First data set of H2O/HDO columns from TROPOMI

Andreas Schneider1, Tobias Borsdorff1, Joost aan de Brugh1, Franziska Aemisegger2, Dietrich G. Feist3,4, Rigel Kivi5, Frank Hase6, Matthias Schneider6, and Jochen Landgraf1 1Earth science group, SRON Netherlands Institute for Space Research, Utrecht, the Netherlands 2Atmospheric Dynamics group, Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland 3Deutsches Zentrum für Luftund Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany 4Max Planck Institute for Biogeochemistry, Jena, Germany 5Finnish Meteorological Institute, Sodankylä, Finland 6Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe Institute of Technology, Karlsruhe, Germany Correspondence: Andreas Schneider (a.schneider@sron.nl)

depends on the thermodynamic conditions of the environment. The relative abundance of a heavy isotopologue with respect to the light isotopologue in an air parcel is therefore dependent on the source region's temperature and relative humidity, the source water's isotopic composition as well as the entire transport history of the air parcel, including all evaporation, 25 condensation and mixing events (e. g. Dansgaard, 1964;Craig and Gordon, 1965). This makes measurements of water vapour isotopologues a unique diagnostic of the hydrological cycle (Dansgaard, 1964) and a valuable benchmark for the evaluation and further development of global and regional circulation models (e. g. Joussaume et al., 1984;Hoffmann et al., 1998;Yoshimura et al., 2008;Risi et al., 2010;Pfahl et al., 2012).
The usual notation to describe the isotopological abundance variations is the relative difference of the ratio of the heavy and Measurements of atmospheric water vapour isotopologues are not very common. In situ observations are performed from aircrafts and balloons (e. g. Rinsland et al., 1984;Dyroff et al., 2010Dyroff et al., , 2015Herman et al., 2014;Sodemann et al., 2017) and 35 on the ground (e.g. Wen et al., 2010;Aemisegger et al., 2012;Bastrikov et al., 2014) (Steinwagner et al., 2007;Payne et al., 2007), the SCanning Imaging Absorption spec-troMeter for Atmospheric CHartographY (SCIAMACHY) instrument on ENVISAT (Frankenberg et al., 2009;Scheepmaker et al., 2015;Schneider et al., 2018), the Infrared Atmospheric Sounding Interferometer (IASI) aboard the MetOP satellite (Herbin et al., 2009;Schneider and Hase, 2011;Lacour et al., 2012) and the Greenhouse Gases Observing Satellite (GOSAT) 50 (Frankenberg et al., 2013;Boesch et al., 2013). On 13th October 2017, the Tropospheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite (Veefkind et al., 2012) was launched. It has a short-wave infrared band in heritage of SCIAMACHY with a spectral range of 2305-2385 nm and a spectral resolution of 0.25 nm, yet with a signal to noise ratio much better than SCIAMACHY and an unprecedented spatial resolution of 7 km × 7 km (in the centre of the swath  (Siddans, 2016). The cloud cover threshold is 1 % for inner field of view and outer field of view. Moreover, soundings with high aerosol load are filtered out by a two-band filter as introduced by Scheepmaker et al. (2016); Hu et al. (2018), which in the present configuration requires that the ratio of retrieved methane in bands with weak and strong absorption (2310-2315 nm and 2363-2373 nm, respectively) is between 75 0.94 and 1.06. Furthermore, scenes with solar zenith angle greater than 75 • are discarded because they are prone to errors due to diffraction effects which are not covered well by the forward model and due to long light paths which mean enhanced sensitivity to inaccuracies in the spectroscopy.
The sensitivity of a retrieved column to changes in a given altitude region is described by the column averaging kernel (Rodgers, 2000). The ideal averaging kernel is unity in all altitudes, but in practice the sensitivity changes with height. Figure 1 80 depicts examples of column averaging kernels for different solar zenith angles. The sensitivity for the two isotopologues are significantly different. For H 2 O, the highest sensitivity is in the lowest layer (where typically most water vapour resides) and decreases with increasing altitude. The sensitivity in the stratosphere is small, however the amount of water vapour in this altitude region is very small and contributes little to the total column. The sensitivity of HDO does not deviate as much from unity as the one of H 2 O. In the lower troposphere it increases slightly with increasing altitude until reaching a maximum 85 depending on solar zenith angle, above which it decreases. The differences in column averaging kernel are due to the different absorption strength of the two isotopologues and mean that a posteriori δ D is sensitive on the profile shapes, particularly of the main isotopologue H 2 O since for that the averaging kernel deviates considerably from unity in higher altitudes.
HDO averaging kernel

Ground-based FTIR data sets
To validate the TROPOMI retrievals, ground-based Fourier transform infrared (FTIR) measurements are used. HDO is a prod-90 uct of NDACC-MUSICA (Barthlott et al., 2017) and TCCON . Seven stations are in both networks: Eureka, Ny Ålesund, Bremen, Karsruhe, Izaña, Wollongong and Lauder. This allows to compare the TCCON and NDACC-MUSICA data products, which reveals that they do not coincide, but have a large difference in δ D of on average 58 ‰ when collocating with a maximal time difference of one hour. An example for Wollongong is plotted in Fig. 2. MUSICA is explicitly created for isotopologue studies, and δ D profiles have been validated against aircraft measurements in an altitude range of 95 2-7 km during a dedicated campaign in summer 2013 Dyroff et al., 2015;Schneider et al., 2016).
However, data is only available until 2014, so that there is no temporal overlap with TROPOMI which has been launched in October 2017. TCCON H 2 O total columns are calibrated with in situ measurements (mainly radiosondes); a so-called aircraft correction factor of 1.0183 is applied to match the reference . However, TCCON HDO is currently not verified and so no correction factor is applied to it. Thus it is assumed that TCCON HDO has to be corrected.

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In order to correct for the discrepancy, the idea is to scale TCCON HDO to match MUSICA δ D. Scaling HDO with a factor a, i. e. c HDO → a c HDO , is equivalent to the linear transformation  Table 1 gives an overview of all stations used. Other stations have too few (less than 5 days) collocated measurements and have thus not been included in the 115 validation study. No altitude correction is applied here. The mentioned collocation criterium for altitude is used to ensure that no bias due to large height difference between station and satellite ground pixel is introduced (cf. Schneider et al., 2018). For each station, daily averages are computed over all collocated measurements.    The results for all TCCON stations are depicted in Fig. 6. The correlation is high for all stations, except for a lower correlation in δ D of 0.58 at Lauder, where the variability in δ D is small. The bias in H 2 O and HDO is small with an average over all stations 125 of 2 · 10 19 molec cm −2 (0.9 %) for H 2 O and −3 · 10 16 molcec cm −2 (−0.7 %) for HDO. The mean bias in δ D is −12 ‰ or 4 %, which is good taken into account that δ D is very sensitive to small errors in H 2 O or HDO.

Demonstration of applications of the data set
An illustration of the TROPOMI retrievals on the global and monthly scale is depicted in Fig. 7 for September 2018. There is no data over the oceans because water is too dark in the short-wave infrared and glint measurements are not taken into account.

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The data gaps in tropical regions are due to persistent clouds. The data quality in terms of noise is significantly better than for a multi-year average of SCIAMACHY observations, cf. Schneider et al. (2018, Fig. 7). In the spatial distribution shown in Fig. 7 the major isotopic effects formulated by Dansgaard (1964) can be recognised. The general latitudinal gradient due to the temperature-dependence of the fractionation effects and progressive rain out of heavy isotopologues, the so-called latitudinal effect, is plain. The continental effect of depletion due to rain out of the heavy isotopologue is visible on all continents including 135 Australia. The altitude effect, which describes depletion above high ground due to lower temperature and increasing rain out, can be seen, for example, over the Andes and the Himalayas.   To demonstrate the quality and the possibilities of the new data set of water vapour isotopologues from TROPOMI, a case study using single overpass results over Europe on 30th July 2018 is presented in Fig. 8. vapour in this region is due to the large-scale subsidence bringing depleted (Fig. 8b) and dry (Fig. 8d) upper tropospheric air towards lower levels. The near-surface divergent wind exports more enriched freshly evaporated moisture that is taken up near the surface towards the edges of the blocking. The anticyclone area is characterised by clear skies (Fig. 8c) with low specific humidity (1-3 g kg −1 at 700 hPa, Fig. 8d), low relative humidity (10-30 % at 700 hPa, Fig. 8e) and high potential temperature associated with the dry subsiding (adiabatically warming) air masses (Fig. 8f). The dry low-level outflow encounters moister 155 and warmer air at the edge of the surface anticyclone, leading to a very strong horizontal gradient of specific and relative humidity (Fig. 8d,e) in the lower troposphere. As a consequence the warm moist air is forced to rise, localised instabilities occur and isolated convective cells develop leading to condensation and the formation of a ring of clouds around the blocking anticyclone. A distinct arc-like feature of enriched total column water vapour at the edge of the anticyclone can be distinguished slightly displaced from the first clouds in the northwest (Fig. 8b). Turbulent mixing and convection injecting more enriched, 160 freshly evaporated moisture advected with the large-scale flow from marine environments (Barents Sea, North Sea and Black Sea) could be the reason for this interesting enriched ring-like water vapour isotopologue pattern. A very depleted cloud free area south of the Ob river with δ D values below −250 ‰ (Fig. 8b) might be connected to anomalously strong descent of northerly continental air masses. In future work, the nature and occurrence of these features should be analysed in more detail including a catalogue of different continental blocking events with observations from TROPOMI.

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Apart from investigations on the water cycle dynamics associated with continental blockings, many other dynamically interesting contexts exist where TROPOMI could present an important added value for further investigations. These comprise among others the region of the heat low over the Sahara (e. g. Schneider et al., 2015;González et al., 2016;Lacour et al., 2017) or continental regions upstream of cold air surges leading to events of strong ocean evaporation along the warm ocean western boundary currents (Aemisegger and Papritz, 2018;Aemisegger and Sjolte, 2018). This work presents a new data set of H 2 O and HDO columns retrieved from TROPOMI short-wave infrared observations.
Scattering is ignored in the forward model so that a strict cloud filtering is necessary, which is performed with collocated VIIRS measurements. The data quality is such that single overpasses yield meaningful results, which is a huge step forward compared to previous missions like SCIAMACHY.

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For validation of the TROPOMI data product, particular attention must be given to the reference data sets. At this stage, there are two data products of ground-based observations of the HDO total column available, provided by the TCCON and NDACC-MUSICA networks. Comparing these two data products for stations in both networks reveals a large bias between the ground-based products of on average 58 ‰ in δ D. NDACC-MUSICA was decidedly developed for water vapour isotopologue studies and is validated in δ D with aircraft measurements, but data are only available until 2014. TCCON provides recent 180 data with temporal overlap with TROPOMI observations and its H 2 O total column data product is validated against in situ measurements, however its HDO data product is not verified. In order to obtain a suitable validation data set, TCCON HDO columns are scaled by a factor of 1.0778 to match the MUSICA δ D over the common observation time period.
Using a collocation radius of 30 km, a maximal altitude difference of 500 m, a field of view of 45 • and a maximal time difference of 2 h, a good agreement between corrected TCCON measurements and collocated TROPOMI observations is found.
The use of the new data set is demonstrated in a case study of an atmospheric blocking event with a single TROPOMI overpass over northeastern Europe on 30th July 2018. Depleted air masses are found in the core of the anticyclone due to subsidence bringing upper tropospheric air towards lower levels. At the edge of the anticyclone a ring of enriched air is 190 observed. A climatological study on the water vapour isotopic signature of continental summer blocking events could provide promising insights into the atmospheric water cycling associated with such systems that frequently lead to heat waves and hot temperature extremes. This case study shows the quality of the new data set and the added value for isotopologue studies, enabling studies on a day-by-day basis with high spatial resolution over continental regions.
Due to the restrictive filter for clear sky scenes, the data coverage is limited. To improve on this, cloudy-sky retrievals over 195 low clouds will be considered in a future work by using a forward model that accounts for scattering. Moreover, a calibration and validation of the TCCON HDO product is necessary. Additionally, it would be beneficial if recent NDACC-MUSICA data would become available. Finally, an improvement in the consistency between the networks would be very valuable.