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The benefits of homogenising snow depth series – Impacts on decadal trends and extremes for Switzerland
The Cryosphere,
2023
DOI:10.5194/tc-17-653-2023
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[2]
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Homogeneity assessment of Swiss snow depth series: comparison of break detection capabilities of (semi-)automatic homogenization methods
The Cryosphere,
2022
DOI:10.5194/tc-16-2147-2022
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[3]
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Homogeneity assessment of Swiss snow depth series: comparison of break detection capabilities of (semi-)automatic homogenization methods
The Cryosphere,
2022
DOI:10.5194/tc-16-2147-2022
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Validation metrics of homogenization techniques on artificially inhomogenized monthly temperature networks in Sweden and Slovenia (1950–2005)
Scientific Reports,
2021
DOI:10.1038/s41598-021-97685-7
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Homogenization and trends analysis of the Belgian historical precipitation time series
International Journal of Climatology,
2021
DOI:10.1002/joc.7129
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Sensitivity of Change-Point Detection and Trend Estimates to GNSS IWV Time Series Properties
Atmosphere,
2021
DOI:10.3390/atmos12091102
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[7]
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Validation metrics of homogenization techniques on artificially inhomogenized monthly temperature networks in Sweden and Slovenia (1950–2005)
Scientific Reports,
2021
DOI:10.1038/s41598-021-97685-7
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[8]
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Homogenization and trends analysis of the Belgian historical precipitation time series
International Journal of Climatology,
2021
DOI:10.1002/joc.7129
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[9]
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Combination of Using Pairwise Comparisons and Composite Reference Series: A New Approach in the Homogenization of Climatic Time Series with ACMANT
Atmosphere,
2021
DOI:10.3390/atmos12091134
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[10]
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Efficiency of Time Series Homogenization: Method Comparison with 12 Monthly Temperature Test Datasets
Journal of Climate,
2021
DOI:10.1175/JCLI-D-20-0611.1
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[11]
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Application of homogenization methods for Ireland's monthly precipitation records: Comparison of break detection results
International Journal of Climatology,
2020
DOI:10.1002/joc.6575
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[12]
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Homogenizing GPS Integrated Water Vapor Time Series: Benchmarking Break Detection Methods on Synthetic Data Sets
Earth and Space Science,
2020
DOI:10.1029/2020EA001121
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[13]
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Development of an Updated Global Land In Situ‐Based Data Set of Temperature and Precipitation Extremes: HadEX3
Journal of Geophysical Research: Atmospheres,
2020
DOI:10.1029/2019JD032263
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[14]
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Removal of bias introduced by considering calendar or rainfall day as 24‐hr period in daily minimum temperature series: Results from
ACMANT
approach
International Journal of Climatology,
2020
DOI:10.1002/joc.6822
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[15]
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Homogenization of instrumental time series of air temperature in Central Italy (1930-2015)
Climate Research,
2019
DOI:10.3354/cr01552
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[16]
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Changes in extreme temperature and precipitation indices: Using an innovative daily homogenized database in Israel
International Journal of Climatology,
2019
DOI:10.1002/joc.6125
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[17]
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Developing Gridded Climate Data Sets of Precipitation for Greece Based on Homogenized Time Series
Climate,
2019
DOI:10.3390/cli7050068
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[18]
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A new method to study inhomogeneities in climate records: Brownian motion or random deviations?
International Journal of Climatology,
2019
DOI:10.1002/joc.6105
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[19]
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The joint influence of break and noise variance on the break detection capability in time series homogenization
Advances in Statistical Climatology, Meteorology and Oceanography,
2018
DOI:10.5194/ascmo-4-1-2018
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[20]
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Effects of undetected data quality issues on climatological analyses
Climate of the Past,
2018
DOI:10.5194/cp-14-1-2018
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[21]
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Inter-comparison of methods to homogenize daily relative humidity
International Journal of Climatology,
2018
DOI:10.1002/joc.5488
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[22]
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Impact of missing data on the efficiency of homogenisation: experiments with ACMANTv3
Theoretical and Applied Climatology,
2018
DOI:10.1007/s00704-018-2488-3
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[23]
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Detecting and adjusting artificial biases of long-term temperature records in Israel
International Journal of Climatology,
2018
DOI:10.1002/joc.5500
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[24]
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Quality control and homogenization of the Belgian historical temperature data
International Journal of Climatology,
2018
DOI:10.1002/joc.5792
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[25]
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Perspectives on Atmospheric Sciences
Springer Atmospheric Sciences,
2017
DOI:10.1007/978-3-319-35095-0_83
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[26]
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Homogenisation of temperature and precipitation time series with ACMANT3: method description and efficiency tests
International Journal of Climatology,
2017
DOI:10.1002/joc.4822
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[27]
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Temperature and precipitation regional climate series over the central Pyrenees during 1910-2013
International Journal of Climatology,
2017
DOI:10.1002/joc.4823
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[28]
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Perspectives on Atmospheric Sciences
Springer Atmospheric Sciences,
2017
DOI:10.1007/978-3-319-35095-0_62
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[29]
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Homogenization and trend analysis of monthly mean and maximum wind speed time series in Finland, 1959-2015
International Journal of Climatology,
2017
DOI:10.1002/joc.5124
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[30]
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Variability modes of precipitation along a Central Mediterranean area and their relations with ENSO, NAO, and other climatic patterns
Atmospheric Research,
2017
DOI:10.1016/j.atmosres.2017.07.031
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[31]
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Homogenization of Ukrainian air temperature data
International Journal of Climatology,
2017
DOI:10.1002/joc.5191
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[32]
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Time series homogenisation of large observational datasets: impact of the number of partner series on efficiency
Climate Research,
2017
DOI:10.3354/cr01488
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[33]
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Review and discussion of homogenisation methods for climate data
Physics and Chemistry of the Earth, Parts A/B/C,
2016
DOI:10.1016/j.pce.2015.08.007
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[34]
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Reconstruction and homogenization of the longest instrumental precipitation series in the Iberian Peninsula (Barcelona, 1786-2014)
International Journal of Climatology,
2016
DOI:10.1002/joc.4537
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[35]
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Detection of inhomogeneities in precipitation time series in Portugal using direct sequential simulation
Atmospheric Research,
2016
DOI:10.1016/j.atmosres.2015.11.014
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[36]
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The Oslo temperature series 1837-2012: homogeneity testing and temperature analysis
International Journal of Climatology,
2015
DOI:10.1002/joc.4223
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[37]
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Homogenization of precipitation time series with ACMANT
Theoretical and Applied Climatology,
2015
DOI:10.1007/s00704-014-1298-5
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[38]
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Comparison of HOMER and ACMANT homogenization methods using a central Pyrenees temperature dataset
Advances in Science and Research,
2015
DOI:10.5194/asr-12-111-2015
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[39]
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Identifying homogenous sub-periods in HadISD
Climate of the Past Discussions,
2014
DOI:10.5194/cpd-10-1567-2014
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[40]
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Pairwise homogeneity assessment of HadISD
Climate of the Past,
2014
DOI:10.5194/cp-10-1501-2014
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[41]
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Investigating uncertainties in global gridded datasets of climate extremes
Climate of the Past,
2014
DOI:10.5194/cp-10-2171-2014
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[42]
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Detection and correction of inhomogeneities in Greek climate temperature series
International Journal of Climatology,
2014
DOI:10.1002/joc.3888
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[43]
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The Ås Temperature Series in Southern Norway–Homogeneity Testing And Climate Analysis
Bulletin of Geography. Physical Geography Series,
2014
DOI:10.2478/bgeo-2014-0001
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[44]
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Efficiencies of Inhomogeneity-Detection Algorithms: Comparison of Different Detection Methods and Efficiency Measures
Journal of Climatology,
2013
DOI:10.1155/2013/390945
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[45]
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Development and testing of homogenisation methods: moving parameter experiments with ACMANT
Advances in Science and Research,
2013
DOI:10.5194/asr-10-43-2013
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[46]
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Tendencias recientes e índices de cambio climático de la temperatura y la precipitación en Andorra, Pirineos (1935-2008)
Pirineos,
2012
DOI:10.3989/Pirineos.2012.167005
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[47]
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The historical pathway towards more accurate homogenisation
Advances in Science and Research,
2012
DOI:10.5194/asr-8-45-2012
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[48]
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Benchmarking homogenization algorithms for monthly data
Climate of the Past,
2012
DOI:10.5194/cp-8-89-2012
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[49]
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Benchmarking monthly homogenization algorithms
Climate of the Past Discussions,
2011
DOI:10.5194/cpd-7-2655-2011
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