TITLE:
A Review on Clustering Methods for Climatology Analysis and Its Application over South America
AUTHORS:
Luana Albertani Pampuch, Rogério Galante Negri, Paul C. Loikith, Cassiano Antonio Bortolozo
KEYWORDS:
Climatology, Clustering Methods, Clustering Regionalization, Reanalysis Data, South America
JOURNAL NAME:
International Journal of Geosciences,
Vol.14 No.9,
September
27,
2023
ABSTRACT: South America’s climatic diversity is a product of
its vast geographical expanse, encompassing tropical to subtropical latitudes.
The variations in precipitation and temperature across the region stem from the
influence of distinct atmospheric systems. While some studies have
characterized the prevailing systems over South America, they often lacked the
utilization of statistical techniques for homogenization. On the other hand,
other research has employed multivariate statistical methods to identify
homogeneous regions regarding temperature and precipitation, but their focus
has been limited to specific areas, such as the south, southeast, and
northeast. Surprisingly, there is a lack of work that compares various
multivariate statistical techniques to determine homogeneous regions across the
entirety of South America concerning temperature and precipitation. This paper
aims to address this gap by comparing three such techniques: Cluster Analysis
(K-means and Ward) and Self Organizing
Maps, using data from different sources for temperature (ERA5, ERA5-Land, and CRU) and precipitation (ERA5,
ERA5-Land, and CPC). Spatial patterns and time series were generated for
each region over the period 1981-2010. The results from this analysis of
spatially homogeneous regions concerning temperature and precipitation have the
potential to significantly benefit climate analysis and forecasts. Moreover, they
can offer valuable insights for various climatological studies, guiding
decision-making processes in diverse fields that rely on climate information,
such as agriculture, disaster management, and water resources planning.