“The planetary heat engine consists of water being evaporated by the sun into water vapour at the equator (storing heat) and transporting it towards the poles on the winds where it is condensed back into a solid or liquid state (releasing heat). Most of what we refer to as “weather,” such as wind, cloud, fog and precipitation is related to this conversion activity. The severity of the weather is often a measure of how much latent heat is released during these activities.”
[The Weather of British Columbia, NAV Canada 2001]
Buildings and Climate Change
“buildings built today need to be designed to work successfully in both the current and future climate, and with the aim of reducing the greenhouse emission burden they place on this and future generations”
[De Wilde and Coley, 2012]
Weather Data for Analysis and Simulations
Building performance simulation studies rely on standard meteorological data that uses synthetic years aggregated (morphed) from past calendar years over a period of several decades, with the purpose to represent typical meteorological patterns (TMY: Typical Meteorological Year). Typical meteorological data sets are available for most large cities around the world. A drawback of TMY years is that they are morphed from years spanning several decades in the past. Therefore, they reflect typical climate from the past, not from the present or the future. However, building performance is affected by future climate, not by typical historical climate. Therefore, the climate data to be used in building performance simulations needs to reflect long-term conditions to which buildings will be exposed in the future (Tian et al. 2018).
In Canada, the standard weather data format is the CWEC (Canadian Weather for Energy Calculations). The CWEC format has been designed to represent CWEC hourly weather conditions that result in approximately average heating and cooling loads in buildings (Numerical Logics 1999). The CWEC are available from Environment Canada.
For obvious reasons, using data from a single year (which may be atypical) is not recommended. However, collecting weather data for a period of time (typically a year, e.g. AMY: Actual Meteorological Year) from the nearest weather station is required for validating building simulation models that have collected building operational data during the same period.
Which weather data set to use? Please read: “Which Weather Data Should You Use for Energy Simulations of Commercial Buildings?” (Crawley D 1998).
Uncertainty quantification/analysis in weather data is recommended to achieve confidence in the building performance results. Main sources of uncertainty in weather data are:
- Spatial uncertainty (weather station location with respect to the building in question). Reducing spatial uncertainty in weather data is particularly critical for micro-climatic analysis for buildings intended to operate under passive natural cooling or mixed-mode ventilation.
- Temporal uncertainty, which is reduced with longer observation/data-collection periods.
- Climate change uncertainty. Tools are readily available that use standard synthetic weather files to forecast climate change weather files that reflect a given (selected) the climate change scenario. IPCC provides data in scenarios projected into the future that can be used for building analyses and simulations. The climate change world weather file generator uses standard synthetic weather files to generate climate change weather files for world-wide locations. The climate change weather files are ready for use in building performance simulations. Another useful tool for considering climate change scenarios in building performance studies is WeatherShift.
Proper quality assurance in the collection of weather data is necessary. Unfortunately, the building industry rarely consider any of the above sources of uncertainty in their studies. More surprisingly, the nature of the weather file used in building simulations is rarely questioned…
Climate Analysis
Climate analysis selects representative local weather data and uses it to analyze prevailing weather conditions affecting designs. It is used for adaptive thermal comfort analyses, and passive analyses, including natural ventilation and solar shading. A well-known tool for climate analysis is the Climate Consultant. It uses an EPW (EnergyPlus 2018) synthetic format file from past data. However, for more current analyses, for the present and the future, recent local weather data can be used, as well as climate change data from the sources above. However, in order to be able to use more current or foretasted data in dynamic building performance simulations, the data has to be converted to EPW format. Following are tools for data conversion to EPW:
Understanding Climate
“The west coast of North America features many examples of local climates that vary markedly over short distances. The Vancouver, Seattle, & San Francisco Bay areas are notable examples. These local climates feature a small coastal littoral backed by mountains & a cold ocean current. Orographic lifting of air on the windward side of the coastal mountains causes increased precipitation with increased elevation on the windward slopes and a rain shadow forms on the leeward sides of the coastal mountains.”
[John E. Hat and Timothy R. Oke (1994). The Climate of Vancouver, 2nd Ed.]
Key references:
- Oke T.R. (1987). Boundary Layer Climates, 2nd Ed. Roudledge.
- Ahrens C.D. (2012). Essentials of Meteorology: An Invitation to the Atmosphere, 6th Ed. BROOKS/COLE.
Outdoor Air Pollution
Can I open the window? Depends on when (season, hour of day), and where you are (region, city, surroundings)… See: Air Quality
References
- Crawley D (1998). “Which Weather Data Should You Use for Energy Simulations of Commercial Buildings?” in ASHRAE Transactions, pp. 498-515, Vol. 104, Pt. 2. Atlanta: ASHRAE.
- De Wilde P and Coley D (2012). The Implications of Climate Change for Buildings, Building and Environment, Vol. 55, pp. 1-7.
- EnergyPlus (2018). EnergyPlus: Weather Data for Simulation
- Numerical Logics. (1999). Canadian Weather for Energy Calculations, Users Manual and CD-ROM. Downsview, Ontario: Environment Canada.
- Tian W, de Wilde P, Li Z, and Yan D. (2018). A Review of uncertainty analysis in building energy assessment, Renewable and Sustainable Energy Reviews, 93 (2018) 285-301.