SUMMARY Diffuse optical tomography, also known as near infrared tomography, has been under investigation, for non-invasive functional imaging of tissue, specifically for the detection and characterization of breast cancer or other soft tissue lesions. Much work has been carried out for accurate modeling and image reconstruction from clinical data. NIRFAST, a modeling and image reconstruction package has been developed, which is capable of single wavelength and multi-wavelength optical or functional imaging from measured data. The theory behind the modeling techniques as well as the image reconstruction algorithms is presented here, and 2D and 3D examples are presented to demonstrate its capabilities. The results show that 3D modeling can be combined with measured data from multiple wavelengths to reconstruct chromophore concentrations within the tissue. Additionally it is possible to recover scattering spectra, resulting from the dominant Mie-type scatter present in tissue. Overall, this paper gives a comprehensive over view of the modeling techniques used in diffuse optical tomographic imaging, in the context of NIRFAST software package.

Weather data are used extensively by building scientists and engineers to study the performance of their designs, help compare design alternatives and ensure compliance with building regulations. Given a changing climate, there is a need to provide data for future years so that practising engineers can investigate the impact of climate change on particular designs and examine any risk the commissioning client might be exposed to. In addition, such files are of use to building scientists in developing generic solutions to problems such as elevated internal temperatures and poor thermal comfort. With the publication of the UK Climate Projections (UKCP09) such data can be created for future years up to 2080 and for various probabilistic projections of climate change by the use of a weather generator. Here, we discuss a method for the creation of future probabilistic reference years for use within thermal models. In addition, a comparison is made with the current set of future weather years based on the UKCIP02 projections. When used within a dynamic thermal simulation of a building, the internal environments created by the current set of future weather files lie within the range of the internal environments created by the probabilistic reference years generated by the weather generator. Hence, the main advantages of the weather generator are seen to lie in its potentially greater spatial resolution, its ability to inform risk analysis and that such files, unlike ones based on observed data, carry no copyright. Practical applications: The methodology presented in this article will allow academics and buildings engineers to create realistic hourly future weather files using the future climate data of UKCP09 weather generator. This will allow the creation of consistent future weather years for use in areas such as building thermal simulation.

This article provides the first comprehensive assessment of methods for the creation of weather variables for use in building simulation. We undertake a critical analysis of the fundamental issues and limitations of each methodology and discusses new challenges, such as how to deal with uncertainty, the urban heat island, climate change and extreme events. Proposals for the next generation of weather files for building simulation are made based on this analysis. A seven-point list of requirements for weather files is introduced and the state-of-the-art compared to this via a mapping exercise. It is found that there are various issues with all current and suggested approaches, but the two areas most requiring attention are the production of weather files for the urban landscape and files specifically designed to test buildings against the criteria of morbidity, mortality and building services system failure. Practical applications: Robust weather files are key to the design of sustainable, healthy and comfortable buildings. This article provides the first comprehensive assessment of their technical requirements to ensure buildings perform well in both current and future climates.

Cities are known to exert a significant influence on their local climate, and are generally warmer than their surroundings. However, climate models generally do not include a representation of urban areas, and so climate projections from models are likely to underestimate temperatures in urban areas. A simple methodology has been developed to calculate the urban heat island (UHI) from a set of gridded temperature data; the UHI may then be added to climate model projections and weather data files. This methodology allows the UHI to be calculated on a monthly basis and downscaled to hourly for addition to weather generator data. The UHI intensities produced are found to be consistent with observed data. Practical application: There is overwhelming consensus amongst the scientific community that the Earth's climate is warming. In addition to the effects of climate change the urban heat island (UHI) effect can increase air temperatures significantly in urban areas above those of the rural areas around them. The proposed methodology for calculating the UHI from a set of gridded temperature data allows the UHI to be added to climate model projections such as UKCP09 or HadRM3 and weather data files. The methodology also allows for the temporal downscaling of the UHI from monthly values to hourly data for use in building thermal simulation software.

Representative, site-specific weather data is a key requirement for building performance simulation. In the UK, such data is available in two formats, Test Reference Years for analysing building services loads under 'typical' year conditions and Design Summer Years for estimating summer discomfort of naturally ventilated and free-running buildings. Currently, Design Summer Years are determined as a complete year based on the rank of the average dry bulb temperature from April to September. The simplicity of this approach does not take into account extreme temperature values in individual months or the incident solar radiation, both of which are however of great significance for the summer overheating performance of a building. This paper analyses the implications of this simplified approach for the resulting data. It is shown that there is no consistent relation between the Design Summer Years and the corresponding Test Reference Years and that, for some sites, building performance simulations using Design Summer Year files deliver unreliable results. Practical application: This paper demonstrates that the current approach for deriving Design Summer Years (DSYs) can lead to data series that are not representative for near-extreme summer conditions at a given location. It highlights that a new approach for deriving near-extreme summer years for building performance simulation is needed in order to overcome the inherent shortfalls of the current DSY data.