TITLE:
Homogeneity of Monthly Mean Air Temperature of the United Republic of Tanzania with HOMER
AUTHORS:
Philbert M. Luhunga, Edmund Mutayoba, Hashim K. Ng’ongolo
KEYWORDS:
Homogenization; HOMER Software Package; ANOVA; Air Temperature
JOURNAL NAME:
Atmospheric and Climate Sciences,
Vol.4 No.1,
January
9,
2014
ABSTRACT:
The long-term climate datasets are widely used in a
variety of climate analyses. These datasets, however, have been adversely
impacted by inhomogeneities caused by, for example relocations of
meteorological station, change of land use cover surrounding the weather
stations, substitution of meteorological station, changes of shelters, changes
of instrumentation due to its failure or damage, and change of observation
hours. If these inhomogeneities are not detected and adjusted properly, the
results of climate analyses using these data can be erroneous. In this paper for the first time, monthly mean air temperatures of the United Republic of Tanzania
are homogenized by using HOMER software package. This software is one of the
most recent homogenization software and exhibited the best results in the
comparative analysis performed within the COST Action ES0601 (HOME). Monthly
mean minimum (TN) and maximum (TX) air temperatures from 1974 to
2012 were used in the analysis. These datasets were obtained from Tanzania
Meteorological Agency (TMA). The analysis reveals a larger
number of artificial break points in TX (12 breaks) than TN (5 breaks) time
series. The homogenization process was assessed by comparing results obtained
with Correlation analysis and Principal Component analysis (PCA) of homogenized
and non-homogenized datasets. Mann-Kendal non-parametric test was used to
estimate the existence, magnitude and statistical significance of potential
trends in the homogenized and non-homogenized time series. Correlation analysis
reveals stronger correlation in homogenized TX than TN in relation to non-homogenized
time series. Results from PCA suggest that the explained variances of the
principal components are higher in homogenized TX than TN in relation to
non-homogenized time series. Mann-Kendal non-parametric test reveals that the
number of statistical significant trend increases higher with
homogenized TX (96%) than TN (67%) in relation to non-homogenized datasets.