Characterizations of Europe's integrated water vapor and assessments of atmospheric reanalyses using more than two decades of ground-based GPS

Abstract

Abstract. Ground-based Global Positioning System (GPS) has been extensively used to retrieve Integrated Water Vapor (IWV) and has been adopted as a unique tool for the assessments of atmospheric reanalyses. In this study, we investigated the multi-temporal-scale variabilities and trends of IWV over Europe by using IWV time series from 108 GPS stations for more than two decades (1994–2018). We then adopted the GPS IWV as a reference to assess six commonly-used atmospheric reanalyses, namely CFSR, ERA5, ERA-Interim, JRA55, MERRA2, and NCEP2. The GPS results show that the diurnal cycles peak within 16:00–24:00 local time with peak-to-peak amplitudes accounting for 2 %–18 % of the daily mean. The diurnal 1-hourly anomalies can be much more intensive with a range of −100 % to 200 %. The annual cycles peak in July and August with maximum values of 17–32 kg m−2. The interannual variations of IWV over Europe are found to be mainly linked to the North Atlantic Oscillation (NAO) and the East Atlantic (EA) patterns. The IWV continues to increase over Europe during the last two decades at 0–0.4 kg m−2 decade−1 in the north and 0.4–1 kg m−2 decade−1 in the south. Regarding the assessments of the reanalyses, the intercomparisons with respect to GPS reveal a general superiority of the newly-released ERA5 IWV product. For instance, ERA5 only has a slight wet bias with a median value of 1 %, whereas the median bias for MERRA2 is 4 %. ERA5, MERRA2, and NCEP2 are the best, second best, and worst performers respectively in modelling the variability of daily IWV time series, with standard deviations of daily IWV differences against GPS by 0.5–1.6, 0.7–2.3, and 1.2–3.0 kg m−2, respectively. Moreover, the daily GPS IWV time series is best correlated with the ERA5 IWV with a median Pearson correlation coefficient of 0.996, whereas the second strongest and weakest median correlations are observed in MERRA2 and NCEP2 with values of 0.991 and 0.971, respectively. Furthermore, the correlations between the IWV trends from the reanalyses and GPS are strongest for ERA5 (0.82), a bit weaker for MERRA2 (0.72), and weakest for NCEP2 (0.52).

Global Spatiotemporal Variability of Integrated Water Vapor Derived from GPS, GOME/SCIAMACHY and ERA-Interim: Annual Cycle, Frequency Distribution and Linear Trends

“…Next, the geographical consistency in the sign and magnitude of the trends between the different datasets (see Figure 10) for different regions in the world was analyzed. For Europe, the different datasets reveal an overall moistening trend, which is a consistent finding from other studies using different IWV datasets and different time periods, e.g., [41][42][43]. Drying trends over Western Australia and moistening trends over the Indian Ocean appear to be consistent features among the three IWV datasets.…”

Trend Changes of the Vegetation Activity in Northeastern East Asia and the Connections with Extreme Climate Indices

“…Different datasets were interpolated to the 1.875 • (longitude) × 1.875 • (latitude) grid to ensure the standardization of all databases on the same spatial resolution and facilitate comparison. All atmospheric reanalysis data, including column-integrated moisture divergence [32] and 500 hPa geopotential height [33], are obtained from ERA5 reanalysis data derived from the European Center for Medium-Range Weather Forecasting (ECMWF) [34]. The vertical integration of the moisture flux corresponds to the horizontal rate of moisture (e.g., water vapor) transport for a column of air extending from the Earth's surface to the top of the atmosphere.…”

Characterizing the tropospheric water vapor spatial variation and trend using 2007–2018 COSMIC radio occultation and ECMWF reanalysis data

“…Various studies have quantified the spatial and temporal variation and trend in atmospheric water vapor using two types of water vapor data: (i) measurements or retrievals from sensor observations and (ii) reanalysis data produced by assimilating various observations. The first data type includes both ground-based in situ and spaceborne observations: longterm radiosonde measurements (Zhai and Eskridge, 1997;Ross and Elliott, 2001;Ho et al, 2010;Zhao et al, 2012;Zhang et al, 2018), weather station data (Dai, 2006), water vapor retrieved from ground-based Global Positioning System (GPS) station data (Kursinski et al, 1997;Bock et al, 2007;Nilsson and Elgered, 2008;Vey et al, 2010;Huang et al, 2013;Chen and Liu, 2016;Yuan et al, 2023), water vapor retrievals from spaceborne radio occultation observations (Ho et al, 2009;Huang et al, 2013Huang et al, , 2018Zhang et al, 2018;Andrisaniand and Vespe, 2020;Gleisner et al, 2022), visible-spectral-range sensor observations Grossi et al, 2015;Borger et al, 2023), microwave observations (Rosenkranz, 2001;Chen and Liu, 2016;Ho et al, 2018;Yadav et al, 2021), and infrared sounder observations (Susskind et al, 2003).…”