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Impact of missing data on the efficiency of homogenisation: experiments with ACMANTv3

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Abstract

The impact of missing data on the efficiency of homogenisation with ACMANTv3 is examined with simulated monthly surface air temperature test datasets. The homogeneous database is derived from an earlier benchmarking of daily temperature data in the USA, and then outliers and inhomogeneities (IHs) are randomly inserted into the time series. Three inhomogeneous datasets are generated and used, one with relatively few and small IHs, another one with IHs of medium frequency and size, and a third one with large and frequent IHs. All of the inserted IHs are changes to the means. Most of the IHs are single sudden shifts or pair of shifts resulting in platform-shaped biases. Each test dataset consists of 158 time series of 100 years length, and their mean spatial correlation is 0.68–0.88. For examining the impacts of missing data, seven experiments are performed, in which 18 series are left complete, while variable quantities (10–70%) of the data of the other 140 series are removed.

The results show that data gaps have a greater impact on the monthly root mean squared error (RMSE) than the annual RMSE and trend bias. When data with a large ratio of gaps is homogenised, the reduction of the upper 5% of the monthly RMSE is the least successful, but even there, the efficiency remains positive. In terms of reducing the annual RMSE and trend bias, the efficiency is 54–91%. The inclusion of short and incomplete series with sufficient spatial correlation in all cases improves the efficiency of homogenisation with ACMANTv3.

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Acknowledgements

The authors thank Kate Willett and her colleagues for giving open access to the temperature database they developed.

Funding

The second author was funded by the Irish Environmental Protection Agency under project 2012-CCRP-FS.11.

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Correspondence to Peter Domonkos.

Appendices

I Gap filling within ACMANTv3

  1. i)

    Concepts and definitions

The dataset consists of N monthly time series of n years length, but the series are incomplete. Series s (s = 1,2,…N) can be presented as

$$ {\mathbf{X}}_{\mathbf{s}}={x}_{s,1},{x}_{s,2},\dots {x}_{s,h}\dots {x}_{s,12n} $$
(A1)

without indicating possible data gaps, h stands for the serial number of month from the beginning of the time series. The relation between h, the serial number of year from the beginning of time series i and the serial number of calendar month m is.

$$ h=12\left(i-1\right)+m\kern0.5em i\in \left\{1,2,\dots n\right\} $$
(A2)

We will denote the cluster of observed values (distinguishing from missing values) of series s with Js, and its sub-cluster for month m with Js,m, and the number of elements in there with Ks and Ks,m, respectively.

Before any other operation with the data, the seasonal cycle is removed by extracting the monthly climatic normal (Us,m) from the observed values, then the deseasonalised series are denoted with As (Eqs. A3A5).

$$ {\mathbf{A}}_{\mathbf{s}}={a}_{s,1},{a}_{s,2},\dots {a}_{s,h}\dots {a}_{s,12n} $$
(A3)
$$ {a}_{s,h}={x}_{s,h}-{U}_{s,m} $$
(A4)
$$ {U}_{s,m}=\frac{1}{K_{s,m}}{\sum}_{{\mathrm{J}}_{\mathrm{s},\mathrm{m}}}{x}_{s,h} $$
(A5)

Missing data of a candidate series (Ag) will be filled with interpolation using the synchronous values of partner series (As). In the selection of partner series and for weighting their contribution, spatial correlations are considered.

The spatial correlation between series g and series s (rg,s) is defined by the Pearson correlation coefficient. The sample size for its calculation includes each h for which both series have observed values. When the sample size is lower than 50, the correlation is zero by our definition.

  1. ii)

    Gap filling

The method of gap filling has remained similar to that in the first version of ACMANT (Domonkos 2011b), but some details have been changed since then.

The interpolation for a missing value of month h0 in the candidate series relies on the synchronous observed values of surrounding stations, but the values of the partner series are tuned to a section mean value characterising the common effect of local climate and possible inhomogeneity effects at the timing of the missing value in the candidate series. For this purpose, window [h1, h2] around h0 is constructed. This window must be wide enough to have sufficient data for the reliable estimation of the difference between the section means for the candidate series Ag and its partner series As, but narrow enough to exclude the effects of temporally distant IHs.

The window width can be regulated by parameterising it directly, or via the minimum number of the value pairs for series g and s. In practice, the window width is varied according to the frequency of missing data around h0 in the time series participating in the interpolation, hence h2 – h1 is functions of both h and s. Table 3 shows various sets of conditions for the window constructions in terms of the half window width (L) and the number of observed value pairs within the window (k). Moving down in Table 3 the conditions soften, and always the strictest conditions allowed by data availability are selected for the window construction.

Table 3 Connections between half window width (L), number of observed value pairs for the candidate series and its partner series (n’) and coefficient of weight correction (c) in the construction of window around the timing of the missing data (h). Always the strictest conditions allowed by data availability are applied

All the series with rg,s ≥ 0.4 are considered as partner series, if they have observed value for month h0. Eq. (A6) shows the tuning of value as,h0 to the candidate series.

$$ {a}_{s,h0}^{\prime }={a}_{s,h0}+\frac{1}{k}{\sum}_{h={h}_1}^{h_2}\left({a}_{g,h}-{a}_{s,h}\right) $$
(A6)

When at least one of the two series do not have observed data (h is not ϵ Jg∩Js), then as,h = ag,h in (6) by definition. The interpolated value will be the weighted average of the tuned values of N* partner series (N* ≤ N − 1). The weights are the squared spatial correlations corrected by coefficient c depending on the window width (Table 3).

When the sum of the corrected weights (p) is lower than 0.4, zero anomaly (ag,h = 0) is presumed for the missing value with a certain or entire weight, according to Eqs. (A7) and (A8).

$$ {a}_{g,h0}=\frac{1}{p}{\sum}_{s=1}^{N^{\ast }}{c}_s{r}_{g,s}^2{a}_{s,h0}^{\prime } $$
(A7)
$$ p=\max \left(0.4,{\sum}_{s=1}^{N^{\ast }}{c}_s{r}_{g,s}^2\right) $$
(A8)

Note that the optimal sample for the calculation of U and r (Huang et al. 1996; Tardivo 2015) may also differ from the sample including all available data and used in this study. However, this difference from the optimal parameterisation likely has a minor effect on the accuracy of interpolation.

The gap filling is performed three times within the homogenisation, first with the use of raw data, then with the use of pre-homogenised data in the second and third stages of the procedure.

II Automatic networking

Appropriately constructed networks for homogenisation with ACMANT have three positive attributes: (i) The candidate series have a sufficient number of highly correlated partner series; (ii) Each section of the candidate series is covered with a sufficient number of synchronous observed data of the partner series; (iii) There is no unnecessary excess of the network size. The algorithm presented here is structured to give solutions with these positive attributes.

The number of partner series and the number of effective partners (see its definition in Sect. 3.4) are denoted with M and F, respectively. The spatial correlations used here (r*) are not the same as those which are used for the interpolation, namely r* is calculated from the first difference (increment) series of the deseasonalised monthly temperatures, and following from how it was introduced to time series homogenisation by Peterson and Easterling (1994).

For the homogenisation of each candidate series, one distinct network is constructed. First, the most highly correlated partner series are selected up to 30 series. When the number of potential partner series with r* ≥ 0.4. is higher than 30, the following steps are performed recursively.

  1. i)

    Possible improvements in F by the inclusion of any further partner series (s) are considered using score S1 for dates of F < 10 (Eqs. A9, A10).

$$ \mathrm{S}1(s)=\sum \limits_{h=1}^{12n}5{r}_s^{\ast 4}{\left(12-{F}_{s,h}^{\ast}\right)}^3 $$
(A9)
$$ {F}^{\ast }=\left\{\begin{array}{c}F\kern0.24em \mathrm{if}\kern0.24em F<10\\ {}12\kern0.24em \mathrm{if}\kern0.24em F\ge 10\end{array}\right\}\kern0.5em \mathrm{for}\kern0.17em \mathrm{every}\;s\;\mathrm{and}\;h $$
(A10)
  1. ii)

    Possible improvements in F by the inclusion of any further partner series are considered using score S2 for decadal sections of the candidate series where F < 10 in at least 25% of the decade. Months belonging to at least one of such decades are denoted with m in Eqs. A11-A12.

$$ \mathrm{S}2(s)=\sum \limits_m5{r}_s^{\ast 4}{\left(20-{F}_{s,m}^{\ast \ast}\right)}^2 $$
(A11)
$$ {F}^{\ast \ast }=\left\{\begin{array}{c}F\kern0.24em \mathrm{if}\kern0.24em F<20\\ {}20\kern0.24em \mathrm{if}\kern0.24em F\ge 20\end{array}\right\}\kern0.5em \mathrm{for}\kern0.17em \mathrm{every}\;s\;\mathrm{and}\;m $$
(A12)
  1. iii)

    The exceedance of M above 30 is penalised with score S3 (Eq. A13).

$$ \mathrm{S}3={\left(M-30\right)}^2 $$
(A13)
  1. iv)

    Summarised score S is calculated for each s (Eq. A14).

$$ \mathrm{S}(s)=\mathrm{S}1(s)+\mathrm{S}2(s)-\mathrm{S}3 $$
(A14)

Then the series with the highest S is selected, and the procedure continues with step i.

If S ≤ 0 for all s, no further partner series is selected, and the procedure terminates.

The development of this algorithm is based on subjective decisions, but the important elements of the procedure can be reasoned well. Frequent occurrence of low F within a relatively short period is considered more destructive to the efficiency of homogenisation than its sporadic occurrences; therefore, higher minimum threshold of F is applied in S2 than in S1. It is more important to raise the smallest F values (if it is possible) than to raise a large number of F values, that is why the second factors of (A9) and (A11) are assigned higher powers. When more series are comparably useful in raising F, it is important to give preference to the one with relatively high correlation, therefore the power of r* is raised by 4. Note that some parameter values are close to those of the networking in PHA (Menne and Williams 2009), as in PHA the 40 best correlating partner series are taken at the first step, and the correlation threshold is 0.5.

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Domonkos, P., Coll, J. Impact of missing data on the efficiency of homogenisation: experiments with ACMANTv3. Theor Appl Climatol 136, 287–299 (2019). https://doi.org/10.1007/s00704-018-2488-3

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