Abstract

This paper presents an extension of the scientific HDO/H2O column data product from the Tropospheric Monitoring Instrument (TROPOMI) including clear-sky and cloudy scenes. The retrieval employs a forward model which accounts for scattering, and the algorithm infers the trace gas column information, surface properties and effective cloud parameters from the observations. The extension to cloudy scenes greatly enhances coverage, particularly enabling data over oceans. The data set is validated against co-located ground-based Fourier transform infrared (FTIR) observations by the Total Carbon Column Observing Network (TCCON). The median bias for clear-sky scenes is 1.4 × 1021 molec cm−2 (2.9 %) in H2O columns and 1.1 × 1017 molec cm−2 (−0.3 %) in HDO columns, which corresponds to −17 ‰ (9.9 %) in a posteriori δD. The bias for cloudy scenes is 4.9 × 1021 molec cm−2 (11 %) in H2O, 1.1 × 1017 molec cm−2 (7.9 %) in HDO, and −20 ‰ (9.7 %) in a posteriori δD. At low-altitude stations in low and middle latitudes the bias is small, and has a larger value at high latitude stations. At high altitude stations, an altitude correction is required to compensate for different partial columns seen by the station and the satellite. The bias in a posteriori δD after altitude correction depends on sensitivity due to shielding by clouds, and on realistic prior profile shapes for both isotopologues. Cloudy scenes generally involve low sensitivity below the clouds, and since the information is filled up by the prior, it plays an important role in these cases. Over oceans, aircraft measurements with the Water Isotope System for Precipitation and Entrainment Research (WISPER) instrument from a field campaign in 2018 are used for validation, yielding a bias of −3.9 % in H2O and −3 ‰ in δD over clouds. To demonstrate the added value of the new data set, a short case study of a cold air outbreak over the Atlantic Ocean in January 2020 is presented, showing the daily evolution of the event with single overpass results.

Highlights

  • Atmospheric moisture strongly controls Earth’s radiative budget and transports energy via latent heat, e.g. from low to high 20 latitudes

  • This paper presents an extension of the scientific HDO/H2O column data product from the Tropospheric Monitoring Instrument (TROPOMI) including clear-sky and cloudy scenes

  • 25 HDO and H2O are observed from space mainly in the thermal infrared spectral range, e. g. by 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) or the Atmospheric Infrared Sounder (AIRS) onboard the NASA Aqua satellite (Worden et al, 2019) which builds on earlier work using the Tropospheric Emission Spectrometer (TES) on the NASA Aura satellite (Worden et al, 2012)

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Summary

Introduction

Atmospheric moisture strongly controls Earth’s radiative budget and transports energy via latent heat, e.g. from low to high 20 latitudes. G. by 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) or the Atmospheric Infrared Sounder (AIRS) onboard the NASA Aqua satellite (Worden et al, 2019) which builds on earlier work using the Tropospheric Emission Spectrometer (TES) on the NASA Aura satellite (Worden et al, 2012) These sounders can observe clear-sky and cloudy scenes over land and oceans, but they are insensitive to the boundary 30 layer. The short-wave infrared (SWIR) spectral range does provide sensitivity to the boundary layer and is suitable to estimate total columns, bodies of water are very dark in the SWIR which makes retrievals over oceans impossible for clear-sky conditions.

Retrieval method
Ground-based measurements by TCCON and co-location criteria
Ground-based measurements by MUSICA-NDACC
Aircraft measurements by the WISPER instrument
Validation
Low-altitude stations
High-altitude stations
MUSICA-NDACC
WISPER aircraft measurements over the ocean
Single overpasses
Findings
Summary and conclusions

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