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The Orbiting Carbon Observatory 2 (OCO-2) carries a hyperspectral A-band sensor that can obtain information about cloud geometric thickness (H). The OCO2CLD-LIDAR-AUX product retrieved H with the aid of collocated CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) lidar data to identify suitable clouds and provide a priori cloud top pressure (Ptop). This collocation is no longer possible, since CALIPSO's coordination flying with OCO-2 has ended, so here we introduce a new cloud flagging and a priori assignment using only OCO-2 data, restricted to ocean footprints where solar zenith angle <45∘. Firstly, a multi-layer perceptron network was trained to identify liquid clouds over the ocean with sufficient optical depth (τ>1) for a valid retrieval, and agreement with MODIS–CALIPSO (Moderate Resolution Imaging Spectroradiometer) is 90.0 %. Secondly, we developed a lookup table to simultaneously retrieve cloud τ, effective radius (re) and Ptop from A-band and CO2 band radiances, with the intention that these will act as the a priori state estimate in a future retrieval. Median Ptop difference vs. CALIPSO is 12 hPa with an inter-decile range of [-11,87]hPa, substantially better than the MODIS–CALIPSO range of [-83,81]hPa. The MODIS–OCO-2 τ difference is 0.8[-3.8,6.9], and re is -0.3[-2.8,2.1]µm. The τ difference is due to optically thick and horizontally heterogeneous cloud scenes. As well as an improved passive Ptop retrieval, this a priori information will allow for a purely OCO-2-based Bayesian retrieval of cloud droplet number concentration (Nd). Finally, our cloud flagging procedure may also be useful for future partial-column above-cloud CO2 abundance retrievals.

This paper introduces the OCO2CLD-LIDAR-AUX product, which uses the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar and the Orbiting Carbon Observatory-2 (OCO-2) hyperspectral A-band spectrometer. CALIPSO provides a prior cloud top pressure (Ptop) for an OCO-2-based retrieval of cloud optical depth, Ptop and cloud geometric thickness expressed in hPa. Measurements are of single-layer liquid clouds over oceans from September 2014 to December 2016 when collocated data are available. Retrieval performance is best for solar zenith angles <45∘ and when the cloud phase classification, which also uses OCO-2's weak CO2 band, is more confident. The highest quality optical depth retrievals agree with those from the Moderate Resolution Imaging Spectroradiometer (MODIS) with discrepancies smaller than the MODIS-reported uncertainty. Retrieved thicknesses are consistent with a substantially subadiabatic structure over marine stratocumulus regions, in which extinction is weighted towards the cloud top. Cloud top pressure in these clouds shows a 4 hPa bias compared with CALIPSO which we attribute mainly to the assumed vertical structure of cloud extinction after showing little sensitivity to the presence of CALIPSO-identified aerosol layers or assumed cloud droplet effective radius. This is the first case of success in obtaining internal cloud structure from hyperspectral A-band measurements and exploits otherwise unused OCO-2 data. This retrieval approach should provide additional constraints on satellite-based estimates of cloud droplet number concentration from visible imagery, which rely on parameterization of the cloud thickness.

The Orbiting Carbon Observatory-2 (OCO-2) carries a hyperspectral A-band sensor that can obtain information about cloud geometric thickness (H). The OCO2CLD-LIDAR-AUX product retrieved H with the aid of collocated CALIPSO lidar data to identify suitable clouds and provide a priori cloud-top pressure (Ptop). This collocation is no longer possible since CALIPSO's coordination flying with OCO-2 has ended, so here we introduce a new cloud flagging and a priori assignment using only OCO-2 data, restricted to ocean footprints where solar zenith angle 1) for a valid retrieval, and agreement with MODIS-CALIPSO is 90.0 %. Secondly, we developed a lookup table to simultaneously retrieve cloud τ, effective radius (re) and Ptop from A-band and CO2 band radiances. Median Ptop difference versus CALIPSO is 12 hPa with interdecile range [−11,87] hPa, substantially better than the MODIS-CALIPSO [−83,81] hPa. The MODIS-OCO-2 τ difference is 0.8 (−3.8,6.9) and re is −0.3 [−2.8,2.1] μm. The tau difference is due to optically thick and horizontally heterogeneous cloud scenes. As well as an improved passive Ptop retrieval, this a priori information will allow a purely OCO-2 based Bayesian retrieval of cloud droplet number concentration (Nd). Finally, our cloud flagging procedure may also be useful for future partial column above-cloud CO2 abundance retrievals.

The Chlorophyll Fluorescence Imaging Spectrometer (CFIS) is an airborne high resolution imaging spectrometer built at NASA's Jet Propulsion Laboratory (JPL) for evaluating solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2). OCO-2 is a NASA mission designed to measure atmospheric CO_2 but one of the novel data products is SIF, retrieved using reductions in the optical depth of Fraunhofer lines in OCO-2’s O_2 A-band, covering 757–775 nm at 0.042 nm spectral resolution. CFIS was specifically designed to retrieve SIF within the wavelength range of OCO-2, but extends further down to 737 nm, nearly maintaining the high spectral resolution of the OCO-2 instrument (0.07 vs. 0.042 nm). Here, we provide an overview of the instrument calibration and performance as well as the retrieval strategy based on non-linear weighted least-squares. To illustrate the retrieval performance using actual flight data, we focus on data acquired over agricultural fields in Mead, Nebraska from an unpressurized Twin Otter (DHC-6) aircraft at a flight altitude of 3000 m above ground level (AGL). Spectral residuals are consistent with expected detector noise, which enables us to compute realistic 1-σ precision errors of 0.5–0.7 W/m^2/sr/μm for typical SIF retrievals, which can be reduced to

Spectra of reflected sunlight in the oxygen A-band contain information about cloud properties such as cloud top pressure, optical depth, and pressure thickness. Here we show, for the first time, that high-spectral-resolution A-band Orbiting Carbon Observatory-2 (OCO-2) spectra respond largely as simulated to the optical properties of water clouds over ocean during November 2015 (N = 184,318) using input cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and the Cloud-Aerosol Lidar with Orthogonal Polarization on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). In A-band continuum channels the standard deviation of simulated minus observed radiance is ±37%. Selecting horizontally homogeneous clouds to mitigate three-dimensional cloud effects and collocation error with the other satellites, the standard deviation of the residuals is reduced to ±18%. Using a look-up table developed from simulations, OCO-2's estimated cloud top pressure for low clouds (Ptop > 680 hPa) has a standard deviation of ±61 hPa relative to CALIPSO retrievals, and bias is dependent on assumed cloud pressure thickness, with our smallest value being −5 hPa. Versus MODIS optical depth, the standard deviation is ±9.0 and the bias is −2.0, although these shrink for clouds of τ

We present an analysis of uncertainties in global measurements of the column averaged dry-air mole fraction of CO2 (XCO2) by the NASA Orbiting Carbon Observatory-2 (OCO-2). The analysis is based on our best estimates for uncertainties in the OCO-2 operational algorithm and its inputs, and uses simulated spectra calculated for the actual flight and sounding geometry, with measured atmospheric analyses. The simulations are calculated for land nadir and ocean glint observations. We include errors in measurement, smoothing, interference, and forward model parameters. All types of error are combined to estimate the uncertainty in XCO2 from single soundings, before any attempt at bias correction has been made. From these results we also estimate the "variable error" which differs between soundings, to infer the error in the difference of XCO2 between any two soundings. The most important error sources are aerosol interference, spectroscopy, and instrument calibration. Aerosol is the largest source of variable error. Spectroscopy and calibration, although they are themselves fixed error sources, also produce important variable errors in XCO2. Net variable errors are usually

This paper introduces the OCO2CLD-LIDAR-AUX product, which uses the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar and the Orbiting Carbon Observatory-2 (OCO-2) hyperspectral A-band spectrometer. CALIPSO provides a prior cloud top pressure ( P top ) for an OCO-2 based retrieval of cloud optical depth, P top and cloud geometric thickness expressed in hPa. Measurements are of single-layer liquid clouds over oceans from September 2014 to December 2016 when collocated data are available. Retrieval performance is best for solar zenith angle 2 band, is more confident. The highest quality optical depth retrievals agree with those from the Moderate Resolution Imaging Spectroradiometer (MODIS) with discrepancies smaller than the MODIS-reported uncertainty. Retrieved thicknesses are consistent with a substantially subadiabatic structure over marine stratocumulus regions, in which extinction is weighted towards the cloud top. Cloud top pressure in these clouds shows a 4 hPa bias compared with CALIPSO which we attribute mainly to the assumed vertical structure of cloud extinction after showing little sensitivity to the presence of CALIPSO-identified aerosol layers or assumed cloud droplet effective radius. This is the first case of success in obtaining internal cloud structure from hyperspectral A-band measurements and exploits otherwise unused OCO-2 data. The data provided by these retrievals provide additional constraints on satellite-based estimates of cloud droplet number concentration from visible imagery, which rely on parameterization of the cloud thickness.

The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO2) with the accuracy, resolution, and coverage needed to quantify CO2 fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014 and has gathered more than 2 years of observations. The v7/v7r operational data products from September 2014 to January 2016 are discussed here. On monthly timescales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO2 dry air mole fraction, XCO2, that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of XCO2 derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes XCO2 enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north–south XCO2 gradient is small. Enhanced XCO2 coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north–south XCO2 gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in XCO2 across the Northern Hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO2. As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart XCO2 changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high-resolution global dataset.

The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO2) with the accuracy, resolution, and coverage needed to quantify CO2 fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014, and joined the 705 km Afternoon Constellation on 3 August 2014. On monthly time scales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO2 dry air mole fraction, XCO2, that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of XCO2 derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes XCO2 enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north-south XCO2 gradient is small. Enhanced XCO2 coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north-south XCO2 gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in XCO2 across the northern hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO2. As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart XCO2 changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high resolution, global data set.

With the advent of dedicated greenhouse gas space-borne spectrometers sporting high resolution spectra in the O2 A-band spectral region (755–774 nm), the retrieval of chlorophyll fluorescence has become feasible on a global scale. If unaccounted for, however, fluorescence can indirectly perturb the greenhouse gas retrievals as it perturbs the oxygen absorption features. As atmospheric CO2 measurements are used to invert net fluxes at the land–atmosphere interface, a bias caused by fluorescence can be crucial as it will spatially correlate with the fluxes to be inverted. Avoiding a bias and retrieving fluorescence accurately will provide additional constraints on both the net and gross fluxes in the global carbon cycle. We show that chlorophyll fluorescence, if neglected, systematically interferes with full-physics multi-band XCO2 retrievals using the O2 A-band. Systematic biases in XCO2 can amount to +1 ppm if fluorescence constitutes 1% to the continuum level radiance. We show that this bias can be largely eliminated by simultaneously fitting fluorescence in a full-physics based retrieval. If fluorescence is the primary target, a dedicated but very simple retrieval based purely on Fraunhofer lines is shown to be more accurate and very robust even in the presence of large scattering optical depths. We find that about 80% of the surface fluorescence is retained at the top-of-atmosphere, even for cloud optical thicknesses around 2–5. We further show that small instrument modifications to future O2 A-band spectrometer spectral ranges can result in largely reduced random errors in chlorophyll fluorescence, paving the way towards a more dedicated instrument exploiting solar absorption features only.

Here, we report preliminary estimates of the column averaged carbon dioxide (CO2) dry air mole fraction, XCO2, retrieved from spectra recorded over land by the Greenhouse gases Observing Satellite, GOSAT (nicknamed "Ibuki"), using retrieval methods originally developed for the NASA Orbiting Carbon Observatory (OCO) mission. After screening for clouds and other known error sources, these retrievals reproduce much of the expected structure in the global XCO2 field, including its variation with latitude and season. However, low yields of retrieved XCO2 over persistently cloudy areas and ice covered surfaces at high latitudes limit the coverage of some geographic regions, even on seasonal time scales. Comparisons of early GOSAT XCO2 retrievals with XCO2 estimates from the Total Carbon Column Observing Network (TCCON) revealed a global, −2% (7–8 parts per million, ppm, with respect to dry air) XCO2 bias and 2 to 3 times more variance in the GOSAT retrievals. About half of the global XCO2 bias is associated with a systematic, 1% overestimate in the retrieved air mass, first identified as a global +10 hPa bias in the retrieved surface pressure. This error has been attributed to errors in the O2 A-band absorption cross sections. Much of the remaining bias and spurious variance in the GOSAT XCO2 retrievals has been traced to uncertainties in the instrument's calibration, oversimplified methods for generating O2 and CO2 absorption cross sections, and other subtle errors in the implementation of the retrieval algorithm. Many of these deficiencies have been addressed in the most recent version (Build 2.9) of the retrieval algorithm, which produces negligible bias in XCO2 on global scales as well as a ~30% reduction in variance. Comparisons with TCCON measurements indicate that regional scale biases remain, but these could be reduced by applying empirical corrections like those described by Wunch et al. (2011b). We recommend that such corrections be applied before these data are used in source sink inversion studies to minimize spurious fluxes associated with known biases. These and other lessons learned from the analysis of GOSAT data are expected to accelerate the delivery of high quality data products from the Orbiting Carbon Observatory-2 (OCO-2), once that satellite is successfully launched and inserted into orbit.

This work describes the NASA Atmospheric CO2 Observations from Space (ACOS) XCO2 retrieval algorithm, and its performance on highly realistic, simulated observations. These tests, restricted to observations over land, are used to evaluate retrieval errors in the face of realistic clouds and aerosols, polarized non-Lambertian surfaces, imperfect meteorology, and uncorrelated instrument noise. We find that post-retrieval filters are essential to eliminate the poorest retrievals, which arise primarily due to imperfect cloud screening. The remaining retrievals have RMS errors of approximately 1 ppm. Modeled instrument noise, based on the Greenhouse Gases Observing SATellite (GOSAT) in-flight performance, accounts for less than half the total error in these retrievals. A small fraction of unfiltered clouds, particularly thin cirrus, lead to a small positive bias of ~0.3 ppm. Overall, systematic errors due to imperfect characterization of clouds and aerosols dominate the error budget, while errors due to other simplifying assumptions, in particular those related to the prior meteorological fields, appear small.

With the advent of dedicated greenhouse-gas space-borne spectrometers sporting high resolution spectra in the O2 A-band spectral region (755–774 nm), the retrieval of chlorophyll fluorescence has become feasible on a global scale. If unaccounted for, however, fluorescence can indirectly perturb the greenhouse gas retrievals as it perturbs the oxygen absorption features. As atmospheric CO2 measurements are used to invert net fluxes at the land-atmosphere interface, a bias caused by fluorescence can be crucial as it will spatially correlate with the fluxes to be inverted. Avoiding a bias and retrieving fluorescence accurately will provide additional constraints on both the net and gross fluxes in the global carbon cycle. We show that chlorophyll fluorescence, if neglected, systematically interferes with full-physics multi-band XCO2 retrievals using the O2 A-band. Systematic biases in XCO2 can amount to +1 ppm if fluorescence constitutes 1% to the continuum level radiance. We show that this bias can be largely eliminated by simultaneously fitting fluorescence in a full-physics based retrieval. If fluorescence is the primary target, a dedicated but very simple retrieval based purely on Fraunhofer lines is shown to be more accurate and very robust even in the presence of large scattering optical depths. We find that about 80% of the surface fluorescence is retained at the top-of-atmosphere even for cloud optical thicknesses around 2–5. We further show that small instrument modifications to future O2 A-band spectrometer spectral ranges can result in largely reduced random errors in chlorophyll fluorescence, paving the way towards a more dedicated instrument exploiting solar absorption features only.

Here, we report preliminary estimates of the column averaged carbon dioxide (CO2) dry air mole fraction, XCO2, retrieved from spectra recorded over land by the Greenhouse gases Observing Satellite, GOSAT (nicknamed "Ibuki"), using retrieval methods originally developed for the NASA Orbiting Carbon Observatory (OCO) mission. After screening for clouds and other known error sources, these retrievals reproduce much of the expected structure in the global XCO2 field, including its variation with latitude and season. However, low yields of retrieved XCO2 over persistently cloudy areas and ice covered surfaces at high latitudes limit the coverage of some geographic regions, even on seasonal time scales. Comparisons of early GOSAT XCO2 retrievals with XCO2 estimates from the Total Carbon Column Observing Network (TCCON) revealed a global, −2% (7–8 parts per million, ppm, with respect to dry air) XCO2 bias and 2 to 3 times more variance in the GOSAT retrievals. About half of the global XCO2 bias is associated with a systematic, 1% overestimate in the retrieved air mass, first identified as a global +10 hPa bias in the retrieved surface pressure. This error has been attributed to errors in the O2 A-band absorption cross sections. Much of the remaining bias and spurious variance in the GOSAT XCO2 retrievals has been traced to uncertainties in the instrument's calibration, oversimplified methods for generating O2 and CO2 absorption cross sections, and other subtle errors in the implementation of the retrieval algorithm. Many of these deficiencies have been addressed in the most recent version (Build 2.9) of the retrieval algorithm, which produces negligible bias in XCO2 on global scales as well as a ∼30% reduction in variance. Comparisons with TCCON measurements indicate that regional scale biases remain, but these could be reduced by applying empirical corrections like those described by Wunch et al. (2011). We recommend that such corrections be applied before these data are used in source sink inversion studies to minimize spurious fluxes associated with known biases. These and other lessons learned from the analysis of GOSAT data are expected to accelerate the delivery of high quality data products from the Orbiting Carbon Observeratory-2 (OCO-2), once that satellite is successfully launched and inserted into orbit.

The NASA Crew Launch Vehicle is a two-stage orbital launcher designed to meet NASA’s current as well as future needs for human space flight. In order to free the designers to explore more possibilities during the design phase, a need exists for the ability to quickly perform simulation on both the baseline vehicle as well as the vehicle after proposed changes due to mission planning, vehicle configuration and avionics changes, proposed new guidance and control algorithms, and any other contingencies the designers may wish to consider. Further, after the vehicle is designed and built, the need will remain for such analysis in the event of future mission planning. An easily reconfigurable, modular, nonlinear six-degree-of-freedom simulation matching NASA Marshall’s in-house highfidelity simulator is created with the ability to quickly perform simulation and analysis of the Crew Launch Vehicle throughout the entire launch profile. Simulation results are presented and discussed, and an example simulation to demonstrate the need for ground vibration testing is shown. I. Introduction HE United States National Aeronautics and Space Administration (NASA) has committed to building the ARES I Crew Launch Vehicle as the man-rated launcher to support the implementation of the Vision for Space Exploration 1 . Preliminary Guidance, Navigation, and Control (GNC these analyses assumed there was insufficient cross-coupling of the vehicle axes to affect stability results. SAVANT uses fully coupled nonlinear equations of motion to run a time-domain simulation to arrive at specific operating points where frequency results are desired. SAVANT is capable of generating both time domain and frequency domain information of both the vehicle integrated stack and Upper Stage flight during ascent flight phases. The tool is used to provide vehicle system and component analysis involving system data for multiple Design Analysis Cycles (DAC) and is able to simulate the effects of component and algorithm modifications in terms of flight performance and robustness requirements.

bd Systems (a subsidiary of SAIC) has developed the Simulation Package for Autonomous Rendezvous Test and ANalysis (SPARTAN), a high-fidelity on-orbit simulation featuring multiple six-degree-of-freedom (6DOF) vehicles. SPARTAN has been developed in a modular fashion in Matlab/Simulink to test next-generation automated rendezvous and docking guidance, navigation,and control algorithms for NASA's new Vision for Space Exploration. SPARTAN includes autonomous state-based mission manager algorithms responsible for sequencing the vehicle through various flight phases based on on-board sensor inputs and closed-loop guidance algorithms, including Lambert transfers, Clohessy-Wiltshire maneuvers, and glideslope approaches The guidance commands are implemented using an integrated translation and attitude control system to provide 6DOF control of each vehicle in the simulation. SPARTAN also includes high-fidelity representations of a variety of absolute and relative navigation sensors that maybe used for NASA missions, including radio frequency, lidar, and video-based rendezvous sensors. Proprietary navigation sensor fusion algorithms have been developed that allow the integration of these sensor measurements through an extended Kalman filter framework to create a single optimal estimate of the relative state of the vehicles. SPARTAN provides capability for Monte Carlo dispersion analysis, allowing for rigorous evaluation of the performance of the complete proposed AR&D system, including software, sensors, and mechanisms. SPARTAN also supports hardware-in-the-loop testing through conversion of the algorithms to C code using Real-Time Workshop in order to be hosted in a mission computer engineering development unit running an embedded real-time operating system. SPARTAN also contains both runtime TCP/IP socket interface and post-processing compatibility with bdStudio, a visualization tool developed by bd Systems, allowing for intuitive evaluation of simulation results. A description of the SPARTAN architecture and capabilities is provided, along with details on the models and algorithms utilized and results from representative missions.

The NASA Crew Launch Vehicle is a two-stage orbital launch vehicle designed to meet NASA’s current as well as future needs for human space flight. In order to free the designers to explore more possibilities during the design phase, a need exists for the ability to quickly perform stability analysis on both the baseline vehicle as well as the vehicle after proposed changes due to mission planning, vehicle configuration and avionics changes, proposed new guidance and control algorithms, and any other contingencies the designers may wish to consider. Further, after the vehicle is designed and built, the need will remain for such analysis in the event of future mission planning. An easily reconfigurable, modular, nonlinear six-degree-of-freedom simulation matching NASA Marshall’s in-house highfidelity simulator is created with the ability to quickly enable stability analysis of the Crew Launch Vehicle throughout the entire launch profile. Stability analysis results are presented and discussed.

A de-coupled sliding mode controller and observer is derived for spacecraft attitude tracking maneuvers and regulation in terms of quaternions. The controller sliding manifold guarantees globally stable asymptotic convergence to the desired time dependent quaternion profile and the quaternion tracking error responds as a linear homogeneous vector differential equation with constant coefficients and desired eigenvalues placement. The full order sliding mode observer is used to avoid quaternion error differentiation noise and to eliminate the need for angular velocity measurement.