Two modes of Gulf Stream variability revealed in two decades of Satellite Altimeter Data

[ES]Charla divulgativa impartida en el Postdoctoal symposium de la Woods Hole oceanographic Institution. Artículo original pulicado en  Journal of Geophysical Research-Oceans

Automated data analysis to rapidly derive and communicate ecological insights from satellite-tag data : A case study of reintroduced red kites

Acknowledgements We thank Andrew Spink (Noldus Information Technology) and the Blogging Birds team members Peter Kindness and Abdul Adeniyi for their valuable contributions to this paper. John Fryxell, Chris Thaxter and Arjun Amar provided valuable comments on an earlier version. The study was part of the Digital Conservation project of dot.rural, the University of Aberdeen’s Digital Economy Research Hub, funded by RCUK (grant reference EP/G066051/1).

Geostationary operational environmental satellite (GOES) data collection system; task 3 & 4 final report.

"TER-157-0003."

Modelling wind direction from satellite scatterometer data

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we apply two novel techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.

Neural network-based wind vector retrieval from satellite scatterometer data

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.

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

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.

Neural network-based wind vector retrieval from satellite scatterometer data

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.

Noise modelling and outlier detection in satellite scatterometer data

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Delayed traveling ionospheric disturbances, especially in equatorial regions, following geomagnetic activity

For the Western-Pacific region spread-F has been found to occur with delays after geomagnetic activity (GA) ranging from 5 to 10 days as station groups are considered from low midlatitudes to equatorial regions. The statistical (superposed-epoch) analyses also indicate that at the equator the spread-F, and therefore associated medium-scale traveling ionospheric disturbances (MS-TIDs) occur with additional delays around 16, 22 and 28 days representing a 6-day modulation of the delay period. These results are compared with similar delays, including the modulation, for D-region enhanced hydroxyl emission (Shefov, 1969). It is proposed that this similarity may be explained by MS-TIDs influencing both the F and D regions as they travel. Long delays of over 20 days are also found near the equator for airglow-measured MS-TIDs (Sobral et al., 1997). These are recorded infrequently and have equatorward motions, while normally eastward motions are measured at the equator. Also in midlatitudes D-region absorption events have been shown (statistically) to have similar long delays after GA. It is suggested that atmospheric gravity waves and associated MS-TIDs may be generated by some of the precipitations responsible for the absorption. The recording of the delayed spread-F events depends on the GA being well below the average levels around sunset on the nights of recording. This implies that lower upper-atmosphere neutral particle densities are necessary.