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 IWV

Water vapor is an abundant natural greenhouse gas in the atmosphere. The knowledge of its variability in time and space is very important with respect to understanding the global climate system

However, some other comparisons examine the IWV

In this paper, we investigated the differences between IWV from GNSS using data products from

GNSS data are the main source of information for the spatial and temporal distribution of water vapor. Thus, the main variable considered is the IWV estimated from the delay caused by the troposphere to the GNSS radio signals during its travel from the satellite to the ground receiver. The total delay projected onto the zenith direction (ZTD) is usually split into two contributions: the hydrostatic delay (ZHD – zenith hydrostatic delay), depending merely on the atmospheric pressure, and the zenith wet delay (ZWD), depending mainly on the humidity. The IWV

The reference database of IWV

Geopotential values at the selected GNSS stations. Values of

Continued.

Location of the GNSS stations (see Tables 1 and 2).

The values of columnar integrated content of water vapor (IWV) as reanalysis products from ERA-Interim

ERA-Interim is the global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). It covers the period from 1979 to present and supersedes the ERA-40 reanalysis. ERA-Interim addresses some of the difficulties of ERA-40 with respect to data assimilation mainly regarding the representation of the hydrological cycle, the quality of the stratospheric circulation and the consistency in terms of reanalyzed geophysical fields

MERRA-2 is the successor of the Modern-Era Retrospective Analysis for Research and Applications (MERRA) from NASA's Global Modeling and Assimilation Office

For this application, we used two different kind of data sets: 2-D values of the IWV from both reanalysis models along with the correspondent geopotential invariant; and three 3-D data sets from ERA-Interim, including the air temperature (

We will use the terms

As previously mentioned, even when both reanalysis models give grid values of the vertical integral of the water vapor, the solution provided by each model is linked to its respective geopotential surface invariant. Nevertheless, elevation differences between the geopotential from each model grid and that computed from the GNSS height must be addressed.

In order to compute the geopotential of the GNSS stations (

For a given GNSS station, the respective geopotential from each of the two reanalysis models resulted from a bilinear interpolation of the respective invariant static geopotential at the four grid points around the GNSS site, referred to as

We will then propose a correction procedure that, by compensating for

Prior to the correction, we analyze the performance of ERA-Interim and MERRA-2 with respect to GNSS.
Thus, although IWV

Differences of the mean values of IWV (

Continued.

In general, regarding Table 2, we can observe that the best agreements between the average IWV values from GNSS and the corresponding average from the models are where the

Conversely, the difference of the model representation of the IWV with respect to GNSS increases as the height differences (

However, other than the abovementioned cases, which can be considered to be critical, the differences are also important at sites with moderate

Note that some MERRA-2 difference values could be a little larger than ERA-Interim values, which would be expected due to the coarser grid. However, this is not a general rule and some stations are in fact better represented by MERRA-2 with

Figure 2a and b show the mean IWV values from GNSS (

It is assumed that these different values of

However the Fig. 2a and b show that there are some cases where

If we take the RNNA station in both maps as a reference (see station number 51 in Fig. 1), and advance towards the south along the Atlantic coast, the behavior of both models is similar.
Both reanalyses are dryer than GNSS, and this same effect is seen in the southern mountainous areas. However, moving along the Atlantic coast from RNNA to the north and up to the Amazon River, we see different behavior in the reanalyses: while ERA-Interim continues underestimating IWV

These findings show that MERRA-2 resulted in wetter conditions than GNSS, whereas ERA-Interim is slightly dryer than GNSS in Central and South America. This is in agreement with the findings of

Finally, the correlation coefficients between

In the following, we proceed to calculate a correction in order to provide a better estimation of the

Thus, we used air temperature (

Recall that the index

Thus, the expression of the pressure at an unknown geopotential

When

Each value of IWV provided by ERA-Interim is the result of the numerical integration of the expression

Thus, the proposed correction can generally be written as

In a given instant, we know the geopotential of the GNSS station and the static geopotential assigned by the NWM to the four grid points surrounding it (

Figure 3 illustrates the application of the correction to an example.
We take just one of the four grid points and assume that both unknowns (

Scheme of the applied correction to the IWV from the ERA-Interim reanalysis for one of the four grid points (

Before analyzing the results of the correction process explained in the previous section, we present a validation of the numerical integration method used.
To this end, we calculate the values of IWV

Mean values of the difference between IWV

Continued.

In order to evaluate the improvements introduced by the correction, Fig. 4 shows the

Values of

In addition, if we focus on the plot area of Fig. 4 that is limited for a

The performance of the proposed correction can also be seen in Fig. 5. Where Fig. 5a, c and e show stations with positive

Residuals of the difference between

We can see that the most important corrections are at BOGT in Bogotá, Colombia, and SANT in Santiago de Chile, Chile. In these examples, the differences (

However the application of this correction, in some cases, should be precautionary. Effectively, sometimes different shortcomings of the model overlap with the height problem; therefore, the proposed correction will not work. For example, in the case of coastal and/or insular stations where two or more grid points are in the ocean: in all of these cases the value of IWV calculated from the bilinear interpolation will be overvalued. Looking at stations near the seashore in more detail (e.g., PARC in Punta Arenas, Chile), where two of the four grid points are in the ocean (see Fig. 6),

Location of GNSS station PARC along with the four grid points around the station. The grid points correspond to ERA-Interim.

The effect of different heights when comparing results from several data sources not only affects the determination of IWV but also impacts other parameters.
For instance,

NWM users commonly utilize the IWV values on a grid and use them to calculate the IWV value at a desired location by way of an interpolation method.
In this work, taking the values of IWV

We analyzed the discrepancies between the vertically integrated water vapor values provided by two reanalysis models (ERA-Interim and MERRA-2) with respect to the IWV

Several authors have reported problems related to the elevation correction for data from the reanalysis models. The artificial bias in the IWV introduced by this altitude difference has previously been reported by

In this work, we proposed an integral correction that compensates IWV for the effect of the geopotential difference between GNSS and the interpolated grid points in the reanalysis model. The results were tested with the respective values from ERA-Interim.
The correction is computed as the numerical integration of the specific humidity where the integral limit is a pressure difference at

Nevertheless, the application of this correction is not advisable at coastal or insular stations in South and Central America due to the fact that the overvaluation of the model near the coast overlaps with the height problem. These results are in agreement with

For this reason, the corrections we propose are always recommended, but they are not advisable at in coastal areas or on islands as at least two grid points of the model are usually in the water.

Data are available at

LIF led the study and contributed to data collection, analysis and interpretation of the results. AMM and MPN cowrote the paper; they also contributed to the statistical analysis and the interpretation of the results. CEB contributed to data collection. All authors read and approved the final paper.

The authors declare that they have no conflict of interest.

We would like to thank the two anonymous reviewers for their valuable comments that highly improved this paper. We would also like to thank the people, organizations and agencies responsible for collecting, computing, maintaining and openly providing the observations and the products employed in this work, including the European Centre for Medium-Range Weather Forecasts (ECMWF) that provided the ERA-Interim reanalysis data (

This research was supported by the National Scientific and Technical Council of Argentina (CONICET; grant no. PIP 112-201201-00292) and La Plata National University (UNLP; project no. 11G/142).

This paper was edited by Vassiliki Kotroni and reviewed by three anonymous referees.