Carbon monoxide total column retrievals from TROPOMI shortwave infrared measurements

Landgraf, Jochen; Brugh, Joost aan de; Scheepmaker, R. A.; Borsdorff, Tobias; Hu, Haili; Houweling, Sander; Butz, A.; Aben, I.; Hasekamp, Otto

doi:10.5194/amt-2016-114

Cited by 20 publications

(40 citation statements)

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“…On 9 November 2017, during the instrument commissioning phase, first radiance measurements of the SWIR spectral range were obtained and since then several days of observations are evaluated. For this purpose, we use the Shortwave Infrared CO Retrieval (SICOR) algorithm, which is developed for the operational processing of TROPOMI data Landgraf, aan de Brugh, Scheepmaker, et al, 2016;Vidot et al, 2012). It retrieves carbon monoxide (CO) total column densities simultaneously with effective cloud parameters (cloud optical thickness cld , cloud center height z cld ) deploying a scattering forward simulation.…”

Section: Introductionmentioning

confidence: 99%

Measuring Carbon Monoxide With TROPOMI: First Results and a Comparison With ECMWF‐IFS Analysis Data

Borsdorff

Brugh

et al. 2018

Geophysical Research Letters

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124

115

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The Tropospheric Monitoring Instrument (TROPOMI) was launched onboard of the European Space Agency's (ESA) Sentinel‐5P satellite. One of the mission's key products is the total column density of carbon monoxide, inferred from TROPOMI's 2.3 μm measurements. Using the operational processing algorithm, we analyze six subsequent days of measurements during the commissioning phase. The TROPOMI product is compared with CO fields from the European Centre for Medium‐Range Weather Forecasts (ECMWF) assimilation system. Globally, a small mean difference between the data sets of 3.2 ± 5.5% with a correlation coefficient of 0.97 is found. The daily global coverage of TROPOMI enables it to capture day‐to‐day evolution of the atmospheric composition. As an example, we discuss the air pollution event of India in November 2017 with high carbon monoxide (CO) concentrations, which partly dispersed when the CO polluted air was transported north alongside the Himalaya to China. The striking agreement and also regional differences with ECMWF indicate new exciting applications for the TROPOMI CO data product.

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Section: Introductionmentioning

confidence: 99%

Measuring Carbon Monoxide With TROPOMI: First Results and a Comparison With ECMWF‐IFS Analysis Data

Borsdorff

Brugh

et al. 2018

Geophysical Research Letters

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124

115

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“…The SWIR measurements of TROPOMI and SCIAMACHY will be similar with respect to spectral coverage and resolution, but TROPOMI will have better spatial resolution (7×7 km 2 ), a larger signal-to-noise ratio, and daily global coverage. Therefore TROPOMI will extend the record of SWIR measurements from space, and because of its similarity to SCIAMACHY, it is possible to use the same CO retrieval algorithm for both instruments and thereby provide a consistent data product for longterm study of CO (Borsdorff et al, 2016;Landgraf et al, 2016b).…”

Section: Introductionmentioning

confidence: 99%

Carbon monoxide column retrieval for clear-sky and cloudy atmospheres: a full-mission data set from SCIAMACHY 2.3 μm reflectance measurements

Borsdorff¹,

Brugh²,

Hu³

et al. 2017

Preprint

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Abstract. We discuss the retrieval of carbon monoxide (CO) vertical column densities from clear-sky and cloud contaminated 2311–2338 nm reflectance spectra measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) from January 2003 until the end of the mission in April 2012. These data was processed with the Shortwave Infrared CO Retrieval algorithm SICOR that we developed for the operational data processing of the Tropospheric Monitoring Instrument (TROPOMI) that will be launched on ESA’s Sentinel-5 Precursor (S5P) mission. This study complements previous work that was limited to clear-sky observations over land. Over the oceans, CO is estimated from cloudy-sky measurements only, which is an important addition to the SCIAMACHY clear-sky CO data set as shown by NDACC and TCCON measurements at coastal sites. For Ny-Ålesund, Lauder, Mauna Loa, and Reunion, a validation of SCIAMACHY clear-sky retrievals is not meaningful because of the high retrieval noise and the few collocations at these sites. This improves significantly when considering cloudy-sky observations, where we find a low mean bias b = ±6.0 ppb and a strong correlation between the validation data set and the SCIAMACHY data sets with a mean Pearson correlation coefficient r = 0.7. Also for land observations, cloudy-sky CO retrievals present an interesting complement to the clear-sky data set, which is less sensitive to the spatial representativeness of the satellite and validation measurement. For example, at the cities Teheran and Beijing the agreement of SCIAMACHY clear-sky CO observations with MOZAIC/IAGOS airborne measurements is poor with a mean bias of b = 171.2 ppb and 57.9 ppb because of local CO pollution, which cannot be captured by SCIAMACHY. The validation improves significantly for cloudy sky retrievals with b = 52.3 ppb and 5.0 ppb, respectively. This is due to a reduced retrieval sensitivity to CO below the cloud and so to the altitude range, which is mostly affected by strong local surface emissions. At the less urbanized region around the airportWindhoek, local CO pollution is less prominent and so MOZAIC/IAGOS measurements agree well with SCIAMACHY clear-sky retrievals with a mean bias of b = 15.5 ppb, but can be even further improved considering cloudy SCIAMACHY observations with a mean CO bias of b = 0.2 ppb. Overall the cloudy-sky CO retrievals from SCIAMACHY short wave infrared measurements present a valuable addition to the clear-sky only data set. Moreover, the study represents the first application of the S5P algorithm for operational CO data processing on cloudy observations prior to the launch of the S5P mission.

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“…2.3. After cloud filtering we use a nonscattering retrieval algorithm, adapted from the Shortwave Infrared CO Retrieval (SICOR) algorithm, which has already been developed as part of ESA's operational CO algorithm and is described by Landgraf et al (2016) in this issue. By using this heritage of TROPOMI's CO processing, we benefit from an algorithm optimized for speed, while also leveraging already existing expertise and software.…”

Section: Retrieval Algorithm Descriptionmentioning

confidence: 99%

“…For low solar zenith angles, µ 0 is corrected for the sphericity of the Earth according to Kasten and Young (1989). F 0 is the solar irradiance inferred from TROPOMI solar measurements (van Deelen et al, 2007;Landgraf et al, 2016) and…”

Section: Forward Model and Averaging Kernelsmentioning

confidence: 99%

“…The implementation of S-LINTRAN for TROPOMI simulations, including the instrument model, is described in detail in Landgraf et al (2016), as the same simulations have been used to assess the performance of the Table 2. Microphysical properties of water and ice clouds: n(r) represents the size distribution type, r eff and v eff are the effective radius and variance of the size distribution, m = n − ik is the refractive index.…”

Section: Measurement Simulationsmentioning

confidence: 99%

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

Scheepmaker

Brugh

et al. 2016

Atmos. Meas. Tech.

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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...

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Carbon monoxide total column retrievals from TROPOMI shortwave infrared measurements

Cited by 20 publications

References 1 publication

Measuring Carbon Monoxide With TROPOMI: First Results and a Comparison With ECMWF‐IFS Analysis Data

Measuring Carbon Monoxide With TROPOMI: First Results and a Comparison With ECMWF‐IFS Analysis Data

Carbon monoxide column retrieval for clear-sky and cloudy atmospheres: a full-mission data set from SCIAMACHY 2.3 μm reflectance measurements

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

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Carbon monoxide total column retrievals from TROPOMI shortwave infrared measurements

Cited by 20 publications

References 1 publication

Measuring Carbon Monoxide With TROPOMI: First Results and a Comparison With ECMWF‐IFS Analysis Data

Measuring Carbon Monoxide With TROPOMI: First Results and a Comparison With ECMWF‐IFS Analysis Data

Carbon monoxide column retrieval for clear-sky and cloudy atmospheres: a full-mission data set from SCIAMACHY 2.3 μm reflectance measurements

HDO and H&lt;sub&gt;2&lt;/sub&gt;O total column retrievals from TROPOMI shortwave infrared measurements

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