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Remote Sens. 2009, 1(3), 606-619; doi:10.3390/rs1030606

Accounting for Uncertainties of the TRMM Satellite Estimates

Department of Civil Engineering, University of Louisiana at Lafayette, PO Box 42291, Lafayette, LA 70504, USA
Author to whom correspondence should be addressed.
Received: 12 August 2009 / Revised: 8 September 2009 / Accepted: 8 September 2009 / Published: 11 September 2009
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Recent advances in the field of remote sensing have led to an increase in available rainfall data on a regional and global scale. Several NASA sponsored satellite missions provide valuable precipitation data. However, the advantages of the data are limited by complications related to the indirect nature of satellite estimates. This study intends to develop a stochastic model for uncertainty analysis of satellite rainfall fields through simulating error fields and imposing them over satellite estimates. In order to examine reliability and performance of the presented model, ensembles of satellite estimates are simulated for a large area across the North and South Carolina. The generated ensembles are then compared with original satellite estimates with respect to statistical properties and spatial dependencies. The results show that the model can be used to describe the uncertainties associated to TRMM multi-satellite precipitation estimates. The presented model is validated using random sub-samples of the observations based on the bootstrap technique. The results indicate that the model performs reasonably well with different numbers of available rain gauges. View Full-Text
Keywords: stochastic simulation; TRMM data; random error; rainfall ensemble; uncertainty analysis; satellite estimates; bootstrap technique stochastic simulation; TRMM data; random error; rainfall ensemble; uncertainty analysis; satellite estimates; bootstrap technique

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

AghaKouchak, A.; Nasrollahi, N.; Habib, E. Accounting for Uncertainties of the TRMM Satellite Estimates. Remote Sens. 2009, 1, 606-619.

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