Ryan Abendroth–CPHC and former Certification Manager at PHIUS–with guidance on selecting datasets for passive modeling.
CPHCs should use the guidelines below to determine which dataset will most accurately represent their current project’s location. Generally, for most projects, one of the existing downloadable datasets will be accurate and appropriate for use with WUFI Passive or the PHPP. In some cases, though, a project will require a more refined dataset customized to a very granular level in terms of location and conditions.
–To start, avoid using data for a location more than fifty linear miles from your project location.
It’s worth noting that even projects within this range may–in some cases–benefit from custom generated data. This is especially true if there are microclimate issues or impacts from geographical features including altitude changes between the project site and the weather station. (Site elevation is a modifier on the climate page in the PHPP that is often overlooked.)
–We recommend using a different/custom dataset if the difference in elevation between the project site and station location is greater than 300-400 feet.
The climate modifier in the PHPP adjusts the data by taking every 1000 feet of elevation change and adjusting it by 3.6 degrees Fahrenheit. We have seen very large discrepancies due to this adjustment because often times, the real world conditions for high elevation changes consist of microclimate situations that are difficult for the linear scaling of the modifier to accurately reflect. If there is not a station location within 300-400 feet of the project site, check for local data. The elevation modifier can also be used to adjust a data set to be in line with local data sources. This is helpful in cases where there may not be a Typical Meteorological Year (TMY3) station for more than 50 miles, or there may be microclimate effects that occur at a given project location that are not able to be accounted for in the base data set. By using the modifier, a dataset can be adjusted up or down to account for the difference in temperatures between the generated data set and local, measured values.
Why accurate data is critical
Having exact sets generated for data points nearest to the project is important because in passive buildings, we are reducing the energy loads so dramatically. Small changes (say 1 degree) in the average temperature throughout the year can have dramatic effects. For a 2000 sq. ft. treated floor area (TFA) building in San Francisco that was meeting passive house criteria, the difference was ~15% for the Annual Heat Demand. This is especially important when considering all of the factors mentioned above. For one project location, I gathered data directly from the station point and then generated a second set based on interpolation through Meteonorm to the exact same coordinates of the station. The result was a variance of +/- 4 degrees Fahrenheit as compared to the base non-interpolated values which equated to ~25%+ difference in Annual Heat Demand in that particular project.
Nothing changed about the location, just the method of generation that was utilized (straight derivation or interpolation). This is the basis behind my insistence on using TMY3 station points whenever possible.
News in the world of Climate File Generation:
The iPHA recently published a tool to generate climate data files for locations where none yet exist. It is an excellent attempt but the fine print recommends use for design only—not certification. This is because the granularity of the tool is only 75 miles by 75 miles, a resolution not small enough for most locations in the United States. It may be relatively accurate in the central plains, but once major geographical features come into play, the microclimate effects will make the iPHA tool only a rough estimate (which reflects the stipulation to use it as a design tool only) due to the spatial resolution being roughly 1 degree about the equator with some data being even less precise (referenced in page 321 of the 16th International Passive House Conference 2012 – Conference Proceedings).
Frequently asked questions:
Is the Climate Data robust enough?
Yes. The passive house verification in WUFI Passive and in PHPP allow architects/designers to design buildings based on two methods, either annual or monthly. The monthly method is the one you want to use for a variety of reasons (more on this later). Because of this, the climate data has been set up to not require very small increments or time steps in the calculation. The actual data sets are a representation of the hourly data from TMY3 sets. It has just been broken down into month-by-month averages instead of a large drawn out set with values every 15 minute or every hour.
What about more exact time steps or hourly values?
If greater specificity is needed in terms of time steps a different program should be used that has dynamic calculation capabilities instead of a standard static model. In many cases, this is not necessary as the passive house verification in WUFI Passive (and PHPP) has been set up to simulate dynamic modeling for passive house buildings. This is made possible because the short term fluctuations should matter less as the lag effect due to super insulation, air-tightness, and thermal mass, provides a buffer against isolated peak conditions.
This past May, the average monthly temperature was 73.2 degrees F, but the PHPP has the temperature as 64.2 degrees F?
Prolonged peak conditions have a large effect in terms of real world performance. However, there is a real difference between weather and climate. The climate is a an average of many years, while the weather is what occurs at any given time. Climate data is unable to predict any given trend in the future weather. Next year, the monthly average for may could be 55F and even out this year’s unseasonable warm spell.
What about climate change? Should we make data for the future?
This is inherently difficult to predict. While many places represent a trend that is most likely warming, there are others where opposite phenomena could occur. Also changing could be the amount of rain, and the corresponding changes in radiation associated with an increase or decrease in cloud cover. Therefore, we should use the data that is available for our area and worry about updating it when new data comes out, but not worry about trying to predict the future.
What about humidity?
Humidity can be determined through the dew point temperature and average temperature within the climate data set. As mentioned earlier, this is a monthly value and not as specific as may be needed for some modeling methods, but should be fine in most climates (more on this from future PHIUS Technical Committee articles).
Where can I see the most up to date list of available data sets?
All 1000+ climate data sets which have already been generated by PHIUS are available to download for PHAUS Professional level members at no charge. All existing climate data sets are shown on this map.
If I need a set generated, how does that work?
First, check the map linked to above to make sure that a suitable climate data set does not already exist for your location. A “custom” data set means that we will generate a new climate data set for you if one does not already exist. If this is the case, inquire with email@example.com to determine the suitability of a site or to have a custom data set generated.
What does a custom generated dataset cost?
Custom data sets cost $75 for everyone, including PHAUS Members and non-members.
Email firstname.lastname@example.org for more information about custom data sets.