Abstract. Atmospheric water vapour has been acknowledged as an essential climate variable. Weather prediction and hazard assessment systems benefit from real-time observations, whereas long-term records contribute to climate studies. Nowadays, ground-based global navigation satellite system (GNSS) products have become widely employed, complementing satellite observations over the oceans. Although the past decade has seen a significant development of the GNSS infrastructure in Central and South America, its potential for atmospheric water vapour monitoring has not been fully exploited. With this in mind, we have performed a regional, 7-year-long and homogeneous analysis, comprising 136 GNSS tracking stations, obtaining high-rate and continuous observations of column-integrated water vapour and troposphere zenith total delay. As a preliminary application for this data set, we have estimated local water vapour trends, their significance, and their relation with specific climate regimes. We have found evidence of drying at temperate regions in South America, at a rate of about 2 % per decade, while a slow moistening of the troposphere over tropical regions is also weakly suggested by our results. Furthermore, we have assessed the regional performance of the empirical model GPT2w to blindly estimate troposphere delays. The model reproduces the observed mean delays fairly well, including their annual and semi-annual variations. Nevertheless, a long-term evaluation has shown systematical biases, up to 20 mm, probably inherited from the underlying atmospheric reanalysis. Additionally, the complete data set has been made openly available as supplementary material.

We compared and analyzed data of vertically Integrated Water Vapor (IWV) from two different re-analysis models (The comparison was performed taking into account the geopotential height differences between each GNSS station and 5 the correspondent values assigned by the models. Thus, the set of GNSS stations was divided into 3 groups: Small, Large and Critical height difference stations. Moreover, the performance of the re-analysis models was also analyzed by using an additional classification of three levels according to the mean IWV (IW V ) value expected at the station: IW V > 30 kg m −2 , 12 kg m −2 IW V 30 kg m −2 and IW V < 12 kg m −2 .Both models (IW V ERA−Interim and IW V M ERRA−2 ) offered a very good representation of the IWV from GNSS values 10 (IW V GN SS ) for stations with a Small height difference (smaller than 100 meters). That is to say, the differences between the mean values of IWV from GNSS (IW V GN SS ) with respect to the IWV averages from both re-analysis models are always below 7 % of the IW V GN SS in the worse case.In general, the discrepancies between the re-analysis models with respect to IW V GN SS raise as the geopotential height difference between the GNSS station and the static geopotential height interpolated from the models grows. Effectively, the 15 difference between IW V GN SS and IWV from the re-analysis models can be as large as 10 kg m −2 for stations with a critical height difference (larger than 500 meters). For this reason, we proposed a numerical correction that compensates the effect of the geopotential height difference and the results were tested with values from ERA-Interim.The suggested correction was successful and reduces the differences |IW V GN SS − IW V ERA−Interim | to less than a 7 % of the mean IW V GN SS values. This strategy is especially recommended for stations that were classified as Critical, most of 20 them located in mountainous areas of South America. In the case of Large height difference stations, the correction procedure is not advisable either for a coastal station and/or stations in islands. Generally in those cases, two or more grid point are on the water. Thus, the interpolated IWV value for the re-analysis model will be overestimated. At one hand, if the geopotential height of the model is smaller than the geopotential height of the GNSS station, the subtracting numerical correction would compensate this overestimation of IWV near the water and thus the strategy will represent an improvement. On the other 25 Water vapor is an abundant natural greenhouse gas of the atmosphere. The knowledge of its variability in time and space is very important to understand the global climate system (Dessler et al., 2008). Most of the regional comparisons of IWV from GNSS are aimed at validating the technique by comparing with radiosonde and radiometers where available. A complete example of this is the work of Van Malderen et al. (2014) who compared IWV GPS (Global Positioning System) with IWV derived from ground-based sun photometers, radiosondes and satel...

Abstract. Commonly, numerical weather model (NWM) users can get the vertically integrated water vapor (IWV) value at a given location from the values at nearby grid points. In this study we used a validated and freely available global navigation satellite system (GNSS) IWV data set to analyze the very well-known effect of height differences. To this end, we studied the behavior of 67 GNSS stations in Central and South America with the prerequisite that they have a minimum of 5 years of data during the period from 2007 to 2013. The values of IWV from GNSS were compared with the respective values from ERA-Interim and MERRA-2 from the same period. Firstly, the total set of stations was compared in order to detect cases in which the geopotential difference between GNSS and NWM required correction. An additive integral correction to the IWV values from ERA-Interim was then proposed. For the calculation of this correction, the multilevel values of specific humidity and temperature given at 37 pressure levels by ERA-Interim were used. The performance of the numerical integration method was tested by accurately reproducing the IWV values at every individual grid point surrounding each of the GNSS sites under study. Finally, considering the IWVGNSS values as a reference, the improvement introduced to the IWVERA-Interim values after correction was analyzed. In general, the corrections were always recommended, but they are not advisable in marine coastal areas or on islands as at least two grid points of the model are usually in the water. In such cases, the additive correction could overestimate the IWV.

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