HDO and H<sub>2</sub>O total column retrievals from TROPOMI shortwave infrared measurements

Abstract

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI) on board the European Space Agency Sentinel-5 Precursor mission is scheduled for launch in the last quarter of 2016. As part of its operational processing the mission will provide CH 4 and CO total columns using backscattered sunlight in the shortwave infrared band (2.3 µm). By adapting the CO retrieval algorithm, we have developed a non-scattering algorithm to retrieve total column HDO and H 2 O from the same measurements under clear-sky conditions. The isotopologue ratio HDO / H 2 O is a powerful diagnostic in the efforts to improve our understanding of the hydrological cycle and its role in climate change, as it provides an insight into the source and transport history of water vapour, nature's strongest greenhouse gas. Due to the weak reflectivity over water surfaces, we need to restrict the retrieval to cloud-free scenes over land. We exploit a novel 2-band filter technique, using strong vs. weak water or methane absorption bands, to prefilter scenes with medium-to-high-level clouds, cirrus or aerosol and to significantly reduce processing time. Scenes with cloud top heights 1 km, very low fractions of high-level clouds or an aerosol layer above a high surface albedo are not filtered out. We use an ensemble of realistic measurement simulations for various conditions to show the efficiency of the cloud filter and to quantify the performance of the retrieval. The single-measurement precision in terms of δD is better than 15-25 ‰ for even the lowest surface albedo (2-4 ‰ for high albedos), while a small bias remains possible of up to ∼ 20 ‰ due to remaining aerosol or up to ∼ 70 ‰ due to remaining cloud contamination. We also present an analysis of the sensitivity towards prior assumptions, which shows that the retrieval has a small but significant sensitivity to the a priori assumption of the atmospheric trace gas profiles. Averaging multiple measurements over time and space, however, will reduce these errors, due to the quasi-random nature of the profile uncertainties. The sensitivity of the retrieval with respect to instrumental parameters within the expected instrument performance is < 3 ‰, which represents only a small contribution to the overall error budget. Spectroscopic uncertainties of the water lines, however, can have a larger and more systematic impact on the performance of the retrieval and warrant further reassessment of the water line parameters. With TROPOMI's high radiometric sensitivity, wide swath (resulting in daily global coverage) and efficient cloud filtering, in combination with a spatial resolution of 7 × 7 km 2 , we will greatly increase the amount of useful data on HDO, H 2 O and their ratio HDO / H 2 O. We showcase the overall performance of the retrieval algorithm and cloud filter with an accurate simulation of TROPOMI measurements from a single overpass over parts of the USA and Mexico, based on MODIS satellite data and realistic conditions for the surface, atmosphere and chemistry (including isotopologues). This shows th...

First data set of H&lt;sub&gt;2&lt;/sub&gt;O/HDO columns from the Tropospheric Monitoring Instrument (TROPOMI)

“…The retrievals are performed with SICOR (short-wave infrared CO retrieval algorithm), which utilises a profilescaling approach and is described in detail by Scheepmaker et al (2016), Landgraf et al (2016) and Borsdorff et al (2014). In the following, the most important features are summarised and the specific setup is given.…”

“…Using the spectral window from 2354.0 to 2380.5 nm (Scheepmaker et al, 2016), the algorithm fits the total columns of H 2 O, HDO, CH 4 and CO as well as a Lambertian surface albedo in the form of a Legendre polynomial of order 1. The isotopologue H 2 18 O is included in the forward model but not fitted.…”

“…With respect to satellites, H 2 O and HDO were first retrieved by Zakharov et al (2004) using thermal infrared measurements from the Interferometric Monitor for Greenhouse gases (IMG) sensor onboard the Advanced Earth Observing Satellite (ADEOS). Later, this was followed by the Tropospheric Emission Spectrometer (TES) on the Earth Observing System (EOS) Aura satellite (Worden et al, 2006), the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) onboard the European Space Agency (ESA)'s environmental satellite (ENVISAT) (Steinwagner et al, 2007;Payne et al, 2007), the SCanning Imaging Absorption spectroMeter for Atmo-spheric CHartographY (SCIAMACHY) instrument on EN-VISAT (Frankenberg et al, 2009;Scheepmaker et al, 2015;Schneider et al, 2018), the Infrared Atmospheric Sounding Interferometer (IASI) onboard the MetOP satellites (Herbin et al, 2009;Schneider and Hase, 2011;Schneider et al, 2016;Lacour et al, 2012), the Greenhouse Gases Observing Satellite (GOSAT) (Frankenberg et al, 2013;Boesch et al, 2013) and the Atmospheric Infrared Sounder (AIRS) onboard the NASA Aqua satellite (Worden et al, 2019). The sensitivity of instruments observing in the thermal infrared (IMG, TES, MIPAS, IASI and AIRS) is very different from that of instruments measuring in the short-wave infrared, such as SCIAMACHY and GOSAT.…”

Carbon monoxide total column retrievals from TROPOMI shortwave infrared measurements

“…The non-scattering retrieval algorithm uses a standard least squares approach to infer the total column of CH 4 , CO, H 2 O and HDO together with a surface albedo A s , its linear dependence on wavelength and a spectral offset. It is described in more detail by Scheepmaker et al (2016). Figure 3 shows the probability density function (PDF) and its cumulative distribution (CPDF) of the difference CH 4 between a non-scattering CH 4 retrieval from observations of Greenhouse Gases Observing Satellite (GOSAT, Kuze et al, 2009) at the 1.6 µm band of the year 2010 over land and ocean and collocated CH 4 columns from TM5 model simulations after optimization using surface measurements (Houweling et al, 2014), relative to the model results.…”

The operational methane retrieval algorithm for TROPOMI

“…Thus, there will be a difference between the H 2 O column retrieved from the strong band and the H 2 O column retrieved from the weak band in the presence of clouds. Based on tests with simulations, we chose 2329-2334 nm as the weak absorption band and 2367-2377 nm as the strong absorption band (see also Scheepmaker et al, 2016). In Fig.…”