Matthew E. Eames

On the creation of future probabilistic design weather years from UKCP09

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.

Wavelength band optimization in spectral near-infrared optical tomography improves accuracy while reducing data acquisition and computational burden

Multispectral near-infrared (NIR) tomographic imaging has the potential to provide information about molecules absorbing light in tissue, as well as subcellular structures scattering light, based on transmission measurements. However, the choice of possible wavelengths used is crucial for the accurate separation of these parameters, as well as for diminishing crosstalk between the contributing chromophores. While multispectral systems are often restricted by the wavelengths of laser diodes available, continuous-wave broadband systems exist that have the advantage of providing broadband NIR spectroscopy data, albeit without the benefit of the temporal data. In this work, the use of large spectral NIR datasets is analyzed, and an objective function to find optimal spectral ranges (windows) is examined. The optimally identified wavelength bands derived from this method are tested using both simulations and experimental data. It is found that the proposed method achieves images as qualitatively accurate as using the full spectrum, but improves crosstalk between parameters. Additionally, the judicious use of these spectral windows reduces the amount of data needed for full spectral tomographic imaging by 50%, therefore increasing computation time dramatically.

Estimation of the urban heat island for UK climate change projections

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.

Comparison of multi-year and reference year building simulations

Buildings are generally modelled for compliance using reference weather years. In the UK these are the test reference year (TRY) used for energy analysis and the design summer year (DSY) used for assessing overheating in the summer. These reference years currently exist for 14 locations around the UK and consist of either a composite year compiled of the most average months from 23 years worth of observed weather data (TRY) or a single contiguous year representing a hot but non-extreme summer (DSY). In this paper, we compare simulations run using the reference years and the results obtained from simulations using the base data sets from which these reference years were chosen. We compare the posterior statistic to the reference year for several buildings examining energy use, internal temperatures, overheating and thermal comfort. We find that while the reference years allow rapid thermal modelling of building designs they are not always representative of the average energy use (TRY) exposed by modelling with many weather years. Also they do not always give an accurate indication of the internal conditions within a building and as such can give a misleading representation of the risk of overheating (DSY). Practical applications: An understanding of the limitations of the current reference years is required to allow creation of updated reference years for building simulation of future buildings. By comparing the reference years to the base data sets of historical data from which they were compiled an understanding of the benefit of multiple simulations in determining risk can be obtained.

An efficient Jacobian reduction method for diffuse optical image reconstruction

Model based image reconstruction in Diffuse Optical Tomography relies on both the numerical accuracy of the forward model as well as the computational speed and efficiency of the inverse model. Most model based image reconstruction algorithms rely on Newton type inversion methods, whereby the inverse of a large Jacobian is approximated. In this work we present an efficient Jacobian reduction method which takes into account the total sensitivity of the imaging domain to the measured boundary data. It is shown using numerical and phantom data that by removing regions within the inverse model whose contribution to the measured data is less than 1%, it has no significant effect upon the estimated inverse problem, but does provide up to a 14 fold improvement in computational time.

Limitations of the CIBSE design summer year approach for delivering representative near-extreme summer weather conditions

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.

The appropriate spatial resolution of future weather files for building simulation

Building thermal modelling packages require weather data to predict representative internal conditions. Typically, around the world, reference weather years of various forms are used which are created from observations at aparticular location. However, it is unlikely that this location is identical to that of the building. This can lead toweather files for coastal locations being applied to inland and upland sites or vice versa. In the UK, the UKCP09 weather generator has the ability to produce weather at a 5 km resolution. Currently, it is unclear how useful this extra spatial resolution will be and it is this question that is addressed here. It is found that for both future and present climate, the spatial variability of the weather is the dominating factor. Although there are geographies where a low spatial resolution can be used, there are regions where a much higher resolution is necessary.

The creation of wind speed and direction data for the use in probabilistic future weather files

Pseudo weather data with a high temporal resolution are of use in many fields including the modelling of agricultural systems, the placement of wind turbines and building thermal simulations. With the publication of the 2009 UK Climate Projections (UKCP09) such data can be created for future years and for various predictions of climate change. Unfortunately such — UKCP09 — data does not include information about wind speed or direction due to a lack of robustness. Here we demonstrate a methodology for generating such wind data on an hourly time grid from a consideration of the potential evapotranspiration reported by the UKCP09 weather generator and information related to the correlation between observed wind speed, direction and time of year. We find our pseudo wind data is consistent with the historic observed wind. Furthermore, when used within a dynamic thermal simulation of a building, the use of such pseudo wind data generates a consistent internal environment in terms of ventilation rates, temperatures and energy use that is indistinguishable from simulations completed using historic observed weather for both single-sided and cross-ventilated buildings. Practical applications: The methodology presented in this paper will allow academics and building engineers to create realistic hourly wind speed and direction data for inclusion with the future climate data of UKCP09. This will allow the creation of consistent future weather years for use in areas such as building thermal simulation.

Generating near-extreme Summer Reference Years for building performance simulation

At present, there is no universally accepted method for deriving near-extreme summer weather data for building performance simulation. Existing data sets such as the Design Summer Years (DSY) used in the UK to estimate summer discomfort in naturally ventilated and free running buildings have been criticised for being inconsistent with the corresponding Test Reference Years (TRY). This paper proposes a method for generating Summer Reference Years (SRY) by adjusting the TRY of a given site with meteorological data in order to represent near-extreme conditions. It takes as the starting point that the TRY is robust, being determined on a monthly basis from the most typical months. Initial simulations for the 14 UK TRY locations show promising results for determining building overheating with the SRY. Practical application : The proposed method for deriving near-extreme summer years from multi-year data and the corresponding ‘typical’ weather year (TRY) of a given site is applicable to locations worldwide and facilitates summer overheating assessment of naturally ventilated and free running buildings. The method helps to overcome the previous shortcomings of near-extreme summer year selection procedures by providing a clear relationship to the underlying TRY.

An update of the UK’s design summer years: Probabilistic design summer years for enhanced overheating risk analysis in building design

Overheating is increasingly becoming a key issue for building design across the world. In the UK, better building fabric performance and warmer weather can increase the risk of overheating events in badly designed buildings. The impacts of these overheating events could be reduced by adapting building designs at an early design stage using building thermal models using appropriate weather data such as a design summer year. In this work, a method to determine probabilistic design summer years will be presented. These years take into account the return periods of actual events, are presented within a probabilistic framework and therefore include a description of the severity of the year at each location. Practical application: Design summer years are designed to be used to optimise building performance in terms of thermal comfort at design stage. This paper demonstrates a method to create probabilistic design summer years which contain a range of overheating events which can be used to inform designers of the overheating risk to occupants. The proposed method is then used to generate new near extreme weather files for the UK.