Abstract
The use of radar data is a powerful tool to improve rainfall spatio-temporal estimation. Geostatistical techniques are well suited to combine both raingage and radar measurements for this purpose. The main problem of this application, particularly in the context of real time estimation, is the definition of a positive definite model of cross-correlation between radar and rainfall. We propose the direct use of the experimental surface variogram, after filtering the spectra and cross-spectra in the frequency domain to ensure positive definiteness of the model. This technique, which has been proposed in the literature, is suitable for its introduction in a real time forecasting system in which fast estimation of the rainfall spatial distribution is needed. A case study shows its application with a real data set corresponding to the Barcelona radar and its watershed pluviographs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cassiraga, E. F., Gómez-Hernández, J. J. (1997). Improved Rainfall Estimation by Integration of Radar Data: A geostatistical approach. In A. Soares, editor, geoENV I-Geostatistics for Environmental Applications, Kluwer Academic Publishers, 363–374.
Creutin, J. D., G. Delrieu, and T. Lebel (1988). Rain measurement by raingage-radar combination: A geostatistical approach. J. Atmos. Oceanic Technol., 5(1), 102–115.
Deutsch, C. and Journel, A. G. (1997). GSLIB: Geostatistical Software Library and User’s Guide. Oxford University Press, New York.
Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228, 113–129.
Krajewski, W. F. (1987). Cokriging radar-rainfall and rain gage data. Journal of Geophysical Research, 92, (D8), 9571–9580.
Seo, D.-J., W. F. Krajewski, and D. S. Bowles (1990a). Stochastic interpolation of rainfall data from rain gages and radar using cokriging. 1. Design of experiments. Water Resources Research, 26 (3), 469–477.
Seo, D.-J., W. F. Krajewski, A. Azimi-Zonooz and D.S. Bowles (1990b). Stochastic interpolation of rainfall data from rain gages and radar using cokriging. 2. Results. Water Resources Research, 26 (5), 915–924.
Sun, X., Mein, R. G., Keenan, T. D. and Elliot, J. F. (2000). Flood estimation using radar and raingauge data. Journal of Hydrology, 239, 4–18.
Yao, T. (1998). Automatic covariance modeling and conditional spectral simulation with Fast Fourier Transform, PhD dissertation, Stanford University, 173 p.
Yao, T. and Journel, A. G. (1998). Automatic modeling of (cross) covariance tables using fast fourier transform, Mathematical Geology, 21(7), 715–739.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Kluwer Academic Publishers
About this paper
Cite this paper
Cassiraga, E.F., Guardiola-Albert, C., Gómez-Hernández, J.J. (2004). Automatic Modeling of Cross-Covariances for Rainfal Estimation Using Raingage and Radar Data. In: Sanchez-Vila, X., Carrera, J., Gómez-Hernández, J.J. (eds) geoENV IV — Geostatistics for Environmental Applications. Quantitative Geology and Geostatistics, vol 13. Springer, Dordrecht. https://doi.org/10.1007/1-4020-2115-1_33
Download citation
DOI: https://doi.org/10.1007/1-4020-2115-1_33
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-2007-0
Online ISBN: 978-1-4020-2115-2
eBook Packages: Springer Book Archive