Neural networks in satellite rainfall estimation

Meteorological Applications

Volumn 11, Issue 1, 2004, Pages 83-91

  Tapiador, F J ^a   Kidd, C ^a   Hsu, K L ^b   Marzano, F ^c  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^b UNIVERSITY OF ARIZONA   (United States)

^c UNIVERSITY OF L'AQUILA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

GEOPHYSICS; IMAGE PROCESSING; RAIN; RAIN GAGES; SATELLITES;

ALGORITHMIC APPROACH; EMPIRICAL ALGORITHMS; ENVIRONMENTAL SCIENCE; GEOPHYSICAL PARAMETERS; NEAR-REAL TIME; NEURAL NETWORKS (NNS); RAINFALL ESTIMATIONS; SATELLITE RAINFALL ESTIMATION;

NEURAL NETWORKS;

EMPIRICAL ANALYSIS; GEOPHYSICAL METHOD; IMAGE CLASSIFICATION; IMAGE PROCESSING; PRECIPITATION ASSESSMENT; PRECIPITATION INTENSITY; PREDICTION; SATELLITE ALTIMETRY;

EID: 2942552158     PISSN: 13504827     EISSN: None     Source Type: Journal    
DOI: 10.1017/S1350482704001173     Document Type: Review

References (52)

1. Anders, U. & Korn, O. (1999) Model selection in neural networks. Neural Networks 12: 309-323.
2. Arkin, P. A. (1979) The relationship between fractional coverage of high cloud and rainfall accumulations during GATE over the B-scale array. Mon. Wea. Rev. 107: 1382-1387.
3. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000) Artificial neural network in hydrology. II: Hydrologic applications. J. Hydrologic Engineering, ASCE 5(2): 124-137.
4. Atkinson, P. M. & Tatnall, A. R. L. (1997) Neural networks in remote sensing - introduction. Int. J. of Remote Sensing 18(4): 699-709.
5. Bellerby, T., Todd, M., Kniveton, D. & Kidd, C. (2000) Rainfall estimation from a combination of TRMM Precipitation Radar and GOES multispectral satellite imagery through the use of an artificial neural network. J. Appl. Meteorol. 39: 2115-2128.
6. Carpenter, G. A., Gjaja, M. N., Gopal, S. & Woodcock, C. E. (1997) ART neural networks for remote sensing: vegetation classification from Landsat TM and terrain data. IEEE Trans. on Geoscience and Remote Sensing 35: 308-325.
7. Cybenko, G. (1989) Approximation by superposition of a sigmoidal function. Math. Control Signals Systems 2: 303-314.
8. Fahlman, S. E. & Lebiere, C. (1990) The Cascade-Correlation Learning Algorithm, Technical Report CMU-CS-90-100.
9. Faure, T., Isaka, H. & Guillemet, B. (2001) Neural network retrieval of cloud parameters of inhomogeneous and fractional clouds. Feasibility study. Remote Sens. Environ. 77: 123-138.
10. Funahashi, K. (1989) On the approximate realization of continuous mappings by neural networks. Neural Networks 2(3): 183-192.
11. Gardner, M. W. & Dorling, S. R. (1998) Artificial neural networks (the multilayer perceptron) - a review of applications in the atmospheric sciences. Atmos. Environ. 32: 2627-2636.
12. Göttsche, F.-M. & Olesen, F. S. (2002) Evolution of neural networks for radiative transfer calculations in the terrestrial infrared. Remote Sens. Environ. 80: 157-164.
13. Govindaraju, R. S. & Rao, A. R. (2000) Artificial Neural Networks in Hydrology. Dordrecht: Kluwer Academic Publishers, 329pp.
14. Grossberg, S. (1969) Some networks that can learn, remember and reproduce any number of complicated space-time patterns. J. Mathematics and Mechanics 19: 53-91.
15. Haykin, S. S. (1999) Neural Networks: A Comprehensive Foundation. 2nd edn. Upper Saddle River,NJ: Prentice Hall.
16. Hollinger, J. P., Pierce, J. L. & Poe, G. A. (1990) SSM/I instrument evaluation. IEEE Trans. on Geosci. and Remote Sensing 28: 781-789.
17. Hopfield, J. J. (1982) Neural networks and physical systems with emergent collective computational abilities. Proc. National Academy of Sciences 79: 2554-2558.
18. Hornik, K., Stinchcombe, M. & White, H. (1989) Multilayer neural networs are universal approximators. Neural Networks 2: 359-366.
19. Hornik, K. (1993) Some new results on neural network approximation. Neural Networks 4(2): 251-257.
20. Hsieh, W. W. & Tang, B. (1998) Applying neural network models to prediction and data analysis in meteorology and oceanography. Bull. Am. Meteorol. Soc. 79: 1855-1870.
21. Hsu, K., Sorooshian, S., Hong, Y. & Gao, X. (2002) Cloud classification and rainfall estimation using GOES imagery. International Conference of Quantitative Precipitation Forecasting, Reading, UK, 2-6 September 2002.
22. Jameson, A. R. & Kostinski, A. B. (2002) What is a raindrop size distribution? Bull. Am. Meteorol. Soc. 82: 1169-1177.
23. Jaynes, E. T. (1990) Probability theory as logic. In P. F. Fougère (ed.), Maximum-Entropy and Bayesian Methods, Dordrecht: Kluwer Academic Publishers.
24. Jensen, C. A., Reed, R. D., Marks II, R. J., El-Sharkawi, M. A., Jung, J.-B., Miyamoto, R. T., Anderson, G. M. & Eggen, C. J. (1999) Inversion of feedforward neural networks: algorithms and applications. Proc. of the IEEE 87: 1536-1549.
25. Kidd, C., Kniveton, D. & Barrett, E. C. (1998) The advantages and disadvantages of statistical derived-empirically calibrated passive microwave algorithms for rainfall estimation. J. Atmos. Sci. 55: 1576-1582.
26. Kohonen, T. (1997) Self-Organizing Maps, 2nd edn. Berlin: Springer.
27. Kolmogorov, A. N. (1963) On the representation of continuous functions of several variables by superposition of continuous functions of one variable and addition. Am. Mathematical Soc. Trans. 28: 55-99.
28. Krasnopolsky, V. M., Gemmill, W. H. & Breaker, L. C. (2000) A neural network multiparameter algorithm for SSM/I ocean retrievals: comparisons and validations. Remote Sens. Environ. 73: 133-142.
29. Krasnopolsky, V. M. & Chevallier, F. (2003) Some neural network applications in environmental sciences. Part II: Advancing computational efficiency of environmental numerical models. Neural Networks 16: 335-348.
30. Krasnopolsky, V. M. & Schiller, H. (2003) Some neural network applications in environmental sciences. Part I: Forward and inverse problems in geophysical remote measurements. Neural Networks 16: 321-334.
31. Kummerow, C., Olson, W. S. & Giglio, L. (1996) A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors. IEEE Trans. Geosci. and Remote Sensing 34: 1213-1232.
32. Levizzani, V., Schmetz, J., Lutz, H. J., Kerkmann, J., Alberoni, P. P. & Cervino, M. (2001) Precipitation estimations from geostationary orbit and prospects for METEOSAT second generation. Meteorol. Appl. 8: 23-41.
33. Li, L., Vivekanandan, J., Chan, C. H. & Tsang, L. (1997) Microwave radiometric technique to retrieve vapor, liquid and ice: Part I. Development of a neural network-based inversion method. Part II. Joint studies of radiometer and radar winter clouds. IEEE Trans. Geosci. and Remote Sensing 35: 224-247.
34. Linden, A. & Kindermann, J. (1989) Inversion of multilayer nets. Proc. Int. Joint Conf. Neural Networks, vol II, Washington, DC, 425-430.
35. Linderman, J. & Linden, A. (1990) Inversion of neural networks by gradient descent. Parallel Comput. 277-286.
36. Maier, H. R. & Dabdy, G. C. (2000) Neural networks for prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ. Modeling and Software 15: 101-124.
37. Mallet, C., Moreau, E., Casagrande, L. & Klapisz, C. (2002) Determination of integrated cloud liquid water path and total precipitable water from SSMA/I data using a neural network algorithm. Int. J. Remote Sensing 23(4): 661-674.
38. Marzano F. S., Fionda, E. & Ciotti, P. (2002) Inversion techniques for ground-based microwave radiometric retrieval of precipitation columnar contents and path attenuation. Proc. IGARSS'02, Toronto (Canada), 24-29 June.
39. Marzano, F. S., Palmacci, M., Cimini, D. & Turk, J. F. (2002) Statistical integration of satellite passive microwave and infrared data for high-temporal sampling retrieval of rainfall. Proc. IGARSS'02, Toronto (Canada), 24-29 June.
40. Murata, N., Yoshizawa, S. & Amari, S. (1994) A criterion for determining the number of parameters in an artificial neural network model. IEEE Trans. Neural Networks 5: 865-872.
41. Murray, A. F. & Edwards, P. J. (1994) Enhanced (MLP) performance and fault tolerance resulting from synaptic weight noise during training. IEEE Trans. Neural Networks 5(5): 792-802.
42. Parker, R. L. (1994) Geophysical Inverse Theory. Princeton: Princeton University Press.
43. Rodin, E. Y. & Amin, M. (1992) Maneuver prediction in air combat via artificial neural networks. Computers and Mathematics With Applications 24(3): 95-112.
44. Rolls, E. T. (1998) Neural Networks and the Brain. Oxford: Oxford University Press.
45. Sarle, W. S. (1994) Neural networks and statistical models. Proc. 19th Annual SAS Users Group International Conference. Cary, NC, SAS Institute, 1538-1550.
46. Shore, J. E. & Johnson, R. W. (1980) Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. IEEE Trans. Inf. Theory, IT-26: 26-39 and IT-29: 942-943.
47. Smith, E. A., Lamm, J., Adler, R., Alishouse, J., Aonashi, K. et al. (1998) Results of WetNet PIP-2 project. J. Atmos. Sci. 55: 1483-1536.
48. Sorooshian, S., Hsu, K. L., Gao, X., Gupta, H. V., Imam, B. & Braithwaite, D. (2000) Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Am. Meteorol. Soc. 81: 2035-2046.
49. Tapiador, F. J., Kidd, C., Levizzani, V. & Marzano, F. S. (2004) A neural networks-based fusion technique to estimate half hourly rainfall estimates at 0.1° resolution from satellite passive microwave and infrared data. J. Appl. Meteorol. 43, 309, pp. 98-116.
50. Turk, F. J., Ebert, E. E., Oh, H.-J. & Sohn, B.-J. (2002) Validation and applications of a realtime global precipitation analysis. Proc. IGARSS, 24-28 June 2002, Toronto, Canada.
51. Wentz, F. J. (1997) A well-calibrated ocean algorithm for special sensor microwave/imager. J. Geophys. Res. 102: 8073-8718.
52. Williams, R. J. (1986) Inverting a connectionist network mapping by backpropagation of error. Proc. 8th Annual Conf. Cognitive Science Society. Hillsdale, NJ: Lawrence Erlbaum, 859-865.