  This paper describes how to analyze the influence of Earth system variables
on the errors when providing temperature forecasts. The initial framework to
get the data has been based on previous research work, which resulted in a very
interesting discovery. However, the aforementioned study only worked on
individual correlations of the variables with respect to the error. This
research work is going to re-use the main ideas but introduce three main
novelties: (1) applying a data science approach by a few representative
locations; (2) taking advantage of the rankings created by Spearman correlation
but enriching them with other metrics looking for a more robust ranking of the
variables; (3) evaluation of the methodology by learning random forest models
for regression with the distinct experimental variations. The main contribution
is the framework that shows how to convert correlations into rankings and
combine them into an aggregate ranking. We have carried out experiments on five
chosen locations to analyze the behavior of this ranking-based methodology. The
results show that the specific performance is dependent on the location and
season, which is expected, and that this selection technique works properly
with Random Forest models but can also improve simpler regression models such
as Bayesian Ridge. This work also contributes with an extensive analysis of the
results. We can conclude that this selection based on the top-k ranked
variables seems promising for this real problem, and it could also be applied
in other domains.
