  Recently, the use of machine learning in meteorology has increased greatly.
While many machine learning methods are not new, university classes on machine
learning are largely unavailable to meteorology students and are not required
to become a meteorologist. The lack of formal instruction has contributed to
perception that machine learning methods are 'black boxes' and thus end-users
are hesitant to apply the machine learning methods in their every day workflow.
To reduce the opaqueness of machine learning methods and lower hesitancy
towards machine learning in meteorology, this paper provides a survey of some
of the most common machine learning methods. A familiar meteorological example
is used to contextualize the machine learning methods while also discussing
machine learning topics using plain language. The following machine learning
methods are demonstrated: linear regression; logistic regression; decision
trees; random forest; gradient boosted decision trees; naive Bayes; and support
vector machines. Beyond discussing the different methods, the paper also
contains discussions on the general machine learning process as well as best
practices to enable readers to apply machine learning to their own datasets.
Furthermore, all code (in the form of Jupyter notebooks and Google Colaboratory
notebooks) used to make the examples in the paper is provided in an effort to
catalyse the use of machine learning in meteorology.
