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Results for “orbiting carbon observatory (OCO)”

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Comparisons of the v11.1 Orbiting Carbon Observatory‐2 (OCO‐2) X CO2 Measurements With GGG2020 TCCON

The Orbiting Carbon Observatory 2 (OCO-2) is NASA's first Earth observation satellite mission dedicated to studying the sources and sinks of carbon dioxide (CO 2 ) on a global scale. The observations of reflected sunlight are inverted in a retrieval algorithm to produce estimates of the dry air mole-fractions of CO 2 (X CO2 ). The OCO-2 Level 2 data release, version 11.1 (v11.1) retrievals from the Atmospheric Carbon Observations from Space (ACOS) algorithm, includes significant improvements in the X CO2 data product compared to older OCO-2 data versions. This work compares the v11.1 X CO2 from OCO-2 against X CO2 estimates collected from a global ground-based network known as the Total Carbon Column Observing Network (TCCON), OCO-2's primary validation source. The OCO-2 project provides a version of the Level 2 data product, called “lite” files that include calibrated and bias-corrected XCO2 values, accessible together with all OCO-2 data products through the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). This work shows that OCO-2 X CO2 observations made between September 2014 and December 2023, after quality filtering and the application of an averaging kernel correction, agree well with coincident TCCON data for all OCO-2 observational modes of land (nadir, glint, target) and ocean (glint). The aggregated, bias-corrected, and quality-filtered absolute average bias values are less than or equal to 0.20 parts per million (ppm) globally for all OCO-2 observation modes, where the biases do not indicate a statistically significant time dependence. The land nadir/glint mode has the lowest bias value of −0.03 ± 0.85 ppm.

54 ENVIRONMENTAL SCIENCES↗

Miniaturized Laser Heterodyne Radiometer (LHR) for Measurements of Greenhouse Gases in the Atmospheric Column

This passive laser heterodyne radiometer (LHR) instrument simultaneously measures multiple trace gases in the atmospheric column including carbon dioxide (CO2) and methane (CH4), and resolves their concentrations at different altitudes. This instrument has been designed to operate in tandem with the passive aerosol sensor currently used in AERONET (an established network of more than 450 ground aerosol monitoring instruments worldwide). Because aerosols induce a radiative effect that influences terrestrial carbon exchange, simultaneous detection of aerosols with these key carbon cycle gases offers a uniquely comprehensive measurement approach. Laser heterodyne radiometry is a technique for detecting weak signals that was adapted from radio receiver technology. In a radio receiver, a weak input signal from a radio antenna is mixed with a stronger local oscillator signal. The mixed signal (beat note, or intermediate frequency) has a frequency equal to the difference between the input signal and the local oscillator. The intermediate frequency is amplified and sent to a detector that extracts the audio from the signal. In the LHR instrument described here, sunlight that has undergone absorption by the trace gas is mixed with laser light at a frequency matched to a trace gas absorption feature in the infrared (IR). Mixing results in a beat signal in the RF (radio frequency) region that can be related to the atmospheric concentration. For a one-second integration, the estimated column sensitivities are 0.1 ppmv for CO2, and <1 ppbv for CH4. In addition to producing a standalone ground measurement product, this instrument could be used to calibrate/validate four Earth observing missions: ASCENDS (Active Sensing of CO2 Emissions over Nights, Days, and Seasons), OCO-2 (Orbiting Carbon Observatory), OCO-3, and GOSAT (Greenhouse gases Observational SATellite). The only network that currently measures CO2 and CH4 in the atmospheric column is TCCON (Total Carbon Column Observing Network), and only two of its 16 operational sites are in the United States. TCCON data is used for validation of GOSAT data, and will be used for OCO-2 validation. While these Fourier-transform spectrometers (FTS) can measure the largest range of trace gases, the network is severely limited due to the high cost and extreme size of these instruments (these occupy small buildings and require personnel for operation). The LHR/AERONET instrument offers a significantly smaller (carry-on luggage size) autonomous instrument that can be incorporated into AERONET s much larger (450 instruments) global network.

Steel, Emily↗

Context Cameras for the Orbiting Carbon Observatory 3 (OCO-3) Instrument

We have developed an adaptable camera platform that takes advantage of screened and qualified commercial off the shelf (COTS) components (CMOS sensor, rad-tolerant microcircuits, lenses) to significantly shorten development time of space-borne visible imaging systems while reducing the overall cost and risk of the development. This modular camera platform is implemented as a suite of two visible-light cameras for the Orbiting Carbon Observatory 3 (OCO-3) instrument at NASA’s Jet Propulsion Laboratory (JPL). The Internal Context Camera (ICC) and External Context Camera (ECC) provide contextual engineering imaging support for the calibration of the OCO-3 instrument, intended to significantly reduce the duration of the mission’s calibration campaign. The OCO-3 mission has taken advantage of the design and qualification work performed by JPL’s Mars 2020 Enhanced Engineering Camera (EECam) team in reusing the qualified COTS CMOS imaging sensor, proven electronic designs, and scalable field-programmable gate array (FPGA) firmware to meet OCO-3’s mission requirements and resource availability. We will be presenting the steps taken by JPL to design, build, and test the Context Cameras for the OCO-3 mission. We will highlight the design deviations from the Mars2020 EECams and discuss the methodology of tailoring the EECam design for the OCO-3 requirements. Camera imaging capabilities, including focal plane and optical performance, will be presented, as well as a summary of the calibration and environmental test results. Finally, we will discuss the potential for future applications for the Context Cameras.

Moore, Douglas↗

Quantification of regional net CO 2 flux errors in the Orbiting Carbon Observatory-2 (OCO-2) v10 model intercomparison project (MIP) ensemble using airborne measurements

Inverse model intercomparison projects (MIPs) provide a chance to assess the uncertainties in inversion estimates arising from various sources. However, accurately quantifying ensemble CO 2 flux errors remains challenging and often relies on the ensemble spread. This study proposes a method for quantifying the errors in regional net surface–atmosphere CO 2 flux estimates from models taken from the Orbiting Carbon Observatory-2 (OCO-2) v10 MIP by using independent airborne CO 2 measurements for the period 2015–2017. We first calculate the root mean square error (RMSE) between the ensemble mean of posterior CO 2 concentrations and airborne observations and then isolate the CO 2 concentration errors caused solely by the ensemble mean of posterior net fluxes by subtracting the observation, representation, and transport errors from seven regions. Our analysis reveals that the flux errors projected onto CO 2 space account for 55 %–85 % of the regional average RMSE over the 3 years, ranging from 0.88 to 1.91 ppm. In five regions, the error estimates based on observations exceed those computed from the ensemble spread of posterior fluxes by a factor of 1.33–1.93, implying an underestimation of the actual flux errors, while their magnitudes are comparable in two regions. The adjoint sensitivity analysis identifies that the underestimation of flux errors is prominent where the magnitudes of fossil fuel emissions exceed those of terrestrial-biosphere fluxes by a factor of 3–31 over the 3 years. This suggests the presence of systematic biases in the inversion estimates associated with errors in the prescribed fossil fuel emissions common to all models. Our study emphasizes the value of airborne measurements for quantifying regional errors in ensemble net CO 2 flux estimates.

54 ENVIRONMENTAL SCIENCES↗

Measuring Atmospheric Carbon Dioxide from the NASA Orbiting Carbon Observatory-2 (OCO-2)

Fossil fuel combustion, deforestation, and other human activities are now adding almost 40 billion tons of carbon dioxide (CO2) to the atmosphere each year. These emissions are superimposed on an active natural carbon cycle that adds about 20 times as much CO2 to the atmosphere annually, and then reabsorbs a comparable amount, along with over half of the CO2 contributed by human activities. Interestingly, the identity and location of the natural “sinks” in the land biosphere and ocean that are responsible for absorbing this anthropogenic CO2 are still poorly understood. Because of this, it is impossible to accurately predict how these sinks will respond to a warming climate. Space based remote sensing provides new tools for studying the sources and sinks of CO2. The NASA Orbiting Carbon Observatory-2 (OCO-2) was launched in July 2014 and was inserted at the head of the 705-km Afternoon Constellation (A-Train) a month later.

Crisp, David↗

Using Orbiting Carbon Observatory-2 (OCO-2) column CO2 retrievals to rapidly detect and estimate biospheric surface carbon flux anomalies

The global carbon cycle is experiencing continued perturbations via increases in atmospheric carbon concentrations, which are partly reduced by terrestrial biosphere and ocean carbon uptake. Greenhouse gas satellites have been shown to be useful in retrieving atmospheric carbon concentrations and observing surface and atmospheric CO2 seasonal-to-interannual variations. However, limited attention has been placed on using satellite column CO2 retrievals to evaluate surface CO2 fluxes from the terrestrial biosphere without advanced inversion models at low latency. Such applications could be useful to monitor, in near real time, biosphere carbon fluxes during climatic anomalies like drought, heatwaves, and floods, before more complex terrestrial biosphere model outputs and/or advanced inversion modelling estimates become available. Here, we explore the ability of Orbiting Carbon Observatory-2 (OCO-2) column-averaged dry air CO2 (XCO2) retrievals to directly detect and estimate terrestrial biosphere CO2 flux anomalies using a simple mass-balance approach. An initial global analysis of surface–atmospheric CO2 coupling and transport conditions reveals that the western US, among a handful of other regions, is a feasible candidate for using XCO2 for detecting terrestrial biosphere CO2 flux anomalies. Using the CarbonTracker model reanalysis as a test bed, we first demonstrate that a well-established mass-balance approach can estimate monthly surface CO2 flux anomalies from XCO2 enhancements in the western United States. The method is optimal when the study domain is spatially extensive enough to account for atmospheric mixing and has favorable advection conditions with contributions primarily from one background region. We find that errors in individual soundings reduce the ability of OCO-2 XCO2 to estimate more frequent, smaller surface CO2 flux anomalies. However, we find that OCO-2 XCO2 can often detect and estimate large surface flux anomalies that leave an imprint on the atmospheric CO2 concentration anomalies beyond the retrieval error/uncertainty associated with the observations. OCO-2 can thus be useful for low-latency monitoring of the monthly timing and magnitude of extreme regional terrestrial biosphere carbon anomalies.

Andrew F. Feldman↗

Future NASA Earth Science Missions

Following on the successful launch of Aura in July 2004, NASA will continue to add to the A-train with the spring 2006 launch of the CALIPSO and Cloudsat missions, followed by the fall 2008 launch of the Orbiting Carbon Observatory (OCO). The CALIPSO mission has three nadir-viewing instruments: a cloud-aerosol lidar for measuring aerosol and cloud height and properties, a 3-channel thermal infrared imaging radiometer, and a wide field cloud camera. The Cloudsat mission has a 94 GHz cloud profiling radar, returning cloud structure vs. height profiles. Orbiting Carbon Observatory (OCO) is a mission focused on precision measurements of total column CO2 using reflected sunlight. These missions and how they relate to the other parts of the A-train will be presented. A brief report from a NASA workshop on future atmospheric science missions will also be presented.

Gleason, James↗

SCIAMACHY and FTS CO2 Retrievals Using the OCO Retrieval Algorithm

The Orbiting Carbon Observatory (OCO) mission will make the first global, space-based measurements of atmospheric C02 with the precision and coverage needed to characterize C02 sources and sinks on regional scales. OCO will make spectrally and spatially highly resolved measurements of reflected sunlight in the 02A -band and two near-infrared C02 bands. To test the OCO retrieval algorithm, SCIAMACHY and ground-based Fourier Transform Spectrometer (FTS) measurements at Park Falls, Wisconsin have been analyzed. Good agreement between SCIAMACHY and FTS C02 columns has been found with SCIAMACHY showing a much larger scatter than FTS measurements. Both SCIAMACHY and FTS overestimate the surface pressure by a few percent which significantly impacts retrieved C02 columns.

remote sensing↗

Space-Based Measurements of CO2 from the Japanese Greenhouse Gases Observing Satellite (GOSAT) and the NASA Orbiting Carbon Observatory-2 (OCO-2) Missions

Space-based remote sensing observations hold substantial promise for future long-term monitoring of CO2 and other greenhouse gases. The principal advantages of space based measurements include: (1) Spatial coverage (especially over oceans and tropical land) (2) Sampling density (needed to resolve CO2 weather). The principal challenge is the need for high precision To reach their full potential, space based CO2 measurements must be validated against surface measurements to ensure their accuracy. The TCCON network is providing the transfer standard There is a need for a long-term vision to establish and address community priorities (1) Must incorporate ground, air, space-based assets and models (2) Must balance calls for new observations with need to maintain climate data records.

Orbiting Carbon Observatory -↗