Water vapour total columns from SCIAMACHY spectra in the 2.36 μm window

Schrijver, H.; Gloudemans, A. M. S.; Frankenberg, Christian; Aben, I.

doi:10.5194/amt-2-561-2009

Cited by 32 publications

(42 citation statements)

References 35 publications

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“…Finally, the surface albedo in the NIR is taken from the MODIS land albedo product in the 841-876 nm channel. For the albedo in the SWIR, the SCIA-MACHY surface albedo product at 2350 nm is used (Schrijver et al, 2009). For simplicity, we assume a constant surface albedo within the NIR and SWIR band in our simulations.…”

Section: Synthetic Measurements: the Global Ensemblementioning

confidence: 99%

The operational methane retrieval algorithm for TROPOMI

et al. 2016

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Abstract. This work presents the operational methane retrieval algorithm for the Sentinel 5 Precursor (S5P) satellite and its performance tested on realistic ensembles of simulated measurements. The target product is the columnaveraged dry air volume mixing ratio of methane (XCH 4 ), which will be retrieved simultaneously with scattering properties of the atmosphere. The algorithm attempts to fit spectra observed by the shortwave and near-infrared channels of the TROPOspheric Monitoring Instrument (TROPOMI) spectrometer aboard S5P.The sensitivity of the retrieval performance to atmospheric scattering properties, atmospheric input data and instrument calibration errors is evaluated. In addition, we investigate the effect of inhomogeneous slit illumination on the instrument spectral response function. Finally, we discuss the cloud filters to be used operationally and as backup.We show that the required accuracy and precision of < 1 % for the XCH 4 product are met for clear-sky measurements over land surfaces and after appropriate filtering of difficult scenes. The algorithm is very stable, having a convergence rate of 99 %. The forward model error is less than 1 % for about 95 % of the valid retrievals. Model errors in the input profile of water do not influence the retrieval outcome noticeably. The methane product is expected to meet the requirements if errors in input profiles of pressure and temperature remain below 0.3 % and 2 K, respectively. We further find that, of all instrument calibration errors investigated here, our retrievals are the most sensitive to an error in the instrument spectral response function of the shortwave infrared channel.

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Section: Synthetic Measurements: the Global Ensemblementioning

confidence: 99%

The operational methane retrieval algorithm for TROPOMI

et al. 2016

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“…Satellite remote sensing of water vapor has led to products retrieved from the visible (e.g., GOME, Wagner et al, 2003;Lang et al, 2007;SCIAMACHY, Noël et al, 2005; GOME-2, Grossi et al, 2015; Ozone Monitoring Instrument (OMI), Wang et al, 2014), near-infrared (e.g., SCIAMACHY, Schrijver et al, 2009;MODIS, Diedrich et al, 2015;MERIS, Lindstrot et al, 2012), infrared (e.g., MODIS, Seemann et al, 2003;AIRS, Bedka et al, 2010;IASI, Pougatchev et al, 2009), microwave (e.g., SSM/I, Schlüssel and Emery, 1990;Wentz, 1997), and GPS radio signals (e.g., Wang et al, 2007;Kishore et al, 2011). These data sets offer the unique opportunity to study water vapor distribution on a global scale.…”

Section: Introductionmentioning

confidence: 99%

Validation and update of OMI Total Column Water Vapor product

Wang¹,

Abad²,

Liu³

et al. 2016

Atmos. Chem. Phys.

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Abstract. The collection 3 Ozone Monitoring Instrument (OMI) Total Column Water Vapor (TCWV) data generated by the Smithsonian Astrophysical Observatory's (SAO) algorithm version 1.0 and archived at the Aura Validation Data Center (AVDC) are compared with NCAR's ground-based GPS data, AERONET's sun-photometer data, and Remote Sensing System's (RSS) SSMIS data. Results show that the OMI data track the seasonal and interannual variability of TCWV for a wide range of climate regimes. During the period from 2005 to 2009, the mean OMI−GPS over land is −0.3 mm and the mean OMI−AERONET over land is 0 mm. For July 2005, the mean OMI−SSMIS over the ocean is −4.3 mm. The better agreement over land than over the ocean is corroborated by the smaller fitting residuals over land and suggests that liquid water is a key factor for the fitting quality over the ocean in the version 1.0 retrieval algorithm. We find that the influence of liquid water is reduced using a shorter optimized retrieval window of 427.7-465 nm. As a result, the TCWV retrieved with the new algorithm increases significantly over the ocean and only slightly over land. We have also made several updates to the air mass factor (AMF) calculation. The updated version 2.1 retrieval algorithm improves the land/ocean consistency and the overall quality of the OMI TCWV data set. The version 2.1 OMI data largely eliminate the low bias of the version 1.0 OMI data over the ocean and are 1.5 mm higher than RSS's "clear" sky SSMIS data in July 2005. Over the ocean, the mean of version 2.1 OMI−GlobVapour is 1 mm for July 2005 and 0 mm for January 2005. Over land, the version 2.1 OMI data are about 1 mm higher than GlobVapour when TCWV < 15 mm and about 1 mm lower when TCWV > 15 mm.

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“…The Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) instrument was operational for measurements of atmospheric compositions from 2002 to 2012, with CO 2 , CH 4 , CO, and H 2 O as four of its target species (Buchwitz et al, 2004(Buchwitz et al, , 2007Schrijver et al, 2009;Schneising et al, 2011). The Greenhouse gases Observing SATellite (GOSAT), developed by the Japan Aerospace Exploration Agency and launched in January 2009, is the world's first mission dedicated to the monitoring of CO 2 and CH 4 (Yokota et al, 2009;Butz et al, 2011;Yoshida et al, 2011).…”

Section: XI Et Al: Simulated Retrievals From Geostationary Orbitmentioning

confidence: 99%

Simulated retrievals for the remote sensing of CO2, CH4, CO, and H2O from geostationary orbit

Natraj

Shia

et al. 2015

Atmos. Meas. Tech.

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Abstract. The Geostationary Fourier Transform Spectrometer (GeoFTS) is designed to measure high-resolution spectra of reflected sunlight in three near-infrared bands centered around 0.76, 1.6, and 2.3 µm and to deliver simultaneous retrievals of column-averaged dry air mole fractions of CO 2 , CH 4 , CO, and H 2 O (denoted XCO 2 , XCH 4 , XCO, and XH 2 O, respectively) at different times of day over North America. In this study, we perform radiative transfer simulations over both clear-sky and all-sky scenes expected to be observed by GeoFTS and estimate the prospective performance of retrievals based on results from Bayesian error analysis and characterization.We find that, for simulated clear-sky retrievals, the average retrieval biases and single-measurement precisions are < 0.2 % for XCO 2 , XCH 4 , and XH 2 O, and < 2 % for XCO, when the a priori values have a bias of 3 % and an uncertainty of 3 %. In addition, an increase in the amount of aerosols and ice clouds leads to a notable increase in the retrieval biases and slight worsening of the retrieval precisions. Furthermore, retrieval precision is a strong function of signal-to-noise ratio and spectral resolution. This simulation study can help guide decisions on the design of the GeoFTS observing system, which can result in cost-effective measurement strategies while achieving satisfactory levels of retrieval precisions and biases. The simultaneous retrievals at different times of day will be important for more accurate estimation of carbon sources and sinks on fine spatiotemporal scales and for studies related to the atmospheric component of the water cycle.

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Water vapour total columns from SCIAMACHY spectra in the 2.36 μm window

Cited by 32 publications

References 35 publications

The operational methane retrieval algorithm for TROPOMI

The operational methane retrieval algorithm for TROPOMI

Validation and update of OMI Total Column Water Vapor product

Simulated retrievals for the remote sensing of CO<sub>2</sub>, CH<sub>4</sub>, CO, and H<sub>2</sub>O from geostationary orbit

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Water vapour total columns from SCIAMACHY spectra in the 2.36 μm window

Cited by 32 publications

References 35 publications

The operational methane retrieval algorithm for TROPOMI

The operational methane retrieval algorithm for TROPOMI

Validation and update of OMI Total Column Water Vapor product

Simulated retrievals for the remote sensing of CO&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt;, CO, and H&lt;sub&gt;2&lt;/sub&gt;O from geostationary orbit

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Simulated retrievals for the remote sensing of CO<sub>2</sub>, CH<sub>4</sub>, CO, and H<sub>2</sub>O from geostationary orbit