scholarly journals MAMAP – a new spectrometer system for column-averaged methane and carbon dioxide observations from aircraft: retrieval algorithm and first inversions for point source emission rates

2011 ◽  
Vol 4 (2) ◽  
pp. 2207-2271 ◽  
Author(s):  
T. Krings ◽  
K. Gerilowski ◽  
M. Buchwitz ◽  
M. Reuter ◽  
A. Tretner ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Point Source ◽  
Integral Method ◽  
Near Infrared ◽  
Retrieval Algorithm ◽  
Detailed Knowledge ◽  
Emission Rates ◽  
Gaussian Plume ◽  
Gaussian Integral ◽  
Source Emission

Abstract. MAMAP is an airborne passive remote sensing instrument designed for measuring columns of methane (CH4) and carbon dioxide (CO2). The MAMAP instrument consists of two optical grating spectrometers: One in the short wave infrared band (SWIR) at 1590–1690 nm to measure CO2 and CH4 absorptions and another one in the near infrared (NIR) at 757–768 nm to measure O2 absorptions for reference purposes. MAMAP can be operated in both nadir and zenith geometry during the flight. Mounted on an airplane MAMAP can effectively survey areas on regional to local scales with a ground pixel resolution of about 29 m × 33 m for a typical aircraft altitude of 1250 m and a velocity of 200 km h−1. The retrieval precision of the measured column relative to background is typically ≲ 1% (1σ). MAMAP can be used to close the gap between satellite data exhibiting global coverage but with a rather coarse resolution on the one hand and highly accurate in situ measurements with sparse coverage on the other hand. In July 2007 test flights were performed over two coal-fired powerplants operated by Vattenfall Europe Generation AG: Jänschwalde (27.4 Mt CO2 yr−1) and Schwarze Pumpe (11.9 Mt CO2 yr−1), about 100 km southeast of Berlin, Germany. By using two different inversion approaches, one based on an optimal estimation scheme to fit Gaussian plume models from multiple sources to the data, and another using a simple Gaussian integral method, the emission rates can be determined and compared with emissions as stated by Vattenfall Europe. An extensive error analysis for the retrieval's dry column results (XCO2 and XCH4) and for the two inversion methods has been performed. Both methods – the Gaussian plume model fit and the Gaussian integral method – are capable of delivering reliable estimates for strong point source emission rates, given appropriate flight patterns and detailed knowledge of wind conditions.

scholarly journals MAMAP – a new spectrometer system for column-averaged methane and carbon dioxide observations from aircraft: retrieval algorithm and first inversions for point source emission rates

2011 ◽  
Vol 4 (9) ◽  
pp. 1735-1758 ◽  
Author(s):  
T. Krings ◽  
K. Gerilowski ◽  
M. Buchwitz ◽  
M. Reuter ◽  
A. Tretner ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Point Source ◽  
Integral Method ◽  
Power Plants ◽  
Retrieval Algorithm ◽  
Detailed Knowledge ◽  
Emission Rates ◽  
Gaussian Plume ◽  
Gaussian Integral ◽  
Source Emission

Abstract. MAMAP is an airborne passive remote sensing instrument designed to measure the dry columns of methane (CH4) and carbon dioxide (CO2). The MAMAP instrument comprises two optical grating spectrometers: the first observing in the short wave infrared band (SWIR) at 1590–1690 nm to measure CO2 and CH4 absorptions, and the second in the near infrared (NIR) at 757–768 nm to measure O2 absorptions for reference/normalisation purposes. MAMAP can be operated in both nadir and zenith geometry during the flight. Mounted on an aeroplane, MAMAP surveys areas on regional to local scales with a ground pixel resolution of approximately 29 m × 33 m for a typical aircraft altitude of 1250 m and a velocity of 200 km h−1. The retrieval precision of the measured column relative to background is typically ≲1% (1σ). MAMAP measurements are valuable to close the gap between satellite data, having global coverage but with a rather coarse resolution, on the one hand, and highly accurate in situ measurements with sparse coverage on the other hand. In July 2007, test flights were performed over two coal-fired power plants operated by Vattenfall Europe Generation AG: Jänschwalde (27.4 Mt CO2 yr−1) and Schwarze Pumpe (11.9 Mt CO2 yr−1), about 100 km southeast of Berlin, Germany. By using two different inversion approaches, one based on an optimal estimation scheme to fit Gaussian plume models from multiple sources to the data, and another using a simple Gaussian integral method, the emission rates can be determined and compared with emissions reported by Vattenfall Europe. An extensive error analysis for the retrieval's dry column results (XCO2 and XCH4) and for the two inversion methods has been performed. Both methods – the Gaussian plume model fit and the Gaussian integral method – are capable of deriving estimates for strong point source emission rates that are within ±10% of the reported values, given appropriate flight patterns and detailed knowledge of wind conditions.

scholarly journals The Impact of Improved Aerosol Priors on Near-Infrared Measurements of Carbon Dioxide

2018 ◽  
Author(s):  
Robert R. Nelson ◽  
Christopher W. O'Dell
Keyword(s):  
Carbon Dioxide ◽  
Near Infrared ◽  
Carbon Sources ◽  
Total Carbon ◽  
Primary Sources ◽  
Retrieval Algorithm ◽  
Sources And Sinks ◽  
Earth Observing System ◽  
Infrared Measurements ◽  
The Impact

Abstract. The Orbiting Carbon Observatory-2 (OCO-2) was launched in 2014 with the goal of measuring the column-averaged dry-air mole fraction of carbon dioxide (XCO2) with sufficient precision and accuracy to infer regional carbon sources and sinks. One of the primary sources of error in near-infrared measurements of XCO2 is the scattering effects of cloud and aerosol layers. In this work, we study the impact of ingesting intelligent aerosol priors from the Goddard Earth Observing System Model, Version 5 (GEOS-5) into the OCO-2 ACOS V8 retrieval algorithm with the objective of reducing the error in XCO2 from real measurements. Multiple levels of both aerosol setup complexity and uncertainty on the aerosol priors were tested, ranging from a mostly unconstrained aerosol optical depth (AOD) setup to ingesting full aerosol profiles with high confidence. We find that using co-located GEOS-5 aerosol types and AODs with low uncertainty results in a small improvement in the retrieved XCO2 against the Total Carbon Column Observing Network relative to V8. In contrast, attempting to use modeled vertical information in the aerosol prior to improve the XCO2 retrieval generally gives poor results, as aerosol models struggle with the vertical placement of aerosol layers. To assess regional differences in XCO2, we compare our results to a global CO2 model validation suite. We find that the GEOS-5 setup performs better than V8 over Northern Africa and Central Asia, with the standard deviation of the XCO2 error reduced from 2.12 ppm to 1.83 ppm, due to a combination of smaller prior AODs and lower prior uncertainty. In general, the use of more intelligent aerosol priors shows promise but is currently restricted by the accuracy of aerosol models.

scholarly journals Atmospheric methane and carbon dioxide from SCIAMACHY satellite data: initial comparison with chemistry and transport models

2004 ◽  
Vol 4 (6) ◽  
pp. 7217-7279 ◽  
Author(s):  
M. Buchwitz ◽  
R. de Beek ◽  
J. P. Burrows ◽  
H. Bovensmann ◽  
T. Warneke ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Reasonable Agreement ◽  
Near Infrared ◽  
Weighting Function ◽  
Lower Surface ◽  
Retrieval Algorithm ◽  
Atmospheric Methane ◽  
Mixing Ratios ◽  
Data Products ◽  
Carbon Dioxide Co2

Abstract. The remote sensing of the atmospheric greenhouse gases methane (CH4) and carbon dioxide (CO2) in the troposphere from instrumentation aboard satellites is a new area of research. In this manuscript, results obtained from observations of the up-welling radiation in the near-infrared by SCIAMACHY (Scanning Imaging Absorption spectroMeter for Atmospheric CHartographY), which flies on board ENVISAT, are presented. Vertical columns of CH4, CO2 and oxygen (O2) have been retrieved and the (air or) O2-normalized CH4 and CO2 column amounts, the dry air column averaged mixing ratios XCH4 and XCO2 derived. In this manuscript the first results, obtained by using the version 0.4 of the Weighting Function Modified (WFM) DOAS retrieval algorithm applied to SCIAMACHY data, are described and compared with global models. This is an important step in assessing the quality and information content of the data products derived from SCIAMACHY observations. This study investigates the behaviour of CO2 and CH4 in the period from January to October 2003. The SCIAMACHY greenhouse gas column amounts and their mixing ratios for cloud free scenes over land are shown to be in reasonable agreement with models. Over the ocean, as a result of the lower surface spectral reflectance and resultant low signal to noise with the exception of sun glint conditions, the accuracy of the individual data products is poorer. The measured methane column amounts agree with the model columns within a few percent. The inter-hemispheric difference of the methane mixing ratios, determined from single day cloud free measurements over land, is in the range 30–110 ppbv and in reasonable agreement with the corresponding model data (48–71 ppbv). For the set of individual measurements the standard deviations of the difference with respect to the models are in the range ~100–200 ppbv (5–10%) and ±14.4 ppmv (3.9%) for XCH

scholarly journals The impact of improved aerosol priors on near-infrared measurements of carbon dioxide

2019 ◽  
Vol 12 (3) ◽  
pp. 1495-1512 ◽  
Author(s):  
Robert R. Nelson ◽  
Christopher W. O'Dell
Keyword(s):  
Carbon Dioxide ◽  
Near Infrared ◽  
Carbon Sources ◽  
Total Carbon ◽  
Primary Sources ◽  
Retrieval Algorithm ◽  
Sources And Sinks ◽  
Earth Observing System ◽  
Infrared Measurements ◽  
The Impact

Abstract. The Orbiting Carbon Observatory-2 (OCO-2) was launched in 2014 with the goal of measuring the column-averaged dry-air mole fraction of carbon dioxide (XCO2) with sufficient precision and accuracy to infer regional carbon sources and sinks. One of the primary sources of error in near-infrared measurements of XCO2 is the scattering effects of cloud and aerosol layers. In this work, we study the impact of ingesting better informed aerosol priors from the Goddard Earth Observing System Model, Version 5 (GEOS-5) into the OCO-2 ACOS V8 retrieval algorithm with the objective of reducing the error in XCO2 from real measurements. Multiple levels of both aerosol setup complexity and uncertainty on the aerosol priors were tested, ranging from a mostly unconstrained aerosol optical depth (AOD) setup to ingesting full aerosol profiles with high confidence. We find that using co-located GEOS-5 aerosol types and AODs with low uncertainty results in a small improvement in the retrieved XCO2 against the Total Carbon Column Observing Network relative to V8. In contrast, attempting to use modeled vertical information in the aerosol prior to improve the XCO2 retrieval generally gives poor results, as aerosol models struggle with the vertical placement of aerosol layers. To assess regional differences in XCO2, we compare our results to a global CO2 model validation suite. We find that the GEOS-5 setup performs better than V8 over northern Africa and central Asia, with the standard deviation of the XCO2 error reduced from 2.12 to 1.83 ppm, due to a combination of smaller prior AODs and lower prior uncertainty. In general, the use of better informed aerosol priors shows promise but may be restricted by the current accuracy of aerosol models.

scholarly journals Retrieval algorithm for CO<sub>2</sub> and CH<sub>4</sub> column abundances from short-wavelength infrared spectral observations by the Greenhouse Gases Observing Satellite

2010 ◽  
Vol 3 (6) ◽  
pp. 4791-4833 ◽  
Author(s):  
Y. Yoshida ◽  
Y. Ota ◽  
N. Eguchi ◽  
N. Kikuchi ◽  
K. Nobuta ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Greenhouse Gases ◽  
Short Wavelength ◽  
Near Infrared ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Detection Methods ◽  
Retrieval Algorithm ◽  
Random Errors ◽  
Cloud Detection

Abstract. The Greenhouse gases Observing SATellite (GOSAT) was launched on 23 January 2009 to monitor the global distributions of carbon dioxide and methane from space. It has operated continuously since then. Here we describe a retrieval algorithm for column abundances of these gases from the short-wavelength infrared spectra obtained by the Thermal And Near infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS). The algorithm consists of three steps. First, cloud-free observational scenes are selected by several cloud-detection methods. Then, column abundances of carbon dioxide and methane are retrieved based on the optimal estimation method. Finally, the retrieval quality is examined to exclude low-quality and/or aerosol-contaminated results. Most of the retrieval random errors come from the instrumental noise. The interferences by auxiliary parameters retrieved simultaneously with gas abundances are small. The evaluated precisions of the retrieved column abundances for single observations are less than 1% in most cases. The interhemispherical differences and the temporal variation patterns of the retrieved column abundances agree well with the current state of knowledge.

scholarly journals The potential of clear-sky carbon dioxide satellite retrievals

2015 ◽  
Vol 8 (12) ◽  
pp. 13039-13072
Author(s):  
R. R. Nelson ◽  
C. W. O'Dell ◽  
T. E. Taylor ◽  
L. Mandrake ◽  
M. Smyth
Keyword(s):  
Carbon Dioxide ◽  
Near Infrared ◽  
Retrieval Algorithm ◽  
Genetic Optimization ◽  
Infrared Observations ◽  
Clear Sky ◽  
Aerosol Scattering ◽  
Scattering Effects ◽  
Thin Cloud ◽  
Rms Errors

Abstract. Since the launch of the Greenhouse Gases Observing Satellite (GOSAT) in 2009, retrieval algorithms designed to infer the column-averaged dry-air mole fraction of carbon dioxide (XCO2) from hyperspectral near-infrared observations of reflected sunlight have been greatly improved. They now generally include the scattering effects of clouds and aerosols, as early work found that absorption-only retrievals, which neglected these effects, often incurred unacceptably large errors, even for scenes with optically thin cloud or aerosol layers. However, these "full-physics" retrievals tend to be computationally expensive and may incur biases from trying to deduce the properties of clouds and aerosols when there are none present. Additionally, algorithms are now available that can quickly and effectively identify and remove most scenes in which cloud or aerosol scattering plays a significant role. In this work, we test the hypothesis that non-scattering, or "clear-sky", retrievals may perform as well as full-physics retrievals for sufficiently clear scenes. Clear-sky retrievals could potentially avoid errors and biases brought about by trying to infer properties of clouds and aerosols when none are present. Clear-sky retrievals are also desirable because they are orders of magnitude faster than full-physics retrievals. Here we use a simplified version of the Atmospheric Carbon Observations from Space (ACOS) XCO2 retrieval algorithm that does not include the scattering and absorption effects of clouds or aerosols. It was found that for simulated Orbiting Carbon Observatory-2 (OCO-2) measurements, the clear-sky retrieval had errors comparable to those of the full-physics retrieval. For real GOSAT data, the clear-sky retrieval had nearly indistinguishable error characteristics over land, but roughly 30–60 % larger errors over ocean, depending on filtration level, compared to the full-physics retrieval. In general, the clear-sky retrieval had XCO2 root-mean-square (RMS) errors of less than 2.0 ppm when adequately filtered through the use of the Data Ordering through Genetic Optimization (DOGO) system. These results imply that non-scattering XCO2 retrievals are potentially much more accurate than previous literature suggests, when employing filtering methods to remove measurements in which scattering can cause significant errors. Additionally, the computational benefits of non-scattering retrievals means they may be useful for certain applications that require large amounts of data but have less stringent error requirements.

scholarly journals Performance of a geostationary mission, geoCARB, to measure CO<sub>2</sub>, CH<sub>4</sub> and CO column-averaged concentrations

2014 ◽  
Vol 7 (4) ◽  
pp. 959-981 ◽  
Author(s):  
I. N. Polonsky ◽  
D. M. O'Brien ◽  
J. B. Kumer ◽  
C. W. O'Dell ◽  
Keyword(s):  
Power Plants ◽  
Near Infrared ◽  
Reliable Estimate ◽  
Emission Rates ◽  
Absorption Bands ◽  
Particulate Filters ◽  
Noise Characteristics ◽  
Gaussian Plume ◽  
Spectral Channels ◽  
Emission Strength

Abstract. GeoCARB is a proposed instrument to measure column averaged concentrations of CO2, CH4 and CO from geostationary orbit using reflected sunlight in near-infrared absorption bands of the gases. The scanning options, spectral channels and noise characteristics of geoCARB and two descope options are described. The accuracy of concentrations from geoCARB data is investigated using end-to-end retrievals; spectra at the top of the atmosphere in the geoCARB bands are simulated with realistic trace gas profiles, meteorology, aerosol, cloud and surface properties, and then the concentrations of CO2, CH4 and CO are estimated from the spectra after addition of noise characteristic of geoCARB. The sensitivity of the algorithm to aerosol, the prior distributions assumed for the gases and the meteorology are investigated. The contiguous spatial sampling and fine temporal resolution of geoCARB open the possibility of monitoring localised sources such as power plants. Simulations of emissions from a power plant with a Gaussian plume are conducted to assess the accuracy with which the emission strength may be recovered from geoCARB spectra. Scenarios for "clean" and "dirty" power plants are examined. It is found that a reliable estimate of the emission rate is possible, especially for power plants that have particulate filters, by averaging emission rates estimated from multiple snapshots of the CO2 field surrounding the plant. The result holds even in the presence of partial cloud cover.

scholarly journals The potential of clear-sky carbon dioxide satellite retrievals

2016 ◽  
Vol 9 (4) ◽  
pp. 1671-1684 ◽  
Author(s):  
Robert R. Nelson ◽  
Christopher W. O'Dell ◽  
Thomas E. Taylor ◽  
Lukas Mandrake ◽  
Mike Smyth
Keyword(s):  
Carbon Dioxide ◽  
Near Infrared ◽  
Total Carbon ◽  
Retrieval Algorithm ◽  
Infrared Observations ◽  
Clear Sky ◽  
Aerosol Scattering ◽  
Scattering Effects ◽  
Thin Cloud ◽  
Mean Square Errors

Abstract. Since the launch of the Greenhouse Gases Observing Satellite (GOSAT) in 2009, retrieval algorithms designed to infer the column-averaged dry-air mole fraction of carbon dioxide (XCO2) from hyperspectral near-infrared observations of reflected sunlight have been greatly improved. They now generally include the scattering effects of clouds and aerosols, as early work found that absorption-only retrievals, which neglected these effects, often incurred unacceptably large errors, even for scenes with optically thin cloud or aerosol layers. However, these “full-physics” retrievals tend to be computationally expensive and may incur biases from trying to deduce the properties of clouds and aerosols when there are none present. Additionally, algorithms are now available that can quickly and effectively identify and remove most scenes in which cloud or aerosol scattering plays a significant role. In this work, we test the hypothesis that non-scattering, or “clear-sky”, retrievals may perform as well as full-physics retrievals for sufficiently clear scenes. Clear-sky retrievals could potentially avoid errors and biases brought about by trying to infer properties of clouds and aerosols when none are present. Clear-sky retrievals are also desirable because they are orders of magnitude faster than full-physics retrievals. Here we use a simplified version of the Atmospheric Carbon Observations from Space (ACOS) XCO2 retrieval algorithm that does not include the scattering and absorption effects of clouds or aerosols. It was found that for simulated Orbiting Carbon Observatory-2 (OCO-2) measurements, the clear-sky retrieval had errors comparable to those of the full-physics retrieval. For real GOSAT data, the clear-sky retrieval had errors 0–20 % larger than the full-physics retrieval over land and errors roughly 20–35 % larger over ocean, depending on filtration level. In general, the clear-sky retrieval had XCO2 root-mean-square errors (RMSEs) of less than 2.0 ppm, relative to Total Carbon Column Observing Network (TCCON) measurements and a suite of CO2 models, when adequately filtered through the use of a custom genetic algorithm filtering system. These results imply that non-scattering XCO2 retrievals are potentially more useful than previous literature suggests, as the filtering methods we employ are able to remove measurements in which scattering can cause significant errors. Additionally, the computational benefits of non-scattering retrievals means they may be useful for certain applications that require large amounts of data but have less stringent error requirements.

Sign in / Sign up

Export Citation Format

Share Document