ALBERT

Description: Imaging of diffractions is a challenge in seismic processing. Standard seismic processing is tuned to enhance reflections. Separation of diffracted from reflected events is frequently used to achieve an optimized image of diffractions. We present a method to effectively separate and image diffracted events in the time domain. The method is based on the common-reflection-surface-based diffraction stacking and the application of a diffraction-filter. The diffraction-filter uses kinematic wavefield attributes determined by the common-reflection-surface approach. After the separation of seismic events, poststack time-migration velocity analysis is applied to obtain migration velocities. The velocity analysis uses a semblance based method of diffraction traveltimes. The procedure is incorporated into the conventional common-reflection-surface workflow. We apply the procedure to 2D synthetic data. The application of the method to simple and complex synthetic data shows promising results.

Description: Time migration is an attractive tool to produce a subsurface image because it is faster and less sensitive to velocities errors than depth migration. However, a highly focused time image is only achievable with well-determined time-migration velocities. Therefore, a refinement of the initial time-migration velocities often is required. We introduced a new technique for prestack time migration, based on the common-migrated-reflector-element stack of common scatterpoint gathers, including an automatic update of time-migration velocities. The common scatterpoint gathers are generated using a new formulation of the double-square-root equation that is parametrized with the common-offset apex time. The common-migrated-reflector-element stack is a multiparameter stacking technique based on the Taylor expansion of traveltimes of time-migrated reflections in the paraxial vicinity of the image ray. Our 2D synthetic and field data examples demonstrated that the proposed method provides updated time-migration velocities that are more robust and have higher resolution compared with the initial time-migration velocities. The prestack time migration method also showed a clear improvement of the focusing of reflections for such geologic features as faults and salt structures.

Description: 〈span〉〈div〉ABSTRACT〈/div〉Assessment of the area of interest on tectonic overprint is a key aspect during various geoscientific exploration endeavors. Conventionally, 3D reflection imaging is used because it provides the highest resolution of the subsurface image. Tectonic features, e.g., faults or fractures, are imaged indirectly by means of discontinuities of the specular reflections. Specular reflections, however, are only a part of the backscattering wavefield. Geologic heterogeneities in the subsurface can act as scattering points or scattering edges, which both evoke diffracted waves. Thus, diffracted waves are a direct seismic response from subsurface heterogeneities, which have a size comparable with the prevailing wavelength or a curvature growing locally to infinity. We investigate a dedicated processing of diffracted waves, which allows direct imaging of such heterogeneities. The method is based on separation of reflections from diffractions with subsequent diffraction focusing. We apply a combination of frequency-wavenumber filtering and an adaptive-subtraction engine to isolate diffractions. Once the reflections are removed from a data set, diffracted waves are focused into their apexes yielding an image of scatter points, which can be interpreted as an indicator for the extent of the degree of inhomogeneities or can be used to map structural elements or fracture density. We use a modified coherence analysis as a focusing tool. Diffractions from the scattering points have a different phase response than diffractions from the edges. These differences can be distinguished and further guide the geologic interpretation.〈/span〉

Description: Surface meltwater on ice shelves can exist as slush, it can pond in lakes or crevasses, or it can flow in surface streams and rivers. The collapse of the Larsen B Ice Shelf in 2002 has been attributed to the sudden drainage of ∼3000 surface lakes and has highlighted the potential for surface water to cause ice-shelf instability. Surface meltwater systems have been identified across numerous Antarctic ice shelves, although the extent to which these systems impact ice-shelf instability is poorly constrained. To better understand the role of surface meltwater systems on ice shelves, it is important to track their seasonal development, monitoring the fluctuations in surface water volume and the transfer of water across ice-shelf surfaces. Here, we use Landsat 8 and Sentinel-2 imagery to track surface meltwater across the Nivlisen Ice Shelf in the 2016–2017 melt season. We develop the Fully Automated Supraglacial-Water Tracking algorithm for Ice Shelves (FASTISh) and use it to identify and track the development of 1598 water bodies, which we classify as either circular or linear. The total volume of surface meltwater peaks on 26 January 2017 at 5.5×107 m3. At this time, 63 % of the total volume is held within two linear surface meltwater systems, which are up to 27 km long, are orientated along the ice shelf's north–south axis, and follow the surface slope. Over the course of the melt season, they appear to migrate away from the grounding line, while growing in size and enveloping smaller water bodies. This suggests there is large-scale lateral water transfer through the surface meltwater system and the firn pack towards the ice-shelf front during the summer.

Description: Seasonal CO2 exchange in the boreal forest plays an important role in the global carbon budget and in driving interannual variability in seasonal cycles of atmospheric CO2. Satellite-based observations from polar orbiting satellites like the Orbiting Carbon Observatory-2 (OCO-2) offer an opportunity to characterize boreal forest seasonal cycles across longitudes with a spatially and temporally rich data set, but data quality controls and biases still require vetting at high latitudes. With the objective of improving data availability at northern, terrestrial high latitudes, this study evaluates quality control methods and biases of OCO-2 retrievals of atmospheric column-averaged dry air mole fractions of CO2 (XCO2) in boreal forest regions. In addition to the standard quality control (QC) filters recommended for the Atmospheric Carbon Observations from Space (ACOS) B8 (B8 QC) and ACOS B9 (B9 QC) OCO-2 retrievals, a third set of quality control filters were specifically tailored to boreal forest observations (boreal QC) with the goal of increasing data availability at high latitudes without sacrificing data quality. Ground-based reference measurements of XCO2 include observations from two sites in the Total Carbon Column Observing Network (TCCON) at East Trout Lake, Saskatchewan, Canada, and Sodankylä, Finland. OCO-2 retrievals were also compared to ground-based observations from two Bruker EM27/SUN Fourier transform infrared spectrometers (FTSs) at Fairbanks, Alaska, USA. The EM27/SUN spectrometers that were deployed in Fairbanks were carefully monitored for instrument performance and were bias corrected to TCCON using observations at the Caltech TCCON site. The B9 QC were found to pass approximately twice as many OCO-2 retrievals over land north of 50∘ N than the B8 QC, and the boreal QC were found to pass approximately twice as many retrievals in May, August, and September as the B9 QC. While boreal QC results in a substantial increase in passable retrievals, this is accompanied by increases in the standard deviations in biases at boreal forest sites from ∼1.4 parts per million (ppm) with B9 QC to ∼1.6 ppm with boreal QC. Total average biases for coincident OCO-2 retrievals at the three sites considered did not consistently increase or decrease with different QC methods, and instead, responses to changes in QC varied according to site and satellite viewing geometries. Regardless of the quality control method used, seasonal variability in biases was observed, and this variability was more pronounced at Sodankylä and East Trout Lake than at Fairbanks. Long-term coincident observations from TCCON, EM27/SUN, and satellites from multiple locations would be necessary to determine whether the reduced seasonal variability in bias at Fairbanks is due to geography or instrumentation. Monthly average biases generally varied between −1 and +1 ppm at the three sites considered, with more negative biases in spring (March, April, and May – MAM) and autumn (September and October – SO) but more positive biases in the summer months (June, July, and August – JJA). Monthly standard deviations in biases ranged from approximately 1.0 to 2.0 ppm and did not exhibit strong seasonal dependence, apart from exceptionally high standard deviation observed with all three QC methods at Sodankylä in June. There was no evidence found to suggest that seasonal variability in bias is a direct result of air mass dependence in ground-based retrievals or of proximity bias from coincidence criteria, but there were a number of retrieval parameters used as quality control filters that exhibit seasonality and could contribute to seasonal dependence in OCO-2 bias. Furthermore, it was found that OCO-2 retrievals of XCO2 without the standard OCO-2 bias correction exhibit almost no perceptible seasonal dependence in average monthly bias at these boreal forest sites, suggesting that seasonal variability in bias is introduced by the bias correction. Overall, we found that modified quality controls can allow for significant increases in passable OCO-2 retrievals with only marginal compromises in data quality, but seasonal dependence in biases still warrants further exploration.

Description: NASA's Orbiting Carbon Observatory-2 (OCO-2) has been measuring carbon dioxide column-averaged dry-air mole fraction, XCO2, in the Earth's atmosphere for over 2 years. In this paper, we describe the comparisons between the first major release of the OCO-2 retrieval algorithm (B7r) and XCO2 from OCO-2's primary ground-based validation network: the Total Carbon Column Observing Network (TCCON). The OCO-2 XCO2 retrievals, after filtering and bias correction, agree well when aggregated around and coincident with TCCON data in nadir, glint, and target observation modes, with absolute median differences less than 0.4 ppm and RMS differences less than 1.5 ppm. After bias correction, residual biases remain. These biases appear to depend on latitude, surface properties, and scattering by aerosols. It is thus crucial to continue measurement comparisons with TCCON to monitor and evaluate the OCO-2 XCO2 data quality throughout its mission.

Description: 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.

Description: The Orbiting Carbon Observatory-3 (OCO-3) is NASA's next instrument dedicated to extending the record of the dry-air mole fraction of column carbon dioxide (XCO2) and solar-induced fluorescence (SIF) measurements from space. The current schedule calls for a launch from the Kennedy Space Center no earlier than April 2019 via a Space-X Falcon 9 and Dragon capsule. The instrument will be installed as an external payload on the Japanese Experimental Module Exposed Facility (JEM-EF) of the International Space Station (ISS) with a nominal mission lifetime of 3 years. The precessing orbit of the ISS will allow for viewing of the Earth at all latitudes less than approximately 52∘, with a ground repeat cycle that is much more complicated than the polar-orbiting satellites that so far have carried all of the instruments capable of measuring carbon dioxide from space. The grating spectrometer at the core of OCO-3 is a direct copy of the OCO-2 spectrometer, which was launched into a polar orbit in July 2014. As such, OCO-3 is expected to have similar instrument sensitivity and performance characteristics to OCO-2, which provides measurements of XCO2 with precision better than 1 ppm at 3 Hz, with each viewing frame containing eight footprints approximately 1.6 km by 2.2 km in size. However, the physical configuration of the instrument aboard the ISS, as well as the use of a new pointing mirror assembly (PMA), will alter some of the characteristics of the OCO-3 data compared to OCO-2. Specifically, there will be significant differences from day to day in the sampling locations and time of day. In addition, the flexible PMA system allows for a much more dynamic observation-mode schedule. This paper outlines the science objectives of the OCO-3 mission and, using a simulation of 1 year of global observations, characterizes the spatial sampling, time-of-day coverage, and anticipated data quality of the simulated L1b. After application of cloud and aerosol prescreening, the L1b radiances are run through the operational L2 full physics retrieval algorithm, as well as post-retrieval filtering and bias correction, to examine the expected coverage and quality of the retrieved XCO2 and to show how the measurement objectives are met. In addition, results of the SIF from the IMAP–DOAS algorithm are analyzed. This paper focuses only on the nominal nadir–land and glint–water observation modes, although on-orbit measurements will also be made in transition and target modes, similar to OCO-2, as well as the new snapshot area mapping (SAM) mode.

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.