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 -