Elementos ASOA para soporte de la metodolog´ıa

future, predictions about the future must be incorporated into weather data; A recent review of the methods for this is given by Guan (2009). One of the simplest methods is to extrapolate statistical historical weather data for a single measure to predict future weather conditions. This has been used to give the change to ―heating degree day‖ (Hulme & Jenkins 2002; Rosenthal & Gruenspecht 1995). This method is simple and fast but gives only coarse information about the future and thus has limited applicability. Of particular interest here are the methods that provide the type of data that can be used in energy simulation. This requires

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further downscaling from a RCM to give predictions at a finer temporal and spatial resolution. Two such methods are described in the next sections.

2.2.2.1 Morphing method

Morphing, or time series adjustment, was the term given to a method of imposing the climate change predictions onto a chosen weather time series representing the current weather

(Belcher et al. 2005). The change to the weather variable is imposed by either a shift or a stretch or both depending on the relevant variable. This methodology was evaluated by

comparing the heating degree day of morphed weather data to that given directly in UKCIP02 (Hulme & Jenkins 2002). It was found that the heating degree days calculated from morphed data corresponded well to the UKCIP02 data and this was thought to providing some

confidence that this method was appropriate for producing future weather data (Belcher et al. 2005). The method has since been used widely due to its simplicity and tools that have been made available for its implementation.

Morphing incorporates the climate change projections into a current weather data time series by one of the following three processes, depending on the variable. These are described as a shift, a linear stretch or combination of both and are presented as follows (Belcher et al. 2005):

 A ‗shift‘ which adds the UKCIP02 predicted absolute monthly mean change

(2.5)

where _{is the future climate variable,} the original present day variable and the absolute monthly change according to UKCIP02. This method is for atmospheric pressure.

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 A ‗linear stretch‘ of hourly weather data parameter by scaling it with the UKCIP02 predicted relative monthly mean change (Belcher & Hacker, 2005).

(2.6)

where _{is the fractional monthly change according to UKCIP02. This method is used for} wind speed.

 A combination of a ‗shift‘ and a ‗stretch‘. An hourly weather data parameter is ‗shifted‘ by adding the UKCIP02 predicted absolute monthly mean change and ‗stretched‘ by the monthly diurnal variation of this parameter:

〈 〉 (2.7)

where 〈 〉is the monthly mean related to the variable , and _{is the ratio of the monthly} variances of and . This method is used for dry bulb temperature. It uses the UKCIP02 predictions for the monthly change of the diurnal mean, minimum and maximum dry bulb temperatures in order to include predicted variations of the diurnal cycle.

As part of a separate study, the program called ―ccgenerator‖ was produced that would carry out the morphing procedure for a desired weather time series and set of change factors (Jentsch & Bahaj 2008). This automated the morphing procedure through an excel

spreadsheet macro, which uses the UKCIP02 climate change factors and a weather data file. The morphing method was used to produce the CIBSE future weather years (CIBSE & Met Office 2009); this incorporated the UKCIP02 projections into the CIBSE set of hourly simulation weather data for 14 sites around the UK (Hacker 2009). As part of the UKCP09 projections a fundamentally different downscaling method was used, stochastic weather

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generators. Stochastic weather generator output was made available that incorporates the probabilistic projections. Stochastic weather generators are discussed in the next section.

2.2.2.2 Stochastic weather generator method

A stochastic weather generator produces synthetic time series of weather that represents both the stochastic or random nature of weather and also key statistical properties of the observed meteorological record such as daily means, variances and covariances, and frequencies. This is in contrast to the deterministic calculation methods employed by, for example the building energy modelling methods described previously. Stochastic weather generators have been widely used in water engineering design and in agricultural, ecosystem and hydrological impact studies. They have been used for the in-filling of missing data or for producing long synthetic weather time series from finite station records (Wilks & Wilby 1999). Weather generators have also more recently been used for the statistical downscaling of regional climate change scenarios and this is what is focussed on here. As part of the BETWIXT project (Harpham & Goodess 2006), hourly stochastically generated future weather data was produced and is available on-line from the ―betwixt‖ website (Betwixt 2008). The weather data incorporates the four generic IPCC SRES emissions scenarios and the UKCIP02 scenarios (Hulme & Jenkins 2002). A version of this weather generator was also used to statistically downscale the UKCP09 projections (Jones et al. 2009).

A stochastic weather generator works by using past hourly meteorological observations at a site to estimate the model parameters. These are then used in a stochastic model to generate streams of hourly weather variables. Precipitation is the primary variable and is generated using the RainClim software, from which all the other variables are derived with regression

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relationships or direct calculations. Most stochastic weather generators focused on

precipitation due to the high importance of rainfall to many environmental processes and due to the complexity of building internally consistent, Multi-variable models (Wilks & Wilby 1999). At least 10 years of observed meteorological data must be available for a site to train the generator and estimate the model parameters. Six UK locations had the appropriate observed data to produce the hourly generated weather data for the BETWIXT project (Betwixt 2008).

The Climatic Research Unit (CRU) hourly weather generator which was used in the BETWIXT project produced data for the following variables; precipitation, temperature, vapour pressure, relative humidity, wind speed and sunshine duration. The data available is for 30 simulated years; these do not correspond to past calendar years due to the stochastic nature of the generator, but the distribution and statistical characteristics of the

aforementioned variables are matched to the training period. 50 runs of each simulation set have been run for validation purposes with the time series from the middle of each

distribution being made available. To incorporate the climate change predictions into the weather generator outputs, ―change factors‖ calculated from the HadRM3H regional climate simulation, were applied to mean daily precipitation. RainClim uses predicted changes to the mean, variability and skewness of precipitation to produce the synthetic data. The CRU daily generator uses changes to the mean and variability of the maximum and minimum

temperature. Hourly data is produced with the assumption that the relationship between daily and hourly variables will stay the same in the future. In this way, the weather generator method was used by the environment agency rainfall and weather impacts generator (Kilsby & Jones 2007). To extend coverage of this generator, beyond the selected sites mentioned previously, continuous coverage across the UK at a 5km resolution is provided.

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A minimum of 100 runs of the desired 30 year time period are recommended when using stochastic weather generator output (Jones et al. 2009). These large amounts of weather data can be processed to produce building simulation weather years such as typical years and design summer years. Two methods for this are studied in this thesis (Watkins et al. 2011; Eames et al. 2011) and these are compared in Chapter 6. Such methods are required to produce weather data usable for thermal building simulation from the large volumes of data that are available as part of the UKCP09 probabilistic projections.