Journal article
A neural network approach to estimating rainfall from spaceborne microwave data
IEEE Transactions on Geoscience and Remote Sensing, Vol.35(5), pp.1079-1093
09/1997
DOI: 10.1109/36.628775
Abstract
Various techniques use microwave (MW) brightness temperature (BT) data, obtained from remote sensing orbiting platforms, to calculate rain rates. The most commonly used techniques are based on regressions or other statistical methods. An emerging tool in rainfall estimation using satellite data is artificial neural networks (NNs), NNs are mathematical models that are capable of learning complex relationships. They consist of highly interconnected, interactive data processing units. NNs are implemented in this study to estimate rainfall, and backpropagation is used as a learning scheme. The inputs for the training phase are BTs and the outputs are rainfall rates, all generated by three-dimensional (3D) simulations based on a 3D stochastic, space-time rainfall model, and a 3D radiative transfer model. Once training is complete the NNs are presented with multi-frequency and polarized (horizontal and vertical) BT data, obtained from the Special Sensor Microwave/Imager (SSM/I) instrument onboard the F10 and F11 polar-orbiting meteorological satellites. Hence, rainrates corresponding to real BT measurements are generated. The rainfall rates are also estimated using a log-linear regression model. Comparison of the two approaches, using simulated data, shows that the NN can represent more accurately the underlying relationship between BT and rainrate than the regression model, Comparison of the rates, estimated by both methods, with radar-estimated rainrates shows that NNs outperform the regression model. This study demonstrates the great potential of NNs in estimating rainfall from remotely sensed data.
Details
- Title: Subtitle
- A neural network approach to estimating rainfall from spaceborne microwave data
- Creators
- D Tsintikidis - Hydrologic Res. Center, San Diego, CA, USAJeffrey L HafermanEmmanouil N AnagnostouWitold F KrajewskiTheodore F Smith
- Resource Type
- Journal article
- Publication Details
- IEEE Transactions on Geoscience and Remote Sensing, Vol.35(5), pp.1079-1093
- Publisher
- IEEE
- DOI
- 10.1109/36.628775
- ISSN
- 0196-2892
- eISSN
- 1558-0644
- Language
- English
- Date published
- 09/1997
- Academic Unit
- Mechanical Engineering; Civil and Environmental Engineering
- Record Identifier
- 9983991937102771
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