Calibration of the InVEST urban cooling model in Twin Cities, USA
You can use git to clone this repository as in:
git clone https://github.com/martibosch/uhi-twin-cities.git
or download as a zip file and extract it.
First of all, conda is required to automatically install all the sofware dependencies. See its installation page and follow the steps to install it in your operating system.
Additionally, GNU Make is used to manage the execution of the calibration workflow, which is usually built-in with Linux and OSX systems. Windows users can install it from the Anaconda prompt as in:
# ACHTUNG: You only need to run this in Windows
conda install -c conda-forge make
Then, from the root of this repository, you can create a conda environment with all the required software dependencies as in:
make create_environment
and then activate it as in:
conda activate uhi-twin-cities
Copy the UCM_CalibrationData
from the Google Drive to the data/raw
directory of this repository so that the directory structure is of the form:
|─ data
| └─ raw
| └─ UCM_CalibrationData
| |─ InVEST_Inputs
| |─ Twine_UHI_2016
| └─ LandSurfaceTemperature2016
|
|─ .gitignore
|─ LICENSE
|─ Makefile
|─ README.md
└─ environment.yml
The calibration of the urban cooling model for the July4-6_2012_DayTemp1.tif
, July4-6_2012_NightTemp1.tif
, JJA_Day_Temp1.tif
, JJA_Night_Temp1.tif
and lst2016_utm_c/hdr.adf
reference temperature rasters can be executed as in:
make calibrate
which will dump the calibrated parameters for each file in the data/processed
directory (which will be created automatically if it does not exist).
Reports for the calibration of the urban cooling model for each of the reference temperature rasters can be obtained as in:
make calibration_reports
which will generate a PDF calibration report and dump it to the reports
directory (which will be created automatically if it does not exist).
Project based on the cookiecutter data science project template. #cookiecutterdatascience