Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> 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&ndash;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&ndash;24:00 local time with peak-to-peak amplitudes accounting for 2&thinsp;%&ndash;18&thinsp;% of the daily mean. The diurnal 1-hourly anomalies can be much more intensive with a range of &minus;100&thinsp;% to 200&thinsp;%. The annual cycles peak in July and August with maximum values of 17&ndash;32&thinsp;kg&thinsp;m<sup>&minus;2</sup>. 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&ndash;0.4&thinsp;kg&thinsp;m<sup>&minus;2</sup>&thinsp;decade<sup>&minus;1</sup> in the north and 0.4&ndash;1&thinsp;kg&thinsp;m<sup>&minus;2</sup>&thinsp;decade<sup>&minus;1</sup> 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&thinsp;%, whereas the median bias for MERRA2 is 4&thinsp;%. 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&ndash;1.6, 0.7&ndash;2.3, and 1.2&ndash;3.0&thinsp;kg&thinsp;m<sup>&minus;2</sup>, 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).

Highlights

  • In this study, Integrated Water Vapor (IWV) time series for the period 1994-2018 were retrieved from continuous Global Positioning System (GPS) observations of 108 ground-based GPS stations in Europe, with an average period of 21 years for those time series

  • The performances of six frequently used global atmospheric reanalyses in Europe were assessed for the first time, namely Climate Forecast System Reanalysis (CFSR), ERA5, ERA-Interim (ERAI), JRA55, MERRA2, and NCEP2

  • (i) The agreement between the daily GPS IWV time series and the six reanalyses are found to be best for ERA5 and worst for NCEP2, with standard deviations of IWV differences of 0.5-1.6 and 1.2-3.0 kg m-2, respectively

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Summary

Introduction

Networks in Europe, such as the European Reference Frame (EUREF) Permanent Global Navigation Satellite System (GNSS) Network (EPN; Bruyninx et al, 2012) Given these benefits, ground-based GPS offers a unique tool to investigate 70 the multiple spatiotemporal scale variabilities of IWV over Europe, especially the associated extreme weather and climate events (Bonafoni et al, 2019). The performances of various reanalyses’ IWV products in Europe have been assessed by many regional/global studies using 90 ground-based GPS data, as the GPS observations are not operationally assimilated by reanalyses (Hagemann et al, 2003; Bock et al, 2005; Heise et al, 2009; Vey et al, 2010; Alshawaf et al, 2018; Parracho et al, 2018; Wang et al, 2020; Yuan et al, 2021).

Data and methods
Reanalysis data
Teleconnection indices
IWV retrievals
Pre-processing
Assessments using KGE
Assessments of diurnal variations
Diurnal cycle
Diurnal anomalies
Annual cycle
Assessments of trends
Findings
Conclusions
Full Text
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