Climate Data—When to Request a Custom Dataset

Ryan Abendroth–CPHC and former Certification Manager at PHIUS–with guidance on Abendroth, headshotselecting 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 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 or visit this webpage for more information about custom data sets.

7 thoughts on “Climate Data—When to Request a Custom Dataset

  1. Ryan,
    This is a great resource thanks, but what’s wrong here. I downloaded set for Montpelier, vt and copied it into the user defined data area on my PHPP, it generated an annual HDD of 6526. That’s nowhere near the actual HDD for Montpelier, Vt – (avg. = 8100 over last 30 years). What’s wrong with this picture?
    Thanks for the help,

  2. [left a similar comment earlier–but it does not appear here; please excuse this if it is redundant]

    I also inserted the new weather data into a PHPP in process, and the verification values moved in a surprising mix of directions. although most values dropped, compared to using the native PHPP values for my region, the specific space heat went from 4.71 to 4.77 ;<(

  3. Another question that I often receive is about the heating and cooling load data being much more mild than expected.

    If you look at ASHRAE (or other)numbers, you may get 2 degrees F as your winter design temperature, but the PHPP gives two options: one of 23 degrees, but cloudy and one of 8 degrees but sunny. This is something I would consider typical and can be attributed to the extra “lag time” experienced by passive houses that allow them to swing through extreme conditions. I have calculated load data for the PHPP using two different methods and both give similar results and in almost all instances are significantly milder than traditional values. The other thing to keep in mind is that the PHPP’s load data are based on a single day (a 24 hour period) and not at a given “worst case” point in time.

  4. Hi All,
    Below is a post I wrote shortly after the post went live, but we had some technical difficulties, So here it is again.

    We have gotten some similar comments via Email and through Facebook. First, I made an informational video about entering datasets into the PHPP. You can find it at the PHAUS site in the members section or at the following link:

    This will only correct a portion of the problem if this is in fact a problem. The other portion of the difference will be due to the fact that HDD are calculated differently in the PHPP compared to normal convention. It has been a while since I looked at this topic and here is what I remember to be true. The HDD in the PHPP use a base of 68F, while most HDD are calculated with a Base of 65 Degrees. The other main difference is the assumptions that need to be made to find a number of heating hours based on a given monthly temperature. If the monthly average is 40 degrees, the PHPP has to have a way of calculating how many heating degree days exist. This is done by taking the amount of days in the month times the amount of hours in a day to get hours in a month. It then multiplies the hours by the temperature difference. This gets trickier in swing months where the month may be split between heating and non heating and the PHPP makes some assumptions here as well (especially on the annual method). There are some other things going on behind the scenes depending on what method (annual or monthly) you are calculating with ( I strongly recommend monthly). I had some information regarding this in the FAQ section earlier, but removed it because I have not had time to double check the HDD calculation for both methods so take this with a grain of salt.

    The important point is that the calculation of HDD’s is different between conventional methods and the PHPP, and that the PHPP is using the correct HDD for its energy balance calculation. Comparisons between the two methods are not always straightforward. I have also been working on a publication about the settings on the Verification Page and that will go deeper into the exact calculation behind the HDD.

    One other thing that I sometimes do to see if a climate data set is matching up with what I expect is by looking at the yearly average temperature, by summing up the monthly averages and dividing it by 12. A yearly average temperature can usually be found online on sites such as or in their historical data. If they are within a degree or less, you can be assured your data set is pretty accurate even if the HDD are a off from what you have seen elsewhere.

  5. Pingback: Climate Data and PHIUS+ 2015 | The Klingenblog

  6. Looking into building a passive house as a test subject for a 20-50 home development. The design and implementation I understand, it is the qualifying that I have difficulty in figuring out the #’s. Anyone give me what the cost can be for a 3rd party qualifier? The only way to really change the industry is through implementation. Make it available in common building practice and it could become mainstream as people see what it can do for them dollar wise, saving the world will then be a bi-product.

  7. Kris,

    Thanks for reaching out. The climate data sets are available free to PHAUS members and can be accessed here:

    For non-PHAUS members, climate data sets can be purchased here:

    The above post has also been updated accordingly.


    Meredith Marsh
    PHIUS Communications Manager

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