New algorithm for generating hourly temperature values using daily maximum, minimum and average values from climate models

The use of building simulation programmes for predicting building performance is increasing all the time especially with the advent of cheap, fast computers. Hourly weather data, in particular outdoor dry bulb temperature (DBT) and solar radiation values, are required for simulation programmes for building performance. When hourly values are not available there are algorithms for generating hourly temperature values from daily values. These use the daily maximum temperature T_MAX, and daily minimum temperature, T_MIN. However, climate prediction models, such as HadCM3 and HadRM3 also provide the daily average dry-bulb temperature T_AVE as well as the daily maximum and minimum. The average temperature is important for selecting weather years and also because the average temperature is often different from the average of the maximum and minimum, assumed in the simpler algorithms. Buildings being designed now will need to perform under future weather conditions with climate change, so the downscaling of daily values from climate prediction models to hourly values is required. This paper describes a new, more accurate algorithm for generating hourly temperature values in the UK that uses all three temperature parameters from climate change models, and demonstrates the improvement of the quality of the generated values against traditional algorithms that use just the daily maxima and minima.

Practical application: The proposed algorithm for generating hourly DBT values from daily maximum, minimum and average values is intended primarily for deriving hourly data for running building simulation programmes, as some weather stations and future climate prediction models only provide daily values of weather parameters. Climate change is affecting all aspects of human life, and as well as being affected by climate change, buildings can also affect the degree of climate change, since well-designed buildings will require less energy to run, thus minimising the amount of carbon dioxide emitted. As the climate is predicted to change significantly in the next 100 years, if buildings are designed to last, it is important for designers to know how buildings will respond and perform then. Building simulation programmes are useful for this, but they require hourly weather data which are not provided by most climate prediction models. By having a quality-assured algorithm for down-scaling the raw daily values to hourly values, data from climate prediction models, such as HadCM3 and HadRM3 can be used for building simulations for any location in the world.

The hourly DBTs derived from using the algorithm suggested in this paper may also be used in conjunction with other hourly weather data, such as wet-bulb temperature, solar irradiance, cloud cover and wind speed, derived from the same dataset.