Skip to main content

Neural Network tools for Satellite Rainfall Estimation

  • Chapter
Measuring Precipitation From Space

Part of the book series: Advances In Global Change Research ((AGLO,volume 28))

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

7 References

  • 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.

    Article  Google Scholar 

  • Birkhoff, G. and S. Mac Lane, S., 1996: Affine geometry. In A Survey of Modern Algebra, 5th edn. New York: Macmillan, pp. 268-275.

    Google Scholar 

  • Göttsche, F.-M. and F. S. Olesen, 2002: Evolution of neural networks for radiative transfer calculations in the terrestrial infrared. Remote Sens. Environ., 80, 157-164.

    Article  Google Scholar 

  • Haykin, S. S., 1999: Neural Networks: A Comprehensive Foundation, 2nd dn. Upper Saddle River, NJ: Prentice-Hall

    Google Scholar 

  • Horn, B. K. P. and B. G. Schunck, 1981: Determining optical flow. Artificial Intelligence, 17, 185-203.

    Article  Google Scholar 

  • Hornik, K., M. Stinchcombe, and H. White, 1989: Multilayer neural networks are universal approximators. Neural Networks, 2, 359-366.

    Article  Google Scholar 

  • Hsu, K., S. Sorooshian, Y. Hong, and X. Gao, 2002: Cloud classification and rainfall estimation using GOES imagery. Int. Conf. Quantitative Precipitation Forecasting, Reading, UK, 2-6 September.

    Google Scholar 

  • Kolmogorov, A. N., 1963: On the representation of continuous functions of several variables by superposition of continuous functions of one variable and addition. Amer. Mathematical Soc. Trans., 28, 55-99.

    Google Scholar 

  • Krasnopolsky, V. M. and F. Chevallier, 2003: Some neural network applications in environmental sciences. Part II: Advancing computational efficiency of environmental numerical models. Neural Networks, 16, 335-348.

    Article  Google Scholar 

  • Krasnopolsky, V. M. and H. Schiller, 2003: Some neural network applications in environmental sciences. Part I: Forward and inverse problems in geophysical remote measurements. Neural Networks, 16, 321-334.

    Article  Google Scholar 

  • Kummerow, C., W. S. Olson, and L. Giglio, 1996: A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors. IEEE Trans. Geosci. and Remote Sensing, 34, 1213-1232.

    Article  Google Scholar 

  • Sarle, W. S., 1994: Neural networks and statistical models. Proc. 19 th Annual SAS Users Group International Conference. Cary, NC, SAS Institute, 1538-1550.

    Google Scholar 

  • Sorooshian, S., K. L. Hsu, X. Gao, H. V. Gupta, B. Imam, and D. Braithwaite, 2000: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81, 2035-2046.

    Article  Google Scholar 

  • Tapiador, F. J., C. Kidd, V. Levizzani, and F. S. Marzano, 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. Meteor., 43, 576-594.

    Article  Google Scholar 

  • Tapiador, F. J., C. Kidd, K. L. Hsu, and F. S. Marzano, 2004: Neural networks in satellite rainfall estimation. Meteorol. Appl., 11, 1-9.

    Article  Google Scholar 

  • Turk, F. J., E. E. Ebert, H.-J. Oh, and B. J. Sohn, 2002: Validation and applications of a realtime global precipitation analysis. Proc. IGARSS, 24-28 June 2002, Toronto, Canada.

    Google Scholar 

  • Vicente, G. A., R. A. Scofield, and W. P. Menzel, 1998: The operational GOES infrared rainfall estimation technique. Bull. Amer. Meteor. Soc., 79, 1883-1898.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer

About this chapter

Cite this chapter

Tapiador, F.J., Kidd, C., Levizzani, V., Marzano, F.S. (2007). Neural Network tools for Satellite Rainfall Estimation. In: Levizzani, V., Bauer, P., Turk, F.J. (eds) Measuring Precipitation From Space. Advances In Global Change Research, vol 28. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-5835-6_12

Download citation

Publish with us

Policies and ethics