On the reduction of trend errors by the ANOVA joint correction scheme used in homogenization of climate station records

Lindau, Ralf; Venema, Victor

doi:10.1002/joc.5728

Cited by 25 publications

(19 citation statements)

References 20 publications

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“…All of the four methods will be examined for a medium‐sized station network and two of the four will also be examined with a denser station network. Note that although the final purpose of homogenization is not the break detection but the removal of non‐climatic biases from the observed data, the break detection is an important step (Venema et al ., 2012; Lindau and Venema, 2013, 2016), and break detection errors may seriously affect the final accuracy of homogenization products (Lindau and Venema, 2018a). Note also that in homogenization tasks including the exhaustive analysis of station history, the skill of break detection may have a large and direct impact on the accuracy of the homogenization results.…”

Section: Introductionmentioning

confidence: 99%

Application of homogenization methods for Ireland's monthly precipitation records: Comparison of break detection results

Coll

Domonkos

Guijarro

et al. 2020

Intl Journal of Climatology

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Time series homogenization for 299 of the available precipitation records for the island of Ireland (IENet) was performed. Four modern relative homogenization methods, that is, HOMER, ACMANT, CLIMATOL and AHOPS were applied to this network of station series where contiguous intact monthly records range from 30 to 70 years within the base period 1941–2010. Break detection results are compared between homogenization methods, and coincidences with available documentary information (metadata) were analysed. The lowest (highest) number of breaks were detected with HOMER (ACMANT). Large differences of break frequency were found, namely ACMANT and AHOPS detected 8 times as many breaks than HOMER, while the break frequency with CLIMATOL was intermediate. Also, the ratio of series classified to be homogeneous varies widely between the methods. It is 85% with HOMER, 60% with CLIMATOL, 31% with AHOPS, while only 22% with ACMANT. In a further experiment, all the available time series for Ireland and Northern Ireland, (910 series) were used with ACMANT and CLIMATOL to explore the stability of break frequency for the same 299 series examined in the base experiment. While overall break frequency slightly increased (by 6–13%), the break positions notably changed for individual time series. The number of breaks changed for 59% (23%) of the series with ACMANT (CLIMATOL). For the breaks detected coincidentally by at least three methods including ACMANT and CLIMATOL in the base experiment, the second experiment confirmed the break positions in 86–87% of the breaks. The consequences of these results in relation to the reliability of statistical homogenization are discussed. Sometimes markedly different step functions provide comparable good approaches. However, the accuracy of homogenized time series cannot be related directly to the instability of break detection results.

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Section: Introductionmentioning

confidence: 99%

Application of homogenization methods for Ireland's monthly precipitation records: Comparison of break detection results

Coll

Domonkos

Guijarro

et al. 2020

Intl Journal of Climatology

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“…The variance of an RD process is in principle equal to σ δ 2 , the variance of the random numbers used to create the segment levels. In this estimation, we neglect the slightly increased effective variance due to the assumed different lengths of the segments, which are caused by the stochastic occurrence of the breaks (Lindau and Venema, ). Nevertheless, the jump heights between two levels consist of the sum of two of such independent random numbers so that their variance σ β 2 is twice as large.

{Var}_{RD} = σ_{δ}^{2} = \frac{{σ_{β}}^{2}}{2} .

…”

Section: The Different Characteristics Of Brownian Motion and Random mentioning

confidence: 99%

“…Even more important are the different effects, the two breaks types will have on the trend. Lindau and Venema () investigated the trend introduced by RD breaks. Assuming zero mean jump sizes, they found that the error variance of the trend decreases with the number of breaks.…”

Section: The Different Characteristics Of Brownian Motion and Random mentioning

confidence: 99%

A new method to study inhomogeneities in climate records: Brownian motion or random deviations?

Lindau

Venema

2019

Intl Journal of Climatology

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Climate data are affected by inhomogeneities due to historical changes in the way the measurements were performed. Understanding these inhomogeneities is important for accurate estimates of long‐term changes in the climate. These inhomogeneities are typically characterized by the number of breaks and the size of the jumps or the variance of the break signal, but a full characterization of the break signal also includes its temporal behaviour. This study develops a method to distinguish between two types of breaks: random deviations from a baseline and Brownian motion. Strength and frequency of both break types are estimated by using the variance of the spatiotemporal differences in the time series of two nearby stations as input. Thus, the result is directly obtained from the data without running a homogenization algorithm to estimate the break signal from the data. This opens the possibility to determine the total number of breaks and not only that of the significantly large ones. The application to German temperature observations suggests generally small inhomogeneities dominated by random deviations from a baseline. U.S. stations, on the other hand, also show the characteristics of a strong Brownian‐motion‐type component.

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“…The variance of an RD process is in principle equal to V G 2 , the variance of the random numbers used to create the segment levels. In this estimation we neglect the slightly increased effective variance due to the assumed different lengths of the segments, which are caused by the stochastic occurrence of the breaks (Lindau and Venema, 2018b). Nevertheless, the jump heights between two levels consist of the sum of two of such independent random numbers so that their variance V E 2 is twice as large.…”

Section: The Different Characteristics Of Brownian Motion and Random mentioning

confidence: 99%

A new method to study inhomogeneities in climate records: Brownian Motion or Random Deviations?

Lindau¹,

Venema²

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Climate data is affected by inhomogeneities due to historical changes in the way the measurements were performed. Understanding these inhomogeneities is important for accurate estimates of long-term changes in the climate. These inhomogeneities are typically characterized by the number of breaks and the size of the jumps or the variance of the break signal, but a full characterization of the break signal also includes its temporal behavior. This study develops a method to distinguish between two types of breaks: random deviations from a baseline and Brownian motion. Strength and frequency of both break types are estimated by using the variance of the spatiotemporal differences in the time series of two nearby stations as input. Thus, the result is directly obtained from the data without running a homogenization algorithm to estimate the break signal from the data. This opens the possibility to determine the total number of breaks and not only that of the significantly large ones. The application to German temperature observations suggests generally small inhomogeneities dominated by random deviations from a baseline. US stations, on the other hand, show also the characteristics of a strong Brownian motion type component.

show abstract

On the reduction of trend errors by the ANOVA joint correction scheme used in homogenization of climate station records

Cited by 25 publications

References 20 publications

Application of homogenization methods for Ireland's monthly precipitation records: Comparison of break detection results

Application of homogenization methods for Ireland's monthly precipitation records: Comparison of break detection results

A new method to study inhomogeneities in climate records: Brownian motion or random deviations?

A new method to study inhomogeneities in climate records: Brownian Motion or Random Deviations?

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