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Maria Carmen Beltrano1 , Simona Sorrenti1
CRA-CMA National Council for Agricultural Research –Unit for Climatology and Agrometeorology
Via del Caravita 7a, I-00186 Rome; ph. +39066195311; fax +390669531215,
The inhomogeneities in meteorological time series that appear as abrupt discontinuities, gradual
changes, or changes in variability often depend to changes in the observing system, changes in
instrumentation or in exposure, station relocation or replacement of sensors, or, more, to the
application of new calibration corrections.
If it is important to have improvements in measuring techniques, it is also important to have
information, known as metadata — that is information on data — that tells station history and often
can explain the occurrence, kind and the time of discontinuities. Only after clearing up
inhomogeneities in time series caused by operational changes in observing systems we can apply
appropriate statistical programs to link the previous dataset with the new dataset in an
homogeneous databases with a high degree of confidence.
We show a case of study about the utility of metadata in a preliminary detection of discontinuities in
the thermometric series of the urban weather observatory of Roman College founded at the end of
18th century, located in a historical building in the heart of Rome.
This study is carrying out within the research program “AGROSCENARI” financed by Ministry for
Agriculture, Alimentation and Forestry (D.M. 8608/7303/2008).
Key words: breakpoints in time series, metadata.
In the headquarters of the Unit for Climatology and Agro-Meteorology Research (CRA-CMA), is
located the oldest meteorological observatory of Rome, known as "Roman College Observatory".
Its institution, due to the Jesuits who then occupied the premises of the College, began in mid1500, and was then known in Europe for its scientific value. At the Centre, observations, started
with regularity in 1782, and continue to be performed always in the same site, using more and
more modern methods of observation, always comparable with the previous one. In effect, the
scientific importance of Roman College Observatory rely on the length and consistency of the
series that is a very valuable database for studies about Rome climate and urban climatic changes
depending from urbanization.
Studies about long historical data series are carrying out to identify climatic trend, expression of a
probable climate change. However, to carry out a correct climatic analysis, it need to know also the
history of weather station, to clean the data set from mistakes linked to external factors. In fact, in
the last decade the scientific community has become more aware of the fact that the real climate
signal in original series of meteorological data generally is hidden behind non-climatic noise
caused by station relocation, changes in instruments and instrument screens, changes in
observation times, observers, and observing routines, algorithms for the calculation of means, and
so on. So at present time the statement that time series of meteorological data cannot be used for
climate research without a clear knowledge about the state of the data in terms of homogeneity
( has a very large agreement. In the context of
climate change, to interpret correctly inhomogeneities which appear as abrupt discontinuities,
gradual changes, or changes in variability present in meteorological time series are very important.
There are different ways for solving homogeneity problems, and the choice of the most suitable
one is not treated in this study; we try to interpret the data-set characteristics -metadata availabilitybefore to apply any homogenization system. Having diagrams and original records permits to
obtain many important information about station history, useful to reconstruct observatory life and
to read unclear data. Regarding Roman College Observatory we maintain in the paper archive all
records and original diagrams since the start of 20th Century.
In this work we present a case study about the research to explain and understand causes of
discontinuities in the historical series of temperature minimum and maximum of the Roman College
Observatory, with the metadata help.
Fig. 1. Calandrelli Tower, headquarters of Roman College Observatory in Rome
Materials and methods
The daily minimum and maximum temperature series of the Roman College Observatory of the
period 1901-1999 stored in the National Agrometeorological Data Base (BDAN), the computerized
archive of the Unit for Climatology and Agrometeorology Research (CMA), are analyzed. CMA
manages the agro-meteorological monitoring network (RAN), that consists in about 130 automatic
weather stations located in national territory; CMA also manages several traditional monitoring
stations including Roman College Observatory. Measures detected by Agrometeorological and
traditional stations are stored in the National Agrometeorological Data Base (BDAN), the
computerized archive of CMA.
The first step was to analyze the series discontinuities starting from daily minimum and maximum
temperature series of the Roman College Observatory of the period 1901-1999 extracted from
The specific advantage of discontinuities analysis is to attract attention about climatic and non
reason that are the origin of the climate evolution. We adopted the discontinuities analysis by flat
step regression. The aim of the flat-step regression is the detection of breakpoints in a time series
defining a partition in stationary climatic sub-periods described by two different mean values of
temperature. This approach permits to define the climatic normal levels before and after
breakpoints, where the mean values minimize gaps.
For each parameter and each year the annual mean was calculated. The detection of breakpoint
has been carried out by the “STRUCCHANGE” package in the software R. (Zeleis et al. 2003).
Consider the standard regression model
yi = xiT βi + ui
i = 1,….,n
yi is the observation of the dependent variable at time i,
xi is a vector of regressors,
βi is the k-dimensional vector of regression coefficient and
ui is an error term.
Assuming the existence of m breakpoints, that is the existence of m+1 partitions with a constant
value of the regression coefficients , the optimal position of these is determined by minimizing the
residual sum of squares
m 1
RSS (i1 ,...., im )   rss (i j 1  1, i j )
j 1
rss (i j 1  1, i j ) is the residual sum of squares of the j partition.
The optimal number of breaks can be determined by minimizing an information criterion. Bai and
Perron (2003) argue that the Bayesian Information Criterion (BIC) is a suitable selection (Zeileis et
al., 2002, 2003).
Individuated breakpoint years, several studies are researched to compare the results of our
investigation. Also news and information about station change have been researched in archive, to
understand if the climate change shown by statistical analysis were depending to mistakes in
station management or due to particular meteorological events. The results of this preliminary
analysis will be able to clean the series from mistakes due to the management, so, it will be
possible to go on to the series homogenization in rigorous way.
To identify change points we’ve analyzed three time series: temperature minimum (Tn), maximum
(Tx) and mean (T) of the Roman College Observatory since 1901 to 1999.
The breakpoints are detected in several years (table 1).
Table 1.
Discontinuity years
Minimum temperature (Tn)
1922, 1949, 1984
Maximum temperature (Tx)
1919, 1937, 1967, 1981
Mean temperature (T)
1919, 1967, 1984
In graphics Figures 1-3 we represented the temperature series breakpoints by dotted lines. The
whole period means are represented by the green horizontal lines, and the blue horizontal lines
represent the mean of the sub-periods detected. The lowest horizontal red lines shows the
confidence range of 90%.
Fig. 2. The annual mean temperature during period 1901-1999 at Roman College Observatory
In the Figure 3 and Figure 4 the results of the discontinuity points for the minimum and maximum
annual data are shown.
The mean and minimum temperature show the biggest difference between the whole mean and
the sub-periods means at the beginning and at the end of the series with change points detected
around the early twenties and the middle eighties, while the most obvious difference for the
maximum series is that occurred during the seventies where the mean is 17.8 while considering
the whole period a mean value of 20.5 has been detected.
For minimum temperatures each sub-period identified by breakpoints presents an increase of
mean values. In the last one there is an important increase of 0,87 °C. For the rise observed in the
third sub-period, it is possible to conjecture that at the end of second world war and up to the
second half of ’60 years there is in Italy the economic boom, heating systems in the city have
begun to spread, giving rise to the phenomenon known as "heat island".
Fig. 3. The annual minimum temperature during period 1901-1999 at Roman College Observatory
Fig. 4. The annual maximum temperature during period 1901-1999 at Roman College Observatory
For maximum temperatures we note a fluctuating behavior with sub-periods mean values up or
down to the whole mean. The lowest mean values of the maximum temperature detected in ’70
years seem to be structural because they coincide with all evidences known in scientific studies as
“cold period” (IPCC, 2001; Esposito S.).
Probably the change point dated ‘1984’ can be explained both with the structural change carried
out in many studies (Werner et al. 2000, IPCC, 2007) and with the observer change.
The research of the Observatory metadata was difficult: in the paper archive we don’t find useful
information about eventual changes in instrumentation and in observing routines. The results
obtained in analysis of dates of observer change (Table 2) show an interesting corresponding
between break point referred to minimum and mean temperature in 1984 and the observer change
at the end of 1983, that have better to investigate in a comparison with the signal registered in the
same period in other stations.
Table 2.
Dates of Observer change
---May 1937
September 1964
November 1983
January 1991
June 1997
To identify existing trend, expression of possible climate change in studying thermometric series,
ever need a work of data homogenization. We think that a rigorous work of data homogenizations
need before a preliminary study of each historical series, also with the help of metadata. In fact we
think is important to clean the observation series from all mistakes derived from causes non
dependent to meteorological behavior.
We present the study case of the minimum and maximum temperatures series detected at the
Roman College Observatory in Roma until 1901 to 1999. We studied data series adopting the
discontinuities analysis by flat step regression that detect breakpoints in a time series, dividing the
series in stationary climatic sub-periods described by different mean values of temperature. Then
we tried to interpret breakpoints in looking for metadata information regarding Roman College
Observatory. Unfortunately we didn’t find useful information, except dates of observer change.
The biggest problems in analyzing for homogenization of a single historical data set is to find
metadata, useful information about management station: in fact also in a principal observatory like
Roman College, which is also the CMA observatory, with a long historical tradition, there are many
problems in order to understand the real climate signal in original series of meteorological data.
Generally non-climatic noise is an important cause in the interpretation of the quality of the data in
terms of homogeneity.
So far metadata haven’t carried out indications about the series fluctuation, but the future aim of
this work is to investigate more deeply the discontinuities results. This work can be considered a
first step for detection and correction of the inhomogeneities in the historical series of the Roman
Observatory. Future development will include the study of different paper archive and will use other
proxy variable that could be a useful instrument for the explanation of the identified discontinuities.
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