This small project uses simple machine learning to bias correct temperature, dew point, and 10m wind speed data from the HRRR, GFS, and ECMWF forecasts.
In order to run the jupyter notebook below place the "data/" file containing the station data in this directory.
The contents can be described as the following:
A slide deck containing a thorough presentation of my findings.
A Jupyter Notebook containing the entirety of the assignment, from analyses, modeling, and plot generations.
A python script containing the classes of functions I built to use for the analysis.
Results indicated that by using the Gradient Boosted Trees algorithm to bias correct temperature, dew point, and 10m wind speeds produced forecasts that were on average 33% more accurate than previously.
