Sensitivity of Change-Point Detection and Trend Estimates to GNSS IWV Time Series Properties

Nguyen, Khanh Ninh; Quarello, Annarosa; Bock, Olivier; Lebarbier, Émilie

doi:10.3390/atmos12091102

Cited by 11 publications

(9 citation statements)

References 46 publications

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“…We use integrated water vapour (IWV) derived from Global Navigation Satellite System (GNSS) measurements (Bock, 2019) and from the fifth ECMWF reanalysis (ERA5; Hersbach et al, 2020). Because the global GNSS data set is relatively sparse, the reanalysis is used as a reference to form the target minus reference difference series Nguyen et al, 2021;Ning et al, 2016;Quarello et al, 2022;Van Malderen et al, 2020). The GNSS minus reanalysis data are segmented with the 'GNSSseg' method developed by (Quarello, 2020) which is based on a penalized likelihood approach.…”

Section: Introductionmentioning

confidence: 99%

“…With real data, the situation is different. Several past studies highlighted the presence of abrupt changes in the mean of GNSS series (Bock et al, 2014;Ning et al, 2016;Parracho et al, 2018;Vey et al, 2009) or in the reanalysis data (Nguyen et al, 2021;Ning et al, 2016;Parracho et al, 2018;Schroeder et al, 2016). Inhomogeneities in the GNSS series can be due to equipment changes, changes in the station's environment, or changes in the data processing procedure (Nguyen et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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A statistical method for the attribution of change‐points in segmented Integrated Water Vapor difference time series

Nguyen,

Bock,

Lebarbier

2024

Intl Journal of Climatology

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Many segmentation or change‐point detection methods for homogenizing climate time series compare candidate station data with reference data to eliminate common climate signals and more efficiently detect spurious, non‐climatic changes. One drawback is that it is difficult to decide whether the detected change‐point is due to the candidate series or to the reference. A so‐called attribution procedure is typically applied in a post‐processing step for each detected change‐point. This article describes a new statistical method for the attribution of change‐points detected in Global Navigation Satellite System (GNSS) minus reanalysis series of integrated water vapour. It requires at least one nearby station with similar GNSS and reanalysis data. Six series of differences are formed from the four base series (BS) and are tested for a significant jump at the time of the change‐point detected in the candidate station. The six test results are analysed with a statistical predictive rule to attribute the change‐point to one, or several, of the four BS. Original aspects of our method are: (1) the significance test, which is based on a generalized linear regression approach, taking both heteroscedasticity and autocorrelation into account; (2) the predictive rule, which uses a machine learning method and is constructed from the test results obtained with the real data by using a resampling strategy. Four popular machine learning methods have been compared using cross‐validation and the best one was applied to a real data set (49 main stations with 114 change‐points). The results depend on the choice of the test significance level and the aggregation method combining the prediction results when several nearby stations are available. We find that 62% of the change‐points are attributed to GNSS, 19% to the reanalysis, and 10% are due to coincident detections.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A statistical method for the attribution of change‐points in segmented Integrated Water Vapor difference time series

Nguyen,

Bock,

Lebarbier

2024

Intl Journal of Climatology

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“…to artificial trends of up to 0.15 kg/(m 2 year). Nguyen et al (2021) show the effect of different a priori models for the hydrostatic zenith delay that are as critical as the quality of the antenna/radome calibrations and applied mapping functions.…”

Section: Introductionmentioning

confidence: 99%

On the Effect of Antenna Calibration Errors on Geodetic Estimates

Kersten

Krzan

Dawidowicz

et al. 2022

International Association of Geodesy Symposia

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This paper addresses an approach to assess the impact of phase centre correction errors of selected receiving antennas in the Polish ASG-Eupos network using GNSS processing strategies such as zero differencing and double differencing. The objective is to characterise the nature of the error patterns of GNSS receiver antennas and to understand their impact on GNSS derived integrated water vapour and geodetic estimates. A semi-analytical approach for characterising variants of error patterns is applied. Differences of up to +12 mm between type-mean and individual receiver antenna calibrations of current antenna models on the ionosphere-free linear combination are identified for repeatable pattern deformations. The analyses show that repeatable effects on tropospheric estimates of up to 8 mm – which corresponds to approx. 1.2 kg/m2 – occur even though only 5 mm variations were applied to the pattern. The results of our analysis show a strong correlation with the type of error patterns that affect the estimates differently. Due to the complex relationship between datum settings, processing strategy, baseline orientation and satellite sky distribution, artefacts in GNSS processing models and their effects must to be modelled in order to achieve a better understanding in the context of GNSS networks and GNSS meteorology.

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“…The change points detection and trend investigation in time series are of interest in many sectors ranging from genetic to finance [19]. In the environmental field they have a widespread applicability due to their significant implications for pollution control and for environmental decision-making [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Change Points Detection and Trend Analysis to Characterize Changes in Meteorologically Normalized Air Pollutant Concentrations

Gagliardi

Andenna

2021

Atmosphere

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Identifying changes in ambient air pollution levels and establishing causation is a research area of strategic importance to assess the effectiveness of air quality interventions. A major challenge in pursuing these objectives is represented by the confounding effects of the meteorological conditions which easily mask or emphasize changes in pollutants concentrations. In this study, a methodological procedure to analyze changes in pollutants concentrations levels after accounting for changes in meteorology over time was developed. The procedure integrated several statistical tools, such as the change points detection and trend analysis that are applied to the pollutants concentrations meteorologically normalized using a machine learning model. Data of air pollutants and meteorological parameters, collected over the period 2013–2019 in a rural area affected by anthropic emissive sources, were used to test the procedure. The joint analysis of the obtained results with the available metadata allowed providing plausible explanations of the observed air pollutants behavior. Consequently, the procedure appears promising in elucidating those changes in the air pollutant levels not easily identifiable in the original data, supplying valuable information to identify an atmospheric response after an intervention or an unplanned event.

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Sensitivity of Change-Point Detection and Trend Estimates to GNSS IWV Time Series Properties

Cited by 11 publications

References 46 publications

A statistical method for the attribution of change‐points in segmented Integrated Water Vapor difference time series

A statistical method for the attribution of change‐points in segmented Integrated Water Vapor difference time series

On the Effect of Antenna Calibration Errors on Geodetic Estimates

Change Points Detection and Trend Analysis to Characterize Changes in Meteorologically Normalized Air Pollutant Concentrations

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