Open AccessLocally weighted scatter‐plot smoothing for analysing temperature changes and patterns in A ustralia
Author(s) -
Wanishsakpong Wandee,
Notodiputro Khairil Anwar
Publication year - 2018
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.1702
Subject(s) - smoothing , exponential smoothing , scatter plot , cluster (spacecraft) , environmental science , plot (graphics) , loess , statistics , regression analysis , regression , linear regression , mathematics , meteorology , geography , computer science , geology , geomorphology , programming language
The mean maximum monthly temperature data were recorded at 112 stations in Australia. The data (1990–2015) were downloaded from the Australian Bureau of Meteorology (BOM) website. Missing values were imputed using regression models based on information from the nearest stations, as well as the time periods. The data were deseasonalized to remove seasonal variations and then cluster analysis techniques were used to group the stations into six clusters. For each cluster, locally weighted scatter‐plot smoothing (LOESS) and double exponential smoothing (DES) were used to analyse temperature changes and patterns. The results showed that LOESS produced better fits as well as smoother curves compared with the DES. The trends in temperature were increasing in all clusters, whereas the patterns showed periodicity of the temperatures.