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| Titel |
MOnthly TEmperature DAtabase of Spain 1951-2010: MOTEDAS. (1) Quality control |
| VerfasserIn |
Dhais Peña-Angulo, Nicola Cortesi, Claudia Simolo, Peter Stepanek, Michele Brunetti, Jose Carlos González-Hidalgo |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250086302
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| Publikation (Nr.) |
 EGU/EGU2014-137.pdf |
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| Zusammenfassung |
| The HIDROCAES project (Impactos Hidrológicos del Calentamiento Global en España,
Spanish Ministery of Research CGL2011-27574-C02-01) is focused on the high
resolution in the Spanish continental land of the warming processes during the
1951-2010. To do that the Department of Geography (University of Zaragoza, Spain), the
Hydrometeorological Service (Brno Division, Chezck Republic) and the ISAC-CNR
(Bologna, Italy) are developing the new dataset MOTEDAS (MOnthly TEmperature
DAtabase of Spain), from which we present a collection of poster to show (1) the general
structure of dataset and quality control; (2) the analyses of spatial correlation of
monthly mean values of maximum (Tmax) and minimum (Tmin temperature; (3)
the reconstruction processes of series and high resolution grid developing; (4) the
first initial results of trend analyses of annual, seasonal and monthly range mean
values.
MOTEDAS has been created after exhaustive analyses and quality control of
the original digitalized data of the Spanish National Meteorological Agency
(Agencia Estatal de Meteorología, AEMET).
Quality control was applied without any prior reconstruction, i.e. on original
series. Then, from the total amount of series stored at AEMet archives (more
than 4680) we selected only those series with at least 10 years of data (i.e. 120
months, 3066 series) to apply a quality control and reconstruction processes (see
Poster MOTEDAS 3). Length of series was Tmin, upper and lower thresholds of absolute data,
etc), and by comparison with reference series (see Poster MOTEDAS 3,
about reconstruction). Anomalous data were considered when difference
between Candidate and Reference series were higher than three times
the interquartile distance. The total amount of monthly suspicious data
recognized and discarded at the end of this analyses was 7832 data for
Tmin, and 8063 for Tmax data; they represent less than 0,8% of original
total monthly data, for both Tmax and Tmin. No spatial pattern was
detected in the suspicious data; month by month Tmin shows maximum
detection in summer months, while Tmax does not show any monthly
pattern.
Secondly, the homogeneity analyses was performed on the list of series free of
anomalous data by using an arrays of test (SNHT, Bivariate, T de Student and
Pettit) after new reference series calculated with data free of anomalous. The
tests were applied at monthly, seasonal and annual scale (i.e. 17 times
per method). Statistical inhomogeneity detections were accepted as
follows:
Three annual detections (monthly, seasonal, annual) must be found in
SNHT or Bivariate test.
The total amount of detections by the four tests was greater than 5%
of the total possible detection per year.
Before any correction we examined the Candidate and reference series
chart.
Proclim and Anclim software were used during all the processes
The total amount of series affected by inhomogeneities was 1013 (Tmax) and 1011
(Tmin), i.e. 1/3 of original series was considered as inhomogeneous. We
notice that identified inhomogeneous series in Tmax and Tmin usually do not
coincide. This apparently small amount of series compared with previous work
could be originated because of the mean length of series is around 15-20
years.
References.
Stepánek P. 2008a. AnClim – software for time series analysis (for Windows
95/NT). Department of Geography, Faculty of Natural Sciences, MU, Brno, 1.47
B.
Stepánek P.. 2008b. ProClimDB – Software for Processing Climatological Datasets.
CHMI, Regional office, Brno. |
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