Note on rotational-Raman scattering in the O&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; A- and B-bands

Vasilkov, A. P.; Joiner, Joanna; Spurr, Robert

doi:10.5194/amt-6-981-2013

Cited by 29 publications

(23 citation statements)

References 26 publications

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“…This systematic error estimate, however, does not include potential other error contributions such as intensity offsets (due to imperfect calibration). It also does not include possible errors due to rotational Raman scattering (RRS), which are expected to be small but may not be entirely negligible (Vasilkov et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Carbon Monitoring Satellite (CarbonSat): assessment of atmospheric CO2 and CH4 retrieval errors by error parameterization

et al. 2013

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Abstract. Carbon Monitoring Satellite (CarbonSat) is one of two candidate missions for ESA's Earth Explorer 8 (EE8) satellite to be launched around the end of this decade. The overarching objective of the CarbonSat mission is to improve our understanding of natural and anthropogenic sources and sinks of the two most important anthropogenic greenhouse gases (GHGs) carbon dioxide (CO 2 ) and methane (CH 4 ). The unique feature of CarbonSat is its "GHG imaging capability", which is achieved via a combination of high spatial resolution (2 km × 2 km) and good spatial coverage (wide swath and gap-free across-and along-track ground sampling). This capability enables global imaging of localized strong emission source, such as cities, power plants, methane seeps, landfills and volcanos, and likely enables better disentangling of natural and anthropogenic GHG sources and sinks. Source-sink information can be derived from the retrieved atmospheric column-averaged mole fractions of CO 2 and CH 4 , i.e. XCO 2 and XCH 4 , by inverse modelling. Using the most recent instrument and mission specification, an error analysis has been performed using the Bremen optimal EStimation DOAS (BESD/C) retrieval algorithm. We assess the retrieval performance for atmospheres containing aerosols and thin cirrus clouds, assuming that the retrieval forward model is able to describe adequately all relevant scattering properties of the atmosphere. To compute the errors for each single CarbonSat observation in a one-year period, we have developed an error parameterization scheme comprising six relevant input parameters: solar zenith angle, surface albedo in two bands, aerosol and cirrus optical depth, and cirrus altitude variations. Other errors, e.g. errors resulting from aerosol type variations, are partially quantified but not yet accounted for in the error parameterization. Using this approach, we have generated and analysed one year of simulated CarbonSat observations. Using this data set we estimate that systematic errors are for the overwhelming majority of cases (≈ 85 %) below 0.3 ppm for XCO 2 (below 0.5 ppm for 99.5 %) and below 2 ppb for XCH 4 (below 4 ppb for 99.3 %). We also show that the single-measurement precision is typically around 1.2 ppm for XCO 2 and 7 ppb for XCH 4 (1σ ). The number of quality-filtered observations over cloud-and ice-free land surfaces is in the range of 33 to 47 million per month depending on season. Recently it has been shown that terrestrial vegetation chlorophyll fluorescence (VCF) emission needs to be considered for accurate XCO 2 retrieval. We therefore retrieve VCF from clear Fraunhofer lines located around 755 nm and show that CarbonSat will provide valuable information on VCF. We estimate that the VCF single-measurement precision is approximately 0.3 mW m −2 nm −1 sr −1 (1σ ).

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Section: Discussionmentioning

confidence: 99%

Carbon Monitoring Satellite (CarbonSat): assessment of atmospheric CO2 and CH4 retrieval errors by error parameterization

et al. 2013

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“…RRS effects are generally small, though not negligible, at the wavelengths of interest (Vasilkov et al, 2013). The effects of O 2 A-band dayglow emissions in the upper atmosphere are expected to be small .…”

Section: Simulated Radiances and Irradiancesmentioning

confidence: 91%

“…Retrieval algorithms have been developed to retrieve some or all of these parameters using O 2 A-band radiances (e.g., Guanter et al, 2007;O'Dell et al, 2012). Other factors such as filling-in from rotational Raman scattering are typically not accounted for owing to a high degree of complexity resulting from interactions with Mie scattering and dependences on other parameters (Vasilkov et al, 2013). Neglect of these effects may produce errors in such algorithms.…”

Section: Generation Of Atmospheric Pcsmentioning

confidence: 99%

Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2

et al. 2013

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Abstract. Globally mapped terrestrial chlorophyll fluorescence retrievals are of high interest because they can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. Previous satellite retrievals of fluorescence have relied solely upon the filling-in of solar Fraunhofer lines that are not significantly affected by atmospheric absorption. Although these measurements provide near-global coverage on a monthly basis, they suffer from relatively low precision and sparse spatial sampling. Here, we describe a new methodology to retrieve global far-red fluorescence information; we use hyperspectral data with a simplified radiative transfer model to disentangle the spectral signatures of three basic components: atmospheric absorption, surface reflectance, and fluorescence radiance. An empirically based principal component analysis approach is employed, primarily using cloudy data over ocean, to model and solve for the atmospheric absorption. Through detailed simulations, we demonstrate the feasibility of the approach and show that moderate-spectral-resolution measurements with a relatively high signal-to-noise ratio can be used to retrieve far-red fluorescence information with good precision and accuracy. The method is then applied to data from the Global Ozone Monitoring Instrument 2 (GOME-2). The GOME-2 fluorescence retrievals display similar spatial structure as compared with those from a simpler technique applied to the Greenhouse gases Observing SATellite (GOSAT). GOME-2 enables global mapping of far-red fluorescence with higher precision over smaller spatial and temporal scales than is possible with GOSAT. Near-global coverage is provided within a few days. We are able to show clearly for the first time physically plausible variations in fluorescence over the course of a single month at a spatial resolution of 0.5 • × 0.5 • . We also show some significant differences between fluorescence and coincident normalized difference vegetation indices (NDVI) retrievals.

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“…It was shown, that other sources of in-filling such as rotational or vibrational Raman scattering do not play an important role in the Near InfraRed (NIR; Vasilkov et al, 2013) and maximum values will hardly exceed 0.1% in-filling of the solar and telluric absorption lines.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Terrestrial Plant Fluorescence Based on the In-Filling of Far-Red Fraunhofer Lines Using SCIAMACHY Observations

Khosravi

Vountas

Розанов

et al. 2015

Front. Environ. Sci.

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Chlorophyll fluorescence is directly linked to the photosynthetic efficiency of plants. As satellite-based remote sensing has been shown to have the potential to derive global information about fluorescence it has become subject of various recently published studies stimulating an upsurge in this research field. This manuscript presents a simple and fast retrieval method for solar induced terrestrial plant fluorescence (SIF) which relies on only a few prerequisites. The spaced based remote sensing spectrometers used in this work typically exhibit an additive spectral feature, which is not fluorescence. This is often accompanying the actual SIF retrieval and can significantly deteriorate the results. To account for this effect a correction method has been developed and is combined with the retrieval. The method has been applied to 10 years of SCIAMACHY data with promising results. The retrieved SIF values are lying between 0 and 4 mW [m −2 sr −1 nm −1 ]. However, most of the retrieved values are not exceeding 1.5 [m −2 sr −1 nm −1 ], agreeing with previous studies on the subject. Results have been retrieved for SCIAMACHY spatial resolution of 240 30 km 2 × and gridded to 80 arc minutes. A clear seasonal variation could be shown utilizing 10 years of SCIAMACHY data (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012). In absence of large area ground based validation data a final judgment of the results presented is not feasible. However, a direct comparison to data of others was showing similar results for most areas.

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Note on rotational-Raman scattering in the O<sub>2</sub> A- and B-bands

Cited by 29 publications

References 26 publications

Carbon Monitoring Satellite (CarbonSat): assessment of atmospheric CO<sub>2</sub> and CH<sub>4</sub> retrieval errors by error parameterization

Carbon Monitoring Satellite (CarbonSat): assessment of atmospheric CO<sub>2</sub> and CH<sub>4</sub> retrieval errors by error parameterization

Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2

Retrieval of Terrestrial Plant Fluorescence Based on the In-Filling of Far-Red Fraunhofer Lines Using SCIAMACHY Observations

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Note on rotational-Raman scattering in the O&lt;sub&gt;2&lt;/sub&gt; A- and B-bands

Cited by 29 publications

References 26 publications

Carbon Monitoring Satellite (CarbonSat): assessment of atmospheric CO&lt;sub&gt;2&lt;/sub&gt; and CH&lt;sub&gt;4&lt;/sub&gt; retrieval errors by error parameterization

Carbon Monitoring Satellite (CarbonSat): assessment of atmospheric CO&lt;sub&gt;2&lt;/sub&gt; and CH&lt;sub&gt;4&lt;/sub&gt; retrieval errors by error parameterization

Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2

Retrieval of Terrestrial Plant Fluorescence Based on the In-Filling of Far-Red Fraunhofer Lines Using SCIAMACHY Observations

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Product

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Note on rotational-Raman scattering in the O<sub>2</sub> A- and B-bands

Carbon Monitoring Satellite (CarbonSat): assessment of atmospheric CO<sub>2</sub> and CH<sub>4</sub> retrieval errors by error parameterization

Carbon Monitoring Satellite (CarbonSat): assessment of atmospheric CO<sub>2</sub> and CH<sub>4</sub> retrieval errors by error parameterization