### Abstract

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

Pages (from-to) | 206-217 |

Number of pages | 12 |

Journal | Neural Computing and Applications |

Volume | 8 |

Issue number | 3 |

DOIs | |

Publication status | Published - Aug 1999 |

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### Keywords

- conditional probability density estimation
- mixture density network
- multi-layer perceptron
- periodic variables
- scatterometer
- wind vectors

### Cite this

*Neural Computing and Applications*,

*8*(3), 206-217. https://doi.org/10.1007/s005210050023

}

*Neural Computing and Applications*, vol. 8, no. 3, pp. 206-217. https://doi.org/10.1007/s005210050023

**Neural network-based wind vector retrieval from satellite scatterometer data.** / Cornford, Dan; Nabney, Ian T.; Bishop, Christopher M.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Neural network-based wind vector retrieval from satellite scatterometer data

AU - Cornford, Dan

AU - Nabney, Ian T.

AU - Bishop, Christopher M.

PY - 1999/8

Y1 - 1999/8

N2 - Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results.

AB - Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results.

KW - conditional probability density estimation

KW - mixture density network

KW - multi-layer perceptron

KW - periodic variables

KW - scatterometer

KW - wind vectors

UR - http://www.springerlink.com/content/03fqqkfnhpugv9dr/

UR - http://www.scopus.com/inward/record.url?scp=0033240927&partnerID=8YFLogxK

U2 - 10.1007/s005210050023

DO - 10.1007/s005210050023

M3 - Article

VL - 8

SP - 206

EP - 217

JO - Neural Computing and Applications

JF - Neural Computing and Applications

SN - 0941-0643

IS - 3

ER -