SEARCH

SEARCH BY CITATION

References

  • Ali, M. M., D. Swain, and R. A. Weller (2004), Estimation of ocean subsurface thermal structure from surface parameters: A neural network approach, Geophys. Res. Lett., 31, L20308, doi:10.1029/2004GL021192.
  • Baik, J.-J., and J.-S. Paek (2000), A neural network model for predicting typhoon intensity, J. Meteorol. Soc. Jpn., 78, 857869.
  • Bell, G. J. (1979), Operational forecasting of tropical cyclones, Aust. Meteorol. Mag., 27, 249258.
  • Bourras, D., and W. T. Liu (2003), Evaluation of latent heat flux fields from satellites and models during SEMAPHORE, J. Appl. Meteorol., 42, 227239.
  • Carr, L. E.III, and R. L. Elsberry (2000), Dynamical tropical cyclone track forecast errors. part I: Tropical region error sources, Weather Forecasting, 15, 641661.
  • Chen, C. H., J. D. Lee, and M. C. Lin (2000), Classification of underwater signals using neural networks, Tamkang J. Sci. Eng., 3(1), 3148.
  • Elsberry, R. L. (1995), Tropical cyclone motion, in Global Perspectives on Tropical Cyclones, edited by R. L. Elsberry, Trop. Cyclone Programme Rep., no. 693, pp. 106197, World Meteorol. Organ., Geneva, Switz.
  • Gupta, A. (2006), Current status of tropical cyclone track prediction techniques and forecast errors, Mausam, 57(1), 151158.
  • Heming, J. T., J. C. L. Chan, and A. M. Radford (1995), A new scheme for the initialization of tropical cyclones in the UK Meteorological Office global model, Meteorol. Appl., 2, 171184.
  • India Meteorological Department (2000), Report on cyclonic disturbances over north Indian Ocean during 1999, Reg. Spec. Meteorol. Cent. Trop. Cyclones, New Delhi.
  • India Meteorological Department (2003), Report on cyclonic disturbances over north Indian Ocean during 2002, Reg. Spec. Meteorol. Cent. Trop. Cyclones, New Delhi.
  • Jain, S., and M. M. Ali (2006), Estimation of sound speed profiles using artificial neural network, IEEE Trans. Geosci. Remote Sens. Lett., 3(4), 467470.
  • Lam, C. Y. (1993), Operational tropical cyclone forecasting from the perspective of a small weather service, in Tropical Cyclone Disasters, pp. 530541, Peking Univ. Press, Beijing.
  • Mohanty, U. C., and A. Gupta (1997), Deterministic methods for prediction of tropical cyclone tracks, Mausam, 48(2), 257272.
  • Nannariello, J., and F. Fricke (1998), Reverberation time predictions using neural network analysis, J. Acoust. Soc. Am., 103(5), 3065, doi:10.1121/1.422835.
  • Neumann, C. J., and G. S. Mandal (1978), Statistical prediction of tropical storm motion over the Bay of Bengal and Arabian Sea, Indian J. Meteorol. Hydrol. Geophys., 29(3), 487500.
  • Neumann, C. J., and E. A. Randrianarison (1976), Statistical prediction of tropical cyclone motion over the southwest Indian Ocean, Mon. Weather Rev., 104, 7685.
  • Pozzi, M., B. A. Malmgren, and S. Monechi (2000), Sea surface temperature and isotopic reconstruction from nannoplankton data using artificial neural networks, Palaeontol. Electron., 3(2), 414.
  • Ramirez, N. D., and J. M. Castro (2006), A transfer function model to predict hurricane intensity, paper presented at 27th Conference on Hurricanes and Tropical Meteorology, Am. Meteorol. Soc., Monterey, Calif., 23 – 28 Apr.
  • Ramirez, N. D., and A. Veneros (2004), Upper air information and neural networks to estimate hurricane intensity, paper presented at 26th Conference on Hurricanes and Tropical Meteorology, Am. Meteorol. Soc., Miami, Fla., 2 – 7 May.
  • Richaume, P., F. Badran, M. Crepon, C. Mejía, H. Roquet, and S. Thiria (2000), Neural network wind retrieval from ERS-1 scatterometer data, J. Geophys. Res., 105, 87378751.
  • Schroeder, T., J. Fischer, M. Schaale, and F. Fell (2002), Artificial-neural-network-based atmospheric correction algorithm: Application to MERIS data, Proc. SPIE Int. Soc. Opt. Eng., 4892, 124132.
  • Smith, D. E. (1958), History of Mathematics, vol. 2, 302 pp., Dover, Mineola, N. Y.