Bibliografie VÚGTK
Homogenizing GPS Integrated Water Vapor Time Series: Benchmarking Break Detection Methods on Synthetic Data Sets
Eliaš, Michal

Source: Earth and Space Science : 7. (5), 20 s. ISSN: 2333-5084

Publication type: článek v časopise
Extent20 stran

Link: https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2020EA001121
Annotation:
We assess the performance of different break detection methods on three sets of benchmarkdata sets, each consisting of 120 daily time series of integrated water vapor differences. These differencesare generated from the Global Positioning System (GPS) measurements at 120 sites worldwide, and thenumerical weather prediction reanalysis (ERAInterim) integrated water vapor output, which serves as thereference series here. The benchmark includes homogeneous and inhomogeneous sections with addednonclimatic shifts (breaks) in the latter. Three different variants of the benchmark time series are produced,with increasing complexity, by adding autoregressive noise of the rst order to the white noise model and theperiodic behavior and consecutively by adding gaps and allowing nonclimatic trends. The purpose of this“complex experiment” is to examine the performance of break detection methods in a more realistic casewhen the reference series are not homogeneous. We evaluate the performance of break detection methodswith skill scores, centered root mean square errors (CRMSE), and trend differences relative to the trends ofthe homogeneous series. We found that most methods underestimate the number of breaks and have asignicant number of false detections. Despite this, the degree of CRMSE reduction is signicant (roughlybetween 40% and 80%) in the easy to moderate experiments, with the ratio of trend bias reduction is evenexceeding the 90% of the raw data error. For the complex experiment, the improvement ranges between 15%and 35% with respect to the raw data, both in terms of RMSE and trend estimations.

Citation: ELIAS, Michal, E. POTTIAUX, A. KLOS, et al. Homogenizing GPS Integrated Water Vapor Time Series: Benchmarking Break Detection Methods on Synthetic Data Sets. Earth and Space Science [online]. 2020, 7(5) [cit. 2021-07-29]. ISSN 2333-5084. Dostupné z: doi:10.1029/2020EA001121

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