Intercomparison of Arctic ground-based XH2O observations from COCCON, TCCON and NDACC, and application of COCCON XH2O for IASI and TROPOMI validation

Intercomparison of Arctic ground-based XH2O observations from COCCON, TCCON and NDACC, and application of COCCON XH2O for IASI and TROPOMI validation Qiansi Tu, Frank Hase, Thomas Blumenstock, Matthias Schneider, Andreas Schneider, Rigel Kivi, Pauli Heikkinen, Benjamin Ertl, Christopher Diekmann, Farahnaz Khosrawi, Michael Sommer, 5 Tobias Borsdorff, Uwe Raffalski Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe, Germany SRON Netherlands Institute for Space Research, Utrecht, the Netherlands Finnish Meteorological Institute, Sodankylä, Finland Karlsruhe Institute of Technology, SCC, Karlsruhe, Germany 10 GRUAN Lead Centre, Deutscher Wetterdienst, Lindenberg, Germany Swedish Institute of Space Physics, Kiruna, Sweden


Introduction
Water vapor (H2O) is among the most abundant gases in the atmosphere and nearly 99 % of the water vapor exists in the troposphere. It has a residence time of about nine days in the atmosphere (Trenberth, 1998) and absorbs approximately 57 % 45 in the long-wave radiation and 72 % in the short-wave in clear skies (Kiehl and Trenberth, 1997). Due to its numerous absorption, atmospheric water vapor also acts as an important greenhouse gas (GHG). It plays a key role in the Earth's energy budget and the large-scale circulation, and thus the climate of our planet (Allan, 2012). Because the equilibrium water vapor pressure increases rapidly with temperature, the increasing atmospheric concentration of water vapor further amplifies warming due to its added radiative absorption (Soden et al., 2002). Therefore, assessing and quantifying atmospheric water 50 vapor is essential to constrain weather and climate models.
The distribution of water vapor in the atmosphere is characterized by very high spatial and temporal variability relevant to weather and climate (Vogelmann et al., 2015). Satellite-borne instruments have the advantage of global coverage but lack of vertical or long-term information (Dupuy et al., 2016). Additionally, satellite water vapor data contain inherent uncertainties and must be validated before use (Loew et al., 2017). On the other hand, independent ground-based remote sensing instruments 55 and in situ sensors do not give access to the global scale but can provide very accurate and long-term data, which can be used for satellite validation. Many studies have investigated water vapor comparisons among satellite and ground-based instruments (Schneider and Hase, 2011;Dupuy et al., 2016;Borger et al., 2018;Trieu et al., 2019), but there are only few studies that focus on the Arctic region (Pałm et al., 2010;Buehler et al., 2012;Alraddawi et al., 2018).
In this work, we present a comparison of column-averaged dry-air mole fractions of water vapor (XH2O) retrievals from 60 three ground-based networks, two space-borne satellites and vertical profiles from in situ radiosondes near Arctic region. This paper is organized as follows: Sect. 2 gives a description of the campaign sites and lists the details of the instruments. The datasets used in this study and coincidence criteria will be presented in Sect. 3. Results are discussed in Sect. 4 and 5, followed by conclusions in Sect. 6. https://doi.org/10.5194/amt-2020-378 Preprint. Discussion started: 28 September 2020 c Author(s) 2020. CC BY 4.0 License.

TCCON network
The Total Carbon Column Observing Network (TCCON) is a worldwide network of ground-based Fourier transform infrared (FTIR) spectrometers, measuring solar absorption spectra in the near infrared region. The TCCON network is designed to provide accurate and long-term time series of column-averaged dry-air mole fractions of greenhouse gases and other atmospheric constituents for carbon cycle studies and satellite validation (Wunch et al., 2011). The TCCON measurements 70 have very high precision due to the minimal effect from surface properties and aerosols (Wunch et al., 2017). The TCCON sites are distributed globally, but with higher density in Europe, Northern America and eastern Asia. Its costs and high demand for qualified personnel and infrastructure hinder its expansion. Remote sites or regions with high or low surface albedo, which are also interesting for satellite and model validation are generally poorly covered by the TCCON stations.
TCCON data are retrieved by the non-linear least-squares fitting algorithm (GFIT). The GFIT algorithm scales a priori 75 water vapor profiles from the National Centers for Environmental Prediction reanalysis data (https://www.ncdc.noaa.gov/dataaccess/model-data/model-datasets/reanalysis-1-reanalysis-2) to obtain a best-fit synthetic spectrum for the measured spectrum (Wunch et al., 2011).

COCCON network and FRM4GHG campaign
Recently, a cheaper, robust and portable ground-based FTIR spectrometer (EM27/SUN) has been developed by the Karlsruhe 80 Institute of Technology (KIT) in cooperation with the company Bruker , Frey et al. (2015) and Hase et al. (2015Hase et al. ( , 2016). The EM27/SUN instruments are characterized with good quality, robustness and reliability in several successful field campaigns Klappenbach et al., 2015;Chen et al., 2016;Butz et al., 2017;Vogel et al., 2019;Sha et al., 2019;Tu et al., 2020) and their excellent level of performance opens up the chance of supporting the TCCON network. The spectrometers are commercially available since spring 2014 and today about 60 spectrometers are in operation around the 85 world. KIT initiated the COllaborative Carbon Column Observing Network (COCCON, https://www.imkasf.kit.edu/english/COCCON.php) as a framework for proper operation of the EM27/SUN spectrometer  and for ensuring common standards for data analysis. Each COCCON spectrometer (including the spectrometers operated in Kiruna and Sodankylä as used in this study) is checked and calibrated at the calibration facility operated in Karlsruhe (using a TCCON spectrometer and the primary EM27/SUN spectrometer unit) before shipment to the observation site. For the purpose 90 of COCCON data processing, a preprocessing tool and the non-linear least-squares fitting algorithm PROFFAST have been created with the support of ESA (projects COCCON-PROCEEDS and COCCON-PROCEEDS II). The EM27/SUN spectrometer offers a low spectral resolution of 0.5 cm -1 and consequently has little vertical resolution capability. Therefore, a simple least-squares fit performing a scaling retrieval of the a priori profile is generally appropriate and sufficient.  . The dataset from this campaign will also serve for the TROPOspheric Monitoring Instrument (TROPOMI) validation and the campaign provides a guideline for establishing portable low-resolution FTIR spectrometers for complementing the TCCON network.

MUSICA NDACC products
The quality assessment study is also complemented by the MUSICA (MUlti-platform remote Sensing of Isotopologues for 105 investigating the Cycle of Atmospheric water) dataset, including ground-based remote sensing, space-based remote sensing and in situ measurements (Schneider et al., 2012;Schneider et al., 2016). In this study, we focus on the first dataset.
Currently, there are 12 NDACC (Network for the Detection of Atmospheric Composition Change, http://www.acd.ucar.edu/irwg/, Kurylo and Zander, 2000;De Mazière et al., 2018) stations for which MUSICA NDACC datasets were created (Barthlott et al., 2017). The MUSICA NDACC retrievals are made by fitting 9 different spectral 110 microwindows with water vapor signatures in the spectral region between 2655 and 3055 cm -1 using the PROFFIT retrieval code (PROFile FIT, Hase et al. 2004). The retrieval consists of an optimal estimation of the vertical distribution of H2O, together with the ratios of different water vapor isotopologues. For all the retrievals a constant unique a priori profile is used, which is a global mean water vapor state, i.e. there is no variability in the a priori. This has the advantage that all detected variability is induced by the observation, but at the cost that the a priori profile can be rather off from the actual atmospheric 115 state, which is a minor issue given the vertical sensitivity as revealed by the profile averaging kernels (Barthlott et al., 2017).
Here we use MUSICA NDACC from the NDACC data base and perform a simple bias correction. This correction consists in adding A*c to the original data, where A is the averaging kernel in logarithmic scale and c a profile vector with the entry -0.12 for all altitudes. This means that we consider a bias of +12% in the original MUSICA H2O data.
The PROFFIT code has been successfully used for many years in the ground-based FTIR community for evaluating high-120 resolution solar absorption spectra (Hase et al., 2004;Schneider and Hase, 2011). NDACC enables monitoring the distribution of a large variety of atmospheric trace gases in the mid-infrared region with Bruker HR120/5 FTIR spectrometers, including water vapor at a very high precision (Schneider et al., 2006;Schneider and Hase, 2009;Schneider et al., 2010a). The Kiruna NDACC observations are well representative for high latitudes and the MUSICA NDACC Kiruna data are available for the whole period from 1996 to present . 125

MUSICA IASI products
The Infrared Atmospheric Sounding Interferometer (IASI) is the primary payload carried on the EUMETSAT's MetOp series of polar-orbiting satellites (Clerbaux, 2009), which provides a near-global distribution of observations with high https://doi.org/10.5194/amt-2020-378 Preprint. Discussion started: 28 September 2020 c Author(s) 2020. CC BY 4.0 License. resolution and accuracy twice a day. There are currently three IASI instruments in operation, which were launched in 2006, 2012 and 2018, respectively. The primary intent of IASI is to provide information on atmospheric temperature and water vapor, 130 although several additional trace gases are accessible in the spectra.
The MUSICA NDACC retrievals are based on the retrieval code PROFFIT while the MUSICA IASI retrievals are based on a nadir version of PROFFIT (Schneider and Hase, 2009). The retrieval uses a single broad spectral window between 1190 and 1400 cm -1 and consists of a simultaneous optimal estimation of the vertical distribution of H2O, the ratio of different water vapor isotopologues, N2O, CH4, and HNO3. Atmospheric temperature is also fitted but constrained strongly to the EUMETSAT 135 IASI Level 2 temperature product. For more details on the MUSICA IASI retrievals and the validation of the MUSICA IASI H2O product please refer to Schneider et al. (2016) and Borger et al. (2018). The version presented here is an update as described by Borger et al. (2018) using a priori profiles for the gases under consideration, which depend on season and latitude.
This dependency is determined from WACCM model simulations (a more detailed description of the new MUSICA IASI retrieval version is currently in preparation for ESSD). 140

In situ radiosonde
Atmospheric profiles are regularly measured by meteorological radiosondes. In January -March 2017 Vaisala RS92 radiosondes (Dirksen et al., 2014) were launched at Sodankylä.  (Madonna et al., 2020). Vaisala RS41 radiosonde contributes to improvements in accuracy and data consistency and its specifications for combined measurement uncertainties are 0.3 °C for temperature below 16 km and 0.4 °C above, 4 % RH for relative humidity, 1.0 hPa for pressure 155 larger than 100 hPa and 10 m for geopotential height (https://www.vaisala.com/sites/default/files/documents/RS-Comparison-White-Paper-B211317EN.pdf).

Ground-based datasets
In the present study, all datasets are obtained during the period from 2017 to 2019. We used the MUSICA NDACC XH2O data 160 from Kiruna (Barthlott et al., 2017) and the latest version GGG2014 TCCON XH2O data from Sodankylä (Kivi and Heikkinen, 2016;Kivi et al., 2017) as references.
The COCCON and TCCON XH2O is computed as the ratio of the co-retrieved total column of O2 to an assumed dry-air mole fraction of O2 equal to 0.2095 (Wunch et al., 2015): The total column of O2 is not available from the NDACC network. Therefore, the XH2O is calculated by dividing the total 165 column of H2O by the total column of dry air and the latter is computed from the surface pressure (P s ) recorded at a local weather station: where is the surface pressure at Kiruna ground level, and 2 are the molecular masses of dry air (~28.96 g • mol −1 ) and water vapor (~18 g • mol −1 ), respectively, and 2 are the total column amount of dry air and water vapor, and ( ) is the latitude-dependent surface acceleration due to gravity. 170 The co-located COCCON measurements from Kiruna and Sodankylä are coincident with NDACC and TCCON measurement with hourly mean, respectively. Due to the high measurement frequency, the TCCON and COCCON measurements offer opportunities for estimating the water vapor intraday variability. To reduce the residual uncertainties introduced by airmass dependence, empirical corrections are applied by TCCON (Wunch et al., 2015) and COCCON. The solar zenith angle (SZA) range has been limited to 80°. This also reduces the collocation error with the satellite borne thermal 175 nadir sounder IASI, whose typical nadir angle range is 0° -48.3°.

Space-borne datasets
For space-borne data, a similar geophysical collocation criterion is used in this study as used in Schneider et al. (2020). The MUSICA IASI and TROPOMI data are collected within a radius of 30 km of the COCCON sites in Kiruna and Sodankylä with an acceptance cone of 45° width in the FTIR viewing direction and all ground-based measurements acquired within 30 180 min of each satellite overpass (before or after). To reduce the altitude effect, the satellite data are filtered for ground pixels with altitudes between 350 and 500 m a.s.l. for Kiruna and 100 and 250 m a.s.l. for Sodankylä, respectively (Kiruna and Sodankylä stations are located at 420 and 189 m a.s.l., respectively). For the TROPOMI data we also applied additional filters https://doi.org/10.5194/amt-2020-378 Preprint. Discussion started: 28 September 2020 c Author(s) 2020. CC BY 4.0 License.
for reducing the effects of clouds and high aerosol. The cloud cover is limited to 1 % for both the inner and outer field of view (Schneider et al., 2020). The soundings with high aerosol load are filtered out with a two-band methane filter, when the 185 difference between the retrieved methane in weak and strong absorption is larger than 6 % (Scheepmaker et al., 2016;Hu et al., 2018).
It is noted that the seasonal variation of atmospheric water vapor is large; therefore, we report the bias and also relative bias (in percent) for one pair of XH2O datasets. The relative bias is calculated as below:

Volume mixing ratio of H2O derived from radiosonde measurements 190
The relative humidity (RH) measured by the in situ radiosondes is equivalent to the partial pressure of H2O divided by the saturation vapor pressure of H2O: where P H 2 O is the partial pressure of H2O, e s (T) is the saturation vapor pressure which only depends on temperature. Here we use the empirical equation from Hyland and Wexler (1983) to present : where denotes the temperature in Kelvin. 195 The volume mixing ratio (VMR) of H2O ( 2 ) is defined as the ratio of partial pressure of H2O ( 2 ) relative to the total pressure ( ): Combing Equations (5) -(7) allows the VMR of H2O to be computed from radiosonde measurements of relative humidity, temperature and pressure: where denotes the relative humidity, denotes the saturation vapor pressure and P denotes the total pressure. 200

Comparison of Arctic COCCON XH2O
We determine the precision by an intercomparison between COCCON and ground-based FTIR datasets. The XH2O observed by the COCCON instruments at two sites is first compared with co-located ground-based MUSICA NDACC retrievals for https://doi.org/10.5194/amt-2020-378 Preprint. Discussion started: 28 September 2020 c Author(s) 2020. CC BY 4.0 License.
Kiruna and with TCCON retrievals for Sodankylä, respectively. Furthermore, in situ radiosonde profiles at Sodankylä are used to investigate the a priori profile shape, and to evaluate TCCON and COCCON XH2O data.   Figure 2 gives the correlation of XH2O measured by the COCCON instrument against coincident MUSICA NDACC retrievals for the Kiruna site (left) and against coincident TCCON retrievals for the Sodankylä site (right). In general, the two datasets at each site show quite good agreement and overall consistency, with R 2 value of 0.9992 for Kiruna and 0.9997 for Sodankylä.

Comparison between COCCON and co-located ground-based FTIR instruments 225
The second and seventh columns in Table 1    https://doi.org/10.5194/amt-2020-378 Preprint. Discussion started: 28 September 2020 c Author(s) 2020. CC BY 4.0 License. Figure 4 shows the XH2O relative bias of MUSICA NDACC and TCCON compared to COCCON with respect to SZA over the whole period studied at the two stations. The absolute values of the relative bias between COCCON and MUSICA NDACC at Kiruna (left) show no obvious sensitivity to the SZA. However, the absolute value of the relative bias between COCCON and TCCON at Sodankylä becomes slightly larger with increasing SZA. This is because the TCCON and COCCON 245 instruments have different spectral resolutions, which results in different absorption strengths and column averaging kernels (AVK) . Furthermore, the AVK also shows different sensitivity to the SZA of the measurement (Wunch et al, 2011). The AVK describes the sensitivity of the H2O retrieval to a perturbation in the H2O column at a given altitude.
An AVK close to unity indicates maximum retrieval accuracy and an AVK larger or smaller than 1 indicate overestimation and underestimation of the true H2O column amount, respectively. 250    When directly comparing the measurements from different remote sensing instruments, the differing observing system characteristics, particularly vertical sensitivity and a priori profiles should be considered (Rodgers and Connor, 2003). The COCCON instrument uses the same a priori H2O VMR profiles as used in the TCCON network, derived from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) 6-hourly reanalysis data. A daily profile has been generated from the NCEP/NCAR data, interpolated to latitude, longitude and local solar noon time at 275 the site. We refer to the TCCON a priori profiles as "MAP" files, following the naming convention used for the TCCON processing. MUSICA NDACC uses a single global climatology profile as a priori, which has no seasonal variations. https://doi.org/10.5194/amt-2020-378 Preprint. Discussion started: 28 September 2020 c Author(s) 2020. CC BY 4.0 License.
To assess the impact of the a priori profiles on the retrievals, the MAP a priori profiles used in TCCON and COCCON are applied to the MUSICA NDACC retrievals and IASI retrievals. We use MUSICA NDACC (MAP) and IASI (MAP) to represent the corresponding XH2O to distinguish the official MUSICA NDACC and MUSICA IASI data. The MUSICA IASI 280 (MAP) will be discussed in Section 5.1.
The correlation between MUSICA NDACC and MUSICA NDACC (MAP) and the time series of their relative bias are presented in Figure 6 (left panel). The MUSICA NDACC (MAP) XH2O shows an excellent agreement with the MUSICA NDACC, with an R 2 value of 0.9998. When using the MAP profiles, the XH2O retrievals are 2.57 % (standard deviation: 2.85 %) wetter than the MUSICA NDACC retrievals and higher relative biases are found early in the year when the atmosphere 285 is dryer. This a priori choice increases slightly the correlation between MUSICA NDACC (MAP) and COCCON retrievals with the R 2 value of 0.9989 and consequently the relative difference decreases to -0.92 % (standard deviation: 4.31%). This small change indicates that MUSICA NDACC XH2O has little sensitivity to the a priori information and the bias between MUSICA NDACC and COCCON is mainly due to the choices for the calibration of the XH2O data product by either network.

H2O VMR profiles from in situ radiosonde at Sodankylä site
The comparison of the a priori H2O profile used for the different instruments with in situ climatological knowledge of H2O 295 vertical variability yields a precision estimate for the retrievals. In this section, the MAP a priori profiles used in TCCON and COCCON are discussed using Vaisala RS41 radiosonde profiles as a reference.
The radiosondes are daily launched and the coincident MAP and radiosonde profiles cover nearly the whole year. Since most of the H2O is located in the troposphere, we here present the profiles below 10 km. Figure 7  The time series of XH2O from integrating the a priori MAP profiles and radiosonde profiles between 0.2 and 15 km is presented in Figure 8. These integrated-profile XH2O shows similar seasonal variability as observed by COCCON and TCCON at Sodankylä. A good agreement is found for the integrated XH2O between MAP and radiosonde profiles, with a relative bias 310 of 4.32 % and a R 2 value of 0.9900 (Figure 9 (a)), indicating that the modeled MAP profiles can well represent the vertical variability of H2O in the real atmosphere. Compared to radiosonde, both COCCON and TCCON measure drier XH2O with mean biases of 1.66 % and 5.63 %, respectively (Figure 9 (b), (c)). TCCON shows a slightly higher variation (standard deviation) than COCCON and a tendency of a higher bias in higher XH2O. The small bias between COCCON and radiosonde indicates that COCCON is capable to serve for the validation of space-borne XH2O measurements with useful accuracy. The 315 estimated bias of MUSICA NDACC at Kiruna with respect to radiosonde is about -4.86 %. https://doi.org/10.5194/amt-2020-378 Preprint. Discussion started: 28 September 2020 c Author(s) 2020. CC BY 4.0 License.

Comparison between COCCON and MUSICA IASI
The agreements between the IASI and COCCON retrievals at two sites are presented in Figure 10. (standard deviation) than the coincident data between COCCON and ground-based MUSICA NDACC and TCCON. This is mainly because the IASI data is collected from a wide area around the ground-based FTIR stations and the varying altitudes also introduce additional biases. Partly cloudy conditions introduce biases to the IASI measurements, even if the cloud filter is applied. The fourth and eighth columns in Table 1 list the statistics between the coincident MUSICA IASI and COCCON 345 XH2O data at two sites.

Comparison between TROPOMI and COCCON
The correlation plots between TROPOMI and COCCON XH2O data at Kiruna and Sodankylä sites are presented in Figure 12 and the sixth and tenth columns in Table 1

Summary and conclusions
In this paper, we compare the column-averaged dry-air mole fractions of water vapor (XH2O) retrievals from COCCON and MUSICA NDACC at Kiruna and from COCCON and TCCON at Sodankylä during the period of 2017 -2019. Additionally, we evaluate the vertical water vapor VMR profiles (MAP) as a priori used in COCCON and TCCON retrievals with in situ 380 profiles retrieved from the radiosonde Vaisala RS41.
We found a very good agreement between the COCCON and the MUSICA NDACC data at Kiruna and between the COCCON and the TCCON data at Sodankylä. The COCCON retrievals have a tendency toward a wet bias of (-49.20 ± 58.61) ppm ((-3.33 ± 3.37) %) at Kiruna and (-56.32 ± 45.63) ppm ((-3.44 ± 1.77) %) at Sodankylä. The relative bias between COCCON and MUSICA shows no obvious sensitivity to the SZA, whereas, the absolute value of the relative bias between 385 COCCON and TCCON becomes slightly larger with increasing SZA over 70°. This is because the TCCON and COCCON instruments have different spectral resolutions, which results in different absorption strengths and also averaging kernels .
The a priori profiles play an important role in the retrievals. The in situ measured vertical H2O VMR profiles performed at Sodankylä provide the possibility to evaluate the a priori profiles (MAP) used in both COCCON and TCCON datasets. Since 390 the shapes of profiles are similar over the years, we investigate the profiles in 2018 as an example. The difference between the MAP and radiosonde profiles varies with season and height, and higher differences are found near the surface in summer. The XH2O amounts integrated from MAP and radiosonde profiles between 0.2 km to 15 km show a very good agreement (R 2 = 0.9900), indicating that MAP profiles are reasonably modeled and represent the vertical variability well. There is a small dry https://doi.org/10.5194/amt-2020-378 Preprint.  (-7.75 %) at Sodankylä. The higher biases between COCCON and satellites is mainly because the satellite data are collected from a wide area around the ground-based FTIR stations and the varying altitudes also add a bias.
To assess the effect of the a priori profile, we also use the MAP profiles as a priori profiles for retrieving the MUSICA During this field campaign the low-cost and portable COCCON instrument shows its advantage of robustness, stability and reliability, which provides the potential to complement the TCCON network and for satellite validation. This is the first published study where COCCON XH2O is compared with MUSICA NDACC and TCCON retrievals, and for MUSICA IASI and TROPOMI validation. Therefore, COCCON instrumentation may also serve as validation tool for space-borne XH2O 415 measurements, additionally to XCO2 and XCH4.