Air Temperature Maximum Blending Distance uncertainty

Dear all,

I am trying to use the urban cooling model in a part of my dissertation and I am currently refining the urban cooling parameters. I’m a little bit skeptical about the “Air Temperature Maximum Blending Distance” parameter that the model requieres. I’ve look out for some questions about air mixing here in the forum and I found a topic that recommended reading Shatz & Kucharik 2015 and Shatz & Kucharik 2014 but I’m still having difficulties. I was wondering, as it is a very uncertain parameter, if the developers or someone who has used the model could make any recommendations on how to extrapolate this air mixing parameter from some data that is easier to find in the literature, such as the wind speed for example. Any idea will also helpful!

Wear a face mask and be safe
Best regards!

Carson

Hi @chris , I wonder if you have any experience here?

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Hi Carson,

Thanks for the question! This is indeed a point of uncertainty in the modeling. Typically we default to 600m from the Schatz and Kucharik papers, and I would also point you to a recent publication that uses a different model but provides a similar distance parameter linking land use to air temperature change: Lonsdorf et al. 2021 https://doi.org/10.1016/j.landurbplan.2020.104022.

I like the idea of tying the parameter to a prevailing wind speed, which to my knowledge has not been done before! As it stands, the parameter is a simplification of air blending dynamics since it does not take into account wind speed, direction, or any actual wind modeling–this allows the model to function on relatively simple data inputs. If you have access to ground-truthed air temperature data, you can train some of these inputs (including the air mixing parameter) using a script built by Marti Bosch, a PhD student at EPFL in Switzerland, which is currently under review for publication. Here’s a link to the github repository:

We have been using this script to refine the model in Minneapolis, Minnesota and Paris, France, with air blending distance being a primary driver of model accuracy. I will try to look into any correlations with typical wind speeds in those cities, as that relationship is intriguing, but for now the above publications and model training code are all I can offer on this specific parameter.

Happy modeling!
Chris

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