Cloud droplet size and liquid water path retrievals from zenith radiance measurements: examples from the Atmospheric Radiation Measurement Program and the Aerosol Robotic Network

The ground-based Atmospheric Radiation Measurement Program (ARM) and NASA Aerosol Robotic Net- work (AERONET) routinely monitor clouds using zenith ra- diances at visible and near-infrared wavelengths. Using the transmittance calculated from such measurements, we have developed a new retrieval method for cloud effective droplet size and conducted extensive tests for non-precipitating liquid water clouds. The underlying principle is to combine a liquid-water-absorbing wavelength (i.e., 1640 nm) with a non-water-absorbing wavelength for acquiring information on cloud droplet size and optical depth. For simulated stratocumulus clouds with liquid water path less than 300 g m−2 and horizontal resolution of 201 m, the retrieval method underestimates the mean effective radius by 0.8μm, with a root-mean-squared error of 1.7 μm and a relative deviation of 13%. For actual observations with a liquid water path less than 450 g m−2 at the ARM Oklahoma site during 2007– 2008, our 1.5-min-averaged retrievals are generally larger by around 1 μm than those from combined ground-based cloud radar and microwave radiometer at a 5-min temporal resolution. We also compared our retrievals to those from combined shortwave flux and microwave observations for relatively homogeneous clouds, showing that the bias between these two retrieval sets is negligible, but the error of 2.6 μm and the relative deviation of 22 % are larger than those found in our simulation case. Finally, the transmittance-based cloud effective droplet radii agree to better than 11 % with satellite observations and have a negative bias of 1 μm. Overall, the retrieval method provides reasonable cloud effective radius estimates, which can enhance the cloud products of both ARM and AERONET.

Greater Amman: metropolitan growth and scenarios for sustainable urban development

This paper reviews the development of Greater Amman, Jordan noting that the vast urban expansion that has occurred over the last fifty years has led to the desertification of rare fertile lands, following the fragmented and scattered territorial expansion of the city. The future scenario for planning in Greater Amman is analyzed in respect of proposals outlined in the Metropolitan Growth Plan of 2008, which assumes a rapid population growth from 2,200,000 persons in 2006, to approximately 6,500,000 by 2025. The concentration of more than 39 per cent of the national population of Jordan in Greater Amman threatens the transformation of former distinct settlement pattern into a distinctive continuous urban zone, aggravating problems of infrastructural provision, water needs, agricultural lands, and leaving unresolved problems of land inflation, poor urban standards and housing shortages. In conclusion, the environmental implications of the Amman Metropolitan Growth Plan are analysed, and it is suggested that an alternative approach is needed, based on clear principles of sustainable urban development.

City futures: exploring urban retrofit and sustainable transitions

Cities are responsible for up to 70% of global carbon emissions and 75% of global energy consumption. By 2050 it is estimated that 70% of the world's population will live in cities. The critical challenge for contemporary urbanism, therefore, is to understand how to develop the knowledge, capacity and capability for public agencies, the private sector and multiple users in city-regions (i.e. the city and its wider hinterland) to re-engineer systemically their built environment and urban infrastructure in response to climate change and resource constraints. To inform transitions to urban sustainability, key stakeholders' perceptions were sought though a participatory backcasting and scenario foresight process in order to illuminate challenging but realistic socio-technical scenarios for the systemic retrofit of core UK city-regions. The challenge of conceptualizing complex urban transitions is explored across multiple socio-technical ‘regimes’ (housing, non-domestic buildings, urban infrastructure), scales (building, neighbourhood, city-region), and domains (energy, water, use of resources) within a participatory process. The development of three archetypal ‘guiding visions’ of retrofit city-regional futures developed through this process are discussed, along with the contribution that such foresight processes might play in ‘opening up’ the governance and strategic navigation of urban sustainability.

Sustainable urban metabolism as a link between bio-physical sciences and urban planning: the BRIDGE project

Urban metabolism considers a city as a system with flows of energy and material between it and the environment. Recent advances in bio-physical sciences provide methods and models to estimate local scale energy, water, carbon and pollutant fluxes. However, good communication is required to provide this new knowledge and its implications to endusers (such as urban planners, architects and engineers). The FP7 project BRIDGE (sustainaBle uRban plannIng Decision support accountinG for urban mEtabolism) aimed to address this gap by illustrating the advantages of considering these issues in urban planning. The BRIDGE Decision Support System (DSS) aids the evaluation of the sustainability of urban planning interventions. The Multi Criteria Analysis approach adopted provides a method to cope with the complexity of urban metabolism. In consultation with targeted end-users, objectives were defined in relation to the interactions between the environmental elements (fluxes of energy, water, carbon and pollutants) and socioeconomic components (investment costs, housing, employment, etc.) of urban sustainability. The tool was tested in five case study cities: Helsinki, Athens, London, Florence and Gliwice; and sub-models were evaluated using flux data selected. This overview of the BRIDGE project covers the methods and tools used to measure and model the physical flows, the selected set of sustainability indicators, the methodological framework for evaluating urban planning alternatives and the resulting DSS prototype.

Climate and more sustainable cities: climate information for improved planning and management of cities (producers/capabilities perspective)

In the last two decades substantial advances have been made in the understanding of the scientific basis of urban climates. These are reviewed here with attention to sustainability of cities, applications that use climate information, and scientific understanding in relation to measurements and modelling. Consideration is given from street (micro) scale to neighbourhood (local) to city and region (meso) scale. Those areas where improvements are needed in the next decade to ensure more sustainable cities are identified. High-priority recommendations are made in the following six strategic areas: observations, data, understanding, modelling, tools and education. These include the need for more operational urban measurement stations and networks; for an international data archive to aid translation of research findings into design tools, along with guidelines for different climate zones and land uses; to develop methods to analyse atmospheric data measured above complex urban surfaces; to improve short-range, high-resolution numerical prediction of weather, air quality and chemical dispersion through improved modelling of the biogeophysical features of the urban land surface; to improve education about urban meteorology; and to encourage communication across scientific disciplines at a range of spatial and temporal scales.

Sustainable social housing: examination of global best practice

This report reviews best social housing schemes from around the world, and makes recommendations for a more sustainable approach.

Sustainable construction: exploring the capabilities of Nigerian construction firms

As the built environment accounts for much of the world's emissions, resource consumption and waste, concerns remain as to how sustainable the sector is. Understanding how such concerns can be better managed is complex, with a range of competing agendas and institutional forces at play. This is especially the case in Nigeria where there are often differing priorities, weak regulations and institutions to deal with this challenge. Construction firms are in competition with each other in a market that is growing in size and sophistication yearly. The business case for sustainability has been argued severally in literature. However, the capability of construction firms with respect to sustainability in Nigeria has not been studied. This paper presents the preliminary findings of an exploratory multi-case study carried out to understand the firm's views on sustainability as a source of competitive advantage. A international firm and a lower medium-sized indigenous firm were selected for this purpose. Qualitative interviews were conducted with top-level management of both firms, with key themes from the sustainable construction and dynamic capabilities literature informing the case study protocol. The interviews were transcribed and analysed with the use of NVivo software. The findings suggest that the multinational firm is better grounded in sustainability knowledge. Although the level of awareness and demand for sustainable construction is generally very poor, few international clients are beginning to stimulate interest in sustainable buildings. This has triggered both firms to build their capabilities in that regard, albeit in an unhurried manner. Both firms agree on the potentials of market-driven sustainability in the long term. Nonetheless, more drastic actions are required to accelerate the sustainable construction agenda in Nigeria.

Using proxies to calculate the carbon impact of investment into electricity network assets

Replacement and upgrading of assets in the electricity network requires financial investment for the distribution and transmission utilities. The replacement and upgrading of network assets also represents an emissions impact due to the carbon embodied in the materials used to manufacture network assets. This paper uses investment and asset data for the GB system for 2015-2023 to assess the suitability of using a proxy with peak demand data and network investment data to calculate the carbon impacts of network investments. The proxies are calculated on a regional basis and applied to calculate the embodied carbon associated with current network assets by DNO region. The proxies are also applied to peak demand data across the 2015-2023 period to estimate the expected levels of embodied carbon that will be associated with network investment during this period. The suitability of these proxies in different contexts are then discussed, along with initial scenario analysis to calculate the impact of avoiding or deferring network investments through distributed generation projects. The proxies were found to be effective in estimating the total embodied carbon of electricity system investment in order to compare investment strategies in different regions of the GB network.

Effects of a multi-session Cognitive Bias Modification program on interpretative biases and social anxiety symptoms in a sample of Iranian socially-anxious students

This study examines the effects of a multi-session Cognitive Bias Modification (CBM) program on interpretative biases and social anxiety in an Iranian sample. Thirty-six volunteers with a high score on social anxiety measures were recruited from a student population and randomly allocated into the experimental and control groups. In the experimental group, participants received 4 sessions of positive CBM for interpretative biases (CBM-I) over 2 weeks in the laboratory. Participants in the control condition completed a neutral task matched the active CBM-I intervention in format and duration but did not encourage positive disambiguation of socially ambiguous scenarios. The results indicated that after training the positive CBM-I group exhibited more positive (and less negative) interpretations of ambiguous scenarios and less social anxiety symptoms relative to the control condition at both 1 week post-test and 7 weeks follow-up. It is suggested that clinical trials are required to establish the clinical efficacy of this intervention for social anxiety.

CDASH: a cloud-enabled program for structure solution from powder diffraction data

The simulated annealing approach to crystal structure determination from powder diffraction data, as implemented in the DASH program, is readily amenable to parallelization at the individual run level. Very large scale increases in speed of execution can be achieved by distributing individual DASH runs over a network of computers. The CDASH program delivers this by using scalable on-demand computing clusters built on the Amazon Elastic Compute Cloud service. By way of example, a 360 vCPU cluster returned the crystal structure of racemic ornidazole (Z0 = 3, 30 degrees of freedom) ca 40 times faster than a typical modern quad-core desktop CPU. Whilst used here specifically for DASH, this approach is of general applicability to other packages that are amenable to coarse-grained parallelism strategies.

Research agenda for the built environment in Latin America

This paper disseminates the outcomes of a series of interdisciplinary and multi-sector research seminars that focused on current development problems in a region of fast urban growth. Qualitative data was collected during round table discussions and workshops involving practitioners and government officials from some of the largest economies in Latin America. The authors then grouped these discussions into coherent themes and framed them into current scholarly debates. After assessing the suitability of theory to respond to practice, the paper concludes with four key areas for further research, with the final aim to encourage more scholarly analysis that can better inform development policy in emerging economies.

An approximate dynamic programming approach for improving accuracy of lossy data compression by Bloom filters

Bloom filters are a data structure for storing data in a compressed form. They offer excellent space and time efficiency at the cost of some loss of accuracy (so-called lossy compression). This work presents a yes-no Bloom filter, which as a data structure consisting of two parts: the yes-filter which is a standard Bloom filter and the no-filter which is another Bloom filter whose purpose is to represent those objects that were recognised incorrectly by the yes-filter (that is, to recognise the false positives of the yes-filter). By querying the no-filter after an object has been recognised by the yes-filter, we get a chance of rejecting it, which improves the accuracy of data recognition in comparison with the standard Bloom filter of the same total length. A further increase in accuracy is possible if one chooses objects to include in the no-filter so that the no-filter recognises as many as possible false positives but no true positives, thus producing the most accurate yes-no Bloom filter among all yes-no Bloom filters. This paper studies how optimization techniques can be used to maximize the number of false positives recognised by the no-filter, with the constraint being that it should recognise no true positives. To achieve this aim, an Integer Linear Program (ILP) is proposed for the optimal selection of false positives. In practice the problem size is normally large leading to intractable optimal solution. Considering the similarity of the ILP with the Multidimensional Knapsack Problem, an Approximate Dynamic Programming (ADP) model is developed making use of a reduced ILP for the value function approximation. Numerical results show the ADP model works best comparing with a number of heuristics as well as the CPLEX built-in solver (B&B), and this is what can be recommended for use in yes-no Bloom filters. In a wider context of the study of lossy compression algorithms, our researchis an example showing how the arsenal of optimization methods can be applied to improving the accuracy of compressed data.