TRAINING SESSION ON HOMOGENISATION METHOD

INTERREG IIIB Alpine Space Project FORALPS

Bologna, 17th-18th May 2005

Maurizio Maugeri, University of Milan

Our approach to homogenisation

Within this context, in the year 2000, a research program with the aim of better investigating the impact of data quality

and homogeneity issues on the detection of Italian temperature and precipitation

trends in the last two centuries was set up

Final goal: revise and update the results presented in Maugeri and Nanni (1998), Buffoni et al. (1999) and Brunetti et al. (2000).

The program has been developed both within EU project ALP-IMP and within National project CLIMAGRI – Climate and Agriculture, Ministry for Agriculture and Forests.

Principal steps of the program

Data and metadata recovery

Homogeneity testing and record adjusting

Data Analysis

Understanding local versus larger scale

Why spend more time on data and metadata recovery?

Invention of some of the principal meteorological instruments

Introduction of the first synoptic network

Six series beginning in the 18th century: Bologna, Milano, Roma, Padova, Palermo and Torino

Italy is well placed in the field of long term records

So, over the last 3 centuries a huge amount of data and metadata has been collected in Italian data archives

The importance of these data has been known for a long time…….

Cantù V. and Narducci P. (1967) Lunghe serie di osservazioni meteorologiche. Rivista di Meteorologia Aeronautica, Anno XXVII, n. 2, 71-79.

Eredia F. (1908) Le precipitazioni atmosferiche in Italia dal 1880 al 1905. In: Annali dell'Ufficio Centrale di Meteorologia. Serie II, Vol. XXVII, anno 1905, Rome.

Eredia F. (1919) Osservazioni pluviometriche raccolate a tutto l'anno 1915 dal R. Ufficio Centrale di Meteorologia e Geodinamica. Ministero dei Lavori Pubblici, Rome.

Eredia F. (1925) Osservazioni pluviometriche raccolate nel quinquennio 1916-1920 dal R. Ufficio Centrale di Meteorologia e Geodinamica. Ministero dei Lavori Pubblici, Rome.

Mennella C. 1967. Il Clima d'Italia. Napoli: Fratelli Conti Editori, 724 pp.

Millosevich (1882) Sulla distribuzione della pioggia in Italia. In: Annali dell'Ufficio Centrale di Meteorologia. Serie II, Vol. III, anno 1881, Rome.

Millosevich (1885) Appendice alla memoria sulla pioggia in Italia. In: Annali dell'Ufficio Centrale di Meteorologia. Serie II, Vol. V, anno 1883, Rome.

Narducci, P., 1991: Bibliografia Climatologica Italiana, Consiglio Nazionale dei Geometri, Roma.

… but until a few years ago only a small amount of the data was available in digital format…

Precipitazioni Temperature Stazione

Lunghezza serie Dati mancanti

(%) Lunghezza serie

Dati mancanti (%)

Reggio Calabria 1878-1972 19.8 1878-1972 19.2 Palermo 1874-1973 0.7 1876-1973 0.1 Perugia 1874-1973 21.2 1876-1973 1.3 Torino 1866-1969 10.2 1870-1969 17.9

Padova 1877-1968 5.0 1877-1968 4.7 Alessandria 1875-1970 16.8 1878-1970 14.7

Arezzo 1879-1972 17.7 1879-1972 19.0 Belluno 1879-1986 7.7 1879-1966 6.9 Rovigo 1879-1976 12.4 1879-1966 13.0 L’Aquila 1879-1973 8.5 1879-1973 15.2

Reggio Emilia 1879-1970 10.8 1879-1970 23.8 Napoli 1865-1969 2.7 1870-1969 9.3 Cagliari 1879-1971 4.0 1879-1971 2.5 Mantova 1880-1973 11.5 1880-1973 11.4 Sassari 1876-1971 12.1 1876-1971 8.8

Siracusa 1874-1973 12.5 1878-1973 11.4 Roma 1862-1999 6.2 1870-1999 0.4 Pesaro 1876-1992 11.6 1876-1992 7.8 Bologna 1879-1988 10.3 1879-1988 14.8 Ferrara 1879-1974 17.9 1879-1974 16.6 Parma 1878-1994 6.3 1878-1994 2.5

Piacenza 1875-1999 6.0 1878-1999 9.4 Pavia 1883-1991 23.8 1870-1979 26.1 Cuneo 1879-1993 12.5 1879-1993 12.4

Messina 1881-1974 12.2 1881-1974 10.7 Livorno 1876-1994 10.3 1870-1994 11.8

Adapted from: Anzaldi C., Mirri L. and Trevisan V., 1980: Archivio Storico delle osservazioni meteorologiche, Pubblicazione CNR AQ/5/27, Roma.

…and no attempts were made to collect the metadata

Data and metadata collection: air temperatureSTATION CODE LON (º) LAT (º) z (m)

Min/Max (daily)

Min/Max (monthly)

Mean (monthly)

ALESSANDRIA ALE 8.63 44.92 98 1854-1985 1854-1985 1854-1985AOSTA AOS 7.30 45.73 544 - 1891-2003 1840-2003L'AQUILA AQU 13.40 42.35 753 1879-2003 1869-2003 1869-2003AREZZO ARE 12.00 43.45 274 1879-2003 1876-2003 1876-2003BELLUNO BEL 12.25 46.12 404 1879-2003 1875-2003 1875-2003BOLOGNA BOL 11.25 44.48 60 1814-2003 1814-2003 1814-2003BOLZANO BOZ 11.33 46.50 272 - - 1850-2003BRÁ BRA 7.87 44.70 290 - - 1862-1970BRIXEN BRI 11.65 46.72 569 - - 1865-2003CAGLIARI CAG 9.15 39.20 55 1879-2003 1879-2003 1879-2003CASTROVILLARI CAR 16.20 39.80 353 1925-2002 1925-2002 1925-2002CATANIA CAT 15.11 37.50 75 1901-2003 1901-2003 1901-2003CHIAVARI CHA 9.30 40.30 5 - - 1883-2002COSENZA COS 16.25 39.28 250 1925-2002 1925-2002 1925-2002CATANZARO CTA 16.58 38.90 343 1924-2002 1924-2002 1924-2002CUNEO CUN 7.50 44.40 536 1879-2003 1879-2003 1879-2003DOMODOSSOLA DOM 8.27 46.10 300 - - 1872-1997FERRARA FER 11.50 44.82 15 1879-2003 1865-2003 1865-2003FIRENZE FIR 11.30 43.80 51 1889-2003 1878-2003 1878-2003FOGGIA FOG 15.52 41.45 80 1901-2003 1901-2003 1901-2003FOSSANO FOS 8.38 44.57 351 - - 1874-1973GENOVA GEN 9.00 44.40 21 1833-2003 1833-2003 1833-2003IMPERIA IMP 8.02 43.87 54 - 1875-2003 1875-2003LIVORNO LIV 10.25 43.55 3 1870-2001 1865-2001 1865-2001LOCARNO LOC 8.79 46.17 379 - 1935-1997 1864-1997LUGANO LUG 8.97 46.00 276 - 1901-1997 1864-1997MANTOVA MAN 10.75 45.15 51 1828-2003 1828-2003 1828-2003MESSINA MES 15.50 38.20 54 1881-2003 1881-2003 1881-2003MILANO MIL 9.00 45.47 64 1763-2003 1763-2003 1763-2003MONTE MARIA MMA 10.49 46.74 1323 - - 1857-2003NAPOLI NAP 14.25 40.88 149 1870-2003 1865-2003 1865-2003NIZZA NIZ 7.20 43.65 4 - - 1806-2003PADOVA PAD 11.75 45.40 14 1774-2003 1774-2003 1774-2003PALERMO PAL 13.35 38.10 71 1876-2003 1876-2003 1876-2003PARMA PAR 10.25 44.80 57 1878-2003 1872-2003 1872-2003PAVIA PAV 9.25 45.17 75 1870-2002 1865-2002 1861-2002PERUGIA PER 12.50 43.10 520 1876-2003 1865-2003 1865-2003PESARO PES 13.00 43.87 11 1871-2003 1871-2003 1871-2003PIACENZA PIA 9.75 45.02 50 1878-2003 1871-2003 1871-2003POTENZA POT 15.82 40.63 826 1924-2002 1924-2002 1924-2002POLA PUL 13.87 44.86 30 - - 1864-2003REGGIO CALABRIA RCA 15.65 38.10 15 1878-2002 1878-2002 1878-2002REGGIO EMILIA REM 10.75 44.70 62 1879-2003 1866-2003 1866-2003RIVA TORBOLE RIV 10.83 45.88 70 - - 1869-2003ROVERETO ROE 11.05 45.87 206 - - 1862-2003ROMA ROM 12.47 41.90 56 1862-2003 1862-2003 1862-2003ROVIGO ROV 11.75 45.05 9 1879-2003 1879-2003 1879-2003ROSSANO RSS 16.62 39.55 300 1925-1997 1925-1997 1925-1997SASSARI SAS 8.60 40.72 224 1876-2003 1874-2003 1874-2003SAN BERNARDO SBE 7.18 45.87 2472 - - 1818-1998SIRACUSA SIR 15.28 37.05 23 1878-2003 1878-2003 1878-2003TARANTO TAR 17.30 40.45 22 1901-2003 1901-2003 1901-2003TORINO MONCALIERI TOM 7.70 45.00 238 - 1865-2003 1864-2003TORINO TOR 7.75 45.05 275 1753-2003 1753-2003 1753-2003TORTONA TOT 8.87 44.88 199 - - 1892-1965TRENTO TRE 11.12 46.07 199 - - 1816-2003TRIESTE TRI 13.75 45.65 11 - 1883-2003 1841-2003TROPEA TRO 15.88 38.67 51 1924-2002 1924-2002 1924-2002UDINE UDI 13.20 46.00 51 - 1803-2003 1803-2003VALLOMBROSA VAL 11.00 43.72 955 1872-2003 1872-2003 1872-2003VALSINNI VAS 16.42 40.15 250 1924-2002 1924-2002 1924-2002VENEZIA VEN 12.25 45.43 21 1900-2002 1900-2002 1900-2002

Data and metadata collection: air temperature

Data and metadata collection: precipitationSTATION CODE LON (º) LAT (º) z (m)

Precipitation (daily)

Precipitation (monthly)

ALESSANDRIA ALE 8.63 44.92 98 1857-1986 1857-1986ANDRIA AND 16.28 41.23 151 - 1921-1996AOSTA AOS 7.30 45.73 544 - 1841-2003L'AQUILA AQU 13.40 42.35 753 1879-2003 1874-2003AREZZO ARE 12.00 43.45 274 1879-2003 1876-2003ASTI AST 8.20 44.90 158 - 1881-1993BARLETTA BAE 16.27 41.33 20 - 1921-1996BALMÈ BAL 7.22 45.32 1432 - 1913-2003BARDONECCHIA BAR 6.70 45.08 1340 - 1913-2002BELLUNO BEL 12.25 46.12 404 1879-2003 1875-2003BENEVENTO BEN 14.80 41.12 177 - 1870-1996BOLOGNA BOL 11.25 44.48 60 1813-2003 1813-2003BORGOMANERO BOR 8.45 45.70 317 - 1881-1996BOLZANO BOZ 11.33 46.50 272 1921-2003 1856-2003BRÁ BRA 7.87 44.70 290 1862-2003 1862-2003BRIXEN BRI 11.65 46.72 569 1921-2003 1878-2003BRINDISI BRN 17.93 40.65 28 - 1877-2000CASTELLANETA CAE 16.93 40.63 245 - 1877-1996CAGLIARI CAG 9.15 39.20 55 1879-2003 1853-2003CANOSA CAO 15.90 41.13 154 - 1922-1996CASALE MONFERRATO CAS 8.50 45.13 113 - 1870-2003CATANIA CAT 15.11 37.50 75 1921-2003 1892-2003CAVOUR CAV 7.37 44.73 290 - 1879-1993CENTALLO CEN 7.60 44.50 417 - 1883-1988CERIGNOLA CER 15.88 41.27 124 - 1922-1996CHIVASSO CHI 7.85 45.17 221 - 1892-1988COSENZA COS 16.25 39.28 250 1916-2002 1873-2002CRISPIANO CRI 17.23 40.60 265 - 1916-1996CROTONE CRO 17.12 39.08 6 1916-2002 1916-2002CATANZARO CTA 16.58 38.90 343 1916-2002 1868-2002CUNEO CUN 7.50 44.40 536 1879-2003 1877-2003DOMODOSSOLA DOM 8.27 46.10 300 1872-1998 1872-1998FENESTRELLE FEN 7.06 45.04 1200 - 1912-1997FERRARA FER 11.50 44.82 15 1879-2003 1865-2003FIRENZE FIR 11.30 43.80 51 1860-2003 1860-2003FOGGIA FOG 15.52 41.45 80 1901-2003 1873-2003FOSSANO FOS 8.38 44.57 351 - 1875-1997GALATINA GAL 18.15 40.17 73 - 1923-1996GALLIPOLI GAP 17.98 40.05 31 - 1877-1996GENOVA GEN 9.00 44.40 21 1833-2003 1833-2003GINOSA GIN 16.75 40.58 257 - 1887-1996GINOSA SCALO GIS 16.75 40.58 5 - 1928-1996IMPERIA IMP 8.02 43.87 54 - 1876-2003IVREA IVR 7.91 45.46 267 - 1837-2002LATIANO LAT 17.72 40.55 98 - 1925-1996LECCE LEC 18.17 40.35 78 - 1875-2000LESINA LES 15.35 41.87 5 - 1928-1998LIVORNO LIV 10.25 43.55 3 1876-2002 1857-2002LIZZANO LIZ 17.45 40.38 67 - 1916-1996LOCARNO LOC 8.79 46.17 379 1901-2002 1886-2002LOMBRIASCO LOM 7.65 44.84 239 - 1913-1999LOCOROTONDO LOR 17.33 40.75 420 - 1829-1996LUGANO LUG 8.97 46.00 276 1901-2002 1861-2002MANFREDONIA MAF 15.92 41.62 2 - 1921-1996MAGLIE MAG 18.30 40.12 77 - 1908-1996MANTOVA MAN 10.75 45.15 51 1840-2003 1840-2003

STATION CODE LON (º) LAT (º) z (m)Precipitation

(daily)Precipitation

(monthly)

MASSAFRA MAS 17.12 40.58 116 - 1881-1997MATERA MAT 16.62 40.68 401 1916-2002 1916-2002MESSINA MES 15.50 38.20 54 1881-2003 1866-2003METAPONTO MET 16.82 40.37 3 1918-2000 1918-2000MILANO MIL 9.00 45.47 64 1858-2003 1764-2003MINERVINO LECCESE MIN 18.42 40.08 98 - 1926-1996MONTE MARIA MMA 10.49 46.74 1323 1923-2003 1858-2003MONCALVO MOC 8.25 45.05 297 - 1889-1988MONDOVI MOD 7.82 44.04 44 - 1866-1995NAPOLI NAP 14.25 40.88 149 1866-2003 1821-2003NARDÒ NAR 18.02 40.18 43 - 1923-1996NIZZA NIZ 7.20 43.65 4 - 1865-2002NOVI LIGURE NOL 8.78 44.78 186 - 1880-1979NOVOLI NOO 18.05 40.38 37 - 1924-1996NOVARA NOV 8.62 45.45 181 - 1875-1996OTRANTO OTR 18.50 40.13 52 - 1879-1996OVADA OVA 8.65 44.62 187 - 1913-1996PADOVA PAD 11.75 45.40 14 1877-2002 1750-2002PALERMO PAL 13.35 38.10 71 1797-2003 1797-2003PARMA PAR 10.25 44.80 57 1878-2003 1833-2003PAVIA PAV 9.25 45.17 75 1873-2002 1812-2002PERUGIA PER 12.50 43.10 520 1874-2003 1811-2003PESARO PES 13.00 43.87 11 1871-2003 1866-2003PIACENZA PIA 9.75 45.02 50 1875-2003 1872-2003POTENZA POT 15.82 40.63 826 1916-2002 1879-2002PRESICCE PRE 18.27 39.90 114 - 1877-1996POLA PUL 13.87 44.86 30 - 1864-2002REGGIO CALABRIA RCA 15.65 38.10 15 1878-2002 1877-2002REGGIO EMILIA REM 10.75 44.70 62 1879-2003 1867-2003RIVA TORBOLE RIV 10.83 45.88 70 1921-2003 1869-2003ROVERETO ROE 11.05 45.87 206 1921-2003 1864-2003ROMA ROM 12.47 41.90 56 1862-2003 1782-2003ROVIGO ROV 11.75 45.05 9 1879-2003 1878-2003SASSARI SAS 8.60 40.72 224 1876-2003 1876-2003SAN BERNARDO SBE 7.18 45.87 2472 - 1864-1997SILANDRO SIL 10.77 46.63 706 1921-1999 1921-1999SIRACUSA SIR 15.28 37.05 23 1874-2003 1869-2003SAN MARCO SMA 15.62 41.72 560 - 1921-1998SAN PIETRO SPI 18.13 40.30 160 - 1923-1996STROPPO STR 7.12 44.50 1087 - 1913-1996TARANTO TAR 17.30 40.45 22 1901-2003 1877-2003TAVIANO TAV 18.08 39.98 61 - 1885-1996TORINO MONCALIERI TOM 7.70 45.00 238 - 1864-2003TORINO TOR 7.75 45.05 275 1802-2003 1802-2003TORTONA TOT 8.87 44.88 199 - 1873-1998TRENTO TRE 11.12 46.07 199 1921-2003 1864-2003TRIESTE TRI 13.75 45.65 11 - 1841-2003TROPEA TRO 15.88 38.67 51 1916-2002 1916-2002UDINE UDI 13.20 46.00 51 - 1803-2003URBINO URB 12.62 43.72 451 1916-2000 1850-2000VALLOMBROSA VAL 11.00 43.72 955 1872-2003 1872-2003VARALLO VAR 8.25 45.82 454 - 1871-1995VENEZIA VEN 12.25 45.43 21 1900-2003 1836-2003VICO GARGANICO VIC 15.95 41.90 450 - 1922-1998VIESTE VIE 16.17 41.88 25 - 1921-1998

Data and metadata collection: precipitation

Data and metadata collection: other variables… the activities are still in progress (EU project ALP-IMP)… …They concern air pressure, cloud cover, humidity and snow…

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SNOW (HS: snow at ground; HN: fresh snow) daily / monthly dataAbout 15 records of northern Italy

HUMIDITY (i.e. dry / wet temperatures). Daily data - 2 records

1951-2004 PERIOD:All variables available in digital formatItalian Air Force data-set.

AIR PRESSURE (secular records)

CLOUD COVER (secular records)

The role of national and international projectsCLIMAGRI (MiPAF), ALP-IMP (EU), COFIN e FIRB (MIUR)

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Data and metadata collection: metadata

For full details; see CLIMAGRI project WEB site (www.climagri.it)

Metadata collection was performed with two main objectives:

i) to understand the evolution of the Italian meteorological network

ii) to reconstruct the “history” of all the stations of the data-set.

The research on the history of the single stations was performed both by analysing a large amount of grey literature and by means of the UCEA archive.

All information was summarized in a card for each data series.

Each card is divided into three parts. In the first part all the information obtained from the literature is reported. In the second part there are abstracts from the epistolary correspondence between the stations and the Central Office. In the third part the sources of the data used to construct the record are summarized.

Metadata: for every station

• Abstracts of all published papers (grey literature) • Abstracts of the correspondence between the

observatories and the Central Office • Position• Data sources• Data availability• Other notes

For more details; see CLIMAGRI project WEB site

1) Make a synthesis of the metadata and study the impact of possible changes

2) Perform an initial homogenisation by means of direct methodologies

3) Perform a final homogenisation by means of indirect methodologies

Homogenisation: principal steps

We developed a method consisting in:

Metadata: for every station

1765 1770 1775 1780 1785 1790 1795 1800 1805 1810 1815 1820 1825 1830 1835 1840 1845 1850 1855 1860 1865 1870 1875 1880 1885 1890 1895 1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

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1: Carlini's 1835 reorganisation; 2: Other displacements of the instruments; 3: Changes in the meteorological screen and in its management; 4: Improvements introduced by Cesaris; 5: Substitutions of the barometer and changes in instrumental correction; 6: Effects of standardisation due to development of national and international meteorological networks; 7: Changes in the observer; they are included only until 1880 as later on standardisation causes the measures to be less dependent from the observer; 8: Interruption of Brera observatory series; 9: Changes in observation hours; 10: Urban heat island development.

Maugeri, M., Buffoni, L., Chlistovsky, F., 2002: Daily Milan temperature and pressure series (1763-1998): history of the observations and data and metadata recovery, Climatic Change, 53, 101-117.

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Corrections applied to Milan daily air pressure data to eliminate the bias introduced by calculating daily means using observations taken at A: 8 a.m., 2 p.m. and 7. p.m. and B: sunrise and mid-afternoon. The corrections A apply to the period December 1st, 1932 - December 31st, 1987, corrections B to 1763-1834.

Corrections by means of metadata:

an example

Maugeri, M., Buffoni, L., Delmonte, B., Fassina, A., 2002: Daily Milan

temperature and pressure series (1763-1998): completing and homogenising the

data, Climatic Change, 53, 119-149.

The indirect methods make use of meteorological data from neighbouring stations.

Formally, data of a given series can be represented as a sum of more terms. Be X(t) the meteorological variable’s value X at the time t. Therefore it can be written:

 

X(t) = N + A(t) + IH(t) (t = 1, 2,..., n) (1)

 

where N is X’s normal value (it is defined by considering the mean value over a suitable time interval like, for example, the period 1961-1990), A(t) is the anomaly related to the instant t (it defines the departure of the variable X from its normal value) and IH(t) is the possible inhomogeneity lying in the measured value X(t) (in the simplest case, IH(t) is a step function that equals to 0 until the inhomogeneity-inducing event takes place, and then that equals to a constant value which represents the effect of the inhomogeneity in fact).

By using an analogous notation, a reference series which is constituted, for example, by the data of a neighbouring station can be written as follows:

 

Homogenisation by means of indirect methods

 

R(t) = N’ + A’(t) + IH’(t) (t = 1, 2,..., n) (2)

 

If the two series belong to the same climatic area, it can be assumed that A(t) = A’(t) for each value of t. Moreover, if you postulate the reference series as homogeneous, it will be always true that IH’(t) = 0.

Therefore, the series of the differences will be:

 

Z(t) = X(t) - R(t) = (N - N’) + IH(t) (t = 1, 2,..., n) (3)

 

In other terms it can be assumed that, unless there are possible inhomogeneities, the series of the differences must result as constant. The same approach is followed for the series of the ratios. The latter approach is particularly used for precipitation series. Possible deviations from Z(t) constant path are therefore assumed as being due to inhomogeneities.

Homogenisation by means of indirect methods

 

The application of indirect methodologies is actually much more complicated than what the previous relations seem to suggest.

In fact, whenever in a relation like the (3) there is a signal which is characterised by one or more steps, it is usually very hard to understand whether it is due to the station under exam or to the station used as a reference. Then, if you consider not too short periods, it can also happen that both the stations present some significant inhomogeneities and that there are several step-shaped signals.

So, the question of the identification of a reference series

is actually very problematic…

Homogenisation by means of indirect methods

How do we select the reference series?

A procedure that rejects the a priori existence of homogeneous reference series is used.

Each series is tested against each other series in subgroups of 10 series.

Subsequently, the break signals of one series against all others are collected in a decision matrix and the breaks are assigned to the single series according to metadata and/or to probability.

The comparison between a test series and a reference series can be

performed by a number of different mathematical techniques.

We use of them:

the Craddock homogeneity test

How do we compare the test and the reference series?

One among the most commonly used statistical tests is the Craddock test. At first it was developed for analysing the precipitation series and subsequently it has been widely updated, improved and extended to thermometric records. It accumulates the normalized differences between two series (a and b) according to one of the following formulas:

where the mean values of the series are calculated over the entire period in which the comparison is performed and where the choice of the proper formula depends on the underlying hypothesis, such as on considering as a constant the difference either the ratio between stations of the same area.

Homogenisation: the Craddock statistical test

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Homogenisation: the Craddock statistical test

TEST SERIES ST

REFERENCE SERIES SR

Complete

Homogeneous

Highly correlated with ST

IT MUST BE:

Basic hypothesys

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Difference between the two series is constant

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ESTIMATION OF ST BY MEANS OF SR (Rici)

HOMOGENEITY TEST

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See also the example presented on the “craddock.xls” Excel File.

In order to display the ability of the Craddock homogeneity test to identify some typical inhomogeneities, we have made use of records generated by means of random numbers. In particular, we have generated some records with the features of Milan yearly mean temperature and yearly total precipitation.

TEMPERATURE

Series length: 240 data

Average: 13.3 °C

St. Dev.: 0.9 °C

PRECIPITATION

Series length: 240 data

Average: 1015 mm

St. Dev.: 202 mm

Then we have applied the Craddock test to A) some pairs of completely random temperature/precipitation records and B) some pairs of records obtained partially from random series and in part from the series to test itself (i.e. we introduce a 0.7 correlation between the pair of series to subject to the Craddock homogeneity test). Then we have added to the series some typical errors as step functions, trends, …

All results are displayed in the Excel files Craddock_TMED_1+2 and Craddock_PREC_1+2.

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Homogenisation: statistical test and metadataCraddock test - Bologna precipitation record

“All’inizio del 1857 a questo pluviometro, ridotto in cattivo stato pel lungo uso, ne venne sostituito un altro di migliore costruzione, e lavorato con molta precisione...”

Introduction of a new pluviometer (Fuess recorder): “... fu collocato a cura del prof Bernardo Dessau nel periodo 1900-1903 ...”

Change in data origin: from “Osservatorio Astronomico” to “Istituto Idrografico”

News about a damage to the pluviometer. In corrispondence with repairing the damage, the cause of the underestimation of precipitation has been removed for the period 1900-1928

Basic problem: what has to be corrected?

a) All the periods found by statistical methods

b) Only the periods for which there is evidence in metadata

The problem is, in part, still open

Our methodology:

Wide use of statistical methods (especially for air temperature)Critical analysis in the light of metadata

The CLIMAGRI project