ALBERT

Description: 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.

Description: Evaluating and attributing uncertainties in total column atmospheric CO2 measurements (XCO2) from the OCO-2 instrument is critical for testing hypotheses related to the underlying processes controlling XCO2 and for developing quality flags needed to choose those measurements that are usable for carbon cycle science.Here we test the reported uncertainties of version 7 OCO-2 XCO2 measurements by examining variations of the XCO2 measurements and their calculated uncertainties within small regions (∼  100 km  ×  10.5 km) in which natural CO2 variability is expected to be small relative to variations imparted by noise or interferences. Over 39 000 of these small neighborhoods comprised of approximately 190 observations per neighborhood are used for this analysis. We find that a typical ocean measurement has a precision and accuracy of 0.35 and 0.24 ppm respectively for calculated precisions larger than  ∼  0.25 ppm. These values are approximately consistent with the calculated errors of 0.33 and 0.14 ppm for the noise and interference error, assuming that the accuracy is bounded by the calculated interference error. The actual precision for ocean data becomes worse as the signal-to-noise increases or the calculated precision decreases below 0.25 ppm for reasons that are not well understood. A typical land measurement, both nadir and glint, is found to have a precision and accuracy of approximately 0.75 and 0.65 ppm respectively as compared to the calculated precision and accuracy of approximately 0.36 and 0.2 ppm. The differences in accuracy between ocean and land suggests that the accuracy of XCO2 data is likely related to interferences such as aerosols or surface albedo as they vary less over ocean than land. The accuracy as derived here is also likely a lower bound as it does not account for possible systematic biases between the regions used in this analysis.

Description: The Orbiting Carbon Observatory-2 (OCO-2) carries and points a three-channel imaging grating spectrometer designed to collect high-resolution, co-boresighted spectra of reflected sunlight within the molecular oxygen (O2) A-band at 0.765 microns and the carbon dioxide (CO2) bands at 1.61 and 2.06 microns. These measurements are calibrated and then combined into soundings that are analyzed to retrieve spatially resolved estimates of the column-averaged CO2 dry-air mole fraction, XCO2. Variations of XCO2 in space and time are then analyzed in the context of the atmospheric transport to quantify surface sources and sinks of CO2. This is a particularly challenging remote-sensing observation because all but the largest emission sources and natural absorbers produce only small (

Description: The Orbiting Carbon Observatory-2 (OCO-2) carries and points a three-channel imaging grating spectrometer designed to collect high-resolution, co-boresighted spectra of reflected sunlight within the molecular oxygen (O2) A-band at 0.765 microns and the carbon dioxide (CO2) bands at 1.61 and 2.06 microns. These measurements are calibrated and then combined into soundings that are analyzed to retrieve spatially resolved estimates of the column-averaged CO2 dry air mole fraction, XCO2. Variations of XCO2 in space and time are then analyzed in the context of the atmospheric transport to quantify surface sources and sinks of CO2. This is particularly challenging remote sensing observations because the all but the largest emission sources and natural absorbers produce only small (

Description: 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.

Description: Evaluating and attributing uncertainties in total column atmospheric CO2 measurements (XCO2) from the OCO-2 instrument is critical for testing hypotheses related to the underlying processes controlling XCO2 and for developing quality flags needed to choose those measurements that are usable for carbon cycle science. Here we test the reported uncertainties of Version 7 OCO-2 XCO2 measurements by examining variations of the XCO2 measurements and their calculated uncertainties within small regions (~ 100 km x 10.5 km) in which CO2 variability is expected to be small relative to variations imparted by noise or interferences. Over 39 000 of these “small neighborhoods” comprised of approximately 190 observations per neighborhood are used for this analysis. We find that a typical ocean measurement should have a precision and accuracy of 0.35 and 0.24 ppm respectively for calculated precisions larger than ~ 0.25 ppm. These values are approximately consistent with the calculated errors of 0.33 and 0.14 ppm for the noise and interference error (assuming that the accuracy is bounded by the calculated interference error). The actual precision for ocean data becomes worse as the signal-to-noise increases or the calculated precision decreases below 0.25 ppm for reasons that not well understood. A typical land measurement (both nadir and glint) is found to have a precision and accuracy of approximately 0.75 ppm and 0.65 ppm respectively as compared to the calculated precision and accuracy of approximately 0.36 ppm and 0.2 ppm. However, this precision includes the effects of synoptic variability in the total column that could be as high as 0.5 ppm during the summer drawdown period. The accuracy is likely related to interferences such as aerosols or surface albedo and is a lower bound as it is evaluated by comparing gradients in OCO-2 estimates of XCO2 to expected gradients across the region and not by direct comparison to well-calibrated XCO2 measurements from the ground network.

Description: Achieving consistently high levels of productivity for surface exploration missions has been a challenge for Mars missions. While the rovers have made major discoveries and accomplished a large number of objectives, they often require a great deal of effort from the operations teams and achievingobjectives can take longer than anticipated. This paper describes the early stages of a multi-year project to investigate solutions for enhancing surface mission productivity. A primary focus of this early stage is to conduct in-depth studies of Mars Science Laboratory science campaigns to gain a deeper understanding of the factors that impact productivity, and to use this understanding to identify potential changes to flight software and ground operations practices to increase productivity. We present the science campaigns we have selected along with a conceptual model of how surface missions achieve objectives that is used to guide the study. We also provide some early thoughts on the technologies, and their interactions, which we believe will play an important role in addressing surface mission productivity challenges.We are in the early stages of a multi-year project to studyand address productivity challenges of future surface missions. We have identified campaigns from the MSL missionfor study which we believe will yield valuable informationabout the nature of surface mission productivity challenges.Based on preliminary analysis from the data collected weanticipate that the lessons from these case studies will helpdevelop and mature our concepts for changes to flight andground systems to address these challenges.While the focus of our work is on Mars rover missions, webelieve the concepts in the work will be applicable to a variety of in-situ explorers, including Venus, and Titan, as wellas orbital missions, such as the Europa orbiter. These missions will also benefit from the ability to adapt and respondto the latest state of the spacecraft and its environment.

Description: No abstract available

Description: A key component of Mars exploration is the operation of robotic instruments on the surface, such as those on board the Mars Exploration Rovers, the Mars Science Laboratory (MSL), and the planned Mars 2020 Rover. As the instruments carried by these rovers have become more advanced, the area targeted by some instruments becomes smaller, revealing more fine-grained details about the geology and chemistry of rocks on the surface. However, thermal fluctuations, rover settling or slipping, and inherent inaccuracies in pointing mechanisms all lead to pointing error that is on the order of the target size (several millimeters) or larger. We show that given a target located on a previously acquired image, the rover can align this with a new image to visually locate the target and refine the current pointing. Due to round-trip communication constraints, this visual targeting must be done efficiently on board the rover using relatively limited computing hardware. We employ existing ORB features for landmark-based image registration, describe and theoretically justify a novel approach to filtering false landmark matches, and employ a random forest classifier to automatically reject failed alignments. We demonstrate the efficacy of our approach using over 3,800 images acquired by Remote Micro-Imager on board the Curiosity rover.

Description: We describe the near real-time transient-source discovery engine for the intermediate Palomar Transient Factory (iPTF), currently in operations at the Infrared Processing and Analysis Center (IPAC), Caltech. We coin this system the IPAC/iPTF Discovery Engine (or IDE). We review the algorithms used for PSF-matching, image subtraction, detection, photometry, and machine-learned (ML) vetting of extracted transient candidates. We also review the performance of our ML classifier. For a limiting signal-to-noise ratio of 4 in relatively unconfused regions, bogus candidates from processing artifacts and imperfect image subtractions outnumber real transients by approximately equal to 10:1. This can be considerably higher for image data with inaccurate astrometric and/or PSF-matching solutions. Despite this occasionally high contamination rate, the ML classifier is able to identify real transients with an efficiency (or completeness) of approximately equal to 97% for a maximum tolerable false-positive rate of 1% when classifying raw candidates. All subtraction-image metrics, source features, ML probability-based real-bogus scores, contextual metadata from other surveys, and possible associations with known Solar System objects are stored in a relational database for retrieval by the various science working groups. We review our efforts in mitigating false-positives and our experience in optimizing the overall system in response to the multitude of science projects underway with iPTF.