Physical oceanography and current meter data from mooring and CTD measurements at Fram Strait

Current meters measured temperature and velocity on 12 moorings from 1997 to 2014 in the deep Fram Strait between Svalbard and Greenland at the only deep passage from the Nordic Seas to the Arctic Ocean. The sill depth in Fram Strait is 2545 m. The observed temperatures vary between the colder Greenland Sea Deep Water and the warmer Eurasian Basin Deep Water. Both end members show a linear warming trend of 0.11±0.02°C/decade (GSDW) and 0.05±0.01°C/decade (EBDW) in agreement with the deep water warming observed in the basins to the north and south. At the current warming rates, GSDW and EBDW will reach the same temperature of -0.71°C in 2020. The deep water on the approximately 40 km wide plateau near the sill in Fram Strait is a mixture of the two end members with both contributing similar amounts. This water mass is continuously formed by mixing in Fram Strait and subsequently exported out of Fram Strait. Individual measurements are approximately normally distributed around the average of the two end members. Meridionally, the mixing is confined to the plateau region. Measurements less than 20 km to the north and south have properties much closer to the properties in the respective basins (Eurasian Basin and Greenland Sea) than to the mixed water on the plateau. The temperature distribution around Fram Strait indicates that the mean flow cannot be responsible for the deep water exchange across the sill. Rather, a coherence analysis shows that energetic mesoscale flows with periods of approximately 1-2 weeks advect the deep water masses across Fram Strait. These flows appear to be barotropically forced by upper ocean mesoscale variability. We conclude that these mesoscale flows make Fram Strait a hot spot of deep water mixing in the Arctic Mediterranean. The fate of the mixed water is not clear, but after the 1990s, it does not reflect the properties of Norwegian Sea Deep Water. We propose that it currently mostly fills the deep Greenland Sea.

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."

Machinery's data sheet series

"compiled from Machinery's monthly data sheets and arranged with explanatory notes."

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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(Table 1) Date, deployment location, tag longevity, and satellite data of humpback whales (Megaptera novaeangliae) from the Cook Islands

Humpback whales (Megaptera novaeangliae) undertake extensive seasonal migrations from summer feeding areas in high latitudes to winter mating and calving grounds in tropical waters (Clapham and Mead 1999, http://www.jstor.org/stable/3504352). In the Southern Hemisphere, seven populations are recognized by the International Whaling Commission (IWC). In this study, we report the movements of seven whales satellite-tagged in the Cook Islands, including the first documented migration to an antarctic feeding ground. In September 2006 and 2007 we attached Argos satellite-monitored tags to eight humpback whales of various sex and behavioral classes. All whales were tagged in the nearshore waters of Rarotonga (the largest island in the Cooks group).

Benthic and substrate cover data derived from a time series of photo-transect surveys for the Eastern Banks, Moreton Bay Australia, 2004-2015

This paper describes seagrass species and percentage cover point-based field data sets derived from georeferenced photo transects. Annually or biannually over a ten year period (2004-2015) data sets were collected using 30-50 transects, 500-800 m in length distributed across a 142 km**2 shallow, clear water seagrass habitat, the Eastern Banks, Moreton Bay, Australia. Each of the eight data sets include seagrass property information derived from approximately 3000 georeferenced, downward looking photographs captured at 2-4 m intervals along the transects. Photographs were manually interpreted to estimate seagrass species composition and percentage cover (Coral Point Count excel; CPCe). Understanding seagrass biology, ecology and dynamics for scientific and management purposes requires point-based data on species composition and cover. This data set, and the methods used to derive it are a globally unique example for seagrass ecological applications. It provides the basis for multiple further studies at this site, regional to global comparative studies, and, for the design of similar monitoring programs elsewhere.