On the creation of future probabilistic design weather years from UKCP09

Abstract

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.

The impact of regulations on overheating risk in dwellings

“…With the goal of addressing this, the PROMETHEUS project at the University of Exeter developed a range of weather data files based on the UKCP09 predictions which represent a range of probabilities (10 th , 50 th and 90 th percentile for low, medium and high scenarios) for three timescale (2030s, 2050s and 2080s) producing both Test Reference Years (TRYs) to represent a 'normal' year and Design Summer Years (DSYs) to represent a 'near extreme' year. The methods used to develop the files are detailed by Eames et al (2011). Several studies considering domestic overheating have used these weather data files (for example see Coley et al, 2012, Gupta & Gregg, 2012, Mavrogianni et al, 2012 and although the methods used to develop the files reduces the computing needed some considerations remain in relation to how the files are implemented in climate change impact studies.…”

“…As noted by Eames, Kershaw and Coley (2011) overheating could have severe health implications and as such could result in uninhabitable buildings that are technically obsolete due to the 'locked-in ' (de Wilde & Tian, 2011) impacts of climate change. The paper, supported by detailed probabilistic predictions, demonstrates the level of overheating that could be experienced depending on the standard or regulation the dwelling is constructed to and suggests how an alternative, more robust approach, to overheating risk assessment could be developed.…”

Zone Modelling and Visualisation: Keys to the Design of Low Carbon Buildings

“…This should partly address concern about 'black box' results where the computer delivers a set of uncomprehendable numbers. Possible adaption to changed climate could also be modelled with the inclusion of future weather data now being developed [19].…”

A proposed method for generating high resolution current and future climate data for Passivhaus design

“…Such methods are limited by the spatial distribution of the baseline TRY datasets and knowledge of the amplitude of the climate change input signals which were typically derived from 50km (or coarser) grid models. In addition to using a much higher spatial resolution the more recent PROMETHEUS files include probabilistic prediction of the future wind speed and direction which was absent from many earlier climate generator models [23].…”

“…In order to consider the most typical month where multiple variables are concerned a weighted index may be applied to each key variable. Typically dry bulb temperature, global irradiation and wind speed are selected as the key variables in a TRY and are given an equal weighting [23]. By multiplying the weighting by the FS statistic for each variable and then summing the products the overall 'typical' month may be selected as the one with the lowest weighted FS, using the following equation.…”