WPS orchestration using the Taverna workbench: The eScience approach

eScience is an umbrella concept which covers internet technologies, such as web service orchestration that involves manipulation and processing of high volumes of data, using simple and efficient methodologies. This concept is normally associated with bioinformatics, but nothing prevents the use of an identical approach for geoinfomatics and OGC (Open Geospatial Consortium) web services like WPS (Web Processing Service). In this paper we present an extended WPS implementation based on the PyWPS framework using an automatically generated WSDL (Web Service Description Language) XML document that replicates the WPS input/output document structure used during an Execute request to a server. Services are accessed using a modified SOAP (Simple Object Access Protocol) interface provided by PyWPS, that uses service and input/outputs identifiers as element names. The WSDL XML document is dynamically generated by applying XSLT (Extensible Stylesheet Language Transformation) to the getCapabilities XML document that is generated by PyWPS. The availability of the SOAP interface and WSDL description allows WPS instances to be accessible to workflow development software like Taverna, enabling users to build complex workflows using web services represented by interconnecting graphics. Taverna will transform the visual representation of the workflow into a SCUFL (Simple Conceptual Unified Flow Language) based XML document that can be run internally or sent to a Taverna orchestration server. SCUFL uses a dataflow-centric orchestration model as opposed to the more commonly used orchestration language BPEL (Business Process Execution Language) which is process-centric.

Transfer Across the Air-Sea Interface

The efficiency of transfer of gases and particles across the air-sea interface is controlled by several physical, biological and chemical processes in the atmosphere and water which are described here (including waves, large- and small-scale turbulence, bubbles, sea spray, rain and surface films). For a deeper understanding of relevant transport mechanisms, several models have been developed, ranging from conceptual models to numerical models. Most frequently the transfer is described by various functional dependencies of the wind speed, but more detailed descriptions need additional information. The study of gas transfer mechanisms uses a variety of experimental methods ranging from laboratory studies to carbon budgets, mass balance methods, micrometeorological techniques and thermographic techniques. Different methods resolve the transfer at different scales of time and space; this is important to take into account when comparing different results. Air-sea transfer is relevant in a wide range of applications, for example, local and regional fluxes, global models, remote sensing and computations of global inventories. The sensitivity of global models to the description of transfer velocity is limited; it is however likely that the formulations are more important when the resolution increases and other processes in models are improved. For global flux estimates using inventories or remote sensing products the accuracy of the transfer formulation as well as the accuracy of the wind field is crucial.

Coccolithophore bloom detection in the north east Atlantic using SeaWiFS: Algorithm description, application and sensitivity analysis

Coccolithophores are the largest source of calcium carbonate in the oceans and are considered to play an important role in oceanic carbon cycles. Current methods to detect the presence of coccolithophore blooms from Earth observation data often produce high numbers of false positives in shelf seas and coastal zones due to the spectral similarity between coccolithophores and other suspended particulates. Current methods are therefore unable to characterise the bloom events in shelf seas and coastal zones, despite the importance of these phytoplankton in the global carbon cycle. A novel approach to detect the presence of coccolithophore blooms from Earth observation data is presented. The method builds upon previous optical work and uses a statistical framework to combine spectral, spatial and temporal information to produce maps of coccolithophore bloom extent. Validation and verification results for an area of the north east Atlantic are presented using an in situ database (N = 432) and all available SeaWiFS data for 2003 and 2004. Verification results show that the approach produces a temporal seasonal signal consistent with biological studies of these phytoplankton. Validation using the in situ coccolithophore cell count database shows a high correct recognition rate of 80% and a low false-positive rate of 0.14 (in comparison to 63% and 0.34 respectively for the established, purely spectral approach). To guide its broader use, a full sensitivity analysis for the algorithm parameters is presented.