For many of the models, there are recommendations for calibration methods. Can anyone suggest ways of calibrating the Urban Cooling model ?
We currently have a paper under review that is helpful in this respect. The preprint is available here: https://gmd.copernicus.org/preprints/gmd-2020-174/
As part of this paper lead author Marti Bosch developed a calibration method for the urban cooling method in Python that is available on his GitHub: https://github.com/martibosch/invest-ucm-calibration. The user’s guide is available here: https://invest-ucm-calibration.readthedocs.io/en/latest/user-guide.html
Data for the paper is also available: https://github.com/martibosch/lausanne-heat-islands
I hope this is useful to you!
I am stuck in the calibration step! Unfortunately I am not familiar with Phyton (I use R) so I cannot really navigate successfully through the calibration steps in GitHub.
Is there any “easier” way to calibrate the model? I have data from weather stations and I am running a correlation analysis between the observed and predicted values but I am afraid it won’t be enough.
I also have the Land Surface Temperature raster of my AOI but I am not sure how to compare LST with the predicted Tair.
Any suggestions will be greatly appreciated!