Sharing and visualizing environmental data using Virtual Globes

Virtual globe technology holds many exciting possibilities for environmental science. These easy-to-use, intuitive systems provide means for simultaneously visualizing four-dimensional environmental data from many different sources, enabling the generation of new hypotheses and driving greater understanding of the Earth system. Through the use of simple markup languages, scientists can publish and consume data in interoperable formats without the need for technical assistance. In this paper we give, with examples from our own work, a number of scientific uses for virtual globes, demonstrating their particular advantages. We explain how we have used Web Services to connect virtual globes with diverse data sources and enable more sophisticated usage such as data analysis and collaborative visualization. We also discuss the current limitations of the technology, with particular regard to the visualization of subsurface data and vertical sections.

GODIVA2: interactive visualization of environmental data on the Web

GODIVA2 is a dynamic website that provides visual access to several terabytes of physically distributed, four-dimensional environmental data. It allows users to explore large datasets interactively without the need to install new software or download and understand complex data. Through the use of open international standards, GODIVA2 maintains a high level of interoperability with third-party systems, allowing diverse datasets to be mutually compared. Scientists can use the system to search for features in large datasets and to diagnose the output from numerical simulations and data processing algorithms. Data providers around Europe have adopted GODIVA2 as an INSPIRE-compliant dynamic quick-view system for providing visual access to their data.

A Web Map Service implementation for the visualization of multidimensional gridded environmental data

We describe ncWMS, an implementation of the Open Geospatial Consortium’s Web Map Service (WMS) specification for multidimensional gridded environmental data. ncWMS can read data in a large number of common scientific data formats – notably the NetCDF format with the Climate and Forecast conventions – then efficiently generate map imagery in thousands of different coordinate reference systems. It is designed to require minimal configuration from the system administrator and, when used in conjunction with a suitable client tool, provides end users with an interactive means for visualizing data without the need to download large files or interpret complex metadata. It is also used as a “bridging” tool providing interoperability between the environmental science community and users of geographic information systems. ncWMS implements a number of extensions to the WMS standard in order to fulfil some common scientific requirements, including the ability to generate plots representing timeseries and vertical sections. We discuss these extensions and their impact upon present and future interoperability. We discuss the conceptual mapping between the WMS data model and the data models used by gridded data formats, highlighting areas in which the mapping is incomplete or ambiguous. We discuss the architecture of the system and particular technical innovations of note, including the algorithms used for fast data reading and image generation. ncWMS has been widely adopted within the environmental data community and we discuss some of the ways in which the software is integrated within data infrastructures and portals.

The world at one's fingertips: interactive interpretation of environmental data

This chapter introduces the latest practices and technologies in the interactive interpretation of environmental data. With environmental data becoming ever larger, more diverse and more complex, there is a need for a new generation of tools that provides new capabilities over and above those of the standard workhorses of science. These new tools aid the scientist in discovering interesting new features (and also problems) in large datasets by allowing the data to be explored interactively using simple, intuitive graphical tools. In this way, new discoveries are made that are commonly missed by automated batch data processing. This chapter discusses the characteristics of environmental science data, common current practice in data analysis and the supporting tools and infrastructure. New approaches are introduced and illustrated from the points of view of both the end user and the underlying technology. We conclude by speculating as to future developments in the field and what must be achieved to fulfil this vision.

The Environmental Data Abstraction Library (EDAL): a modular approach to processing and visualising large environmental data

The Environmental Data Abstraction Library provides a modular data management library for bringing new and diverse datatypes together for visualisation within numerous software packages, including the ncWMS viewing service, which already has very wide international uptake. The structure of EDAL is presented along with examples of its use to compare satellite, model and in situ data types within the same visualisation framework. We emphasize the value of this capability for cross calibration of datasets and evaluation of model products against observations, including preparation for data assimilation.

Preliminary ethnoarchaeological research on modern animal husbandry in Bestansur, Iraqi Kurdistan: integrating animal, plant and environmental data

This paper presents preliminary results from an ethnoarchaeological study of animal husbandry in the modern village of Bestansur, situated in the lower Zagros Mountains of Iraqi Kurdistan. This research explores how modern families use and manage their livestock within the local landscape and identifies traces of this use. The aim is to provide the groundwork for future archaeological investigations focusing on the nearby Neolithic site of Bestansur. This is based on the premise that modern behaviours can suggest testable patterns for past practices within the same functional and ecological domains. Semi-structured interviews conducted with villagers from several households provided large amounts of information on modern behaviours that helped direct data collection, and which also illustrate notable shifts in practices and use of the local landscape over time. Strontium isotope analysis of modern plant material demonstrates that a measurable variation exists between the alluvial floodplain and the lower foothills, while analysis of modern dung samples shows clear variation between sheep/goat and cow dung, in terms of numbers of faecal spherulites. These results are specific to the local environment of Bestansur and can be used for evaluating and contextualising archaeological evidence as well as providing modern reference material for comparative purposes.

Storing and manipulating environmental big data with JASMIN

JASMIN is a super-data-cluster designed to provide a high-performance high-volume data analysis environment for the UK environmental science community. Thus far JASMIN has been used primarily by the atmospheric science and earth observation communities, both to support their direct scientific workflow, and the curation of data products in the STFC Centre for Environmental Data Archival (CEDA). Initial JASMIN configuration and first experiences are reported here. Useful improvements in scientific workflow are presented. It is clear from the explosive growth in stored data and use that there was a pent up demand for a suitable big-data analysis environment. This demand is not yet satisfied, in part because JASMIN does not yet have enough compute, the storage is fully allocated, and not all software needs are met. Plans to address these constraints are introduced.

Automated pre-processing strategies for species occurrence data used in biodiversity modelling

To construct Biodiversity richness maps from Environmental Niche Models (ENMs) of thousands of species is time consuming. A separate species occurrence data pre-processing phase enables the experimenter to control test AUC score variance due to species dataset size. Besides, removing duplicate occurrences and points with missing environmental data, we discuss the need for coordinate precision, wide dispersion, temporal and synonymity filters. After species data filtering, the final task of a pre-processing phase should be the automatic generation of species occurrence datasets which can then be directly ’plugged-in’ to the ENM. A software application capable of carrying out all these tasks will be a valuable time-saver particularly for large scale biodiversity studies.

MOLES3: implementing an ISO standards driven data catalogue

ISO19156 Observations and Measurements (O&M) provides a standardised framework for organising information about the collection of information about the environment. Here we describe the implementation of a specialisation of O&M for environmental data, the Metadata Objects for Linking Environmental Sciences (MOLES3). MOLES3 provides support for organising information about data, and for user navigation around data holdings. The implementation described here, “CEDA-MOLES”, also supports data management functions for the Centre for Environmental Data Archival, CEDA. The previous iteration of MOLES (MOLES2) saw active use over five years, being replaced by CEDA-MOLES in late 2014. During that period important lessons were learnt both about the information needed, as well as how to design and maintain the necessary information systems. In this paper we review the problems encountered in MOLES2; how and why CEDA-MOLES was developed and engineered; the migration of information holdings from MOLES2 to CEDA-MOLES; and, finally, provide an early assessment of MOLES3 (as implemented in CEDA-MOLES) and its limitations. Key drivers for the MOLES3 development included the necessity for improved data provenance, for further structured information to support ISO19115 discovery metadata export (for EU INSPIRE compliance), and to provide appropriate fixed landing pages for Digital Object Identifiers (DOIs) in the presence of evolving datasets. Key lessons learned included the importance of minimising information structure in free text fields, and the necessity to support as much agility in the information infrastructure as possible without compromising on maintainability both by those using the systems internally and externally (e.g. citing in to the information infrastructure), and those responsible for the systems themselves. The migration itself needed to ensure continuity of service and traceability of archived assets.

Storm prediction research and its application to the oil/gas industry

The accurate prediction of storms is vital to the oil and gas sector for the management of their operations. An overview of research exploring the prediction of storms by ensemble prediction systems is presented and its application to the oil and gas sector is discussed. The analysis method used requires larger amounts of data storage and computer processing time than other more conventional analysis methods. To overcome these difficulties eScience techniques have been utilised. These techniques potentially have applications to the oil and gas sector to help incorporate environmental data into their information systems

A theoretical adaptive model of thermal comfort – Adaptive Predicted Mean Vote (aPMV)

This paper presents in detail a theoretical adaptive model of thermal comfort based on the “Black Box” theory, taking into account factors such as culture, climate, social, psychological and behavioural adaptations, which have an impact on the senses used to detect thermal comfort. The model is called the Adaptive Predicted Mean Vote (aPMV) model. The aPMV model explains, by applying the cybernetics concept, the phenomena that the Predicted Mean Vote (PMV) is greater than the Actual Mean Vote (AMV) in free-running buildings, which has been revealed by many researchers in field studies. An Adaptive coefficient (λ) representing the adaptive factors that affect the sense of thermal comfort has been proposed. The empirical coefficients in warm and cool conditions for the Chongqing area in China have been derived by applying the least square method to the monitored onsite environmental data and the thermal comfort survey results.

The benefits of a compute cluster approach to high spatial resolution biodiversity richness modelling: projecting the impact of climate change on Mediterranean flora

High spatial resolution environmental data gives us a better understanding of the environmental factors affecting plant distributions at fine spatial scales. However, large environmental datasets dramatically increase compute times and output species model size stimulating the need for an alternative computing solution. Cluster computing offers such a solution, by allowing both multiple plant species Environmental Niche Models (ENMs) and individual tiles of high spatial resolution models to be computed concurrently on the same compute cluster. We apply our methodology to a case study of 4,209 species of Mediterranean flora (around 17% of species believed present in the biome). We demonstrate a 16 times speed-up of ENM computation time when 16 CPUs were used on the compute cluster. Our custom Java ‘Merge’ and ‘Downsize’ programs reduce ENM output files sizes by 94%. The median 0.98 test AUC score of species ENMs is aided by various species occurrence data filtering techniques. Finally, by calculating the percentage change of individual grid cell values, we map the projected percentages of plant species vulnerable to climate change in the Mediterranean region between 1950–2000 and 2020.

The climate and environment of Byzantine Anatolia: integrating science, history, and archaeology

The integration of high-resolution archaeological, textual, and environmental data with longer-term, low-resolution data affords greater precision in identifying some of the causal relationships underlying societal change. Regional and microregional case studies about the Byzantine world—in particular, Anatolia, which for several centuries was the heart of that world—reveal many of the difficulties that researchers face when attempting to assess the influence of environmental factors on human society. The Anatolian case challenges a number of assumptions about the impact of climatic factors on socio-political organization and medium-term historical evolution, highlighting the importance of further collaboration between historians, archaeologists, and climate scientists.