Fusion of radar and rain gage measurements for an accurate estimation of rainfall

Journal of Geophysical Research Atmospheres

Volumn 104, Issue D24, 1999, Pages 31437-31450

  Matsoukas, Christos ^a,b   Islam, Shafiqul ^a,b   Kothari, Ravi ^a  

^a University of Cincinnati   (United States)

^b UNIVERSITY OF CINCINNATI   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MEASUREMENT METHOD; PRECIPITATION ASSESSMENT; RADAR; RAINGAUGE;

OKLAHOMA; UNITED STATES;

EID: 0033611326     PISSN: 01480227     EISSN: None     Source Type: Journal    
DOI: 10.1029/1999JD900487     Document Type: Article

References (19)

1. Barron, A.R., Universal approximation bounds for superposition of a sigmoidal function, IEEE Trans. Inf. Theor., 39, 930-945, 1993.
2. Bishop, C.M., and C. Legleye, Training with noise is equivalent to Tikhonov regularization, Neural Comput., 7(1), 108-116, 1995.
3. Brandes, E.A., Optimizing rainfall estimates with the aid of radar, J. Appl. Meteorol., 14(7), 1339-1345, 1975.
4. Crawford, K.C., Considerations for the design of a hydrologic data network using multivariate sensors, Water Resour. Res., 15(6), 1752-1762, 1979.
5. Cybenko, G., Approximation by superpositions of a sigmoidal function, Math. Control Signals Syst., 2, 303-314, 1989.
6. Duchon, C.E., T.M. Renkevens, and W.L. Crosson, Estimation of daily area-average rainfall during the CaPE experiment in central Florida, J. Appl. Meteorol., 34, 2704-2714, 1995.
7. Eddy, A., Objective analysis of convective scale rainfall using gages and radar, J. Hydrol., 44, 125-134, 1979.
8. Groisman, P.Y., and D.R. Legates, The accuracy of United States precipitation data, Bull. Am. Meteorol. Soc., 75, 215-227, 1994.
9. Hsu, K.-L., X. Gao, S. Sorooshian, and H.V. Gupta, Precipitation estimation from remotely sensed information using artificial neural networks, J. Appl. Meteorol., 36, 1176-1190, 1997.
10. Huffman, G., R. Adler, B. Rudolf, U. Schneider, and P. Keehn, Global precipitation estimates based on a technique for combining satellite-based estimates, rain gauge analysis and NWP model precipitation information, J. Clim., V8, 1284-1295, 1995.
11. Journel, A., and C.J. Huijbregts, Mining Geostatistics, Academic, San Diego, Calif., 1978.
12. Krajewski, W.F., Cokriging radar-rainfall and rain gage data, J. Geophys. Res., 92, 9571-9580, 1987.
13. Rodriguez-Iturbe, I. and J.M. Mejia, The design of rainfall networks in time and space, Water Resour. Res., 10, 713-728, 1974.
14. Rumelhart, D., G. Hinton, and R. Williams, Learning representations by backpropagation errors, Nature, 323, 533-536, 1986.
15. Seo, D.-J., W.F. Krajewski, and D.S. Bowles, Stochastic interpolation of rainfall data from rain gages and radar using cokriging. 1. Design of experiments, Water Resour. Res., 26(3), 469-477, 1990a.
16. Seo, D.-J., W.F. Krajewski, A. Azimi-Zonooz, and D.S. Bowles, Stochastic interpolation of rainfall data from rain gages and radar using cokriging. 2. Results, Water Resour. Res., 26(5), 915-924, 1990b.
17. Smith, J.A., D.-J. Seo, M.L. Baeck, and M.D. Hudlow, An intercomparison study of NEXRAD precipitation estimates, Water. Resour. Res., 32(7), 2035-2045, 1996.
18. Wilson, J.W., and E.A. Brandes, Radar measurement of rainfall - a summary, Bull. Am. Meteorol. Soc., 60(9), 1048-1058, 1979.
19. Zawadzki, I.I., On radar-rain gage comparison, J. Appl. Meteorol., 14, 1430-1436, 1975.