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Benchmarking homogenization algorithms for monthly data
- Source :
- Climate of the past (Online) 8 (2012): 89–115., info:cnr-pdr/source/autori:Venema V. K. C., O. Mestre, E. Aguilar, I. Auer, J. A. Guijarro, P. Domonkos, G. Vertacnik, T. Szentimrey, P. Stepanek, P. Zahradnicek, J. Viarre, G. Müller-Westermeier, M. Lakatos, C. N. Williams, M. J. Menne, R. Lindau, D. Rasol, E. Rustemeier, K. Kolokythas, T. Marinova, L. Andresen, F. Acquaotta, S. Fratianni, S. Cheval, M. Klancar, M. Brunetti, C. Gruber, M. Prohom Duran, T. Likso, P. Esteban, and T. Brandsma/titolo:Benchmarking homogenization algorithms for monthly data/doi:/rivista:Climate of the past (Online)/anno:2012/pagina_da:89/pagina_a:115/intervallo_pagine:89–115/volume:8, ARCIMIS. Archivo Climatológico y Meteorológico Institucional (AEMET), Agencia Estatal de Meteorología (AEMET), Climate of the Past, Vol 8, Iss 1, Pp 89-115 (2012)
- Publication Year :
- 2012
- Publisher :
- Copernicus GmbH, 2012.
-
Abstract
- The COST (European Cooperation in Science and Technology) Action ES0601: advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random independent break-type inhomogeneities with normally distributed breakpoint sizes were added to the simulated datasets. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added. Participants provided 25 separate homogenized contributions as part of the blind study. After the deadline at which details of the imposed inhomogeneities were revealed, 22 additional solutions were submitted. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training the users on homogenization software was found to be very important. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that automatic algorithms can perform as well as manual ones.
- Subjects :
- Validation study
Climate Time Series
Computer science
lcsh:Environmental protection
Stratigraphy
Homogenization (climate)
COST Action
Precipitation
lcsh:Environmental pollution
monthly homogenization algorithms
real inhomeogeneities
inserted inhomogeneities
blind testing
Temperature records
lcsh:TD169-171.8
Surface climate network
Precipitation records
lcsh:Environmental sciences
lcsh:GE1-350
Homogenization
Global and Planetary Change
Multiplicative function
Temperature
Paleontology
Benchmarking
Integrated approach
Missing data
Nonlinear system
lcsh:TD172-193.5
Outlier
Instrumental climate records
Algorithm
Subjects
Details
- ISSN :
- 18149332
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- Climate of the Past
- Accession number :
- edsair.doi.dedup.....b960635f8e168b03768a4e1eaf16a1f7