scholarly journals CO2 Retrieval over Clouds from the OCO Mission: Model Simulations and Error Analysis

2009 ◽  
Vol 26 (6) ◽  
pp. 1090-1104 ◽  
Author(s):  
Jérôme Vidot ◽  
Ralf Bennartz ◽  
Christopher W. O’Dell ◽  
René Preusker ◽  
Rasmus Lindstrot ◽  
...  

Abstract Spectral characteristics of the future Orbiting Carbon Observatory (OCO) sensor, which will be launched in January 2009, were used to infer the carbon dioxide column-averaged mixing ratio over liquid water clouds over ocean by means of radiative transfer simulations and an inversion process based on optimal estimation theory. Before retrieving the carbon dioxide column-averaged mixing ratio over clouds, cloud properties such as cloud optical depth, cloud effective radius, and cloud-top pressure must be known. Cloud properties were not included in the prior in the inversion but are retrieved within the algorithm. The high spectral resolution of the OCO bands in the oxygen absorption spectral region around 0.76 μm, the weak CO2 absorption band around 1.61 μm, and the strong CO2 absorption band around 2.06 μm were used. The retrieval of all parameters relied on an optimal estimation technique that allows an objective selection of the channels needed to reach OCO’s requirement accuracy. The errors due to the radiometric noise, uncertainties in temperature profile, surface pressure, spectral shift, and presence of cirrus above the liquid water clouds were quantified. Cirrus clouds and spectral shifts are the major sources of errors in the retrieval. An accurate spectral characterization of the OCO bands and an effective mask for pixels contaminated by cirrus would mostly eliminate these errors.

2020 ◽  
Vol 12 (1) ◽  
pp. 172 ◽  
Author(s):  
Vito Romaniello ◽  
Claudia Spinetti ◽  
Malvina Silvestri ◽  
Maria Fabrizia Buongiorno

The measurements of gas concentrations in the atmosphere are recently developed thanks to the availability of gases absorbing spectral channels in space sensors and strictly depending on the instrument performances. In particular, measuring the sources of carbon dioxide is of high interest to know the distribution, both spatial and vertical, of this greenhouse gas and quantify the natural/anthropogenic sources. The present study aims to understand the sensitivity of the CO2 absorption band at 4.8 µm to possibly detect and measure the spatial distribution of emissions from point sources (i.e., degassing volcanic plumes, fires, and industrial emissions). With the aim to define the characteristics of future multispectral imaging space radiometers, the performance of the CO2 4.8 µm absorption band was investigated. Simulations of the “Top of Atmosphere” (TOA) radiance have been performed by using real input data to reproduce realistic scenarios on a volcanic high elevation point source (>2 km): actual atmospheric background of CO2 (~400 ppm) and vertical atmospheric profiles of pressure, temperature, and humidity obtained from probe balloons. The sensitivity of the channel to the CO2 concentration has been analyzed also varying surface temperatures as environmental conditions from standard to high temperature. Furthermore, response functions of operational imaging sensors in the middle wave infrared spectral region were used. The channel width values of 0.15 µm and 0.30 µm were tested in order to find changes in the gas concentration. Simulations provide results about the sensitivity necessary to appreciate carbon dioxide concentration changes considering a target variation of 10 ppm in gas column concentration. Moreover, the results show the strong dependence of at-sensor radiance on the surface temperature: radiances sharply increase, from 1 Wm−2sr−1µm−1 (in the “standard condition”) to >1200 Wm−2sr−1µm−1 (in the warmest case) when temperatures increase from 300 to 1000 K. The highest sensitivity has been obtained considering the channel width equal to 0.15 µm with noise equivalent delta temperature (NEDT) values in the range from 0.045 to 0.56 K at surface temperatures ranging from 300 to 1000 K.


2010 ◽  
Vol 3 (1) ◽  
pp. 209-232 ◽  
Author(s):  
M. Reuter ◽  
M. Buchwitz ◽  
O. Schneising ◽  
J. Heymann ◽  
H. Bovensmann ◽  
...  

Abstract. An optimal estimation based retrieval scheme for satellite based retrievals of XCO2 (the dry air column averaged mixing ratio of atmospheric CO2) is presented enabling accurate retrievals also in the presence of thin clouds. The proposed method is designed to analyze near-infrared nadir measurements of the SCIAMACHY instrument in the CO2 absorption band at 1580 nm and in the O2-A absorption band at around 760 nm. The algorithm accounts for scattering in an optically thin cirrus cloud layer and at aerosols of a default profile. The scattering information is mainly obtained from the O2-A band and a merged fit windows approach enables the transfer of information between the O2-A and the CO2 band. Via the optimal estimation technique, the algorithm is able to account for a priori information to further constrain the inversion. Test scenarios of simulated SCIAMACHY sun-normalized radiance measurements are analyzed in order to specify the quality of the proposed method. In contrast to existing algorithms for SCIAMACHY retrievals, the systematic errors due to cirrus clouds with optical thicknesses up to 1.0 are reduced to values below 4 ppm for most of the analyzed scenarios. This shows that the proposed method has the potential to reduce uncertainties of SCIAMACHY retrieved XCO2 making this data product potentially useful for surface flux inverse modeling.


2019 ◽  
Author(s):  
Jean-Loup Bertaux ◽  
Alain Hauchecorne ◽  
Franck Lefèvre ◽  
François-Marie Breon ◽  
Laurent Blanot ◽  
...  

Abstract. Monitoring CO2 from space is essential to characterize the spatio/temporal distribution of this major greenhouse gas, and quantify its sources and sinks. The mixing ratio of CO2 to dry air can be derived from the CO2/O2 column ratio. The O2 column is usually derived form its absorption signature on the solar reflected spectra over the O2 A-band (i.e. OCO-2, Tanso/Gosat, Tansat). As a result of atmospheric scattering, the atmospheric path length varies with the aerosols load, their vertical distribution, and their optical properties. The spectral distance between the O2 A-band (0.76 µm) and the CO2 absorption band (1.6 µm) results in significant uncertainties due to the varying spectral properties of the aerosols over the globe. There is another O2 absorption band at 1.27 µm with weaker lines than in the A-band. As the wavelength is much nearer to the CO2 and CH4 bands, there is less uncertainty when using it as a proxy of the atmospheric path length to the CO2 and CH4 bands. This O2 band is used by the TCCON network implemented for the validation of space-based GHG observations. However, this absorption band is contaminated by the spontaneous emission of the excited molecule O2*, which is produced by the photo-dissociation of O3 molecules in the stratosphere and mesosphere. From a satellite looking nadir, this emission has a similar shape as the absorption signal that is used. In the frame of the CNES MicroCarb project, scientific studies have been performed in 2016–2018 to explore the problems associated to this O2* airglow contamination and methods to correct it. A theoretical synthetic spectrum of the emission was derived from a new approach, based on A21 Einstein coefficients information contained in the line-by-line HITRAN 2016 data base. The shape of our synthetic spectrum is fully validated when compared to O2* airglow spectra observed by SCIAMACHY/ENVISAT in limb viewing. We have designed an inversion scheme of SCIAMACHY limb viewing spectra, allowing to determine the vertical distribution of the Volume Emision Rate of the O2* airglow. The VER profiles and corresponding integrated nadir intensities were both compared to a model of the emission based on the chemical-transport model REPROBUS. The airglow intensities depend mostly on the Solar Zenith Angle (both in model and data) and the model underestimate the observed emission by ∼ 15 %. This is fully confirmed with SCIAMACHY nadir viewing measurements over the oceans: in such conditions, we have disentangled and retrieved the nadir O2* emission in spite of the moderate spectral resolving power (∼ 860). It is shown that with the MicroCarb spectral resolution power (25,000) and SNR, the contribution of the O2* emission at 1.27 µm to the observed spectral radiance in nadir viewing may be disentangled from the lower atmosphere/ground absorption signature with a great accuracy. Simulations with 4ARCTIC radiative transfer inversion tool have shown that the CO2 mixing ratio may be retrieved with the accuracy required for quantifying the CO2 natural sources and sinks (pressure level error ≤ 1 hPa, XCO2 accuracy better than 0.4 ppmv) with only the O2 1.27 µm band. As a result of these studies (at an intermediate phase), it was decided to include this band (B4) in the MicroCarb design, while keeping the O2 A band for reference (B1). Our approach is very similar (likely identical), to the approach of Sun et al. (2018) who also analysed the potential of the O2 1.27 µm band and concluded favourably for GHG monitoring from space. We advocate for the inclusion of this O2 band on other GHG monitoring future space missions, such as GOSAT-3 and EU/ESA CO2-M missions, for a better GHG retrieval.


2017 ◽  
Author(s):  
Stephanie P. Rusli ◽  
David P. Donovan ◽  
Herman W. J. Russchenberg

Abstract. Despite the importance of radar reflectivity (Z) measurements in the retrieval of liquid water cloud properties, it remains non-trivial to interpret Z due to the possible presence of drizzle droplets within the clouds. So far, there has been no published work that utilizes Z to identify the presence of drizzle above the cloud base in an optimized and a physically-consistent manner. In this work, we develop a retrieval technique that exploits the synergy of different remote sensing systems to carry out this task and to subsequently profile the microphysical properties of the cloud and drizzle in a unified framework. This is accomplished by using ground-based measurements of Z, lidar attenuated backscatter below as well as above the cloud base, and microwave brightness temperatures. Fast physical forward models coupled to cloud and drizzle structure parametrization are used in an optimal estimation type framework in order to retrieve the best-estimate for the cloud and drizzle property profiles. The cloud retrieval is first evaluated using synthetic signals generated from large-eddy simulation output to verify the forward models used in the retrieval procedure and the vertical parametrization of the liquid water content. From this exercise it is found that, on average, the cloud properties can be retrieved within 5 % of the mean truth. The full cloud-drizzle retrieval method is then applied to a selected ACCEPT campaign dataset collected in Cabauw, The Netherlands. An assessment of the retrieval products is performed using three independent methods from the literature, each was specifically developed to retrieve only the cloud properties, the drizzle properties below the cloud base, or the drizzle fraction within the cloud, respectively. One-to-one comparisons, taking into account the uncertainties or limitations of each retrieval, show that our results are generally consistent with what is derived using the three independent methods.


2021 ◽  
Author(s):  
David Donovan ◽  
Gerd-Jan van Zadelhoff ◽  
Ping Wang ◽  
Dorit Huber

<p><span><span>ALADIN (Atmospheric Laser Doppler Instrument) is the world’s first space-based Doppler wind lidar. It is a direct detection system operating at 355 nm. ALADIN’s primary products are atmospheric line-of-sight winds. </span></span><span><span>Wind-profiles are derived from the Doppler shift of the backscattered signals. Using a variation of the High Spectral Resolution Lidar technique (HSRL), two detection channels are used, a `Mie ‘-channel and a `Rayleigh’-channel. Cloud/aerosol information is also present in the signals, however, ALADIN’s design is optimized for wind observations. </span></span></p><p><span><span>ATLID (</span></span><span><span>Atmospheric Lidar) </span></span><span><span>is the lidar to be embarked on the Earth Clouds and Radiation Explorer (EarthCARE) mission. EarthCARE is a joint ESA-JAXA mission and will embark a cloud/aerosol lidar (ATLID), a cloud-profiling Radar (CPR) a multispectral cloud/aerosol imager (MSI) and a three—view broad-band radiometer (BBR). Both ALADIN and ATLID are HSRL systems, however, ATLID does not measure winds and is optimized exclusively for cloud and aerosol observations. In particular, compared to ALADIN, ATLID has a higher spatial resolution, measures the depolarization of the return signal and has a much cleaner Rayleigh- Mie backscatter signal separation. </span></span></p><p><span><span>With regards to the retrieval of aerosol and cloud properties both lidars face similar challenges. Amongst, these is the fact that the SNR ratio of the backscatter signals is low compared to terrestrial signal, this creates esp. large difficulties when using direct standard HSRL inversion methods. Along-track averaging can increase the SNR, however, the presence of clouds and other inhomogeneities will lead to often very large biases in the retrieved extinction and backscatters if not accounted for in an appropriate manner.</span></span></p><p><span><span>Over the past several years, cloud/aerosol algorithms have been developed for ATLID that have focused on the challenge of making accurate retrievals of cloud and aerosol extinction and backscatter specifically addressing the low SNR nature of the lidar signals and the need for intelligent binning/averaging of the data. Two of these ATLID processors are A-FM (ATLID featuremask) and A-PRO (ATLID profile processor)</span></span></p><p><span><span>A-FM uses techniques inspired from the field of image processing to detect the presence of targets at high resolution while A-PRO (using A-FM as input) preforms a multi-scale optimal-estimation technique in order to retrieve both aerosol and cloud extinction and backscatter </span></span><span><span>profiles.</span></span></p><p><span><span>Versions of the A-FM and A-PRO processors have been developed for Aeolus (called AEL-FM and AEL-PRO, respectively). Prototype codes exist and preliminary versions are in the process of being introduced into the L2</span></span><span><span>a</span></span><span><span> operational processor. In this presentation AEL-FM and AEL-PRO will be described and preliminary results presented and discussed.</span></span></p><p> </p>


2021 ◽  
Author(s):  
Niklas Bohn ◽  
David Thompson ◽  
Nimrod Carmon ◽  
Jouni Susiluoto ◽  
Michael Turmon ◽  
...  

<p>Snow and ice melt processes are key variables in Earth energy-balance and hydrological modeling. Their quantification facilitates predictions of meltwater runoff and distribution and availability of fresh water. Furthermore, they are indicators of climate change and control the balance of the Earth's ice sheets. These processes decrease the surface reflectance with unique spectral patterns due to the accumulation of liquid water and light absorbing particles (LAP), making imaging spectroscopy a powerful tool to measure and map this phenomenon. Here we present a new method to retrieve snow grain size, liquid water fraction, and LAP mass mixing ratio from airborne and space borne imaging spectroscopy acquisitions. This methodology is based on a simultaneous retrieval of atmospheric and surface parameters using optimal estimation (OE), a retrieval technique which leverages prior knowledge and measurement noise in the inversion and also produces uncertainty estimates. We exploit statistical relationships between surface reflectance spectra and snow and ice properties to estimate their most probable quantities given the reflectance. To test this new algorithm we conducted a sensitivity analysis based on simulated top-of-atmosphere radiance spectra using the upcoming EnMAP orbital imaging spectroscopy mission, demonstrating an accurate estimation performance of snow and ice surface properties. An additional validation experiment using in-situ measurements of glacier algae mass mixing ratio and surface reflectance from the Greenland Ice Sheet yields promising results. Finally, we evaluated the retrieval capacity for all snow and ice properties with an AVIRIS-NG acquisition from the Greenland Ice Sheet demonstrating this approach’s potential and suitability for upcoming orbital imaging spectroscopy missions.</p>


2009 ◽  
Vol 2 (5) ◽  
pp. 2483-2538 ◽  
Author(s):  
M. Reuter ◽  
M. Buchwitz ◽  
O. Schneising ◽  
J. Heymann ◽  
H. Bovensmann ◽  
...  

Abstract. An optimal estimation based retrieval scheme for satellite based measurements of XCO2 (the column averaged mixing ratio of atmospheric CO2) is presented enabling accurate retrievals also in the presence of thin clouds. The proposed method is designed to analyze near-infrared nadir measurements of the SCIAMACHY instrument in the CO2 absorption band at 1580 nm and in the O2-A absorption band at around 760 nm. The algorithm accounts for scattering in an optically thin cirrus cloud layer and at aerosols of a default profile. The scattering information is mainly obtained from the O2-A band and a merged fit windows approach enables the transfer of information between the O2-A and the CO2 band. Via the optimal estimation technique, the algorithm is able to account for a priori information to further constrain the inversion. Test scenarios of simulated SCIAMACHY sun-normalized radiance measurements are analyzed in order to specify the quality of the proposed method. In contrast to existing algorithms, the systematic errors due to cirrus clouds with optical thicknesses up to 1.0 are reduced to values typically below 4 ppm. This shows that the proposed method has the potential to reduce uncertainties of SCIAMACHY retrieved XCO2 making this data product useful for surface flux inverse modeling.


2017 ◽  
Vol 10 (12) ◽  
pp. 4777-4803 ◽  
Author(s):  
Stephanie P. Rusli ◽  
David P. Donovan ◽  
Herman W. J. Russchenberg

Abstract. Despite the importance of radar reflectivity (Z) measurements in the retrieval of liquid water cloud properties, it remains nontrivial to interpret Z due to the possible presence of drizzle droplets within the clouds. So far, there has been no published work that utilizes Z to identify the presence of drizzle above the cloud base in an optimized and a physically consistent manner. In this work, we develop a retrieval technique that exploits the synergy of different remote sensing systems to carry out this task and to subsequently profile the microphysical properties of the cloud and drizzle in a unified framework. This is accomplished by using ground-based measurements of Z, lidar attenuated backscatter below as well as above the cloud base, and microwave brightness temperatures. Fast physical forward models coupled to cloud and drizzle structure parameterization are used in an optimal-estimation-type framework in order to retrieve the best estimate for the cloud and drizzle property profiles. The cloud retrieval is first evaluated using synthetic signals generated from large-eddy simulation (LES) output to verify the forward models used in the retrieval procedure and the vertical parameterization of the liquid water content (LWC). From this exercise it is found that, on average, the cloud properties can be retrieved within 5 % of the mean truth. The full cloud–drizzle retrieval method is then applied to a selected ACCEPT (Analysis of the Composition of Clouds with Extended Polarization Techniques) campaign dataset collected in Cabauw, the Netherlands. An assessment of the retrieval products is performed using three independent methods from the literature; each was specifically developed to retrieve only the cloud properties, the drizzle properties below the cloud base, or the drizzle fraction within the cloud. One-to-one comparisons, taking into account the uncertainties or limitations of each retrieval, show that our results are consistent with what is derived using the three independent methods.


2020 ◽  
Author(s):  
Yanmeng Bi ◽  
Qian Wang ◽  
Zhongdong Yang ◽  
Chengbao Liu ◽  
Chao Lin ◽  
...  

Abstract. The spectra measured by the Atmospheric Carbon dioxide Grating Spectrometer (ACGS) carried by the China global carbon dioxide observation satellite (TanSat) in the band of 0.76 μm, 1.61 μm and 2.06 μm can be used for the retrieval of carbon dioxide (CO2) concentrations by fitting the observations and simulations using the optimal estimation algorithm. Accurately detecting the change of the center wavelength is highly important because of its very high spectral resolution and accuracy requirement for product retrieval. The variations of center wavelength for all three bands of ACGS have been calculated on the locations of the individual solar absorption lines by comparing the solar-viewing measurements and the high resolution solar reference spectrum. The variations with magnitudes less than 10 % of the spectral resolution for each band have been detected. The changes are probably caused by vibration and the instrument status difference between the ground and space, especially temperature variation on orbit. In addition to solar lines, the entire atmospheric spectra simulated by radiative transfer model can be used as the reference spectrum to determine the wavelength change by fitting the measured and simulated spectra. The change of wavelength determined by atmospheric spectra is closely consistent with that by solar lines. Both schemes described here can be used not only for monitoring spectral stability but also to gain spectral knowledge prior to the level-2 product processing. These minor temporal changes of wavelength on orbit should be corrected in the product retrieval.


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