Focusing on commercial office real estate as both a manifestation of and a conduit of cross-border capital flows, this paper refers to the concepts of topology and topography in a theoretical and empirical exploration of contemporary 'network economy' spatial implications for the 'polycentric urban region' (PUR). A body of research has cast doubt on the normative European representation of the multi-centre PUR as a balanced, sustainable spatial development model. Yet, the model has continued to be propagated in European territorial strategy and has been influential internationally. Academic perspectives and qualitative evidence reviewed in the paper shed a light on mutual dependencies and recursive relations between network economy global structural processes, international office real estate investment practices mediated by city governments, and the spatial configuration of density. Commercial investment and city planning actor practices chime with urban agglomeration, spatial concentration and density. Quantitative evidence of associations between urban density and office real estate investment returns and capital flows is found. It is concluded that network economy topology, politics and the city are in a dialectical relationship with the PUR territorial governance agenda for spatially balanced regional development.

The energy sector is undergoing a major transformation. This transformation in the energy sector is taking place against a variable and changing climate. Given the weather-and climate-dependency of both renewable energy and demand (even in the case of large storage uptake), it is important to develop robust climate-based tools to advise energy planners and policy makers. This talk will describe how the EU Copernicus Climate Change Service (C3S) European Climatic Energy Mixes (ECEM) project can assist in this energy transformation. ECEM is producing, in close collaboration with prospective users, a proof-of-concept climate service, or demonstrator, whose purpose is to enable the energy industry and policy makers to assess how well different energy supply mixes in Europe will meet demand, over different time horizons (from seasonal to long-term decadal planning), focusing on the role climate has on the mixes. Energy time series were modelled and computed at country level and daily time step for the whole of Eu-rope. Bias-adjusted Essential Climate Variables from ERA-Interim over a European domain for 1979-2016 produced by ECEM were used to calculate energy variables, namely electricity demand and generation from wind, solar and hydro power. Different approaches were adopted depending on the target variable, and the availability of measured data to calibrate and validate the energy models. Examples from the C3S ECEM Demonstrator – an interactive and visual tool which allows users to view and explore energy supply and demand profiles for each European country (and for ca 100 clusters) and generation type, as well as climate variables, in map and/or time series format – will be used to illustrate the power of climate services in supporting the energy industry. The C3S ECEM Demonstrator is being developed according to user needs and integrates the energy and climate variables being produced in C3S ECEM on historic, seasonal forecast and climate projection timescales. Data can also be downloaded directly from the Demonstrator. The presentation will conclude with an outlook for future developments of climate services for the energy sector, and what benefits they could provide to the energy industry and decision makers at different levels.

The EU Copernicus Climate Change Service (C3S) European Climatic Energy Mixes (ECEM) has produced, in close collaboration with prospective users, a proof-of-concept climate service, or Demonstrator, designed to enable the energy industry and policy makers assess how well different energy supply mixes in Europe will meet demand, over different time horizons (from seasonal to long-term decadal planning), focusing on the role climate has on the mixes. The concept of C3S ECEM, its methodology and some results are presented here. The first part focuses on the construction of reference data sets for climate variables based on the ERA-Interim reanalysis. Subsequently, energy variables were created by transforming the bias-adjusted climate variables using a combination of statistical and physically-based models. A comprehensive set of measured energy supply and demand data was also collected, in order to assess the robustness of the conversion to energy variables. Climate and energy data have been produced both for the historical period (1979–2016) and for future projections (from 1981 to 2100, to also include a past reference period, but focusing on the 30 year period 2035–2065). The skill of current seasonal forecast systems for climate and energy variables has also been assessed. The C3S ECEM project was designed to provide ample opportunities for stakeholders to convey their needs and expectations, and assist in the development of a suitable Demonstrator. This is the tool that collects the output produced by C3S ECEM and presents it in a user-friendly and interactive format, and it therefore constitutes the essence of the C3S ECEM proof-of-concept climate service.

Virtual globe technology holds many exciting possibilities for environmental science. These easyto-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.

The CF (Climate and Forecast) metadata conventions are designed to promote the creation, processing, and sharing of climate and forecasting data using Network Common Data Form (netCDF) files and libraries. The CF conventions provide a description of the physical meaning of data and of their spatial and temporal properties, but they depend on the netCDF file encoding which can currently only be fully understood and interpreted by someone familiar with the rules and relationships specified in the conventions documentation. To aid in development of CF-compliant software and to capture with a minimal set of elements all of the information contained in the CF conventions, we propose a formal data model for CF which is independent of netCDF and describes all possible CF-compliant data. Because such data will often be analysed and visualised using software based on other data models, we compare our CF data model with the ISO 19123 coverage model, the Open Geospatial Consortium CF netCDF standard, and the Unidata Common Data Model. To demonstrate that this CF data model can in fact be implemented, we present cf-python, a Python software library that conforms to the model and can manipulate any CF-compliant dataset.

Abstract. The CF (Climate and Forecast) metadata conventions are designed to promote the creation, processing and sharing of climate and forecasting data using Network Common Data Form (netCDF) files and libraries. The CF conventions provide a description of the physical meaning of data and of their spatial and temporal properties, but they depend on the netCDF file encoding which can currently only be fully understood and interpreted by someone familiar with the rules and relationships specified in the conventions documentation. To aid in development of CF-compliant software and to capture with a minimal set of elements all of the information contained in the CF conventions, we propose a formal data model for CF which is independent of netCDF and describes all possible CF-compliant data. Because such data will often be analysed and visualised using software based on other data models, we compare the CF data model with the ISO 19123 coverage model, the Open Geospatial Consortium CF netCDF standard and the Unidata Common Data Model. To demonstrate that the CF data model can in fact be implemented, we present cf-python, a Python software library that conforms to the model and can manipulate any CF-compliant dataset.

The energy sector is undergoing a major transformation. This transformation in the energy sector is taking place against a variable and changing climate. Given the weather-and climate-dependency of both renewable energy and demand (even in the case of large storage uptake), it is important to develop robust climate-based tools to advise energy planners and policy makers. The EU Copernicus Climate Change Service (C3S) European Climatic Energy Mixes (ECEM) is producing, in close collaboration with prospective users, a proof-of-concept climate service, or demonstrator, whose purpose is to enable the energy industry and policy makers to assess how well different energy supply mixes in Europe will meet demand, over different time horizons (from seasonal to long-term decadal planning), focusing on the role climate has on the mixes. This presentation will provide details about the calibration of climate variables over the historic period (last ca. 40 years) derived from reanalyses that was necessary in order to reduce the bias in variables relevant to the energy sector, such as wind speed and solar radiation. The conversion of climate variables into energy variables for wind power, solar power and hydropower, by means of both statistical methods and physical methods where possible, and for various European countries is also presented. An assessment of seasonal climate forecasts for use by the energy sector over the European domain will also be discussed.

New methods need to be developed to handle the increasing size of data sets in atmospheric science - traditional analysis scripts often inefficiently read and process the data. NetCDF4 is a common file format used in atmospheric and ocean sciences, and Python is widely used in atmospheric and ocean science data analysis. The aim of this work is to provide insight into which read patterns and sizes are most effective when using the netCDF4-python library. Quantitative information on these would be useful information for scientists, library developers, and data managers. Three different read patterns were compared to simulate different types of reads: sequential, strided, and random, with each tested across three file systems - Panasas, Lustre, and GPFS. Read rate and standard deviation were measured using Python and C, reading from plain binary files and NetCDF4 files. Read performance for netCDF4-python was compared with the performance of native Python, the C NetCDF library, and the C Posix library. As expected, comparison between the different read modes shows that access pattern and read size significantly affect achieved performance. The results also show read performance profiles that are similar for the C, C NetCDF, and Python tests, however netCDF4-python performs less efficiently.

AbstractFor users of climate services, the ability to quickly determine the datasets that best fit one’s needs would be invaluable. The volume, variety, and complexity of climate data makes this judgment difficult. The ambition of CHARMe (Characterization of metadata to enable high-quality climate services) is to give a wider interdisciplinary community access to a range of supporting information, such as journal articles, technical reports, or feedback on previous applications of the data. The capture and discovery of this “commentary” information, often created by data users rather than data providers, and currently not linked to the data themselves, has not been significantly addressed previously. CHARMe applies the principles of Linked Data and open web standards to associate, record, search, and publish user-derived annotations in a way that can be read both by users and automated systems. Tools have been developed within the CHARMe project that enable annotation capability for data delivery systems ...

Geospatial information of many kinds, from topographic maps to scientific data, is increasingly being made available through web mapping services. These allow georeferenced map images to be served from data stores and displayed in websites and geographic information systems, where they can be integrated with other geographic information. The Open Geospatial Consortium’s Web Map Service (WMS) standard has been widely adopted in diverse communities for sharing data in this way. However, current services typically provide little or no information about the quality or accuracy of the data they serve. In this paper we will describe the design and implementation of a new “quality-enabled” profile of WMS, which we call “WMS-Q”. This describes how information about data quality can be transmitted to the user through WMS. Such information can exist at many levels, from entire datasets to individual measurements, and includes the many different ways in which data uncertainty can be expressed. We also describe proposed extensions to the Symbology Encoding specification, which include provision for visualizing uncertainty in raster data in a number of different ways, including contours, shading and bivariate colour maps. We shall also describe new open-source implementations of the new specifications, which include both clients and servers.