasting, 16 , 600610.

  • Masters, T., 1993: Practical Neural Network Recipes in C++. Academic Press, 493 pp.

  • McGinnis, D. L., 1994: Predicting snowfall from synoptic circulation: A comparison of linear regression and neural network in methodologies. Neural Nets: Applications in Geography, B. Hewitson and R. G. Crane, Eds., Kluwer Academic Publishers, 79–99.

    • Search Google Scholar
    • Export Citation
  • Mercer, A., M. Richman, H. Bluestein, and J. Brown, 2008: Statistical modeling of downslope windstorms in Boulder, Colorado. Wea. Forecasting, 23 , 11761194.

    • Search Google Scholar
    • Export Citation
  • Miniscloux, F., J. D. Creutin, and S. Anquetin, 2001: Geostatical analysis of orographic rainbands. J. Appl. Meteor., 40 , 18351854.

  • Minns, A., and M. Hall, 1996: Artificial neural networks as rainfall-runoff models. Hydrol. Sci. J., 41 , 399417.

  • Navone, H. D., and H. A. Ceccatto, 1994: Predicting Indian monsoon rainfall: A neural network approach. Climate Dyn., 10 , 305312.

  • Noilhan, J., and S. Planton, 1989: A simple parameterization of land surface processes for meteorological models. Mon. Wea. Rev., 117 , 536549.

    • Search Google Scholar
    • Export Citation
  • Pankiewicz, G. S., 1995: Pattern recognition techniques for the identification of cloud and cloud systems. Meteor. Appl., 2 , 257271.

  • Peak, J., and P. Tag, 1994: Segmentation of satellite imagery using hierarchical thresholding and neural networks. J. Appl. Meteor., 33 , 605616.

    • Search Google Scholar
    • Export Citation
  • Pointin, Y., D. Ramond, and J. Fournet-Fayard, 1988: Radar differential reflectivity ZDR: A real-case evaluation of errors induced by antenna characteristics. J. Atmos. Oceanic Technol., 5 , 416423.

    • Search Google Scholar
    • Export Citation
  • Ripley, B., 1994: Neural networks and related methods of classification. J. Roy. Stat. Soc., 56 , 409456.

  • Ripley, B., 1996: Pattern Recognition and Neural Networks. Cambridge University Press, 403 pp.

  • Rogers, L., and F. Dowla, 1994: Optimization of groundwater remediation using artificial neural networks with parallel solute transport modelling. Water Resour. Res., 30 , 457481.

    • Search Google Scholar
    • Export Citation
  • Rumelhart, D. E., G. E. Hinton, and R. J. Williams, 1986a: Learning internal representations by error propagation. Foundations, D. E. Rumelhart and J. L. McClelland, Eds., Vol. 1, Parallel Distributed Processing, MIT Press, 318–362.

    • Search Google Scholar
    • Export Citation
  • Smith, R. B., 1979: The influence of mountains on the atmosphere. Advances in Geophysics, Vol. 21, Academic Press, 87–230.

  • Stanley, J., 1988: Introduction of Neural Networks: Computer Simulations of Biological Intelligence. California Scientific Software, 222 pp.

    • Search Google Scholar
    • Export Citation
  • Stein, J., E. Richard, J. P. Lafore, J. P. Pinty, N. Asencio, and S. Cosma, 2000: High-resolution non-hydrostatic simulations of flash-flood episodes with grid-nesting and ice-phase parametrization. Meteor. Atmos. Phys., 72 , 203221.

    • Search Google Scholar
    • Export Citation
  • Venugopal, V., and W. Baets, 1994: Neural networks and statistical techniques in marketing research: A conceptual comparison. Mark. Intell. Plann., 12 , 3038.

    • Search Google Scholar
    • Export Citation
  • Verdecchia, M., G. Visconti, F. D’Andrea, and S. Tibaldi, 1996: A neural network approach for blocking recognition. Geophys. Res. Lett., 23 , 20812084.

    • Search Google Scholar
    • Export Citation
  • Weichert, A., and G. Bürger, 1998: Linear versus nonlinear techniques in downscaling. Climate Res., 10 , 8393.

  • Weigend, A., D. Rumelhart, and B. Huberman, 1990: Predicting the future: A connectionist approach. Int. J. Neural Syst., 1 , 193209.

  • Wierenga, B., and J. Kluytmans, 1994: Neural nets versus marketing models in time series analysis: A simulation study. Marketing: Its Dynamics and Challenges, J. Bloemer, J. Lemmink, and H. Kasper, Eds., EMAC, 1139–1153.

    • Search Google Scholar
    • Export Citation
  • Yates, E., 2006: Convection en région Cévennes-Vivarais: Etude de données pluviométriques, simulations numériques et validation multi-échelles. Ph.D. thesis, Grenoble Polytechnic Institute, 235 pp.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 179 38 1
PDF Downloads 52 23 3