Benjamin Leighton
email: ben.leighton@csiro.au

Simon Cox

Nicholas J Car

Matthew P Stenson

Jamie Vleeshouwer

Jonathan Hodge

A Best of Both Worlds Approach to Complex, Efficient, Time Series Data Delivery

Keywords: time, series, timeseries, SOS, OGC, sensor, database

Point time series are a key data-type for the description of real or modelled environmental phenomena. Delivering this data in useful ways can be challenging when the data volume is large, when computational work (such as aggregation, subsetting, or re-sampling) needs to be performed, or when complex metadata is needed to place data in context for understanding. Some aspects of these problems are especially relevant to the environmental domain: large sensor networks measuring continuous environmental phenomena sampling frequently over long periods of time generate very large datasets, and rich metadata is often required to understand the context of observations. Nevertheless, timeseries data, and most of these challenges, are prevalent beyond the environmental domain, for example in financial and industrial domains.

A review of recent technologies illustrates an emerging trend toward high performance, lightweight, databases specialized for time series data. These databases tend to have non-existent or minimalistic formal metadata capacities. In contrast, the environmental domain boasts standards such as the Sensor Observation Service (SOS) that have mature and comprehensive metadata models but existing implementations have had problems with slow performance.

In this paper we describe our hybrid approach to achieve efficient delivery of large time series datasets with complex metadata. We use three subsystems within a single system-of-systems: a proxy (Python), an efficient time series database (InfluxDB) and a SOS implementation (52 North SOS). Together these present a regular SOS interface. The proxy processes standard SOS queries and issues them to the either 52 North SOS or to InfluxDB for processing. Responses are returned directly from 52 North SOS or indirectly from InfluxDB via Python proxy where they are processed into WaterML. This enables the scalability and performance advantages of the time series database to be married with the sophisticated metadata handling of SOS. Testing indicates that a recent version of 52 North SOS configured with a Postgres/PostGIS database performs well but an implementation incorporating InfluxDB and 52 North SOS in a hybrid architecture performs approximately 12 times faster.

Time Series Data, Lots is Hard to Manage

Time series data is frequently used to represent environmental properties and processes and is fundamental in environmental science. Hey and Trefethen [START_REF] Hey | The data deluge: An e-science perspective[END_REF], describe and predict a scientific data deluge. Managing a deluge of time series data so that it can be discovered, understood, accessed and used effectively, is an ongoing challenge.

A simple time series of numerical values requires context to be useful. For example observational time series often include a unit of measure, and some description of an observed feature. Metadata provides a richer description and context and allows more complex queries to filter or group data. Furthermore standards for metadata mean data can be more readily exchanged because compatible systems and data sets can be identified and interfaced.

Very large amounts of data can be collected by sensors or generated computationally. Metadata can further increase the size and complexity of a dataset. Better analysis and use can be made of large time series data that are richly described. Such descriptions are more computationally expensive to process, require more storage, and consume more network bandwidth.

To address these challenges we review and compare some technologies for storing, delivering and managing time series data. We introduce a hybrid approach that merges a lightweight time series database with a standardized metadata-rich service for delivering sensor observations. This approach provides good performance for a both large dataset with rich metadata. In testing this approach performs faster than a standalone combined data and metadata service.

Sensor Observation Service for Management

Standards and models for interacting with and describing observations help address the problem of management, and effective use, of time series data.

The open geospatial consortium sensor observation service (SOS) [START_REF]Portal.opengeospatial.org: OGC Sensor Observation Service Interface Standard[END_REF] describes a standard way of managing and querying observation data. Services compliant to this standard are capable of returning stored observations and contextual information about related real world features, sensors, and observational procedures. SOS compliant services can provide observation records that conform to the Open Geospatial Consortium's (OGC) Observations and Measurements standard (O&M) [START_REF] Cox | Geographic information: observations and measurements[END_REF] . O&M provides a flexible observations model including information about an observed feature, property, and the procedure associated with making an observation. SOS implementations also provide for complex descriptions of sensors, for example, using Sen-sorML [START_REF]OGC SensorML: Model and XML Encoding Standard[END_REF].

SensorCloud [START_REF] Peters | SensorCloud RESTful API[END_REF] is an architecture and associated application that performs functions similar to those of described by the SOS standard. SensorCloud specifies a service for delivering sensor data and metadata through REST like URL requests. Sen-sorCloud encodes observations and related data using a JSON format. Sensor metadata is closely modelled on the StarFL [START_REF] Malewski | StarFL--a modularised metadata language for sensor descriptions[END_REF] format. SensorCloud services can provide information on networks of sensors, sensor platforms, sensors, phenomena reported by sensors, sensor calibrations and can deliver observations produced by a sensor. The SensorCloud services also provide create and insert operations for networks, sensor, platform, and observations data.

Both SensorCloud and OGC SOS standards describe ways of structuring and delivering time series data and related metadata in the scientific domain.

Other Databases

Beyond the scientific domain there are many databases and services for the storage and use of time series data. There is a continuum between specialised time series databases and more general purpose databases. Numerous specialized time series databases exist and many are in active development. Development of time series databases is driven by a broad need to access observations but not always in a scientific context. For example OpenTSDB [START_REF] Opentsdb | OpenTSDB -A Distributed, Scalable Monitoring System[END_REF], while somewhat general purpose, is typically used to monitor various I.T system metrics. Similarly Cube [START_REF]Cube[END_REF] is generic but was developed for use cases related to monitoring customer data, website, and system performance. Time Series databases readily integrate with other systems and often provide web service based interfaces. REST interfaces and JSON provide rapid integration with JavaScript frameworks and thus ease integration with web based visualisation tools. KariosDB [START_REF]kairosdb -Fast scalable time series database -Google Project Hosting[END_REF] is a rewrite of, and intended to be an improvement on, OpenTSDB. It provides a REST interface alongside other interfaces including a GUI and a telnet interface. Cube is built on MongoDB [START_REF]Mongodb[END_REF] and provides a REST like interface. In-fluxDB [START_REF]Influxdb.com: InfluxDB -Open Source Time Series, Metrics, and Analytics Database[END_REF] provides a REST interface.

More general purpose databases are also used for time series storage. MongoDB provides the back end database for SensorCloud as well as Cube. PostgreSQL [START_REF]PostgreSQL: The world's most advanced open source database[END_REF] is one of the possible back-ends for 52 North SOS [START_REF] Hollmann | North SOS 4[END_REF]. Cassandra [START_REF]The Apache Cassandra Project[END_REF] can be readily adapted to store time series data. Cois [START_REF] Cois | Large-Scale Data Collection and Real-Time Analytics Using Redis -O'Reilly Radar[END_REF] describes a hybrid approach using Redis [START_REF]Redis[END_REF] and PostgreSQL for an environmental database prioritizing real time event detection. InfluxDB can use a variety of embedded backend databases.

Databases may be optimized for write versus read operations, can be highly normalised and efficient, or may enable rapid prototyping and querying of unstructured or loosely structured data.

Standards Support and Interoperability

SensorCloud and OGC SOS-compliant systems provide rich metadata models. In contrast most other time series databases provide minimal metadata and none provided any time series-specific standards conformance. Non-relational databases, like MongoDB, provide storage of structured documents thus support arbitrary metadata schemas. Relational databases like PostgreSQL support table structures that may accommodate arbitrary metadata schemas. Neither MongoDB nor PostgreSQL provide a built in metadata model for time series data. Specialised time series database typically provide limited and inflexible metadata capabilities that do not conform to recognised standards. OpenTSDB provides some very basic support through unique identifiers [17]. KariosDB annotates values with tags and a name [START_REF]PushingData -kairosdb -Pushing data into KairosDB -Fast scalable time series database -Google Project Hosting[END_REF]. Cube is much more flexible and supports arbitrary structured JSON data at each data point. In InfluxDB, a value is effectively a row consisting of multiple columns and thus simple metadata structures can be accommodated.

Performance Considerations for Rich Metadata Systems

Standards for interoperability and metadata are particularly important in the scientific domain. Standards provide a basis for a scientific infrastructure where data can be discovered, compared, reused, and through which experiments can be replicated [19][20]. Thus time series databases and services that conform to comprehensive metadata and encoding standard have an advantage over more general database systems and time series specific databases. Performance is always an important consideration: the ability to rapidly query, analyze and retrieve time series data is a requirement for a number of time series use cases. For example, exploratory interactive visualization of time series data is possible if data points can be quickly retrieved, processed and displayed. Similarly fast responses ensure that access to time series data is not a bottleneck in analysis, or when used as an input to a model. An ongoing concern with the SOS standard is the performance of implementations. The well-known 52 North SOS implementation provides conformance to the SOS 2.0 standard and includes a number of extension methods and formats. Recent releases have addressed performance issues however older 52 North SOS implementations have suffered from numerous performance issues. Results [START_REF] Tan | SOS 2.0 Performance Test[END_REF] from performance tests for various older versions of 52 North SOS indicate that under a load of 10 concurrent requests every 5 seconds, SOS performs quite slowly. For example a GetObservation request, the average response time for a request for a yearly data set having approximately 500,000 records was 191 seconds. Similar performance issues with scaling SOS to millions of records for thousands of sensors have been discussed in the SOS community [START_REF]Fwd: ODIP-3 Prototype SOS -Google Groups[END_REF].

Performance has been greatly improved in 52 North SOS version 4.1 [START_REF] Hollmann | North SOS 4[END_REF] however with dataset sizes likely to increase more than linearly, revolutionary performance change will be needed in the longer term. Large performance gains may be possible through alternative approaches to data storage, that behind a SOS interface, leverage dedicated time series databases.

An Alternative Architecture

A good candidate for comparison with 52 North SOS's large data capacity is Influx DB. InfluxDB is a "Timeseries, events and metrics" database [START_REF]InfluxDB Documentation[END_REF]. InfluxDB is easy to deploy and is packaged with an embedded backend database. Unlike 52 North SOS, InfluxDB lacks any native metadata model and provides no standards-compliant interface.

We developed a hybrid data/metadata architecture, combining InfluxDB with 52 North SOS. We tested an implementation of this architecture to determine whether it provide better performance than a standalone 52 North SOS instance.

Similar Hybrid architectures have been previously proposed. Cox [START_REF] Cox | No Title[END_REF][Fig 1 .] describes scenarios in which SOS services consume data from other OGC standard Web Feature Services and Web Coverage Services. Bröring et. al. [START_REF] Broring | Interaction patterns for bridging the gap between sensor networks and the Sensor Web[END_REF] describe patterns for bridging sensors and sensor delivery services in hybrid like architectures. In particular Bröring describes using Twitter as a middleware layer to store sensor metadata.

Fig. 1. Hybrid OGC architectures (adapted from Cox[24])

A prototype SOS service has also been developed that, through a heterogeneous systems architecture, provided a partially SOS-compliant service using a Web Feature Service (WFS) back-end [START_REF] Golodoniuc | ThinSOS < SISS4BoM < SEEGrid[END_REF]. Our hybrid architecture aims to meet multiple use cases. It provides a rich SOS implementation for standards compliant interoperability to enable sophisticated queries. For a subset of queries it provides SOS-compliant responses faster than 52 North SOS configured with a Postgres/PostGIS backend database. The system should enable exploration of data across the temporal domain with a minimum of delay to facilitate use cases such as real time interactive visualisation.

Figure 2 presents a high-level view of the hybrid architecture: SOS requests and responses are handled via a hybrid internet proxy. The hybrid proxy forwards certain requests to InfluxDB while others not able to be handled by InfluxDB are redirected to 52 North SOS.

A test implementation of the hybrid proxy is capable of processing and forwarding only a small subset of SOS queries to InfluxDB: SOS 2.0 requests provided as key value pairs (as URL arguments) where the response format is specified as WaterML2. This subset includes the main data delivery request GetObservation when WaterML2 format is used. The hybrid proxy can process queries that vary by real world feature or property of interest for a feature and can handle a temporal filter that specify results between varying start and end times. We compared the performance of a realistic 52 North SOS instance as a standalone service to a test implementation the hybrid proxy architecture incorporating the same 52 North SOS instance alongside InfluxDB.

The 52 North SOS version tested was 4.1 and the particular instance contained approximately 14 million weather station records stored in a backend a Postgres/PostGIS. Two configurations were tested: one in which a query was sent to a standalone 52 North SOS instance and another in which the query was sent to a hybrid proxy configuration combining 52 North SOS and InfluxDB. In the hybrid proxy configuration the 52 North SOS dataset was replicated into a parallel InfluxDB instance (version 0.7.3).

In both configurations the test used a SOS-compliant query specified as a URL with key value arguments. The test query varied between the standalone SOS instance and hybrid proxy implementation by the system specifying URL part only with key value pair arguments and format remaining the same. The test machines varied in specification and no attempt was made to compensate for hardware differences or network latencies. However our estimates are that the server 52 North SOS virtual machine was significantly more powerful than the Hybrid test virtual machine and the InfluxDB virtual machine. Therefore we expect that performance results are biased in favor of the standalone 52 North SOS instance.

The test query in both configurations retrieved approximately 52,230 hourly average air temperature records for a weather station between 19-11-2010 at midnight and 13-11-2013 at 13:00.

In both test configurations the request was sent using the cURL application. The cURL "w" parameter and a template file were used to generate response timing information. Server processing time was considered to be the time between time_pretransfer and time_starttransfer. This approach minimised noise related to network overheads and time taken to transport response data. Responses are listed in Table 1 The first response was considered an outlier and was significantly slower than subsequent queries for both systems. It was assumed this was because parts of the databases are cached into memory after the first response. In our test the hybrid approach using InfluxDB generated responses 12 times faster on average than 52 North SOS. Our results are indicative of a significant performance benefit through the hybrid approach nevertheless further study could provide more certainty and help remove confounding factors such as differing machine and network performance.

In addition to high performance a hybrid approach offers other advantages. It maintains rich metadata capabilities. The hybrid proxy forwards SOS queries that can't be handled by InfluxDB to 52 North SOS. In our example InfluxDB replicates time series data stored in SOS but doesn't replicate any metadata. For example, In-fluxDB cannot provide a response to a SOS DescribeSensor request because it doesn't store any information about the sensors used to produce a time series. The hybrid proxy can identify queries that can't be handled by InfluxDB and redirect these to the 52 North SOS instance. Thus the hybrid proxy handles a subset of queries quickly via InfluxDB but can also respond to the broader set of possible SOS requests by redirecting these to 52 North SOS. This approach improves performance while maintaining a rich metadata model. Additionally, a more general approach should be possible interfacing to other data stores. These kinds of systems could leverage existing data ser-vices and adapt those to return SOS responses. This improves the reuse of data and reduces the need to duplicate data across multiple systems. In turn this "future proofs" SOS installations against reimplementation if and when data loads require new backend database implementations.

Conclusion

Time series data is prolific. There are many systems to help manage, store and deliver time series data. In the scientific domain some systems also provide metadata capabilities that provide more context for understanding and analysing data. Beyond the scientific domain, many other time series databases and services are being developed. These typically have poor metadata capabilities but may be more scalable and provide faster performance. Near real time visualisation and other use cases requiring rapid retrieval and exploration of data would benefit from a system that is standards based, provides rich metadata and performs fast. The 52 North SOS implementation provides a standards based service for management and storage of time series data along with extensive metadata capabilities. Newer versions of 52 North SOS have improved performance. To investigate whether further performance gains were possible we developed a hybrid architecture that combined 52 North SOS with InfluxDB, a lightweight dedicated time series database. We compared the performance of a standalone 52 North SOS instance and a prototype implementation of the hybrid architecture that coupled the 52 North SOS instance with InfluxDB. Under our tests performance was approximately 12 times faster in the hybrid system. There are performance benefits using a hybrid architecture compared to a standalone 52 North SOS instance other advantages maybe that loose coupling between components readily allows integration of new technologies and reuse of data in existing deployments.

Fig. 2 .

 2 Fig. 2. High-level architecture of a hybrid SOS implementation

Table 1 .

 1 below. Response times for 52 North standalone and 52 North Influx DB hybrid

	Run	Hybrid	52 North
			SOS
	First	0.45s	8.485s
	Run 1	0.377s	4.891s
	Run 2	0.31s	4.594s
	Run 3	0.336s	4.282s
	Run Average	0.368s	4.589s
	(excluding first result)


Simon N Gallant
email: simon.gallant@csiro.au

Rebecca K Schmidt
email: becky.schmidt@csiro.au

Nicholas J Car
email: nicholas.car@csiro.au

Implementing a Glossary and Vocabulary Service in an Interdisciplinary Environmental Assessment for Decision Makers

Keywords: glossaries, web services, interoperable information systems

When delivering scientific information for decision makers, it is important to define and use appropriate terminology to ensure scientific credibility and good communication. A glossary with terms from authoritative sources for specific domains can increase the usefulness and reusability of information for decision makers as the information can be more easily used without adaptation or translation. Linked Data principles and semantic web-based vocabulary tools provide mechanisms for delivering formalised glossaries via vocabulary services for use in integrated products, both documents and information platforms.

Issues to consider when implementing a glossary and vocabulary service are covered: persuading stakeholders to accept standard external terms and gain agreement on unique terminology; requirements for gathering, controlling and maintaining terminology in a glossary to ensure transparency and persistence; formalising a glossary as a standards-based vocabulary; and efficiently implementing this glossary via automation.

Introduction

This paper explores the implementation of a glossary service in the Bioregional Assessment Programme (the Programme) [START_REF]Overview of the Bioregional Assessment Programme[END_REF][START_REF] Barrett | Methodology for bioregional assessments of the impacts of coal seam gas and coal mining development on water resources[END_REF]. The Programme provides information on the ecology, hydrology, geology and hydrogeology of specified bioregions with explicit assessment of the potential direct, indirect and cumulative impacts of coal seam gas and coal mining development on water resources. This scientific information will be available for all interested parties, including Australian and state government regulators, industry, community and the general public, when considering coal seam gas and coal mining developments. The Programme is delivering over 150 products, mostly scientific reports, over the course of three years. A key requirement for these products is a high standard of scientific and editorial quality including the consistent use of terminology. For plain-English words, this is straightforward as existing literature can be used. For more technical language, standards must be agreed upon and recorded, including both nega-tive and positive instruction ('Use bore not well in the context of groundwater. Use well not bore in the context of oil or gas.'). Through interacting with experts in many fields, a Programme-specific language list has been developed, which guides the writing, integration and quality assurance of content for all Programme staff.

The limitation of this list is its simplicity. When an author knows precisely what they wish to say, but not exactly which word to use, this list provides authority. What the list cannot do is help multiple authors agree on what they mean, nor inform readers as to that meaning. For the Programme to publish its products in a way that is truly useful to and accessible by the public, the way words and concepts are used needs to be discoverable by readers. For this, a controlled, authoritative glossary service is proposed.

This paper provides a short background on the Programme, Linked Data, ontologies and controlled vocabularies in order to establish the context of the work. The processes by which terminology, both individual words and entire sets of words from particular authorities, is agreed and governed are described, as is the architecture for automatically building product-specific glossaries. Finally, the costs and benefits of such a service are discussed, as well as the implications for multiple-use services such as this, with particular reference to the difficulties of (i) conflicting requirements, (ii) multiple-context reporting and (iii) doing something rather than nothing.

Background

The Bioregional Assessment Programme

A bioregional assessment is a scientific analysis of the ecology, hydrology, geology and hydrogeology of a particular geographic location, with explicit assessment of the potential direct, indirect and cumulative impacts of coal seam gas and large coal mining development on water resources [START_REF]Overview of the Bioregional Assessment Programme[END_REF][START_REF] Barrett | Methodology for bioregional assessments of the impacts of coal seam gas and coal mining development on water resources[END_REF]. The Programme undertakes these assessments for a range of stakeholders including the Independent Expert Scientific Committee on Coal Seam Gas and Large Coal Mining Development (IESC), Australian and state government regulators, industry, community and the general public. The outputs are a suite of scientific products for each of the geographic locations currently being studied, delivered both as documents and via an information platform. The Programme team spans both scientific disciplines and research agencies with four main collaborators: the Australian Government Department of the Environment; the Bureau of Meteorology; the Commonwealth Scientific and Industrial Research Organisation (CSIRO); and Geoscience Australia.

Linked Data, Ontologies and Controlled Vocabularies

In this Programme, multiple agencies contribute and multiple fields of research are involved so information from a diverse range of sources must be integrated. Semantic web [START_REF] Berners-Lee | The Semantic Web[END_REF] technologies such as standardised vocabularies 1 and Linked Data [START_REF] Heath | Linked Data: Evolving the Web into a Global Data Space[END_REF], are designed with heterogeneous data integration in mind and are thus of great utility to this Programme. Terms from a range of authorities in a range of formats can be integrated for a single purpose, then placed within semantic web vocabularies. The delivery of them as Linked Data assists this greatly. By using Linked Data, terms become properties of objects that are identified using Uniform Resource Identifiers (URIs) 2 meaning they can be linked to and information about them 'dereferenced' (looked up) by following their URI. This allows the term owners (the authorities or acting on behalf of the authorities regarding their definition) to deliver them at a single point of truth and in both human-and machine-readable formats, enabling unambiguous references (links) to individual terms within text (documents, webpages). If standardised concept ontologies, such as the Simple Knowledge Organization System (SKOS) [START_REF] Miles | Skos simple knowledge organization system -reference[END_REF], are used for vocabularies, multiple properties for terms may be recorded, not simply textual definitions. SKOS allows the mapping of terms between vocabularies using a range of relationships such as broad, close and exact. This allows for nuanced relationships between the constructed glossary and other known, trusted vocabularies. Software tools, such as the Spatial Information Services Stack Vocabulary Service (SISSVoc, [START_REF] Cox | Vocabulary services to support scientific data interoperability[END_REF]), deliver controlled vocabularies with formalised relationships between terms defined using SKOS as Linked Data. Other vocabulary delivery tools do exist, such as the Australian National Data Service (ANDS) Controlled Vocabulary Service [7], but controlled vocabularies are more commonly delivered in informal ways without standardised information models (ontologies) and without formal data formats. For example, the Australian Government's Interactive Functions Thesaurus (AGIFT) http://agift.naa.gov.au/ delivers its controlled vocabulary via regular webpages.

3

Glossary and Vocabulary Services

Persuasion and Approvals

Editorial quality is required to be of a very high standard for products of the Programme. Part of ensuring high editorial quality is ensuring that language is consistent both within and between products. This required consistency in language spans the choice of terminology to the way that concepts are used. The Programme's interdisciplinary nature has made this particularly challenging as experts from different disciplines have different ways of expressing similar concepts and different uses for the same terms. Two approaches have therefore been taken: (i) to use an external authority for definitions wherever possible, and (ii) to discuss, collate and socialise a language list which is governed by Programme management. External authorities are valuable as they provide a point of truth, once contributors agree that it is appropriate. For instance, Programme members have agreed to use the Australian Oxford dictionary [START_REF][END_REF] avoiding many arguments over terminology (such as whether to use 'modeling' or 'modelling'). In the experience of the Programme, the following is necessary to gain agreement to use an external authority:

1. the majority of Programme staff have access to that authority 2. that authority includes a sufficiently volume of terms to make it worthwhile using 3. the majority of Programme scientific leaders already agree with the majority of terms.

Similar projects have devised related language lists, for example for the Sustainable Yields projects [START_REF]Sustainable Yields Projects[END_REF] and the Great Artesian Basin Water Resource Assessment [START_REF] Ahmad | Editing conventions for authors and editors. A document in the CSIRO Great Artesian Basin Water Resource Assessment reporting tools series[END_REF]. In addition, the BA methodology [START_REF] Barrett | Methodology for bioregional assessments of the impacts of coal seam gas and coal mining development on water resources[END_REF], Australian Government's Style manual [START_REF]Australian Government: Style manual for authors[END_REF] and the Australian Oxford dictionary [START_REF][END_REF] were accepted as authorities. This provided the Programme with a sufficiently comprehensive language list to begin with, which was endorsed by Programme management as part of the development of the products, thus developing a first-pass list of approved terms.

As the Programme progressed it became clear that it was important to move beyond simply specifying a list of approved terms, but rather to give definitions. This is best practice when writing, particularly in interdisciplinary projects where readers and co-authors from different disciplines might have different meanings for the same word. This confusion needs to be avoided within the single context of a product and the broader context of the whole Programme. Some middle ground between two disciplines must be determined, or one discipline must use a different word. This problem cannot be solved by the imposition of a rule based upon personal preferences -it can only be solved by having conversations with the scientists involved and coming to agreement. For efficiency, the discussion ideally would start with determining external authorities that each discipline accepts so that glossaries from them could be adopted, then it would move to the task of defining individual terms that are not already defined in any external authority. External authorities that will be considered by the Programme include: the METOTERM database [START_REF]METEOTERM database[END_REF], the Australian Water Information Dictionary [13], and the Water Quality vocabulary developed for the Bioregional Assessment Framework [START_REF] Simons | Water Quality vocabularies for the Bioregional Assessment Framework[END_REF].

A method of discourse based in participatory research methods was used to facilitate this agreement. The involvement in the decision-making processes of parties those decisions will affect has been encouraged since the 1950s and it has been long argued (for example, [START_REF] Arnstein | A Ladder of Citizen Participation[END_REF]) that increasing this involvement will improve outcomes. The persuasion work done is best described as a Partnership [START_REF] Arnstein | A Ladder of Citizen Participation[END_REF]: groups of scientists are given the power to negotiate, trade-off and come to agreement both with each other and with the editing and managerial teams, but the editing and management teams retain the power to formalise these decisions. Once decisions are formalised, they are presumed to be fixed.

Governance

What are the requirements for the process to gather, version control and maintain terminology in a glossary in order to ensure transparency and long-term persistence? The governance of terminology in the Programme is managed from the time an undefined term is identified right through until that term is published in the glossary.

At the first step, identification, the term is entered in the glossary as 'proposed, awaiting editing'. The definition of the term is then raised with those concerned (editors, authors, managers, subject matter experts), and a member of the glossary team facilitates the discussion. The glossary team member revises the definition in the glossary, changing its status to 'edited, awaiting approval'. Edited terms are then submitted to Programme management for acceptance at which point they gain a status of 'approved'.

The glossary will hold a large number of terms in various stages at any given time. Various status-based subsets of these terms will be shown through the use of different views. Only 'approved' terms will be available in the Public view, only 'edited, awaiting approval' terms will be visible in the ForApproval view, and all terms will be visible in the Management view. All views are able to show multiple definitions for a term, but this functionality is intended to be used infrequently. It is expected, that the Public view would only ever show the most recent definition.

The Public view can be used as an authoritative list of approved terminology, fulfilling the function of enforcing consistency. An agreed list of terms is a necessary part of publishing products with a high standard of scientific and editorial quality with transparency. Using the glossary as a way of tracking the changes and approvals of the way terms are used makes it possible to ensure that the products of the Programme are consistent in their terminology, with a transparent process for defining, approving and possibly redefining terms.

This process to submit and accept terms is informed by the Geographic Information standard ISO19135 [16] and future efforts will be made to harmonise the term stage naming with existing semantic web ontologies that handle resource lifecycles.

The management and governance of the glossary is important due to its multiple requirements: (i) the glossary is the complete list of terms for which rules on usage and spelling have been made and (ii) the glossary is an audited and controlled list of important terms and their definitions. While those terms that fall under (ii) can be included in (i), the reverse is not practicable. Therefore, there is conflict in the use, governance and maintenance responsibilities and requirements for the glossary as a whole. To resolve this issue, it is helpful to simplify the idea of 'the glossary': the glossary is a structured way of storing terms, some of which may have agreed definitions. The necessary additional maturity and complexity that comes from having multiple lists of terms can therefore be solved by using filter criteria for different views. An Authors view can then be presented, showing the complete list of approved words, without definitions even when they exist (addressing (i)), and the Public and ForApproval views show only those important, defined terms (addressing (ii)).

3.3

Formalising the Glossary as a Standards-Based Vocabulary

Once glossary terms have been identified and even before definitions are agreed upon, a URI for each term is generated. These are intuitive when designed well and take the following form for the Programme's terms: http://{ProgrammsDataWebsiteAddress}/glossary/term/{term-label} With a URI and the text of the term (known as a prefLabel in SKOS) now known, the bare-minimum requirements of SKOS have been achieved. Once the term's definition is settled, that is added as a definition and SKOS relationships such as broader, narrower or exact can be determined. This information can easily be managed in the same media -a spreadsheet or database -as used to store the term's status and other information required by governance.

Once SKOS data are stored, a computer script can be used to automatically load the terms and their properties into a vocabulary service such as SISSVoc for delivery on the web.

Automated Implementation

The Public view of the glossary can also be used to generate product-specific glossaries in multiple formats.

The products are written using standard document preparation software which is easily available to all Programme contributors. For the traditional delivery of documents, a print-style glossary is required: a list of terms at the end of a product, with a definition for each. Where defined terms exist in the product, they should be linked to their individual location in the glossary through text indicating the reference. For the web-based delivery, the Public view of the entire glossary should be accessible through a link on any page and, where defined terms exist in these products, they should be hyperlinked to their individual location in the online glossary.

The production of both of these forms can be automated using computer scripts, which manipulate documents and can read information from web services. Postprocessing of document files can identify defined terms used, inject referencing links and auto-assemble the print-style glossary. Similar processing can take place for marked-up files for web-based products.

Discussion and Conclusion

The primary cost involved in setting up a glossary service such as the one described here is time: a great deal of it is required in both system development and administration, as well as the discourse that is necessary to gain agreement on terms. The benefits of having a glossary, however, should not be understated. The glossary is a binding context for Programme products. When a reader encounters a term they find ambiguous they are able to find its definition and can be sure that it will still be defined in the same way every time it is used. Thus, the clarity of written communication in Programme products is greatly increased and the trust that a reader places in Programme products is improved.

It is important to note that the Programme is large enough for such an activity to be worth undertaking. Relative to the total time for which the Programme will run (3 years) and the number of people involved (more than 160), the time and people required to develop and maintain the glossary system is reasonable. The same would not be true for a small project with few staff. However, the system and processes are relatively easily transferable to other projects, thus expanding the benefits without much additional cost.

The Programme is reliant on credible, authoritative external sources of definitions: if external authorities for glossary terms are not available or are not accepted by the disciplinary scientists, the cost in developing and maintaining a glossary (by writing hundreds of definitions) may not offset the benefits.

Using one glossary service to fulfil many functions adds to the maturity of the Programme. In addition to improving communication and increasing trust, the problem of conflicting requirements is solved in part: instead of having a language list for authors and editors, a printed glossary for readers and a series of meeting minutes indicating managerial approval of terms -all of which must be aligned -the Programme is able to include all the necessary information in a single store and then provide the filtered information as subsets of the whole.

The difficulties of multiple-context reporting have not been fully addressed by the Programme. While the goal of having both complete, cover-to-cover reports and 'chunks' of online context is admirable, the shift of context both for authors and readers is difficult. It is believed that by providing a centralised service that ensures consistent definitions for terms, some of this context shift can be avoided.

Beginning development despite fluctuating and conflicting requirements has been valuable, and parts of the system can be implemented while others mature. The need to develop the glossary system incrementally has prompted the involvement of some Programme staff earlier than would have been anticipated, which should increase buy-in from many Programme staff. This is the most important outcome of the glossary development for if it is not valued it will not be used, and a glossary that is not used is not a glossary at all.

adfa, p. 1, 2011. © Springer-Verlag Berlin Heidelberg 2011

Acknowledgements. This work was funded by the Bioregional Assessment Programme, a scientific collaboration between the Australian Government Department of the Environment, Bureau of Meteorology, the Commonwealth Scientific and Industrial Research Organisation (CSIRO), and Geoscience Australia. For more information visit http://bioregionalassessments.gov.au.


Alena Oulehlová
email: alena.oulehlova@unob.cz

Jiří Barta
email: jiri.barta@unob.cz

Hana Malachová
email: hana.malachova@unob.cz

Jiří F Urbánek
email: jiri.urbanek@unob.cz

Requirement Engineering for Emergency Simulations

Keywords: critical energy infrastructure, emergency management, information systems, security, simulation tool

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Timeliness, completeness, objectivity, reliability, and accuracy are essential units to measure the effectiveness and efficiency of decision-making process in emergency or crisis situations. Monitoring and information tools play key role -in obtaining such information. Natural and anthropogenic hazard information represents statistics and other quantitative or qualitative data that are used by authorities. These authoritative bodies include, but not limited to, the emergency management authorities, individuals, households, non-governmental organizations, companies and scientific or research institutions. The same figure may render different information for different entities at the same time.

The basic information scheme about sources of danger and their transmission is similar to other information systems. This information scheme typically contains description of the transmission protocol through which the information is being transported, and the format of the information itself. One typical information format is a composition of three components: the sender, the recipient, and the information payload.

Prediction, origin and course of specific natural hazards are recorded with monitoring tools of individual environmental components managed by public authorities. Information systems in the Czech Republic that focus primarily on natural disasters caused by atmospheric changes with strongest effects are the longest and most thoroughly monitored.

One of the tasks carried out by the state under normal conditions and particularly in crisis situations is ensuring the functioning and protection of key system components and services. This task has been implemented by the state since its formation. However, with the growth of intensity and occurrence of anthropogenic and natural threats and impacts on society, pressure increases for the state to improve the quality of its protection and resilience, and to reduce vulnerability. Individual systems and services, due to their importance to the society, have begun to be referred to as critical infrastructure.

One of the earliest establishment of national critical infrastructure protection occurred in the United States of America. Stemming from the country's comprehensive legal definition of the subjects of critical infrastructure, USA has now been one of the leading countries in critical infrastructure protection. Germany and the Great Britain later followed suit as they begun to deal with similar issues. The discussion on the critical infrastructure protection at European level was launched at the beginning of the 21st century after incidents of several crisis situations (floods, terrorist attacks, etc.). Current documents of European critical infrastructure protection (1) were created on principles of proportionality, subsidiarity, complementarity, confidentiality and cooperation among stakeholders. Based on application of principle of subsidiarity, the critical infrastructure in the European Union is divided into National Critical Infrastructure and European Critical Infrastructure.

2

Critical Energy Infrastructure in the Czech Republic There are 228 subjects of critical energy infrastructure in the electricity sector at the national level: 58 subjects in the production of electricity subsector, 133 subjects in the transmission system subsector, and 37 subjects in the distribution system subsector.

Simulation Tools for Training Cooperation by Subjects of Critical Infrastructure

Although ensuring proper function of energy infrastructure is crucial for the European Union, there are other sectors that play significant role in the system of critical infrastructure protection (3). This project, entitled "Research and development of simulation tools for training cooperation of actors in emergency management by subjects of critical infrastructure (SIMEX)", focuses on the roles and responsibilities of the subjects in the critical energy infrastructure. SIMEX project was selected from a competition held by the Technology Agency of the Czech Republic to support applied research and experimental development called "ALFA". It is classified in the sub-program of energy resources and environmental protection and preservation. The project started in September 2014. To meet the project objectives, it is necessary to identify the needs of individual stakeholders on mutual communication, quality and level of information exchange, potential threats and the state of hardware and software among stakeholders.

Without an in-depth initial requirements analysis of individual stakeholders it is not possible to create simulation software for common training of the Integrated Rescue System subjects of critical infrastructure that can improve coordination, cooperation, operability and interoperability of intervening units. The secondary objective of the project is to investigate the impacts of failure of critical energy infrastructure on environment, mitigation and prevention strategies for negative consequences from natural disasters, and the impact of protection and preservation of the environment.

Primary and Secondary Stakeholders

Requirement engineering is employed in order to achieve the project objectives [START_REF]A Framework for Software Product Line Practice, Version 5.0[END_REF][START_REF] Sutcliffe | Requirements Engineering[END_REF]. At the currently ongoing first stage of requirement engineering, the project has identified primary and secondary stakeholders for energy critical infrastructure. Table 1 shows an overview of stakeholders who influence mitigation, solution and elimination of consequences of extraordinary event and crisis situation caused by failure of critical energy infrastructure. Identification of stakeholders was conducted in accordance to the recommendations of ISO CSR the Stakeholder Engagement Manual [START_REF]The Stakeholder Engagement Manual Volume I: The guide to practitioners´ perspectives on stakeholder engagement[END_REF], and The Stakeholder Engagement Manual Volume II [START_REF]The Stakeholder Engagement Manual Volume II: The practitioner´s handbook on stakeholder engagement[END_REF], focusing on emergency management in subjects of critical energy infrastructure. Identifying contracting party at the primary stakeholders is meant to be generic because specific names of legal entities and individuals are stated in the emergency preparedness plan of each given subject. The subject is contracted with them for specific range of services and assistance that will be performed in case of extraordinary events and crisis situations.

Requirements Engineering: A Communication With Stakeholders

Based on the definition of stakeholders, we began to communicate with them. In the first step there we addressed the state authorities (Ministry of the Interior of the Czech Republic, Ministry of the Interior -General Directorate of Fire Rescue Service of the Czech Republic, and the Ministry of Industry and Trade). Fundamental requirements for protection of subject of critical infrastructure, realization and practices of liaison exercises related to failure of some element of critical infrastructure, were found by interviewing responsible people. Public administration authorities pointed out several requirements that the simulation software for the joint training of the Integrated Rescue System and subjects of critical infrastructure should provide.

Requirements for the simulation software in terms of further research could be divided into the following groups:

• Requirements for core functions of the system; • Interface requirements -interface with other systems, user interface, software, hardware and communication interfaces; • Non-functional requirements -product requirements, external resources, etc.;

• Other requirements -including legislative requirements, software multilingualism, etc.

In the second phase there is established communication with the liaison safety staff in particular subjects. There are structured and controlled interviews with the liaison security staff. Questions for structured and controlled interviews are divided a number of particular areas: emergency management and crisis preparedness plans, preparation and realization of training simulating failure of element of critical energy infrastructure, and impacts of the critical energy infrastructure on environment. In case of unsuccessful communication with the liaison security staff, we have prepared a checklist that represents basis qualitative method of risk management.

Simulation Software for Training

For a successful creation of the simulation software for liaison training to work in real time with real geo-data and models of effects of extraordinary event effects or crisis situation, it is necessary to create good quality scenarios of extraordinary event and crisis situations process. Creating of such scenarios will require wide spectrum of requirements from different interfaces, non-functional and other requirements. The first step for creating the scenario is hazard (threats) identification concerning element of critical energy infrastructure. Qualitative, semi-quantitative or quantitative methods of risk analysis will be used to verify the correctness of created registry of danger for element of critical infrastructure in the emergency preparedness plan.

To determine technological threats and their impact, we will use risk analysis methods What-if, Fault Tree Analysis (FTA), Event Tree Analysis (ETA), Failure Mode and Effects Analysis (FMEA), a Hazard and Operability Study (HAZOP), Human Reliability Analysis (HRA), Chemical Exposure Index. For the sector of critical energy infrastructure, a special method RAMCAP Plus was developed. All Hazards Risk and Resilience Prioritizing Critical Infrastructures use RAMCAP Plus approach (9).

This method is based on the general procedure of risk management [START_REF] Bozek | General Procedure of Risk Management[END_REF]. RAMCAP Plus, in addition, evaluates the environmental threats and mutual dependencies of regional threats. Evaluation of impact of environmental threats is based on semi-quantitative evaluation using historical data on frequency of natural threats. In relation to technological risk analysis and modelling of leakage, scatter, discharge, evaporation, explosion, and fire, many software tools were developed to facilitate the application of the above mentioned methods. These applicable methods include HAZOP Manager Version 7.0, Fault Tree +, EPRI HRA Calculator®, Security Risk Scorecard, Property Security Risk Survey, SFÉRA-ENERGY and other.

Proposed Solution of Simulator

To ensure the main function of proposed simulator for solution of extraordinary events of critical infrastructure subject [START_REF] Rehak | Preference Risk Assessment of Electric Power Critical Infrastructure[END_REF], it will be necessary to take up real information about the occurrence of extraordinary event and its spread via interfaces that are available and used not only by the Integrated Rescue System. This information has at its disposal the sector of critical infrastructure and Emergency Services. Table 2 summarizes main information systems providing information about the dangers of natural extraordinary events, environmental impacts, and secondary sources of danger in case of spreading of extraordinary event or crisis situations on the environment.

From executed analysis of information and data provided by various information and monitoring systems in the area of environmental security imply that on the interface level the simulator will use data from CZRAD, EUMETSAT, Air Quality Information System (AQIS) and the Water Information System.

Radar Network CZRAD is used for detection of precipitation clouds (storms up to 250 km). It can be used to estimate the instantaneous precipitation intensity to about 150 km away from the radar [START_REF]Radar net CZRAD[END_REF]. Real-time view of radar images of rainfall, in combination with images of lightning discharges and ward measurements, information on weather in a place where an element of critical energy infrastructure will be made available. Information system Voda EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) provides data on weather, climate, and environment. It has a system of meteorological satellites that observe the atmosphere, ocean and land surface [START_REF]Monitoring weather and climate from space[END_REF]. It also provides the latest data to the Czech Hydrometeorological Institute that uses it for rigorous prediction of weather forecast and issuing warnings.

Air Quality Information System (AQIS) monitors, evaluate, publish and archive data on air quality for territory of the Czech Republic through automatic air pollution monitoring stations. Local fluctuation of air pollution caused by accidents, extraordinary event and crisis situations (especially smog situations) are due to stations immediately recorded.

Water Information System "Voda" provides information about condition and flows on rivers, water levels in reservoirs, rainfall and water quality, water planning, register state of surface-and groundwater. Information in Voda Portal is provided by a wide range of authorities of Ministry of Agriculture and Ministry of Environment.

Apart from the above mentioned information systems, we recommend to incorporate interface with the Integrated Warning Service System of the Czech Hydro mete-orological system. The Integrated Warning Service System issues alert for 32 dangerous phenomena, divided into 8 groups (temperature, wind, snow, frost, storm, rain, floods and fires). Alerts on storms, rains and floods are issued in cooperation with Prediction and Warning Service. Alerts on risk of flooding are already linked to the aforementioned Water Information System.

Publicly available information services provide detailed data on climate, soil, and hydrological drought that belong among the key data for the energy sector. Another optional system from the interface is Informational System POVIS (Flood Information System) that is developed for flood protection and flood authorities. The System contains digital flood plans and books. The flood issue is added by Hydroecological Information System VÚV THM (HEIS VÚV).

National information systems are connected to external systems abroad, especially with neighbouring states for risk of flooding, or systems of European Union (Mete-oAlarm, Floods Portal EFAs,) and system of international organizations, where Czech Republic is a member. Some elements of European Critical Infrastructures are connected to the system of Critical Infrastructure Warning Information Network (CIWIN) [START_REF]Monitoring weather and climate from space[END_REF]. Warning Network was established by the European Commission in order to secure the exchange of best practices and provides a platform for the exchange of rapid alerts about threats and vulnerabilities.

Conclusion

SIMEX project is currently at the initial stage at the first phase of requirement engineering. The aim is to find out all necessary functions that the simulation tool should obtain for cooperative training of emergency management actors at subjects of critical infrastructure. We initially have identified primary and secondary stakeholders.

Communication was later started with the primary stakeholders through a guided interview and checklist. At present, the results are being evaluated. For the exact definition of requirements and elimination of uncertainties in the area of user feedback, a workshop will be organized. This will be where this project puts the most pressure on the next phase.

In definition of simulation interface, there were defined broad spectrum of information and monitoring systems for technological and natural risks that could be used for creation and processing of real simulation scenarios that will subsequently be validated by training of subjects. This fact will significantly contribute to the increase reliability, portability and interoperability in real life scenario with a view to reducing environmental impacts, loss of human lives and damage to property.

Table 1 .

 1 Summary of identified stakeholders in emergency management in subjects of critical energy infrastructure.

	Primary stakeholders	Secondary stakeholders
	Security Liaison employee	Ministry of Defence -Army of the
	Staff	Czech Republic (ensuring of selected
	Parties (suppliers of spare parts, com-	objects)
	ponents, technologies, contracted	State institution (e.g. the Government,
	subjects providing help in a crisis	National Security Council, The Czech
	situation).	Environmental Inspectorate, Energy
	Emergency services of Integrated	Regulatory Office, Administration of
	Rescue System	State Material Reserves, District Secu-
	Other emergency services of Integrat-	rity Council, Czech hydrometeorolog-
	ed Rescue System	ical Institute)
	Ministry of Interior, Ministry of Inte-	Media
	rior -the General Directorate of Fire	Civil associations
	Rescue Service of the Czech Republic	(NGO -Non-Governmental Organiza-
	Ministry of Interior of the Czech Re-	tion)
	public -Policy of the Czech Republic	Banks
	(ensuring of selected objects)
	Ministry of Industry and Trade
	Influenced Customers
	Influenced Suppliers
	Owners and Subject Investors
	External processor of crisis prepared-
	ness plan

Table 2 .

 2 Summary of significant information systems in the area of environmental safety and their operators1

	Operator	Name of information system
	Czech Hydrometeorological Insti-	AMIS
	tute
	Czech Hydrometeorological Insti-	AWOS AVIMET
	tute
	Czech Hydrometeorological Insti-	CZRAD
	tute
	Czech Hydrometeorological Insti-	EUMETSAT
	tute
	Czech Hydrometeorological Insti-	Information system of the air quality (ISKO)
	tute
	Ministry of the Environment of the	Integrated Pollution Register
	Czech Republic
	Ministry of the Environment of the	Unified Information System for the Environ-
	Czech Republic	ment (JISŽP)
	Ministry of the Environment of the	Information system SEA
	Czech Republic
	Ministry of the Environment of the	Information system EIA
	Czech Republic
	Ministry of the Environment of the	Integrated system of reporting compliance
	Czech Republic
	Ministry of Agriculture of the
	Czech Republic

Adapted in accordance with Act No. 174/2014 Coll., on significant information systems and their underlying criteria (12).


Jiye Zeng
email: zeng@nies.go.jp

Hideaki Nakajima

Yukihiro Nojiri

Shin-Ichiro Nakaoka

Reconstructing the Carbon Dioxide Absorption Patterns of World Oceans using a Feed-Forward Neural Network: Software Implementation and Employment Techniques

Keywords: CO2, climate, neural network, ocean, software

Oceans play a major role in the global carbon budget, absorbing approximately 27% of anthropogenic carbon dioxide (CO2). As the degree to which an ocean can serve as a carbon sink is determined by the partial pressure of CO2 in the surface water, it is critical to obtain an accurate estimate of the spatial distributions of CO2 and its temporal variation on a global scale. However, this is extremely challenging due to insufficient measurements, large seasonal variability, and short spatial de-correlation scales. This paper presents an open source software package that implements a feed-forward neural network and a back-propagation training algorithm to solve a problem with one output variable and a large number of training patterns. We discuss the employment of the neural network for global ocean CO2 mapping.

Introduction

Recent changes in the global climate are closely related to increasing atmospheric levels of anthropogenic greenhouse gases [START_REF] Ciais | Carbon and Other Biogeochemical Cycles[END_REF], particularly carbon dioxide (CO2).

According to the global carbon budget 2013 report [START_REF] Quéré | Global Carbon Budget 2013[END_REF], the total global carbon emissions increased from 3.9 GtC/yr in 1959 to 10.7 GtC/yr in 2012, of which approximately 43% remained in the atmosphere, 30% was taken by terrestrial sinks, and 27% was absorbed by the oceans. The uncertainty of the magnitude of the ocean sink is largely due to the uncertainty in estimating the CO2 at the ocean's surface, given that the flux at the air-water interface is determined by the difference in the partial pressure of CO2 between the two mediums, the transfer coefficient, and the wind at the surface [START_REF] Wanninkhof | Relationship between Wind-Speed and Gas-Exchange over the Ocean[END_REF]. Although the latter two parameters encompass some uncertainty, the CO2 in the surface ocean determines whether the oceans are a carbon sink or source. The most challenging obstacle in estimating the capacity of the oceans as a carbon sink is the insufficiency of measurement data. Using the 1×1 gridded dataset from the Surface Ocean CO2 Atlas1 (SOCAT) version 2 [START_REF] Bakker | An update to the Surface Ocean CO2 Atlas (SOCAT version 2)[END_REF], which is the most complete and quality-controlled dataset, we calculated that no more than 4% of the surface waters of the world's oceans had been sampled in any single month between 1990 and 2011. This motivated us to examine robust and reliable methods for interpolating the available CO2 data, both spatially and temporarily. However, most existing models use a basin scale (e.g., [START_REF] Zeng | A comparison of Delta pCO2 distributions in the northern North Pacific using results from a commercial vessel in 1995-1999[END_REF][START_REF] Lefevre | A comparison of multiple regression and neural network techniques for mapping in situ pCO2 data[END_REF][START_REF] Chierici | Biogeochemical processes as drivers of surface fCO(2) in contrasting provinces in the subarctic North Pacific Ocean[END_REF][START_REF] Sarma | Basin-scale pCO2 distribution using satellite sea surface temperature, Chla, and climatological salinity in the North Pacific in spring and summer[END_REF][START_REF] Jamet | Estimation of the oceanic pCO2 in the North Atlantic from VOS lines in-situ measurements: parameters needed to generate seasonally mean maps[END_REF][START_REF] Friedrich | Neural network-based estimates of North Atlantic surface pCO(2) from satellite data: A methodological study[END_REF][START_REF] Telszewski | Estimating the monthly pCO2 distribution in the North Atlantic using a self-organizing neural network[END_REF][START_REF] Takamura | Seasonal and Inter-Annual Variations in pCO2sea and Air-Sea CO2 Fluxes in Mid-Latitudes of the Western and Eastern North Pacific during 1999-2006: Recent Results Utilizing Voluntary Observation Ships[END_REF][START_REF] Landschützer | A Neural Network-based Estimate of the Seasonal to Interannual Variability of the Atlantic Ocean Carbon Sink[END_REF][START_REF] Nakaoka | Estimating temporal and spatial variation of ocean surface pCO2 in the North Pacific using a Self Organizing Map neural network technique[END_REF]) as the spatial de-correlation length scale of CO2 on the order of 100 km, which is about 10 times smaller than the marine atmosphere CO2 [START_REF] Li | Global aurocorrelation scales of the partial pressure of oceanic CO2[END_REF], and the seasonal changes in the surface ocean CO2 can be 100 μatm or more [START_REF] Wanninkhof | Global ocean carbon uptake: magnitude, variability and trends[END_REF]. The most used product to estimate the global ocean CO2 levels is the monthly climatology data of [START_REF] Takahashi | Climatological mean and decadal change in surface ocean pCO2, and net sea-air CO2 flux over the global oceans[END_REF], which uses a 5×4 degree mesh.

This work presents open-source software that implements a feed-forward neural network (FNN) specifically developed for modeling environmental problems such as reconstructing the global ocean CO2. The software has been used by [START_REF] Zeng | A Global Surface Ocean fCO2 Climatology Based on a Feed-Forward Neural Network[END_REF] to produce monthly maps of the climatology of CO2 with a spatial resolution of 1×1 degrees. We discuss possible techniques to extend the work of [START_REF] Zeng | A Global Surface Ocean fCO2 Climatology Based on a Feed-Forward Neural Network[END_REF] to reconstruct the time variant distribution of the global ocean CO2 from 1990 to 2011.

Method

Software Package

Our software package 2 implements the FNN shown Figure 1. The FNN comprises three layers: input, hidden, and output. A neuron in the input layer takes the value of an input variable and passes it to all neurons in the hidden layer. A neuron in the hidden or output layer adds a bias to the weighted sum of all of the outputs from its upstream neurons and transforms the sum to yield its output:

∑ = - + = + = = M i upstream i i x y w b x e x f y 1 , 1 1 ) ( (1)
The sigmoid transform function was chosen because it increases monotonically from 0 to 1 for x from -∞ to +∞ and the derivative of y with respect to x yields a simple form that affiliates the so-called back-propagation [START_REF] Rumelhart | Learning Representations by Back-Propagating Errors[END_REF] algorithm for training the FNN. In training, the algorithm changes the weight and bias parameters proportionally according to the negative gradient of the error cost function, i.e., the sum of squared error between the network output y and the target d: where P is the total number of training patterns (a pattern comprises a set of data of all input variables). In the simplest form, the algorithm uses the first-order derivative of the cost function as the gradient g to find the minima in the error space:

e J w g T F = ∇ = ) ( (3)
where the vector w includes both weight and bias parameters and J is the Jacobian matrix. Assuming w has N elements, J has the form of

                    ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂
- = ∂ ∂ = - = ∂ ∂ ∂ ∂ - = ∂ ∂ - = ∂ ∂ - = ∂ ∂ ∂ ∂ - = ∂ ∂ - = ∂ ∂ δ δ δ (5)
and the partial differentiations by the weights and bias of the jth hidden neuron for the ith input neuron can be expressed as

- = ∂ ∂ ∂ ∂ ∂ ∂ = - = ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ - = ∂ ∂ - = ∂ ∂ - = ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ - = ∂ ∂ - = ∂ ∂ δ δ δ δ (6)
Updating neuron parameters for the tth training epoch (a complete loop through all training patterns) can be expressed as

g w w w w α + - = ∆ + - = ) 1 ( ) ( ) 1 ( ) ( t t t t (7)
where α is the learning rate with a value between 0 and 1. The convergence of training the FNN by the gradient expressed in equation ( 3) is slow. The Levenberg-Marquardt algorithm (see [START_REF] Lecun | Efficient backprop[END_REF][START_REF] Wilamowski | Improved Computation for Levenberg-Marquardt Training[END_REF]) improves the convergence speed by considering the second-order derivative of the cost function, resulting in a new estimation for the gradient as

e J I J J g T T 1 ) ( - + = µ (8)
where µ is the regularization constant and I is a unit matrix. In our implementation, we set µ proportional to the root mean square error: We call β the regularization coefficient.

We implemented several methods in the software to achieve efficient computing and simple usage. First, the method of [START_REF] Wilamowski | Improved Computation for Levenberg-Marquardt Training[END_REF] was used to eliminate the necessity of storing a large Jacobian matrix in the memory. Second, the software scales all input variable data to have a zero mean and unit standard deviation and scales the target variable data to vary from 0.1 to 0.9. This method not only improves convergence speed but also makes it possible to use a default α and β for a variety of cases. Third, the software allows a user to use a text file and select any given number of columns in any order as input and output variables.

CO2 Model

Our basic model equation defines the fugacity of CO2 (fCO2) as a nonlinear function of month (MON), latitude (LAT), and longitude (LON):

) , , ( 2
LON LAT MON F fCO = (10)
Because of insufficient measurements, we introduced sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll-a concentration (CHL) as proxy variables in space and time. Therefore, the model equation becomes The model equation establishes the relationship between the eight input variables and the one output variable of the neural network.

Employment Issues

Two issues often cloud the proper use of an FNN: landing on a local minimum in training and choosing the "right" number of hidden neurons. The first issue can be detected using a different random number seeds to initialize the weight and bias parameters, or using different learning rates and regularization parameters. We have experimented with the Levenberg-Marquardt algorithm with these approaches and found that landing on a local minimum rarely occurred in our case. The second issue is notorious. It is well known that an FNN can approximate any finite function given a sufficiently large number of hidden neurons [START_REF] Blum | Approximation-Theory and Feedforward Networks[END_REF][START_REF] Hornik | Approximation Capabilities of Multilayer Feedforward Networks[END_REF]. That means that if the fCO2 values are unique in corresponding to unique values of MON, LAT, LON, SST, SSS, and CHL, equation ( 12) can be fitted accurately with a larger number of hidden neurons. An FNN configured as such remembers all training patterns and thus loses the capability to filter out erroneous information in measurement data. Unfortunately, there is no theoretically approved rule for choosing the right number of hidden neurons [START_REF] Svozil | Introduction to multi-layer feed-forward neural networks[END_REF]. Our criterion for selecting the number is that the standard deviation of the difference between model outputs and observations (std1) is no larger than the standard deviations of repeated fCO2 measurements averaged over all months and grid boxes (std2) by 20%, i.e., |std1-std2|/std2<0.2.

An FNN configuration needs validation before it can be used to make predictions. Our approach uses 90% of the randomly selected data points for training and the remaining 10% for validation [START_REF] Zeng | A Global Surface Ocean fCO2 Climatology Based on a Feed-Forward Neural Network[END_REF]. Once a configuration is validated, we used all data points to train the FNN to obtain a final product for prediction.

Results

We succeeded in using the FNN to model the mean global rate of increase in CO2 fugacity at the ocean's surface [START_REF] Zeng | A Global Surface Ocean fCO2 Climatology Based on a Feed-Forward Neural Network[END_REF], yielding a value of 1.5 µatm/yr, which is in agreement with the commonly accepted value. Prior to this work, the rate was esti-mated from a collection of rates [START_REF] Takahashi | Climatological mean and decadal change in surface ocean pCO2, and net sea-air CO2 flux over the global oceans[END_REF] that vary greatly. Using our rate, we were able to obtain the monthly climatology maps of CO2 for the reference year of 2000 [START_REF] Zeng | A Global Surface Ocean fCO2 Climatology Based on a Feed-Forward Neural Network[END_REF]. The results agree well with both measurements and the values reported by [START_REF] Takahashi | Climatological mean and decadal change in surface ocean pCO2, and net sea-air CO2 flux over the global oceans[END_REF], which is the most used resource for calculating ocean sinks. Extending the work of Zeng et al. [START_REF] Zeng | A Global Surface Ocean fCO2 Climatology Based on a Feed-Forward Neural Network[END_REF], we reconstructed monthly CO2 maps for 1990-2011 (see the examples in Fig. 2). Two new steps were taken to improve the mapping. First, we excluded the LAT and LON variables in equation ( 12) to obtain a first guess of CO2 for areas where measurements were scarce, i.e., the Southern Pacific Ocean. As a large spatial gap may lead to overestimating the nonlinear interpolation of the FNN, we used the first guess for gap-filling every 10 degrees. Second, a version of the FNN without CHL was used to estimate CO2 for areas where CHL data were not available. This step completes the coverage of the global oceans. The yielded product provides an alternative for estimating the time-variant ocean carbon sinks for 1990-2011.

Conclusions

Our results show that the FNN is an effective model for reconstructing global ocean CO2 sinks. As the model only establishes the dependence of the output variable on the input variables, and makes no assumption on the explicit expression of the model equation, it's straightforward to employ in practice. However, users must be aware of its nonlinear property and take appropriate steps to prevent over-fitting and overshooting.

  the output neuron and a training pattern, the partial differentiation by the weights and bias for the jth hidden neuron can be derived as

Fig. 1 .

 1 Fig. 1. Illustration of the feed forward neural network. Neurons in different layers are fully connected, but no connection exists among neurons of the same layer.

  setting for long-term modeling, MON and LON must be transformed to account for their circular properties. As a result, the complete model equation be-

Fig. 2 .

 2 Fig. 2. Distributions of CO2 fugacity (µatm) in 1990 and 2011. The blank areas were either covered by ice or the depth is less than 500 m, which is our criterion for identifying open oceans.

http://www.socat.info/ adfa, p. 1,

© Springer-Verlag Berlin Heidelberg 2011

We made efforts to minimize the user interaction required, and the FNN converges quickly and steadily. In our experiments, the error of the cost function decreased steadily and changed very slightly after about 100 epochs for over 100,000 training patterns. The software only requires an ordinary PC to obtain results in 10 to 30 min for large datasets. We expect the software to be used for solving environmental problems similar to the carbon sink example discussed herein.