Neural network-based wind vector retrieval from satellite scatterometer data

Dan Cornford, Ian T. Nabney, Christopher M. Bishop

Research output: Working paperTechnical report

Abstract

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.
Original languageEnglish
Place of PublicationBirmingham
PublisherAston University
Number of pages16
ISBN (Print)NCRG99/003
Publication statusPublished - 26 Jan 1999

Fingerprint

scatterometer
wind direction
ocean
wind velocity
weather forecasting
modeling

Keywords

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

Cite this

Cornford, D., Nabney, I. T., & Bishop, C. M. (1999). Neural network-based wind vector retrieval from satellite scatterometer data. Birmingham: Aston University.
Cornford, Dan ; Nabney, Ian T. ; Bishop, Christopher M. / Neural network-based wind vector retrieval from satellite scatterometer data. Birmingham : Aston University, 1999.
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keywords = "conditional probability density estimation, mixture density network, multi-layer perceptron, periodic variables, scatterometer, wind vectors",
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Cornford, D, Nabney, IT & Bishop, CM 1999 'Neural network-based wind vector retrieval from satellite scatterometer data' Aston University, Birmingham.

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

Birmingham : Aston University, 1999.

Research output: Working paperTechnical report

TY - UNPB

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

AU - Cornford, Dan

AU - Nabney, Ian T.

AU - Bishop, Christopher M.

PY - 1999/1/26

Y1 - 1999/1/26

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

M3 - Technical report

SN - NCRG99/003

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

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Cornford D, Nabney IT, Bishop CM. Neural network-based wind vector retrieval from satellite scatterometer data. Birmingham: Aston University. 1999 Jan 26.