Intercomparison of homogenization techniques for precipitation data

Beaulieu, Claudie; Seidou, Ousmane; Ouarda, Taha B. M. J.; Zhang, Xuebin; Boulet, Gilles; Yagouti, Abderrahmane

doi:10.1029/2006wr005615

Cited by 61 publications

(93 citation statements)

References 17 publications

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“…The ZTD parameter is now customarily derived from the processing of the GPS observations but its consistency in time is adversely affected by a number of processes, particularly discontinuities that may or may not stem from real geophysical or climatic signals (Beaulieu et al, 2008;Gazeaux et al, 2011;Gazeaux et al, 2013;Bock et al, 2014;Griffiths and Ray, 2015). The source of these discontinuities can have many origins but is mostly related to hardware changes (receiver, 5 antenna or antenna cable), changes in the observation procedures (e.g.…”

Section: Homogenization Of Ztd Time Seriesmentioning

confidence: 99%

Noise characteristics in Zenith Total Delay from homogeneously reprocessed GPS time series

Kłos

Hunegnaw

Teferle

et al. 2016

Preprint

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Abstract. Zenith Total Delay (ZTD) time series, derived from the re-processing of Global Positioning System (GPS) data, provide valuable information for the evaluation of global atmospheric reanalysis products such as ERA-Interim. Identifying 10 the correct noise characteristics in the ZTD time series is an important step to assess the 'true' magnitude of ZTD trend uncertainties. The ZTD residual time series for 1995-2015 are generated from our homogeneously re-processed and homogenized GPS time series from over 700 globally distributed stations classified into five major climate zones. The annual peak of ZTD data ranges between 10 and 150 mm with the smallest values for the polar and Alpine zone. The amplitudes of daily curve fall between 0 and 12 mm with the greatest variations for the dry zone. The autoregressive process 15 of fourth order plus white noise model were found to be optimal for ZTD series. The tropical zone has the largest amplitude of autoregressive noise (9.59 mm) and the greatest amplitudes of white noise (13.00 mm). All climate zones have similar median coefficients of AR(1) (0.80±0.05) with a minimum for polar and Alpine, which has the highest coefficients of AR (2) (0.27±0.01) and AR(3) (0.11±0.01) and clearly different from the other zones considered. We show that 53 of 120 examined trends became insignificant, when the optimum noise model was employed, compared to 11 insignificant trends for pure 20 white noise. The uncertainty of the ZTD trends may be underestimated by a factor of 3 to 12 compared to the white noise only assumption.

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Section: Homogenization Of Ztd Time Seriesmentioning

confidence: 99%

Noise characteristics in Zenith Total Delay from homogeneously reprocessed GPS time series

Kłos

Hunegnaw

Teferle

et al. 2016

Preprint

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“…Such techniques have been developed for in situ climate data for example (e.g. Peterson et al, 1998;Beaulieu et al, 2008).…”

Section: Trend Detection In Presence Of Autocorrelation and Discontinmentioning

confidence: 99%

Factors challenging our ability to detect long-term trends in ocean chlorophyll

et al. 2013

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Global climate change is expected to affect the ocean's biological productivity. The most comprehensive information available about the global distribution of contemporary ocean primary productivity is derived from satellite data. Large spatial patchiness and interannual to multidecadal variability in chlorophyll a concentration challenges efforts to distinguish a global, secular trend given satellite records which are limited in duration and continuity. The longest ocean color satellite record comes from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), which failed in December 2010. The Moderate Resolution Imaging Spectroradiometer (MODIS) ocean color sensors are beyond their originally planned operational lifetime. Successful retrieval of a quality signal from the current Visible Infrared Imager Radiometer Suite (VIIRS) instrument, or successful launch of the Ocean and Land Colour Instrument (OLCI) expected in 2014 will hopefully extend the ocean color time series and increase the potential for detecting trends in ocean productivity in the future. Alternatively, a potential discontinuity in the time series of ocean chlorophyll a, introduced by a change of instrument without overlap and opportunity for cross-calibration, would make trend detection even more challenging. In this paper, we demonstrate that there are a few regions with statistically significant trends over the ten years of SeaWiFS data, but at a global scale the trend is not large enough to be distinguished from noise. We quantify the degree to which red noise (autocorrelation) especially challenges trend detection in these observational time series. We further demonstrate how discontinuities in the time series at various points would affect our ability to detect trends in ocean chlorophyll a. We highlight the importance of maintaining continuous, climate-quality satellite data records for climate-change detection and attribution studies

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“…where y t is the response variable, m represents the overall mean, 3 t are the normally distributed (N ) random errors with mean 0 and overall variance s 2 , t represents the time, n is the number of observations, and m 1 and m 2 are the means before and after the unknown change point at time p. These models have been used widely to detect artificial shifts in temperature, pressure or precipitation series [13,[32][33][34][35][36]. Undocumented changes in the measurement procedures can be detected by applying change-point techniques to series of ratios or differences between the observations at several neighbouring sites.…”

Section: (A) Mean And/or Variancementioning

confidence: 99%

“…We compute the hit rates, which we define as: the percentage of cases in the 2000 synthetic series for which the model with a shift in the mean is selected and the shift detected is located at most 2 years away from the real shift in 1990. The 2 years rule was used in several studies comparing techniques for the detection of artificial shifts in temperature and precipitation [35,66,83] and provides a measure of the power of detection of the technique. Figure 4b presents the results of this simulation study.…”

Section: (C) Length Of Time Series and Shift Detectionmentioning

confidence: 99%

Change-point analysis as a tool to detect abrupt climate variations

Beaulieu

Chen

Sarmiento

2012

Phil. Trans. R. Soc. A.

Self Cite

139

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Recently, there have been an increasing number of studies using change-point methods to detect artificial or natural discontinuities and regime shifts in climate. However, a major drawback with most of the currently used change-point methods is the lack of flexibility (able to detect one specific type of shift under the assumption that the residuals are independent). As temporal variations in climate are complex, it may be difficult to identify change points with very simple models. Moreover, climate time series are known to exhibit autocorrelation, which corresponds to a model misspecification if not taken into account and can lead to the detection of non-existent shifts. In this study, we extend a method known as the informational approach for change-point detection to take into account the presence of autocorrelation in the model. The usefulness and flexibility of this approach are demonstrated through applications. Furthermore, it is highly desirable to develop techniques that can detect shifts soon after they occur for climate monitoring. To address this, we also carried out a simulation study in order to investigate the number of years after which an abrupt shift is detectable. We use two decision rules in order to decide whether a shift is detected or not, which represents a trade-off between increasing our chances of detecting a shift and reducing the risk of detecting a shift while in reality there is none. We show that, as of now, we have good chances to detect an abrupt shift with a magnitude that is larger than that of the standard deviation in the series of observations. For shifts with a very large magnitude (three times the standard deviation), our simulation study shows that after only 4 years the probabilities of shift detection reach nearly 100 per cent. This reveals that the approach has potential for climate monitoring.

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Intercomparison of homogenization techniques for precipitation data

Cited by 61 publications

References 17 publications

Noise characteristics in Zenith Total Delay from homogeneously reprocessed GPS time series

Noise characteristics in Zenith Total Delay from homogeneously reprocessed GPS time series

Factors challenging our ability to detect long-term trends in ocean chlorophyll

Change-point analysis as a tool to detect abrupt climate variations

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