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.
|Number of pages||11|
|Journal||Neural Computing and Applications|
|Publication status||Published - 1 Dec 1999|
- Conditional probability density estimation, Mixture density network, Multi-layer perceptron, Periodic variables, Scatterometer, Wind vectors