### Abstract

Original language | English |
---|---|

Place of Publication | Birmingham, UK |

Publisher | Aston University |

Number of pages | 58 |

ISBN (Print) | NCRG/99/005 |

Publication status | Published - 1999 |

### Fingerprint

### Keywords

- Satellite
- European Space Agency
- polar orbit
- scatterometer
- ripples
- local winds
- local inversion
- forward model
- mapping
- cost function
- density networks
- conditional probability density functions
- joint probability distribution
- kernels
- Gaussian mixture model
- geophysical knowledge
- ambiguity removal
- Bayesian framework

### Cite this

*First year qualifying report: neural networks for extracting wind vectors from satellite scatterometer data*. Birmingham, UK: Aston University.

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**First year qualifying report: neural networks for extracting wind vectors from satellite scatterometer data.** / Evans, David J.

Research output: Working paper › Project report

TY - UNPB

T1 - First year qualifying report: neural networks for extracting wind vectors from satellite scatterometer data

AU - Evans, David J.

PY - 1999

Y1 - 1999

N2 - The ERS-1 Satellite was launched in July 1991 by the European Space Agency into a polar orbit at about km800, carrying a C-band scatterometer. A scatterometer measures the amount of radar back scatter generated by small ripples on the ocean surface induced by instantaneous local winds. Operational methods that extract wind vectors from satellite scatterometer data are based on the local inversion of a forward model, mapping scatterometer observations to wind vectors, by the minimisation of a cost function in the scatterometer measurement space.par This report uses mixture density networks, a principled method for modelling conditional probability density functions, to model the joint probability distribution of the wind vectors given the satellite scatterometer measurements in a single cell (the `inverse' problem). The complexity of the mapping and the structure of the conditional probability density function are investigated by varying the number of units in the hidden layer of the multi-layer perceptron and the number of kernels in the Gaussian mixture model of the mixture density network respectively. The optimal model for networks trained per trace has twenty hidden units and four kernels. Further investigation shows that models trained with incidence angle as an input have results comparable to those models trained by trace. A hybrid mixture density network that incorporates geophysical knowledge of the problem confirms other results that the conditional probability distribution is dominantly bimodal.par The wind retrieval results improve on previous work at Aston, but do not match other neural network techniques that use spatial information in the inputs, which is to be expected given the ambiguity of the inverse problem. Current work uses the local inverse model for autonomous ambiguity removal in a principled Bayesian framework. Future directions in which these models may be improved are given.

AB - The ERS-1 Satellite was launched in July 1991 by the European Space Agency into a polar orbit at about km800, carrying a C-band scatterometer. A scatterometer measures the amount of radar back scatter generated by small ripples on the ocean surface induced by instantaneous local winds. Operational methods that extract wind vectors from satellite scatterometer data are based on the local inversion of a forward model, mapping scatterometer observations to wind vectors, by the minimisation of a cost function in the scatterometer measurement space.par This report uses mixture density networks, a principled method for modelling conditional probability density functions, to model the joint probability distribution of the wind vectors given the satellite scatterometer measurements in a single cell (the `inverse' problem). The complexity of the mapping and the structure of the conditional probability density function are investigated by varying the number of units in the hidden layer of the multi-layer perceptron and the number of kernels in the Gaussian mixture model of the mixture density network respectively. The optimal model for networks trained per trace has twenty hidden units and four kernels. Further investigation shows that models trained with incidence angle as an input have results comparable to those models trained by trace. A hybrid mixture density network that incorporates geophysical knowledge of the problem confirms other results that the conditional probability distribution is dominantly bimodal.par The wind retrieval results improve on previous work at Aston, but do not match other neural network techniques that use spatial information in the inputs, which is to be expected given the ambiguity of the inverse problem. Current work uses the local inverse model for autonomous ambiguity removal in a principled Bayesian framework. Future directions in which these models may be improved are given.

KW - Satellite

KW - European Space Agency

KW - polar orbit

KW - scatterometer

KW - ripples

KW - local winds

KW - local inversion

KW - forward model

KW - mapping

KW - cost function

KW - density networks

KW - conditional probability density functions

KW - joint probability distribution

KW - kernels

KW - Gaussian mixture model

KW - geophysical knowledge

KW - ambiguity removal

KW - Bayesian framework

M3 - Project report

SN - NCRG/99/005

BT - First year qualifying report: neural networks for extracting wind vectors from satellite scatterometer data

PB - Aston University

CY - Birmingham, UK

ER -