Analysis of global water vapour trends from satellite measurements in the visible spectral range

Mieruch, Sebastian; Noël, Stefan; Bovensmann, Heinrich; Burrows, John P.

doi:10.5194/acpd-7-11761-2007

Cited by 22 publications

(46 citation statements)

References 28 publications

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“…Comparing the trend results derived here with those shown in Mieruch et al (2008) results in small differences, indicating that the expansion of the data to 2007 influences the trends very slightly.…”

Section: A Estimating Trends From Time Seriesmentioning

confidence: 49%

“…For simplicity anomalies are calculated by subtracting the seasonal means from 1996 to 2007 from the monthly mean water vapor time series in this manuscript. In Mieruch (2010) the trends are shown to be invariant with respect to the choice of the two methods for deseasonalizing. Accordingly, we add the respective overall means to the data.…”

Section: A Estimating Trends From Time Seriesmentioning

confidence: 89%

“…Global GOME-SCIAMACHY water vapor trends have been calculated for the time span from 1996 to 2006 in Mieruch et al (2008), where the methods have been adopted and slightly expanded from Weatherhead et al (1998). We have extended the trend analysis, which now includes the time from 1996 to 2007.…”

Section: A Estimating Trends From Time Seriesmentioning

confidence: 99%

“…As shown by Mieruch et al (2008) the autocorrelated noise N 1t is transformed to white noise « 1t using the AR(1). The autocorrelations are accommodated by the variables in Eq.…”

Section: A Estimating Trends From Time Seriesmentioning

confidence: 99%

“…In the publications of Mieruch et al (2008) and Weatherhead et al (1998) the seasonal component is described as a Fourier series and subtracted from the measurements to yield deseasonalized data. For simplicity anomalies are calculated by subtracting the seasonal means from 1996 to 2007 from the monthly mean water vapor time series in this manuscript.…”

Section: A Estimating Trends From Time Seriesmentioning

confidence: 99%

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A New Method for the Comparison of Trend Data with an Application to Water Vapor

et al. 2011

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Global total column water vapor trends have been derived from both the Global Ozone Monitoring Experiment (GOME) and the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite data and from globally distributed radiosonde measurements, archived and quality controlled by the Deutscher Wetterdienst (DWD).The control of atmospheric water vapor amount by the hydrological cycle plays an important role in determining surface temperature and its response to the increase in man-made greenhouse effect. As a result of its strong infrared absorption, water vapor is the most important naturally occurring greenhouse gas. Without water vapor, the earth surface temperature would be about 20 K lower, making the evolution of life, as we know it, impossible. The monitoring of water vapor and its evolution in time is therefore of utmost importance for our understanding of global climate change. Comparisons of trends derived from independent water vapor measurements from satellite and radiosondes facilitate the assessment of the significance of the observed changes in water vapor.In this manuscript, the authors have compared observed water vapor change and trends, derived from independent instruments, and assessed the statistical significance of their differences. This study deals with an example of the Behrens-Fisher problem, namely, the comparison of samples with different means and different standard deviations, applied to trends from time series.Initially the Behrens-Fisher problem for the derivation of the consolidated change and trends is solved using standard (frequentist) hypothesis testing by performing the Welch test. Second, a Bayesian model selection is applied to solve the Behrens-Fisher problem by integrating the posterior probabilities numerically by using the algorithm Differential Evolution Markov Chain (DEMC). Additionally, an analytical approximative solution of the Bayesian posterior probabilities is derived by means of a quadratic Taylor series expansion applied in a computationally efficient manner to large datasets. The two statistical methods used in the study yield similar results for the comparison of the water vapor changes and trends from the different measurements, yielding a consolidated and consistent behavior.

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Section: A Estimating Trends From Time Seriesmentioning

confidence: 49%

Section: A Estimating Trends From Time Seriesmentioning

confidence: 89%

Section: A Estimating Trends From Time Seriesmentioning

confidence: 99%

“…As shown by Mieruch et al (2008) the autocorrelated noise N 1t is transformed to white noise « 1t using the AR(1). The autocorrelations are accommodated by the variables in Eq.…”

Section: A Estimating Trends From Time Seriesmentioning

confidence: 99%

Section: A Estimating Trends From Time Seriesmentioning

confidence: 99%

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A New Method for the Comparison of Trend Data with an Application to Water Vapor

et al. 2011

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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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Comparison of decadal global water vapor changes derived from independent satellite time series

et al. 2014

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Citation:Mieruch, S., M. Schröder, S. Noël, and J. Schulz (2014), Comparison of decadal global water vapor changes derived from independent satellite time series, J. Geophys. Res. Atmos., 119, 12,489-12,499, doi:10.1002 Abstract We analyze trends in total column water vapor (TCWV) retrieved from independent satellite observations and retrieval schemes. GOME-SCIAMACHY (Global Ozone Monitoring Experiment-SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) measurements are carried out in the visible part of the solar spectrum and present a partly cloud-corrected climatology that is available over land and ocean. The HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data) product, provided by EUMETSAT's Satellite Application Facility on Climate Monitoring is based on passive microwave observations from the Special Sensor Microwave/Imager. It also includes the TCWV from cloudy pixels but is only available over oceans. The common observation time period is between 1996 and 2005. Due to the relatively short length of the period, the strong interannual variability with strong contributions from El Niño and La Niña events and the strong anomaly at the start of the common period, caused by the 1997/1998 El Niño, the observed trends should not be interpreted as long-term climate trends. After subtraction of average seasonality from monthly gridded data, a linear model and a level shift model have been fitted to the HOAPS and GOME-SCIAMACHY data, respectively. Autocorrelation and cross correlation of fit residuals are accounted for in assessing uncertainties in trends. The trends observed in both time series agree within uncertainty margins. This agreement holds true for spatial patterns, magnitudes, and global averages. The consistency increases confidence in the reliability of the trends because the methods, spectral range, and observation technique as well as the satellites and their orbits are completely independent of each other. The similarity of the trends in both data sets is an indication of sufficient stability in the observations for the time period of ≈ 10 years.

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Analysis of global water vapour trends from satellite measurements in the visible spectral range

Cited by 22 publications

References 28 publications

A New Method for the Comparison of Trend Data with an Application to Water Vapor

A New Method for the Comparison of Trend Data with an Application to Water Vapor

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

Comparison of decadal global water vapor changes derived from independent satellite time series

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