Climate Data Trajectories

In an earlier post, we focused on simpler ways to interact with climate data. By using a flexible interface, a designer can potentially identify visual patterns and parametric relationships in weather files. Since the previous post, we’ve worked with LMN’s sustainability team and learned of a recent Building Green lecture regarding EPW files. The presentation was by Christoph Reinhart of MIT, and in it he referred to some compelling research by the University of Southampton: the Climate Change World Weather File Generator.

This is a robust body of work which uses various models to predict and interpolate future climates based on global warming trends. With a basic Excel format, a designer can import a weather file with TMY Data and export a weather file for the years 2020, 2050, and 2080:


With these weather files, an architect or engineer may run energy simulations for future climates in addition to current ones.  There’s great potential here, which invariably creates a new challenge for the designer: how will a building adapt to a changing climate? And how should we design building systems accordingly?  In order to make these decisions, an architect may want to first grasp exactly what those changes are.  This is reason enough for us to update the Climate Data Visualizer to consider future conditions.

Since the University of Southampton’s research allows one to create a future weather file, we can plot current climates as well as their trajectories. The intention with this is to allow designers to comprehend climate change through visual cues and interaction.  As a simplified example, the interface below provides average conditions for days, month, weeks and seasons. Each node represents a data point from the four imported climate files, and the direction of each arrow denotes the passing of time:
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With the Seattle example above, we can immediately observe relationships based on the University of Southampton’s research. For example, temperature fluctuations are predicted to be more severe during summer months. These are of course crude findings which are not intended for making hard-lined conclusions. The interface is instead meant to show us which portions of data we want to explore further. A few other findings are outlined in the images below:



We plan on running this interface at the beginning of projects to take a look at a building’s lifespan. And while this interface was designed to study future climates, it can also be used to visualize other weather files. The need for comparing weather files comes up frequently since architectural sites are often placed between multiple weather stations; and it can become difficult to determine which weather station has readings more appropriate for the site. Using this interface, one can visualize up to four weather stations in the vicinity and consider their discrepancies. Below is an example of three adjacent weather stations in a mountainous region (Vail, CO):
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In this case, it’s important to notice the ranges on the extents of the X and Y axes. When parameters are changed, the X and Y axes will rescale to the extent of all plotted values. This is to allow one to see patterns in data. For example, one may notice that a nearby weather station has consistently lower temperature readings than another one. However if the axis domain is not substantial, the discrepancy may not be relevant.

With these scaled domains in mind, consider that the visual output of contrasting climates may be similar to that of adjacent climates. Below is a look at Seattle vs. Sydney, Australia. You can start to see some jumbled drawings when adjusting the X and Y axes, and the axis domains are of course more signficant. The intention here is pattern recognition rather than chart consistency. Admittedly, patterns will be difficult to find in this case:
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This interface serves as an introduction to an approach for studying current and future climates. The design of it is not complete, and it would benefit from allowing the user better control over time ranges (nighttime vs. daytime comparison would offer insights into environmental systems for example). And while the chart currently displays average data, it would be a quick update to offer highs and lows as well. The diagram can also be changed to an hourly time range, but the interaction becomes significantly slower (there are 8,760 rows of data in each climate file).

In conclusion, we highly recommend downloading the Climate Change World Weather File Generator, as it offers some very interesting predictions for the future of our climates. And feel free to use our future climate interface (for generic EPW files) by clicking here.