Evaluation of the atmospheric water vapor content in a regional climate model using ground‐based GPS measurements

Ning, Tong; Elgered, Gunnar; Willén, Ulrika; Johansson, Jan M.

doi:10.1029/2012jd018053

Cited by 64 publications

(58 citation statements)

References 58 publications

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“…Similar results were also reported before by Ning et al (2013). However, few inconsistences were noticed.…”

Section: Discussionsupporting

confidence: 80%

“…smaller than its error. Diurnal peaks for other stations from Europe agree to within 2 hours with those shown in Ning et al (2013).…”

Section: Discussionsupporting

confidence: 70%

“…However, few inconsistences were noticed. A diurnal peak for station VILL (Villanueva, Spain) falls at 1 a.m. in this study, while those happened at 6 p.m. in Ning et al (2013). However, the diurnal amplitude for VILL is insignificant, i.e.…”

Section: Discussionmentioning

confidence: 55%

“…In this case we adopt ordinary least-squares (LS) and estimate the covariance of fitted parameters as in equation (5). This approach is widely used in ZTD/IWV estimations in terms of climate applications (e.g., Jin et al, 2007;Flouzat et al, 2009;Morland et al, 2009;Ning et al, 2013;Bock et al, 2014). Nilsson and Elgered (2008) applied additional covariance as a function of time so as not to assume a pure white noise process.…”

Section: Noise Analysis Of Ztdmentioning

confidence: 99%

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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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“…Similar results were also reported before by Ning et al (2013). However, few inconsistences were noticed.…”

Section: Discussionsupporting

confidence: 80%

“…smaller than its error. Diurnal peaks for other stations from Europe agree to within 2 hours with those shown in Ning et al (2013).…”

Section: Discussionsupporting

confidence: 70%

Section: Discussionmentioning

confidence: 55%

Section: Noise Analysis Of Ztdmentioning

confidence: 99%

See 2 more Smart Citations

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

Kłos

Hunegnaw

Teferle

et al. 2016

Preprint

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“…The estimates of amplitudes and phase shifts of seasonal components match the seasonal signals presented in Jin et al (2007). The daily curve phase shifts also matched the values presented by Ning et al (2013) with a tolerance of 2 h.…”

Section: Discussionsupporting

confidence: 50%

Statistical significance of trends in Zenith Wet Delay from re-processed GPS solutions

et al. 2018

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Long series of Zenith Wet Delay (ZWD) obtained as part of a homogeneous re-processing of Global Positioning System solutions constitute a reliable set of data to be assimilated into climate models. The correct stochastic properties, i.e. the noise model of these data, have to be identified to assess the real value of ZWD trend uncertainties since assuming an inappropriate noise model may lead to over-or underestimated error bounds leading to statistically insignificant trends. We present the ZWD time series for 1995-2017 for 120 selected globally distributed stations. The deterministic model in the form of a trend and significant seasonal signals were removed prior to the noise analysis. We examined different stochastic models and compared them to widely assumed white noise (WN). A combination of the autoregressive process of first-order plus WN (AR(1) + WN) was proven to be the preferred stochastic representation of the ZWD time series over the generally assumed white-noise-only approach. We found that for 103 out of 120 considered stations, the AR(1) process contributed to the AR(1) + WN model in more than 50% with noise amplitudes between 9 and 68 mm. As soon as the AR(1) + WN model was employed, 43 trend estimates became statistically insignificant, compared to 5 insignificant trend estimates for a white-noise-only model. We also found that the ZWD trend uncertainty may be underestimated by 5-14 times with median value of 8 using the white-noise-only assumption. Therefore, we recommend that AR(1) + WN model is employed before tropospheric trends are to be determined with the greatest reliability.

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A high-quality, homogenized, global, long-term (1993-2008) DORIS precipitable water data set for climate monitoring and model verification

Bock

Willis

Wang

et al. 2014

J. Geophys. Res. Atmos.

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For the first time a high-quality, consistent, global, long-term data set of zenith tropospheric delay (ZTD) and precipitable water (PW) is produced from Doppler orbitography radiopositioning integrated by satellite (DORIS) measurements at 81 sites. The data set was screened using a two-level procedure. First, postprocessing information is used to apply range checks and outlier checks to ZTD and formal error estimates. Second, outliers are detected by comparing DORIS ZTD with European Centre for Medium-Range Weather Forecasts reanalysis (ERA-Interim) data. These procedures reject 3% and 1% of the data, respectively. A linear drift is evidenced in the screened DORIS ZTD data compared to ERA-Interim and Global Positioning System (GPS) data, which potentially results from biases introduced by the progressive replacement of Alcatel antennas with Starec antennas. The DORIS PW is homogenized by applying a bias correction computed form comparison with ERA-Interim data each time station equipment is changed. The homogenized DORIS data are in excellent agreement with GPS data (correlation of 0.98 and standard deviation of differences of 1.5 kg m À2) and with ERA-Interim and satellite PW data (correlation > 0.95 and standard deviation of differences < 2.7 kg m À2). The agreement with radiosonde data is less good. Preliminary results of water vapor trends and variability are shown for 31 sites with more than 10 years of data. Good consistency is found between DORIS PW trends and ERA-Interim trends, which demonstrates the high potential of the DORIS PW data set for climate monitoring and model verification. The final DORIS PW data set is freely available in the supporting information.

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Evaluation of the atmospheric water vapor content in a regional climate model using ground‐based GPS measurements

Cited by 64 publications

References 58 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

Statistical significance of trends in Zenith Wet Delay from re-processed GPS solutions

A high-quality, homogenized, global, long-term (1993-2008) DORIS precipitable water data set for climate monitoring and model verification

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