Arctic trends of integrated water vapor were analyzed based on four reanalyses and radiosonde data over 1979–2016. Averaged over the region north of 70°N, the Arctic experiences a robust moistening trend that is smallest in March (0.07 ± 0.06 mm decade−1) and largest in August (0.33 ± 0.18 mm decade−1), according to the reanalyses’ median and over the 38 years. While the absolute trends are largest in summer, the relative ones are largest in winter. Superimposed on the trend is a pronounced interannual variability. Analyzing overlapping 30-yr subsets of the entire period, the maximum trend has shifted toward autumn (September–October), which is related to an accelerated trend over the Barents and Kara Seas. The spatial trend patterns suggest that the Arctic has become wetter overall, but the trends and their statistical significance vary depending on the region and season, and drying even occurs over a few regions. Although the reanalyses are consistent in their spatiotemporal trend patterns, they substantially disagree on the trend magnitudes. The summer and the Nordic and Barents Seas, the central Arctic Ocean, and north-central Siberia are the season and regions of greatest differences among the reanalyses. We discussed various factors that contribute to the differences, in particular, varying sea level pressure trends, which lead to regional differences in moisture transport, evaporation trends, and differences in data assimilation. The trends from the reanalyses show a close agreement with the radiosonde data in terms of spatiotemporal patterns. However, the scarce and nonuniform distribution of the stations hampers the assessment of central Arctic trends.
Abstract. Ground-based GNSS (Global Navigation Satellite System) has efficiently been used since the 1990s as a meteorological observing system. Recently scientists have used GNSS time series of precipitable water vapor (PWV) for climate research. In this work, we compare the temporal trends estimated from GNSS time series with those estimated from European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-Interim) data and meteorological measurements. We aim to evaluate climate evolution in Germany by monitoring different atmospheric variables such as temperature and PWV. PWV time series were obtained by three methods: (1) estimated from ground-based GNSS observations using the method of precise point positioning, (2) inferred from ERA-Interim reanalysis data, and (3) determined based on daily in situ measurements of temperature and relative humidity. The other relevant atmospheric parameters are available from surface measurements of meteorological stations or derived from ERA-Interim. The trends are estimated using two methods: the first applies least squares to deseasonalized time series and the second uses the Theil-Sen estimator. The trends estimated at 113 GNSS sites, with 10 to 19 years temporal coverage, vary between −1.5 and 2.3 mm decade −1 with standard deviations below 0.25 mm decade −1 . These results were validated by estimating the trends from ERA-Interim data over the same time windows, which show similar values. These values of the trend depend on the length and the variations of the time series. Therefore, to give a mean value of the PWV trend over Germany, we estimated the trends using ERA-Interim spanning from 1991 to 2016 (26 years) at 227 synoptic stations over Germany. The ERA-Interim data show positive PWV trends of 0.33 ± 0.06 mm decade −1 with standard errors below 0.03 mm decade −1 . The increment in PWV varies between 4.5 and 6.5 % per degree Celsius rise in temperature, which is comparable to the theoretical rate of the ClausiusClapeyron equation.
(2015), Constructing accurate maps of atmospheric water vapor by combining interferometric synthetic aperture radar and GNSS observations, J. Geophys. Res. Atmos., 120, 1391-1403, doi:10.1002 Abstract Over the past 20 years, repeat-pass spaceborne interferometric synthetic aperture radar (InSAR) has been widely used as a geodetic technique to generate maps of the Earth's topography and to measure the Earth's surface deformation. In this paper, we present a new approach to exploit microwave data from InSAR, particularly Persistent Scatterer InSAR (PSI), and Global Navigation Satellite Systems (GNSS) to derive maps of the absolute water vapor content in the Earth's atmosphere. Atmospheric water vapor results in a phase shift in the InSAR interferogram, which if successfully separated from other phase components provides valuable information about its distribution. PSI produces precipitable water vapor (PWV) difference maps of a high spatial density, which can be inverted using the least squares method to retrieve PWV maps at each SAR acquisition time. These maps do not contain the absolute (total) PWV along the signal path but only a part of it. The components eliminated by forming interferograms or phase filtering during PSI data processing are reconstructed using GNSS phase observations. The approach is applied to build maps of absolute PWV by combining data from InSAR and GNSS over the region of Upper Rhine Graben in Germany and France. For validation, we compared the derived PWV maps with PWV maps measured by the optical sensor MEdium-Resolution Imaging Spectrometer. The results show strong spatial correlation with values of uncertainty of less than 1.5 mm. Continuous grids of PWV are then produced by applying the kriging geostatistical interpolation technique that exploits the spatial correlations between the PWV observations.
We assess the performance of different break detection methods on three sets of benchmark data sets, each consisting of 120 daily time series of integrated water vapor differences. These differences are generated from the Global Positioning System (GPS) measurements at 120 sites worldwide, and the numerical weather prediction reanalysis (ERA-Interim) integrated water vapor output, which serves as the reference series here. The benchmark includes homogeneous and inhomogeneous sections with added nonclimatic shifts (breaks) in the latter. Three different variants of the benchmark time series are produced, with increasing complexity, by adding autoregressive noise of the first order to the white noise model and the periodic 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 case when the reference series are not homogeneous. We evaluate the performance of break detection methods with skill scores, centered root mean square errors (CRMSE), and trend differences relative to the trends of the homogeneous series. We found that most methods underestimate the number of breaks and have a significant number of false detections. Despite this, the degree of CRMSE reduction is significant (roughly between 40% and 80%) in the easy to moderate experiments, with the ratio of trend bias reduction is even exceeding 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.
For more than two decades, Global Positioning System (GPS) tropospheric delays have successfully been exploited to monitor the tropospheric water vapor in near real time and reprocessing mode. Although reprocessed data are considered reliable for climatic research, it is important to address the often present gaps, inhomogeneities, and to use a proper model to describe the stochastic part of the time series so that trustworthy trends are estimated. Having relatively long time series, daily reprocessed tropospheric Zenith Total Delay, precipitable water vapor (PWV), and gradients from the Tide Gauge benchmark monitoring network are used in this work to estimate climatic trends. We use a first-order autoregressive model AR(1) to describe the residuals after the trend estimation so that a correct trend uncertainty is estimated. Using the same model, we obtain the number of years of PWV data required to estimate statistically significant trends. For comparison, we produce tropospheric parameters at each Tide Gauge station based on ERA-Interim refractivity fields. We found that 83% of 64 GPS stations show a positive PWV trend below 1 mm/decade independent of the time interval, with approximately half of the trends indicated significant. There is a strong correlation (86%) between the Global Navigation Satellite Systems and ERA-Interim PWV trends. The trends tend to increase when moving east and south on the European map. The results show a percentage change of PWV of 3-8% per a degree Celsius rise in temperature. The number of years required to detect significant PWV trends varies between 30 and 40 years.
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